From acfb01b156d4cbbfc482b15f2ab556fa7faa7589 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Mon, 13 Dec 2021 17:14:24 +0800 Subject: [PATCH 01/55] test --- .../PyTorch\345\256\211\350\243\205\346\214\207\345\215\227.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/docs/zh/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227.md" index e5f4cc926f5..d4dd6209eb1 100644 --- "a/docs/zh/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227.md" +++ "b/docs/zh/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227.md" @@ -11,7 +11,7 @@ - [安装“torch-\*.whl ”提示“torch 1.5.0xxxx”与“torchvision”所依赖的版本不匹配](#安装-torch--whl-提示-torch-1-5-0xxxx-与-torchvision-所依赖的版本不匹配md)

简介

-用户在准备相关环境进行基于PyTorch框架模型的开发、运行时，可以选择在服务器中手动编译安装PyTorch框架相关模块。 +用户在准备相关环境进行基于PyTorch框架模型的开发、运行时，可以选择在服务器中手动编译安装PyTorch框架相关模块。 **图 1** 环境准备流程图 -- Gitee From 63b178233c6237b8e18eeff8f4e76d66d663e14e Mon Sep 17 00:00:00 2001 From: pengzirong Date: Tue, 14 Dec 2021 12:00:47 +0800 Subject: [PATCH 02/55] =?UTF-8?q?=E5=90=8C=E6=AD=A51.5.0=20fd0700df1eca64d?= =?UTF-8?q?01571072bb51d56143e3efde3=20=20=20=20=20!2877=20=E5=8D=95?= =?UTF-8?q?=E5=85=83=E6=B5=8B=E8=AF=95=E7=AE=97=E5=AD=90=E5=87=BA=E9=94=99?= =?UTF-8?q?=E6=97=B6=E5=8A=A0=E5=85=A5resnet18=E6=B5=8B=E8=AF=95=20=20=20?= =?UTF-8?q?=20=20Merge=20pull=20request=20!2877=20from=20=E9=BE=9A?= =?UTF-8?q?=E4=BF=8A=E5=BC=BA/master=20--=20=20=20=20=20!2873=20=E3=80=90?= =?UTF-8?q?=E9=80=82=E9=85=8D=E3=80=91=E8=87=AA=E5=AE=9A=E4=B9=89=E6=8E=A5?= =?UTF-8?q?=E5=8F=A3npu=5Fnms=5Frotated=E9=80=82=E9=85=8D=20=20=20=20=20Me?= =?UTF-8?q?rge=20pull=20request=20!2873=20from=20=E9=99=88=E5=BA=86/nms=20?= =?UTF-8?q?--=20=20=20=20=20!2871=20Task:=20clean=20codecheck=20warnings?= =?UTF-8?q?=20=20=20=20=20Merge=20pull=20request=20!2871=20from=20?= =?UTF-8?q?=E8=B5=B5=E5=85=89=E7=81=BF/master=20=E5=90=8C=E6=AD=A51.8.1=20?= =?UTF-8?q?558f739f09d9f9422ec0f8e283c9f80ab62b42e5=20=20=20=20=20!2872=20?= =?UTF-8?q?=E5=88=A0=E9=99=A4pytorch181=20test/test=5Fnpu/=E7=9B=AE?= =?UTF-8?q?=E5=BD=95=E4=B8=8B=E9=87=8D=E5=A4=8D=E7=9A=84testcase=20=20=20?= =?UTF-8?q?=20=20Merge=20pull=20request=20!2872=20from=20=E9=BE=9A?= =?UTF-8?q?=E4=BF=8A=E5=BC=BA/pytorch1.8.1=20--=20=20=20=20=20!2870=20Feat?= =?UTF-8?q?ure:=20add=20NPU=20AMP=20testcases=20=20=20=20=20Merge=20pull?= =?UTF-8?q?=20request=20!2870=20from=20=E8=B5=B5=E5=85=89=E7=81=BF/pytorch?= =?UTF-8?q?1.8.1?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- patch/pytorch1.5.0_npu.patch | 225 ++++++------- patch/pytorch1.8.1_npu.patch | 184 +++++------ pytorch1.5.0/access_control_test.py | 10 +- .../src/ATen/native/native_functions.yaml | 5 + .../ATen/native/npu/NmsRotatedKernelNpu.cpp | 62 ++++ .../function/anchor_generator.py | 6 +- .../optimized_lib/module/channel_shuffle.py | 6 +- .../npu/optimized_lib/module/deform_conv.py | 6 +- .../npu/optimized_lib/module/dropout.py | 4 +- .../test_network_ops/test_nms_rotated.py | 59 ++++ pytorch1.8.1/test/test_npu/test__Ixor__.py | 129 -------- pytorch1.8.1/test/test_npu/test___iand__.py | 138 -------- .../test/test_npu/test___rshift__.Scalar.py | 60 ---- .../test__nnpack_spatial_convolution.py | 144 --------- .../test/test_npu/test_adaptive_avg_pool1d.py | 66 ---- .../test/test_npu/test_adaptive_avg_pool2d.py | 74 ----- .../test_adaptive_avg_pool2d_backward.py | 71 ---- .../test/test_npu/test_adaptive_max_pool1d.py | 75 ----- pytorch1.8.1/test/test_npu/test_addbmm.py | 114 ------- pytorch1.8.1/test/test_npu/test_addcdiv.py | 269 ---------------- .../test_affine_grid_generator_backward.py | 72 ----- .../test_npu/test_amp/common_device_type.py | 23 ++ .../test/test_npu/test_amp/common_utils.py | 29 ++ .../test/test_npu/test_amp/test_amp.py | 302 ++++++++++++++++++ .../test/test_npu/test_amp/util_test.py | 22 ++ pytorch1.8.1/test/test_npu/test_argsort.py | 71 ---- pytorch1.8.1/test/test_npu/test_asin.py | 66 ---- pytorch1.8.1/test/test_npu/test_baddbmm.py | 105 ------ .../test/test_npu/test_bartlett_window.py | 82 ----- pytorch1.8.1/test/test_npu/test_batch_norm.py | 79 ----- pytorch1.8.1/test/test_npu/test_bilinear.py | 157 --------- .../test_binary_cross_entropy_backward.py | 157 --------- .../test_binary_cross_entropy_with_logits.py | 212 ------------ pytorch1.8.1/test/test_npu/test_bincount.py | 67 ---- .../test/test_npu/test_blackman_window.py | 96 ------ pytorch1.8.1/test/test_npu/test_cast_Byte.py | 66 ---- pytorch1.8.1/test/test_npu/test_cast_Char.py | 67 ---- pytorch1.8.1/test/test_npu/test_cast_Float.py | 66 ---- pytorch1.8.1/test/test_npu/test_cast_Half.py | 80 ----- pytorch1.8.1/test/test_npu/test_cast_Int.py | 84 ----- pytorch1.8.1/test/test_npu/test_cast_Long.py | 67 ---- pytorch1.8.1/test/test_npu/test_cast_Short.py | 67 ---- pytorch1.8.1/test/test_npu/test_col2im.py | 56 ---- pytorch1.8.1/test/test_npu/test_conv1d.py | 92 ------ pytorch1.8.1/test/test_npu/test_conv_tbc.py | 65 ---- .../test/test_npu/test_conv_tbc_backward.py | 101 ------ .../test/test_npu/test_conv_transpose2d.py | 76 ----- .../test/test_npu/test_convolution.py | 90 ------ .../test_npu/test_convolution_backward.py | 96 ------ .../test_convolution_backward_input.py | 106 ------ .../test_convolution_backward_weight.py | 110 ------- ...t_convolution_transpose_backward_weight.py | 114 ------- pytorch1.8.1/test/test_npu/test_copy.py | 141 -------- pytorch1.8.1/test/test_npu/test_cos.py | 66 ---- pytorch1.8.1/test/test_npu/test_cosh.py | 149 --------- .../test/test_npu/test_cosinesimilarity.py | 84 ----- .../test_cudnn_convolution_backward_bias.py | 95 ------ 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mode 100644 pytorch1.8.1/test/test_npu/test_threshold_v2_d.py delete mode 100644 pytorch1.8.1/test/test_npu/test_trapz_dx.py delete mode 100644 pytorch1.8.1/test/test_npu/test_trapz_x.py delete mode 100644 pytorch1.8.1/test/test_npu/test_triangular_solve.py delete mode 100644 pytorch1.8.1/test/test_npu/test_upsample_bicubic2d_backward.py delete mode 100644 pytorch1.8.1/test/test_npu/test_upsample_linear1d.py delete mode 100644 pytorch1.8.1/test/test_npu/test_zeros_like.py diff --git a/patch/pytorch1.5.0_npu.patch b/patch/pytorch1.5.0_npu.patch index e66bae45207..bcf87a32433 100644 --- a/patch/pytorch1.5.0_npu.patch +++ b/patch/pytorch1.5.0_npu.patch @@ -1,6 +1,6 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/CMakeLists.txt pytorch-develop-150/aten/CMakeLists.txt --- pytorch-v1.5.0/aten/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/CMakeLists.txt 2021-12-11 23:02:22.532077032 +0800 ++++ pytorch-develop-150/aten/CMakeLists.txt 2021-12-14 12:00:40.705545026 +0800 @@ -22,8 +22,10 @@ set(ATen_CPU_INCLUDE) set(ATen_THIRD_PARTY_INCLUDE) @@ -51,7 +51,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_CPU_DEPENDENCY_LIBS ${ATen_CPU_DEPENDENCY_LIBS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/CMakeLists.txt pytorch-develop-150/aten/src/ATen/CMakeLists.txt --- pytorch-v1.5.0/aten/src/ATen/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/CMakeLists.txt 2021-12-11 23:02:22.532077032 +0800 ++++ pytorch-develop-150/aten/src/ATen/CMakeLists.txt 2021-12-14 12:00:40.705545026 +0800 @@ -67,6 +67,9 @@ FILE(GLOB native_quantized_h "native/quantized/*.h" "native/quantized/cpu/*.h") FILE(GLOB native_cpu_h "native/cpu/*.h") @@ -129,7 +129,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_QUANTIZED_SRCS ${ATen_QUANTIZED_SRCS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/core/dispatch/DispatchTable.h pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h --- pytorch-v1.5.0/aten/src/ATen/core/dispatch/DispatchTable.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h 2021-12-11 23:02:22.536077046 +0800 ++++ pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h 2021-12-14 12:00:40.709545051 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -170,7 +170,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/function_wrapper.py pytorch-develop-150/aten/src/ATen/function_wrapper.py --- pytorch-v1.5.0/aten/src/ATen/function_wrapper.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/function_wrapper.py 2021-12-11 23:02:22.536077046 +0800 ++++ pytorch-develop-150/aten/src/ATen/function_wrapper.py 2021-12-14 12:00:40.709545051 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -354,7 +354,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for option in declaration['options']: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/gen.py pytorch-develop-150/aten/src/ATen/gen.py --- pytorch-v1.5.0/aten/src/ATen/gen.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/gen.py 2021-12-11 23:02:22.536077046 +0800 ++++ pytorch-develop-150/aten/src/ATen/gen.py 2021-12-14 12:00:40.709545051 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -512,7 +512,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= generate_outputs() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/BatchLinearAlgebra.cpp pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp --- pytorch-v1.5.0/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-12-11 23:02:22.540077061 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-12-14 12:00:40.713545077 +0800 @@ -680,7 +680,7 @@ std::tuple triangular_solve_out(Tensor& result, Tensor& clone_A, const Tensor& self, const Tensor& A, bool upper, bool transpose, bool unitriangular) { @@ -524,7 +524,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return std::tuple(result, clone_A); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/cpu/Activation.cpp pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp --- pytorch-v1.5.0/aten/src/ATen/native/cpu/Activation.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp 2021-12-11 23:02:22.540077061 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp 2021-12-14 12:00:40.713545077 +0800 @@ -339,20 +339,20 @@ void hardsigmoid_backward_kernel(TensorIterator& iter) { @@ -552,7 +552,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/Memory.cpp pytorch-develop-150/aten/src/ATen/native/Memory.cpp --- pytorch-v1.5.0/aten/src/ATen/native/Memory.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/Memory.cpp 2021-12-11 23:02:22.540077061 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/Memory.cpp 2021-12-14 12:00:40.713545077 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -614,7 +614,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= detail::computeStorageSize(self.sizes(), self.strides()), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/native_functions.yaml pytorch-develop-150/aten/src/ATen/native/native_functions.yaml --- pytorch-v1.5.0/aten/src/ATen/native/native_functions.yaml 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/native_functions.yaml 2021-12-11 23:02:22.548077089 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/native_functions.yaml 2021-12-14 12:00:40.721545127 +0800 @@ -1,6 +1,5 @@ # See README.md in this directory for more guidance @@ -6679,7 +6679,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: slow_conv_dilated3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1) -> Tensor python_module: nn -@@ -6577,57 +8245,457 @@ +@@ -6577,57 +8245,462 @@ dispatch: CPU: col2im_out_cpu CUDA: col2im_out_cuda @@ -6863,6 +6863,11 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + variants: function, method + npu_dispatch_only: + NPU: nms_v4_npu ++ ++- func: npu_nms_rotated(Tensor self, Tensor scores, float iou_threshold, float scores_threshold=0, int max_output_size=-1, int mode=0) -> (Tensor, Tensor) ++ variants: function, method ++ npu_dispatch_only: ++ NPU: nms_rotated_npu + +- func: npu_lstm(Tensor input, Tensor weight, Tensor bias, Tensor seqMask, Tensor h, Tensor c, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first, bool flagSeq, bool direction) -> (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor) + variants: function @@ -7139,7 +7144,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + NPU: rotated_overlaps_npu diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S --- pytorch-v1.5.0/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-12-11 23:02:22.564077148 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-12-14 12:00:40.733545202 +0800 @@ -659,14 +659,14 @@ SUB x1, x1, 4 @@ -7165,7 +7170,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CMP x1, 2 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorCompare.cpp pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorCompare.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp 2021-12-11 23:02:22.540077061 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp 2021-12-14 12:00:40.713545077 +0800 @@ -64,7 +64,7 @@ Tensor isinf(const Tensor &self) { @@ -7177,7 +7182,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return AT_DISPATCH_FLOATING_TYPES_AND_HALF(self.scalar_type(), "isinf", [&]() { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorFactories.cpp pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorFactories.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp 2021-12-11 23:02:22.540077061 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp 2021-12-14 12:00:40.713545077 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7222,7 +7227,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorProperties.cpp pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorProperties.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp 2021-12-11 23:02:22.540077061 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp 2021-12-14 12:00:40.713545077 +0800 @@ -87,6 +87,7 @@ if (self.is_contiguous(memory_format)) { return self; @@ -7233,7 +7238,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= "preserve memory format is unsupported by the contiguous operator"); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/UpSampleBicubic2d.cpp pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp --- pytorch-v1.5.0/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-12-11 23:02:22.540077061 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-12-14 12:00:40.713545077 +0800 @@ -26,7 +26,7 @@ const scalar_t* in = &idata[output_y * input_width + output_x]; scalar_t* out = &odata[output_y * output_width + output_x]; @@ -7245,7 +7250,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= out += output_width * output_height; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native_parse.py pytorch-develop-150/aten/src/ATen/native_parse.py --- pytorch-v1.5.0/aten/src/ATen/native_parse.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native_parse.py 2021-12-11 23:02:22.576077191 +0800 ++++ pytorch-develop-150/aten/src/ATen/native_parse.py 2021-12-14 12:00:40.709545051 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -7283,7 +7288,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= msg = '''Exception raised in processing function: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/preprocess_declarations.py pytorch-develop-150/aten/src/ATen/preprocess_declarations.py --- pytorch-v1.5.0/aten/src/ATen/preprocess_declarations.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/preprocess_declarations.py 2021-12-11 23:02:22.576077191 +0800 ++++ pytorch-develop-150/aten/src/ATen/preprocess_declarations.py 2021-12-14 12:00:40.709545051 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -7315,7 +7320,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/templates/TensorBody.h pytorch-develop-150/aten/src/ATen/templates/TensorBody.h --- pytorch-v1.5.0/aten/src/ATen/templates/TensorBody.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/templates/TensorBody.h 2021-12-11 23:02:22.576077191 +0800 ++++ pytorch-develop-150/aten/src/ATen/templates/TensorBody.h 2021-12-14 12:00:40.741545253 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7348,7 +7353,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/templates/TensorMethods.h pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h --- pytorch-v1.5.0/aten/src/ATen/templates/TensorMethods.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h 2021-12-11 23:02:22.576077191 +0800 ++++ pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h 2021-12-14 12:00:40.741545253 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7382,7 +7387,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/CMakeLists.txt pytorch-develop-150/aten/src/TH/CMakeLists.txt --- pytorch-v1.5.0/aten/src/TH/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/CMakeLists.txt 2021-12-11 23:02:22.580077205 +0800 ++++ pytorch-develop-150/aten/src/TH/CMakeLists.txt 2021-12-14 12:00:40.741545253 +0800 @@ -48,6 +48,11 @@ ${CMAKE_CURRENT_SOURCE_DIR} PARENT_SCOPE) @@ -7397,7 +7402,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/generic/THStorage.cpp pytorch-develop-150/aten/src/TH/generic/THStorage.cpp --- pytorch-v1.5.0/aten/src/TH/generic/THStorage.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/generic/THStorage.cpp 2021-12-11 23:02:22.580077205 +0800 ++++ pytorch-develop-150/aten/src/TH/generic/THStorage.cpp 2021-12-14 12:00:40.741545253 +0800 @@ -1,9 +1,32 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7506,7 +7511,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/generic/THStorage.h pytorch-develop-150/aten/src/TH/generic/THStorage.h --- pytorch-v1.5.0/aten/src/TH/generic/THStorage.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/generic/THStorage.h 2021-12-11 23:02:22.580077205 +0800 ++++ pytorch-develop-150/aten/src/TH/generic/THStorage.h 2021-12-14 12:00:40.741545253 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7545,7 +7550,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/CMakeLists.txt pytorch-develop-150/c10/CMakeLists.txt --- pytorch-v1.5.0/c10/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/CMakeLists.txt 2021-12-11 23:02:22.592077248 +0800 ++++ pytorch-develop-150/c10/CMakeLists.txt 2021-12-14 12:00:40.749545303 +0800 @@ -63,6 +63,14 @@ message(STATUS "don't use NUMA") endif() @@ -7574,7 +7579,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # not checked in diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Backend.h pytorch-develop-150/c10/core/Backend.h --- pytorch-v1.5.0/c10/core/Backend.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Backend.h 2021-12-11 23:02:22.592077248 +0800 ++++ pytorch-develop-150/c10/core/Backend.h 2021-12-14 12:00:40.749545303 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7671,7 +7676,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case Backend::CUDA: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Device.cpp pytorch-develop-150/c10/core/Device.cpp --- pytorch-v1.5.0/c10/core/Device.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Device.cpp 2021-12-11 23:02:22.592077248 +0800 ++++ pytorch-develop-150/c10/core/Device.cpp 2021-12-14 12:00:40.749545303 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7711,7 +7716,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= types.begin(), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Device.h pytorch-develop-150/c10/core/Device.h --- pytorch-v1.5.0/c10/core/Device.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Device.h 2021-12-11 23:02:22.592077248 +0800 ++++ pytorch-develop-150/c10/core/Device.h 2021-12-14 12:00:40.749545303 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7746,7 +7751,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return type_ == DeviceType::CPU; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DeviceType.cpp pytorch-develop-150/c10/core/DeviceType.cpp --- pytorch-v1.5.0/c10/core/DeviceType.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DeviceType.cpp 2021-12-11 23:02:22.592077248 +0800 ++++ pytorch-develop-150/c10/core/DeviceType.cpp 2021-12-14 12:00:40.749545303 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7786,7 +7791,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return false; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DeviceType.h pytorch-develop-150/c10/core/DeviceType.h --- pytorch-v1.5.0/c10/core/DeviceType.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DeviceType.h 2021-12-11 23:02:22.592077248 +0800 ++++ pytorch-develop-150/c10/core/DeviceType.h 2021-12-14 12:00:40.749545303 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7829,7 +7834,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= constexpr DeviceType kXLA = DeviceType::XLA; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DispatchKey.cpp pytorch-develop-150/c10/core/DispatchKey.cpp --- pytorch-v1.5.0/c10/core/DispatchKey.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DispatchKey.cpp 2021-12-11 23:02:22.592077248 +0800 ++++ pytorch-develop-150/c10/core/DispatchKey.cpp 2021-12-14 12:00:40.749545303 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7861,7 +7866,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::SparseCPUTensorId: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DispatchKey.h pytorch-develop-150/c10/core/DispatchKey.h --- pytorch-v1.5.0/c10/core/DispatchKey.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DispatchKey.h 2021-12-11 23:02:22.592077248 +0800 ++++ pytorch-develop-150/c10/core/DispatchKey.h 2021-12-14 12:00:40.749545303 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7893,7 +7898,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Storage.h pytorch-develop-150/c10/core/Storage.h --- pytorch-v1.5.0/c10/core/Storage.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Storage.h 2021-12-11 23:02:22.592077248 +0800 ++++ pytorch-develop-150/c10/core/Storage.h 2021-12-14 12:00:40.749545303 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7927,7 +7932,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/StorageImpl.cpp pytorch-develop-150/c10/core/StorageImpl.cpp --- pytorch-v1.5.0/c10/core/StorageImpl.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/StorageImpl.cpp 2021-12-11 23:02:22.592077248 +0800 ++++ pytorch-develop-150/c10/core/StorageImpl.cpp 2021-12-14 12:00:40.749545303 +0800 @@ -1 +1,18 @@ #include + @@ -7949,7 +7954,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +} // namespace c10 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/StorageImpl.h pytorch-develop-150/c10/core/StorageImpl.h --- pytorch-v1.5.0/c10/core/StorageImpl.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/StorageImpl.h 2021-12-11 23:02:22.592077248 +0800 ++++ pytorch-develop-150/c10/core/StorageImpl.h 2021-12-14 12:00:40.749545303 +0800 @@ -1,11 +1,55 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8076,7 +8081,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/TensorImpl.h pytorch-develop-150/c10/core/TensorImpl.h --- pytorch-v1.5.0/c10/core/TensorImpl.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/TensorImpl.h 2021-12-11 23:02:22.592077248 +0800 ++++ pytorch-develop-150/c10/core/TensorImpl.h 2021-12-14 12:00:40.749545303 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8144,7 +8149,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/TensorOptions.h pytorch-develop-150/c10/core/TensorOptions.h --- pytorch-v1.5.0/c10/core/TensorOptions.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/TensorOptions.h 2021-12-11 23:02:22.592077248 +0800 ++++ pytorch-develop-150/c10/core/TensorOptions.h 2021-12-14 12:00:40.749545303 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8185,7 +8190,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } else if (tid == DispatchKey::HIPTensorId) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/cuda/CMakeLists.txt pytorch-develop-150/c10/cuda/CMakeLists.txt --- pytorch-v1.5.0/c10/cuda/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/cuda/CMakeLists.txt 2021-12-11 23:02:22.592077248 +0800 ++++ pytorch-develop-150/c10/cuda/CMakeLists.txt 2021-12-14 12:00:40.753545329 +0800 @@ -24,6 +24,7 @@ CUDACachingAllocator.cpp impl/CUDAGuardImpl.cpp @@ -8204,7 +8209,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= torch_cuda_based_add_library(c10_cuda ${C10_CUDA_SRCS} ${C10_CUDA_HEADERS}) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/macros/Export.h pytorch-develop-150/c10/macros/Export.h --- pytorch-v1.5.0/c10/macros/Export.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/macros/Export.h 2021-12-11 23:02:22.592077248 +0800 ++++ pytorch-develop-150/c10/macros/Export.h 2021-12-14 12:00:40.753545329 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8331,7 +8336,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/caffe2/CMakeLists.txt pytorch-develop-150/caffe2/CMakeLists.txt --- pytorch-v1.5.0/caffe2/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/caffe2/CMakeLists.txt 2021-12-11 23:02:22.596077263 +0800 ++++ pytorch-develop-150/caffe2/CMakeLists.txt 2021-12-14 12:00:40.753545329 +0800 @@ -32,6 +32,7 @@ # Add source, includes, and libs to lists list(APPEND Caffe2_CPU_SRCS ${ATen_CPU_SRCS}) @@ -8486,7 +8491,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Call again since Caffe2_HIP_INCLUDE is extended with ATen include dirs. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/.clang-format pytorch-develop-150/.clang-format --- pytorch-v1.5.0/.clang-format 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/.clang-format 2021-12-11 23:02:22.528077018 +0800 ++++ pytorch-develop-150/.clang-format 2021-12-14 12:00:40.701545000 +0800 @@ -84,5 +84,4 @@ SpacesInSquareBrackets: false Standard: Cpp11 @@ -8497,7 +8502,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/BuildVariables.cmake pytorch-develop-150/cmake/BuildVariables.cmake --- pytorch-v1.5.0/cmake/BuildVariables.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/BuildVariables.cmake 2021-12-11 23:02:22.668077522 +0800 ++++ pytorch-develop-150/cmake/BuildVariables.cmake 2021-12-14 12:00:40.809545683 +0800 @@ -11,6 +11,7 @@ # CMakeLists.txt files under each folder respectively. set(Caffe2_CPU_SRCS) @@ -8524,7 +8529,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # symbols. However, if the lib is whole linked in caffe2 lib, we don't want diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Codegen.cmake pytorch-develop-150/cmake/Codegen.cmake --- pytorch-v1.5.0/cmake/Codegen.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Codegen.cmake 2021-12-11 23:02:22.668077522 +0800 ++++ pytorch-develop-150/cmake/Codegen.cmake 2021-12-14 12:00:40.809545683 +0800 @@ -191,13 +191,14 @@ file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt generated_cpp) file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt-cuda cuda_generated_cpp) @@ -8555,7 +8560,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endif() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Dependencies.cmake pytorch-develop-150/cmake/Dependencies.cmake --- pytorch-v1.5.0/cmake/Dependencies.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Dependencies.cmake 2021-12-11 23:02:22.668077522 +0800 ++++ pytorch-develop-150/cmake/Dependencies.cmake 2021-12-14 12:00:40.809545683 +0800 @@ -1509,6 +1509,13 @@ ENDIF(NOT C_HAS_THREAD) endif() @@ -8572,7 +8577,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Summary.cmake pytorch-develop-150/cmake/Summary.cmake --- pytorch-v1.5.0/cmake/Summary.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Summary.cmake 2021-12-11 23:02:22.668077522 +0800 ++++ pytorch-develop-150/cmake/Summary.cmake 2021-12-14 12:00:40.809545683 +0800 @@ -134,6 +134,7 @@ if(NOT "${SELECTED_OP_LIST}" STREQUAL "") message(STATUS " SELECTED_OP_LIST : ${SELECTED_OP_LIST}") @@ -8583,7 +8588,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endfunction() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/TorchConfig.cmake.in pytorch-develop-150/cmake/TorchConfig.cmake.in --- pytorch-v1.5.0/cmake/TorchConfig.cmake.in 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/TorchConfig.cmake.in 2021-12-11 23:02:22.668077522 +0800 ++++ pytorch-develop-150/cmake/TorchConfig.cmake.in 2021-12-14 12:00:40.809545683 +0800 @@ -112,6 +112,11 @@ list(APPEND TORCH_LIBRARIES ${TORCH_CUDA_LIBRARIES}) endif() @@ -8598,7 +8603,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(TORCH_CXX_FLAGS "-D_GLIBCXX_USE_CXX11_ABI=@GLIBCXX_USE_CXX11_ABI@") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/CMakeLists.txt pytorch-develop-150/CMakeLists.txt --- pytorch-v1.5.0/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/CMakeLists.txt 2021-12-11 23:02:22.528077018 +0800 ++++ pytorch-develop-150/CMakeLists.txt 2021-12-14 12:00:40.701545000 +0800 @@ -205,6 +205,10 @@ option(USE_TBB "Use TBB" OFF) option(ONNX_ML "Enable traditional ONNX ML API." ON) @@ -8665,7 +8670,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-missing-braces") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/CONTRIBUTING.zh.md pytorch-develop-150/CONTRIBUTING.zh.md --- pytorch-v1.5.0/CONTRIBUTING.zh.md 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-150/CONTRIBUTING.zh.md 2021-12-11 23:02:22.528077018 +0800 ++++ pytorch-develop-150/CONTRIBUTING.zh.md 2021-12-14 12:00:40.701545000 +0800 @@ -0,0 +1,228 @@ +# PyTorch贡献指南 +- [贡献者许可协议](#贡献者许可协议.md) @@ -8897,7 +8902,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/.dockerignore pytorch-develop-150/.dockerignore --- pytorch-v1.5.0/.dockerignore 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/.dockerignore 2021-12-11 23:02:22.528077018 +0800 ++++ pytorch-develop-150/.dockerignore 2021-12-14 12:00:40.701545000 +0800 @@ -1,257 +1 @@ -# READ THIS BEFORE YOU REFACTOR ME -# @@ -9173,7 +9178,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/requirements.txt pytorch-develop-150/requirements.txt --- pytorch-v1.5.0/requirements.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/requirements.txt 2021-12-11 23:02:22.592077248 +0800 ++++ pytorch-develop-150/requirements.txt 2021-12-14 12:00:40.701545000 +0800 @@ -4,4 +4,13 @@ requests setuptools @@ -9193,7 +9198,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/setup.py pytorch-develop-150/setup.py --- pytorch-v1.5.0/setup.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/setup.py 2021-12-11 23:02:22.592077248 +0800 ++++ pytorch-develop-150/setup.py 2021-12-14 12:00:40.701545000 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -9296,7 +9301,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= 'python/serialized_test/data/operator_test/*.zip', diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/derivatives.yaml pytorch-develop-150/tools/autograd/derivatives.yaml --- pytorch-v1.5.0/tools/autograd/derivatives.yaml 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/derivatives.yaml 2021-12-11 23:02:23.284079745 +0800 ++++ pytorch-develop-150/tools/autograd/derivatives.yaml 2021-12-14 12:00:41.445549703 +0800 @@ -107,6 +107,10 @@ # # NB: The parameter names here MUST be consistent with the parameter names @@ -9421,7 +9426,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/dump_utils.py pytorch-develop-150/tools/autograd/dump_utils.py --- pytorch-v1.5.0/tools/autograd/dump_utils.py 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/dump_utils.py 2021-12-11 23:02:23.284079745 +0800 ++++ pytorch-develop-150/tools/autograd/dump_utils.py 2021-12-14 12:00:41.445549703 +0800 @@ -0,0 +1,312 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# All rights reserved. @@ -9737,7 +9742,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + return prepare_to_check_overflow, overflow_dump_inputs diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_autograd_functions.py pytorch-develop-150/tools/autograd/gen_autograd_functions.py --- pytorch-v1.5.0/tools/autograd/gen_autograd_functions.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_autograd_functions.py 2021-12-11 23:02:23.288079759 +0800 ++++ pytorch-develop-150/tools/autograd/gen_autograd_functions.py 2021-12-14 12:00:41.445549703 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -9943,7 +9948,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_python_functions.py pytorch-develop-150/tools/autograd/gen_python_functions.py --- pytorch-v1.5.0/tools/autograd/gen_python_functions.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_python_functions.py 2021-12-11 23:02:23.288079759 +0800 ++++ pytorch-develop-150/tools/autograd/gen_python_functions.py 2021-12-14 12:00:41.445549703 +0800 @@ -1,3 +1,20 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -9985,7 +9990,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= 'value': argname, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_variable_type.py pytorch-develop-150/tools/autograd/gen_variable_type.py --- pytorch-v1.5.0/tools/autograd/gen_variable_type.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_variable_type.py 2021-12-11 23:02:23.288079759 +0800 ++++ pytorch-develop-150/tools/autograd/gen_variable_type.py 2021-12-14 12:00:41.445549703 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -10166,7 +10171,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/Functions.cpp pytorch-develop-150/tools/autograd/templates/Functions.cpp --- pytorch-v1.5.0/tools/autograd/templates/Functions.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/Functions.cpp 2021-12-11 23:02:23.288079759 +0800 ++++ pytorch-develop-150/tools/autograd/templates/Functions.cpp 2021-12-14 12:00:41.445549703 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2021 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10247,7 +10252,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto sparse = sparse_.coalesce(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/python_torch_functions.cpp pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp --- pytorch-v1.5.0/tools/autograd/templates/python_torch_functions.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp 2021-12-11 23:02:23.288079759 +0800 ++++ pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp 2021-12-14 12:00:41.445549703 +0800 @@ -22,7 +22,7 @@ #include "torch/csrc/autograd/generated/variable_factories.h" #include "torch/csrc/utils/structseq.h" @@ -10331,7 +10336,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/python_variable_methods.cpp pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp --- pytorch-v1.5.0/tools/autograd/templates/python_variable_methods.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp 2021-12-11 23:02:23.288079759 +0800 ++++ pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp 2021-12-14 12:00:41.445549703 +0800 @@ -15,7 +15,13 @@ #include "torch/csrc/cuda/Stream.h" #include "torch/csrc/cuda/Event.h" @@ -10426,7 +10431,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"has_names", (PyCFunction)THPVariable_has_names, METH_NOARGS, NULL}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/VariableType.cpp pytorch-develop-150/tools/autograd/templates/VariableType.cpp --- pytorch-v1.5.0/tools/autograd/templates/VariableType.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/VariableType.cpp 2021-12-11 23:02:23.288079759 +0800 ++++ pytorch-develop-150/tools/autograd/templates/VariableType.cpp 2021-12-14 12:00:41.445549703 +0800 @@ -1,7 +1,29 @@ +// Copyright (c) 2021 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10459,7 +10464,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/VariableType.h pytorch-develop-150/tools/autograd/templates/VariableType.h --- pytorch-v1.5.0/tools/autograd/templates/VariableType.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/VariableType.h 2021-12-11 23:02:23.288079759 +0800 ++++ pytorch-develop-150/tools/autograd/templates/VariableType.h 2021-12-14 12:00:41.445549703 +0800 @@ -1,3 +1,20 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10491,7 +10496,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= const at::Tensor & unpack(const Tensor & t, const char * name, int pos); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/build_variables.bzl pytorch-develop-150/tools/build_variables.bzl --- pytorch-v1.5.0/tools/build_variables.bzl 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/build_variables.bzl 2021-12-11 23:02:23.284079745 +0800 ++++ pytorch-develop-150/tools/build_variables.bzl 2021-12-14 12:00:41.441549678 +0800 @@ -46,6 +46,7 @@ "torch/csrc/autograd/functions/utils.cpp", "torch/csrc/autograd/input_buffer.cpp", @@ -10577,7 +10582,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def grad(outputs: _TensorOrTensors, inputs: _TensorOrTensors, grad_outputs: Optional[_TensorOrTensors]=..., retain_graph: Optional[bool]=..., create_graph: bool=..., only_inputs: bool=..., allow_unused: bool=...) -> Tuple[Tensor, ...]: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/autograd/profiler.py pytorch-develop-150/torch/autograd/profiler.py --- pytorch-v1.5.0/torch/autograd/profiler.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/autograd/profiler.py 2021-12-11 23:02:23.288079759 +0800 ++++ pytorch-develop-150/torch/autograd/profiler.py 2021-12-14 12:00:41.445549703 +0800 @@ -1,8 +1,25 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -11054,7 +11059,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ''.join(result) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/CMakeLists.txt pytorch-develop-150/torch/CMakeLists.txt --- pytorch-v1.5.0/torch/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/CMakeLists.txt 2021-12-11 23:02:23.288079759 +0800 ++++ pytorch-develop-150/torch/CMakeLists.txt 2021-12-14 12:00:41.445549703 +0800 @@ -97,6 +97,7 @@ ${TORCH_SRC_DIR}/csrc/tensor/python_tensor.cpp ${TORCH_SRC_DIR}/csrc/utils.cpp @@ -11086,7 +11091,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endif() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/engine.cpp pytorch-develop-150/torch/csrc/autograd/engine.cpp --- pytorch-v1.5.0/torch/csrc/autograd/engine.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/engine.cpp 2021-12-11 23:02:23.296079788 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/engine.cpp 2021-12-14 12:00:41.453549753 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11238,7 +11243,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto event = c10::Event{c10::DeviceType::CUDA}; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/function.h pytorch-develop-150/torch/csrc/autograd/function.h --- pytorch-v1.5.0/torch/csrc/autograd/function.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/function.h 2021-12-11 23:02:23.296079788 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/function.h 2021-12-14 12:00:41.453549753 +0800 @@ -11,6 +11,7 @@ #include @@ -11260,7 +11265,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // probably operate with names. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/functions/tensor.cpp pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp --- pytorch-v1.5.0/torch/csrc/autograd/functions/tensor.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp 2021-12-11 23:02:23.296079788 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp 2021-12-14 12:00:41.453549753 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11292,7 +11297,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= /*non_blocking=*/false, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/init.cpp pytorch-develop-150/torch/csrc/autograd/init.cpp --- pytorch-v1.5.0/torch/csrc/autograd/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/init.cpp 2021-12-11 23:02:23.296079788 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/init.cpp 2021-12-14 12:00:41.457549779 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11335,7 +11340,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= m.def("_enable_profiler", enableProfiler); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/input_buffer.cpp pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp --- pytorch-v1.5.0/torch/csrc/autograd/input_buffer.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp 2021-12-11 23:02:23.296079788 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp 2021-12-14 12:00:41.457549779 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11387,7 +11392,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto& old_var = buffer[pos]; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/profiler.cpp pytorch-develop-150/torch/csrc/autograd/profiler.cpp --- pytorch-v1.5.0/torch/csrc/autograd/profiler.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/profiler.cpp 2021-12-11 23:02:23.296079788 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/profiler.cpp 2021-12-14 12:00:41.457549779 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11627,7 +11632,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CUDAStubs::~CUDAStubs() = default; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/profiler.h pytorch-develop-150/torch/csrc/autograd/profiler.h --- pytorch-v1.5.0/torch/csrc/autograd/profiler.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/profiler.h 2021-12-11 23:02:23.296079788 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/profiler.h 2021-12-14 12:00:41.457549779 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11763,7 +11768,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/python_variable.cpp pytorch-develop-150/torch/csrc/autograd/python_variable.cpp --- pytorch-v1.5.0/torch/csrc/autograd/python_variable.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/python_variable.cpp 2021-12-11 23:02:23.296079788 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/python_variable.cpp 2021-12-14 12:00:41.457549779 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11817,7 +11822,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"is_complex", (getter)THPVariable_is_complex, nullptr, nullptr, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/python_variable_indexing.cpp pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp --- pytorch-v1.5.0/torch/csrc/autograd/python_variable_indexing.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-11 23:02:23.296079788 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-14 12:00:41.457549779 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11848,7 +11853,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= at::Device self_device = self_.device(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/record_function.cpp pytorch-develop-150/torch/csrc/autograd/record_function.cpp --- pytorch-v1.5.0/torch/csrc/autograd/record_function.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/record_function.cpp 2021-12-11 23:02:23.296079788 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/record_function.cpp 2021-12-14 12:00:41.457549779 +0800 @@ -154,6 +154,12 @@ } } @@ -11882,7 +11887,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/record_function.h pytorch-develop-150/torch/csrc/autograd/record_function.h --- pytorch-v1.5.0/torch/csrc/autograd/record_function.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/record_function.h 2021-12-11 23:02:23.296079788 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/record_function.h 2021-12-14 12:00:41.457549779 +0800 @@ -44,6 +44,9 @@ // Default constructor is used with before function called afterwards RecordFunction() {} @@ -11946,7 +11951,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (torch::autograd::profiler::needsInputs()) { \ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/utils/wrap_outputs.h pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h --- pytorch-v1.5.0/torch/csrc/autograd/utils/wrap_outputs.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h 2021-12-11 23:02:23.296079788 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h 2021-12-14 12:00:41.457549779 +0800 @@ -168,6 +168,45 @@ return r.release(); } @@ -11995,7 +12000,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (!r) throw python_error(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/VariableTypeManual.cpp pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp --- pytorch-v1.5.0/torch/csrc/autograd/VariableTypeManual.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp 2021-12-11 23:02:23.296079788 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp 2021-12-14 12:00:41.453549753 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12029,7 +12034,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (!t.defined()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/comm.cpp pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/comm.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp 2021-12-11 23:02:23.296079788 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp 2021-12-14 12:00:41.457549779 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12135,7 +12140,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= while (!in_flight.empty()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/init.cpp pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp 2021-12-11 23:02:23.296079788 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp 2021-12-14 12:00:41.457549779 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12192,7 +12197,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= .def("is_success", &::c10d::ProcessGroup::Work::isSuccess) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/reducer.cpp pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/reducer.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp 2021-12-11 23:02:23.296079788 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp 2021-12-14 12:00:41.457549779 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12327,7 +12332,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/DynamicTypes.cpp pytorch-develop-150/torch/csrc/DynamicTypes.cpp --- pytorch-v1.5.0/torch/csrc/DynamicTypes.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/DynamicTypes.cpp 2021-12-11 23:02:23.288079759 +0800 ++++ pytorch-develop-150/torch/csrc/DynamicTypes.cpp 2021-12-14 12:00:41.449549729 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12376,7 +12381,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return it->second; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/Generator.cpp pytorch-develop-150/torch/csrc/Generator.cpp --- pytorch-v1.5.0/torch/csrc/Generator.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/Generator.cpp 2021-12-11 23:02:23.292079774 +0800 ++++ pytorch-develop-150/torch/csrc/Generator.cpp 2021-12-14 12:00:41.449549729 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12444,7 +12449,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #endif diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/serialization.cpp pytorch-develop-150/torch/csrc/generic/serialization.cpp --- pytorch-v1.5.0/torch/csrc/generic/serialization.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/serialization.cpp 2021-12-11 23:02:23.300079803 +0800 ++++ pytorch-develop-150/torch/csrc/generic/serialization.cpp 2021-12-14 12:00:41.461549804 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12544,7 +12549,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/Storage.cpp pytorch-develop-150/torch/csrc/generic/Storage.cpp --- pytorch-v1.5.0/torch/csrc/generic/Storage.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/Storage.cpp 2021-12-11 23:02:23.300079803 +0800 ++++ pytorch-develop-150/torch/csrc/generic/Storage.cpp 2021-12-14 12:00:41.461549804 +0800 @@ -1,7 +1,25 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12623,7 +12628,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for (Py_ssize_t i = 0; i < length; i++) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/StorageMethods.cpp pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp --- pytorch-v1.5.0/torch/csrc/generic/StorageMethods.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp 2021-12-11 23:02:23.300079803 +0800 ++++ pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp 2021-12-14 12:00:41.461549804 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12671,7 +12676,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"_write_file", (PyCFunction)THPStorage_(writeFile), METH_VARARGS, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/Module.cpp pytorch-develop-150/torch/csrc/Module.cpp --- pytorch-v1.5.0/torch/csrc/Module.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/Module.cpp 2021-12-11 23:02:23.292079774 +0800 ++++ pytorch-develop-150/torch/csrc/Module.cpp 2021-12-14 12:00:41.449549729 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12832,7 +12837,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto set_module_attr = [&](const char* name, PyObject* v, bool incref = true) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/tensor/python_tensor.cpp pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp --- pytorch-v1.5.0/torch/csrc/tensor/python_tensor.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp 2021-12-11 23:02:23.308079832 +0800 ++++ pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp 2021-12-14 12:00:41.469549855 +0800 @@ -1,18 +1,35 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13209,7 +13214,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +} // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/init.cpp pytorch-develop-150/torch/csrc/utils/init.cpp --- pytorch-v1.5.0/torch/csrc/utils/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/init.cpp 2021-12-11 23:02:23.308079832 +0800 ++++ pytorch-develop-150/torch/csrc/utils/init.cpp 2021-12-14 12:00:41.469549855 +0800 @@ -1,7 +1,13 @@ #include #include @@ -13374,7 +13379,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/init.h pytorch-develop-150/torch/csrc/utils/init.h --- pytorch-v1.5.0/torch/csrc/utils/init.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/init.h 2021-12-11 23:02:23.308079832 +0800 ++++ pytorch-develop-150/torch/csrc/utils/init.h 2021-12-14 12:00:41.469549855 +0800 @@ -8,4 +8,7 @@ void initThroughputBenchmarkBindings(PyObject* module); @@ -13385,7 +13390,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/python_arg_parser.h pytorch-develop-150/torch/csrc/utils/python_arg_parser.h --- pytorch-v1.5.0/torch/csrc/utils/python_arg_parser.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/python_arg_parser.h 2021-12-11 23:02:23.308079832 +0800 ++++ pytorch-develop-150/torch/csrc/utils/python_arg_parser.h 2021-12-14 12:00:41.473549881 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13420,7 +13425,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return at::Device(device_str); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_layouts.cpp pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_layouts.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp 2021-12-11 23:02:23.308079832 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp 2021-12-14 12:00:41.473549881 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13451,7 +13456,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= registerLayoutObject((THPLayout*)strided_layout, at::Backend::QuantizedCPU); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_new.cpp pytorch-develop-150/torch/csrc/utils/tensor_new.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_new.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_new.cpp 2021-12-11 23:02:23.308079832 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_new.cpp 2021-12-14 12:00:41.473549881 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13587,7 +13592,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= " or ", c10::DispatchKey::XLATensorId, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_types.cpp pytorch-develop-150/torch/csrc/utils/tensor_types.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_types.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_types.cpp 2021-12-11 23:02:23.308079832 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_types.cpp 2021-12-14 12:00:41.473549881 +0800 @@ -1,58 +1,91 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13800,7 +13805,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def get_rng_state(): ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/distributed/distributed_c10d.py pytorch-develop-150/torch/distributed/distributed_c10d.py --- pytorch-v1.5.0/torch/distributed/distributed_c10d.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/distributed/distributed_c10d.py 2021-12-11 23:02:23.312079846 +0800 ++++ pytorch-develop-150/torch/distributed/distributed_c10d.py 2021-12-14 12:00:41.473549881 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -13881,7 +13886,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/__init__.py pytorch-develop-150/torch/__init__.py --- pytorch-v1.5.0/torch/__init__.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/__init__.py 2021-12-11 23:02:23.288079759 +0800 ++++ pytorch-develop-150/torch/__init__.py 2021-12-14 12:00:41.445549703 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -13924,7 +13929,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/lib/c10d/CMakeLists.txt pytorch-develop-150/torch/lib/c10d/CMakeLists.txt --- pytorch-v1.5.0/torch/lib/c10d/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/lib/c10d/CMakeLists.txt 2021-12-11 23:02:23.312079846 +0800 ++++ pytorch-develop-150/torch/lib/c10d/CMakeLists.txt 2021-12-14 12:00:41.473549881 +0800 @@ -28,6 +28,10 @@ option(USE_C10D_NCCL "USE C10D NCCL" ON) endif() @@ -13977,7 +13982,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= copy_header(ProcessGroupMPI.hpp) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/lib/libshm/CMakeLists.txt pytorch-develop-150/torch/lib/libshm/CMakeLists.txt --- pytorch-v1.5.0/torch/lib/libshm/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/lib/libshm/CMakeLists.txt 2021-12-11 23:02:23.312079846 +0800 ++++ pytorch-develop-150/torch/lib/libshm/CMakeLists.txt 2021-12-14 12:00:41.477549905 +0800 @@ -37,8 +37,11 @@ SET_TARGET_PROPERTIES(shm PROPERTIES PREFIX "lib" @@ -14034,7 +14039,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -_maybe_indices_t = _scalar_or_tuple_2_t[Tensor] diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/functional.py pytorch-develop-150/torch/nn/functional.py --- pytorch-v1.5.0/torch/nn/functional.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/functional.py 2021-12-11 23:02:23.312079846 +0800 ++++ pytorch-develop-150/torch/nn/functional.py 2021-12-14 12:00:41.477549905 +0800 @@ -1611,7 +1611,7 @@ else: output = input.matmul(weight.t()) @@ -14057,7 +14062,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -from . import parallel as parallel diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/batchnorm.py pytorch-develop-150/torch/nn/modules/batchnorm.py --- pytorch-v1.5.0/torch/nn/modules/batchnorm.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/batchnorm.py 2021-12-11 23:02:23.316079861 +0800 ++++ pytorch-develop-150/torch/nn/modules/batchnorm.py 2021-12-14 12:00:41.477549905 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14089,7 +14094,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= self.register_parameter('running_var', None) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/module.py pytorch-develop-150/torch/nn/modules/module.py --- pytorch-v1.5.0/torch/nn/modules/module.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/module.py 2021-12-11 23:02:23.316079861 +0800 ++++ pytorch-develop-150/torch/nn/modules/module.py 2021-12-14 12:00:41.477549905 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14249,7 +14254,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return t.to(device, dtype if t.is_floating_point() else None, non_blocking, memory_format=convert_to_format) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/normalization.py pytorch-develop-150/torch/nn/modules/normalization.py --- pytorch-v1.5.0/torch/nn/modules/normalization.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/normalization.py 2021-12-11 23:02:23.316079861 +0800 ++++ pytorch-develop-150/torch/nn/modules/normalization.py 2021-12-14 12:00:41.477549905 +0800 @@ -128,13 +128,14 @@ """ __constants__ = ['normalized_shape', 'eps', 'elementwise_affine'] @@ -14318,7 +14323,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - module_kwargs: Optional[Any] = ...) -> Tensor: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/parallel/distributed.py pytorch-develop-150/torch/nn/parallel/distributed.py --- pytorch-v1.5.0/torch/nn/parallel/distributed.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/parallel/distributed.py 2021-12-11 23:02:23.316079861 +0800 ++++ pytorch-develop-150/torch/nn/parallel/distributed.py 2021-12-14 12:00:41.477549905 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14673,7 +14678,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def remove_weight_norm(module: T_module, name: str = ...) -> T_module: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/onnx/symbolic_opset9.py pytorch-develop-150/torch/onnx/symbolic_opset9.py --- pytorch-v1.5.0/torch/onnx/symbolic_opset9.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/onnx/symbolic_opset9.py 2021-12-11 23:02:23.316079861 +0800 ++++ pytorch-develop-150/torch/onnx/symbolic_opset9.py 2021-12-14 12:00:41.481549931 +0800 @@ -1621,14 +1621,23 @@ slices = [sym_help._slice_helper(g, w, axes=[0], starts=[x * n], ends=[y * n]) for x, y in intervals] return g.op('Concat', *slices, axis_i=0) @@ -14751,7 +14756,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, params: _params_t, lr: float=..., lr_decay: float=..., weight_decay: float=..., initial_accumulator_value: float=..., eps: float=...) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/optim/adamax.py pytorch-develop-150/torch/optim/adamax.py --- pytorch-v1.5.0/torch/optim/adamax.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/optim/adamax.py 2021-12-11 23:02:23.316079861 +0800 ++++ pytorch-develop-150/torch/optim/adamax.py 2021-12-14 12:00:41.481549931 +0800 @@ -80,8 +80,8 @@ exp_inf.mul_(beta2).unsqueeze(0), grad.abs().add_(eps).unsqueeze_(0) @@ -14928,7 +14933,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, params: _params_t, lr: float=..., betas: Tuple[float, float]=..., eps: float=...) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/serialization.py pytorch-develop-150/torch/serialization.py --- pytorch-v1.5.0/torch/serialization.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/serialization.py 2021-12-11 23:02:23.288079759 +0800 ++++ pytorch-develop-150/torch/serialization.py 2021-12-14 12:00:41.445549703 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15012,7 +15017,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def location_tag(storage): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/storage.py pytorch-develop-150/torch/storage.py --- pytorch-v1.5.0/torch/storage.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/storage.py 2021-12-11 23:02:23.288079759 +0800 ++++ pytorch-develop-150/torch/storage.py 2021-12-14 12:00:41.445549703 +0800 @@ -7,6 +7,7 @@ class _StorageBase(object): @@ -15032,7 +15037,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= else: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/tensor.py pytorch-develop-150/torch/tensor.py --- pytorch-v1.5.0/torch/tensor.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/tensor.py 2021-12-11 23:02:23.288079759 +0800 ++++ pytorch-develop-150/torch/tensor.py 2021-12-14 12:00:41.445549703 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15094,7 +15099,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def __reversed__(self): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/_tensor_str.py pytorch-develop-150/torch/_tensor_str.py --- pytorch-v1.5.0/torch/_tensor_str.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/_tensor_str.py 2021-12-11 23:02:23.288079759 +0800 ++++ pytorch-develop-150/torch/_tensor_str.py 2021-12-14 12:00:41.445549703 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15148,7 +15153,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= has_default_dtype = self.dtype in (torch.get_default_dtype(), torch.int64, torch.bool) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/data/dataloader.py pytorch-develop-150/torch/utils/data/dataloader.py --- pytorch-v1.5.0/torch/utils/data/dataloader.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/data/dataloader.py 2021-12-11 23:02:23.320079875 +0800 ++++ pytorch-develop-150/torch/utils/data/dataloader.py 2021-12-14 12:00:41.481549931 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15357,7 +15362,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, sampler: Sampler[int], batch_size: int, drop_last: bool) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/data/_utils/pin_memory.py pytorch-develop-150/torch/utils/data/_utils/pin_memory.py --- pytorch-v1.5.0/torch/utils/data/_utils/pin_memory.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/data/_utils/pin_memory.py 2021-12-11 23:02:23.320079875 +0800 ++++ pytorch-develop-150/torch/utils/data/_utils/pin_memory.py 2021-12-14 12:00:41.481549931 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15418,7 +15423,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/__init__.py pytorch-develop-150/torch/utils/__init__.py --- pytorch-v1.5.0/torch/utils/__init__.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/__init__.py 2021-12-11 23:02:23.320079875 +0800 ++++ pytorch-develop-150/torch/utils/__init__.py 2021-12-14 12:00:41.481549931 +0800 @@ -1,6 +1,9 @@ from __future__ import absolute_import, division, print_function, unicode_literals @@ -15431,7 +15436,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def set_module(obj, mod): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/_utils.py pytorch-develop-150/torch/_utils.py --- pytorch-v1.5.0/torch/_utils.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/_utils.py 2021-12-11 23:02:23.288079759 +0800 ++++ pytorch-develop-150/torch/_utils.py 2021-12-14 12:00:41.445549703 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. diff --git a/patch/pytorch1.8.1_npu.patch b/patch/pytorch1.8.1_npu.patch index cb2b697aa6b..46bd2390c14 100644 --- a/patch/pytorch1.8.1_npu.patch +++ b/patch/pytorch1.8.1_npu.patch @@ -1,6 +1,6 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/CMakeLists.txt pytorch-develop-181/aten/CMakeLists.txt --- pytorch-v1.8.1/aten/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/CMakeLists.txt 2021-12-11 23:02:27.432094690 +0800 ++++ pytorch-develop-181/aten/CMakeLists.txt 2021-12-14 12:00:45.657576320 +0800 @@ -22,9 +22,11 @@ set(ATen_CPU_INCLUDE) set(ATen_THIRD_PARTY_INCLUDE) @@ -52,7 +52,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_CPU_DEPENDENCY_LIBS ${ATen_CPU_DEPENDENCY_LIBS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/autocast_mode.h pytorch-develop-181/aten/src/ATen/autocast_mode.h --- pytorch-v1.8.1/aten/src/ATen/autocast_mode.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/autocast_mode.h 2021-12-11 23:02:27.436094704 +0800 ++++ pytorch-develop-181/aten/src/ATen/autocast_mode.h 2021-12-14 12:00:45.661576346 +0800 @@ -5,7 +5,7 @@ namespace { @@ -64,7 +64,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/CMakeLists.txt pytorch-develop-181/aten/src/ATen/CMakeLists.txt --- pytorch-v1.8.1/aten/src/ATen/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/CMakeLists.txt 2021-12-11 23:02:27.432094690 +0800 ++++ pytorch-develop-181/aten/src/ATen/CMakeLists.txt 2021-12-14 12:00:45.657576320 +0800 @@ -85,6 +85,10 @@ file(GLOB native_quantized_h "native/quantized/*.h" "native/quantized/cpu/*.h") file(GLOB native_cpu_h "native/cpu/*.h") @@ -115,7 +115,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_HIP_SRCS ${ATen_HIP_SRCS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/Dispatcher.h pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/Dispatcher.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h 2021-12-11 23:02:27.440094718 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h 2021-12-14 12:00:45.665576370 +0800 @@ -417,6 +417,11 @@ } } @@ -130,7 +130,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.cpp pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-12-11 23:02:27.440094718 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-12-14 12:00:45.665576370 +0800 @@ -6,6 +6,7 @@ namespace c10 { @@ -141,7 +141,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= std::unordered_set not_observed_ops = { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.h pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-12-11 23:02:27.440094718 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-12-14 12:00:45.665576370 +0800 @@ -1,12 +1,16 @@ #pragma once @@ -161,7 +161,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/OperatorEntry.h pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-12-11 23:02:27.440094718 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-12-14 12:00:45.665576370 +0800 @@ -10,6 +10,7 @@ #include #include @@ -172,7 +172,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #include diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/VariableFallbackKernel.cpp pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp --- pytorch-v1.8.1/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-12-11 23:02:27.436094704 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-12-14 12:00:45.661576346 +0800 @@ -48,4 +48,8 @@ m.fallback(torch::CppFunction::makeFallthrough()); } @@ -184,7 +184,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/Convolution.cpp pytorch-develop-181/aten/src/ATen/native/Convolution.cpp --- pytorch-v1.8.1/aten/src/ATen/native/Convolution.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/Convolution.cpp 2021-12-11 23:02:27.452094762 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/Convolution.cpp 2021-12-14 12:00:45.677576447 +0800 @@ -603,7 +603,9 @@ const Tensor& input, const Tensor& weight, const Tensor& bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, @@ -198,7 +198,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= at::Tensor _convolution( diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/Memory.cpp pytorch-develop-181/aten/src/ATen/native/Memory.cpp --- pytorch-v1.8.1/aten/src/ATen/native/Memory.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/Memory.cpp 2021-12-11 23:02:27.456094776 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/Memory.cpp 2021-12-14 12:00:45.681576471 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -249,7 +249,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= detail::computeStorageNbytes( diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/native_functions.yaml pytorch-develop-181/aten/src/ATen/native/native_functions.yaml --- pytorch-v1.8.1/aten/src/ATen/native/native_functions.yaml 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/native_functions.yaml 2021-12-11 23:02:27.488094891 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/native_functions.yaml 2021-12-14 12:00:45.713576674 +0800 @@ -2073,6 +2073,8 @@ dispatch: CPU, CUDA: isnan @@ -489,7 +489,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +- func: npu_apply_adam.out(Scalar beta1_power, Scalar beta2_power, Scalar lr, Scalar beta1, Scalar beta2, Scalar epsilon, Tensor grad, bool? use_locking, bool? use_nesterov, *, Tensor(a!) var, Tensor(b!) m, Tensor(c!) v) -> (Tensor(a!), Tensor(b!), Tensor(c!)) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/NPUVariableMethodStubs.cpp pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp --- pytorch-v1.8.1/aten/src/ATen/native/NPUVariableMethodStubs.cpp 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp 2021-12-11 23:02:27.456094776 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp 2021-12-14 12:00:45.681576471 +0800 @@ -0,0 +1,464 @@ +#include "ATen/native/npu/common/FormatCastHelper.h" +#include "ATen/native/npu/frame/FormatHelper.h" @@ -1873,7 +1873,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -} diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/TensorFactories.cpp pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp --- pytorch-v1.8.1/aten/src/ATen/native/TensorFactories.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp 2021-12-11 23:02:27.468094819 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp 2021-12-14 12:00:45.693576548 +0800 @@ -1,3 +1,20 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -1906,7 +1906,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= namespace { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h --- pytorch-v1.8.1/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-12-11 23:02:27.524095021 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-12-14 12:00:45.749576901 +0800 @@ -1,19229 +1,19229 @@ -// -// Copyright (c) 2017-2020 Advanced Micro Devices, Inc. All rights reserved. @@ -40368,7 +40368,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +#endif // #ifdef VMA_IMPLEMENTATION diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/record_function.cpp pytorch-develop-181/aten/src/ATen/record_function.cpp --- pytorch-v1.8.1/aten/src/ATen/record_function.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/record_function.cpp 2021-12-11 23:02:27.528095035 +0800 ++++ pytorch-develop-181/aten/src/ATen/record_function.cpp 2021-12-14 12:00:45.753576927 +0800 @@ -400,6 +400,9 @@ rf_tls_.tls_record_function_enabled_ = enable; } @@ -40381,7 +40381,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (rf_tls_ptr->tls_record_function_enabled_) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/record_function.h pytorch-develop-181/aten/src/ATen/record_function.h --- pytorch-v1.8.1/aten/src/ATen/record_function.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/record_function.h 2021-12-11 23:02:27.528095035 +0800 ++++ pytorch-develop-181/aten/src/ATen/record_function.h 2021-12-14 12:00:45.753576927 +0800 @@ -2,6 +2,7 @@ #include @@ -40417,7 +40417,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // scope - record scope that this function tracks diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/templates/TensorBody.h pytorch-develop-181/aten/src/ATen/templates/TensorBody.h --- pytorch-v1.8.1/aten/src/ATen/templates/TensorBody.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/templates/TensorBody.h 2021-12-11 23:02:27.528095035 +0800 ++++ pytorch-develop-181/aten/src/ATen/templates/TensorBody.h 2021-12-14 12:00:45.753576927 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40450,7 +40450,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= bool is_xpu() const; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/templates/TensorMethods.cpp pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp --- pytorch-v1.8.1/aten/src/ATen/templates/TensorMethods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp 2021-12-11 23:02:27.528095035 +0800 ++++ pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp 2021-12-14 12:00:45.753576927 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40495,7 +40495,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/Utils.cpp pytorch-develop-181/aten/src/ATen/Utils.cpp --- pytorch-v1.8.1/aten/src/ATen/Utils.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/Utils.cpp 2021-12-11 23:02:27.436094704 +0800 ++++ pytorch-develop-181/aten/src/ATen/Utils.cpp 2021-12-14 12:00:45.661576346 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40539,7 +40539,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/TH/generic/THStorage.cpp pytorch-develop-181/aten/src/TH/generic/THStorage.cpp --- pytorch-v1.8.1/aten/src/TH/generic/THStorage.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/TH/generic/THStorage.cpp 2021-12-11 23:02:27.532095050 +0800 ++++ pytorch-develop-181/aten/src/TH/generic/THStorage.cpp 2021-12-14 12:00:45.757576951 +0800 @@ -1,9 +1,32 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40652,7 +40652,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/TH/generic/THStorage.h pytorch-develop-181/aten/src/TH/generic/THStorage.h --- pytorch-v1.8.1/aten/src/TH/generic/THStorage.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/TH/generic/THStorage.h 2021-12-11 23:02:27.532095050 +0800 ++++ pytorch-develop-181/aten/src/TH/generic/THStorage.h 2021-12-14 12:00:45.757576951 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40691,7 +40691,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/CMakeLists.txt pytorch-develop-181/c10/CMakeLists.txt --- pytorch-v1.8.1/c10/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/CMakeLists.txt 2021-12-11 23:02:27.548095107 +0800 ++++ pytorch-develop-181/c10/CMakeLists.txt 2021-12-14 12:00:45.769577028 +0800 @@ -79,6 +79,14 @@ message(STATUS "don't use NUMA") endif() @@ -40720,7 +40720,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # not checked in diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Backend.h pytorch-develop-181/c10/core/Backend.h --- pytorch-v1.8.1/c10/core/Backend.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Backend.h 2021-12-11 23:02:27.548095107 +0800 ++++ pytorch-develop-181/c10/core/Backend.h 2021-12-14 12:00:45.769577028 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40814,7 +40814,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Device.cpp pytorch-develop-181/c10/core/Device.cpp --- pytorch-v1.8.1/c10/core/Device.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Device.cpp 2021-12-11 23:02:27.548095107 +0800 ++++ pytorch-develop-181/c10/core/Device.cpp 2021-12-14 12:00:45.769577028 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40845,7 +40845,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= types.begin(), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Device.h pytorch-develop-181/c10/core/Device.h --- pytorch-v1.8.1/c10/core/Device.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Device.h 2021-12-11 23:02:27.548095107 +0800 ++++ pytorch-develop-181/c10/core/Device.h 2021-12-14 12:00:45.769577028 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40880,7 +40880,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= bool is_cpu() const noexcept { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DeviceType.cpp pytorch-develop-181/c10/core/DeviceType.cpp --- pytorch-v1.8.1/c10/core/DeviceType.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DeviceType.cpp 2021-12-11 23:02:27.548095107 +0800 ++++ pytorch-develop-181/c10/core/DeviceType.cpp 2021-12-14 12:00:45.769577028 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40920,7 +40920,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return false; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DeviceType.h pytorch-develop-181/c10/core/DeviceType.h --- pytorch-v1.8.1/c10/core/DeviceType.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DeviceType.h 2021-12-11 23:02:27.548095107 +0800 ++++ pytorch-develop-181/c10/core/DeviceType.h 2021-12-14 12:00:45.769577028 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40962,7 +40962,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= constexpr DeviceType kMSNPU = DeviceType::MSNPU; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKey.cpp pytorch-develop-181/c10/core/DispatchKey.cpp --- pytorch-v1.8.1/c10/core/DispatchKey.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKey.cpp 2021-12-11 23:02:27.548095107 +0800 ++++ pytorch-develop-181/c10/core/DispatchKey.cpp 2021-12-14 12:00:45.769577028 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41011,7 +41011,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::PrivateUse1: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKey.h pytorch-develop-181/c10/core/DispatchKey.h --- pytorch-v1.8.1/c10/core/DispatchKey.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKey.h 2021-12-11 23:02:27.548095107 +0800 ++++ pytorch-develop-181/c10/core/DispatchKey.h 2021-12-14 12:00:45.769577028 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41058,7 +41058,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= PrivateUse3_PreAutograd = AutogradPrivateUse3, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKeySet.cpp pytorch-develop-181/c10/core/DispatchKeySet.cpp --- pytorch-v1.8.1/c10/core/DispatchKeySet.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKeySet.cpp 2021-12-11 23:02:27.548095107 +0800 ++++ pytorch-develop-181/c10/core/DispatchKeySet.cpp 2021-12-14 12:00:45.769577028 +0800 @@ -11,6 +11,7 @@ DispatchKey::XLA, DispatchKey::NestedTensor, @@ -41078,7 +41078,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::AutogradXPU: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKeySet.h pytorch-develop-181/c10/core/DispatchKeySet.h --- pytorch-v1.8.1/c10/core/DispatchKeySet.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKeySet.h 2021-12-11 23:02:27.548095107 +0800 ++++ pytorch-develop-181/c10/core/DispatchKeySet.h 2021-12-14 12:00:45.769577028 +0800 @@ -193,6 +193,7 @@ DispatchKey::AutogradCPU, DispatchKey::AutogradCUDA, @@ -41089,7 +41089,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= DispatchKey::AutogradPrivateUse1, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Storage.h pytorch-develop-181/c10/core/Storage.h --- pytorch-v1.8.1/c10/core/Storage.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Storage.h 2021-12-11 23:02:27.548095107 +0800 ++++ pytorch-develop-181/c10/core/Storage.h 2021-12-14 12:00:45.769577028 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41123,7 +41123,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/StorageImpl.h pytorch-develop-181/c10/core/StorageImpl.h --- pytorch-v1.8.1/c10/core/StorageImpl.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/StorageImpl.h 2021-12-11 23:02:27.548095107 +0800 ++++ pytorch-develop-181/c10/core/StorageImpl.h 2021-12-14 12:00:45.769577028 +0800 @@ -1,12 +1,42 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41183,7 +41183,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/TensorImpl.h pytorch-develop-181/c10/core/TensorImpl.h --- pytorch-v1.8.1/c10/core/TensorImpl.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/TensorImpl.h 2021-12-11 23:02:27.548095107 +0800 ++++ pytorch-develop-181/c10/core/TensorImpl.h 2021-12-14 12:00:45.769577028 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41226,7 +41226,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ts.has(DispatchKey::SparseCPU) || diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/TensorOptions.h pytorch-develop-181/c10/core/TensorOptions.h --- pytorch-v1.8.1/c10/core/TensorOptions.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/TensorOptions.h 2021-12-11 23:02:27.548095107 +0800 ++++ pytorch-develop-181/c10/core/TensorOptions.h 2021-12-14 12:00:45.769577028 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41267,7 +41267,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } else if (tid == DispatchKey::QuantizedXPU) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/macros/Export.h pytorch-develop-181/c10/macros/Export.h --- pytorch-v1.8.1/c10/macros/Export.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/macros/Export.h 2021-12-11 23:02:27.552095121 +0800 ++++ pytorch-develop-181/c10/macros/Export.h 2021-12-14 12:00:45.773577053 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41336,7 +41336,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #define C10_API_ENUM C10_API diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/caffe2/CMakeLists.txt pytorch-develop-181/caffe2/CMakeLists.txt --- pytorch-v1.8.1/caffe2/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/caffe2/CMakeLists.txt 2021-12-11 23:02:27.556095136 +0800 ++++ pytorch-develop-181/caffe2/CMakeLists.txt 2021-12-14 12:00:45.781577104 +0800 @@ -76,6 +76,7 @@ # Add source, includes, and libs to lists list(APPEND Caffe2_CPU_SRCS ${ATen_CPU_SRCS}) @@ -41461,7 +41461,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Call again since Caffe2_HIP_INCLUDE is extended with ATen include dirs. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/BuildVariables.cmake pytorch-develop-181/cmake/BuildVariables.cmake --- pytorch-v1.8.1/cmake/BuildVariables.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/BuildVariables.cmake 2021-12-11 23:02:27.632095409 +0800 ++++ pytorch-develop-181/cmake/BuildVariables.cmake 2021-12-14 12:00:45.853577558 +0800 @@ -11,6 +11,7 @@ # CMakeLists.txt files under each folder respectively. set(Caffe2_CPU_SRCS) @@ -41485,7 +41485,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # symbols. However, if the lib is whole linked in caffe2 lib, we don't want diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Codegen.cmake pytorch-develop-181/cmake/Codegen.cmake --- pytorch-v1.8.1/cmake/Codegen.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Codegen.cmake 2021-12-11 23:02:27.632095409 +0800 ++++ pytorch-develop-181/cmake/Codegen.cmake 2021-12-14 12:00:45.853577558 +0800 @@ -208,13 +208,14 @@ file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt generated_cpp) file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt-cuda cuda_generated_cpp) @@ -41518,7 +41518,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= function(append_filelist name outputvar) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Dependencies.cmake pytorch-develop-181/cmake/Dependencies.cmake --- pytorch-v1.8.1/cmake/Dependencies.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Dependencies.cmake 2021-12-11 23:02:27.636095424 +0800 ++++ pytorch-develop-181/cmake/Dependencies.cmake 2021-12-14 12:00:45.853577558 +0800 @@ -1771,6 +1771,13 @@ endif(NOT C_HAS_THREAD) endif() @@ -41535,7 +41535,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Summary.cmake pytorch-develop-181/cmake/Summary.cmake --- pytorch-v1.8.1/cmake/Summary.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Summary.cmake 2021-12-11 23:02:27.636095424 +0800 ++++ pytorch-develop-181/cmake/Summary.cmake 2021-12-14 12:00:45.853577558 +0800 @@ -127,6 +127,7 @@ message(STATUS " USE_MKLDNN_CBLAS : ${USE_MKLDNN_CBLAS}") endif() @@ -41554,7 +41554,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= message(STATUS " Private Dependencies : ${Caffe2_DEPENDENCY_LIBS}") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/TorchConfig.cmake.in pytorch-develop-181/cmake/TorchConfig.cmake.in --- pytorch-v1.8.1/cmake/TorchConfig.cmake.in 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/TorchConfig.cmake.in 2021-12-11 23:02:27.636095424 +0800 ++++ pytorch-develop-181/cmake/TorchConfig.cmake.in 2021-12-14 12:00:45.853577558 +0800 @@ -158,6 +158,11 @@ list(APPEND TORCH_LIBRARIES ${TORCH_CUDA_LIBRARIES}) endif() @@ -41569,7 +41569,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(TORCH_CXX_FLAGS "-D_GLIBCXX_USE_CXX11_ABI=@GLIBCXX_USE_CXX11_ABI@") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/CMakeLists.txt pytorch-develop-181/CMakeLists.txt --- pytorch-v1.8.1/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/CMakeLists.txt 2021-12-11 23:02:27.424094661 +0800 ++++ pytorch-develop-181/CMakeLists.txt 2021-12-14 12:00:45.649576269 +0800 @@ -261,6 +261,10 @@ "USE_DISTRIBUTED" OFF) option(USE_TBB "Use TBB" OFF) @@ -41613,7 +41613,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if(APPLE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/setup.py pytorch-develop-181/setup.py --- pytorch-v1.8.1/setup.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/setup.py 2021-12-11 23:02:27.648095467 +0800 ++++ pytorch-develop-181/setup.py 2021-12-14 12:00:45.869577659 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -41682,7 +41682,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= 'utils/benchmark/utils/valgrind_wrapper/*.h', diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/derivatives.yaml pytorch-develop-181/tools/autograd/derivatives.yaml --- pytorch-v1.8.1/tools/autograd/derivatives.yaml 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/derivatives.yaml 2021-12-11 23:02:28.148097266 +0800 ++++ pytorch-develop-181/tools/autograd/derivatives.yaml 2021-12-14 12:00:46.353580717 +0800 @@ -1976,3 +1976,7 @@ - name: nonzero(Tensor self) -> Tensor @@ -41693,7 +41693,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + mat2: npu_bmm_v2_mat2_backward(grad, self, mat2, mat2.sizes()) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/gen_python_functions.py pytorch-develop-181/tools/autograd/gen_python_functions.py --- pytorch-v1.8.1/tools/autograd/gen_python_functions.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/gen_python_functions.py 2021-12-11 23:02:28.148097266 +0800 ++++ pytorch-develop-181/tools/autograd/gen_python_functions.py 2021-12-14 12:00:46.353580717 +0800 @@ -1,3 +1,20 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -41717,7 +41717,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # The bindings are generated as methods on python_variable or functions on the diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/Functions.cpp pytorch-develop-181/tools/autograd/templates/Functions.cpp --- pytorch-v1.8.1/tools/autograd/templates/Functions.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/Functions.cpp 2021-12-11 23:02:28.152097280 +0800 ++++ pytorch-develop-181/tools/autograd/templates/Functions.cpp 2021-12-14 12:00:46.353580717 +0800 @@ -14,6 +14,35 @@ namespace torch { namespace autograd { namespace generated { @@ -41756,7 +41756,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }}} // namespace torch::autograd::generated diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/python_torch_functions.cpp pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp --- pytorch-v1.8.1/tools/autograd/templates/python_torch_functions.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp 2021-12-11 23:02:28.152097280 +0800 ++++ pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp 2021-12-14 12:00:46.353580717 +0800 @@ -30,7 +30,7 @@ #include "torch/csrc/autograd/generated/variable_factories.h" #include "torch/csrc/utils/structseq.h" @@ -41792,7 +41792,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return torch::range(start, end, step, options); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/python_variable_methods.cpp pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp --- pytorch-v1.8.1/tools/autograd/templates/python_variable_methods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp 2021-12-11 23:02:28.152097280 +0800 ++++ pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp 2021-12-14 12:00:46.353580717 +0800 @@ -20,7 +20,13 @@ #ifdef USE_CUDA #include "torch/csrc/cuda/Event.h" @@ -41860,7 +41860,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"has_names", THPVariable_has_names, METH_NOARGS, NULL}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/build_variables.bzl pytorch-develop-181/tools/build_variables.bzl --- pytorch-v1.8.1/tools/build_variables.bzl 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/build_variables.bzl 2021-12-11 23:02:28.152097280 +0800 ++++ pytorch-develop-181/tools/build_variables.bzl 2021-12-14 12:00:46.353580717 +0800 @@ -362,6 +362,7 @@ libtorch_cuda_core_sources = [ "torch/csrc/CudaIPCTypes.cpp", @@ -41879,7 +41879,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= "torch/csrc/utils/python_arg_parser.cpp", diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/codegen/gen.py pytorch-develop-181/tools/codegen/gen.py --- pytorch-v1.8.1/tools/codegen/gen.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/codegen/gen.py 2021-12-11 23:02:28.156097294 +0800 ++++ pytorch-develop-181/tools/codegen/gen.py 2021-12-14 12:00:46.357580743 +0800 @@ -815,7 +815,7 @@ core_fm = make_file_manager(core_install_dir) cpu_fm = make_file_manager(options.install_dir) @@ -41927,7 +41927,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= main() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/codegen/model.py pytorch-develop-181/tools/codegen/model.py --- pytorch-v1.8.1/tools/codegen/model.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/codegen/model.py 2021-12-11 23:02:28.156097294 +0800 ++++ pytorch-develop-181/tools/codegen/model.py 2021-12-14 12:00:46.357580743 +0800 @@ -79,6 +79,7 @@ SparseHIP = auto() SparseXPU = auto() @@ -42069,7 +42069,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/autograd/profiler.py pytorch-develop-181/torch/autograd/profiler.py --- pytorch-v1.8.1/torch/autograd/profiler.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/autograd/profiler.py 2021-12-11 23:02:28.160097309 +0800 ++++ pytorch-develop-181/torch/autograd/profiler.py 2021-12-14 12:00:46.361580767 +0800 @@ -37,14 +37,17 @@ class EventList(list): """A list of Events (for pretty printing)""" @@ -42748,7 +42748,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ''.join(result) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_C/_autograd.pyi pytorch-develop-181/torch/_C/_autograd.pyi --- pytorch-v1.8.1/torch/_C/_autograd.pyi 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_C/_autograd.pyi 2021-12-11 23:02:28.156097294 +0800 ++++ pytorch-develop-181/torch/_C/_autograd.pyi 2021-12-14 12:00:46.357580743 +0800 @@ -9,14 +9,17 @@ CUDA = ... NVTX = ... @@ -42769,7 +42769,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= class ProfilerConfig: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/CMakeLists.txt pytorch-develop-181/torch/CMakeLists.txt --- pytorch-v1.8.1/torch/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/CMakeLists.txt 2021-12-11 23:02:28.156097294 +0800 ++++ pytorch-develop-181/torch/CMakeLists.txt 2021-12-14 12:00:46.357580743 +0800 @@ -131,6 +131,20 @@ endif() @@ -42793,7 +42793,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= list(APPEND TORCH_PYTHON_SRCS ${GENERATED_THNN_CXX_CUDA}) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/engine.cpp pytorch-develop-181/torch/csrc/autograd/engine.cpp --- pytorch-v1.8.1/torch/csrc/autograd/engine.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/engine.cpp 2021-12-11 23:02:28.164097323 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/engine.cpp 2021-12-14 12:00:46.365580793 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -42890,7 +42890,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto outputs = call_function(graph_task, func, inputs); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/functions/tensor.cpp pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp --- pytorch-v1.8.1/torch/csrc/autograd/functions/tensor.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp 2021-12-11 23:02:28.164097323 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp 2021-12-14 12:00:46.365580793 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -42922,7 +42922,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= /*non_blocking=*/false, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/init.cpp pytorch-develop-181/torch/csrc/autograd/init.cpp --- pytorch-v1.8.1/torch/csrc/autograd/init.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/init.cpp 2021-12-11 23:02:28.164097323 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/init.cpp 2021-12-14 12:00:46.365580793 +0800 @@ -52,6 +52,7 @@ .value("Disabled", ProfilerState::Disabled) .value("CPU", ProfilerState::CPU) @@ -42965,7 +42965,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= .value("OPENCL", c10::DeviceType::OPENCL) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/input_buffer.cpp pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp --- pytorch-v1.8.1/torch/csrc/autograd/input_buffer.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp 2021-12-11 23:02:28.164097323 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp 2021-12-14 12:00:46.365580793 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43017,7 +43017,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto& old_var = buffer[pos]; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.cpp pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp --- pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp 2021-12-11 23:02:28.164097323 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp 2021-12-14 12:00:46.365580793 +0800 @@ -147,7 +147,7 @@ constexpr const CUDAStubs* default_stubs_addr = &default_stubs; // Constant initialization, so it is guaranteed to be initialized before @@ -43260,7 +43260,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CUDAStubs::~CUDAStubs() = default; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.h pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h --- pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h 2021-12-11 23:02:28.164097323 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h 2021-12-14 12:00:46.365580793 +0800 @@ -19,6 +19,8 @@ #include // for gettimeofday() #endif @@ -43449,7 +43449,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= std::vector&& remoteProfiledEvents); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/python_variable.cpp pytorch-develop-181/torch/csrc/autograd/python_variable.cpp --- pytorch-v1.8.1/torch/csrc/autograd/python_variable.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/python_variable.cpp 2021-12-11 23:02:28.164097323 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/python_variable.cpp 2021-12-14 12:00:46.369580818 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43503,7 +43503,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"is_mkldnn", (getter)THPVariable_is_mkldnn, nullptr, nullptr, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/python_variable_indexing.cpp pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp --- pytorch-v1.8.1/torch/csrc/autograd/python_variable_indexing.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-11 23:02:28.164097323 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-14 12:00:46.369580818 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43536,7 +43536,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/distributed/c10d/init.cpp pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp --- pytorch-v1.8.1/torch/csrc/distributed/c10d/init.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp 2021-12-11 23:02:28.168097337 +0800 ++++ pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp 2021-12-14 12:00:46.369580818 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43703,7 +43703,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= torch::class_<::c10d::DistributedC10d>("dist_c10d", "frontend") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/DynamicTypes.cpp pytorch-develop-181/torch/csrc/DynamicTypes.cpp --- pytorch-v1.8.1/torch/csrc/DynamicTypes.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/DynamicTypes.cpp 2021-12-11 23:02:28.160097309 +0800 ++++ pytorch-develop-181/torch/csrc/DynamicTypes.cpp 2021-12-14 12:00:46.361580767 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43752,7 +43752,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return it->second; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/Generator.cpp pytorch-develop-181/torch/csrc/Generator.cpp --- pytorch-v1.8.1/torch/csrc/Generator.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/Generator.cpp 2021-12-11 23:02:28.160097309 +0800 ++++ pytorch-develop-181/torch/csrc/Generator.cpp 2021-12-14 12:00:46.361580767 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43799,7 +43799,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= " is not supported for torch.Generator() api."); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/serialization.cpp pytorch-develop-181/torch/csrc/generic/serialization.cpp --- pytorch-v1.8.1/torch/csrc/generic/serialization.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/serialization.cpp 2021-12-11 23:02:28.172097352 +0800 ++++ pytorch-develop-181/torch/csrc/generic/serialization.cpp 2021-12-14 12:00:46.373580844 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43897,7 +43897,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/Storage.cpp pytorch-develop-181/torch/csrc/generic/Storage.cpp --- pytorch-v1.8.1/torch/csrc/generic/Storage.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/Storage.cpp 2021-12-11 23:02:28.172097352 +0800 ++++ pytorch-develop-181/torch/csrc/generic/Storage.cpp 2021-12-14 12:00:46.373580844 +0800 @@ -1,7 +1,25 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43977,7 +43977,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for (Py_ssize_t i = 0; i < length; i++) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/StorageMethods.cpp pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp --- pytorch-v1.8.1/torch/csrc/generic/StorageMethods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp 2021-12-11 23:02:28.172097352 +0800 ++++ pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp 2021-12-14 12:00:46.373580844 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44057,7 +44057,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/Module.cpp pytorch-develop-181/torch/csrc/Module.cpp --- pytorch-v1.8.1/torch/csrc/Module.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/Module.cpp 2021-12-11 23:02:28.160097309 +0800 ++++ pytorch-develop-181/torch/csrc/Module.cpp 2021-12-14 12:00:46.361580767 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44183,7 +44183,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (incref) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/tensor/python_tensor.cpp pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp --- pytorch-v1.8.1/torch/csrc/tensor/python_tensor.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp 2021-12-11 23:02:28.196097439 +0800 ++++ pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp 2021-12-14 12:00:46.397580995 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44221,7 +44221,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/python_arg_parser.h pytorch-develop-181/torch/csrc/utils/python_arg_parser.h --- pytorch-v1.8.1/torch/csrc/utils/python_arg_parser.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/python_arg_parser.h 2021-12-11 23:02:28.200097453 +0800 ++++ pytorch-develop-181/torch/csrc/utils/python_arg_parser.h 2021-12-14 12:00:46.401581021 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44256,7 +44256,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return at::Device(device_str); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_layouts.cpp pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_layouts.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp 2021-12-11 23:02:28.200097453 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp 2021-12-14 12:00:46.401581021 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44287,7 +44287,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= PyObject *sparse_coo_layout = THPLayout_New(at::Layout::Sparse, "torch.sparse_coo"); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_new.cpp pytorch-develop-181/torch/csrc/utils/tensor_new.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_new.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_new.cpp 2021-12-11 23:02:28.200097453 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_new.cpp 2021-12-14 12:00:46.401581021 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44419,7 +44419,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= dispatch_key); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_types.cpp pytorch-develop-181/torch/csrc/utils/tensor_types.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_types.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_types.cpp 2021-12-11 23:02:28.200097453 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_types.cpp 2021-12-14 12:00:46.401581021 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44469,7 +44469,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/distributed/distributed_c10d.py pytorch-develop-181/torch/distributed/distributed_c10d.py --- pytorch-v1.8.1/torch/distributed/distributed_c10d.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/distributed/distributed_c10d.py 2021-12-11 23:02:28.200097453 +0800 ++++ pytorch-develop-181/torch/distributed/distributed_c10d.py 2021-12-14 12:00:46.401581021 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -44560,7 +44560,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= prefix_store, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/__init__.py pytorch-develop-181/torch/__init__.py --- pytorch-v1.8.1/torch/__init__.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/__init__.py 2021-12-11 23:02:28.156097294 +0800 ++++ pytorch-develop-181/torch/__init__.py 2021-12-14 12:00:46.357580743 +0800 @@ -675,3 +675,11 @@ # class usage. We add these lines here to preserve backward compatibility. quantized_lstm = torch.ops.aten.quantized_lstm @@ -44576,7 +44576,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/CMakeLists.txt pytorch-develop-181/torch/lib/c10d/CMakeLists.txt --- pytorch-v1.8.1/torch/lib/c10d/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/CMakeLists.txt 2021-12-11 23:02:28.208097482 +0800 ++++ pytorch-develop-181/torch/lib/c10d/CMakeLists.txt 2021-12-14 12:00:46.409581071 +0800 @@ -27,6 +27,10 @@ option(USE_C10D_NCCL "USE C10D NCCL" ON) endif() @@ -44629,7 +44629,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= copy_header(ProcessGroupMPI.hpp) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/comm.cpp pytorch-develop-181/torch/lib/c10d/comm.cpp --- pytorch-v1.8.1/torch/lib/c10d/comm.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/comm.cpp 2021-12-11 23:02:28.208097482 +0800 ++++ pytorch-develop-181/torch/lib/c10d/comm.cpp 2021-12-14 12:00:46.409581071 +0800 @@ -12,6 +12,26 @@ class BroadcastWork { @@ -44718,7 +44718,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= while (!in_flight.empty()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/reducer.cpp pytorch-develop-181/torch/lib/c10d/reducer.cpp --- pytorch-v1.8.1/torch/lib/c10d/reducer.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/reducer.cpp 2021-12-11 23:02:28.208097482 +0800 ++++ pytorch-develop-181/torch/lib/c10d/reducer.cpp 2021-12-14 12:00:46.409581071 +0800 @@ -18,6 +18,18 @@ namespace c10d { namespace { @@ -44910,7 +44910,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // A bucket with one or more dense tensors needs to be unflattened. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/libshm/CMakeLists.txt pytorch-develop-181/torch/lib/libshm/CMakeLists.txt --- pytorch-v1.8.1/torch/lib/libshm/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/libshm/CMakeLists.txt 2021-12-11 23:02:28.208097482 +0800 ++++ pytorch-develop-181/torch/lib/libshm/CMakeLists.txt 2021-12-14 12:00:46.409581071 +0800 @@ -41,8 +41,11 @@ set_target_properties(shm PROPERTIES PREFIX "lib" @@ -44926,7 +44926,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # https://github.com/libgit2/libgit2/issues/2128#issuecomment-35649830 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/batchnorm.py pytorch-develop-181/torch/nn/modules/batchnorm.py --- pytorch-v1.8.1/torch/nn/modules/batchnorm.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/batchnorm.py 2021-12-11 23:02:28.212097496 +0800 ++++ pytorch-develop-181/torch/nn/modules/batchnorm.py 2021-12-14 12:00:46.413581096 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -44948,7 +44948,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= from ._functions import SyncBatchNorm as sync_batch_norm diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/module.py pytorch-develop-181/torch/nn/modules/module.py --- pytorch-v1.8.1/torch/nn/modules/module.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/module.py 2021-12-11 23:02:28.212097496 +0800 ++++ pytorch-develop-181/torch/nn/modules/module.py 2021-12-14 12:00:46.413581096 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45074,7 +45074,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if convert_to_format is not None and t.dim() == 4: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/normalization.py pytorch-develop-181/torch/nn/modules/normalization.py --- pytorch-v1.8.1/torch/nn/modules/normalization.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/normalization.py 2021-12-11 23:02:28.212097496 +0800 ++++ pytorch-develop-181/torch/nn/modules/normalization.py 2021-12-14 12:00:46.413581096 +0800 @@ -167,8 +167,11 @@ init.zeros_(self.bias) @@ -45091,7 +45091,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return '{normalized_shape}, eps={eps}, ' \ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/parallel/distributed.py pytorch-develop-181/torch/nn/parallel/distributed.py --- pytorch-v1.8.1/torch/nn/parallel/distributed.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/parallel/distributed.py 2021-12-11 23:02:28.212097496 +0800 ++++ pytorch-develop-181/torch/nn/parallel/distributed.py 2021-12-14 12:00:46.413581096 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45123,7 +45123,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= output = self.module(*inputs[0], **kwargs[0]) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/serialization.py pytorch-develop-181/torch/serialization.py --- pytorch-v1.8.1/torch/serialization.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/serialization.py 2021-12-11 23:02:28.220097524 +0800 ++++ pytorch-develop-181/torch/serialization.py 2021-12-14 12:00:46.421581146 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45212,7 +45212,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= See also: `saving-loading-tensors` diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/storage.py pytorch-develop-181/torch/storage.py --- pytorch-v1.8.1/torch/storage.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/storage.py 2021-12-11 23:02:28.220097524 +0800 ++++ pytorch-develop-181/torch/storage.py 2021-12-14 12:00:46.421581146 +0800 @@ -8,6 +8,7 @@ class _StorageBase(object): _cdata: Any @@ -45232,7 +45232,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= else: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/tensor.py pytorch-develop-181/torch/tensor.py --- pytorch-v1.8.1/torch/tensor.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/tensor.py 2021-12-11 23:02:28.220097524 +0800 ++++ pytorch-develop-181/torch/tensor.py 2021-12-14 12:00:46.421581146 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45294,7 +45294,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def __reversed__(self): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_tensor_str.py pytorch-develop-181/torch/_tensor_str.py --- pytorch-v1.8.1/torch/_tensor_str.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_tensor_str.py 2021-12-11 23:02:28.156097294 +0800 ++++ pytorch-develop-181/torch/_tensor_str.py 2021-12-14 12:00:46.361580767 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45338,7 +45338,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # TODO: add an API to map real -> complex dtypes diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/utils/data/dataloader.py pytorch-develop-181/torch/utils/data/dataloader.py --- pytorch-v1.8.1/torch/utils/data/dataloader.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/utils/data/dataloader.py 2021-12-11 23:02:28.228097553 +0800 ++++ pytorch-develop-181/torch/utils/data/dataloader.py 2021-12-14 12:00:46.429581197 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45399,7 +45399,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= pin_memory_thread.start() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/utils/data/_utils/pin_memory.py pytorch-develop-181/torch/utils/data/_utils/pin_memory.py --- pytorch-v1.8.1/torch/utils/data/_utils/pin_memory.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/utils/data/_utils/pin_memory.py 2021-12-11 23:02:24.308083438 +0800 ++++ pytorch-develop-181/torch/utils/data/_utils/pin_memory.py 2021-12-14 12:00:42.445556024 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45445,7 +45445,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # logic of this function. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_utils.py pytorch-develop-181/torch/_utils.py --- pytorch-v1.8.1/torch/_utils.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_utils.py 2021-12-11 23:02:28.156097294 +0800 ++++ pytorch-develop-181/torch/_utils.py 2021-12-14 12:00:46.361580767 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. diff --git a/pytorch1.5.0/access_control_test.py b/pytorch1.5.0/access_control_test.py index 003de69a863..060d531828b 100644 --- a/pytorch1.5.0/access_control_test.py +++ b/pytorch1.5.0/access_control_test.py @@ -176,7 +176,7 @@ def exec_ut(ut_files): return ret_status -if __name__ == "__main__": +def main(): cur_dir = os.path.abspath(os.path.dirname(__file__)) modify_files = os.path.join(cur_dir, 'modify_files.txt') test_mgr = TestMgr() @@ -188,4 +188,12 @@ if __name__ == "__main__": test_mgr.print_ut_files() ret = exec_ut(ut_files) + if ret and DEFAULT_UT_FILE not in ut_files: + print("***** start resnet18:") + os.chdir(cur_dir) + exec_ut([DEFAULT_UT_FILE]) sys.exit(ret) + + +if __name__ == "__main__": + main() diff --git a/pytorch1.5.0/src/aten/src/ATen/native/native_functions.yaml b/pytorch1.5.0/src/aten/src/ATen/native/native_functions.yaml index f80527c3544..df964967d06 100644 --- a/pytorch1.5.0/src/aten/src/ATen/native/native_functions.yaml +++ b/pytorch1.5.0/src/aten/src/ATen/native/native_functions.yaml @@ -8425,6 +8425,11 @@ variants: function, method npu_dispatch_only: NPU: nms_v4_npu + +- func: npu_nms_rotated(Tensor self, Tensor scores, float iou_threshold, float scores_threshold=0, int max_output_size=-1, int mode=0) -> (Tensor, Tensor) + variants: function, method + npu_dispatch_only: + NPU: nms_rotated_npu - func: npu_lstm(Tensor input, Tensor weight, Tensor bias, Tensor seqMask, Tensor h, Tensor c, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first, bool flagSeq, bool direction) -> (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor) variants: function diff --git a/pytorch1.5.0/src/aten/src/ATen/native/npu/NmsRotatedKernelNpu.cpp b/pytorch1.5.0/src/aten/src/ATen/native/npu/NmsRotatedKernelNpu.cpp new file mode 100644 index 00000000000..653abc41138 --- /dev/null +++ b/pytorch1.5.0/src/aten/src/ATen/native/npu/NmsRotatedKernelNpu.cpp @@ -0,0 +1,62 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. +// All rights reserved. +// +// Licensed under the BSD 3-Clause License (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// https://opensource.org/licenses/BSD-3-Clause +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "ATen/native/npu/utils/OpAdapter.h" + +namespace at { +namespace native { +using namespace at::native::npu; + +tuple nms_rotated_npu( + const Tensor& dets, + const Tensor& scores, + double iouThreshold, + double scoreThreshold, + int64_t maxOutputSize, + int64_t mode) { + SmallVector selectedIndexSize = {dets.size(0)}; + SmallVector selectedNumSize = {1}; + + Tensor selectedIndex = OpPreparation::ApplyTensor(selectedIndexSize, dets.options().dtype(at::kInt), dets); + Tensor selectedNum = OpPreparation::ApplyTensor(selectedNumSize, dets.options().dtype(at::kInt), dets); + + // the Op only support fp32 currently! + auto originDtype = dets.scalar_type(); + Tensor detsCast = dets; + Tensor scoresCast = scores; + if(originDtype != at::ScalarType::Float){ + detsCast = dets.npu_dtype_cast(at::kFloat); + scoresCast = scores.npu_dtype_cast(at::kFloat); + } + + OpCommand cmd; + cmd.Name("PolyNMS") + .Input(detsCast) + .Input(scoresCast) + .Output(selectedIndex) + .Output(selectedNum) + .Attr("iou_threshold", (float)iouThreshold) + .Attr("score_threshold", (float)scoreThreshold) + .Attr("max_output_size", maxOutputSize) + .Attr("mode", mode) + .Run(); + + Tensor selectedInd = selectedIndex.slice(0, 0, selectedNum.item().toLong()); + return std::tie(selectedInd, selectedNum); +} + +} // namespace native +} // namespace at diff --git a/pytorch1.5.0/src/torch/contrib/npu/optimized_lib/function/anchor_generator.py b/pytorch1.5.0/src/torch/contrib/npu/optimized_lib/function/anchor_generator.py index adc2d2f4592..2cfe178b286 100644 --- a/pytorch1.5.0/src/torch/contrib/npu/optimized_lib/function/anchor_generator.py +++ b/pytorch1.5.0/src/torch/contrib/npu/optimized_lib/function/anchor_generator.py @@ -52,7 +52,7 @@ def npu_single_level_responsible_flags(featmap_size, return flags -if __name__ == "__main__": +def main(): featmap_sizes = [[10, 10], [20, 20], [40, 40]] stride = [[32, 32], [16, 16], [8, 8]] gt_bboxes = torch.randint(0, 512, size=(128, 4)) @@ -68,3 +68,7 @@ if __name__ == "__main__": stride[i], num_base_anchors) print(out.shape, out.max(), out.min()) + + +if __name__ == "__main__": + main() diff --git a/pytorch1.5.0/src/torch/contrib/npu/optimized_lib/module/channel_shuffle.py b/pytorch1.5.0/src/torch/contrib/npu/optimized_lib/module/channel_shuffle.py index e36776689d5..4c4e3dd2cc5 100644 --- a/pytorch1.5.0/src/torch/contrib/npu/optimized_lib/module/channel_shuffle.py +++ b/pytorch1.5.0/src/torch/contrib/npu/optimized_lib/module/channel_shuffle.py @@ -163,7 +163,7 @@ class IndexSelectHalfImplementation(torch.autograd.Function): return out1, out2, None, None, None, None -if __name__ == '__main__': +def main(): device = 'cpu' if device.startswith('npu'): @@ -188,3 +188,7 @@ if __name__ == '__main__': tescase(split_shuffle=True) tescase(split_shuffle=False) + + +if __name__ == '__main__': + main() diff --git a/pytorch1.5.0/src/torch/contrib/npu/optimized_lib/module/deform_conv.py b/pytorch1.5.0/src/torch/contrib/npu/optimized_lib/module/deform_conv.py index 40ac77b6104..5dd38263e8d 100644 --- a/pytorch1.5.0/src/torch/contrib/npu/optimized_lib/module/deform_conv.py +++ b/pytorch1.5.0/src/torch/contrib/npu/optimized_lib/module/deform_conv.py @@ -220,7 +220,7 @@ class ModulatedDeformConv(nn.Module): DCNv2 = ModulatedDeformConv -if __name__ == "__main__": +def main(): x = torch.randn(2, 32, 7, 7) model = DCNv2(32, 32, 3, 2, 1) @@ -232,3 +232,7 @@ if __name__ == "__main__": l = o.sum() l.backward() print(l) + + +if __name__ == "__main__": + main() diff --git a/pytorch1.5.0/src/torch/contrib/npu/optimized_lib/module/dropout.py b/pytorch1.5.0/src/torch/contrib/npu/optimized_lib/module/dropout.py index 3f41fca5b5f..c00eaba73ba 100644 --- a/pytorch1.5.0/src/torch/contrib/npu/optimized_lib/module/dropout.py +++ b/pytorch1.5.0/src/torch/contrib/npu/optimized_lib/module/dropout.py @@ -74,7 +74,7 @@ class DropoutV2(nn.Module): return x -if __name__ == '__main__': +def main(): torch.npu.set_device('npu:0') x = torch.randn(1, 2, 2, 2).npu() @@ -95,3 +95,5 @@ if __name__ == '__main__': print(o) +if __name__ == '__main__': + main() diff --git a/pytorch1.5.0/test/test_npu/test_network_ops/test_nms_rotated.py b/pytorch1.5.0/test/test_npu/test_network_ops/test_nms_rotated.py new file mode 100644 index 00000000000..45cc5e07735 --- /dev/null +++ b/pytorch1.5.0/test/test_npu/test_network_ops/test_nms_rotated.py @@ -0,0 +1,59 @@ +# Copyright (c) 2020, Huawei Technologies.All rights reserved. +# +# Licensed under the BSD 3-Clause License (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://opensource.org/licenses/BSD-3-Clause +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +from common_utils import TestCase, run_tests +from common_device_type import dtypes, instantiate_device_type_tests + +class TestNmsRotated(TestCase): + def npu_op_exec(self, det, score): + # det, score, iou_threshold, score_threshold, max_output_size, mode + output1, output2 = torch.npu_nms_rotated(det.npu(), score.npu(), 0.2, 0, -1, 1) + return output1, output2 + + def test_nms_rotated_float32(self, device): + det = torch.tensor([[1.0382e+03, 3.1657e+02, 1.1556e+03, 4.4303e+02, 2.3674e+00], + [1.1503e+03, 3.0598e+02, 1.2602e+03, 4.3456e+02, 3.2729e-01], + [1.1508e+03, 3.0652e+02, 1.2607e+03, 4.3472e+02, 5.1713e-01], + [1.1518e+03, 3.0781e+02, 1.2622e+03, 4.3448e+02, 3.9718e-01], + [1.1748e+03, 3.0202e+02, 1.2859e+03, 4.3915e+02, 1.8112e+00], + [1.1711e+03, 3.0480e+02, 1.2868e+03, 4.3551e+02, 2.1171e+00], + [1.1673e+03, 3.0675e+02, 1.2889e+03, 4.3194e+02, 2.5968e+00], + [1.2741e+03, 3.0181e+02, 1.3823e+03, 4.3036e+02, 2.0379e+00], + [1.2741e+03, 3.0286e+02, 1.3836e+03, 4.2940e+02, 2.2072e+00], + [1.2733e+03, 3.0382e+02, 1.3855e+03, 4.2846e+02, 2.0921e+00], + [1.2935e+03, 3.0517e+02, 1.3961e+03, 4.3137e+02, 2.9583e+00], + [1.4076e+03, 3.2173e+02, 1.4930e+03, 4.2714e+02, 2.6099e+00], + [1.4097e+03, 3.2496e+02, 1.4934e+03, 4.2651e+02, 3.0967e+00], + [1.4097e+03, 3.2569e+02, 1.4935e+03, 4.2632e+02, 2.5553e+00], + [1.0279e+03, 3.1883e+02, 1.1412e+03, 4.4646e+02, 1.2030e+00], + [1.0275e+03, 3.1776e+02, 1.1408e+03, 4.4641e+02, 1.2732e+00], + [1.0289e+03, 3.1694e+02, 1.1407e+03, 4.4510e+02, 9.4897e-01], + [1.0372e+03, 3.1233e+02, 1.1477e+03, 4.4521e+02, 1.4125e+00], + [1.0370e+03, 3.1564e+02, 1.1487e+03, 4.4317e+02, 1.6109e+00], + [1.0367e+03, 3.1682e+02, 1.1510e+03, 4.4020e+02, 1.4112e+00]]) + score = torch.tensor([0.9910, 0.9854, 0.9972, 0.9930, 0.4282, 0.5092, 0.6532, 0.9965, 0.9989, + 0.9976, 0.3144, 0.9874, 0.9980, 0.9967, 0.9698, 0.9824, 0.9474, 0.9856, 0.9964, 0.9926]) + + expect_output1 = torch.tensor([ 8, 12, 2, 18], dtype=torch.int32) + expect_output2 = torch.tensor([4], dtype=torch.int32) + + npu_output1, npu_output2 = self.npu_op_exec(det, score) + + self.assertRtolEqual(expect_output1, npu_output1.cpu()) + self.assertRtolEqual(expect_output2, npu_output2.cpu()) + +instantiate_device_type_tests(TestNmsRotated, globals(), except_for='cpu') +if __name__ == "__main__": + run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test__Ixor__.py b/pytorch1.8.1/test/test_npu/test__Ixor__.py deleted file mode 100644 index 1e101aa7d9c..00000000000 --- a/pytorch1.8.1/test/test_npu/test__Ixor__.py +++ /dev/null @@ -1,129 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class Testixor(TestCase): - def generate_data(self, min_d, max_d, shape, dtype): - input1 = np.random.uniform(min_d, max_d, shape).astype(dtype) - input2 = np.random.uniform(min_d, max_d, shape).astype(dtype) - - #modify from numpy.ndarray to torch.tensor - npu_input1 = torch.from_numpy(input1) - npu_input2 = torch.from_numpy(input2) - - return npu_input1, npu_input2 - - def generate_bool_data(self, min_d, max_d, shape): - input1 = np.random.uniform(min_d, max_d, shape) - input2 = np.random.uniform(min_d, max_d, shape) - input1 = input1.reshape(-1) - input2 = input2.reshape(-1) - for i in range(len(input1)): - if input1.any() < 0.5: - input1[i] = 0 - for i in range(len(input2)): - if input2.any() < 0.5: - input2[i] = 0 - input1 = input1.astype(np.bool) - input2 = input2.astype(np.bool) - input1 = input1.reshape(shape) - input2 = input2.reshape(shape) - #modify from numpy.ndarray to torch.tensor - npu_input1 = torch.from_numpy(input1) - npu_input2 = torch.from_numpy(input2) - - return npu_input1, npu_input2 - - def generate_single_data(self, min_d, max_d, shape, dtype): - input1 = np.random.uniform(min_d, max_d, shape).astype(dtype) - npu_input1 = torch.from_numpy(input1) - - return npu_input1 - - def generate_single_bool_data(self, min_d, max_d, shape): - input1 = np.random.uniform(min_d, max_d, shape) - input1 = input1.reshape(-1) - for i in range(len(input1)): - if input1[i] < 0.5: - input1[i] = 0 - input1 = input1.astype(np.bool) - input1 = input1.reshape(shape) - npu_input1 = torch.from_numpy(input1) - return npu_input1 - - def generate_three_data(self, min_d, max_d, shape, dtype): - input1 = np.random.uniform(min_d, max_d, shape).astype(dtype) - input2 = np.random.uniform(min_d, max_d, shape).astype(dtype) - input3 = np.random.uniform(min_d, max_d, shape).astype(dtype) - - npu_input1 = torch.from_numpy(input1) - npu_input2 = torch.from_numpy(input2) - npu_input3 = torch.from_numpy(input3) - - return npu_input1, npu_input2, npu_input3 - - def npu_op_exec_out(self, input1, input2, input3): - input1 = input1.to("npu") - input2 = input2.to("npu") - output = input3.to("npu") - input1.__ixor__(input2, out=output) - output = output.to("cpu") - output = output.numpy() - return output - - def cpu_op_exec_out(self, input1, input2, input3): - output = input3 - input1.__ixor__(input2, out=output) - output = output.numpy() - return output - - def npu_op_exec_scalar_out(self, input1, input2, input3): - output = input3.to("npu") - input1 = input1.to("npu") - input2 = torch.tensor(input2) - input2 = input2.to("npu") - input1.__ixor__(input2, out=output) - output = output.to("cpu") - output = output.numpy() - return output - - def test__ixor__int32(self, device): - npu_input1, npu_input2 = self.generate_data(0, 100, (2,3), np.int32) - cpu_output = self.cpu_op_exec_out(npu_input1, npu_input2,npu_input1) - npu_output = self.npu_op_exec_out(npu_input1, npu_input2,npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test__ixor__int32_scalar(self, device): - npu_input1, npu_input2 = self.generate_data(0, 100, (2,3), np.int32) - cpu_output = self.cpu_op_exec_out(npu_input1, 1, npu_input1) - npu_output = self.npu_op_exec_scalar_out(npu_input1, 1, npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test__ixor__float32_out(self, device): - npu_input1, npu_input2, npu_input3 = self.generate_three_data(0, 100, (4, 3), np.int32) - cpu_output = self.cpu_op_exec_out(npu_input1, npu_input2, npu_input3) - npu_output = self.npu_op_exec_out(npu_input1, npu_input2, npu_input3) - self.assertRtolEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(Testixor, globals(), except_for='cpu') -if __name__ == '__main__': - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test___iand__.py b/pytorch1.8.1/test/test_npu/test___iand__.py deleted file mode 100644 index d16107c8be5..00000000000 --- a/pytorch1.8.1/test/test_npu/test___iand__.py +++ /dev/null @@ -1,138 +0,0 @@ -# Copyright (c) 2020 Huawei Technologies Co., Ltd -# Copyright (c) 2019, Facebook CORPORATION. -# All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class Test__Iand__(TestCase): - - def generate_bool_data(self, shape): - input1 = np.random.uniform(0, 1, shape).astype(np.float32) - input1 = input1 < 0.5 - npu_input1 = torch.from_numpy(input1) - - return npu_input1 - - def generate_data(self, min_d, max_d, shape, dtype): - input1 = np.random.uniform(min_d, max_d, shape).astype(dtype) - input2 = np.random.uniform(min_d, max_d, shape).astype(dtype) - - # modify from numpy.ndarray to torch.tensor - npu_input1 = torch.from_numpy(input1) - npu_input2 = torch.from_numpy(input2) - - return npu_input1, npu_input2 - - - def generate_single_data(self, min_d, max_d, shape, dtype): - input1 = np.random.uniform(min_d, max_d, shape).astype(dtype) - npu_input1 = torch.from_numpy(input1) - - return npu_input1 - - def generate_scalar(self, min_d, max_d): - scalar = np.random.uniform(min_d, max_d) - return scalar - - def generate_int_scalar(self, min_d, max_d): - scalar = np.random.randint(min_d, max_d) - return scalar - - def cpu_op_exec(self, input1, input2): - input1 = input1.to("cpu") - input2 = input2.to("cpu") - output = input1.__iand__(input2) - output = output.to("cpu") - output = output.numpy() - return output - - def cpu_op_exec_scalar(self, input1, input2): - input1 = input1.to("cpu") - output = input1.__iand__(input2) - output = output.to("cpu") - output = output.numpy() - return output - - def npu_op_exec(self, input1, input2): - input1 = input1.to("npu") - input2 = input2.to("npu") - output = input1.__iand__(input2) - output = output.to("cpu") - output = output.numpy() - return output - - def npu_op_exec_scalar(self, input1, input2): - input1 = input1.to("npu") - output = input1.__iand__(input2) - output = output.to("cpu") - output = output.numpy() - return output - - def test___iand___bool(self, device): - npu_input1, npu_input2 = self.generate_bool_data((3, 5)), self.generate_bool_data((3, 5)) - cpu_output = self.cpu_op_exec(npu_input1, npu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2) - self.assertRtolEqual(cpu_output, npu_output) - - def test___iand___int16(self, device): - npu_input1, npu_input2= self.generate_data(0, 100, (4, 3), np.int16) - cpu_output = self.cpu_op_exec(npu_input1, npu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2) - cpu_output = cpu_output.astype(np.int32) - npu_output = npu_output.astype(np.int32) - self.assertRtolEqual(cpu_output, npu_output) - - def test___iand___int32(self, device): - npu_input1, npu_input2= self.generate_data(0, 100, (4, 3), np.int32) - cpu_output = self.cpu_op_exec(npu_input1, npu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2) - cpu_output = cpu_output.astype(np.int32) - npu_output = npu_output.astype(np.int32) - self.assertRtolEqual(cpu_output, npu_output) - - def test___iand___scalar_bool(self, device): - npu_input1 = self.generate_bool_data((3, 5)) - cpu_output = self.cpu_op_exec_scalar(npu_input1, True) - npu_output = self.npu_op_exec_scalar(npu_input1, True) - self.assertRtolEqual(cpu_output, npu_output) - - def test___iand___scalar_int16(self, device): - npu_input1 = self.generate_single_data(0, 100, (4, 3), np.int16) - cpu_output = self.cpu_op_exec_scalar(npu_input1, 1) - npu_output = self.npu_op_exec_scalar(npu_input1, 1) - cpu_output = cpu_output.astype(np.int32) - npu_output = npu_output.astype(np.int32) - self.assertRtolEqual(cpu_output, npu_output) - - def test___iand___scalar_int32(self, device): - npu_input1 = self.generate_single_data(0, 100, (4, 3), np.int32) - cpu_output = self.cpu_op_exec_scalar(npu_input1, 1) - npu_output = self.npu_op_exec_scalar(npu_input1, 1) - cpu_output = cpu_output.astype(np.int32) - npu_output = npu_output.astype(np.int32) - self.assertRtolEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(Test__Iand__, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:1") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test___rshift__.Scalar.py b/pytorch1.8.1/test/test_npu/test___rshift__.Scalar.py deleted file mode 100644 index ac2cbd9e70b..00000000000 --- a/pytorch1.8.1/test/test_npu/test___rshift__.Scalar.py +++ /dev/null @@ -1,60 +0,0 @@ -# Copyright (c) 2020 Huawei Technologies Co., Ltd -# Copyright (c) 2019, Facebook CORPORATION. -# All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestRshiftScalar(TestCase): - - def cpu_op_exec(self, input, other): - output = input.__rshift__(other) - return output.numpy() - - def npu_op_exec(self, input, other): - output = input.__rshift__(other).npu() - return output.cpu().numpy() - - - def test_cast_Char_common_shape_format(self, device): - shape_format = [ - [[np.int64, -1, (4, 3)]], - [[np.int32, -1, (4, 3, 1)]], - [[np.int8, -1, (2, 3)]], - [[np.float32, -1, (4, 3, 1)]], - [[np.float16, -1, (4, 3, 1)]], - [[np.uint8, -1, (4, 3, 1)]] - ] - other_list = [0, 1, -1, 1.5, -1.5, 10, -10, 100, -100, 1000000, -1000000] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item[0], -100, 100) - cpu_input = cpu_input.to(torch.float32) - for other in other_list: - cpu_output = self.cpu_op_exec(cpu_input, other) - npu_output = self.npu_op_exec(npu_input, other) - cpu_output = cpu_output.astype(np.float16) - self.assertRtolEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(TestRshiftScalar, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:3") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test__nnpack_spatial_convolution.py b/pytorch1.8.1/test/test_npu/test__nnpack_spatial_convolution.py deleted file mode 100644 index daaed945793..00000000000 --- a/pytorch1.8.1/test/test_npu/test__nnpack_spatial_convolution.py +++ /dev/null @@ -1,144 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor -import unittest - -class TestNnpackSpatialConvolution(TestCase): - - def generate_data(self, min_d, max_d, N, C0, Hi, Wi, C1, Hw, Ww, dtype): - input_shape = (N, C0, Hi, Wi) - input_x = np.random.uniform(min_d, max_d, input_shape).astype(dtype) - weight_shape = (C1, C0, Hw, Ww) - weight = np.random.uniform(min_d, max_d, weight_shape).astype(dtype) - input_x = torch.from_numpy(input_x) - weight = torch.from_numpy(weight) - bias = np.zeros(C1).astype(dtype) - bias = torch.from_numpy(bias) - padding = tuple(np.ones(2).astype(np.int)) - return input_x, weight, bias, padding - - @unittest.skipIf(not torch._nnpack_available(),"NNPACK unavailable") - def cpu_op_exec(self, input_x, weight, bias, padding): - flag = 0 - if input_x.dtype == torch.float16: - input_x = input_x.to(torch.float32) - weight = weight.to(torch.float32) - bias = bias.to(torch.float32) - flag = 1 - output = torch._nnpack_spatial_convolution( - input_x, weight, bias, padding) - if flag == 1: - output = output.to(torch.float16) - output = output.numpy() - return output - - @unittest.skipIf(not torch._nnpack_available(),"NNPACK unavailable") - def npu_op_exec(self, input_x, weight, bias, padding): - flag = 0 - if input_x.dtype == torch.float16: - input_x = input_x.to(torch.float32) - weight = weight.to(torch.float32) - bias = bias.to(torch.float32) - flag = 1 - input_x = input_x.to("npu") - weight = weight.to("npu") - bias = bias.to("npu") - output = torch._nnpack_spatial_convolution( - input_x, weight, bias, padding) - output = output.to("cpu") - if flag == 1: - output = output.to(torch.float16) - output = output.numpy() - return output - - - def test__nnpack_spatial_convolution_float16_1(self, device): - input_x, weight, bias, padding = self.generate_data( - #min_d, max_d, N, C0, Hi, Wi, C1, Hw, Ww, dtype - -2, 2, 1, 3, 4, 4, 2, 2, 2, np.float16) - cpu_output = self.cpu_op_exec(input_x, weight, bias, padding) - npu_output = self.npu_op_exec(input_x, weight, bias, padding) - self.assertRtolEqual(cpu_output, npu_output) - - def test__nnpack_spatial_convolution_float16_2(self, device): - input_x, weight, bias, padding = self.generate_data( - #min_d, max_d, N, C0, Hi, Wi, C1, Hw, Ww, dtype - -50, 50, 1, 3, 5, 5, 5, 2, 2, np.float16) - cpu_output = self.cpu_op_exec(input_x, weight, bias, padding) - npu_output = self.npu_op_exec(input_x, weight, bias, padding) - self.assertRtolEqual(cpu_output, npu_output) - - def test__nnpack_spatial_convolution_float16_3(self, device): - input_x, weight, bias, padding = self.generate_data( - #min_d, max_d, N, C0, Hi, Wi, C1, Hw, Ww, dtype - -50, 50, 1, 5, 1024, 1024, 5, 8, 8, np.float16) - cpu_output = self.cpu_op_exec(input_x, weight, bias, padding) - npu_output = self.npu_op_exec(input_x, weight, bias, padding) - self.assertRtolEqual(cpu_output, npu_output) - - def test__nnpack_spatial_convolution_float16_4(self, device): - input_x, weight, bias, padding = self.generate_data( - #min_d, max_d, N, C0, Hi, Wi, C1, Hw, Ww, dtype - -100, 100, 1, 5, 1024, 1024, 5, 8, 8, np.float16) - cpu_output = self.cpu_op_exec(input_x, weight, bias, padding) - npu_output = self.npu_op_exec(input_x, weight, bias, padding) - self.assertRtolEqual(cpu_output, npu_output) - - - def test__nnpack_spatial_convolution_float32_1(self, device): - input_x, weight, bias, padding = self.generate_data( - #min_d, max_d, N, C0, Hi, Wi, C1, Hw, Ww, dtype - -2, 2, 1, 3, 4, 4, 2, 2, 2, np.float32) - cpu_output = self.cpu_op_exec(input_x, weight, bias, padding) - npu_output = self.npu_op_exec(input_x, weight, bias, padding) - self.assertRtolEqual(cpu_output, npu_output) - - def test__nnpack_spatial_convolution_float32_2(self, device): - input_x, weight, bias, padding = self.generate_data( - #min_d, max_d, N, C0, Hi, Wi, C1, Hw, Ww, dtype - -50, 50, 1, 3, 4, 4, 2, 2, 2, np.float32) - cpu_output = self.cpu_op_exec(input_x, weight, bias, padding) - npu_output = self.npu_op_exec(input_x, weight, bias, padding) - self.assertRtolEqual(cpu_output, npu_output) - - def test__nnpack_spatial_convolution_float32_3(self, device): - input_x, weight, bias, padding = self.generate_data( - #min_d, max_d, N, C0, Hi, Wi, C1, Hw, Ww, dtype - -50, 50, 1, 5, 512, 512, 5, 8, 8, np.float32) - cpu_output = self.cpu_op_exec(input_x, weight, bias, padding) - npu_output = self.npu_op_exec(input_x, weight, bias, padding) - self.assertRtolEqual(cpu_output, npu_output) - - def test__nnpack_spatial_convolution_float32_4(self, device): - input_x, weight, bias, padding = self.generate_data( - #min_d, max_d, N, C0, Hi, Wi, C1, Hw, Ww, dtype - -100, 100, 1, 5, 512, 512, 5, 8, 8, np.float32) - cpu_output = self.cpu_op_exec(input_x, weight, bias, padding) - npu_output = self.npu_op_exec(input_x, weight, bias, padding) - self.assertRtolEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(TestNnpackSpatialConvolution, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:1") - run_tests() - - diff --git a/pytorch1.8.1/test/test_npu/test_adaptive_avg_pool1d.py b/pytorch1.8.1/test/test_npu/test_adaptive_avg_pool1d.py deleted file mode 100644 index 44f92176967..00000000000 --- a/pytorch1.8.1/test/test_npu/test_adaptive_avg_pool1d.py +++ /dev/null @@ -1,66 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import torch.nn as nn -import numpy as np -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestAdaptiveAvgPool1d(TestCase): - def cpu_op_exec(self, input, output_size): - m = nn.AdaptiveAvgPool1d(output_size) - output= m(input) - return output.numpy() - - def npu_op_exec(self, input, output_size): - m = nn.AdaptiveAvgPool1d(output_size).npu() - output = m(input) - return output.cpu().numpy() - - def test_AdaptiveAvgPool1d_shape_format_fp16(self, device): - shape_format = [ - [np.float16, 0, (64, 10, 16)], - [np.float16, 1, (256, 2048, 8)], - [np.float16, 3, (32, 16, 16)] - ] - output_list = [(4), (3), (1)] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item, 1, 10) - for output_size in output_list: - cpu_output = self.cpu_op_exec(cpu_input, output_size) - npu_output = self.npu_op_exec(npu_input, output_size) - self.assertRtolEqual(cpu_output, npu_output) - - def test_AdaptiveAvgPool1d_shape_format_fp32(self, device): - shape_format = [ - [np.float32, 0, (64, 10, 16)], - [np.float32, 1, (256, 2048, 8)], - [np.float32, 3, (32, 16, 16)] - ] - output_list = [(4), (3), (1)] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item, 1, 10) - for output_size in output_list: - cpu_output = self.cpu_op_exec(cpu_input, output_size) - npu_output = self.npu_op_exec(npu_input, output_size) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestAdaptiveAvgPool1d, globals(), except_for="cpu") -if __name__ == "__main__": - torch.npu.set_device("npu:6") - run_tests() - - diff --git a/pytorch1.8.1/test/test_npu/test_adaptive_avg_pool2d.py b/pytorch1.8.1/test/test_npu/test_adaptive_avg_pool2d.py deleted file mode 100644 index 45aca180e5b..00000000000 --- a/pytorch1.8.1/test/test_npu/test_adaptive_avg_pool2d.py +++ /dev/null @@ -1,74 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import torch.nn as nn -import numpy as np -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestAdaptiveAvgPool2d(TestCase): - def cpu_op_exec(self, input, output_size): - m = nn.AdaptiveAvgPool2d(output_size) - output= m(input) - return output.numpy() - - def npu_op_exec(self, input, output_size): - m = nn.AdaptiveAvgPool2d(output_size).npu() - output = m(input) - return output.cpu().numpy() - - def test_adaptiveAvgPool2d_shape_format_fp16(self, device): - format_list = [0, 3] - shape_list = [(32, 16, 16), - (16, 1024, 256), - (1024, 464, 11, 9), - (1, 2048, 15, 15)] - shape_format = [ - [np.float16, i, j] for i in format_list for j in shape_list - ] - # TODO(Ascend): tbe operator has problem in precision and (x, 1) case and so on. - output_list = [(4, 4), (3, 5), (1), (1, None), (None, 2)] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item, 0, 100) - cpu_input = cpu_input.to(torch.float32) - for output_size in output_list: - cpu_output = self.cpu_op_exec(cpu_input, output_size) - npu_output = self.npu_op_exec(npu_input, output_size) - cpu_output = cpu_output.astype(np.float16) - self.assertRtolEqual(cpu_output, npu_output) - - def test_adaptiveAvgPool2d_shape_format_fp32(self, device): - format_list = [0, 3] - shape_list = [(32, 16, 16), - (16, 1024, 256), - (1024, 464, 11, 9), - (1, 2048, 15, 15)] - shape_format = [ - [np.float16, i, j] for i in format_list for j in shape_list - ] - output_list = [(4, 4), (3, 5), (1), (1, None), (None, 2)] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item, 0, 100) - for output_size in output_list: - cpu_output = self.cpu_op_exec(cpu_input, output_size) - npu_output = self.npu_op_exec(npu_input, output_size) - self.assertRtolEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(TestAdaptiveAvgPool2d, globals(), except_for="cpu") -if __name__ == "__main__": - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_adaptive_avg_pool2d_backward.py b/pytorch1.8.1/test/test_npu/test_adaptive_avg_pool2d_backward.py deleted file mode 100644 index 9d09b5ee938..00000000000 --- a/pytorch1.8.1/test/test_npu/test_adaptive_avg_pool2d_backward.py +++ /dev/null @@ -1,71 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -from torch.nn import functional as F -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestAdaptiveAvgPool2dBackward(TestCase): - - def cpu_op_exec(self, input_x, input_grad): - input_x.requires_grad_(True) - m = torch.nn.AdaptiveAvgPool2d(input_grad) - output = m(input_x) - output.backward(output) - out = input_x.grad - return out - - def npu_op_exec(self, input_x, input_grad): - input_x.requires_grad_(True) - m = torch.nn.AdaptiveAvgPool2d(input_grad) - output = m(input_x) - output.backward(output) - out = input_x.grad.cpu() - return out - - def test_adaptiveAvgPool2d_backward_1(self, device): - cpu_input = torch.randn((1, 8, 9), dtype=torch.float32) - npu_input = cpu_input - output_size = np.array((2, 3)) - cpu_output = self.cpu_op_exec(cpu_input, output_size) - npu_output = self.npu_op_exec(npu_input, output_size) - self.assertRtolEqual(cpu_output.numpy(), npu_output.numpy()) - - def test_adaptiveAvgPool2d_backward_2(self, device): - cpu_input = torch.randn((1, 3, 3, 3), dtype=torch.float32) - npu_input = cpu_input - output_size = np.array((2, 2)) - cpu_output = self.cpu_op_exec(cpu_input, output_size) - npu_output = self.npu_op_exec(npu_input, output_size) - - self.assertRtolEqual(cpu_output.numpy(), npu_output.numpy()) - - def test_adaptiveAvgPool2d_backward_fp16(self, device): - input_x = np.random.uniform(0, 1, (1, 3, 6, 6)).astype(np.float16) - cpu_input = torch.from_numpy(input_x) - npu_input = cpu_input - output_size = np.array((5, 5)) - cpu_input = cpu_input.to(torch.float32) - cpu_output = self.cpu_op_exec(cpu_input, output_size) - npu_output = self.npu_op_exec(npu_input, output_size) - cpu_output = cpu_output.to(torch.float16) - self.assertRtolEqual(cpu_output.numpy(), npu_output.numpy()) - -instantiate_device_type_tests(TestAdaptiveAvgPool2dBackward, globals(), except_for="cpu") -if __name__ == "__main__": - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_adaptive_max_pool1d.py b/pytorch1.8.1/test/test_npu/test_adaptive_max_pool1d.py deleted file mode 100644 index a8b1ea91cc8..00000000000 --- a/pytorch1.8.1/test/test_npu/test_adaptive_max_pool1d.py +++ /dev/null @@ -1,75 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import torch.nn as nn -import numpy as np -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestAdaptiveMaxPool1d(TestCase): - def cpu_op_exec(self, input, output_size): - m = nn.AdaptiveMaxPool1d(output_size) - output= m(input) - return output.numpy() - - def npu_op_exec(self, input, output_size): - m = nn.AdaptiveMaxPool1d(output_size).npu() - output = m(input) - return output.cpu().numpy() - - def test_adaptiveMaxPool1d_shape_format_fp16(self, device): - format_list = [0, 3] - shape_list = [(32, 16, 16), - (16, 1024, 256), - (1024, 464, 11), - (1, 2048, 15)] - shape_format = [ - [np.float16, i, j] for i in format_list for j in shape_list - ] - - output_list = [4, 3, 1, 2] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item, 0, 100) - cpu_input = cpu_input.to(torch.float32) - for output_size in output_list: - cpu_output = self.cpu_op_exec(cpu_input, output_size) - npu_output = self.npu_op_exec(npu_input, output_size) - cpu_output = cpu_output.astype(np.float16) - self.assertRtolEqual(cpu_output, npu_output) - - def test_adaptiveMaxPool1d_shape_format_fp32(self, device): - format_list = [0, 3] - shape_list = [(32, 16, 16), - (16, 1024, 256), - (1024, 464, 11), - (1, 2048, 15)] - shape_format = [ - [np.float32, i, j] for i in format_list for j in shape_list - ] - output_list = [4, 3, 1, 2] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item, 0, 100) - for output_size in output_list: - cpu_output = self.cpu_op_exec(cpu_input, output_size) - npu_output = self.npu_op_exec(npu_input, output_size) - self.assertRtolEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(TestAdaptiveMaxPool1d, globals(), except_for="cpu") -if __name__ == "__main__": - torch.npu.set_device("npu:1") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_addbmm.py b/pytorch1.8.1/test/test_npu/test_addbmm.py deleted file mode 100644 index 14ed9e9f6e8..00000000000 --- a/pytorch1.8.1/test/test_npu/test_addbmm.py +++ /dev/null @@ -1,114 +0,0 @@ -import torch -import numpy as np -import sys -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestAddbmm(TestCase): - def generate_scalar(self, dtype, min_d, max_d): - if dtype == "float32": - scalar = np.random.uniform(min_d, max_d) - if dtype == "int32": - scalar = np.random.randint(min_d, max_d) - return scalar - - def cpu_op_exec(self, input1, input2, input3, scalar1, scalar2): - output = torch.addbmm(input1, input2, input3, beta=scalar1, alpha=scalar2) - output = output.numpy() - return output - - def npu_op_exec(self, input1, input2, input3, scalar1, scalar2): - input1 = input1.to("npu") - input2 = input2.to("npu") - input3 = input3.to("npu") - output = torch.addbmm(input1, input2, input3, beta=scalar1, alpha=scalar2) - output = output.to("cpu") - output = output.numpy() - return output - - def npu_op_exec_out(self, input1, input2, input3, scalar1, scalar2, input4): - input1 = input1.to("npu") - input2 = input2.to("npu") - input3 = input3.to("npu") - output = input4.to("npu") - torch.addbmm(input1, input2, input3, beta=scalar1, alpha=scalar2, out=output) - output = output.to("cpu") - output = output.numpy() - return output - - def npu_op_exec_inplace(self, input1, input2, input3, scalar1, scalar2): - input1 = input1.to("npu") - input2 = input2.to("npu") - input3 = input3.to("npu") - input1.addbmm_(input2, input3, beta=scalar1, alpha=scalar2) - output = input1.to("cpu") - output = output.numpy() - return output - - - def cpu_op_transpose_exec(self, input1, input2, input3, scalar1, scalar2): - input3_t = np.transpose(input3,(0,2,1)) - output = torch.addbmm(input1, input2, input3_t, beta=scalar1, alpha=scalar2) - output = output.numpy() - return output - - def npu_op_transpose_exec(self, input1, input2, input3, scalar1, scalar2): - input1 = input1.to("npu") - input2 = input2.to("npu") - input3 = input3.to("npu") - input3_t = np.transpose(input3,(0,2,1)) - output = torch.addbmm(input1, input2, input3_t, beta=scalar1, alpha=scalar2) - output = output.to("cpu") - output = output.numpy() - return output - - def test_addbmm(self, device): - shape_format = [ - [[np.float32, 0, [3, 5]], [np.float32, 0, [10, 3, 4]], [np.float32, 0, [10, 4, 5]], "float32"], - [[np.int32, 0, [3, 5]], [np.int32, 0, [10, 3, 4]], [np.int32, 0, [10, 4, 5]], "int32"] - ] - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 0, 100) - cpu_input2, npu_input2 = create_common_tensor(item[1], 0, 100) - cpu_input3, npu_input3 = create_common_tensor(item[2], 0, 100) - cpu_input4, npu_input4 = create_common_tensor(item[0], 0, 100) - - scalar1 = self.generate_scalar(item[3], 0, 10) - scalar2 = self.generate_scalar(item[3], 0, 10) - - cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2, cpu_input3, scalar1, scalar2) - npu_output = self.npu_op_exec(npu_input1, npu_input2, npu_input3, scalar1, scalar2) - - npu_output1 = self.npu_op_exec_out(npu_input1, npu_input2, npu_input3, scalar1, scalar2, npu_input4) - npu_output2 = self.npu_op_exec_inplace(npu_input1, npu_input2, npu_input3, scalar1, scalar2) - - self.assertRtolEqual(cpu_output, npu_output) - self.assertRtolEqual(cpu_output, npu_output1) - self.assertRtolEqual(cpu_output, npu_output2) - - def test_addbmm_transpose(self, device): - shape_format = [ - [[np.float32, 0, [4, 5]], [np.float32, 0, [10, 4, 7]], [np.float32, 0, [10, 5, 7]], "float32"], - [[np.int32, 0, [4, 5]], [np.int32, 0, [10, 4, 7]], [np.int32, 0, [10, 5, 7]], "int32"] - ] - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 0, 100) - cpu_input2, npu_input2 = create_common_tensor(item[1], 0, 100) - cpu_input3, npu_input3 = create_common_tensor(item[2], 0, 100) - - scalar1 = self.generate_scalar(item[3], 0, 10) - scalar2 = self.generate_scalar(item[3], 0, 10) - - cpu_transpose_output = self.cpu_op_transpose_exec(cpu_input1, cpu_input2, cpu_input3, scalar1, scalar2) - npu_transpose_output = self.npu_op_transpose_exec(npu_input1, npu_input2, npu_input3, scalar1, scalar2) - - self.assertRtolEqual(cpu_transpose_output, npu_transpose_output) - - -instantiate_device_type_tests(TestAddbmm, globals(), except_for='cpu') -if __name__ == "__main__": - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_addcdiv.py b/pytorch1.8.1/test/test_npu/test_addcdiv.py deleted file mode 100644 index ae33fda7514..00000000000 --- a/pytorch1.8.1/test/test_npu/test_addcdiv.py +++ /dev/null @@ -1,269 +0,0 @@ -# Copyright (c) 2020 Huawei Technologies Co., Ltd -# Copyright (c) 2019, Facebook CORPORATION. -# All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestAddcdiv(TestCase): - def test_addcdiv(self, device): - def _test_addcdiv(a, alpha, b, c): - actual = torch.addcdiv(a, b, c, value=alpha) - # implementation of addcdiv downcasts alpha. arithmetic ops don't. - if not actual.dtype.is_floating_point: - alpha = int(alpha) - expected = a + (alpha * b) / c - # print(expected) - # print(actual) - self.assertTrue(torch.allclose(expected.to("cpu"), actual.to("cpu"), equal_nan=True)) - - with self.maybeWarnsRegex( - UserWarning, "This overload of addcdiv is deprecated"): - self.assertEqual(actual.to("cpu"), torch.addcdiv(a, alpha, b, c).to("cpu")) - - def non_zero_rand(size, dtype, device): - if dtype.is_floating_point: - a = torch.rand(size=size, dtype=dtype, device="cpu") - a = a.to("npu") # torch.rand()在npu暂未适配 - elif dtype == torch.uint8: - a = torch.randint(1, 5, size=size, dtype=dtype, device=device) - else: - a = torch.randint(-5, 5, size=size, dtype=dtype, device=device) - # return a + (a == 0).type(dtype) #add 方法有些问题，先注释不使用 - return a.type(dtype) - - for dtype in torch.testing.get_all_math_dtypes(device): - # print(dtype, " : ", device) - if dtype in [torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64, torch.float64]: - continue - _test_addcdiv( - non_zero_rand((2, 2), dtype=dtype, device=device), - 0.5, - non_zero_rand((2, 2), dtype=dtype, device=device), - non_zero_rand((2, 2), dtype=dtype, device=device)) - - def generate_data(self, min, max, shape, dtype): - input1 = np.random.uniform(min, max, shape).astype(dtype) - input2 = np.random.uniform(min, max, shape).astype(dtype) - input3 = np.random.uniform(min, max, shape).astype(dtype) - - # 将numpy.ndarray转换为torch.tensor - npu_input1 = torch.from_numpy(input1) - npu_input2 = torch.from_numpy(input2) - npu_input3 = torch.from_numpy(input3) - - return npu_input1, npu_input2, npu_input3 - - def generate_single_data(self, min, max, shape, dtype): - input = np.random.uniform(min, max, shape).astype(dtype) - npu_input = torch.from_numpy(input) - return npu_input - - def generate_scalar(self, min, max): - scalar = np.random.uniform(min, max) - return scalar - - def generate_int_scalar(self, min, max): - scalar = np.random.randint(min, max) - return scalar - - def test_addcdiv_float32(self, device): - def cpu_op_exec(input1, input2, input3, scalar): - output = torch.addcdiv(input1, input2, input3, value=scalar) - return output - - def npu_op_exec(input1, input2, input3, scalar): - input1 = input1.to("npu") - input2 = input2.to("npu") - input3 = input3.to("npu") - output = torch.addcdiv(input1, input2, input3, value=scalar) - output = output.to("cpu") - return output - - npu_input1, npu_input2, npu_input3 = self.generate_data(1, 100, (5, 3), np.float32) - scalar = self.generate_scalar(1, 10) - cpu_output = cpu_op_exec(npu_input1, npu_input2, npu_input3, scalar) - npu_output = npu_op_exec(npu_input1, npu_input2, npu_input3, scalar) - self.assertEqual(cpu_output, npu_output) - - - def test_addcdiv_float32_out(self, device): - def cpu_op_exec_out(input1, input2, input3, scalar, input4): - output = input4 - torch.addcdiv(input1, input2, input3, value=scalar, out=output) - output = output.numpy() - return output - - def npu_op_exec_out(input1, input2, input3, scalar, input4): - input1 = input1.to("npu") - input2 = input2.to("npu") - input3 = input3.to("npu") - output = input4.to("npu") - torch.addcdiv(input1, input2, input3, value=scalar, out=output) - output = output.to("cpu") - output = output.numpy() - return output - - npu_input1, npu_input2, npu_input3 = self.generate_data(1, 100, (5, 3), np.float32) - scalar = self.generate_scalar(1, 10) - npu_input4 = self.generate_single_data(1, 100, (5, 3), np.float32) - cpu_output = cpu_op_exec_out(npu_input1, npu_input2, npu_input3, scalar, npu_input4) - npu_output = npu_op_exec_out(npu_input1, npu_input2, npu_input3, scalar, npu_input4) - self.assertEqual(cpu_output, npu_output) - - def test_addcdiv_float32_broadcast(self, device): - def cpu_op_exec(input1, input2, input3, scalar): - output = torch.addcdiv(input1, input2, input3, value=scalar) - return output - - def npu_op_exec(input1, input2, input3, scalar): - input1 = input1.to("npu") - input2 = input2.to("npu") - input3 = input3.to("npu") - output = torch.addcdiv(input1, input2, input3, value=scalar) - output = output.to("cpu") - return output - - npu_input1 = self.generate_single_data(1, 100, (5, 3, 1), np.float32) - npu_input2 = self.generate_single_data(1, 100, (5, 1, 5), np.float32) - npu_input3 = self.generate_single_data(1, 100, (1, 1, 5), np.float32) - scalar = self.generate_scalar(1, 10) - cpu_output = cpu_op_exec(npu_input1, npu_input2, npu_input3, scalar) - npu_output = npu_op_exec(npu_input1, npu_input2, npu_input3, scalar) - # self.assertEqual(cpu_output, npu_output) - self.assertRtolEqual(cpu_output, npu_output) - - def test_addcdiv_inp_contiguous_float32(self, device): - def cpu_op_inp_contiguous_exec(input1, input2, input3, scalar): - input1.addcdiv_(input2, input3, value=scalar) - output = input1.numpy() - return output - - def npu_op_inp_contiguous_exec(input1, input2, input3, scalar): - input1 = input1.to("npu") - input2 = input2.to("npu") - input3 = input3.to("npu") - input1.addcdiv_(input2, input3, value=scalar) - output = input1.to("cpu") - output = output.numpy() - return output - - npu_input1, npu_input2, npu_input3 = self.generate_data(1, 100, (5, 3), np.float32) - cpu_input1 = copy.deepcopy(npu_input1) - cpu_input2 = copy.deepcopy(npu_input2) - cpu_input3 = copy.deepcopy(npu_input3) - scalar = self.generate_int_scalar(1, 10) - cpu_output = cpu_op_inp_contiguous_exec(cpu_input1, cpu_input2, cpu_input3, scalar) - npu_output = npu_op_inp_contiguous_exec(npu_input1, npu_input2, npu_input3, scalar) - self.assertEqual(cpu_output, npu_output) - - def test_addcdiv_inp_input1_noncontiguous_float32(self, device): - def cpu_op_inp_input1_noncontiguous_exec(input1, input2, input3, scalar): - input1_strided = input1.as_strided([2, 2], [1, 2], 2) - input1_strided.addcdiv_(input2, input3, value=scalar) - output = input1.numpy() - return output - - def npu_op_inp_input1_noncontiguous_exec(input1, input2, input3, scalar): - input1 = input1.to("npu") - input2 = input2.to("npu") - input3 = input3.to("npu") - input1_as_strided = input1.as_strided([2, 2], [1, 2], 2) - input1_as_strided.addcdiv_(input2, input3, value=scalar) - output = input1.to("cpu") - output = output.numpy() - return output - - npu_input1 = self.generate_single_data(1, 100, (4, 3), np.float32) - npu_input2 = self.generate_single_data(1, 100, (2, 2), np.float32) - npu_input3 = self.generate_single_data(1, 100, (2, 2), np.float32) - cpu_input1 = copy.deepcopy(npu_input1) - cpu_input2 = copy.deepcopy(npu_input2) - cpu_input3 = copy.deepcopy(npu_input3) - scalar = self.generate_int_scalar(1, 10) - cpu_output = cpu_op_inp_input1_noncontiguous_exec(cpu_input1, cpu_input2, cpu_input3, scalar) - npu_output = npu_op_inp_input1_noncontiguous_exec(npu_input1, npu_input2, npu_input3, scalar) - self.assertEqual(cpu_output, npu_output) - - def test_addcdiv_inp_input2_noncontiguous_float32(self, device): - def cpu_op_inp_input2_noncontiguous_exec(input1, input2, input3, scalar): - input2_strided = input2.as_strided([2, 2], [1, 2], 2) - input1.addcdiv_(input2_strided, input3, value=scalar) - output = input1.numpy() - return output - - def npu_op_inp_input2_noncontiguous_exec(input1, input2, input3, scalar): - input1 = input1.to("npu") - input3 = input3.to("npu") - input2 = input2.to("npu") - input2_as_strided = input2.as_strided([2, 2], [1, 2], 2) - input1.addcdiv_(input2_as_strided, input3, value=scalar) - output = input1.to("cpu") - output = output.numpy() - return output - - npu_input1 = self.generate_single_data(1, 100, (2, 2), np.float32) - npu_input2 = self.generate_single_data(1, 100, (4, 3), np.float32) - npu_input3 = self.generate_single_data(1, 100, (2, 2), np.float32) - cpu_input1 = copy.deepcopy(npu_input1) - cpu_input2 = copy.deepcopy(npu_input2) - cpu_input3 = copy.deepcopy(npu_input3) - scalar = self.generate_int_scalar(1, 10) - cpu_output = cpu_op_inp_input2_noncontiguous_exec(cpu_input1, cpu_input2, cpu_input3, scalar) - npu_output = npu_op_inp_input2_noncontiguous_exec(npu_input1, npu_input2, npu_input3, scalar) - self.assertEqual(cpu_output, npu_output) - - def test_addcdiv_inp_input3_noncontiguous_float32(self, device): - def cpu_op_inp_input3_noncontiguous_exec(input1, input2, input3, scalar): - input3_strided = input3.as_strided([2, 2], [1, 2], 2) - input1.addcdiv_(input2, input3_strided, value=scalar) - output = input1.numpy() - return output - - def npu_op_inp_input3_noncontiguous_exec(input1, input2, input3, scalar): - input1 = input1.to("npu") - input2 = input2.to("npu") - input3 = input3.to("npu") - input3_as_strided = input3.as_strided([2, 2], [1, 2], 2) - input1.addcdiv_(input2, input3_as_strided, value=scalar) - output = input1.to("cpu") - output = output.numpy() - return output - - npu_input1 = self.generate_single_data(1, 100, (2, 2), np.float32) - npu_input2 = self.generate_single_data(1, 100, (2, 2), np.float32) - npu_input3 = self.generate_single_data(1, 100, (4, 3), np.float32) - cpu_input1 = copy.deepcopy(npu_input1) - cpu_input2 = copy.deepcopy(npu_input2) - cpu_input3 = copy.deepcopy(npu_input3) - scalar = self.generate_int_scalar(1, 10) - cpu_output = cpu_op_inp_input3_noncontiguous_exec(cpu_input1, cpu_input2, cpu_input3, scalar) - npu_output = npu_op_inp_input3_noncontiguous_exec(npu_input1, npu_input2, npu_input3, scalar) - self.assertEqual(cpu_output, npu_output) - - - - - - -instantiate_device_type_tests(TestAddcdiv, globals(), except_for="cpu") - -if __name__ == "__main__": - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_affine_grid_generator_backward.py b/pytorch1.8.1/test/test_npu/test_affine_grid_generator_backward.py deleted file mode 100644 index f06cd9882ad..00000000000 --- a/pytorch1.8.1/test/test_npu/test_affine_grid_generator_backward.py +++ /dev/null @@ -1,72 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -from torch.nn import functional as F -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestAffineGridGeneratorBackward(TestCase): - def test_affine_grid_generator_backward_common_shape(self, device): - shape_list = [[100, 2, 3], [10, 2, 3]] - shape_format = [ - [np.float32, -1, j] for j in shape_list - ] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item, 0, 1) - size = torch.Size((item[2][0], 2, 28, 2)) - cpu_input.requires_grad = True - cpu_output = self.cpu_op_exec(cpu_input, size) - npu_input.requires_grad = True - npu_output = self.npu_op_exec(npu_input, size) - self.assertRtolEqual(cpu_output, npu_output) - - def test_affine_grid_generator_backward_fp16(self, device): - shape_list = [[100, 2, 3], [10, 2, 3]] - shape_format = [ - [np.float16, -1, j] for j in shape_list - ] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item, 0, 1) - cpu_input = cpu_input.to(torch.float32) - npu_input = npu_input.to(torch.float32) - size = torch.Size((item[2][0], 2, 28, 2)) - cpu_input.requires_grad = True - cpu_output = self.cpu_op_exec(cpu_input, size) - npu_input.requires_grad = True - npu_output = self.npu_op_exec(npu_input, size) - self.assertRtolEqual(cpu_output.astype(np.float16), npu_output.astype(np.float16)) - - def cpu_op_exec(self, input, size): - out = F.affine_grid(input, size, True) - input.requires_grad = True - grad_output = torch.ones(out.size(), dtype=torch.float) - out.backward(gradient=grad_output) - output = input.grad.numpy() - return output - - def npu_op_exec(self, input, size): - input.requires_grad = True - out = F.affine_grid(input, size, True) - grad_output = torch.ones(out.size(), dtype=torch.float).npu() - out.backward(gradient=grad_output) - output = input.grad.to("cpu").numpy() - return output - -instantiate_device_type_tests(TestAffineGridGeneratorBackward, globals(), except_for="cpu") -if __name__ == "__main__": - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_amp/common_device_type.py b/pytorch1.8.1/test/test_npu/test_amp/common_device_type.py new file mode 100644 index 00000000000..0dee03c1fcf --- /dev/null +++ b/pytorch1.8.1/test/test_npu/test_amp/common_device_type.py @@ -0,0 +1,23 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. +# All rights reserved. +# +# Licensed under the BSD 3-Clause License (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://opensource.org/licenses/BSD-3-Clause +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import sys + +common_path = os.path.dirname("../common/") +if common_path not in sys.path: + sys.path.append(common_path) +from common_device_type_new import dtypes, instantiate_device_type_tests, formats \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_amp/common_utils.py b/pytorch1.8.1/test/test_npu/test_amp/common_utils.py new file mode 100644 index 00000000000..9de8fc29154 --- /dev/null +++ b/pytorch1.8.1/test/test_npu/test_amp/common_utils.py @@ -0,0 +1,29 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. +# All rights reserved. +# +# Licensed under the BSD 3-Clause License (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://opensource.org/licenses/BSD-3-Clause +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +r"""Importing this file must **not** initialize CUDA context. test_distributed +relies on this assumption to properly run. This means that when this is imported +no CUDA calls shall be made, including torch.cuda.device_count(), etc. + +torch.testing._internal.common_cuda.py can freely initialize CUDA context when imported. +""" +import os +import sys + +common_path = os.path.dirname("../common/") +if common_path not in sys.path: + sys.path.append(common_path) +from common_utils_new import TestCase, run_tests diff --git a/pytorch1.8.1/test/test_npu/test_amp/test_amp.py b/pytorch1.8.1/test/test_npu/test_amp/test_amp.py new file mode 100644 index 00000000000..33c0535fd27 --- /dev/null +++ b/pytorch1.8.1/test/test_npu/test_amp/test_amp.py @@ -0,0 +1,302 @@ +# Copyright (c) 2021, Huawei Technologies.All rights reserved. +# +# Licensed under the BSD 3-Clause License (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://opensource.org/licenses/BSD-3-Clause +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from itertools import chain +import pickle + +import torch +from torch.npu.amp import NpuGradScaler, NpuAutocast + +from common_utils import TestCase, run_tests +from common_device_type import instantiate_device_type_tests +from util_test import create_common_tensor + +class TestAmp(TestCase): + def make_device_overflow(self): + float_tensor = torch.tensor([40000.0], dtype=torch.float16).npu() + float_tensor = float_tensor + float_tensor + + def test_grad_scaling_scale(self, device): + scaler = NpuGradScaler(init_scale=2.) + t0 = torch.full((1,), 4.0, dtype=torch.float32, device="npu") + t1 = torch.full((1,), 4.0, dtype=torch.float32, device="npu") + # Create some nested iterables of tensors on different devices. + outputs = (t1.clone(), (t0.clone(), t1.clone()), [t0.clone(), (t1.clone(), t0.clone())]) + outputs = scaler.scale(outputs) + self.assertTrue(outputs[0] == 8.0 and outputs[1][0] == 8.0 and outputs[1][1] == 8.0 and + outputs[2][0] == 8.0 and outputs[2][1][0] == 8.0 and outputs[2][1][1] == 8.0) + self.assertTrue(scaler._scale.device == t1.device) + + def test_grad_scaling_state_dict(self, device): + for lazy_init_scale in True, False: + s0 = NpuGradScaler(init_scale=3., growth_factor=4., backoff_factor=.5, growth_interval=2) + s1 = NpuGradScaler(init_scale=6., growth_factor=7., backoff_factor=.8, growth_interval=1) + + # sets a random value for load_state_dict to overwrite + s1._init_growth_tracker = 7 + + if lazy_init_scale: + # Dummy scale() call to ensure the scale tensor is lazily initialized. + s1.scale(torch.full((1,), 4.0, dtype=torch.float32, device="npu")) + self.assertTrue(isinstance(s1._scale, torch.npu.FloatTensor)) + + s1.load_state_dict(s0.state_dict()) + + self.assertTrue(s1.get_scale() == 3.) + self.assertTrue(s1.get_growth_factor() == 4.) + self.assertTrue(s1.get_backoff_factor() == .5) + self.assertTrue(s1.get_growth_interval() == 2) + self.assertTrue(s1._init_growth_tracker == 0) + + def _create_scaling_models_optimizers(self, device="npu"): + # Create a module+optimizer that will use scaling, and a control module+optimizer + # that will not use scaling, against which the scaling-enabled module+optimizer can be compared. + mod_control = torch.nn.Sequential(torch.nn.Linear(8, 8), torch.nn.Linear(8, 8)).to(device=device) + mod_scaling = torch.nn.Sequential(torch.nn.Linear(8, 8), torch.nn.Linear(8, 8)).to(device=device) + for c, s in zip(mod_control.parameters(), mod_scaling.parameters()): + s.data.copy_(c.data) + + opt_control = torch.optim.SGD(mod_control.parameters(), lr=1.0) + opt_scaling = torch.optim.SGD(mod_scaling.parameters(), lr=1.0) + + return mod_control, mod_scaling, opt_control, opt_scaling + + def _create_scaling_case(self, device="npu", dtype=torch.float): + data = [(torch.randn((8, 8), dtype=dtype, device=device), torch.randn((8, 8), dtype=dtype, device=device)), + (torch.randn((8, 8), dtype=dtype, device=device), torch.randn((8, 8), dtype=dtype, device=device)), + (torch.randn((8, 8), dtype=dtype, device=device), torch.randn((8, 8), dtype=dtype, device=device)), + (torch.randn((8, 8), dtype=dtype, device=device), torch.randn((8, 8), dtype=dtype, device=device))] + + loss_fn = torch.nn.MSELoss().npu() + + skip_iter = 2 + + return self._create_scaling_models_optimizers(device=device) + (data, loss_fn, skip_iter) + + # _run_scaling_case generalizes some single-optimizer test logic to avoid too much copy-pasting below. + def _run_scaling_case(self, run, unskipped, skipped, atol=1e-7): + # Ensure scaling can be disabled without changing user control flow. + for enabled in True, False: + mod_control, mod_scaling, opt_control, opt_scaling, data, loss_fn, skip_iter = self._create_scaling_case() + + # For functionality, test with a modest initial scale, and an unrealistically-large growth factor + # so any potential errors with the growth factor handling will be magnified. + scaler = NpuGradScaler(init_scale=128., growth_factor=2.0, enabled=enabled, growth_interval=1) + + _ = run(data, mod_control, opt_control, scaler, loss_fn, skip_iter, False) + ret = run(data, mod_scaling, opt_scaling, scaler, loss_fn, skip_iter, True) + + # Allows run() to optionally return a different scaler instance. + scaler = ret if ret else scaler + + # If scaling was enabled, the scale factor should have been multiplied by the growth factor + # len(data) - skipped times and the backoff factor "skipped" times. + if enabled: + net_growth = scaler.get_growth_factor()**unskipped if unskipped > 0 else 1.0 + net_backoff = scaler.get_backoff_factor()**skipped if skipped > 0 else 1.0 + self.assertTrue(scaler.get_scale() == (128. * net_growth * net_backoff)) + else: + self.assertTrue(scaler.get_scale() == 1.0) + + for c, s in zip(mod_control.parameters(), mod_scaling.parameters()): + c = c.cpu().to(torch.float).detach().numpy() + s = s.cpu().to(torch.float).detach().numpy() + self.assertRtolEqual(c, s, atol) + + # Compares no scaling + no autocasting against scaling + autocasting. + def test_grad_scaling_autocast(self, device): + try_pickle = False + + def run(data, model, optimizer, scaler, loss_fn, skip_iter, try_scaling_api): + for i, (input_data, target) in enumerate(data): + optimizer.zero_grad() + with NpuAutocast(enabled=try_scaling_api): + output = model(input_data) + loss = loss_fn(output, target) + if try_scaling_api: + scaler.scale(loss).backward() + if i == skip_iter and scaler.is_enabled(): + self.make_device_overflow() + scaler.step(optimizer) + scaler.update() + if try_pickle: + scaler = pickle.loads(pickle.dumps(scaler)) + else: + loss.backward() + if (not scaler.is_enabled()) or (i != skip_iter): + optimizer.step() + return scaler + + # sets atol=1e-3 because we're comparing pure fp32 arithmetic vs a mixture of fp16 and fp32 + self._run_scaling_case(run, unskipped=3, skipped=1, atol=1e-3) + # this will be picked up by try_pickle within run(): + try_pickle = True + self._run_scaling_case(run, unskipped=3, skipped=1, atol=1e-3) + + def test_grad_scaling_clipping(self, device): + def run(data, model, optimizer, scaler, loss_fn, skip_iter, try_scaling_api): + max_norm = 0.2 # A reasonable value that actually has an effect, based on printouts of grads + for i, (input_data, target) in enumerate(data): + optimizer.zero_grad() + output = model(input_data) + loss = loss_fn(output, target) + if try_scaling_api: + scaler.scale(loss).backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm * scaler.get_scale()) + if i == skip_iter and scaler.is_enabled(): + self.make_device_overflow() + scaler.step(optimizer) + scaler.update() + else: + loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm) + if (not scaler.is_enabled()) or (i != skip_iter): + optimizer.step() + + self._run_scaling_case(run, unskipped=3, skipped=1, atol=1e-6) + + def test_grad_scaling_clipping_separate_unscale(self, device): + def run(data, model, optimizer, scaler, loss_fn, skip_iter, try_scaling_api): + max_norm = 0.2 # A reasonable value that actually has an effect, based on printouts of grads + for i, (input_data, target) in enumerate(data): + optimizer.zero_grad() + output = model(input_data) + loss = loss_fn(output, target) + if try_scaling_api: + scaler.scale(loss).backward() + if i == skip_iter and scaler.is_enabled(): + self.make_device_overflow() + scaler.unscale_(optimizer) + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm) + scaler.step(optimizer) + scaler.update() + else: + loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm) + if (not scaler.is_enabled()) or (i != skip_iter): + optimizer.step() + + self._run_scaling_case(run, unskipped=3, skipped=1) + + def test_grad_scaling_penalty(self, device): + def run(data, model, optimizer, scaler, loss_fn, skip_iter, try_scaling_api): + for i, (input_data, target) in enumerate(data): + optimizer.zero_grad() + output = model(input_data) + loss = loss_fn(output, target) + + if try_scaling_api: + grad_params = torch.autograd.grad(scaler.scale(loss), + model.parameters(), create_graph=True) + inv_scale = 1. / scaler.get_scale() + grad_params = [p * inv_scale for p in grad_params] + else: + grad_params = torch.autograd.grad(loss, model.parameters(), create_graph=True) + + grad_norm = 0 + for grad in grad_params: + grad_norm += grad.pow(2).sum() + grad_norm = grad_norm.sqrt() + loss = loss + grad_norm + + if try_scaling_api: + scaler.scale(loss).backward() + if i == skip_iter and scaler.is_enabled(): + self.make_device_overflow() + scaler.step(optimizer) + scaler.update() + else: + loss.backward() + if (not scaler.is_enabled()) or (i != skip_iter): + optimizer.step() + + self._run_scaling_case(run, unskipped=3, skipped=1) + + def test_grad_scaling_accumulation(self, device): + def run(data, model, optimizer, scaler, loss_fn, skip_iter, try_scaling_api): + iters_to_accumulate = 2 + for i, (input_data, target) in enumerate(data): + output = model(input_data) + loss = loss_fn(output, target) + loss = loss / iters_to_accumulate + if try_scaling_api: + scaler.scale(loss).backward() + else: + loss.backward() + if (i + 1) % iters_to_accumulate == 0: + if try_scaling_api: + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + else: + optimizer.step() + optimizer.zero_grad() + + self._run_scaling_case(run, unskipped=2, skipped=0) + + def test_grad_scaling_multiple(self, device): + # Tests gradient scaling with 2 models and 2 optimizers that both receive gradients from 2 losses. + # Some of the logic here cannot reuse the generic helper functions created for the 1-optimizer cases. + for enabled in True, False: + mod_control0, mod_scaling0, opt_control0, opt_scaling0, data, loss_fn, skip_iter = \ + self._create_scaling_case() + mod_control1, mod_scaling1, opt_control1, opt_scaling1 = \ + self._create_scaling_models_optimizers() + + scaler = NpuGradScaler(init_scale=128., growth_factor=2.0, enabled=enabled, growth_interval=1) + + def run(model0, model1, optimizer0, optimizer1, try_scaling_api): + for i, (input_data, target) in enumerate(data): + optimizer0.zero_grad() + optimizer1.zero_grad() + output0 = model0(input_data) + output1 = model1(input_data) + loss0 = loss_fn(0.3 * output0 + 0.7 * output1, target) + loss1 = loss_fn(0.6 * output0 - 0.4 * output1, target) + + if try_scaling_api: + scaler.scale(loss0).backward(retain_graph=True) + scaler.scale(loss1).backward() + if i == skip_iter and scaler.is_enabled(): + self.make_device_overflow() + + # As an additional stress test, separately unscale for one of the optimizers. + scaler.unscale_(optimizer0) + + scaler.step(optimizer0) + scaler.step(optimizer1) + scaler.update() + else: + loss0.backward(retain_graph=True) + loss1.backward() + if (not scaler.is_enabled()) or (i != skip_iter): + optimizer0.step() + optimizer1.step() + + run(mod_control0, mod_control1, opt_control0, opt_control1, False) + run(mod_scaling0, mod_scaling1, opt_scaling0, opt_scaling1, True) + + # The loss scale should have been multiplied by the growth factor 3 times and the backoff factor once. + self.assertTrue(scaler.get_scale() == (128. * scaler.get_growth_factor()**3 * + scaler.get_backoff_factor()**1) if enabled else 1.0) + + for c, s in zip(chain(mod_control0.parameters(), mod_control1.parameters()), + chain(mod_scaling0.parameters(), mod_scaling1.parameters())): + c = c.cpu().to(torch.float).detach().numpy() + s = s.cpu().to(torch.float).detach().numpy() + self.assertRtolEqual(c, s, 1e-7) + +instantiate_device_type_tests(TestAmp, globals(), except_for='cpu') +if __name__ == "__main__": + run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_amp/util_test.py b/pytorch1.8.1/test/test_npu/test_amp/util_test.py new file mode 100644 index 00000000000..90eab062ed9 --- /dev/null +++ b/pytorch1.8.1/test/test_npu/test_amp/util_test.py @@ -0,0 +1,22 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. +# All rights reserved. +# +# Licensed under the BSD 3-Clause License (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://opensource.org/licenses/BSD-3-Clause +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +import sys + +common_path = os.path.dirname("../common/") +if common_path not in sys.path: + sys.path.append(common_path) +from util_test_new import create_common_tensor diff --git a/pytorch1.8.1/test/test_npu/test_argsort.py b/pytorch1.8.1/test/test_npu/test_argsort.py deleted file mode 100644 index bdf7b8af323..00000000000 --- a/pytorch1.8.1/test/test_npu/test_argsort.py +++ /dev/null @@ -1,71 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestArgsort(TestCase): - def cpu_op_exec(self, input, dim, descending): - output = torch.argsort(input, dim, descending) - output = output.numpy() - output = output.astype("int32") - return output - - def cpu_fp16_op_exec(self, input, dim, descending): - input = input.to(torch.float32) - output = torch.argsort(input, dim, descending) - output = output.numpy() - output = output.astype("int32") - return output - - def npu_op_exec(self, input, dim, descending): - output = torch.argsort(input, dim, descending) - output = output.to("cpu") - output = output.numpy() - output = output.astype("int32") - return output - - def test_argsort_shape_format_fp32(self, device): - shape_format = [ - [[np.float32, -1, (1, 12, 5, 8)], -1, False], - [[np.float32, -1, (2, 3, 13)], 2, True], - [[np.float32, -1, (5, 20)], 1, False], - [[np.float32, -1, (1,)], 0, False] - ] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item[0], -100, 100) - cpu_output = self.cpu_op_exec(cpu_input, item[1], item[2]) - npu_output = self.npu_op_exec(npu_input, item[1], item[2]) - self.assertRtolEqual(cpu_output, npu_output) - - def test_argsort_shape_format_fp16(self, device): - shape_format = [ - #[[np.float16, -1, (2, 31, 15, 7)], -2, False], - [[np.float16, -1, (2, 5, 23)], 1, False], - [[np.float16, -1, (5, 12)], -1, True], - [[np.float16, -1, (1, 1)], 0, False] - ] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item[0], -100, 100) - cpu_output = self.cpu_fp16_op_exec(cpu_input, item[1], item[2]) - npu_output = self.npu_op_exec(npu_input, item[1], item[2]) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestArgsort, globals(), except_for="cpu") -if __name__ == "__main__": - torch.npu.set_device("npu:5") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_asin.py b/pytorch1.8.1/test/test_npu/test_asin.py deleted file mode 100644 index 54e32964b87..00000000000 --- a/pytorch1.8.1/test/test_npu/test_asin.py +++ /dev/null @@ -1,66 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestAsin(TestCase): - - def cpu_op_exec(self,input1): - output = torch.asin(input1) - output = output.numpy() - return output - - def npu_op_exec(self,input1): - output = torch.asin(input1) - output = output.to("cpu") - output = output.numpy() - return output - - def npu_op_exec_out(self,input1, input2): - torch.asin(input1, out=input2) - output = input2.to("cpu") - output = output.numpy() - return output - - def test_asin_common_shape_format(self, device): - shape_format = [ - [[np.float32, 0, (5,3)]], - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], -1, 1) - cpu_output = self.cpu_op_exec(cpu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_asin_out_common_shape_format(self, device): - shape_format = [ - [[np.float32, 0, (4,3)], [np.float32, 0, (4,3)]], - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], -1, 1) - cpu_input2, npu_input2 = create_common_tensor(item[1], -1, 1) - cpu_output = self.cpu_op_exec(cpu_input1) - npu_output = self.npu_op_exec_out(npu_input1, npu_input2) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestAsin, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:6") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_baddbmm.py b/pytorch1.8.1/test/test_npu/test_baddbmm.py deleted file mode 100644 index 2502c4c36ee..00000000000 --- a/pytorch1.8.1/test/test_npu/test_baddbmm.py +++ /dev/null @@ -1,105 +0,0 @@ -# Copyright (c) 2020 Huawei Technologies Co., Ltd -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestBaddBmm(TestCase): - def generate_scalar(self, dtype, min, max): - if dtype == "float32": - scalar = np.random.uniform(min, max) - if dtype == "float16": - scalar = np.random.uniform(min, max) - if dtype == "int32": - scalar = np.random.randint(min, max) - return scalar - - def cpu_op_exec(self, input1, input2, input3, scalar1, scalar2): - output = torch.baddbmm(input1, input2, input3, beta=scalar1, alpha=scalar2) - output = output.numpy() - return output - - def cpu_op_exec_(self, input1, input2, input3, scalar1, scalar2): - input1.baddbmm_(input2, input3, beta=scalar1, alpha=scalar2) - input1 = input1.numpy() - return input1 - - def npu_op_exec(self, input1, input2, input3, scalar1, scalar2): - output = torch.baddbmm(input1, input2, input3, beta=scalar1, alpha=scalar2) - output = output.to("cpu") - output = output.numpy() - return output - - def npu_op_exec_(self, input1, input2, input3, scalar1, scalar2): - input1.baddbmm_(input2, input3, beta=scalar1, alpha=scalar2) - input1 = input1.to("cpu") - input1 = input1.numpy() - return input1 - - def test_baddbmm_common_shape_format(self, device): - shape_format = [ - [[np.float32, -1, (1, 3, 5)], [np.float32, -1, (1, 3, 4)], [np.float32, -1, (1, 4, 5)], "float32"], - [[np.float32, -1, (6, 4, 3)], [np.float32, -1, (6, 4, 5)], [np.float32, -1, (6, 5, 3)], "float32"], - [[np.float32, -1, (175, 455, 22)], [np.float32, -1, (175, 455, 116)], [np.float32, -1, (175, 116, 22)], "float32"], - [[np.float32, -1, (25, 56, 12)], [np.float32, -1, (25, 56, 51)], [np.float32, -1, (25, 51, 12)], "float32"] - ] - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100) - cpu_input2, npu_input2 = create_common_tensor(item[1], 1, 100) - cpu_input3, npu_input3 = create_common_tensor(item[2], 1, 100) - scalar1 = self.generate_scalar(item[3], 0, 10) - scalar2 = self.generate_scalar(item[3], 0, 10) - cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2, cpu_input3, scalar1, scalar2) - npu_output = self.npu_op_exec(npu_input1, npu_input2, npu_input3, scalar1, scalar2) - self.assertRtolEqual(cpu_output, npu_output) - cpu_output_ = self.cpu_op_exec_(cpu_input1, cpu_input2, cpu_input3, scalar1, scalar2) - npu_output_ = self.npu_op_exec_(npu_input1, npu_input2, npu_input3, scalar1, scalar2) - self.assertRtolEqual(cpu_output_, npu_output_) - - def test_baddbmm_float16_shape_format(self, device): - def cpu_op_exec_fp16(input1, input2, input3, scalar1, scalar2): - input1 = input1.to(torch.float32) - input2 = input2.to(torch.float32) - input3 = input3.to(torch.float32) - output = torch.baddbmm(input1, input2, input3, beta=scalar1, alpha=scalar2) - output = output.numpy() - output = output.astype(np.float16) - return output - - shape_format = [ - [[np.float16, -1, (1, 3, 5)], [np.float16, -1, (1, 3, 4)], [np.float16, -1, (1, 4, 5)], "float16"], - [[np.float16, -1, (500, 40, 300)], [np.float16, -1, (500, 40, 500)], [np.float16, -1, (500, 500, 300)], "float16"], - [[np.float16, -1, (175, 455, 22)], [np.float16, -1, (175, 455, 116)], [np.float16, -1, (175, 116, 22)], "float16"], - [[np.float16, -1, (25, 21, 11)], [np.float16, -1, (25, 21, 34)], [np.float16, -1, (25, 34, 11)], "float16"], - ] - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100) - cpu_input2, npu_input2 = create_common_tensor(item[1], 1, 100) - cpu_input3, npu_input3 = create_common_tensor(item[2], 1, 100) - scalar1 = self.generate_scalar(item[3], 0, 10) - scalar2 = self.generate_scalar(item[3], 0, 10) - cpu_output = cpu_op_exec_fp16(cpu_input1, cpu_input2, cpu_input3, scalar1, scalar2) - npu_output = self.npu_op_exec(npu_input1, npu_input2, npu_input3, scalar1, scalar2) - self.assertRtolEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(TestBaddBmm, globals(), except_for='cpu') -if __name__ == "__main__": - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_bartlett_window.py b/pytorch1.8.1/test/test_npu/test_bartlett_window.py deleted file mode 100644 index 2cfa2aefb34..00000000000 --- a/pytorch1.8.1/test/test_npu/test_bartlett_window.py +++ /dev/null @@ -1,82 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestBartlettWindow(TestCase): - - def cpu_op_exec_length(self, length): - output = torch.bartlett_window(length, dtype=torch.float32) - output = output.numpy() - return output - - def cpu_op_exec_periodic(self, length, periodic): - output = torch.bartlett_window(length, periodic, dtype=torch.float32) - output = output.numpy() - return output - - def npu_op_exec_length(self, length): - d = torch.device("npu") - output = torch.bartlett_window(length, device=d) - output = output.to("cpu") - output = output.numpy() - return output - - def npu_op_exec_periodic(self, length, periodic): - d = torch.device("npu") - output = torch.bartlett_window(length, periodic, device=d) - output = output.to("cpu") - output = output.numpy() - return output - - def subtest_bartlett_window_length(self, length): - cpu_output = self.cpu_op_exec_length(length) - npu_output = self.npu_op_exec_length(length) - self.assertRtolEqual(cpu_output, npu_output) - - def subtest_bartlett_window_periodic(self, length, periodic): - cpu_output = self.cpu_op_exec_periodic(length, periodic) - npu_output = self.npu_op_exec_periodic(length, periodic) - self.assertRtolEqual(cpu_output, npu_output) - - def test_bartlett_window(self, device): - self.subtest_bartlett_window_length(0) - self.subtest_bartlett_window_length(78) - self.subtest_bartlett_window_length(6) - self.subtest_bartlett_window_length(1) - self.subtest_bartlett_window_length(345632) - self.subtest_bartlett_window_length(4214748) - self.subtest_bartlett_window_length(6784) - self.subtest_bartlett_window_length(214748) - self.subtest_bartlett_window_periodic(214748, True) - self.subtest_bartlett_window_periodic(214748, False) - self.subtest_bartlett_window_periodic(6, True) - self.subtest_bartlett_window_periodic(6, False) - self.subtest_bartlett_window_periodic(1, True) - self.subtest_bartlett_window_periodic(1, False) - self.subtest_bartlett_window_periodic(0, False) - self.subtest_bartlett_window_periodic(0, True) - - -instantiate_device_type_tests(TestBartlettWindow, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:0") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_batch_norm.py b/pytorch1.8.1/test/test_npu/test_batch_norm.py deleted file mode 100644 index 93e785ec0c6..00000000000 --- a/pytorch1.8.1/test/test_npu/test_batch_norm.py +++ /dev/null @@ -1,79 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor -#affine = False,目前测试报错。所以本UT未做affine=False测试 -class TestBatchNorm(TestCase): - def cpu_op_exec(self, input1, num_features, affine): - flag = False - if input1.dtype == torch.float16: - input1 = input1.to(torch.float32) - flag = True - m = torch.nn.BatchNorm2d(num_features, affine=affine) - output = m(input1) - if flag: - output = output.to(torch.float16) - output_cpu = output.detach().numpy() - return output_cpu - - def npu_op_exec_new(self, input1, num_features,affine): - m = torch.nn.BatchNorm2d(num_features, affine=affine) - m = m.to("npu") - output = m(input1) - output = output.to("cpu").detach().numpy() - return output - - def test_batchnorm_shape_format(self, device): - shape_format = [ - [[np.float32, -1, (10, 32, 35, 45)], True], - [[np.float32, -1, (256, 100, 7, 7)], True], - [[np.float32, -1, (256, 100, 14, 14)], True], - [[np.float32, -1, (10, 56, 28, 28)], True], - [[np.float32, 0, (10, 50, 14, 14)], True], - [[np.float32, 3, (10, 24, 50, 50)], True], - [[np.float32, 3, (10, 56, 56, 56)], True], - [[np.float32, 3, (10, 100, 7, 7)], True], - [[np.float32, -1, (10, 10, 28, 28)], True], - [[np.float32, -1, (10, 150, 28, 28)], True], - [[np.float32, -1, (10, 200, 7, 7)], True], - [[np.float32, -1, (10, 100, 14, 14)], True], - [[np.float16, -1, (256, 100, 7, 7)], True], - [[np.float16, -1, (256, 100, 14, 14)], True], - [[np.float16, -1, (10, 56, 28, 28)], True], - [[np.float16, 0, (10, 50, 14, 14)], True], - [[np.float16, 3, (10, 24, 50, 50)], True], - [[np.float16, 3, (10, 56, 56, 56)], True], - [[np.float16, 3, (10, 100, 7, 7)], True], - [[np.float16, -1, (10, 10, 28, 28)], True], - [[np.float16, -1, (10, 150, 28, 28)], True], - [[np.float16, -1, (10, 200, 7, 7)], True], - [[np.float16, -1, (10, 100, 14, 14)], True] - ] - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 0, 10) - cpu_output = self.cpu_op_exec(cpu_input1, item[0][2][1], item[1]) - npu_output = self.npu_op_exec_new(npu_input1, item[0][2][1], item[1]) - self.assertRtolEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(TestBatchNorm, globals(), except_for="cpu") -if __name__ == "__main__": - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_bilinear.py b/pytorch1.8.1/test/test_npu/test_bilinear.py deleted file mode 100644 index dbb919e5a74..00000000000 --- a/pytorch1.8.1/test/test_npu/test_bilinear.py +++ /dev/null @@ -1,157 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class test_bilinear(TestCase): - def cpu_op_exec(self, input1, input2, weight, bias): - if input1.dtype == torch.float16: - input1 = input1.to(torch.float32) - input2 = input2.to(torch.float32) - outputs = torch.nn.functional.bilinear(input1, input2, weight, bias) - outputs = outputs.detach().numpy() - return outputs - - def npu_op_exec(self, input1, input2, weight, bias): - outputs = torch.nn.functional.bilinear(input1, input2, weight, bias) - outputs = outputs.cpu().detach().numpy() - return outputs - - def test_add_common_shape_format1(self, device): - shape_format = [ - [[np.float32, -1, (10,30)], [np.float32, -1, (10, 40)], [np.float32, -1, (5, 30, 40)], - [np.float32, -1, (5,)]], - [[np.float32, -1, (100, 30)], [np.float32, -1, (100, 40)], [np.float32, -1, (5, 30, 40)], - [np.float32, -1, (5,)]], - [[np.float32, -1, (100, 30)], [np.float32, -1, (100, 40)], [np.float32, -1, (5, 30, 40)],], - [[np.float32, -1, (10, 30, 40, 30)], [np.float32, -1, (10, 30, 40, 30)], - [np.float32, -1, (30, 30, 30)], - [np.float32, -1, (30,)]], - [[np.float32, -1, (100,3)], [np.float32, -1, (1000, 4)], [np.float32, -1, (5, 3, 4)], - [np.float32, -1, (5,)]], - [[np.float16, -1, (2, 1, 1, 1)], [np.float16, -1, (2, 1, 1, 1)], [np.float16, -1, (5, 1, 1)], - [np.float16, -1, (5,)]], - [[np.float16, -1, (2, 50)], [np.float16, -1, (2, 50)], [np.float16, -1, (5, 50, 50)], - [np.float16, -1, (2, 4)]], - [[np.float16, -1, (2, 3)], [np.float16, -1, (2, 4)], [np.float16, -1, (2, 3, 4)],], - [[np.float16, -1, (2, 3)], [np.float16, -1, (2, 4)], [np.float16, -1, (4, 3, 4)], - [np.float16, -1, (4,)]], - ] - for item in shape_format: - bias = [None, None] - cpu_input1, npu_input1 = create_common_tensor(item[0], 0, 1) - cpu_input2, npu_input2 = create_common_tensor(item[1], 0, 1) - cpu_input3, npu_input3 = create_common_tensor(item[2], 0, 1) - if len(item)>3: - cpu_input4, npu_input4 = create_common_tensor(item[3], 0, 1) - bias = [cpu_input4, npu_input4] - cpu_outputs = self.cpu_op_exec(cpu_input1, cpu_input2, cpu_input3, bias[0]) - npu_outputs = self.npu_op_exec(npu_input1, npu_input2, npu_input3, bias[1]) - self.assertRtolEqual(cpu_outputs, npu_outputs) - - def test_add_common_shape_format2(self, device): - shape_format = [ - [[np.int32, -1, (10,30)], [np.int32, -1, (10, 40)], [np.int32, -1, (5, 30, 40)], - [np.int32, -1, (5,)]], - [[np.int32, -1, (100,30)], [np.int32, -1, (100, 40)], [np.int32, -1, (50, 30, 40)], - [np.int32, -1, (50,)]], - [[np.int32, -1, (100,30)], [np.int32, -1, (100, 40)], [np.int32, -1, (50, 30, 40)],], - [[np.int32, -1, (1, 1, 1, 1)], [np.int32, -1, (1, 1, 1, 1)], [np.int32, -1, (1, 1, 1)], - [np.int32, -1, (1,)]] - ] - for item in shape_format: - bias = [None, None] - cpu_input1, npu_input1 = create_common_tensor(item[0], 0, 1) - cpu_input2, npu_input2 = create_common_tensor(item[1], 0, 1) - cpu_input3, npu_input3 = create_common_tensor(item[2], 0, 1) - if len(item)>3: - cpu_input4, npu_input4 = create_common_tensor(item[3], 0, 1) - bias = [cpu_input4, npu_input4] - cpu_outputs = self.cpu_op_exec(cpu_input1, cpu_input2, cpu_input3, bias[0]) - npu_outputs = self.npu_op_exec(npu_input1, npu_input2, npu_input3, bias[1]) - self.assertRtolEqual(cpu_outputs, npu_outputs) - - def test_add_common_shape_format3(self, device): - shape_format = [ - [[np.float32, 0, (10,30)], [np.float32, 0, (10, 40)], [np.float32, 0, (5, 30, 40)], - [np.float32, 0, (5,)]], - [[np.float32, 0, (100, 30)], [np.float32, 0, (100, 40)], [np.float32, 0, (5, 30, 40)], - [np.float32, 0, (5,)]], - [[np.float32, 0, (100, 30)], [np.float32, 0, (100, 40)], [np.float32, 0, (5, 30, 40)],], - [[np.float32, 0, (10, 30, 40, 30)], [np.float32, 0, (10, 30, 40, 30)], - [np.float32, 0, (30, 30, 30)], - [np.float32, 0, (30,)]], - [[np.float32, 0, (100,3)], [np.float32, 0, (1000, 4)], [np.float32, 0, (5, 3, 4)], - [np.float32, 0, (5,)]], - [[np.float16, 0, (2, 1, 1, 1)], [np.float16, 0, (2, 1, 1, 1)], [np.float16, 0, (5, 1, 1)], - [np.float16, 0, (5,)]], - [[np.float16, 0, (2, 50)], [np.float16, 0, (2, 50)], [np.float16, 0, (5, 50, 50)], - [np.float16, 0, (2, 4)]], - [[np.float16, 0, (2, 3)], [np.float16, 0, (2, 4)], [np.float16, 0, (2, 3, 4)],], - [[np.float16, 0, (2, 3)], [np.float16, 0, (2, 4)], [np.float16, 0, (4, 3, 4)], - [np.float16, 0, (4,)]], - ] - for item in shape_format: - bias = [None, None] - cpu_input1, npu_input1 = create_common_tensor(item[0], 0, 1) - cpu_input2, npu_input2 = create_common_tensor(item[1], 0, 1) - cpu_input3, npu_input3 = create_common_tensor(item[2], 0, 1) - if len(item)>3: - cpu_input4, npu_input4 = create_common_tensor(item[3], 0, 1) - bias = [cpu_input4, npu_input4] - cpu_outputs = self.cpu_op_exec(cpu_input1, cpu_input2, cpu_input3, bias[0]) - npu_outputs = self.npu_op_exec(npu_input1, npu_input2, npu_input3, bias[1]) - self.assertRtolEqual(cpu_outputs, npu_outputs) - - def test_add_common_shape_format4(self, device): - shape_format = [ - [[np.float32, 3, (10,30)], [np.float32, 3, (10, 40)], [np.float32, 3, (5, 30, 40)], - [np.float32, 3, (5,)]], - [[np.float32, 3, (100, 30)], [np.float32, 3, (100, 40)], [np.float32, 3, (5, 30, 40)], - [np.float32, 3, (5,)]], - [[np.float32, 3, (100, 30)], [np.float32, 3, (100, 40)], [np.float32, 3, (5, 30, 40)],], - [[np.float32, 3, (10, 30, 40, 30)], [np.float32, 3, (10, 30, 40, 30)], - [np.float32, 3, (30, 30, 30)], - [np.float32, 3, (30,)]], - [[np.float32, 29, (100,3)], [np.float32, 29, (1000, 4)], [np.float32, 29, (5, 3, 4)], - [np.float32, 29, (5,)]], - [[np.float16, 29, (2, 1, 1, 1)], [np.float16, 29, (2, 1, 1, 1)], [np.float16, 29, (5, 1, 1)], - [np.float16, 29, (5,)]], - [[np.float16, 29, (2, 50)], [np.float16, 29, (2, 50)], [np.float16, 29, (5, 50, 50)], - [np.float16, 29, (2, 4)]], - [[np.float16, 29, (2, 3)], [np.float16, 29, (2, 4)], [np.float16, 29, (2, 3, 4)],], - [[np.float16, 29, (2, 3)], [np.float16, 29, (2, 4)], [np.float16, 29, (4, 3, 4)], - [np.float16, 29, (4,)]], - ] - for item in shape_format: - bias = [None, None] - cpu_input1, npu_input1 = create_common_tensor(item[0], 0, 1) - cpu_input2, npu_input2 = create_common_tensor(item[1], 0, 1) - cpu_input3, npu_input3 = create_common_tensor(item[2], 0, 1) - if len(item)>3: - cpu_input4, npu_input4 = create_common_tensor(item[3], 0, 1) - bias = [cpu_input4, npu_input4] - cpu_outputs = self.cpu_op_exec(cpu_input1, cpu_input2, cpu_input3, bias[0]) - npu_outputs = self.npu_op_exec(npu_input1, npu_input2, npu_input3, bias[1]) - self.assertRtolEqual(cpu_outputs, npu_outputs) - -instantiate_device_type_tests(test_bilinear, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:5") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_binary_cross_entropy_backward.py b/pytorch1.8.1/test/test_npu/test_binary_cross_entropy_backward.py deleted file mode 100644 index bf65962d5cd..00000000000 --- a/pytorch1.8.1/test/test_npu/test_binary_cross_entropy_backward.py +++ /dev/null @@ -1,157 +0,0 @@ -# Copyright (c) 2020 Huawei Technologies Co., Ltd -# Copyright (c) 2019, Facebook CORPORATION. -# All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import copy -import torch.nn as nn -import numpy as np -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor -from torch._C import _infer_size - -class TestBinaryCrossEntropyBackward(TestCase): - def generate_data(self, min_val, max_val, shape, dtype): - x = np.random.uniform(min_val, max_val, shape).astype(dtype) - x = torch.from_numpy(x) - return x - - def cpu_op_exec(self, input1, target, weight, reduction="mean"): - float16flag = False - if input1.dtype == torch.float16: - input1 = input1.to(torch.float32) - target = target.to(torch.float32) - float16flag = True - if weight is not None: - weight = weight.to(torch.float32) - input1.requires_grad_(True) - cpu_output = torch.nn.functional.binary_cross_entropy(input1, target, weight=weight, size_average=None, reduce=None, reduction=reduction) - input_cpu = cpu_output.detach().numpy() - if reduction == 'none': - w = torch.ones_like(input1) - cpu_output.backward(w) - else: - cpu_output.backward() - res = input1.grad - res = res.numpy() - if float16flag: - input_cpu = input_cpu.astype(np.float16) - res = res.astype(np.float16) - return input_cpu, res - - def npu_op_exec(self, input1, target, weight, format = -1,reduction="mean"): - input1 = input1.npu() - target = target.npu() - if format != -1: #转npu_format - input1 = input1.npu_format_cast(format) - target = target.npu_format_cast(format) - if weight is not None: - weight = weight.npu() - weight = weight.npu_format_cast(format) - else: - if weight is not None: - weight = weight.npu() - input1.requires_grad_(True) - npu_output = torch.nn.functional.binary_cross_entropy(input1, target, weight=weight, size_average=None, reduce=None, reduction=reduction) - npu_input = npu_output.cpu() - npu_input = npu_input.detach().numpy() - if reduction == 'none': - w = torch.ones_like(input1) - npu_output.backward(w) - else: - npu_output.backward() - res = input1.grad.cpu() - res = res.numpy() - return npu_input, res - - def test_binary_cross_entropy_backward_float16(self, device): - format_list = [0, 2, 3] - shape_list = [[1024], [32, 1024], [32, 8, 1024]] - reduction_list = ["none", "mean", "sum"] - shape_format = [ - [np.float32, i, j, k] for i in shape_list for j in reduction_list for k in format_list - ] - for item in shape_format: - input1 = self.generate_data(0, 1, item[1], item[0]) - target = self.generate_data(0, 2, item[1], item[0]) - cpu_input1 = copy.deepcopy(input1) - cpu_target = copy.deepcopy(target) - weight = None - cpu_output, cpu_grad = self.cpu_op_exec(cpu_input1, cpu_target, weight, reduction=item[2]) - npu_output, npu_grad = self.npu_op_exec(input1, target, weight, format = item[3], reduction=item[2]) - self.assertRtolEqual(cpu_output, npu_output) - self.assertRtolEqual(cpu_grad, npu_output) - - def test_binary_cross_entropy_backward_float32(self, device): - format_list = [0, 2, 3] - shape_list = [[1024], [32, 1024], [32, 8, 1024]] - reduction_list = ["none", "mean", "sum"] - shape_format = [ - [np.float32, i, j, k] for i in shape_list for j in reduction_list for k in format_list - ] - for item in shape_format: - input1 = self.generate_data(0, 1, item[1], item[0]) - target = self.generate_data(0, 2, item[1], item[0]).int().to(torch.float32) - cpu_input1 = copy.deepcopy(input1) - cpu_target = copy.deepcopy(target) - weight = None - cpu_output, cpu_grad = self.cpu_op_exec(cpu_input1, cpu_target, weight, reduction=item[2]) - npu_output, npu_grad = self.npu_op_exec(input1, target, weight, format = item[3], reduction=item[2]) - self.assertRtolEqual(cpu_output, npu_output) - self.assertRtolEqual(cpu_grad, npu_grad) - - def test_binary_cross_entropy_backward_with_weight_float16(self, device): - format_list = [0, 2, 3] - shape_list = [[1024], [32, 1024], [32, 8, 1024]] - reduction_list = ["none", "mean", "sum"] - shape_format = [ - [np.float32, i, j, k] for i in shape_list for j in reduction_list for k in format_list - ] - for item in shape_format: - input1 = self.generate_data(0, 1, item[1], item[0]) - target = self.generate_data(0, 2, item[1], item[0]) - weight = self.generate_data(0, 1, item[1], item[0]) - cpu_input1 = copy.deepcopy(input1) - cpu_target = copy.deepcopy(target) - cpu_weight = copy.deepcopy(weight) - cpu_output, cpu_grad = self.cpu_op_exec(cpu_input1, cpu_target, cpu_weight, reduction=item[2]) - npu_output, npu_grad = self.npu_op_exec(input1, target, weight, format = item[3], reduction=item[2]) - self.assertRtolEqual(cpu_output, npu_output) - self.assertRtolEqual(cpu_grad, npu_grad) - - def test_binary_cross_entropy_backward_with_weight_float32(self, device): - format_list = [0, 2, 3] - shape_list = [[1024], [32, 1024], [32, 8, 1024]] - reduction_list = ["none", "mean", "sum"] - shape_format = [ - [np.float32, i, j, k] for i in shape_list for j in reduction_list for k in format_list - ] - for item in shape_format: - input1 = self.generate_data(0, 1, item[1], item[0]) - target = self.generate_data(0, 1, item[1], item[0]) - weight = self.generate_data(0, 1, item[1], item[0]) - cpu_input1 = copy.deepcopy(input1) - cpu_target = copy.deepcopy(target) - cpu_weight = copy.deepcopy(weight) - cpu_output, cpu_grad = self.cpu_op_exec(cpu_input1, cpu_target, cpu_weight, reduction=item[2]) - npu_output, npu_grad = self.npu_op_exec(input1, target, weight, format = item[3], reduction=item[2]) - self.assertRtolEqual(cpu_output, npu_output) - self.assertRtolEqual(cpu_grad, npu_grad) - -instantiate_device_type_tests(TestBinaryCrossEntropyBackward, globals(), except_for="cpu") -if __name__ == "__main__": - torch.npu.set_device("npu:1") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_binary_cross_entropy_with_logits.py b/pytorch1.8.1/test/test_npu/test_binary_cross_entropy_with_logits.py deleted file mode 100644 index 8da175a47e1..00000000000 --- a/pytorch1.8.1/test/test_npu/test_binary_cross_entropy_with_logits.py +++ /dev/null @@ -1,212 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import copy -import torch.nn as nn -import numpy as np -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - - -class TestBinaryCrossEntropyWithLogits(TestCase): - - def generate_two_input(self, lower, upper, shape, dtype): - x = np.random.uniform(lower, upper, shape).astype(dtype) - y = np.random.uniform(lower, upper, shape).astype(dtype) - - npu_input = torch.from_numpy(x) - target_input = torch.from_numpy(y) - - return npu_input, target_input - - def generate_one_input(self, lower, upper, shape, dtype): - x = np.random.uniform(lower, upper, shape).astype(dtype) - npu_input = torch.from_numpy(x) - return npu_input - - def cpu_op_exec(self, input1, target, weight=None, pos_weight=None, reduction="mean"): - criterion = torch.nn.BCEWithLogitsLoss(weight=weight, pos_weight=pos_weight, - reduction=reduction) - res = criterion(input1, target) - return res.numpy() - - def npu_op_exec(self, input1, target, weight=None, pos_weight=None, reduction="mean"): - input1 = input1.to("npu") - target = target.to("npu") - if weight is not None: - weight = weight.to("npu") - if pos_weight is not None: - pos_weight = pos_weight.to("npu") - - criterion = torch.nn.BCEWithLogitsLoss(weight=weight, pos_weight=pos_weight, - reduction=reduction) - criterion = criterion.to("npu") - res = criterion(input1, target) - res = res.to("cpu") - return res.numpy() - - def cpu_op_func_exec(self, input1, target, weight=None, pos_weight=None, reduction="mean"): - res = torch.nn.functional.binary_cross_entropy_with_logits(input1, target, weight=weight, pos_weight=pos_weight, - reduction=reduction) - return res.numpy() - - def npu_op_func_exec(self, input1, target, weight=None, pos_weight=None, reduction="mean"): - input1 = input1.to("npu") - target = target.to("npu") - if weight is not None: - weight = weight.to("npu") - if pos_weight is not None: - pos_weight = pos_weight.to("npu") - - res = torch.nn.functional.binary_cross_entropy_with_logits(input1, target, weight=weight, pos_weight=pos_weight, - reduction=reduction) - res = res.to("cpu") - return res.numpy() - - def test_binary_cross_with_logits_float32(self, device): - for shape, weight_shape, pos_weight_shape, reduction in [ - ((10, 64), None, None, "mean"), - ((10, 64), (10, 1), None, "mean"), - ((10, 64), None, (64,), "mean"), - ((10, 64), None, None, "none"), - ((10, 64), (10, 1), None, "none"), - ((10, 64), None, (64,), "none"), - ((10, 64), None, None, "sum"), - ((10, 64), (10, 1), None, "sum"), - ((10, 64), None, (64,), "sum"), - ((10, 64), (10, 64), (10, 64), "mean"), - ((10, 64), (10, 64), (10, 64), "sum"), - ((10, 64), (10, 64), (10, 64), "none") - ]: - input1 = self.generate_one_input(0, 10, shape, np.float32) - target = torch.empty(shape, dtype=torch.float32).random_(2) - weight = None - pos_weight = None - if weight_shape is not None: - weight = self.generate_one_input(0, 10, weight_shape, np.float32) - if pos_weight_shape is not None: - pos_weight = self.generate_one_input(0, 10, pos_weight_shape, np.float32) - cpu_output = self.cpu_op_exec(input1, target, weight=weight, pos_weight=pos_weight, reduction=reduction) - npu_output = self.npu_op_exec(input1, target, weight=weight, pos_weight=pos_weight, reduction=reduction) - self.assertRtolEqual(cpu_output, npu_output) - - def test_binary_cross_with_logits_float16(self, device): - for shape, weight_shape, pos_weight_shape, reduction in [ - ((10, 64), None, None, "mean"), - ((10, 64), (10, 1), None, "mean"), - ((10, 64), None, (64,), "mean"), - ((10, 64), None, None, "none"), - ((10, 64), (10, 1), None, "none"), - ((10, 64), None, (64,), "none"), - ((10, 64), None, None, "sum"), - ((10, 64), (10, 1), None, "sum"), - ((10, 64), None, (64,), "sum"), - ((10, 64), (10, 64), (10, 64), "sum"), - ((10, 64), (10, 64), (10, 64), "mean"), - ((10, 64), (10, 64), (10, 64), "none") - ]: - input1 = self.generate_one_input(0, 10, shape, np.float16) - target = torch.empty(shape, dtype=torch.float16).random_(2) - input_32 = input1.type(torch.float32) - target_32 = target.type(torch.float32) - weight = None - weight_32 = None - pos_weight = None - pos_weight_32 = None - - if weight_shape is not None: - weight = self.generate_one_input(0, 10, weight_shape, np.float16) - weight_32 = weight.type(torch.float32) - if pos_weight_shape is not None: - pos_weight = self.generate_one_input(0, 10, pos_weight_shape, np.float16) - pos_weight_32 = pos_weight.type(torch.float32) - - npu_output = self.npu_op_exec(input1, target, weight=weight, pos_weight=pos_weight, reduction=reduction) - cpu_output = self.cpu_op_exec(input_32, target_32, weight=weight_32, pos_weight=pos_weight_32, - reduction=reduction) - cpu_output = cpu_output.astype(np.float16) - self.assertRtolEqual(cpu_output, npu_output) - - def test_binary_cross_with_logits_function_float32(self, device): - for shape, weight_shape, pos_weight_shape, reduction in [ - ((10, 64), None, None, "mean"), - ((10, 64), (10, 1), None, "mean"), - ((10, 64), None, (64,), "mean"), - ((10, 64), None, None, "none"), - ((10, 64), (10, 1), None, "none"), - ((10, 64), None, (64,), "none"), - ((10, 64), None, None, "sum"), - ((10, 64), (10, 1), None, "sum"), - ((10, 64), None, (64,), "sum"), - ((10, 64), (10, 64), (10, 64), "mean"), - ((10, 64), (10, 64), (10, 64), "sum"), - ((10, 64), (10, 64), (10, 64), "none") - ]: - input1 = self.generate_one_input(0, 2, shape, np.float32) - target = torch.empty(shape, dtype=torch.float32).random_(2) - weight = None - pos_weight = None - if weight_shape is not None: - weight = self.generate_one_input(0, 2, weight_shape, np.float32) - if pos_weight_shape is not None: - pos_weight = self.generate_one_input(0, 2, pos_weight_shape, np.float32) - cpu_output = self.cpu_op_func_exec(input1, target, weight=weight, pos_weight=pos_weight, reduction=reduction) - npu_output = self.npu_op_func_exec(input1, target, weight=weight, pos_weight=pos_weight, reduction=reduction) - self.assertRtolEqual(cpu_output, npu_output) - - def test_binary_cross_with_logits_function_float16(self, device): - for shape, weight_shape, pos_weight_shape, reduction in [ - ((10, 64), None, None, "mean"), - ((10, 64), (10, 1), None, "mean"), - ((10, 64), None, (64,), "mean"), - ((10, 64), None, None, "none"), - ((10, 64), (10, 1), None, "none"), - ((10, 64), None, (64,), "none"), - ((10, 64), None, None, "sum"), - ((10, 64), (10, 1), None, "sum"), - ((10, 64), None, (64,), "sum"), - ((10, 64), (10, 64), (10, 64), "sum"), - ((10, 64), (10, 64), (10, 64), "mean"), - ((10, 64), (10, 64), (10, 64), "none") - ]: - input1 = self.generate_one_input(0, 2, shape, np.float16) - target = torch.empty(shape, dtype=torch.float16).random_(2) - input_32 = input1.type(torch.float32) - target_32 = target.type(torch.float32) - weight = None - weight_32 = None - pos_weight = None - pos_weight_32 = None - - if weight_shape is not None: - weight = self.generate_one_input(0, 2, weight_shape, np.float16) - weight_32 = weight.type(torch.float32) - if pos_weight_shape is not None: - pos_weight = self.generate_one_input(0, 2, pos_weight_shape, np.float16) - pos_weight_32 = pos_weight.type(torch.float32) - - npu_output = self.npu_op_func_exec(input1, target, weight=weight, pos_weight=pos_weight, reduction=reduction) - cpu_output = self.cpu_op_func_exec(input_32, target_32, weight=weight_32, pos_weight=pos_weight_32, - reduction=reduction) - - cpu_output = cpu_output.astype(np.float16) - self.assertRtolEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(TestBinaryCrossEntropyWithLogits, globals(), except_for="cpu") -if __name__ == "__main__": - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_bincount.py b/pytorch1.8.1/test/test_npu/test_bincount.py deleted file mode 100644 index 3c59f2e7eed..00000000000 --- a/pytorch1.8.1/test/test_npu/test_bincount.py +++ /dev/null @@ -1,67 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestBincount(TestCase): - def cpu_op_exec(self, input, weights): - output = torch.bincount(input,weights) - output = output.numpy() - return output - - def npu_op_exec(self, input, weights): - output = torch.bincount(input,weights) - output = output.to("cpu") - output = output.numpy() - return output - - def test_bincount_common_shape_format(self, device): - shape_format = [ - [[np.int16, -1, (1,)], 0], - [[np.int16, -1, (18,)], 1], - [[np.int16, -1, (32,), 2]], - [[np.int16, -1, (100,), 3]], - [[np.int32, -1, (10,)], 0], - [[np.int32, -1, (8,)], 1], - [[np.int32, -1, (32,), 2]], - [[np.int32, -1, (124,), 3]], - [[np.int64, -1, (1,)], 0], - [[np.int64, -1, (8,)], 1], - [[np.int64, -1, (32,), 2]], - [[np.int64, -1, (100,), 3]], - [[np.uint8, -1, (11,)], 0], - [[np.uint8, -1, (80,)], 1], - [[np.uint8, -1, (320,), 2]], - [[np.uint8, -1, (1024,), 3]], - [[np.uint8, -1, (11,)], 0], - [[np.uint8, -1, (18,)], 1], - [[np.uint8, -1, (32,), 2]], - [[np.uint8, -1, (100,), 3]], - - ] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item[0], -1, 1) - cpu_weights, npu_weights = create_common_tensor(item[0], -1, 1) - cpu_output = self.cpu_op_exec(cpu_input, cpu_weights) - npu_output = self.npu_op_exec(npu_input, npu_weights) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestBincount, globals(), except_for="cpu") -if __name__ == "__main__": - torch.npu.set_device("npu:6") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_blackman_window.py b/pytorch1.8.1/test/test_npu/test_blackman_window.py deleted file mode 100644 index 8a600bb0805..00000000000 --- a/pytorch1.8.1/test/test_npu/test_blackman_window.py +++ /dev/null @@ -1,96 +0,0 @@ -# Copyright (c) 2020 Huawei Technologies Co., Ltd - -# All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestBlackmanWindow(TestCase): - - def cpu_op_exec(self, window_length): - output = torch.blackman_window(window_length) - output = output.numpy() - return output - - def npu_op_exec(self, window_length): - output = torch.blackman_window(window_length, device='npu') - output = output.to('cpu') - output = output.numpy() - return output - - def cpu_op_exec_periodic(self, window_length, periodic): - output = torch.blackman_window(window_length, periodic) - output = output.numpy() - return output - - def npu_op_exec_periodic(self, window_length, periodic): - output = torch.blackman_window(window_length, periodic, device='npu') - output = output.to('cpu') - output = output.numpy() - return output - - def cpu_op_exec_out(self, window_length, periodic, out): - torch.blackman_window(window_length, periodic, out=out) - output = out.numpy() - return output - - def npu_op_exec_out(self, window_length, periodic, out): - out = out.to('npu') - torch.full(window_length, periodic, out=out) - output = out.to('cpu') - output = output.numpy() - return output - - def test_blackman_window(self, device): - shape_format = [ - [0, torch.float32], - [1, torch.float32], - [7, torch.float32], - [12, torch.float32], - [0, torch.float16], - [1, torch.float16], - [7, torch.float16], - [12, torch.float16]] - for item in shape_format: - cpu_output = self.cpu_op_exec(item[0]) - npu_output = self.npu_op_exec(item[0]) - self.assertRtolEqual(cpu_output, npu_output) - - def test_blackman_window_periodic(self, device): - shape_format = [ - [0, False, torch.float32], - [1, False, torch.float32], - [7, False, torch.float32], - [12, False, torch.float32], - [0, False, torch.float16], - [1, False, torch.float16], - [7, False, torch.float16], - [12, False, torch.float16]] - for item in shape_format: - cpu_output = self.cpu_op_exec_periodic(item[0], item[1]) - npu_output = self.npu_op_exec_periodic(item[0], item[1]) - self.assertRtolEqual(cpu_output, npu_output) - - - -instantiate_device_type_tests(TestBlackmanWindow, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:6") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_cast_Byte.py b/pytorch1.8.1/test/test_npu/test_cast_Byte.py deleted file mode 100644 index c06faec1580..00000000000 --- a/pytorch1.8.1/test/test_npu/test_cast_Byte.py +++ /dev/null @@ -1,66 +0,0 @@ -# Copyright (c) 2020 Huawei Technologies Co., Ltd -# Copyright (c) 2019, Facebook CORPORATION. -# All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor,compare_res_new - - -class TestCastByte(TestCase): - - def generate_data(self, minValue, maxValue, shape, dtype): - input1 = np.random.uniform(minValue, maxValue, shape).astype(dtype) - # modify from numpy.ndarray to torch.tensor - npu_input1 = torch.from_numpy(input1) - return npu_input1 - - - def cpu_op_exec(self, input1): - output = torch._cast_Byte(input1) - output = output.numpy() - return output - - - def npu_op_exec(self, input1): - input1 = input1.to("npu") - output = torch._cast_Byte(input1.int()) - output = output.to("cpu") - output = output.numpy() - return output - - - def test__cast_Byte_common_shape_format(self, device): - shape_format = [ - [[np.int32, -1, (4, 3, 1)]], - [[np.int8, -1, (2, 3)]], - [[np.float32, -1, (4, 3, 1)]], - [[np.float16, -1, (4, 3, 1)]], - [[np.uint8, -1, (4, 3, 1)]] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], -100, 100) - cpu_output = self.cpu_op_exec(cpu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(TestCastByte, globals(), except_for='cpu') -if __name__ == "__main__": - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_cast_Char.py b/pytorch1.8.1/test/test_npu/test_cast_Char.py deleted file mode 100644 index b933d62ce2c..00000000000 --- a/pytorch1.8.1/test/test_npu/test_cast_Char.py +++ /dev/null @@ -1,67 +0,0 @@ -# Copyright (c) 2020 Huawei Technologies Co., Ltd -# Copyright (c) 2019, Facebook CORPORATION. -# All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestCastChar(TestCase): - - def generate_data(self, minValue, maxValue, shape, dtype): - input1 = np.random.uniform(minValue, maxValue, shape).astype(dtype) - # modify from numpy.ndarray to torch.tensor - npu_input1 = torch.from_numpy(input1) - return npu_input1 - - - def cpu_op_exec(self, input1): - output = torch._cast_Char(input1) - output = output.numpy() - return output - - - def npu_op_exec(self, input1): - input1 = input1.to("npu") - output = torch._cast_Char(input1) - output = output.to("cpu") - output = output.numpy() - return output - - - def test_cast_Char_common_shape_format(self, device): - shape_format = [ - [[np.int64, -1, (4, 3)]], - [[np.int32, -1, (4, 3, 1)]], - [[np.int8, -1, (2, 3)]], - [[np.float32, -1, (4, 3, 1)]], - [[np.float16, -1, (4, 3, 1)]], - [[np.uint8, -1, (4, 3, 1)]] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], -100, 100) - cpu_output = self.cpu_op_exec(cpu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(TestCastChar, globals(), except_for='cpu') -if __name__ == "__main__": - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_cast_Float.py b/pytorch1.8.1/test/test_npu/test_cast_Float.py deleted file mode 100644 index 37c3a285c8d..00000000000 --- a/pytorch1.8.1/test/test_npu/test_cast_Float.py +++ /dev/null @@ -1,66 +0,0 @@ -# Copyright (c) 2020 Huawei Technologies Co., Ltd -# Copyright (c) 2019, Facebook CORPORATION. -# All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestCastFloat(TestCase): - - def generate_data(self, minValue, maxValue, shape, dtype): - input1 = np.random.uniform(minValue, maxValue, shape).astype(dtype) - # modify from numpy.ndarray to torch.tensor - npu_input1 = torch.from_numpy(input1) - return npu_input1 - - - def cpu_op_exec(self, input1): - output = torch._cast_Float(input1) - output = output.numpy() - return output - - - def npu_op_exec(self, input1): - input1 = input1.to("npu") - output = torch._cast_Float(input1) - output = output.to("cpu") - output = output.numpy() - return output - - - def test_cast_Float_common_shape_format(self, device): - shape_format = [ - [[np.int32, -1, (4, 3, 1)]], - [[np.int8, -1, (2, 3)]], - [[np.float32, -1, (4, 3, 1)]], - [[np.float16, -1, (4, 3, 1)]], - [[np.uint8, -1, (4, 3, 1)]] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], -100, 100) - cpu_output = self.cpu_op_exec(cpu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(TestCastFloat, globals(), except_for='cpu') -if __name__ == "__main__": - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_cast_Half.py b/pytorch1.8.1/test/test_npu/test_cast_Half.py deleted file mode 100644 index 41a22cb78e3..00000000000 --- a/pytorch1.8.1/test/test_npu/test_cast_Half.py +++ /dev/null @@ -1,80 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy - -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class Testcast_Half(TestCase): - - def generate_single_data(self, min_d, max_d, shape, dtype): - input1 = np.random.uniform(min_d, max_d, shape).astype(dtype) - # modify from numpy.ndarray to torch.tensor - npu_input1 = torch.from_numpy(input1) - return npu_input1 - def cpu_op_exec(self, input1): - output = torch._cast_Half(input1) - output = output.numpy() - return output - - def npu_op_exec(self, input1): - input1 = input1.to("npu") - output = torch._cast_Half(input1) - output = output.to("cpu") - output = output.numpy() - return output - def test_cast_Half_float16(self, device): - def cpu_op_exec_fp16(input1): - input1 = input1.to(torch.float32) - output = torch._cast_Half(input1) - output = output.numpy() - output = output.astype(np.float16) - return output - npu_input1 = self.generate_single_data(0, 100, (5,3), np.float16) - cpu_output = cpu_op_exec_fp16(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_cast_Half_float32(self, device): - npu_input1 = self.generate_single_data(0, 100, (4,3), np.float32) - cpu_output = self.cpu_op_exec(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_cast_Half_int32(self, device): - npu_input1 = self.generate_single_data(0, 100, (4,3), np.int32) - cpu_output = self.cpu_op_exec(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_cast_Half_int8(self, device): - npu_input1 = self.generate_single_data(0, 100, (4,3,2), np.int8) - cpu_output = self.cpu_op_exec(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_cast_Half_uint8(self, device): - npu_input1 = self.generate_single_data(0, 100, (4,3,2), np.uint8) - cpu_output = self.cpu_op_exec(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(Testcast_Half, globals(), except_for='cpu') -if __name__ == '__main__': - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_cast_Int.py b/pytorch1.8.1/test/test_npu/test_cast_Int.py deleted file mode 100644 index 936c703ab14..00000000000 --- a/pytorch1.8.1/test/test_npu/test_cast_Int.py +++ /dev/null @@ -1,84 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy - -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class Testcast_Int(TestCase): - - def generate_single_data(self, min_d, max_d, shape, dtype): - input1 = np.random.uniform(min_d, max_d, shape).astype(dtype) - # modify from numpy.ndarray to torch.tensor - npu_input1 = torch.from_numpy(input1) - return npu_input1 - - def cpu_op_exec(self, input1): - output = torch._cast_Int(input1) - output = output.numpy() - return output - - def npu_op_exec(self, input1): - input1 = input1.to("npu") - output = torch._cast_Int(input1) - output = output.to("cpu") - output = output.numpy() - return output - - def test_cast_Int_float16(self, device): - def cpu_op_exec_fp16(input1): - input1 = input1.to(torch.float32) - output = torch._cast_Int(input1) - output = output.numpy() - return output - - npu_input1 = self.generate_single_data(0, 100, (5, 3), np.float16) - cpu_output = cpu_op_exec_fp16(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_cast_Int_float32(self, device): - npu_input1 = self.generate_single_data(0, 100, (4, 3), np.float32) - cpu_output = self.cpu_op_exec(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_cast_Int_int32(self, device): - npu_input1 = self.generate_single_data(0, 100, (4, 3), np.int32) - cpu_output = self.cpu_op_exec(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_cast_Int_int8(self, device): - npu_input1 = self.generate_single_data(0, 100, (4, 3, 2), np.int8) - cpu_output = self.cpu_op_exec(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_cast_Int_uint8(self, device): - npu_input1 = self.generate_single_data(0, 100, (4, 3, 2), np.uint8) - cpu_output = self.cpu_op_exec(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(Testcast_Int, globals(), except_for='cpu') -if __name__ == '__main__': - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_cast_Long.py b/pytorch1.8.1/test/test_npu/test_cast_Long.py deleted file mode 100644 index 9b5abb19919..00000000000 --- a/pytorch1.8.1/test/test_npu/test_cast_Long.py +++ /dev/null @@ -1,67 +0,0 @@ -# Copyright (c) 2020 Huawei Technologies Co., Ltd -# Copyright (c) 2019, Facebook CORPORATION. -# All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestCastLong(TestCase): - - def generate_data(self, minValue, maxValue, shape, dtype): - input1 = np.random.uniform(minValue, maxValue, shape).astype(dtype) - # modify from numpy.ndarray to torch.tensor - npu_input1 = torch.from_numpy(input1) - return npu_input1 - - - def cpu_op_exec(self, input1): - output = torch._cast_Long(input1) - output = output.numpy() - return output - - - def npu_op_exec(self, input1): - input1 = input1.to("npu") - output = torch._cast_Long(input1) - output = output.to("cpu") - output = output.numpy() - return output - - - def test_cast_Long_common_shape_format(self, device): - shape_format = [ - [[np.bool, -1, (4, 3, 1)]], - [[np.int32, -1, (4, 3, 1)]], - [[np.int8, -1, (2, 3)]], - [[np.float32, -1, (4, 3, 1)]], - [[np.float16, -1, (4, 3, 1)]], - [[np.uint8, -1, (4, 3, 1)]] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], -100, 100) - cpu_output = self.cpu_op_exec(cpu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(TestCastLong, globals(), except_for='cpu') -if __name__ == "__main__": - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_cast_Short.py b/pytorch1.8.1/test/test_npu/test_cast_Short.py deleted file mode 100644 index c90c9dd47b8..00000000000 --- a/pytorch1.8.1/test/test_npu/test_cast_Short.py +++ /dev/null @@ -1,67 +0,0 @@ -# Copyright (c) 2020 Huawei Technologies Co., Ltd -# Copyright (c) 2019, Facebook CORPORATION. -# All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestCastShort(TestCase): - - def generate_data(self, minValue, maxValue, shape, dtype): - input1 = np.random.uniform(minValue, maxValue, shape).astype(dtype) - # modify from numpy.ndarray to torch.tensor - npu_input1 = torch.from_numpy(input1) - return npu_input1 - - - def cpu_op_exec(self, input1): - output = torch._cast_Short(input1) - output = output.numpy() - return output - - - def npu_op_exec(self, input1): - input1 = input1.to("npu") - output = torch._cast_Short(input1) - output = output.to("cpu") - output = output.numpy() - return output - - - def test_cast_Short_common_shape_format(self, device): - shape_format = [ - [[np.bool, -1, (4, 3, 1)]], - [[np.int32, -1, (4, 3, 1)]], - [[np.int8, -1, (2, 3)]], - [[np.float32, -1, (4, 3, 1)]], - [[np.float16, -1, (4, 3, 1)]], - [[np.uint8, -1, (4, 3, 1)]] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], -100, 100) - cpu_output = self.cpu_op_exec(cpu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(TestCastShort, globals(), except_for='cpu') -if __name__ == "__main__": - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_col2im.py b/pytorch1.8.1/test/test_npu/test_col2im.py deleted file mode 100644 index c0455837375..00000000000 --- a/pytorch1.8.1/test/test_npu/test_col2im.py +++ /dev/null @@ -1,56 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestCol2ImBackward(TestCase): - - def cpu_op_exec(self,input1, output_size, ksizes, strides, dilates, padding): - output = torch._C._nn.col2im(input1, output_size, ksizes, dilates, padding, strides) - output = output.numpy() - return output - - def npu_op_exec(self, input1,output_size, ksizes, strides, dilates,padding): - output = torch._C._nn.col2im(input1, output_size, ksizes, dilates, padding, strides) - output = output.to("cpu") - output = output.numpy() - return output - - def test_col2im_shape_format(self, device): - shape_format = [ - [ [np.float32, 0, (4,4)], (4,5), (2,2), (2,2), (1,1), (0,0)], - [ [np.float32, 3, (2, 8,30 )], (4,5), (2,2), (1,1), (1,1), (1,1)], - [ [np.float32, 4, ( 12, 20)], (12,6), (2,3), (1,1), (2,2), (0,0)], - [ [np.float32, 29, ( 1,12, 12)], (4,5), (2,2), (1,1), (1,1), (0,0)], - [ [np.float16, 29, ( 1,12, 12)], (4,5), (2,2), (1,1), (1,1), (0,0)], - ] - - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item[0], 1, 20) - cpu_output = self.cpu_op_exec(cpu_input, item[1], item[2], item[3], item[4], item[5]) - npu_output = self.npu_op_exec(npu_input, item[1], item[2], item[3], item[4], item[5]) - self.assertRtolEqual(cpu_output, npu_output) - - - -instantiate_device_type_tests(TestCol2ImBackward, globals(), except_for="cpu") -if __name__ == "__main__": - torch.npu.set_device("npu:5") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_conv1d.py b/pytorch1.8.1/test/test_npu/test_conv1d.py deleted file mode 100644 index f780431e4f6..00000000000 --- a/pytorch1.8.1/test/test_npu/test_conv1d.py +++ /dev/null @@ -1,92 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestConv1d(TestCase): - def op_exec_cpu(self, input, weight, in_channels, out_channels, kernel_size, padding=0, stride=1, dilation=1, bias=True): - m = torch.nn.Conv1d(in_channels, out_channels, kernel_size, stride, padding, dilation, bias=bias, groups=1) - m.weight.data = weight - output = m(input) - output = output.detach().numpy() - return output - - def op_exec_npu(self, input, weight, in_channels, out_channels, kernel_size, padding=0, stride=1, dilation=1, bias=True): - m = torch.nn.Conv1d(in_channels, out_channels, kernel_size, stride, padding, dilation, bias=bias, groups=1) - m.weight.data = weight - m = m.to("npu") - output = m(input) - output = output.to("cpu") - output = output.detach().numpy() - return output - - def test_conv1d_shape_format(self, device): - shape_format = [ - [[np.float32, 3, (256, 32, 1, 1)], [np.float32, 3, (8, 32, 1, 1)], 0, (1, 1), (1, 1), (8)], - [[np.float32, 3, [256, 32, 112, 112]], [np.float32, 0, [16, 32, 1, 1]], 0, 1, 1, None], - [[np.float32, 0, [256, 3, 224, 224]], [np.float32, 0, [32, 3, 3, 3]], 0, [2, 2], 1, None], - [[np.float32, 3, (2, 3, 3, 3)], [np.float32, 0, (3, 1, 3, 3)], 3, 1, 1, 1], - [[np.float32, 3, [1024, 232, 7, 7]], [np.float32, 4, [232, 232, 1, 1]], 0, 1, 1, True], - ] - - for item in shape_format: - input_cpu, input_npu = create_common_tensor(item[0], -2, 2) - weight_cpu, weight_npu = create_common_tensor(item[1], -2, 2) - kernel_size = (item[1][2][2], item[1][2][3]) - cpu_output = self.op_exec_cpu(input_cpu, weight_cpu, item[0][2][1], item[1][2][0], kernel_size=kernel_size, - padding=item[2], stride=item[3], dilation=item[4], bias=item[5]) - weight_npu = weight_npu.to("cpu") - npu_output = self.op_exec_npu(input_npu, weight_npu, item[0][2][1], item[1][2][0], kernel_size=kernel_size, - padding=item[2], stride=item[3], dilation=item[4], bias=item[5]) - self.assertRtolEqual(cpu_output, npu_output) - - def test_conv1d_shape_format_float16(self, device): - def cpu_op_exec_fp16(input, weight, in_channels, out_channels, kernel_size, padding=0, stride=1, dilation=1, bias=True): - input = input.to(torch.float32) - weight = weight.to(torch.float32) - m = torch.nn.Conv1d(in_channels, out_channels, kernel_size, stride, padding, dilation, bias=bias, groups=1) - m.weight.data = weight - output = m(input) - output = output.detach().numpy() - output = output.astype(np.float16) - return output - - shape_format = [ - [[np.float16, 3, (256, 32, 1, 1)], [np.float16, 3, (8, 32, 1, 1)], 0, (1, 1), (1, 1), (8)], - [[np.float16, 3, [256, 32, 112, 112]], [np.float16, 0, [16, 32, 1, 1]], 0, 1, 1, None], - [[np.float16, 0, [256, 3, 224, 224]], [np.float16, 0, [32, 3, 3, 3]], 0, [2, 2], 1, None], - [[np.float16, 3, (2, 3, 3, 3)], [np.float16, 0, (3, 1, 3, 3)], 3, 1, 1, 1], - [[np.float16, 3, [1024, 232, 7, 7]], [np.float16, 4, [232, 232, 1, 1]], 0, 1, 1, True], - ] - - for item in shape_format: - input_cpu, input_npu = create_common_tensor(item[0], -2, 2) - weight_cpu, weight_npu = create_common_tensor(item[1], -2, 2) - kernel_size = (item[1][2][2], item[1][2][3]) - cpu_output = cpu_op_exec_fp16(input_cpu, weight_cpu, item[0][2][1], item[1][2][0], kernel_size=kernel_size, - padding=item[2], stride=item[3], dilation=item[4], bias=item[5]) - weight_npu = weight_npu.to("cpu") - npu_output = self.op_exec_npu(input_npu, weight_npu, item[0][2][1], item[1][2][0], kernel_size=kernel_size, - padding=item[2], stride=item[3], dilation=item[4], bias=item[5]) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestConv1d, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:6") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_conv_tbc.py b/pytorch1.8.1/test/test_npu/test_conv_tbc.py deleted file mode 100644 index aeb8eca4a25..00000000000 --- a/pytorch1.8.1/test/test_npu/test_conv_tbc.py +++ /dev/null @@ -1,65 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -import torch.nn as nn -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestConvTbc(TestCase): - - def op_exec_cpu(self, input1, weight, bias, pad): - cpu_output = torch.conv_tbc(input1, weight, bias, pad) - cpu_output = cpu_output.numpy().astype('float16') - print("===cpu_output===") - print(cpu_output) - return cpu_output - - def op_exec_npu(self, input1, weight, bias, pad): - input1 = input1.to("npu") - weight = weight.to("npu") - bias = bias.to("npu") - npu_output = torch.conv_tbc(input1, weight, bias, pad) - npu_output = npu_output.to("cpu") - npu_output = npu_output.numpy().astype('float16') - print("===npu_output===") - print(npu_output) - return npu_output - - def test_conv_tbc_shape_format(self, device): - inputs = np.random.uniform(0, 2, [5, 1, 2]) - npu_input = torch.from_numpy(inputs.astype('float16')) - cpu_input = torch.from_numpy(inputs) - weights = np.random.uniform(0, 2, [1, 2, 2]) - npu_weight = torch.from_numpy(weights.astype('float16')) - cpu_weight = torch.from_numpy(weights) - bias = np.random.uniform(0, 2, [2]) - npu_bias = torch.from_numpy(bias.astype('float16')) - cpu_bias = torch.from_numpy(bias) - pad = 1 - cpu_output = self.op_exec_cpu(cpu_input, cpu_weight, cpu_bias, pad) - npu_output = self.op_exec_npu(npu_input, npu_weight, npu_bias, pad) - res = abs((cpu_output - npu_output)/cpu_output) - print(res) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestConvTbc, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:1") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_conv_tbc_backward.py b/pytorch1.8.1/test/test_npu/test_conv_tbc_backward.py deleted file mode 100644 index 8297bb2ea77..00000000000 --- a/pytorch1.8.1/test/test_npu/test_conv_tbc_backward.py +++ /dev/null @@ -1,101 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestConvTbcBackward(TestCase): - weight_grad = [] - input_grad = [] - - def getWeightGrad(self, grad): - self.weight_grad.append(grad.to("cpu")) - - def getInputGrad(self, grad): - self.input_grad.append(grad.to("cpu")) - - def cpu_op_exec(self, input1, weight1, bias1, pad): - input1.requires_grad = True - input1.register_hook(lambda grad: self.getInputGrad(grad)) - weight1.requires_grad = True - weight1.register_hook(lambda grad: self.getWeightGrad(grad)) - bias1.requires_grad = True - cpuOutput = torch.conv_tbc(input1, weight1, bias1, pad) - tmp = torch.ones_like(cpuOutput) - cpuOutput.backward(tmp) - cpuOutput = cpuOutput.detach().numpy() - return cpuOutput, bias1.grad - - def npu_op_exec(self, input1, weight1, bias1, pad): - input1.requires_grad = True - input1.register_hook(lambda grad: self.getInputGrad(grad)) - weight1.requires_grad = True - weight1.register_hook(lambda grad: self.getWeightGrad(grad)) - bias1.requires_grad = True - npuOutput = torch.conv_tbc(input1, weight1, bias1, pad) - tmp = torch.ones_like(npuOutput) - tmp = tmp.to("npu") - npuOutput.backward(tmp) - npuOutput = npuOutput.to("cpu") - npuOutput = npuOutput.detach().numpy() - return npuOutput, bias1.grad.to("cpu") - - def test_conv_tbc_backward_shape_format(self, device): - - shape_format = [ # input(TBC1), weight(Lc1c0), bias(c0), pad - [[np.float16, -1, (5, 1, 2)], [np.float16, -1, (1, 2, 2)], [np.float16, -1, (2)], 0], - [[np.float32, -1, (5, 2, 2)], [np.float32, -1, (2, 2, 2)], [np.float32, -1, (2)], 1], - [[np.float16, -1, (256, 8, 1)], [np.float16, -1, (10, 1, 1)], [np.float16, -1, (1)], 0], - [[np.float16, -1, [232, 23, 7]], [np.float16, -1, [23, 7, 8]], [np.float16, -1, [8]], 1], - [[np.float32, -1, [10, 2, 4]], [np.float32, -1, [2, 4, 2]], [np.float32, -1, [2]], 1], - [[np.float32, -1, [167, 243, 219]], [np.float32, -1, [37, 219, 216]], [np.float32, -1, [216]], 1], - [[np.float16, -1, [155, 96, 16]], [np.float16, -1, [88, 16, 67]], [np.float16, -1, [67]], 1], - [[np.float32, -1, [220, 269, 55]], [np.float32, -1, [33, 55, 292]], [np.float32, -1, [292]], 1], - [[np.float32, -1, [250, 278, 38]], [np.float32, -1, [80, 38, 81]], [np.float32, -1, [81]], 0], - [[np.float16, -1, [150, 1, 20]], [np.float16, -1, [35, 20, 4]], [np.float16, -1, [4]], 1], - [[np.float16, -1, [10, 2, 2]], [np.float16, -1, [3, 2, 3]], [np.float16, -1, [3]], 0], - ] - - for item in shape_format: - self.input_grad.clear() - self.weight_grad.clear() - cpu_input1, npu_input1 = create_common_tensor(item[0], 0, 10) - if cpu_input1.dtype == torch.float16: - cpu_input1 = cpu_input1.to(torch.float32) - cpu_weight, npu_weight = create_common_tensor(item[1], 0, 10) - if cpu_weight.dtype == torch.float16: - cpu_weight = cpu_weight.to(torch.float32) - cpu_bias, npu_bias = create_common_tensor(item[2], 0, 10) - if cpu_bias.dtype == torch.float16: - cpu_bias = cpu_bias.to(torch.float32) - cpu_output, cpu_dBias = self.cpu_op_exec(cpu_input1, cpu_weight, cpu_bias, item[3]) - npu_output, npu_dBias = self.npu_op_exec(npu_input1, npu_weight, npu_bias, item[3]) - cpu_output = cpu_output.astype(npu_output.dtype) - self.input_grad[0] = self.input_grad[0].to(self.input_grad[1].dtype) - self.weight_grad[0] = self.weight_grad[0].to(self.weight_grad[1].dtype) - cpu_dBias = cpu_dBias.to(npu_dBias.dtype) - self.assertRtolEqual(cpu_output, npu_output) - self.assertRtolEqual(cpu_dBias, npu_dBias) - self.assertRtolEqual(self.input_grad[0].numpy(), self.input_grad[1].numpy()) - self.assertRtolEqual(self.weight_grad[0].numpy(), self.weight_grad[1].numpy()) - -instantiate_device_type_tests(TestConvTbcBackward, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:1") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_conv_transpose2d.py b/pytorch1.8.1/test/test_npu/test_conv_transpose2d.py deleted file mode 100644 index e62981ef9af..00000000000 --- a/pytorch1.8.1/test/test_npu/test_conv_transpose2d.py +++ /dev/null @@ -1,76 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -import torch.nn as nn -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestConvTranspose2d(TestCase): - def cpu_op_exec(self, input, weight): - cpu_output = torch.nn.functional.conv_transpose2d(input, weight,bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) - cpu_output = cpu_output.numpy() - return cpu_output - - def cpu_op_exec_fp16(self, input, weight): - input = input.to(torch.float32) - weight = weight.to(torch.float32) - cpu_output = torch.nn.functional.conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) - cpu_output = cpu_output.numpy() - cpu_output = cpu_output.astype(np.float16) - - return cpu_output - - def npu_op_exec(self, input, weight): - input = input.to("npu") - weight = weight.to("npu") - npu_output = torch.nn.functional.conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) - npu_output = npu_output.to("cpu").numpy() - - return npu_output - - def test_conv_transpose2d(self, device): - shape_format = [ # input, weight - [[np.float16, 3, [1024, 116, 14, 14]], [np.float16, 4, [116, 116, 1, 1]]], - [[np.float16, 3, [1024, 58, 28, 28]], [np.float16, 3, [58, 58, 1, 1]]], - [[np.float16, 4, [1024, 3, 224, 224]], [np.float16, 4, [3, 3, 3, 3]]], - [[np.float16, 0, [1024, 116, 14, 14]], [np.float16, 4, [116, 116, 1, 1]]], - [[np.float16, 3, [1024, 232, 7, 7]], [np.float16, 4, [232, 232, 1, 1]]], - [[np.float16, 4, [1024, 58, 28, 28]], [np.float16, 4, [58, 58, 1, 1]]], - [[np.float16, 0, [1024, 24, 56, 56]], [np.float16, 4, [24, 24, 1, 1]]], - [[np.float32, 0, [256, 128, 7, 7]], [np.float32, 4, [128, 128, 3, 3]]], - [[np.float32, 4, [256, 3, 224, 224]], [np.float32, 4, [3, 3, 7, 7]]], - [[np.float32, 3, [2, 3, 3, 3]], [np.float32, 4, [3, 1, 3, 3]]], - [[np.float32, 3, [1024, 232, 7, 7]], [np.float32, 4, [232, 232, 1, 1]]], - ] - for item in shape_format: - input_cpu, input_npu = create_common_tensor(item[0], 0, 10) - weight_cpu, weight_npu = create_common_tensor(item[1], 0, 10) - if input_cpu.dtype == torch.float16: - cpu_output = self.cpu_op_exec_fp16(input_cpu, weight_cpu) - else: - cpu_output = self.cpu_op_exec(input_cpu, weight_cpu) - npu_output = self.npu_op_exec(input_npu, weight_npu) - # fp32精度不足，放宽对其精度要求 - self.assertRtolEqual(cpu_output, npu_output, prec=1.e-1) - - -instantiate_device_type_tests(TestConvTranspose2d, globals(), except_for='cpu') -if __name__ == "__main__": - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_convolution.py b/pytorch1.8.1/test/test_npu/test_convolution.py deleted file mode 100644 index 59236c16fd8..00000000000 --- a/pytorch1.8.1/test/test_npu/test_convolution.py +++ /dev/null @@ -1,90 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestCudnnConvolution(TestCase): - def cpu_op_exec(self, input, weight, in_channels, out_channels, kernel_size, padding=0, stride=1, dilation=1, bias=True): - m = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, bias=bias, groups=1) - m.weight.data = weight - output = m(input) - output = output.detach().numpy() - return output - - def npu_op_exec(self, input, weight, in_channels, out_channels, kernel_size, padding=0, stride=1, dilation=1, bias=True): - m = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, bias=bias, groups=1) - m.weight.data = weight - output = m(input) - weight = weigh.to("cpu") - output = output.to("cpu") - output = output.detach().numpy() - return output - - def test_cudnn_convolution_shape_format(self, device): - shape_format = [ - [[np.float32, 3, (256, 32, 1, 1)], [np.float32, 3, (8, 32, 1, 1)], 0, (1, 1), (1, 1), (8)], - [[np.float32, 3, [256, 32, 112, 112]], [np.float32, 0, [16, 32, 1, 1]], 0, 1, 1, True], - [[np.float32, 0, [256, 3, 224, 224]], [np.float32, 0, [32, 3, 3, 3]], 0, [2, 2], 1, None], - [[np.float32, 3, [256, 128, 7, 7]], [np.float32, 4, [32, 128, 3, 3]], (1, 1), 1, 1, True], - [[np.float32, 0, [256, 3, 224, 224]], [np.float32, 4, [64, 3, 7, 7]], [3, 3], [2, 2], 1, None], - [[np.float32, 3, (2, 3, 3, 3)], [np.float32, 0, (3, 1, 3, 3)], 3, 1, 1, 1], - ] - - for item in shape_format: - input_cpu, input_npu = create_common_tensor(item[0], 0, 10) - weight_cpu, weight_npu = create_common_tensor(item[1], 0, 10) - kernel_size = (item[1][2][2], item[1][2][3]) - cpu_output = self.cpu_op_exec(input_cpu, weight_cpu, item[0][2][1], item[1][2][0], kernel_size=kernel_size, - padding=item[2], stride=item[3], dilation=item[4], bias=item[5]) - npu_output = self.npu_op_exec(input_npu, weight_npu, item[0][2][1], item[1][2][0], kernel_size=kernel_size, - padding=item[2], stride=item[3], dilation=item[4], bias=item[5]) - self.assertRtolEqual(cpu_output, npu_output) - - def test_cudnn_convolution_float16_shape_format(self, device): - def cpu_op_exec_fp16(input, weight, in_channels, out_channels, kernel_size, padding=0, stride=1, dilation=1, bias=True): - weight = weight.to(torch.float32) - input = input.to(torch.float32) - m = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, bias=bias, groups=1) - m.weight.data = weight - output = m(input) - output = output.detach().numpy() - output = output.astype(np.float16) - return output - shape_format = [ - [[np.float16, 3, (2, 3, 3, 3)], [np.float16, 0, (3, 1, 3, 3)], 3, 1, 1, 1], - [[np.float16, 3, [1024, 232, 7, 7]], [np.float16, 4, [232, 232, 1, 1]], 0, 1, 1, True], - [[np.float16, 0, [1024, 116, 14, 14]], [np.float16, 4, [116, 116, 1, 1]], 0, 1, 1, None], - [[np.float16, 0, [1024, 58, 28, 28]], [np.float16, 4, [58, 58, 1, 1]], 0, 1, 1, True], - [[np.float16, 0, [1024, 3, 224, 224]], [np.float16, 4, [24, 3, 3, 3]], 0, [2, 2], 1, None], - ] - for item in shape_format: - input_cpu, input_npu = create_common_tensor(item[0], 0, 10) - weight_cpu, weight_npu = create_common_tensor(item[1], 0, 10) - weight_cpu = weight_cpu.to(torch.float32) - kernel_size = (item[1][2][2], item[1][2][3]) - cpu_output = cpu_op_exec_fp16(input_cpu, weight_cpu, item[0][2][1], item[1][2][0], kernel_size=kernel_size, - padding=item[2], stride=item[3], dilation=item[4], bias=item[5]) - npu_output = self.npu_op_exec(input_npu, weight_npu, item[0][2][1], item[1][2][0], kernel_size=kernel_size, - padding=item[2], stride=item[3], dilation=item[4], bias=item[5]) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestCudnnConvolution, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:6") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_convolution_backward.py b/pytorch1.8.1/test/test_npu/test_convolution_backward.py deleted file mode 100644 index ded1c9a232a..00000000000 --- a/pytorch1.8.1/test/test_npu/test_convolution_backward.py +++ /dev/null @@ -1,96 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -import torch.nn as nn -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestConv2dBackward(TestCase): - weight_grad = [] - input_grad = [] - - def getWeightGrad(self, grad): - self.weight_grad.append(grad.to("cpu")) - - def getInputGrad(self, grad): - self.input_grad.append(grad.to("cpu")) - - def cpu_op_exec(self, input1, weight, padding = 0, stride = 1, bias1 = None): - input1.requires_grad = True - input1.register_hook(lambda grad: self.getInputGrad(grad)) - weight.requires_grad = True - weight.register_hook(lambda grad: self.getWeightGrad(grad)) - bias1.requires_grad = True - - res_forward = nn.functional.conv2d(input1, weight, bias1, stride, padding) - grads = torch.ones_like(res_forward).float() - res_forward.backward(grads, retain_graph=True) - res_forward = res_forward.detach().numpy() - return res_forward - - def npu_op_exec(self, input1, weight, padding = 0, stride = 1, bias1 = None): - input1.requires_grad = True - input1.register_hook(lambda grad: self.getInputGrad(grad)) - weight.requires_grad = True - weight.register_hook(lambda grad: self.getWeightGrad(grad)) - bias1 = bias1.to("npu") - bias1.requires_grad = True - - res_forward = nn.functional.conv2d(input1, weight, bias1, stride, padding) - grads = torch.ones_like(res_forward).float() - grads = grads.to("npu") - res_forward.backward(grads, retain_graph=True) - res_forward = res_forward.to("cpu") - res_forward = res_forward.detach().numpy() - return res_forward - - def test_conv2d_backward_shape_format(self, device): - shape_format = [ # input, weight, padding, stride - [[np.float32, 0, (1, 4, 5, 5)], [np.float32, 0, (4, 4, 3, 3)], 0, (1, 1)], - [[np.float32, 0, (1, 8, 3, 3)], [np.float32, 0, (8, 8, 1, 1)], 0, (2, 1)], - [[np.float32, 0, (1024, 2048, 6, 6)], [np.float32, 0, (2048, 2048, 3, 3)], 0, (1, 2)], - [[np.float32, 0, (512, 256, 4, 4)], [np.float32, 0, (256, 256, 2, 2)], 0, (2, 2)], - [[np.float32, 0, (128, 4, 3, 3)], [np.float32, 0, (4, 4, 2, 2)], 0, (3, 1)], - [[np.float32, 0, (2, 64, 3, 3)], [np.float32, 0, (64, 64, 3, 3)], 0, (1, 3)], - [[np.float32, 0, (64, 2, 8, 8)], [np.float32, 0, (2, 2, 1, 1)], 0, (3, 3)], - [[np.float32, 0, (32, 16, 4, 4)], [np.float32, 0, (16, 16, 3, 3)], 0, (2, 1)], - [[np.float32, 0, (1024, 8, 3, 3)], [np.float32, 0, (8, 8, 1, 1)], 0, (1, 2)], - [[np.float32, 0, (1, 8, 512, 512)], [np.float32, 0, (8, 8, 3, 3)], 0, (2, 2)], - [[np.float32, 0, (1, 2, 1, 1)], [np.float32, 0, (1, 1, 2, 2)], 0, (1, 1)], - ] - - for item in shape_format: - self.weight_grad.clear() - self.input_grad.clear() - cpu_input1, npu_input1 = create_common_tensor(item[0], -2, 2) - cpu_input2, npu_input2 = create_common_tensor(item[1], -2, 2) - cpu_bias = torch.randn(item[1][2][0]) - npu_bias = copy.deepcopy(cpu_bias) - cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2, item[2], item[3], cpu_bias) - npu_output = self.npu_op_exec(npu_input1, npu_input2, item[2], item[3], npu_bias) - - self.assertRtolEqual(cpu_output, npu_output) - self.assertRtolEqual(self.input_grad[0], self.input_grad[1]) - self.assertRtolEqual(self.weight_grad[0], self.weight_grad[1]) - - -instantiate_device_type_tests(TestConv2dBackward, globals(), except_for='cpu') -if __name__ == "__main__": - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_convolution_backward_input.py b/pytorch1.8.1/test/test_npu/test_convolution_backward_input.py deleted file mode 100644 index 233a18a4694..00000000000 --- a/pytorch1.8.1/test/test_npu/test_convolution_backward_input.py +++ /dev/null @@ -1,106 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestCudnnConvolutionBackwardInput(TestCase): - def cpu_op_exec(self, input1, weight, stride, padding, dilation, groups): - input1.requires_grad = True - res_forward = torch._convolution(input1, - weight, - bias=None, - stride=stride, - padding=padding, - dilation=dilation, - transposed=False, - output_padding=(0, 0), - groups=groups, - benchmark=True, - deterministic=True, - cudnn_enabled=True) - grads = torch.ones_like(res_forward).float() - res_forward.backward(grads, retain_graph=True) - res_forward = res_forward.detach().numpy() - gradinput = input1.grad - return res_forward, gradinput - - def npu_op_exec(self, input1, weight, stride, padding, dilation, groups): - input1.requires_grad = True - weight = weight.to("npu") - res_forward = torch._convolution(input1, - weight, - bias=None, - stride=stride, - padding=padding, - dilation=dilation, - transposed=False, - output_padding=(0, 0), - groups=groups, - benchmark=True, - deterministic=True, - cudnn_enabled=True) - grads = torch.ones_like(res_forward).float() - grads = grads.to("npu") - res_forward.backward(grads, retain_graph=True) - res_forward = res_forward.to("cpu") - res_forward = res_forward.detach().numpy() - gradinput = input1.grad.to("cpu") - return res_forward, gradinput - - def test_cudnn_convolution_backward_input_shape_format(self, device): - shape_format = [ # input, weight, stride, padding, dilation, groups - [[np.float16, 0, (1, 4, 5, 5)], [np.float16, 0, (4, 4, 3, 3)], - (1, 1), (1, 1), (1, 1), 1], - [[np.float32, 0, [256, 3, 224, 224]], - [np.float32, 0, [32, 3, 3, 3]], [2, 2], [0, 0], [1, 1], 1], - [[np.float16, 3, (256, 8, 1, 1)], [np.float16, 3, (8, 8, 1, 1)], - (1, 1), (0, 0), (1, 1), 1], - [[np.float16, 3, [1024, 232, 7, 7]], - [np.float16, 4, [232, 232, 1, 1]], (1, 1), (0, 0), (1, 1), 1], - [[np.float32, 0, (1, 4, 5, 5)], [np.float32, 0, (4, 4, 3, 3)], - (1, 1), (1, 1), (1, 1), 1] - ] - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], -2, 2) - if cpu_input1.dtype == torch.float16: - cpu_input1 = cpu_input1.to(torch.float32) - cpu_input2, npu_input2 = create_common_tensor(item[1], -2, 2) - if cpu_input2.dtype == torch.float16: - cpu_input2 = cpu_input2.to(torch.float32) - cpu_output, cpu_dinput = self.cpu_op_exec(cpu_input1, cpu_input2, - item[2], item[3], - item[4], item[5]) - npu_output, npu_dinput = self.npu_op_exec(npu_input1, npu_input2, - item[2], item[3], - item[4], item[5]) - cpu_output = cpu_output.astype(npu_output.dtype) - cpu_dinput = npu_dinput.to(npu_dinput.dtype) - self.assertRtolEqual(cpu_output, npu_output) - self.assertRtolEqual(cpu_dinput, npu_dinput) - - -instantiate_device_type_tests(TestCudnnConvolutionBackwardInput, - globals(), - except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:1") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_convolution_backward_weight.py b/pytorch1.8.1/test/test_npu/test_convolution_backward_weight.py deleted file mode 100644 index de421a95520..00000000000 --- a/pytorch1.8.1/test/test_npu/test_convolution_backward_weight.py +++ /dev/null @@ -1,110 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestCudnnConvolutionBackwardWeight(TestCase): - weight_grad = [] - - def getWeightGrad(self, grad): - self.weight_grad.append(grad.to("cpu")) - - def cpu_op_exec(self, input1, weight, stride, padding, dilation, groups): - weight.requires_grad = True - res_forward = torch._convolution(input1, - weight, - bias=None, - stride=stride, - padding=padding, - dilation=dilation, - transposed=False, - output_padding=(0, 0), - groups=groups, - benchmark=True, - deterministic=True, - cudnn_enabled=True) - grads = torch.ones_like(res_forward).float() - res_forward.backward(grads, retain_graph=True) - res_forward = res_forward.detach().numpy() - gradweight = weight.grad - return res_forward, gradweight - - def npu_op_exec(self, input1, weight, stride, padding, dilation, groups): - weight.requires_grad = True - input1 = input1.to("npu") - res_forward = torch._convolution(input1, - weight, - bias=None, - stride=stride, - padding=padding, - dilation=dilation, - transposed=False, - output_padding=(0, 0), - groups=groups, - benchmark=True, - deterministic=True, - cudnn_enabled=True) - grads = torch.ones_like(res_forward).float() - grads = grads.to("npu") - res_forward.backward(grads, retain_graph=True) - res_forward = res_forward.to("cpu") - res_forward = res_forward.detach().numpy() - gradweight = weight.grad.to("cpu") - return res_forward, gradweight - - def test_cudnn_convolution_backward_weight_shape_format(self, device): - shape_format = [ # input, weight, stride, padding, dilation, groups - [[np.float16, 0, (1, 4, 5, 5)], [np.float16, 0, (4, 4, 3, 3)], - (1, 1), (1, 1), (1, 1), 1], - [[np.float32, 0, [256, 3, 224, 224]], - [np.float32, 0, [32, 3, 3, 3]], [2, 2], [0, 0], [1, 1], 1], - [[np.float16, 3, (256, 8, 1, 1)], [np.float16, 3, (8, 8, 1, 1)], - (1, 1), (0, 0), (1, 1), 1], - [[np.float16, 3, [1024, 232, 7, 7]], - [np.float16, 4, [232, 232, 1, 1]], (1, 1), (0, 0), (1, 1), 1], - [[np.float32, 0, (1, 4, 5, 5)], [np.float32, 0, (4, 4, 3, 3)], - (1, 1), (1, 1), (1, 1), 1] - ] - - for item in shape_format: - self.weight_grad.clear() - cpu_input1, npu_input1 = create_common_tensor(item[0], -2, 2) - if cpu_input1.dtype == torch.float16: - cpu_input1 = cpu_input1.to(torch.float32) - cpu_input2, npu_input2 = create_common_tensor(item[1], -2, 2) - if cpu_input2.dtype == torch.float16: - cpu_input2 = cpu_input2.to(torch.float32) - cpu_output, cpu_dweight = self.cpu_op_exec(cpu_input1, cpu_input2, item[2], - item[3], item[4], item[5]) - npu_output, npu_dweight = self.npu_op_exec(npu_input1, npu_input2, item[2], - item[3], item[4], item[5]) - cpu_output = cpu_output.astype(npu_output.dtype) - cpu_dweight = cpu_dweight.to(npu_dweight.dtype) - self.assertRtolEqual(cpu_output, npu_output) - self.assertRtolEqual(cpu_dweight, npu_dweight) - - -instantiate_device_type_tests(TestCudnnConvolutionBackwardWeight, - globals(), - except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:1") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_convolution_transpose_backward_weight.py b/pytorch1.8.1/test/test_npu/test_convolution_transpose_backward_weight.py deleted file mode 100644 index 76fc807c716..00000000000 --- a/pytorch1.8.1/test/test_npu/test_convolution_transpose_backward_weight.py +++ /dev/null @@ -1,114 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestCudnnConvolutionTransposeBackwardWeight(TestCase): - weight_grad = [] - - def getWeightGrad(self, grad): - self.weight_grad.append(grad.to("cpu")) - - def cpu_op_exec(self, input1, weight, stride, padding, dilation, groups): - weight.requires_grad = True - weight.register_hook(lambda grad: self.getWeightGrad(grad)) - res_forward = torch._convolution(input1, - weight, - bias=None, - stride=stride, - padding=padding, - dilation=dilation, - transposed=True, - output_padding=(0, 0), - groups=groups, - benchmark=True, - deterministic=True, - cudnn_enabled=False) - print("===cpu_res_forward===") - print(res_forward) - grads = torch.ones_like(res_forward).float() - res_forward.backward(grads, retain_graph=True) - res_forward = res_forward.detach().numpy() - return res_forward - - def npu_op_exec(self, input1, weight, stride, padding, dilation, groups): - weight.requires_grad = True - weight.register_hook(lambda grad: self.getWeightGrad(grad)) - weight = weight.to("npu") - res_forward = torch._convolution(input1, - weight, - bias=None, - stride=stride, - padding=padding, - dilation=dilation, - transposed=True, - output_padding=(0, 0), - groups=groups, - benchmark=True, - deterministic=True, - cudnn_enabled=False) - print("===npu_res_forward===") - print(res_forward) - grads = torch.ones_like(res_forward).float() - grads = grads.to("npu") - res_forward.backward(grads, retain_graph=True) - res_forward = res_forward.to("cpu") - res_forward = res_forward.detach().numpy() - return res_forward - - def test_cudnn_convolution_transpose_backward_weight_shape_format( - self, device): - shape_format = [ # input, weight, stride, padding, dilation, groups - [[np.float16, 0, (1, 4, 5, 5)], [np.float16, 0, (4, 4, 3, 3)], - (1, 1), (1, 1), (1, 1), 1], - [[np.float16, 3, (256, 8, 1, 1)], [np.float16, 3, (8, 8, 1, 1)], - (1, 1), (0, 0), (1, 1), 1], - [[np.float16, 3, [1024, 232, 7, 7]], - [np.float16, 4, [232, 232, 1, 1]], (1, 1), (0, 0), (1, 1), 1], - # [[np.float32, 0, (1, 4, 5, 5)], [np.float32, 0, (4, 4, 3, 3)], - # (1, 1), (1, 1), (1, 1), 1] - ] - - for item in shape_format: - self.weight_grad.clear() - cpu_input1, npu_input1 = create_common_tensor(item[0], -2, 2) - if cpu_input1.dtype == torch.float16: - cpu_input1 = cpu_input1.to(torch.float32) - cpu_input2, npu_input2 = create_common_tensor(item[1], -2, 2) - if cpu_input2.dtype == torch.float16: - cpu_input2 = cpu_input2.to(torch.float32) - cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2, item[2], - item[3], item[4], item[5]) - npu_output = self.npu_op_exec(npu_input1, npu_input2, item[2], - item[3], item[4], item[5]) - cpu_output = cpu_output.astype(npu_output.dtype) - self.weight_grad[0] = self.weight_grad[0].to( - self.weight_grad[1].dtype) - self.assertRtolEqual(cpu_output, npu_output) - self.assertRtolEqual(self.weight_grad[0], self.weight_grad[1]) - - -instantiate_device_type_tests(TestCudnnConvolutionTransposeBackwardWeight, - globals(), - except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:1") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_copy.py b/pytorch1.8.1/test/test_npu/test_copy.py deleted file mode 100644 index d8c3700b8a9..00000000000 --- a/pytorch1.8.1/test/test_npu/test_copy.py +++ /dev/null @@ -1,141 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestCopy(TestCase): - def test_copy_transpose(self, device): - inputs = torch.randn(2, 3, 5) - cpu_out = inputs.transpose(2, 0) + 1 - inputs = inputs.to("npu") - npu_out = inputs.transpose(2, 0) + 1 - self.assertRtolEqual(cpu_out.detach().numpy(), npu_out.cpu().detach().numpy()) - - def test_copy_permute_nd(self, device): - inputs = torch.randn(2, 5, 6, 9) - cpu_out = inputs.permute(2, 3, 0, 1) + 1 - inputs = inputs.to("npu").npu_format_cast(0) - npu_out = inputs.permute(2, 3, 0, 1) + 1 - self.assertRtolEqual(cpu_out.detach().numpy(), npu_out.cpu().detach().numpy()) - - def test_copy_permute_nd_optimize(self, device): - inputs = torch.randn(32, 64, 15, 20, 1) - cpu_out = inputs.permute(2, 3, 0, 1, 4) + 1 - inputs = inputs.to("npu").npu_format_cast(0) - npu_out = inputs.permute(2, 3, 0, 1, 4) + 1 - self.assertRtolEqual(cpu_out.detach().numpy(), npu_out.cpu().detach().numpy()) - - def test_copy_permute_5hd(self, device): - inputs = torch.from_numpy(np.random.randn(2560,512,1,26).astype(np.float32)) - cpu_out = inputs.permute(2,3,0,1) + 1 - inputs = inputs.to("npu").npu_format_cast(3) - npu_out = inputs.permute(2,3,0,1) + 1 - self.assertRtolEqual(cpu_out.detach().numpy(), npu_out.cpu().detach().numpy()) - - def test_copy_squeeze_permute_nd(self, device): - inputs = torch.from_numpy(np.random.randn(2560,512,1,26).astype(np.float32)) - cpu_out = inputs.squeeze(2).permute(1,2,0) + 1 - inputs = inputs.to("npu").npu_format_cast(0) - npu_out = inputs.squeeze(2).permute(1,2,0) + 1 - self.assertRtolEqual(cpu_out.detach().numpy(), npu_out.cpu().detach().numpy()) - - def test_copy_squeeze_unsqueeze_permute_5hd(self, device): - inputs = torch.from_numpy(np.random.randn(1,512,1,26).astype(np.float32)) - cpu_out = inputs.squeeze().unsqueeze(1).unsqueeze(3).permute(1,3,2,0) + 1 - inputs = inputs.to("npu").npu_format_cast(3) - npu_out = inputs.squeeze().unsqueeze(1).unsqueeze(3).permute(1,3,2,0) + 1 - self.assertRtolEqual(cpu_out.detach().numpy(), npu_out.cpu().detach().numpy()) - - def test_copy_transpose_squeeze_permute_nd(self, device): - inputs = torch.from_numpy(np.random.randn(16,512,1,26).astype(np.float32)) - cpu_out = inputs.transpose(1,3).squeeze().permute(2,1,0) + 1 - inputs = inputs.to("npu").npu_format_cast(0) - npu_out = inputs.transpose(1,3).squeeze().permute(2,1,0) + 1 - self.assertRtolEqual(cpu_out.detach().numpy(), npu_out.cpu().detach().numpy()) - - def test_copy_view_permute_nd(self, device): - inputs = torch.from_numpy(np.random.randn(16,512,1,26).astype(np.float32)) - cpu_out = inputs.view(32,256,1,26).permute(2,1,0,3) + 1 - inputs = inputs.to("npu").npu_format_cast(0) - npu_out = inputs.view(32,256,1,26).permute(2,1,0,3) + 1 - self.assertRtolEqual(cpu_out.detach().numpy(), npu_out.cpu().detach().numpy()) - - def test_copy_narrow_5hd(self, device): - inputs = torch.from_numpy(np.random.randn(256,256,28,28).astype(np.float32)) - cpu_out = torch.narrow(inputs, 1, 224, 32) + 1 - inputs = inputs.to("npu").npu_format_cast(3) - npu_out = torch.narrow(inputs, 1, 224, 32) + 1 - self.assertRtolEqual(cpu_out.detach().numpy(), npu_out.cpu().detach().numpy()) - - def test_copy_narrow_nd(self, device): - inputs = torch.from_numpy(np.random.randn(256,256,28,28).astype(np.float32)) - narrow_1 = torch.narrow(inputs, 1, 224, 32) - cpu_out = torch.narrow(narrow_1, 2, 14, 14) + 1 - inputs = inputs.to("npu").npu_format_cast(0) - narrow_1 = torch.narrow(inputs, 1, 224, 32) - npu_out = torch.narrow(narrow_1, 2, 14, 14) + 1 - self.assertRtolEqual(cpu_out.detach().numpy(), npu_out.cpu().detach().numpy()) - - def test_copy_index_nd(self, device): - inputs = torch.from_numpy(np.random.randn(256,256,28,28).astype(np.float32)) - narrow_1 = torch.narrow(inputs, 1, 32, 192) - cpu_out = narrow_1[0:64, 32:128, 16:24, :] + 1 - inputs = inputs.to("npu").npu_format_cast(0) - narrow_1 = torch.narrow(inputs, 1, 32, 192) - npu_out = narrow_1[0:64, 32:128, 16:24, :] + 1 - self.assertRtolEqual(cpu_out.detach().numpy(), npu_out.cpu().detach().numpy()) - - def test_copy_index_step_nd(self, device): - inputs = torch.from_numpy(np.random.randn(256,256,28,28).astype(np.float32)) - cpu_out = inputs[0:64:2, 32:128:4, :, 6:22] + 1 - inputs = inputs.to("npu").npu_format_cast(0) - npu_out = inputs[0:64:2, 32:128:4, :, 6:22] + 1 - self.assertRtolEqual(cpu_out.detach().numpy(), npu_out.cpu().detach().numpy()) - - def test_copy_chunk(self, device): - inputs = torch.from_numpy(np.random.randn(256,256,28,28).astype(np.float32)) - cpu_out = torch.chunk(inputs, 2, 1) - chunk1_cpu = cpu_out[0] + 1 - chunk2_cpu = cpu_out[1] * 2 - inputs = inputs.to("npu") - npu_out= torch.chunk(inputs, 2, 1) - chunk1_npu = npu_out[0] + 1 - chunk2_npu = npu_out[1] * 2 - self.assertRtolEqual(chunk1_cpu.detach().numpy(), chunk1_npu.cpu().detach().numpy()) - self.assertRtolEqual(chunk2_cpu.detach().numpy(), chunk2_npu.cpu().detach().numpy()) - - def test_copy_split(self, device): - inputs = torch.from_numpy(np.random.randn(256,256,28,28).astype(np.float32)) - cpu_out = torch.chunk(inputs, 12, 2) - chunk1_cpu = cpu_out[0] + 1 - chunk2_cpu = cpu_out[1] * 2 - chunk3_cpu = cpu_out[2].contiguous() - inputs = inputs.to("npu") - npu_out= torch.chunk(inputs, 12, 2) - chunk1_npu = npu_out[0] + 1 - chunk2_npu = npu_out[1] * 2 - chunk3_npu = npu_out[2].contiguous() - self.assertRtolEqual(chunk1_cpu.detach().numpy(), chunk1_npu.cpu().detach().numpy()) - self.assertRtolEqual(chunk2_cpu.detach().numpy(), chunk2_npu.cpu().detach().numpy()) - self.assertRtolEqual(chunk3_cpu.detach().numpy(), chunk3_npu.cpu().detach().numpy()) - - -instantiate_device_type_tests(TestCopy, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:0") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_cos.py b/pytorch1.8.1/test/test_npu/test_cos.py deleted file mode 100644 index 6756d47f57c..00000000000 --- a/pytorch1.8.1/test/test_npu/test_cos.py +++ /dev/null @@ -1,66 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestCos(TestCase): - - def cpu_op_exec(self,input1): - output = torch.cos(input1) - output = output.numpy() - return output - - def npu_op_exec(self,input1): - output = torch.cos(input1) - output = output.to("cpu") - output = output.numpy() - return output - - def npu_op_exec_out(self,input1, input2): - torch.cos(input1, out=input2) - output = input2.to("cpu") - output = output.numpy() - return output - - def test_cos_common_shape_format(self, device): - shape_format = [ - [[np.float32, 0, (5,3)]], - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], -10, 10) - cpu_output = self.cpu_op_exec(cpu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_cos_out_common_shape_format(self, device): - shape_format = [ - [[np.float32, 0, (4,3)], [np.float32, 0, (4,3)]], - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], -10, 10) - cpu_input2, npu_input2 = create_common_tensor(item[1], -10, 10) - cpu_output = self.cpu_op_exec(cpu_input1) - npu_output = self.npu_op_exec_out(npu_input1, npu_input2) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestCos, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:6") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_cosh.py b/pytorch1.8.1/test/test_npu/test_cosh.py deleted file mode 100644 index 1ba58569b75..00000000000 --- a/pytorch1.8.1/test/test_npu/test_cosh.py +++ /dev/null @@ -1,149 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestCosh(TestCase): - def generate_single_data(self, min_d, max_d, shape, dtype): - input1 = np.random.uniform(min_d, max_d, shape).astype(dtype) - npu_input1 = torch.from_numpy(input1) - return npu_input1 - - def cpu_op_exec(self, input1): - output = torch.cosh(input1) - output = output.numpy() - return output - - def cpu_op_exec_fp16(self, input1): - input1 = input1.to(torch.float32) - output = torch.cosh(input1) - output = output.numpy() - output = output.astype(np.float16) - return output - - def npu_op_exec(self, input1): - input1 = input1.to("npu") - output = torch.cosh(input1) - output = output.to("cpu") - output = output.numpy() - return output - - def test_neg_float16_1(self, device): - npu_input1 = self.generate_single_data(-2, 2, ((65535, 1, 1, 1)), np.float16) - cpu_output = self.cpu_op_exec_fp16(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_neg_float16_2(self, device): - npu_input1 = self.generate_single_data(-2, 2, ((1, 1, 1, 8192)), np.float16) - cpu_output = self.cpu_op_exec_fp16(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_neg_float16_3(self, device): - npu_input1 = self.generate_single_data(-2, 2, ((1, 1, 1, 65535)), np.float16) - cpu_output = self.cpu_op_exec_fp16(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_neg_float16_4(self, device): - npu_input1 = self.generate_single_data(-2, 2, ((1, 1, 1, 524288)), np.float16) - cpu_output = self.cpu_op_exec_fp16(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_neg_float16_5(self, device): - npu_input1 = self.generate_single_data(-2, 2, ((1, 1, 1, 786432)), np.float16) - cpu_output = self.cpu_op_exec_fp16(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_neg_float16_6(self, device): - npu_input1 = self.generate_single_data(-5, 5, ((1, 1, 1, 786432)), np.float16) - cpu_output = self.cpu_op_exec_fp16(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_neg_float32_1(self, device): - npu_input1 = self.generate_single_data(-1.1754943508e-38, -1.1754943508e-38, ((1, 31, 149, 2)), np.float32) - cpu_output = self.cpu_op_exec(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_neg_float32_2(self, device): - npu_input1 = self.generate_single_data(-0.000030517578125, 0.000030517578125, ((2, 32, 149, 31)), np.float32) - cpu_output = self.cpu_op_exec(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_neg_float32_3(self, device): - npu_input1 = self.generate_single_data(-9.313225746154785e-10, 9.313225746154785e-10, ((184965, 1)), np.float32) - cpu_output = self.cpu_op_exec(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_neg_float32_4(self, device): - npu_input1 = self.generate_single_data(-3, 3, ((1, 31, 149, 2)), np.float32) - cpu_output = self.cpu_op_exec(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_neg_float32_5(self, device): - npu_input1 = self.generate_single_data(-9.313225746154785e-10, 9.313225746154785e-10, ((1, 31, 149, 2)), np.float32) - cpu_output = self.cpu_op_exec(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_neg_float32_6(self, device): - npu_input1 = self.generate_single_data(-0.000000000000000000000000000000000000011754943508, - 0.000000000000000000000000000000000000011754943508, ((2, 31, 149, 2)), - np.float32) - cpu_output = self.cpu_op_exec(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_neg_float32_7(self, device): - npu_input1 = self.generate_single_data(0.000000000000000000000000000000000000011754943508, - 0.000000000000000000000000000000000000011754943508, ((4, 31, 149, 2)), - np.float32) - cpu_output = self.cpu_op_exec(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_neg_float32_8(self, device): - npu_input1 = self.generate_single_data(-0.000000000000000000000000000000000000011754943508, - -0.000000000000000000000000000000000000011754943508, ((2048, 31, 1, 2)), - np.float32) - cpu_output = self.cpu_op_exec(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_neg_float32_9(self, device): - npu_input1 = self.generate_single_data(-0.000000000000000000000000000000000000011754943508, - 0.000000000000000000000000000000000000011754943508, ((8, 7, 149)), np.float32) - cpu_output = self.cpu_op_exec(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestCosh, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:5") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_cosinesimilarity.py b/pytorch1.8.1/test/test_npu/test_cosinesimilarity.py deleted file mode 100644 index 913acc78e3c..00000000000 --- a/pytorch1.8.1/test/test_npu/test_cosinesimilarity.py +++ /dev/null @@ -1,84 +0,0 @@ -# Copyright 2020 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestCosinesimilarity(TestCase): - - def generate_data(self, min_num, max_num, shape, dtype): - input1 = np.random.uniform(min_num, max_num, shape).astype(dtype) - input2 = np.random.uniform(min_num, max_num, shape).astype(dtype) - - # modify from numpy.ndarray to torch.tensor - npu_input1 = torch.from_numpy(input1) - npu_input2 = torch.from_numpy(input2) - - return npu_input1, npu_input2 - - def cpu_op_exec(self, input_x1, input_x2, dim=1, eps=1e-8): - cos = torch.nn.CosineSimilarity(dim, eps) - res = cos(input_x1, input_x2) - res=res.numpy() - return res - - def npu_op_exec(self, input1, input2, dim=1, eps=1e-8): - input1 = input1.npu() - input2 = input2.npu() - cos = torch.nn.CosineSimilarity(dim, eps) - output = cos(input1, input2) - output = output.cpu() - output = output.numpy() - return output - - def test_cosine_similarity(self, device): - shape_format = [ - [-100, 100, (16, 32), np.float32], - [-100, 100, (2, 4, 8), np.float32], - [-100, 100, (2, 4, 6, 8), np.float32], - [-100, 100, (2, 4, 6, 8, 10), np.float32], - [-100, 100, (2, 4, 6, 8, 10, 12), np.float32], - [-0.000030517578125, 0.000030517578125, (2, 32, 149, 31), np.float32], - [-9.313225746154785e-10, 9.313225746154785e-10, (184965, 1), np.float32], - [-2, 2, (65535, 1, 1, 1), np.float32], - [-2, 2, (1, 1, 1, 8192), np.float32], - [-2, 2, (1, 1, 1, 16384), np.float32], - [-2, 2, (1, 1, 1, 32768), np.float32], - [-2, 2, (1, 1, 1, 65535), np.float32], - [-2, 2, (1, 1, 1, 131072), np.float32], - [-2, 2, (1, 1, 1, 196608), np.float32], - [-2, 2, (1, 1, 1, 262144), np.float32], - [-2, 2, (1, 1, 1, 393216), np.float32], - [-2, 2, (1, 1, 1, 524288), np.float32], - [-2, 2, (1, 1, 1, 655360), np.float32], - [-2, 2, (1, 1, 1, 786432), np.float32], - [0, 0, (2, 4, 16), np.float32], - ] - - def test_cosinesimilarity_float32(self, min, max, shape, dtype, dim=1, eps=1e-8): - cpu_input1, cpu_input2 = self.generate_data(min, max, shape, dtype) - cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2, dim=dim, eps=eps) - npu_output = self.npu_op_exec(cpu_input1, cpu_input2, dim=dim, eps=eps) - self.assertRtolEqual(cpu_output, npu_output) - for item in shape_format: - test_cosinesimilarity_float32(self, item[0], item[1], item[2], item[3]) - -instantiate_device_type_tests(TestCosinesimilarity, globals(), except_for='cpu') -if __name__ == '__main__': - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_cudnn_convolution_backward_bias.py b/pytorch1.8.1/test/test_npu/test_cudnn_convolution_backward_bias.py deleted file mode 100644 index 6e274874701..00000000000 --- a/pytorch1.8.1/test/test_npu/test_cudnn_convolution_backward_bias.py +++ /dev/null @@ -1,95 +0,0 @@ -# Copyright (c) 2020 Huawei Technologies Co., Ltd -# Copyright (c) 2019, Facebook CORPORATION. -# All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import torch.nn as nn -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestCudnnConvolutionBackwardBias(TestCase): - def cpu_op_exec(self, input1): - m = nn.Conv2d(1,8,(2,3),bias=True) - m = m.to(torch.float32) - output = m(input1) - output.backward(torch.ones_like(output), retain_graph = True) - grad = m.bias.grad - return grad.detach().numpy() - - def cpu_op_exec_f16(self, input1): - input1 = input1.to(torch.float32) - m = nn.Conv2d(1,8,(2,3),bias=True) - m = m.to(torch.float32) - output = m(input1) - output.backward(torch.ones_like(output), retain_graph = True) - grad = m.bias.grad - grad = grad.to(torch.float16) - return grad.detach().numpy() - - def npu_op_exec(self, input1): - m = nn.Conv2d(1,8,(2,3),bias=True) - m = m.to("npu") - m = m.to(torch.float32) - output = m(input1) - output = output.to("npu") - inputback = torch.ones_like(output) - output.backward(inputback, retain_graph = True) - output = output.to("cpu") - grad = m.bias.grad - grad = grad.to("cpu") - return grad.detach().numpy() - - def npu_op_exec_f16(self, input1): - m = nn.Conv2d(1,8,(2,3),bias=True) - m = m.to("npu") - input1 = input1.to(torch.float32) - m = m.to(torch.float32) - output = m(input1) - output = output.to("npu") - inputback = torch.ones_like(output) - output.backward(inputback, retain_graph = True) - output = output.to("cpu") - grad = m.bias.grad - grad = grad.to(torch.float16) - grad = grad.to("cpu") - return grad.detach().numpy() - - def test_cudnn_convolution_backward_bias(self, device): - shape_format = [ - [[[np.float32, -1, (10,1,30,32)]], - [[np.float32, -1, (10, 1, 13, 4)]]], - [[[np.float16, -1, (1, 1, 2, 3)]], - [[np.float16, -1, (50, 1, 4, 5)]]] - ] - for item in shape_format[0]: - cpu_input1, npu_input1 = create_common_tensor(item[0], 0, 100) - cpu_output = self.cpu_op_exec(cpu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - for item in shape_format[1]: - cpu_input1, npu_input1 = create_common_tensor(item[0], 0, 100) - cpu_output = self.cpu_op_exec_f16(cpu_input1) - npu_output = self.npu_op_exec_f16(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestCudnnConvolutionBackwardBias, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:2") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_cudnn_convolution_transpose_backward_bias.py b/pytorch1.8.1/test/test_npu/test_cudnn_convolution_transpose_backward_bias.py deleted file mode 100644 index f5271d4197a..00000000000 --- a/pytorch1.8.1/test/test_npu/test_cudnn_convolution_transpose_backward_bias.py +++ /dev/null @@ -1,95 +0,0 @@ -# Copyright (c) 2020 Huawei Technologies Co., Ltd -# Copyright (c) 2019, Facebook CORPORATION. -# All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import torch.nn as nn -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestCudnnConvolutionTransposeBackwardBias(TestCase): - def cpu_op_exec(self, input1): - m = nn.ConvTranspose2d(1,8,(2,3),bias=True) - m = m.to(torch.float32) - output = m(input1) - output.backward(torch.ones_like(output), retain_graph = True) - grad = m.bias.grad - return grad.detach().numpy() - - def cpu_op_exec_f16(self, input1): - input1 = input1.to(torch.float32) - m = nn.ConvTranspose2d(1,8,(2,3),bias=True) - m = m.to(torch.float32) - output = m(input1) - output.backward(torch.ones_like(output), retain_graph = True) - grad = m.bias.grad - grad = grad.to(torch.float16) - return grad.detach().numpy() - - def npu_op_exec(self, input1): - m = nn.ConvTranspose2d(1,8,(2,3),bias=True) - m = m.to("npu") - m = m.to(torch.float32) - output = m(input1) - output = output.to("npu") - inputback = torch.ones_like(output) - output.backward(inputback, retain_graph = True) - output = output.to("cpu") - grad = m.bias.grad - grad = grad.to("cpu") - return grad.detach().numpy() - - def npu_op_exec_f16(self, input1): - m = nn.ConvTranspose2d(1,8,(2,3),bias=True) - m = m.to("npu") - input1 = input1.to(torch.float32) - m = m.to(torch.float32) - output = m(input1) - output = output.to("npu") - inputback = torch.ones_like(output) - output.backward(inputback, retain_graph = True) - output = output.to("cpu") - grad = m.bias.grad - grad = grad.to(torch.float16) - grad = grad.to("cpu") - return grad.detach().numpy() - - def test_cudnn_convolution_transpose_backward_bias(self, device): - shape_format = [ - [[[np.float32, -1, (2, 1, 7, 3)]], - [[np.float32, -1, (10, 1, 13, 4)]]], - [[[np.float16, -1, (1, 1, 2, 3)]], - [[np.float16, -1, (100, 1, 50, 3)]]] - ] - for item in shape_format[0]: - cpu_input1, npu_input1 = create_common_tensor(item[0], 0, 100) - cpu_output = self.cpu_op_exec(cpu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - for item in shape_format[1]: - cpu_input1, npu_input1 = create_common_tensor(item[0], 0, 100) - cpu_output = self.cpu_op_exec_f16(cpu_input1) - npu_output = self.npu_op_exec_f16(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestCudnnConvolutionTransposeBackwardBias, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:2") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_cummin.py b/pytorch1.8.1/test/test_npu/test_cummin.py deleted file mode 100644 index 52938df20aa..00000000000 --- a/pytorch1.8.1/test/test_npu/test_cummin.py +++ /dev/null @@ -1,170 +0,0 @@ -# Copyright (c) 2020 Huawei Technologies Co., Ltd -# Copyright (c) 2019, Facebook CORPORATION. -# All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestCummin(TestCase): - def generate_data(self, min_d, max_d, shape, dtype): - input_x = np.random.uniform(min_d, max_d, shape).astype(dtype) - npu_input = torch.from_numpy(input_x) - return npu_input - - def generate_dimname_data(self, min_d, max_d, shape, dtype): - input_x = np.random.uniform(min_d, max_d, shape).astype(dtype) - npu_input = torch.from_numpy(input_x) - npu_input.names = ['N', 'C', 'H', 'W'] - return npu_input - - def cpu_op_exec(self, input_x, dim): - output, argmin = torch.cummin(input_x, dim) - output = output.numpy() - argmin = argmin.numpy().astype(np.int32) - return output, argmin - - def npu_op_exec(self, input_x, dim): - input1 = input_x.to("npu") - output, argmin = torch.cummin(input1, dim) - output = output.to("cpu") - output = output.numpy() - argmin = argmin.to("cpu") - argmin = argmin.numpy().astype(np.int32) - return output, argmin - - def npu_op_exec_out(self, input_x, dim, output_value, output_argmin): - input_x = input_x.to("npu") - output_value = output_value.to("npu") - output_argmin = output_argmin.to("npu") - torch.cummin(input_x, dim, out=(output_value, output_argmin)) - output_value = output_value.to("cpu") - output_value = output_value.numpy() - output_argmin = output_argmin.to("cpu") - output_argmin = output_argmin.numpy().astype(np.int32) - return output_value, output_argmin - - def test_cummin_3_3_0_float32(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3), np.float32) - cpu_output, cpu_argmin = self.cpu_op_exec(input_x1, 1) - npu_output, npu_argmin = self.npu_op_exec(input_x1, 1) - self.assertRtolEqual(cpu_output, npu_output) - self.assertRtolEqual(cpu_argmin, npu_argmin) - - def test_cummin_3_3_3_0_float32(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3, 3), np.float32) - cpu_output, cpu_argmin = self.cpu_op_exec(input_x1, 0) - npu_output, npu_argmin = self.npu_op_exec(input_x1, 0) - self.assertRtolEqual(cpu_output, npu_output) - self.assertRtolEqual(cpu_argmin, npu_argmin) - - def test_cummin_3_3_3_3_3_3_4_float32(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3, 3, 3, 3, 3), np.float32) - cpu_output, cpu_argmin = self.cpu_op_exec(input_x1, 4) - npu_output, npu_argmin = self.npu_op_exec(input_x1, 4) - self.assertRtolEqual(cpu_output, npu_output) - self.assertRtolEqual(cpu_argmin, npu_argmin) - - def test_cummin_3_3_2_int32(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3), np.int32) - cpu_output, cpu_argmin = self.cpu_op_exec(input_x1, 1) - npu_output, npu_argmin = self.npu_op_exec(input_x1, 1) - self.assertRtolEqual(cpu_output, npu_output) - self.assertRtolEqual(cpu_argmin, npu_argmin) - - def test_cummin_3_3_2_int32_out(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3), np.int32) - output_values = self.generate_data(-1, 1, (3, 3), np.int32) - output_argmin = self.generate_data(-1, 1, (3, 3), np.int32) - cpu_output, cpu_argmin = self.cpu_op_exec(input_x1, 1) - npu_output, npu_argmin = self.npu_op_exec_out(input_x1, 1, output_values, output_argmin) - self.assertRtolEqual(cpu_output, npu_output) - self.assertRtolEqual(cpu_argmin, npu_argmin) - - def test_cummin_3_3_3_3_3_3_2_float16(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3, 3, 3, 3, 3), np.float16) - input_cpu = input_x1.float() - cpu_output, cpu_argmin = self.cpu_op_exec(input_cpu, 2) - cpu_output = cpu_output.astype(np.float16) - npu_output, npu_argmin = self.npu_op_exec(input_x1, 2) - self.assertRtolEqual(cpu_output, npu_output) - self.assertRtolEqual(cpu_argmin, npu_argmin) - - def test_cummin_3_3_3_3_3_3_5_float32(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3, 3, 3, 3, 3), np.float32) - cpu_output, cpu_argmin = self.cpu_op_exec(input_x1, 5) - npu_output, npu_argmin = self.npu_op_exec(input_x1, 5) - self.assertRtolEqual(cpu_output, npu_output) - self.assertRtolEqual(cpu_argmin, npu_argmin) - - def test_cummin_3_3_3_3_3_3_4_float16(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3, 3, 3, 3, 3), np.float16) - input_cpu = input_x1.float() - cpu_output, cpu_argmin = self.cpu_op_exec(input_cpu, 4) - cpu_output = cpu_output.astype(np.float16) - npu_output, npu_argmin = self.npu_op_exec(input_x1, 4) - self.assertRtolEqual(cpu_output, npu_output) - self.assertRtolEqual(cpu_argmin, npu_argmin) - - def test_cummin_3_3_1_out_float32(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3), np.float32) - output_values = self.generate_data(-1, 1, (3, 3), np.float32) - output_argmin = self.generate_data(-1, 1, (3, 3), np.int32) - cpu_output, cpu_argmin = self.cpu_op_exec(input_x1, 1) - npu_output, npu_argmin = self.npu_op_exec_out(input_x1, 1, output_values, output_argmin) - self.assertRtolEqual(cpu_output, npu_output) - self.assertRtolEqual(cpu_argmin, npu_argmin) - - def test_cummin_3_3_3_3_3_2_out_float32(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3, 3, 3, 3), np.float32) - output_values = self.generate_data(-1, 1, (3, 3, 3, 3, 3), np.float32) - output_argmin = self.generate_data(-1, 1, (3, 3, 3, 3, 3), np.int32) - cpu_output, cpu_argmin = self.cpu_op_exec(input_x1, 2) - npu_output, npu_argmin = self.npu_op_exec_out(input_x1, 2, output_values, output_argmin) - self.assertRtolEqual(cpu_output, npu_output) - self.assertRtolEqual(cpu_argmin, npu_argmin) - - def test_cummin_10_10_10_10_10_10_10_2_float32(self, device): - input_x1 = self.generate_data(-1, 1, (10, 10, 10, 10, 10, 10), np.float32) - cpu_output, cpu_argmin = self.cpu_op_exec(input_x1, 2) - npu_output, npu_argmin = self.npu_op_exec(input_x1, 2) - self.assertRtolEqual(cpu_output, npu_output) - self.assertRtolEqual(cpu_argmin, npu_argmin) - - def test_cummin_3_3_3_3_N_out_float32_dimname(self, device): - input_x1 = self.generate_dimname_data(-1, 1, (3, 3, 3, 3), np.float32) - output_values = self.generate_data(-1, 1, (3, 3, 3, 3), np.float32) - output_argmin = self.generate_data(-1, 1, (3, 3, 3, 3), np.int32) - cpu_output, cpu_argmin = self.cpu_op_exec(input_x1, 'N') - npu_output, npu_argmin = self.npu_op_exec_out(input_x1, 'N', output_values, output_argmin) - self.assertRtolEqual(cpu_output, npu_output) - self.assertRtolEqual(cpu_argmin, npu_argmin) - - def test_cummin_3_3_3_3_H_float32_dimname(self, device): - input_x1 = self.generate_dimname_data(-1, 1, (3, 3, 3, 3), np.float32) - cpu_output, cpu_argmin = self.cpu_op_exec(input_x1, 'H') - npu_output, npu_argmin = self.npu_op_exec(input_x1, 'H') - self.assertRtolEqual(cpu_output, npu_output) - self.assertRtolEqual(cpu_argmin, npu_argmin) - - - -instantiate_device_type_tests(TestCummin, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:0") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_cumprod.py b/pytorch1.8.1/test/test_npu/test_cumprod.py deleted file mode 100644 index 976fe8ae070..00000000000 --- a/pytorch1.8.1/test/test_npu/test_cumprod.py +++ /dev/null @@ -1,67 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestCumprod(TestCase): - - def cpu_op_exec(self,input1, dim): - output = torch.cumprod(input1, dim) - output = output.numpy() - return output - - def npu_op_exec(self,input1, dim): - output = torch.cumprod(input1, dim) - output = output.to("cpu") - output = output.numpy() - return output - - def npu_op_exec_out(self,input1, input2, dim): - torch.cumprod(input1, dim, out=input2) - output = input2.to("cpu") - output = output.numpy() - return output - - def test_cumprod_common_shape_format(self, device): - shape_format = [ - [[np.float32, 0, (5,3)]], - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 10) - dim = 0 - cpu_output = self.cpu_op_exec(cpu_input1, dim) - npu_output = self.npu_op_exec(npu_input1, dim) - self.assertRtolEqual(cpu_output, npu_output) - - def test_cumprod_out_common_shape_format(self, device): - shape_format = [ - [[np.float32, 0, (4,3)], [np.float32, 0, (4,3)]], - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 10) - cpu_input2, npu_input2 = create_common_tensor(item[1], 1, 10) - dim = 0 - cpu_output = self.cpu_op_exec(cpu_input1, dim) - npu_output = self.npu_op_exec_out(npu_input1, npu_input2, dim) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestCumprod, globals(), except_for='cpu') -if __name__ == "__main__": - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_cumsum.py b/pytorch1.8.1/test/test_npu/test_cumsum.py deleted file mode 100644 index 0bf59efd436..00000000000 --- a/pytorch1.8.1/test/test_npu/test_cumsum.py +++ /dev/null @@ -1,98 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestCumsum(TestCase): - - def cpu_op_exec(self,input1, dim): - output = torch.cumsum(input1, dim) - output = output.numpy() - return output - - def npu_op_exec(self,input1, dim): - output = torch.cumsum(input1, dim) - output = output.to("cpu") - output = output.numpy() - return output - - def cpu_op_fp16_exec(self,input1, dim): - input1 = input1.to(torch.float32) - output = torch.cumsum(input1, dim) - output = output.numpy() - output = output.astype(np.float16) - return output - - def npu_op_exec_out(self,input1, input2, dim): - torch.cumsum(input1, dim, out=input2) - output = input2.to("cpu") - output = output.numpy() - return output - - def test_cumsum_common_shape_format(self, device): - shape_format = [ - [[[np.float32, 0, (1, 2, 3, 4)]], - [[np.float32, 0, (2, 3, 4)]], - [[np.float32, 0, (3, 4)]]], - [[[np.float16, 0, (1, 2, 3, 4)]], - [[np.float16, 0, (2, 3, 4)]], - [[np.float16, 0, (3, 4)]]], - ] - for item in shape_format[0]: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 4) - dim = 0 - cpu_output = self.cpu_op_exec(cpu_input1, dim) - npu_output = self.npu_op_exec(npu_input1, dim) - self.assertRtolEqual(cpu_output, npu_output) - - for item in shape_format[1]: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 4) - dim = 0 - cpu_output = self.cpu_op_fp16_exec(cpu_input1, dim) - npu_output = self.npu_op_exec(npu_input1, dim) - self.assertRtolEqual(cpu_output, npu_output) - - def test_cumsum_out_common_shape_format(self, device): - shape_format = [ - [[[np.float32, 0, (1, 2, 3, 4)], [np.float32, 0, (1, 2, 3, 4)]], - [[np.float32, 0, (2, 3, 4)], [np.float32, 0, (2, 3, 4)]], - [[np.float32, 0, (3, 4)], [np.float32, 0, (3, 4)]]], - [[[np.float16, 0, (1, 2, 3, 4)], [np.float16, 0, (1, 2, 3, 4)]], - [[np.float16, 0, (2, 3, 4)], [np.float16, 0, (2, 3, 4)]], - [[np.float16, 0, (3, 4)], [np.float16, 0, (3, 4)]]], - ] - for item in shape_format[0]: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 4) - cpu_input2, npu_input2 = create_common_tensor(item[1], 1, 4) - dim = 0 - cpu_output = self.cpu_op_exec(cpu_input1, dim) - npu_output = self.npu_op_exec_out(npu_input1, npu_input2, dim) - self.assertRtolEqual(cpu_output, npu_output) - - for item in shape_format[1]: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 4) - cpu_input2, npu_input2 = create_common_tensor(item[1], 1, 4) - dim = 0 - cpu_output = self.cpu_op_fp16_exec(cpu_input1, dim) - npu_output = self.npu_op_exec_out(npu_input1, npu_input2, dim) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestCumsum, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:5") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_dim_arange.py b/pytorch1.8.1/test/test_npu/test_dim_arange.py deleted file mode 100644 index 4c6eade565b..00000000000 --- a/pytorch1.8.1/test/test_npu/test_dim_arange.py +++ /dev/null @@ -1,72 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestDimArange(TestCase): - def generate_data(self, min_d, max_d, shape, dtype): - input_x = np.random.uniform(min_d, max_d, shape).astype(dtype) - npu_input = torch.from_numpy(input_x) - return npu_input - - def cpu_op_exec(self, input_x, dim): - output = torch._dim_arange(input_x, dim) - output = output.numpy().astype(np.int32) - return output - - def npu_op_exec(self, input_x, dim): - input1 = input_x.to("npu") - output = torch._dim_arange(input1, dim) - output = output.to("cpu") - output = output.numpy() - return output - - def test_dim_arange_3_4_5_0_float32(self, device): - input_x1 = self.generate_data(-1, 1, (3, 4, 5), np.float32) - cpu_output = self.cpu_op_exec(input_x1, 1) - npu_output = self.npu_op_exec(input_x1, 1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_dim_arange_30_40_50_0_float32(self, device): - input_x1 = self.generate_data(-1, 1, (30, 40, 50), np.float32) - cpu_output = self.cpu_op_exec(input_x1, 0) - npu_output = self.npu_op_exec(input_x1, 0) - self.assertRtolEqual(cpu_output, npu_output) - - def test_dim_arange_10_10_10_10_10_10_10_2_float32(self, device): - input_x1 = self.generate_data(-1, 1, (10, 10, 10, 10, 10, 10), np.float32) - cpu_output = self.cpu_op_exec(input_x1, 2) - npu_output = self.npu_op_exec(input_x1, 2) - self.assertRtolEqual(cpu_output, npu_output) - - def test_dim_arange_7_13_22_193_45_2_float16(self, device): - input_x1 = self.generate_data(-1, 1, (7, 13, 22, 193, 45, 2), np.float16) - cpu_output = self.cpu_op_exec(input_x1, 2) - npu_output = self.npu_op_exec(input_x1, 2) - self.assertRtolEqual(cpu_output, npu_output) - - def test_dim_arange_7_13_22_float16(self, device): - input_x1 = self.generate_data(-1, 1, (7, 13, 22), np.float16) - cpu_output = self.cpu_op_exec(input_x1, 0) - npu_output = self.npu_op_exec(input_x1, 0) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestDimArange, globals(), except_for='cpu') -if __name__ == "__main__": - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_diml.py b/pytorch1.8.1/test/test_npu/test_diml.py deleted file mode 100644 index 7a621db2201..00000000000 --- a/pytorch1.8.1/test/test_npu/test_diml.py +++ /dev/null @@ -1,56 +0,0 @@ -import torch -import numpy as np -import sys -import copy -from torch.autograd import Variable -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestDiml(TestCase): - def generate_data(self, min, max, shape, dtype): - input1 = np.random.uniform(min, max, shape).astype(dtype) - npu_input1 = torch.from_numpy(input1) - return npu_input1 - - def cpu_op_exec(self, input1): - input1[0][0] = 5 - input1_sparse = input1.to_sparse() - outut = input1_sparse.indices().size(0) - return outut - - def npu_op_exec(self, input1): - input1 = input1.to("npu") - input1_sparse = input1.to_sparse() - outut = input1_sparse.indices().size(0) - outut = outut.to("cpu") - return outut - - def test_diml_float32_1(self, device): - npu_input1 = self.generate_data(0, 100, (5, 5), np.float32) - cpu_output = self.cpu_op_exec(npu_input1) - # npu_output = self.npu_op_exec(npu_input1) - # self.assertRtolEqual(cpu_output, npu_output) - - def test_diml_float64_1(self, device): - npu_input1 = self.generate_data(0, 100, (10, 5, 5), np.float64) - cpu_output = self.cpu_op_exec(npu_input1) - # npu_output = self.npu_op_exec(npu_input1) - # self.assertRtolEqual(cpu_output, npu_output) - - def test_diml_float64_2(self, device): - npu_input1 = self.generate_data(0, 100, (10, 3, 5, 5), np.float64) - cpu_output = self.cpu_op_exec(npu_input1) - # npu_output = self.npu_op_exec(npu_input1) - # self.assertRtolEqual(cpu_output, npu_output) - - def test_diml_float64_3(self, device): - npu_input1 = self.generate_data(0, 100, (2, 10, 3, 5, 5), np.float64) - cpu_output = self.cpu_op_exec(npu_input1) - # npu_output = self.npu_op_exec(npu_input1) - # self.assertRtolEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(TestDiml, globals(), except_for='cpu') -if __name__ == '__main__': - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_dirichlet_grad.py b/pytorch1.8.1/test/test_npu/test_dirichlet_grad.py deleted file mode 100644 index fc89ee24832..00000000000 --- a/pytorch1.8.1/test/test_npu/test_dirichlet_grad.py +++ /dev/null @@ -1,69 +0,0 @@ -# Copyright (c) 2020 Huawei Technologies Co., Ltd -# Copyright (c) 2019, Facebook CORPORATION. -# All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from torch.autograd import Variable -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestDirichletGrad(TestCase): - def generate_data(self, min, max, shape, dtype): - input1 = np.random.uniform(min, max, shape).astype(dtype) - input2 = np.random.uniform(min, max, shape).astype(dtype) - input3 = np.random.uniform(min, max, shape).astype(dtype) - npu_input1 = torch.from_numpy(input1) - npu_input2 = torch.from_numpy(input2) - npu_input3 = torch.from_numpy(input3) - return npu_input1, npu_input2, npu_input3 - - def cpu_op_exec(self, input1, input2, input3): - output = torch._dirichlet_grad(input1, input2, input3) - return output - - def npu_op_exec(self, input1, input2, input3): - input1 = input1.to("npu") - input2 = input2.to("npu") - input3 = input3.to("npu") - output = torch._dirichlet_grad(input1, input2, input3) - output = output.to("cpu") - return output - - def test_symeig_float(self, device): - npu_input1, npu_input2, npu_input3 = self.generate_data(0, 100, (5, 5), np.float32) - cpu_output1 = self.cpu_op_exec(npu_input1, npu_input2, npu_input3) - # npu_output1 = self.npu_op_exec(npu_input1, npu_input2, npu_input3) - # self.assertRtolEqual(cpu_output1, npu_output1) - npu_input1, npu_input2, npu_input3 = self.generate_data(0, 100, (10, 5, 5), np.float64) - cpu_output2 = self.cpu_op_exec(npu_input1, npu_input2, npu_input3) - # npu_output2 = self.npu_op_exec(npu_input1, npu_input2, npu_input3) - # self.assertRtolEqual(cpu_output2, npu_output2) - npu_input1, npu_input2, npu_input3 = self.generate_data(0, 100, (10, 3, 5, 5), np.float64) - cpu_output3 = self.cpu_op_exec(npu_input1, npu_input2, npu_input3) - # npu_output3 = self.npu_op_exec(npu_input1, npu_input2, npu_input3) - # self.assertRtolEqual(cpu_output3, npu_output3) - npu_input1, npu_input2, npu_input3 = self.generate_data(0, 100, (2, 10, 3, 5, 5), np.float64) - cpu_output4 = self.cpu_op_exec(npu_input1, npu_input2, npu_input3) - # npu_output = self.npu_op_exec(npu_input1, npu_input2, npu_input3) - # self.assertRtolEqual(cpu_output4, npu_output4) - - -instantiate_device_type_tests(TestDirichletGrad, globals(), except_for='cpu') -if __name__ == '__main__': - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_dot.py b/pytorch1.8.1/test/test_npu/test_dot.py deleted file mode 100644 index 74edec12535..00000000000 --- a/pytorch1.8.1/test/test_npu/test_dot.py +++ /dev/null @@ -1,101 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestDot(TestCase): - def generate_data(self, min, max, shape, dtype): - input1 = np.random.uniform(min, max, shape).astype(dtype) - input2 = np.random.uniform(min, max, shape).astype(dtype) - - npu_input1 = torch.from_numpy(input1) - npu_input2 = torch.from_numpy(input2) - - return npu_input1, npu_input2 - - def generate_three_data(self, min, max, shape, dtype): - input1 = np.random.uniform(min, max, shape).astype(dtype) - input2 = np.random.uniform(min, max, shape).astype(dtype) - input3 = np.random.uniform(min, max, shape).astype(dtype) - - npu_input1 = torch.from_numpy(input1) - npu_input2 = torch.from_numpy(input2) - npu_input3 = torch.from_numpy(input3) - - return npu_input1, npu_input2, npu_input3 - - def cpu_op_exec(self, input1, input2): - output = torch.dot(input1, input2) - output = output.numpy() - return output - - def npu_op_exec(self, input1, input2): - input1 = input1.to("npu") - input2 = input2.to("npu") - output = torch.dot(input1, input2) - output = output.to("cpu") - output = output.numpy() - return output - - def npu_op_exec_out(self, input1, input2, input3): - input1 = input1.to("npu") - input2 = input2.to("npu") - output = input3.to("npu") - torch.dot(input1, input2, out=output) - output = output.to("cpu") - output = output.numpy() - return output - - def test_dot_float32(self, device): - npu_input1, npu_input2 = self.generate_data(0, 10, (3) , np.float32) - cpu_output = self.cpu_op_exec(npu_input1, npu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2) - self.assertRtolEqual(cpu_output, npu_output) - - def test_dot_float32_out(self, device): - npu_input1, npu_input2, npu_input3 = self.generate_three_data(0, 10, (3) , np.float32) - cpu_output = self.cpu_op_exec(npu_input1, npu_input2) - npu_output = self.npu_op_exec_out(npu_input1, npu_input2, npu_input3) - self.assertRtolEqual(cpu_output, npu_output) - - def test_dot_float16(self, device): - npu_input1, npu_input2 = self.generate_data(0, 10, (3) , np.float16) - cpu_output = self.cpu_op_exec(npu_input1.float(), npu_input2.float()).astype(np.float16) - npu_output = self.npu_op_exec(npu_input1.float(), npu_input2.float()).astype(np.float16) - self.assertRtolEqual(cpu_output, npu_output) - - def test_dot_float16_out(self, device): - npu_input1, npu_input2, npu_input3 = self.generate_three_data(0, 10, (3) , np.float16) - cpu_output = self.cpu_op_exec(npu_input1.float(), npu_input2.float()).astype(np.float16) - npu_output = self.npu_op_exec_out(npu_input1.float(), npu_input2.float(), npu_input3.float()).astype(np.float16) - self.assertRtolEqual(cpu_output, npu_output) - - def test_big_scale_float32(self, device): - npu_input1, npu_input2 = self.generate_data(0, 10, (10240) , np.float32) - cpu_output = self.cpu_op_exec(npu_input1, npu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestDot, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:3") - run_tests() - diff --git a/pytorch1.8.1/test/test_npu/test_embedding.py b/pytorch1.8.1/test/test_npu/test_embedding.py deleted file mode 100644 index 907516c7312..00000000000 --- a/pytorch1.8.1/test/test_npu/test_embedding.py +++ /dev/null @@ -1,67 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor -import torch.nn.functional as F - -class TestEmbedding(TestCase): - def cpu_op_exec(self, weight, indices): - weight.requires_grad_(True) - out = F.embedding(indices, weight, scale_grad_by_freq=True, padding_idx=37) - return out.detach().numpy() - - def npu_op_exec(self, weight, indices): - weight.requires_grad_(True) - out = F.embedding(indices, weight, scale_grad_by_freq=True, padding_idx=37) - out_npu = out.to("cpu") - return out_npu.detach().numpy() - - def test_shape_format(self, device): - shape_format = [ - [[np.float32, 0, [40,32]], [np.int64, 0, [40]]], - [[np.float32, 0, [40,1024]], [np.int64, 0, [40]]], - [[np.float32, 0, [40000,1024]], [np.int64, 0, [3125]]], - [[np.float32, 0, [40000,1024]], [np.int64, 0, [128,8]]], - [[np.float16, 0, [40,32]], [np.int64, 0, [40]]], - [[np.float16, 0, [40,1024]], [np.int64, 0, [128,8]]], - [[np.float16, 0, [33712,1024]], [np.int64, 0, [64,7]]], - [[np.float32, 3, [40,32]], [np.int64, 0, [40]]], - [[np.float32, 4, [40,1024]], [np.int64, 0, [40]]], - [[np.float32, 2, [40000,1024]], [np.int64, 0, [3125]]], - [[np.float32, 29, [40000,1024]], [np.int64, 0, [128,8]]], - [[np.float16, 3, [40,32]], [np.int64, 0, [40]]], - [[np.float16, 3, [40,1024]], [np.int64, 0, [128,8]]], - [[np.float16, 3, [33712,1024]], [np.int64, 0, [64,7]]] - ] - for item in shape_format: - weight_cpu, weight_npu = create_common_tensor(item[0], 1, 1) - indices_cpu, indices_npu = create_common_tensor(item[1], 0, 1) - - if weight_cpu.dtype == torch.float16: - weight_cpu = weight_cpu.to(torch.float32) - - cpu_out = self.cpu_op_exec(weight_cpu, indices_cpu) - npu_out = self.npu_op_exec(weight_npu, indices_npu) - cpu_out = cpu_out.astype(npu_out.dtype) - - self.assertRtolEqual(cpu_out, npu_out) - -instantiate_device_type_tests(TestEmbedding, globals(), except_for="cpu") -if __name__ == "__main__": - torch.npu.set_device("npu:1") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_embedding_renorm.py b/pytorch1.8.1/test/test_npu/test_embedding_renorm.py deleted file mode 100644 index 51f06efe73e..00000000000 --- a/pytorch1.8.1/test/test_npu/test_embedding_renorm.py +++ /dev/null @@ -1,117 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestEmbeddingRenorm(TestCase): - def generate_data(self, min_d, max_d, shape, dtype): - input1 = np.random.uniform(min_d, max_d, shape).astype(dtype) - npu_input1 = torch.from_numpy(input1) - npu_input2 = torch.LongTensor(np.random.uniform(0,shape[0], int(shape[0]/2,)).astype(np.int32)) - #npu_input2=torch.LongTensor([[0,1,1,0,1],[0,1,1,0,1],[1,0,1,1,2]]) - return npu_input1, npu_input2 - - def cpu_op_exec(self, input1, input2, max_norm, norm_type): - stype = input1.dtype - if stype == torch.float16: - input1 = input1.float() - output = torch.embedding_renorm_(input1, input2, max_norm=max_norm, norm_type=norm_type) - if stype == torch.float16: - output = output.half() - output = output.numpy() - return output - - def npu_op_exec(self, input1, input2, max_norm,norm_type): - input1 = input1.to("npu") - input2 = input2.to("npu") - output = torch.embedding_renorm_(input1, input2, max_norm=max_norm, norm_type=norm_type) - output = output.to("cpu") - output = output.numpy() - return output - - def test_embedding_renorm_float16_2(self, device): - npu_input1, npu_input2 = self.generate_data(0, 100, (5, 3), np.float16) - cpu_input1 = copy.deepcopy(npu_input1) - cpu_input2 = copy.deepcopy(npu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2, 0.1, 2) - cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2, 0.1, 2) - self.assertRtolEqual(cpu_output, npu_output) - - def test_embedding_renorm_float16_0(self, device): - npu_input1, npu_input2 = self.generate_data(0, 100, (10, 4),np.float16) - cpu_input1 = copy.deepcopy(npu_input1) - cpu_input2 = copy.deepcopy(npu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2, 0.2, 0) - cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2, 0.2, 0) - self.assertRtolEqual(cpu_output, npu_output) - - def test_embedding_renorm_float16_1(self, device): - npu_input1, npu_input2 = self.generate_data(0, 100, (3, 3), np.float16) - cpu_input1 = copy.deepcopy(npu_input1) - cpu_input2 = copy.deepcopy(npu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2, 0.5, 1) - cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2, 0.5, 1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_embedding_renorm_float16_10(self, device): - npu_input1, npu_input2 = self.generate_data(0, 100, (4, 6), np.float16) - cpu_input1 = copy.deepcopy(npu_input1) - cpu_input2 = copy.deepcopy(npu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2, 1.0, 10) - cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2, 1.0, 10) - self.assertRtolEqual(cpu_output, npu_output) - - def test_embedding_renorm_float32_2(self, device): - npu_input1, npu_input2 = self.generate_data(0, 100, (5, 3), np.float32) - cpu_input1 = copy.deepcopy(npu_input1) - cpu_input2 = copy.deepcopy(npu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2, 0.1, 2) - cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2, 0.1, 2) - self.assertRtolEqual(cpu_output, npu_output) - - def test_embedding_renorm_float32_0(self, device): - npu_input1, npu_input2 = self.generate_data(0, 100, (10, 4), np.float32) - cpu_input1 = copy.deepcopy(npu_input1) - cpu_input2 = copy.deepcopy(npu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2, 0.2, 0) - cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2, 0.2, 0) - self.assertRtolEqual(cpu_output, npu_output) - - def test_embedding_renorm_float32_1(self, device): - npu_input1, npu_input2 = self.generate_data(0, 100, (3, 3), np.float32) - cpu_input1 = copy.deepcopy(npu_input1) - cpu_input2 = copy.deepcopy(npu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2, 0.5, 1) - cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2, 0.5, 1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_embedding_renorm_float32_10(self, device): - npu_input1, npu_input2 = self.generate_data(0, 100, (4,6), np.float32) - cpu_input1 = copy.deepcopy(npu_input1) - cpu_input2 = copy.deepcopy(npu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2, 1.0, 10) - cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2, 1.0, 10) - self.assertRtolEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(TestEmbeddingRenorm, globals(), except_for='cpu') -if __name__ == "__main__": - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_erfinv.py b/pytorch1.8.1/test/test_npu/test_erfinv.py deleted file mode 100644 index 8eb7e68bfdd..00000000000 --- a/pytorch1.8.1/test/test_npu/test_erfinv.py +++ /dev/null @@ -1,124 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy - -from torch import device -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestErfinv(TestCase): - def cpu_op_exec(self, input_data): - output = torch.erfinv(input_data) - output = output.numpy() - return output - - def npu_op_exec(self, input_data): - output = torch.erfinv(input_data) - output = output.to("cpu") - output = output.numpy() - return output - - def cpu_op_exec_out(self,input1,cpu_out): - torch.erfinv(input1, out = cpu_out) - output = cpu_out.numpy() - return output - - def npu_op_exec_out(self,input1,npu_out): - torch.erfinv(input1, out = npu_out) - output = npu_out.to("cpu") - output = output.numpy() - return output - - def cpu_op_exec_(self, input1): - input1.erfinv_() - output = input1.numpy() - return output - - def npu_op_exec_(self, input1): - input1 = input1.to("npu") - input1.erfinv_() - output = input1.to("cpu") - output = output.numpy() - return output - - def test_erfinv_shape_format(self, device): - shape_format = [ - [np.float32, -1, (2, 3, 4, 5)], - [np.float32, -1, (4, 5, 6, 7)], - [np.float32, -1, (2, 3, 4, 5, 6)], - [np.float16, -1, (2, 3, 4, 5)], - [np.float16, -1, (4, 5, 6, 7)], - [np.float16, -1, (2, 3, 4, 5, 6)] - ] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item, -0.5, 0.5) - if item[0] == np.float16: - cpu_input = cpu_input.to(torch.float32) - cpu_output = self.cpu_op_exec(cpu_input) - npu_output = self.npu_op_exec(npu_input) - if item[0] == np.float16: - cpu_output = cpu_output.astype(np.float16) - self.assertRtolEqual(cpu_output, npu_output, prec=1e-3) - - def test_erfinv_out_shape_format(self, device): - shape_format = [ - [np.float32, -1, (2, 3, 4, 5)], - [np.float32, -1, (4, 5, 6, 7)], - [np.float32, -1, (2, 3, 4, 5, 6)], - [np.float16, -1, (2, 3, 4, 5)], - [np.float16, -1, (4, 5, 6, 7)], - [np.float16, -1, (2, 3, 4, 5, 6)] - ] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item, -0.5, 0.5) - cpu_out, npu_out = create_common_tensor(item, -0.5, 0.5) - if item[0] == np.float16: - cpu_input = cpu_input.to(torch.float32) - cpu_out = cpu_out.to(torch.float32) - cpu_output = self.cpu_op_exec_out(cpu_input, cpu_out) - npu_output = self.npu_op_exec_out(npu_input, npu_out) - if item[0] == np.float16: - cpu_output = cpu_output.astype(np.float16) - self.assertRtolEqual(cpu_output, npu_output, prec=1e-3) - - def test_erfinv__shape_format(self, device): - shape_format = [ - [np.float32, -1, (2, 3, 4, 5)], - [np.float32, -1, (4, 5, 6, 7)], - [np.float32, -1, (2, 3, 4, 5, 6)], - [np.float16, -1, (2, 3, 4, 5)], - [np.float16, -1, (4, 5, 6, 7)], - [np.float16, -1, (2, 3, 4, 5, 6)] - ] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item, -0.5, 0.5) - if item[0] == np.float16: - cpu_input = cpu_input.to(torch.float32) - cpu_output = self.cpu_op_exec_(cpu_input) - npu_output = self.npu_op_exec_(npu_input) - if item[0] == np.float16: - cpu_output = cpu_output.astype(np.float16) - self.assertRtolEqual(cpu_output, npu_output, prec=1e-3) - - -instantiate_device_type_tests(TestErfinv, globals(), except_for="cpu") -if __name__ == "__main__": - torch.npu.set_device("npu:1") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_expm1.py b/pytorch1.8.1/test/test_npu/test_expm1.py deleted file mode 100644 index 52899245f82..00000000000 --- a/pytorch1.8.1/test/test_npu/test_expm1.py +++ /dev/null @@ -1,169 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# coding: utf-8 - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestExpm1(TestCase): - - def cpu_op_exec(self,input1): - output = torch.expm1(input1) - output = output.numpy() - return output - - def npu_op_exec(self,input1): - output = torch.expm1(input1) - output = output.to("cpu") - output = output.numpy() - return output - - def cpu_op_exec_(self,input1): - torch.expm1_(input1) - output = input1.numpy() - return output - - def npu_op_exec_(self,input1): - torch.expm1_(input1) - output = input1.to("cpu") - output = output.numpy() - return output - - def cpu_op_exec_out(self,input1,out): - torch.expm1(input1, out=out) - output = out.numpy() - return output - - def npu_op_exec_out(self,input1, out): - torch.expm1(input1, out=out) - output = out.to("cpu") - output = output.numpy() - return output - def test_expm1_float32_common_shape_format(self, device): - shape_format = [ - [np.float32, -1 , (4, 3)], - [np.float32, -1, (2,4, 3)], - [np.float32, 3, (20, 13)], - [np.float32, 4, (20, 13)], - [np.float32, 29, (20, 13)] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item, 1, 10) - cpu_output = self.cpu_op_exec(cpu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_expm1_float321_common_shape_format(self, device): - shape_format = [ - [np.float32, -1, (4, 3)], - [np.float32, 0 , (4, 3)], - [np.float32, -1, (2,4, 3)], - [np.float32, 3, (20, 13)], - [np.float32, 4, (20, 13)], - [np.float32, 29, (20, 13)] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item, 1, 10) - cpu_output = self.cpu_op_exec_(cpu_input1) - npu_output = self.npu_op_exec_(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_expm1_out_float32_common_shape_format(self, device): - shape_format = [ - [np.float32, -1, (4, 3)], - [np.float32, 0 , (4, 3)], - [np.float32, -1, (2,4, 3)], - [np.float32, 3, (20, 13)], - [np.float32, 4, (20, 13)], - [np.float32, 29, (20, 13)] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item, 1, 10) - cpu_out, npu_out = create_common_tensor(item, 1, 10) - cpu_output = self.cpu_op_exec_out(cpu_input1,cpu_out) - npu_output = self.npu_op_exec_out(npu_input1,npu_out) - self.assertRtolEqual(cpu_output, npu_output) - - def test_expm1_float16_common_shape_format(self, device): - shape_format = [ - [np.float16, -1 , (4, 3)], - [np.float16, -1, (2,4, 3)], - [np.float16, -1, (100, 20, 10)], - [np.float16, 3, (20, 13)], - [np.float16, 4, (20, 13)], - [np.float16, 29, (20, 13)] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item, 1, 10) - if item[0] == np.float16: - cpu_input1 = cpu_input1.to(torch.float32) - cpu_output = self.cpu_op_exec(cpu_input1) - npu_output = self.npu_op_exec(npu_input1) - if item[0] == np.float16: - cpu_output = cpu_output.astype(np.float16) - self.assertRtolEqual(cpu_output, npu_output) - - def test_expm1_float16__common_shape_format(self, device): - shape_format = [ - [np.float16, -1, (4, 3)], - [np.float16, 0 , (4, 3)], - [np.float16, -1, (2,4, 3)], - [np.float16, -1, (100, 20, 10)], - [np.float16, 3, (20, 13)], - [np.float16, 4, (20, 13)], - [np.float16, 29, (20, 13)] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item, 1, 10) - if item[0] == np.float16: - cpu_input1 = cpu_input1.to(torch.float32) - cpu_output = self.cpu_op_exec_(cpu_input1) - npu_output = self.npu_op_exec_(npu_input1) - if item[0] == np.float16: - cpu_output = cpu_output.astype(np.float16) - self.assertRtolEqual(cpu_output, npu_output) - - def test_expm1_out_float16_common_shape_format(self, device): - shape_format = [ - [np.float16, -1, (4, 3)], - [np.float16, 0 , (4, 3)], - [np.float16, -1, (2,4, 3)], - [np.float16, -1, (100, 20, 10)], - [np.float16, 3, (20, 13)], - [np.float16, 4, (20, 13)], - [np.float16, 29, (20, 13)] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item, 1, 10) - cpu_out, npu_out = create_common_tensor(item, 1, 10) - if item[0] == np.float16: - cpu_input1 = cpu_input1.to(torch.float32) - cpu_out = cpu_out.to(torch.float32) - cpu_output = self.cpu_op_exec_out(cpu_input1,cpu_out) - npu_output = self.npu_op_exec_out(npu_input1,npu_out) - if item[0] == np.float16: - cpu_output = cpu_output.astype(np.float16) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestExpm1, globals(), except_for="cpu") - -if __name__ == "__main__": - torch.npu.set_device("npu:0") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_eye.py b/pytorch1.8.1/test/test_npu/test_eye.py deleted file mode 100644 index e642baaa300..00000000000 --- a/pytorch1.8.1/test/test_npu/test_eye.py +++ /dev/null @@ -1,145 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestEye(TestCase): - - def cpu_op_exec(self, shapes): - if shapes[0] == shapes[1]: - output = torch.eye(shapes[0]) - else: - output = torch.eye(shapes[0], shapes[1]) - output = output.numpy() - return output - - def npu_op_exec(self, shapes): - if shapes[0] == shapes[1]: - output = torch.eye(shapes[0], device="npu") - else: - output = torch.eye(shapes[0], shapes[1], device="npu") - output = output.to("cpu") - output = output.numpy() - return output - - def cpu_op_out_exec(self, shapes, out): - if shapes[0] == shapes[1]: - torch.eye(shapes[0], out=out) - else: - torch.eye(shapes[0], shapes[1], out=out) - output = out.numpy() - return output - - def npu_op_out_exec(self, shapes, out): - out = out.to("npu") - if shapes[0] == shapes[1]: - torch.eye(shapes[0], out=out) - else: - torch.eye(shapes[0], shapes[1], out=out) - output = out.to("cpu") - output = output.numpy() - return output - - def test_eye_int32_common_shape_format(self, device): - shape_format = [ - [np.int32, 0, (3563, 4000)], - [np.int32, 0, (1350, 1762)], - ] - for item in shape_format: - cpu_output = self.cpu_op_exec(item[2]) - npu_output = self.npu_op_exec(item[2]) - self.assertRtolEqual(cpu_output, npu_output) - - def test_eye_float32_common_shape_format(self, device): - shape_format = [ - [np.float32, 0, (5, 5)], - [np.float32, 0, (15, 15)], - [np.float32, 0, (3, 5)], - [np.float32, 0, (40, 5)], - [np.float32, 0, (16480, 25890)], - [np.float32, 0, (1350, 1762)], - [np.float32, 0, (352, 4000)], - [np.float32, 0, (3563, 4000)], - [np.float32, 0, (1, 51)], - [np.float32, 0, (1, 173)], - [np.float32, 0, (1, 45000)], - [np.float32, 0, (1, 100000)], - ] - for item in shape_format: - cpu_output = self.cpu_op_exec(item[2]) - npu_output = self.npu_op_exec(item[2]) - self.assertRtolEqual(cpu_output, npu_output) - - def test_eye_out_float32_common_shape_format(self, device): - shape_format = [ - [np.float32, 0, (5, 5)], - [np.float32, 0, (3, 5)], - [np.float32, 0, (1350, 1762)], - [np.float32, 0, (352, 4000)], - [np.float32, 0, (3563, 4000)], - [np.float32, 0, (40000, 40000)] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item, 1, 100) - cpu_output = self.cpu_op_out_exec(item[2], cpu_input1) - npu_output = self.npu_op_out_exec(item[2], npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_eye_out_float32_different_shape_format(self, device): - shape_1 = [np.float32, 0, (4000, 400)] - shape_2 = [np.float32, 0, (4000, 4000)] - cpu_input1 = torch.randn(shape_1[2][0], shape_1[2][1], dtype=torch.float32) - cpu_output = self.cpu_op_out_exec(shape_2[2], cpu_input1) - npu_input1 = torch.randn(shape_2[2][0], shape_2[2][1], dtype=torch.float32) - npu_output = self.npu_op_out_exec(shape_2[2], npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_eye_float16_shape_format(self, device): - def cpu_op_exec_fp16(shapes): - output = torch.eye(shapes[0], shapes[1]) - output = output.numpy() - output = output.astype(np.float16) - return output - - def npu_op_exec_fp16(shapes): - output = torch.eye(shapes[0], shapes[1], device="npu", dtype=torch.float16) - output = output.to("cpu") - output = output.numpy() - return output - - shape_format = [ - [np.float16, 0, (5, 5)], - [np.float16, 0, (3, 5)], - [np.float32, 0, (1350, 1762)], - [np.float32, 0, (352, 4000)], - [np.float32, 0, (3563, 4000)] - ] - - for item in shape_format: - cpu_output = cpu_op_exec_fp16(item[2]) - npu_output = npu_op_exec_fp16(item[2]) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestEye, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:0") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_floor_divide.py b/pytorch1.8.1/test/test_npu/test_floor_divide.py deleted file mode 100644 index b14be0d3801..00000000000 --- a/pytorch1.8.1/test/test_npu/test_floor_divide.py +++ /dev/null @@ -1,106 +0,0 @@ -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor -import random -import math - -class TestFloorDivide(TestCase): -# pylint: disable=unused-variable,unused-argument - - def cpu_op_exec(self, input1, input2): - output = torch.floor_divide(input1,input2) - output = output.numpy() - return output - - def cpu_op_exec_fp16(self, input1, input2): - input1 = input1.to(torch.float32) - input2 = input2.to(torch.float32) - output = torch.floor_divide(input1, input2) - output = output.numpy() - output = output.astype(np.float16) - return output - - def npu_op_exec(self, input1, input2): - input1 = input1.to("npu") - input2 = input2.to("npu") - output = torch.floor_divide(input1,input2) - output = output.to("cpu") - output = output.numpy() - return output - - def test_floor_divide_common_shape_format(self, device): - shape_format = [ - [[np.float32, -1, (4, 3, 3)]], - [[np.float32, -1, (4, 5, 5)]], - [[np.float32, -1, (3, 3, 3)]], - [[np.float32, -1, (4, 4, 4)]], - [[np.float32, -1, (2, 0, 2)]] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 10, 100) - cpu_input2, npu_input2 = create_common_tensor(item[0], 10, 100) - cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2) - self.assertRtolEqual(cpu_output, npu_output) - - def test_floor_divide_float16_shape_format(self, device): - shape_format = [ - [[np.float16, -1, (4, 2, 6, 6)]], - [[np.float16, -1, (4, 2, 8, 8)]], - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100) - cpu_input2, npu_input2 = create_common_tensor(item[0], 1, 100) - cpu_output = self.cpu_op_exec_fp16(cpu_input1, cpu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2) - self.assertRtolEqual(cpu_output, npu_output) - - def test_floor_divide_int32_shape_format(self, device): - shape_format = [ - [[np.int32, -1, (4, 3)]], - [[np.int32, -1, (4, 5)]], - [[np.int32, -1, (3, 3)]], - [[np.int32, -1, (4, 4)]] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 100, 1000) - cpu_input2, npu_input2 = create_common_tensor(item[0], 100, 1000) - cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2) - self.assertRtolEqual(cpu_output, npu_output) - - def test_floor_divide_int8_shape_format(self, device): - shape_format = [ - [[np.int8, -1, (4, 8, 3)]], - [[np.int8, -1, (4, 7, 5)]], - [[np.int8, -1, (3, 6, 3)]], - [[np.int8, -1, (4, 5, 4)]] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 10, 100) - cpu_input2, npu_input2 = create_common_tensor(item[0], 10, 100) - cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2) - self.assertRtolEqual(cpu_output, npu_output) - - def test_floor_divide_uint8_shape_format(self, device): - shape_format = [ - [[np.uint8, -1, (4, 3, 3)]], - [[np.uint8, -1, (4, 5, 5)]], - [[np.uint8, -1, (3, 3, 3)]], - [[np.uint8, -1, (4, 4, 4)]] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 10, 100) - cpu_input2, npu_input2 = create_common_tensor(item[0], 10, 100) - cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestFloorDivide, globals(), except_for='cpu') -if __name__ == '__main__': - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_floordivide.py b/pytorch1.8.1/test/test_npu/test_floordivide.py deleted file mode 100644 index e25ac85e716..00000000000 --- a/pytorch1.8.1/test/test_npu/test_floordivide.py +++ /dev/null @@ -1,148 +0,0 @@ -# Copyright 2020 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestFloorDivide(TestCase): - - def generate_data(self, min, max, shape, dtype): - input1 = np.random.uniform(min, max, shape).astype(dtype) - input2 = np.random.uniform(min, max, shape).astype(dtype) - - #modify from numpy.ndarray to torch.tensor - npu_input1 = torch.from_numpy(input1) - npu_input2 = torch.from_numpy(input2) - - return npu_input1, npu_input2 - - - def generate_three_data(self, min, max, shape, dtype): - input1 = np.random.uniform(min, max, shape).astype(dtype) - input2 = np.random.uniform(min, max, shape).astype(dtype) - input3 = np.random.uniform(min, max, shape).astype(dtype) - - #modify from numpy.ndarray to torch.tensor - npu_input1 = torch.from_numpy(input1) - npu_input2 = torch.from_numpy(input2) - npu_input3 = torch.from_numpy(input3) - - return npu_input1, npu_input2, npu_input3 - - - def cpu_op_exec(self, input1, input2): - output = torch.floor_divide(input1,input2) - output = output.numpy() - return output - - - def npu_op_exec(self, input1, input2): - input1 = input1.to("npu") - input2 = input2.to("npu") - output = torch.floor_divide(input1,input2) - output = output.to("cpu") - output = output.numpy() - return output - - - def npu_op_exec_scalar(self, input1, input2): # - input1 = input1.to("npu") - output = torch.floor_divide(input1,input2) - output = output.to("cpu") - output = output.numpy() - return output - - - def npu_op_exec_out(self, input1, input2, input3): # - input1 = input1.to("npu") - input2 = input2.to("npu") - output = input3.to("npu") - torch.floor_divide(input1, input2, out=output) - output = output.to("cpu") - output = output.numpy() - return output - - - def test_floor_divide_float32(self, device): - npu_input1, npu_input2 = self.generate_data(1, 100, (1, 2), np.float32) - cpu_output = self.cpu_op_exec(npu_input1, npu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2) - self.assertRtolEqual(cpu_output, npu_output) - - - def test_floor_divide_float32_out(self, device): - npu_input1, npu_input2, npu_input3 = self.generate_three_data(1, 100, (1,2), np.float32) - cpu_output = self.cpu_op_exec(npu_input1, npu_input2) - npu_output = self.npu_op_exec_out(npu_input1, npu_input2, npu_input3) - self.assertRtolEqual(cpu_output, npu_output) - - - def test_floor_divide_int32(self, device): - npu_input1, npu_input2 = self.generate_data(1, 100, (1,2), np.int32) - cpu_output = self.cpu_op_exec(npu_input1, npu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2) - self.assertRtolEqual(cpu_output, npu_output) - - def test_floor_divide_int8(self, device): - npu_input1, npu_input2 = self.generate_data(1, 100, (1,2), np.int8) - cpu_output = self.cpu_op_exec(npu_input1, npu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2) - self.assertRtolEqual(cpu_output, npu_output) - - def test_floor_divide_uint8(self, device): - npu_input1, npu_input2 = self.generate_data(1, 100, (1,3), np.uint8) - cpu_output = self.cpu_op_exec(npu_input1, npu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2) - self.assertRtolEqual(cpu_output, npu_output) - - def test_floor_divide_scalar_float32(self, device): - npu_input1, _= self.generate_data(1, 100, (1,3), np.float32) - cpu_output = self.cpu_op_exec(npu_input1, 1) - npu_output = self.npu_op_exec_scalar(npu_input1, 1) - self.assertRtolEqual(cpu_output, npu_output) - - - def npu_uncontiguous_op_exec_scalar(self, input1, input2): # - input1 = input1.to("npu") - input1 = input1.as_strided([2,2], [1,2], 1) - output = torch.floor_divide(input1, input2) - output = output.to("cpu") - output = output.numpy() - return output - - def cpu_uncontiguous_op_exec_scalar(self, input1, input2): # - input1 = input1.as_strided([2,2], [1,2], 1) - output = torch.floor_divide(input1, input2) - output = output.numpy() - return output - - def test_floor_divide_uncontiguous_float32_scalar(self, device): - npu_input1, npu_input2 = self.generate_data(1, 100, (4,3), np.float32) - cpu_input1 = copy.deepcopy(npu_input1) - cpu_output = self.cpu_uncontiguous_op_exec_scalar(cpu_input1, 2) - npu_output = self.npu_uncontiguous_op_exec_scalar(npu_input1, 2) - self.assertRtolEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(TestFloorDivide, globals(), except_for='cpu') -if __name__ == '__main__': - # 当前版本需要调用如下代码 - torch.npu.set_device("npu:6") - run_tests() - diff --git a/pytorch1.8.1/test/test_npu/test_frac.py b/pytorch1.8.1/test/test_npu/test_frac.py deleted file mode 100644 index dcb781a8d36..00000000000 --- a/pytorch1.8.1/test/test_npu/test_frac.py +++ /dev/null @@ -1,151 +0,0 @@ -# Copyright (c) 2020 Huawei Technologies Co., Ltd -# All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# coding: utf-8 -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestFrac(TestCase): - - def generate_data(self, min_d, max_d, shape, dtype): - input1 = np.random.uniform(min_d, max_d, shape).astype(dtype) - input2 = np.random.uniform(min_d, max_d, shape).astype(dtype) - - #modify from numpy.ndarray to torch.tensor - npu_input1 = torch.from_numpy(input1) - npu_input2 = torch.from_numpy(input2) - - return npu_input1, npu_input2 - - - def generate_single_data(self, min_d, max_d, shape, dtype): - input1 = np.random.uniform(min_d, max_d, shape).astype(dtype) - npu_input1 = torch.from_numpy(input1) - - return npu_input1 - - def generate_three_data(self, min_d, max_d, shape, dtype): - input1 = np.random.uniform(min_d, max_d, shape).astype(dtype) - input2 = np.random.uniform(min_d, max_d, shape).astype(dtype) - input3 = np.random.uniform(min_d, max_d, shape).astype(dtype) - - #modify from numpy.ndarray to torch.tensor - npu_input1 = torch.from_numpy(input1) - npu_input2 = torch.from_numpy(input2) - npu_input3 = torch.from_numpy(input3) - - return npu_input1, npu_input2, npu_input3 - - def generate_scalar(self, min_d, max_d): - scalar = np.random.uniform(min_d, max_d) - return scalar - - def generate_int_scalar(self, min_d, max_d): - scalar = np.random.randint( min_d, max_d) - return scalar - - def cpu_op_exec(self, input1): - output = torch.frac(input1) - output = output.numpy() - return output - - def npu_op_exec(self, input1): - #input1 = input1.to("npu") - output = torch.frac(input1) - output = output.to("cpu") - output = output.numpy() - return output - - def cpu_op_exec_(self, input1): - torch.frac_(input1) - output = input1.numpy() - return output - - - def npu_op_exec_(self, input1): - # input1 = input1.to("npu") - torch.frac_(input1) - output = input1.to("cpu") - output = output.numpy() - return output - - - def cpu_op_exec_out(self, input1,out): - torch.frac(input1, out=out) - output = out.numpy() - return output - - - def npu_op_exec_out(self, input1, out): - # input1 = input1.to("npu") - out = out.to("npu") - torch.frac(input1, out=out) - output = out.to("cpu") - output = output.numpy() - return output - - def test_frac_common_shape_format(self, device): - shape_format = [ - [np.float32, -1, (4, 3)], - [np.float32, -1, (4, 3, 1)], - #[np.float16, -1, (2, 3)], - # [np.double, -1, (2, 3)], - - #[np.int32, -1, (4, 3, 1)] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item, 1, 100) - cpu_output = self.cpu_op_exec(cpu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_frac1_common_shape_format(self, device): - shape_format = [ - [np.float32, -1, (4, 3)], - [np.float32, -1, (4, 3, 1)], - #[np.int32, -1, (2, 3)], - #[np.int32, -1, (4, 3, 1)] - ] - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item, 1, 100) - cpu_output = self.cpu_op_exec_(cpu_input1) - npu_output = self.npu_op_exec_(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_frac_out_common_shape_format(self, device): - shape_format = [ - [np.float32, -1, (4, 3)], - [np.float32, -1, (4, 3, 1)], - # [np.int32, -1, (2, 3)], - #[np.int32, -1, (4, 3, 1)] - ] - out = self.generate_single_data(0, 100, (5,3), np.float32) - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item, 1, 100) - cpu_output = self.cpu_op_exec_out(cpu_input1, out) - npu_output = self.npu_op_exec_out(npu_input1, out) - self.assertRtolEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(TestFrac, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:6") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_frobenius_norm.py b/pytorch1.8.1/test/test_npu/test_frobenius_norm.py deleted file mode 100644 index 202974470b3..00000000000 --- a/pytorch1.8.1/test/test_npu/test_frobenius_norm.py +++ /dev/null @@ -1,176 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. - -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestFrobenius_norm(TestCase): - - - def generate_single_data(self, min_d, max_d, shape, dtype): - input1 = np.random.uniform(min_d, max_d, shape).astype(dtype) - npu_input1 = torch.from_numpy(input1) - - return npu_input1 - - def cpu_single_input_op_exec(self, input1): - output = torch.frobenius_norm(input1) - output = output.numpy() - return output - - def cpu_op_exec(self, input1, axis, keep_dim): - output = torch.frobenius_norm(input1, axis, keep_dim) - # output = torch.fmod(input1, input2) - output = output.numpy() - return output - - def npu_single_input_op_exec(self, input1): - input1 = input1.to("npu") - output = torch.frobenius_norm(input1) - output = output.to("cpu") - output = output.numpy() - return output - - def npu_op_exec_tensor_need_to_npu(self, input1, axis, keep_dim): - input1 = input1.to("npu") - output = torch.frobenius_norm(input1, axis, keep_dim) - # output = torch.frobenius_norm(input1, input2) - output = output.to("cpu") - output = output.numpy() - return output - - def test_single_input_format(self, device): - shape_format = [ - [np.float32, -1, (4, 3)], - [np.float32, -1, (2, 3)], - [np.float32, -1, (4, 3)], - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item, 1, 100) - cpu_output = self.cpu_single_input_op_exec(cpu_input1) - print(cpu_output) - npu_output = self.npu_single_input_op_exec(npu_input1) - print(npu_output) - self.assertRtolEqual(cpu_output, npu_output) - - def test_add_common_shape_format(self, device): - shape_format = [ - [np.float32, -1, (4, 3)], - [np.float32, -1, (2, 3)], - [np.float32, -1, (4, 3)], - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item, 1, 100) - cpu_output = self.cpu_op_exec(cpu_input1, [1], False) - npu_output = self.npu_op_exec_tensor_need_to_npu(npu_input1, [1], False) - self.assertRtolEqual(cpu_output, npu_output) - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item, 1, 100) - cpu_output = self.cpu_op_exec(cpu_input1, [0], False) - npu_output = self.npu_op_exec_tensor_need_to_npu(npu_input1, [0], False) - self.assertRtolEqual(cpu_output, npu_output) - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item, 1, 100) - cpu_output = self.cpu_op_exec(cpu_input1, [1], True) - npu_output = self.npu_op_exec_tensor_need_to_npu(npu_input1, [1], True) - self.assertRtolEqual(cpu_output, npu_output) - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item, 1, 100) - cpu_output = self.cpu_op_exec(cpu_input1, [0], True) - npu_output = self.npu_op_exec_tensor_need_to_npu(npu_input1, [0], True) - self.assertRtolEqual(cpu_output, npu_output) - - def test_add_float16_shape_format(self, device): - def cpu_op_exec_fp16(input1, axis, keep_dim): - input1 = input1.to(torch.float32) - output = torch.frobenius_norm(input1, axis, keep_dim) - output = output.numpy() - output = output.astype(np.float16) - return output - - shape_format = [ - [np.float16, -1, (4, 3)], - [np.float16, -1, (4, 1)], - [np.float16,-1,(65535, 1)], - [np.float16, -1, (1, 8192)], - [np.float16, -1, (1, 16384)], - [np.float16, -1, (1, 32768)], - [np.float16, -1, ( 1, 131072)], - [np.float16, -1, (1, 196608)], - [np.float16, -1, (1, 262144)], - [np.float16, -1, (1, 393216)], - [np.float16, -1, (1, 524288)], - [np.float16, -1, (1, 655360)], - [np.float16, -1, (1, 786432)], - ] - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item, 1, 100) - cpu_output = cpu_op_exec_fp16(cpu_input1,[1], True) - npu_output = self.npu_op_exec_tensor_need_to_npu(npu_input1, [1], True) - self.assertRtolEqual(cpu_output, npu_output) - - def test_frobenius_norm__float32_data_range(self, device): - data_range = [ - [-1.1754943508e-38, -1.1754943508e-38], - [-3402823500.0, 3402823500.0], - [-0.000030517578125, 0.000030517578125], - [3402823500, 3402800000], - [-9.313225746154785e-10, 9.313225746154785e-10], - [-3402823500.0, -3402823500.0], - [-3402823500.0, 3402823500.0], - [-9.313225746154785e-10, 9.313225746154785e-10], - [-3402823500.0,-3402823500.0], - [-0.000000000000000000000000000000000000011754943508, 0.000000000000000000000000000000000000011754943508], - [0.000000000000000000000000000000000000011754943508, 0.000000000000000000000000000000000000011754943508], - [-0.000000000000000000000000000000000000011754943508, -0.000000000000000000000000000000000000011754943508], - [-0.000000000000000000000000000000000000011754943508, 0.000000000000000000000000000000000000011754943508] - ] - for item in data_range: - cpu_input1, npu_input1 = create_common_tensor([np.float32, - 1, (1, 31, 149, 2)], item[0], item[1]) - cpu_output = self.cpu_op_exec(cpu_input1, [1], False) - npu_output = self.npu_op_exec_tensor_need_to_npu(npu_input1, [1], False) - self.assertRtolEqual(cpu_output, npu_output) - - for item in data_range: - cpu_input1, npu_input1 = create_common_tensor([np.float32, - 1, (1, 31, 149, 2)], item[0], item[1]) - cpu_output = self.cpu_op_exec(cpu_input1, [-1], False) - npu_output = self.npu_op_exec_tensor_need_to_npu(npu_input1, [-1], False) - self.assertRtolEqual(cpu_output, npu_output) - - for item in data_range: - cpu_input1, npu_input1 = create_common_tensor([np.float32, - 1, (1, 31, 149, 2)], item[0], item[1]) - cpu_output = self.cpu_op_exec(cpu_input1, [-1,0], False) - npu_output = self.npu_op_exec_tensor_need_to_npu(npu_input1, [-1,0], False) - self.assertRtolEqual(cpu_output, npu_output) - - for item in data_range: - cpu_input1, npu_input1 = create_common_tensor([np.float32, - 1, (1, 31, 149, 2)], item[0], item[1]) - cpu_output = self.cpu_op_exec(cpu_input1, [-2,1], False) - npu_output = self.npu_op_exec_tensor_need_to_npu(npu_input1, [-2,1], False) - self.assertRtolEqual(cpu_output, npu_output) -instantiate_device_type_tests(TestFrobenius_norm, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:7") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_full_like.py b/pytorch1.8.1/test/test_npu/test_full_like.py deleted file mode 100644 index 36d5f6378f1..00000000000 --- a/pytorch1.8.1/test/test_npu/test_full_like.py +++ /dev/null @@ -1,88 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import torch -import numpy as np -import sys -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestFullLike(TestCase): - def generate_single_data(self,min_d, max_d, shape, dtype): - input1 = np.random.uniform(min_d, max_d, shape).astype(dtype) - npu_input1 = torch.from_numpy(input1) - - return npu_input1 - - def cpu_op_exec(self,input1, input2): - output = torch.full_like(input1,input2) - #modify from torch.tensor to numpy.ndarray - output = output.numpy() - return output - - def npu_op_exec(self,input1, input2): - input1 = input1.to("npu") - # input2 = input2.to("npu") - output = torch.full_like(input1,input2) - output = output.to("cpu") - output = output.numpy() - return output - - def test_full_like_float16(self,device): - npu_input1=self.generate_single_data(0,100,(4,3),np.float16) - npu_input2=np.random.randint(0,100) - cpu_output=self.cpu_op_exec(npu_input1,npu_input2) - npu_output=self.npu_op_exec(npu_input1,npu_input2) - self.assertRtolEqual(cpu_output, npu_output) - - def test_full_like_float32(self,device): - npu_input1=self.generate_single_data(0,100,(4,3),np.float32) - npu_input2=np.random.randint(0,100) - cpu_output=self.cpu_op_exec(npu_input1,npu_input2) - npu_output=self.npu_op_exec(npu_input1,npu_input2) - self.assertRtolEqual(cpu_output, npu_output) - - def test_full_like_int32(self,device): - npu_input1=self.generate_single_data(0,100,(4,3),np.int32) - npu_input2=np.random.randint(0,100) - cpu_output=self.cpu_op_exec(npu_input1,npu_input2) - npu_output=self.npu_op_exec(npu_input1,npu_input2) - self.assertRtolEqual(cpu_output, npu_output) - - def test_full_like_float_float16(self,device): - npu_input1=self.generate_single_data(0,100,(4,3),np.float16) - npu_input2=np.random.uniform(0,100) - cpu_output=self.cpu_op_exec(npu_input1,npu_input2) - npu_output=self.npu_op_exec(npu_input1,npu_input2) - self.assertRtolEqual(cpu_output, npu_output) - - def test_full_like_float_float32(self,device): - npu_input1=self.generate_single_data(0,100,(4,3),np.float32) - npu_input2=np.random.uniform(0,100) - cpu_output=self.cpu_op_exec(npu_input1,npu_input2) - npu_output=self.npu_op_exec(npu_input1,npu_input2) - self.assertRtolEqual(cpu_output, npu_output) - - def test_full_like_float_int32(self,device): - npu_input1=self.generate_single_data(0,100,(4,3),np.int32) - npu_input2=np.random.uniform(0,100) - cpu_output=self.cpu_op_exec(npu_input1,npu_input2) - npu_output=self.npu_op_exec(npu_input1,npu_input2) - self.assertRtolEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(TestFullLike, globals(), except_for='cpu') -if __name__ == '__main__': - torch.npu.set_device("npu:3") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_gelu.py b/pytorch1.8.1/test/test_npu/test_gelu.py deleted file mode 100644 index 9b338d3e865..00000000000 --- a/pytorch1.8.1/test/test_npu/test_gelu.py +++ /dev/null @@ -1,129 +0,0 @@ -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor -#pylint: disable=unused-argument - -class TestGelu(TestCase): - - def generate_data(self, min_d, max_d, shape, dtype): - input1 = np.random.uniform(min_d, max_d, shape).astype(dtype) - - #modify from numpy.ndarray to torch.tensor - npu_input1 = torch.from_numpy(input1) - return npu_input1 - - def cpu_op_exec(self, input1): - output = torch.nn.functional.gelu(input1) - output = output.numpy() - return output - - def npu_op_exec(self, input1): - input1_npu = input1.to('npu') - output = torch.nn.functional.gelu(input1_npu) - output = output.to("cpu") - output = output.numpy() - return output - - def test_gelu_float32_1(self, device): - input1= self.generate_data(0, 100, (4,3), np.float32) - cpu_input1 = copy.deepcopy(input1) - cpu_output = self.cpu_op_exec(cpu_input1) - npu_output = self.npu_op_exec(input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_gelu_float32_2(self, device): - input1= self.generate_data(0, 1000, (4,3), np.float32) - cpu_input1 = copy.deepcopy(input1) - cpu_output = self.cpu_op_exec(cpu_input1) - npu_output = self.npu_op_exec(input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_gelu_float32_3(self, device): - input1= self.generate_data(0, 1000, (4,3), np.float32) - cpu_input1 = copy.deepcopy(input1) - cpu_output = self.cpu_op_exec(cpu_input1) - npu_output = self.npu_op_exec(input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_gelu_float16_1(self, device): - def cpu_op_exec_fp16(input1): - input1 = input1.to(torch.float32) - output = torch.nn.functional.gelu(input1) - output = output.numpy() - output = output.astype(np.float16) - return output - - def npu_op_exec_fp16(input1): - input1 = input1.to(torch.float32).to('npu') - output = torch.nn.functional.gelu(input1) - output = output.to("cpu") - output = output.numpy().astype(np.float16) - return output - - npu_input1 = self.generate_data(0, 100, (5,3), np.float16) - cpu_input1 = copy.deepcopy(npu_input1) - cpu_output = cpu_op_exec_fp16(cpu_input1) - npu_output = npu_op_exec_fp16(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_gelu_float16_2(self, device): - def cpu_op_exec_fp16(input1): - input1 = input1.to(torch.float32) - output = torch.nn.functional.gelu(input1) - output = output.numpy() - output = output.astype(np.float16) - return output - - def npu_op_exec_fp16(input1): - input1 = input1.to(torch.float32).to('npu') - output = torch.nn.functional.gelu(input1) - output = output.to("cpu") - output = output.numpy().astype(np.float16) - return output - - npu_input1 = self.generate_data(0, 1000, (5,3), np.float16) - cpu_input1 = copy.deepcopy(npu_input1) - cpu_output = cpu_op_exec_fp16(cpu_input1) - npu_output = npu_op_exec_fp16(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_gelu_float16_3(self, device): - def cpu_op_exec_fp16(input1): - input1 = input1.to(torch.float32) - output = torch.nn.functional.gelu(input1) - output = output.numpy() - output = output.astype(np.float16) - return output - - def npu_op_exec_fp16(input1): - input1 = input1.to(torch.float32).to('npu') - output = torch.nn.functional.gelu(input1) - output = output.to("cpu") - output = output.numpy().astype(np.float16) - return output - - npu_input1 = self.generate_data(0, 1000, (3,3), np.float16) - cpu_input1 = copy.deepcopy(npu_input1) - cpu_output = cpu_op_exec_fp16(cpu_input1) - npu_output = npu_op_exec_fp16(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestGelu, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:2") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_gelu_backward.py b/pytorch1.8.1/test/test_npu/test_gelu_backward.py deleted file mode 100644 index 4e05c66b411..00000000000 --- a/pytorch1.8.1/test/test_npu/test_gelu_backward.py +++ /dev/null @@ -1,86 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor -import copy -#pylint: disable=unused-argument - -class TestGeluBackward(TestCase): - - def generate_single_data(self, min_val, max_val, shape, dtype): - input1 = np.random.uniform(min_val, max_val, shape).astype(dtype) - npu_input1 = torch.from_numpy(input1) - return npu_input1 - - def cpu_op_exec(self, input1): - input1.requires_grad_(True) - output = torch.nn.functional.gelu(input1) - z = output.sum() - z.backward() - res = input1.grad - return res.detach() - - def npu_op_exec(self, input1): - input1 = input1.to("npu") - input1.requires_grad = True - output = torch.nn.functional.gelu(input1) - z = output.sum() - z.backward() - res = input1.grad.to("cpu") - return res.detach() - - def test_gelu_backward_float32_1(self, device): - input1= self.generate_single_data(0, 100, (4,3,1,1), np.float32) - cpu_input1 = copy.deepcopy(input1) - cpu_output = self.cpu_op_exec(cpu_input1) - npu_output = self.npu_op_exec(input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_gelu_backward_float32_2(self, device): - input1= self.generate_single_data(0, 100, (4,3,10), np.float32) - cpu_input1 = copy.deepcopy(input1) - cpu_output = self.cpu_op_exec(cpu_input1) - npu_output = self.npu_op_exec(input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_gelu_backward_float32_3(self, device): - input1= self.generate_single_data(0, 100, (400,30,10), np.float32) - cpu_input1 = copy.deepcopy(input1) - cpu_output = self.cpu_op_exec(cpu_input1) - npu_output = self.npu_op_exec(input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_gelu_backward_float32_4(self, device): - input1= self.generate_single_data(-30, 0, (4,4), np.float32) - cpu_input1 = copy.deepcopy(input1) - cpu_output = self.cpu_op_exec(cpu_input1) - npu_output = self.npu_op_exec(input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_gelu_backward_float16(self, device): - input1 = self.generate_single_data(0, 100, (5, 10, 100) , np.float16) - input1 = input1.to(torch.float32) - cpu_input1 = copy.deepcopy(input1) - cpu_output = self.cpu_op_exec(cpu_input1) - npu_output = self.npu_op_exec(input1) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestGeluBackward, globals(), except_for="cpu") -if __name__ == "__main__": - torch.npu.set_device("npu:2") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_ger.py b/pytorch1.8.1/test/test_npu/test_ger.py deleted file mode 100644 index ae5dd4e34e7..00000000000 --- a/pytorch1.8.1/test/test_npu/test_ger.py +++ /dev/null @@ -1,90 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import copy -import torch.nn as nn -import numpy as np -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestGer(TestCase): - - def generate_data(self, min_d, max_d, shape, dtype): - input1 = np.random.uniform(min_d, max_d, shape).astype(dtype) - input2 = np.random.uniform(min_d, max_d, shape).astype(dtype) - - # modify from numpy.ndarray to torch.tensor - npu_input1 = torch.from_numpy(input1) - npu_input2 = torch.from_numpy(input2) - - return npu_input1, npu_input2 - - def cpu_op_exec(self,input1, input2): - output = torch.ger(input1, input2) - output = output.numpy() - return output - - def npu_op_exec(self,input1, input2): - input1 = input1.to("npu") - input2 = input2.to("npu") - output = torch.ger(input1, input2) - output = output.to("cpu") - output = output.numpy() - return output - - def test_add_float32(self, device): - npu_input1, npu_input2 = self.generate_data(0, 100, (4), np.float32) - cpu_output = self.cpu_op_exec(npu_input1, npu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2) - self.assertRtolEqual(cpu_output, npu_output) - - def test_add_float32(self, device): - npu_input1, npu_input2 = self.generate_data(0, 100, (15), np.float32) - cpu_output = self.cpu_op_exec(npu_input1, npu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2) - self.assertRtolEqual(cpu_output, npu_output) - - def test_add_float32(self, device): - npu_input1, npu_input2 = self.generate_data(0, 100, (128), np.float32) - cpu_output = self.cpu_op_exec(npu_input1, npu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2) - self.assertRtolEqual(cpu_output, npu_output) - - - def test_add_float16(self, device): - npu_input1, npu_input2 = self.generate_data(0, 100, (4), np.float16) - cpu_output = self.cpu_op_exec(npu_input1.to(torch.float32), npu_input2.to(torch.float32)) - npu_output = self.npu_op_exec(npu_input1, npu_input2) - self.assertRtolEqual(cpu_output.astype(np.float16), npu_output.astype(np.float16)) - - def test_add_float16(self, device): - npu_input1, npu_input2 = self.generate_data(0, 100, (15), np.float16) - cpu_output = self.cpu_op_exec(npu_input1.to(torch.float32), npu_input2.to(torch.float32)) - npu_output = self.npu_op_exec(npu_input1, npu_input2) - self.assertRtolEqual(cpu_output.astype(np.float16), npu_output.astype(np.float16)) - - def test_add_float16(self, device): - npu_input1, npu_input2 = self.generate_data(0, 100, (128), np.float16) - cpu_output = self.cpu_op_exec(npu_input1.to(torch.float32), npu_input2.to(torch.float32)) - npu_output = self.npu_op_exec(npu_input1, npu_input2) - self.assertRtolEqual(cpu_output.astype(np.float16), npu_output.astype(np.float16)) - - -instantiate_device_type_tests(TestGer, globals(), except_for="cpu") -if __name__ == "__main__": - torch.npu.set_device("npu:2") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_glu.py b/pytorch1.8.1/test/test_npu/test_glu.py deleted file mode 100644 index 85167f0ab8d..00000000000 --- a/pytorch1.8.1/test/test_npu/test_glu.py +++ /dev/null @@ -1,81 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestGlu(TestCase): - def generate_single_data(self, min_d, max_d, shape, dtype): - input1 = np.random.uniform(min_d, max_d, shape).astype(dtype) - npu_input1 = torch.from_numpy(input1) - return npu_input1 - - def cpu_op_exec(self, input_data, dim): - input_data = input_data.to("cpu") - flag = False - if input_data.dtype == torch.float16: - input_data = input_data.to(torch.float32) - flag = True - output = torch.nn.functional.glu(input_data, dim) - - if flag: - output = output.to(torch.float16) - output = output.numpy() - return output - - def npu_op_exec(self, input_data, dim): - input_data = input_data.to("npu") - output = torch.nn.functional.glu(input_data, dim) - output = output.to("cpu") - output = output.numpy() - return output - - def test_put_common_shape_format(self, device): - #pylint:disable=unused-argument - shape_format = [ - [np.float32, (4, 8), -1, 100, 200], - [np.float32, (4, 6, 8), -2, 100, 200], - [np.float32, (44, 6, 8, 4), 3, 0, 1], - [np.float32, (4, 5, 6), 2, 0, 1], - [np.float32, (4, 4, 2, 2, 6, 4), 2, 0, 1], - [np.float32, (4, 2, 1, 5, 8, 10), 0, 0, 1], - [np.float32, (4, 2, 1, 5, 8, 1, 2, 3), 0, 0, 1], - [np.float32, (8, 10, 1, 5, 2, 10), 0, 0, 1], - - [np.float16, (12000, 10), 0, 0, 1], - [np.float16, (6000, 20, 10), 0, 0, 1], - [np.float16, (4, 6), -1, 100, 200], - [np.float16, (2, 2, 3), 1, 100, 200], - [np.float16, (4, 6, 8, 10), 3, 0, 1], - [np.float16, (4, 5, 6), 2, 0, 1], - [np.float16, (22, 3, 35, 34, 10, 2), 0, 1, 10], - [np.float16, (42, 33, 32, 32, 36, 22), -3, 1, 10] - ] - for item in shape_format: - input_data = self.generate_single_data(item[3], item[4], item[1], item[0]) - cpu_output = self.cpu_op_exec(input_data, item[2]) - npu_output = self.npu_op_exec(input_data, item[2]) - self.assertRtolEqual(cpu_output, npu_output, prec16 = 0.002, prec = 0.0002) - - -instantiate_device_type_tests(TestGlu, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:1") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_glugrad.py b/pytorch1.8.1/test/test_npu/test_glugrad.py deleted file mode 100644 index c2e546bd289..00000000000 --- a/pytorch1.8.1/test/test_npu/test_glugrad.py +++ /dev/null @@ -1,91 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy - -from torch import device -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestGluGrad(TestCase): - def cpu_op_exec(self, input_data, dim): - sign = False - if input_data.dtype == torch.float16: - input_data = input_data.to(torch.float32) - sign = True - - input_data.requires_grad = True - data = torch.nn.functional.glu(input_data, dim=dim) - data.backward(torch.ones_like(data)) - cpu_output = input_data.grad - - if sign: - cpu_output = cpu_output.to(torch.float16) - - return cpu_output.to("cpu").numpy() - - def npu_op_exec(self, input_data, dim): - input_data = input_data.to("npu") - input_data.requires_grad = True - data = torch.nn.functional.glu(input_data, dim=dim) - data.backward(torch.ones_like(data)) - npu_output = input_data.grad - - return npu_output.to("cpu").numpy() - - def test_glugrad_shape_format(self, device): - # dtype, format[-1 默认], shape, dim - shape_format_32 = [ - [np.float32, -1, (2, 2, 4), 0], - [np.float32, -1, (4, 6, 10), 1], - [np.float32, -1, (2, 4, 8), 2], - [np.float32, -1, (4, 6), -1], - [np.float32, -1, (2, 2, 4), 2], - [np.float32, -1, (4, 6, 8, 10), -2], - [np.float32, -1, (4, 6, 6), 1], - [np.float32, -1, (6, 20, 10), 1], - ] - - shape_format_16 = [ - [np.float16, -1, (2, 2, 4), 0], - [np.float16, -1, (4, 6, 10), 1], - [np.float16, -1, (2, 4, 8), 2], - [np.float16, -1, (4, 6), -1], - [np.float16, -1, (2, 2, 4), 2], - [np.float16, -1, (4, 6, 8, 10), -2], - [np.float16, -1, (4, 6, 6), 1], - ] - for item in shape_format_32: - cpu_input, npu_input = create_common_tensor(item, -2.0, 2.0) - cpu_output = self.cpu_op_exec(cpu_input, item[3]) - npu_output = self.npu_op_exec(npu_input, item[3]) - eps = 0.0002 if item[0].dtype == torch.float32 else 0.002 - self.assertRtolEqual(cpu_output, npu_output, prec=eps) - - for item in shape_format_16: - cpu_input, npu_input = create_common_tensor(item, -2.0, 2.0) - cpu_output = self.cpu_op_exec(cpu_input, item[3]) - npu_output = self.npu_op_exec(npu_input, item[3]) - eps = 0.0002 if item[0].dtype == torch.float32 else 0.002 - self.assertRtolEqual(cpu_output, npu_output, prec=eps) - -instantiate_device_type_tests(TestGluGrad, globals(), except_for="cpu") -if __name__ == "__main__": - torch.npu.set_device("npu:1") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_grid_sampler_2d.py b/pytorch1.8.1/test/test_npu/test_grid_sampler_2d.py deleted file mode 100644 index 655f548aedc..00000000000 --- a/pytorch1.8.1/test/test_npu/test_grid_sampler_2d.py +++ /dev/null @@ -1,74 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestGridSampler2D(TestCase): - def cpu_op_exec(self, input1, grid): - output = torch.grid_sampler(input1, grid, 0, 0, True) - output = output.numpy() - return output - - def npu_op_exec(self, input1, grid): - output = torch.grid_sampler(input1, grid, 0, 0, True) - output = output.to("cpu") - output = output.numpy() - return output - - def cpu_op_fp16_exec(self, input1, grid): - input1 = input1.to(torch.float32) - grid = grid.to(torch.float32) - output = torch.grid_sampler(input1, grid, 0, 0, True) - output = output.numpy() - output = output.astype(np.float16) - return output - - def test_grid_sampler_2d_shape_format(self, device): - shape_format = [ - [[np.float32, 0, (1,2,4,20)],[np.float32, 0, (1,10,8,2)]], - [[np.float32, 0, (1,4,64, 10)],[np.float32, 0, (1,2,32,2)]], - [[np.float32, 0, (2, 2048, 7, 7)],[np.float32, 0, (2, 2048, 14, 2)]], - [[np.float32, 4, (32, 1, 3, 3)],[np.float32, 4, (32, 20, 30, 2)]], - [[np.float32, 29, (1,2,10, 128)],[np.float32, 4, (1, 10, 5, 2)]] - ] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item[0], 1, 100) - cpu_grid, npu_grid = create_common_tensor(item[1], -1, 1) - cpu_output = self.cpu_op_exec(cpu_input, cpu_grid) - npu_output = self.npu_op_exec(npu_input, npu_grid) - self.assertRtolEqual(cpu_output, npu_output) - - def test_grid_sampler_2d_fp16_shape_format(self, device): - shape_format = [ - [[np.float16, 0, (1,2,4,20)],[np.float16, 0, (1,10,8,2)]], - [[np.float16, 0, (1,4,64, 10)],[np.float16, 0, (1,2,32,2)]], - [[np.float16, 0, (2, 2048, 7, 7)],[np.float16, 0, (2, 2048, 14, 2)]], - [[np.float16, 4, (32, 1, 3, 3)],[np.float16, 4, (32, 20, 30, 2)]], - [[np.float16, 29, (1,2,10, 128)],[np.float16, 4, (1, 10, 5, 2)]] - ] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item[0], 1, 100) - cpu_grid, npu_grid = create_common_tensor(item[1], -1, 1) - cpu_output = self.cpu_op_fp16_exec(cpu_input, cpu_grid) - npu_output = self.npu_op_exec(npu_input, npu_grid) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestGridSampler2D, globals(), except_for="cpu") -if __name__ == "__main__": - torch.npu.set_device("npu:6") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_grid_sampler_2d_backward.py b/pytorch1.8.1/test/test_npu/test_grid_sampler_2d_backward.py deleted file mode 100644 index f5ca5d00307..00000000000 --- a/pytorch1.8.1/test/test_npu/test_grid_sampler_2d_backward.py +++ /dev/null @@ -1,78 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestGridSampler2dBackward(TestCase): - def cpu_op_exec(self, input, sample): - input.requires_grad = True - out = torch.grid_sampler(input, sample, 0, 0, True) - grad_output = torch.ones(out.size(), dtype=torch.float) - out.backward(gradient=grad_output) - output = input.grad.numpy() - return output - - def npu_op_exec(self, input, sample): - input.requires_grad = True - out = torch.grid_sampler(input, sample, 0, 0, True) - grad_output = torch.ones(out.size(), dtype=torch.float).npu() - out.backward(gradient=grad_output) - output = input.grad.to("cpu").numpy() - return output - - def test_grid_sampler_2d_backward_fp32(self, device): - shape_list = [[100, 1, 28, 28], [100, 64, 32, 28]] - shape_format = [ - [np.float32, -1, j] for j in shape_list - ] - sample_format = [np.float32, -1, [100, 1, 1, 2]] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item, 0, 100) - cpu_sample, npu_sample = create_common_tensor(sample_format, -1, 1) - cpu_output = self.cpu_op_exec(cpu_input, cpu_sample) - # npu_output = self.npu_op_exec(npu_input, npu_sample) - # self.assertRtolEqual(cpu_output, npu_output) - - def test_grid_sampler_2d_backward_fp16(self, device): - def cpu_op_fp16_exec(input, sample): - input = input.to(torch.float32) - sample = sample.to(torch.float32) - input.requires_grad = True - out = torch.grid_sampler(input, sample, 0, 0, True) - grad_output = torch.ones(out.size(), dtype=torch.float) - out.backward(gradient=grad_output) - output = input.grad.numpy() - output = output.astype(np.float16) - return output - - shape_list = [[100, 1, 28, 28], [100, 64, 32, 28]] - shape_format = [ - [np.float16, -1, j] for j in shape_list - ] - sample_format = [np.float16, -1, [100, 1, 1, 2]] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item, 0, 100) - cpu_sample, npu_sample = create_common_tensor(sample_format, -1, 1) - cpu_output = cpu_op_fp16_exec(cpu_input, cpu_sample) - # npu_output = self.npu_op_exec(npu_input, npu_sample) - # self.assertRtolEqual(cpu_output, npu_output.astype(np.float16)) - -instantiate_device_type_tests(TestGridSampler2dBackward, globals(), except_for="cpu") -if __name__ == "__main__": - torch.npu.set_device("npu:4") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_group_norm.py b/pytorch1.8.1/test/test_npu/test_group_norm.py deleted file mode 100644 index 3a326b779bb..00000000000 --- a/pytorch1.8.1/test/test_npu/test_group_norm.py +++ /dev/null @@ -1,133 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import sys -import pdb - -import torch -import numpy as np - -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestGroupNormExt(TestCase): - def cpu_output_exec(self, data_format, input_x, scale, offset, - shape, shape_param, num_groups, epsilon = 1e-5): - - input_x = input_x.numpy() - if data_format == "NCHW": - shape_r = [shape[0], - num_groups, - shape[1] // num_groups, - shape[2], - shape[3]] - shape_param_r = \ - [1, num_groups, shape_param[0] // num_groups, 1, 1] - elif data_format == "NHWC": - shape_r = [shape[0], - shape[1], - shape[2], - num_groups, - shape[3] // num_groups] - shape_param_r = \ - [1, 1, 1, num_groups, shape_param[0] // num_groups] - - input_x_r = np.reshape(input_x, shape_r) - scale_r = np.reshape(scale, shape_param_r) - offset_r = np.reshape(offset, shape_param_r) - - if data_format == "NCHW": - reduce_axis = (2, 3, 4) - else: - reduce_axis = (1, 2, 4) - - reduce_elts = 1.0 - for i in reduce_axis: - reduce_elts *= shape_r[i] - - mean_muls = input_x_r / reduce_elts - mean = np.sum(mean_muls, axis = reduce_axis, keepdims = True) - - x_mean_sub = input_x_r - mean - variance_mul = x_mean_sub * x_mean_sub - variance_muls = variance_mul / reduce_elts - variance = np.sum(variance_muls, axis = reduce_axis, keepdims = True) - - normalize_add = variance + epsilon - normalize_sqrt = np.sqrt(normalize_add) - normalize_mul = x_mean_sub / normalize_sqrt - - scale_mul = scale_r * normalize_mul - output = scale_mul + offset_r - output_y = np.reshape(output, shape).numpy() - mean_y = np.reshape(mean, -1) - variance_y = np.reshape(variance, -1) - - return output_y - - def npu_output_exec(self, input_x, scale, offset, num_groups): - npu_input_x = input_x.to("npu") - npu_scale = scale.to("npu") - npu_offset = offset.to("npu") - - output = torch.group_norm( - npu_input_x, num_groups=num_groups, weight=npu_scale, - bias=npu_offset) - - return output - - def test_group_norm_case1(self, device): - shape_format = [ - [[np.float32, 0, (2, 6, 1, 1)], [np.float32, -1, (6,)], 2], - [[np.float32, 0, (8, 6, 4, 4)], [np.float32, -1, (6,)], 2], - [[np.float32, 0, (8, 6, 4, 4)], [np.float32, -1, (6,)], 3], - ] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item[0], -2, 2) - cpu_scale, npu_scale = create_common_tensor(item[1], -2, 2) - cpu_offset, npu_offset = create_common_tensor(item[1], -2, 2) - - cpu_output = self.cpu_output_exec( - 'NCHW', cpu_input, cpu_scale, cpu_offset, item[0][2], - item[1][2], item[2]) - npu_output = self.npu_output_exec( - npu_input, npu_scale, npu_offset, item[2]) - - self.assertRtolEqual(cpu_output, npu_output.to('cpu').numpy()) - - def test_group_norm_case2(self, device): - shape_format = [ - [[np.float32, 0, (2, 6, 1, 1)], [np.float32, -1, (6,)], 2, -2e5, 2e5], - [[np.float32, 0, (8, 6, 4, 4)], [np.float32, -1, (6,)], 2, -2e-38, 2e-38], - [[np.float32, 0, (8, 6, 4, 4)], [np.float32, -1, (6,)], 6, -2e5, 2e5], - [[np.float32, 0, (8, 6, 4, 4)], [np.float32, -1, (6,)], 6, -2e-38, 2e-38], - ] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item[0], item[3], item[4]) - cpu_scale, npu_scale = create_common_tensor(item[1], item[3], item[4]) - cpu_offset, npu_offset = create_common_tensor(item[1], item[3], item[4]) - - cpu_output = self.cpu_output_exec( - 'NCHW', cpu_input, cpu_scale, cpu_offset, item[0][2], - item[1][2], item[2]) - npu_output = self.npu_output_exec( - npu_input, npu_scale, npu_offset, item[2]) - - self.assertRtolEqual(cpu_output, npu_output.to('cpu').numpy()) - -instantiate_device_type_tests(TestGroupNormExt, globals(), except_for='cpu') -if __name__ == '__main__': - torch.npu.set_device("npu:1") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_hamming_window.py b/pytorch1.8.1/test/test_npu/test_hamming_window.py deleted file mode 100644 index 490cf878cbf..00000000000 --- a/pytorch1.8.1/test/test_npu/test_hamming_window.py +++ /dev/null @@ -1,134 +0,0 @@ -# Copyright (c) 2020 Huawei Technologies Co., Ltd - -# All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestHammingWindow(TestCase): - - def cpu_op_exec(self, window_length): - output = torch.hamming_window(window_length) - output = output.numpy() - return output - - def npu_op_exec(self, window_length): - output = torch.hamming_window(window_length, device='npu') - output = output.to('cpu') - output = output.numpy() - return output - - def cpu_op_exec_periodic(self, window_length, periodic): - output = torch.hamming_window(window_length, periodic) - output = output.numpy() - return output - - def npu_op_exec_periodic(self, window_length, periodic): - output = torch.hamming_window(window_length, periodic, device='npu') - output = output.to('cpu') - output = output.numpy() - return output - - def cpu_op_exec_periodic_alpha(self, window_length, periodic, alpha): - output = torch.hamming_window(window_length, periodic, alpha) - output = output.numpy() - return output - - def npu_op_exec_periodic_alpha(self, window_length, periodic, alpha): - output = torch.hamming_window(window_length, periodic, alpha, device='npu') - output = output.to('cpu') - output = output.numpy() - return output - - def cpu_op_exec_periodic_alpha_beta(self, window_length, periodic, alpha, beta): - output = torch.hamming_window(window_length, periodic, alpha, beta) - output = output.numpy() - return output - - def npu_op_exec_periodic_alpha_beta(self, window_length, periodic, alpha, beta): - output = torch.hamming_window(window_length, periodic, alpha, beta, device='npu') - output = output.to('cpu') - output = output.numpy() - return output - - def test_hamming_window(self, device): - shape_format = [ - [0, torch.float32], - [1, torch.float32], - [7, torch.float32], - [12, torch.float32]] - for item in shape_format: - cpu_output = self.cpu_op_exec(item[0]) - npu_output = self.npu_op_exec(item[0]) - self.assertRtolEqual(cpu_output, npu_output) - - def test_hamming_window_periodic(self, device): - shape_format = [ - [0, False, torch.float32], - [1, False, torch.float32], - [7, False, torch.float32], - [12, False, torch.float32]] - for item in shape_format: - cpu_output = self.cpu_op_exec_periodic(item[0], item[1]) - npu_output = self.npu_op_exec_periodic(item[0], item[1]) - self.assertRtolEqual(cpu_output, npu_output) - - def test_hamming_window_periodic_alpha(self, device): - shape_format = [ - [0, True,0.22, torch.float32], - [0, True,2.2, torch.float32], - [1, True, 0.22, torch.float32], - [1, True, 2.0, torch.float32], - [7, True, 0.22, torch.float32], - [7, True, 2.0, torch.float32], - [12, True, 0.22, torch.float32], - [12, True, 2.0, torch.float32], - [0, False, 0.22, torch.float32], - [0, False, 2.2, torch.float32], - [1, False, 2.0, torch.float32], - [7, False, 2.0, torch.float32], - [12, False, 1.1, torch.float32]] - for item in shape_format: - cpu_output = self.cpu_op_exec_periodic_alpha(item[0], item[1], item[2]) - npu_output = self.npu_op_exec_periodic_alpha(item[0], item[1], item[2]) - self.assertRtolEqual(cpu_output, npu_output) - - def test_hammingwindow_periodic_alpha_beta(self, device): - shape_format = [ - [0, True, 0.44, 0.22, torch.float32], - [1, True, 0.44, 0.22, torch.float32], - [7, True, 0.44, 0.22, torch.float32], - [12, True, 0.44, 0.22, torch.float32], - [0, False, 0.44, 0.22, torch.int32], - [1, False, 0.44, 0.22, torch.int32], - [7, False, 0.44, 0.22, torch.int32], - [12, False, 0.44, 0.22, torch.int32], - [7, True, 4.4, 2.2, torch.float32], - [1, True, 4.4, 2.2, torch.float32]] - for item in shape_format: - cpu_output = self.cpu_op_exec_periodic_alpha_beta(item[0], item[1], item[2], item[3]) - npu_output = self.npu_op_exec_periodic_alpha_beta(item[0], item[1], item[2], item[3]) - self.assertRtolEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(TestHammingWindow, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:6") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_hammingwindow.py b/pytorch1.8.1/test/test_npu/test_hammingwindow.py deleted file mode 100644 index e8c954add1d..00000000000 --- a/pytorch1.8.1/test/test_npu/test_hammingwindow.py +++ /dev/null @@ -1,65 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestHammingWindow(TestCase): - def test_hammingwindow(self, device): - shape_format = [ - [7, True, 0.44, 0.22, torch.float32], - [10, False, 0.44, 0.22, torch.float32]] - - for item in shape_format: - cpu_output = torch.hamming_window(item[0], item[1], item[2], item[3], dtype=item[4]).numpy() - npu_output = torch.hamming_window(item[0], item[1], item[2], item[3], dtype=item[4]).cpu().numpy() - self.assertRtolEqual(cpu_output, npu_output) - - - def generate_output_data(self, min, max, shape, dtype): - output_y = np.random.uniform(min, max, shape).astype(dtype) - npu_output_y = torch.from_numpy(output_y) - return npu_output_y - - def cpu_op_exec_out(self, window_length, periodic, alpha, beta, dtype, output_y): - output = output_y - torch.hamming_window(window_length, periodic = periodic, alpha = alpha, beta = beta, dtype = dtype, out = output_y) - output = output.numpy() - return output - - - def npu_op_exec_out(self, window_length, periodic, alpha, beta, dtype, output_y): - output = output_y.to("npu") - torch.hamming_window(window_length, periodic = periodic, alpha = alpha, beta = beta, dtype = dtype, out = output_y) - output = output.to("cpu") - output = output.numpy() - return output - - for item in shape_format: - output_shape = ((item[0]+1) if (item[1]) else item[0]) - - output_y = self.generate_output_data(0, 100, (1, output_shape), np.float32) - cpu_output = self.cpu_op_exec_out(item[0], periodic = item[1], alpha = item[2], beta = item[3], dtype = item[4], output_y = output_y).numpy() - npu_output = self.npu_op_exec_out(item[0], periodic = item[1], alpha = item[2], beta = item[3], dtype = item[4], output_y = output_y).cpu().numpy() - self.assertRtolEqual(cpu_output, npu_output) - - - -instantiate_device_type_tests(TestHammingWindow, globals(), except_for='cpu') -if __name__ == "__main__": - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_hanning_window.py b/pytorch1.8.1/test/test_npu/test_hanning_window.py deleted file mode 100644 index 30fe1d86a03..00000000000 --- a/pytorch1.8.1/test/test_npu/test_hanning_window.py +++ /dev/null @@ -1,87 +0,0 @@ -# Copyright (c) 2020 Huawei Technologies Co., Ltd - -# All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestHannWindow(TestCase): - - def cpu_op_exec(self, window_length): - output = torch.hann_window(window_length) - output = output.numpy() - return output - - def npu_op_exec(self, window_length): - output = torch.hann_window(window_length, device='npu') - output = output.to('cpu') - output = output.numpy() - return output - - def cpu_op_exec_periodic(self, window_length, periodic): - output = torch.hann_window(window_length, periodic) - output = output.numpy() - return output - - def npu_op_exec_periodic(self, window_length, periodic): - output = torch.hann_window(window_length, periodic, device='npu') - output = output.to('cpu') - output = output.numpy() - return output - - - def test_hann_window(self, device): - shape_format = [ - [0, torch.float32], - [1, torch.float32], - [7, torch.float32], - [12, torch.float32], - [0, torch.int32], - [1, torch.int32], - [7, torch.int32], - [12, torch.int32]] - for item in shape_format: - cpu_output = self.cpu_op_exec(item[0]) - npu_output = self.npu_op_exec(item[0]) - self.assertRtolEqual(cpu_output, npu_output) - - def test_hann_window_periodic(self, device): - shape_format = [ - [0, False, torch.float32], - [1, False, torch.float32], - [7, False, torch.float32], - [12, False, torch.float32], - [0, False, torch.int32], - [1, False, torch.int32], - [7, False, torch.int32], - [12, False, torch.int32]] - for item in shape_format: - cpu_output = self.cpu_op_exec_periodic(item[0], item[1]) - npu_output = self.npu_op_exec_periodic(item[0], item[1]) - self.assertRtolEqual(cpu_output, npu_output) - - - - -instantiate_device_type_tests(TestHannWindow, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:6") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_hard_sigmoid_backward.py b/pytorch1.8.1/test/test_npu/test_hard_sigmoid_backward.py deleted file mode 100644 index 816273b144a..00000000000 --- a/pytorch1.8.1/test/test_npu/test_hard_sigmoid_backward.py +++ /dev/null @@ -1,80 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -def cpu_input_grad_hook(grad): - global cpu_input_grad - cpu_input_grad = grad - -def npu_input_grad_hook(grad): - global npu_input_grad - npu_input_grad = grad.cpu() - -class TestHardSigmoidBackward(TestCase): - def generate_data(self, min_d, max_d, shape, dtype): - input_grad = np.random.uniform(min_d, max_d, shape).astype(dtype) - input_x = np.random.uniform(min_d, max_d, shape).astype(dtype) - npu_input_grad = torch.from_numpy(input_grad) - npu_input_x = torch.from_numpy(input_x) - return npu_input_grad, npu_input_x - - def cpu_op_exec(self, input_x, input_grad): - input_x.requires_grad_(True) - input_x.register_hook(cpu_input_grad_hook) - h = torch.nn.Hardsigmoid() - output = h(input_x) - output.backward(input_grad) - - def npu_op_exec(self, input_x, input_grad): - input_x = input_x.to("npu") - input_grad = input_grad.to("npu") - input_x.requires_grad_(True) - input_x.register_hook(npu_input_grad_hook) - h = torch.nn.Hardsigmoid() - output = h(input_x) - output.backward(input_grad) - - def test_hardsigmoidbackward_6_6_float32(self, device): - input_grad, input_x = self.generate_data(-6, 6, (6, 6), np.float32) - self.cpu_op_exec(input_x, input_grad) - self.npu_op_exec(input_x, input_grad) - self.assertRtolEqual(cpu_input_grad, npu_input_grad) - - def test_hardsigmoidbackward_10_10_float32(self, device): - input_grad, input_x = self.generate_data(-6, 6, (10, 10), np.float32) - self.cpu_op_exec(input_x, input_grad) - self.npu_op_exec(input_x, input_grad) - self.assertRtolEqual(cpu_input_grad, npu_input_grad) - - def test_hardsigmoidbackward_100_100_float32(self, device): - input_grad, input_x = self.generate_data(-6, 6, (100, 100), np.float32) - self.cpu_op_exec(input_x, input_grad) - self.npu_op_exec(input_x, input_grad) - self.assertRtolEqual(cpu_input_grad, npu_input_grad) - - def test_hardsigmoidbackward_10_10_10_10_float32(self, device): - input_grad, input_x = self.generate_data(-6, 6, (10, 10, 10, 10), np.float32) - self.cpu_op_exec(input_x, input_grad) - self.npu_op_exec(input_x, input_grad) - self.assertRtolEqual(cpu_input_grad, npu_input_grad) - -instantiate_device_type_tests(TestHardSigmoidBackward, globals(), except_for='cpu') -if __name__ == '__main__': - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_hardshrink.py b/pytorch1.8.1/test/test_npu/test_hardshrink.py deleted file mode 100644 index 1a4044c81af..00000000000 --- a/pytorch1.8.1/test/test_npu/test_hardshrink.py +++ /dev/null @@ -1,75 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestHardShrink(TestCase): - def generate_data(self, min_d, max_d, shape, dtype): - input_x = np.random.uniform(min_d, max_d, shape).astype(dtype) - npu_input = torch.from_numpy(input_x) - return npu_input - - def cpu_op_exec(self, input_x, lambd): - output = torch.nn.functional.hardshrink(input_x, lambd=lambd) - output = output.numpy() - return output.astype(np.float32) - - def npu_op_exec(self, input_x, lambd): - input1 = input_x.to("npu") - output = torch.nn.functional.hardshrink(input1, lambd=lambd) - output = output.to("cpu") - output = output.numpy() - return output - - def test_hardshrink_3_3_float32(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 0.5) - npu_output1 = self.npu_op_exec(input_x1, 0.5) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_hardshrink_100_100_float32(self, device): - input_x1 = self.generate_data(-1, 1, (100, 100), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 0.5) - npu_output1 = self.npu_op_exec(input_x1, 0.5) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_hardshrink_3_3_float16(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3), np.float16) - input_x1_cpu = input_x1.float() - cpu_output1 = self.cpu_op_exec(input_x1_cpu, 0.5).astype(np.float16) - npu_output1 = self.npu_op_exec(input_x1, 0.5) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_hardshrink_100_100_float16(self, device): - input_x1 = self.generate_data(-1, 1, (100, 100), np.float16) - input_x1_cpu = input_x1.float() - cpu_output1 = self.cpu_op_exec(input_x1_cpu, 0.5).astype(np.float16) - npu_output1 = self.npu_op_exec(input_x1, 0.5) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_hardshrink_10_10_10_10_float32(self, device): - input_x1 = self.generate_data(-1, 1, (10, 10, 10, 10), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 0.5) - npu_output1 = self.npu_op_exec(input_x1, 0.5) - self.assertRtolEqual(cpu_output1, npu_output1) - - -instantiate_device_type_tests(TestHardShrink, globals(), except_for='cpu') -if __name__ == "__main__": - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_hardshrink_backward.py b/pytorch1.8.1/test/test_npu/test_hardshrink_backward.py deleted file mode 100644 index 1842c1d890c..00000000000 --- a/pytorch1.8.1/test/test_npu/test_hardshrink_backward.py +++ /dev/null @@ -1,77 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -cpu_input_grad=None -npu_input_grad=None - -def cpu_input_grad_hook(grad): - global cpu_input_grad - cpu_input_grad = grad.numpy() - -def npu_input_grad_hook(grad): - global npu_input_grad - npu_input_grad = grad.cpu().numpy() - -class TestHardShrinkBackward(TestCase): - def generate_data(self, min_d, max_d, shape, dtype): - input_grad = np.random.uniform(min_d, max_d, shape).astype(dtype) - input_x = np.random.uniform(min_d, max_d, shape).astype(dtype) - npu_input_grad = torch.from_numpy(input_grad) - npu_input_x = torch.from_numpy(input_x) - return npu_input_grad, npu_input_x - - def cpu_op_exec(self, input_x, input_grad, lambd): - input_x.requires_grad_(True) - input_x.register_hook(cpu_input_grad_hook) - m = torch.nn.Hardshrink(lambd=lambd) - output = m(input_x) - output.backward(input_grad) - - def npu_op_exec(self, input_x, input_grad, lambd): - input_x = input_x.to("npu") - input_grad = input_grad.to("npu") - input_x.requires_grad_(True) - input_x.register_hook(npu_input_grad_hook) - m = torch.nn.Hardshrink(lambd=lambd).npu() - output = m(input_x) - output.backward(input_grad) - - def test_hardshrink_3_3_float32(self, device): - input_grad, input_x = self.generate_data(-1, 1, (3, 3), np.float32) - self.cpu_op_exec(input_x, input_grad, 0.5) - self.npu_op_exec(input_x, input_grad, 0.5) - self.assertRtolEqual(cpu_input_grad, npu_input_grad) - - def test_hardshrink_100_100_float32(self, device): - input_grad, input_x = self.generate_data(-1, 1, (100, 100), np.float32) - self.cpu_op_exec(input_x, input_grad, 0.5) - self.npu_op_exec(input_x, input_grad, 0.5) - self.assertRtolEqual(cpu_input_grad, npu_input_grad) - - def test_hardshrink_10_10_10_10_float32(self, device): - input_grad, input_x = self.generate_data(-1, 1, (10, 10, 10, 10), np.float32) - self.cpu_op_exec(input_x, input_grad, 0.5) - self.npu_op_exec(input_x, input_grad, 0.5) - self.assertRtolEqual(cpu_input_grad, npu_input_grad) - -instantiate_device_type_tests(TestHardShrinkBackward, globals(), except_for='cpu') -if __name__ == "__main__": - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_hinge_embedding_loss.py b/pytorch1.8.1/test/test_npu/test_hinge_embedding_loss.py deleted file mode 100644 index 222333ccd11..00000000000 --- a/pytorch1.8.1/test/test_npu/test_hinge_embedding_loss.py +++ /dev/null @@ -1,108 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -import torch.nn as nn -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestHingeEmbeddingLoss(TestCase): - def generate_data(self, min_val, max_val, shape, dtype): - x = np.random.uniform(min_val, max_val, shape).astype(dtype) - x = torch.from_numpy(x) - return x - - def op_exec_cpu(self, input1, target, margin, reduction): - cpu_output = torch.hinge_embedding_loss(input1, target, margin, reduction) - cpu_output = cpu_output.numpy() - return cpu_output - - def op_exec_npu(self, input1, target, margin, reduction): - input1 = input1.to("npu") - target = target.to("npu") - npu_output = torch.hinge_embedding_loss(input1, target, margin, reduction) - npu_output = npu_output.to("cpu") - npu_output = npu_output.numpy() - return npu_output - - def test_hinge_embedding_loss_float32_mean(self, device): - input1 = self.generate_data(0, 2, (5, 3), np.float32) - target = self.generate_data(0, 2, (5, 3), np.int32) - target[target < 1] = -1 - cpu_input1 = copy.deepcopy(input1) - cpu_target = copy.deepcopy(target) - margin = 1.0 - reduction = 1 - cpu_output = self.op_exec_cpu(cpu_input1, cpu_target, margin, reduction) - npu_output = self.op_exec_npu(input1, target, margin, reduction) - self.assertRtolEqual(cpu_output, npu_output) - - def test_hinge_embedding_loss_float32_none(self, device): - input1 = self.generate_data(0, 2, (5, 3), np.float32) - target = self.generate_data(0, 2, (5, 3), np.int32) - target[target < 1] = -1 - cpu_input1 = copy.deepcopy(input1) - cpu_target = copy.deepcopy(target) - margin = 1.0 - reduction = 0 - cpu_output = self.op_exec_cpu(cpu_input1, cpu_target, margin, reduction) - npu_output = self.op_exec_npu(input1, target, margin, reduction) - self.assertRtolEqual(cpu_output, npu_output) - - def test_hinge_embedding_loss_float32_sum(self, device): - input1 = self.generate_data(0, 2, (5, 3), np.float32) - target = self.generate_data(0, 2, (5, 3), np.int32) - target[target < 1] = -1 - cpu_input1 = copy.deepcopy(input1) - cpu_target = copy.deepcopy(target) - margin = 1.2 - reduction = 2 - cpu_output = self.op_exec_cpu(cpu_input1, cpu_target, margin, reduction) - npu_output = self.op_exec_npu(input1, target, margin, reduction) - self.assertRtolEqual(cpu_output, npu_output) - - def test_hinge_embedding_loss_float16_mean(self, device): - input1 = self.generate_data(-2, 2, (5, 3), np.float16) - target = self.generate_data(0, 2, (5, 3), np.int32) - target[target < 1] = -1 - cpu_input1 = copy.deepcopy(input1) - cpu_input1 = cpu_input1.float() - cpu_target = copy.deepcopy(target) - margin = 1.0 - reduction = 1 - cpu_output = self.op_exec_cpu(cpu_input1, cpu_target, margin, reduction).astype(np.float16) - npu_output = self.op_exec_npu(input1, target, margin, reduction) - self.assertRtolEqual(cpu_output, npu_output) - - def test_hinge_embedding_loss_int32_sum(self, device): - input1 = self.generate_data(-2, 2, (5, 3), np.int32) - target = self.generate_data(0, 2, (5, 3), np.int32) - target[target < 1] = -1 - cpu_input1 = copy.deepcopy(input1) - cpu_target = copy.deepcopy(target) - margin = 1.2 - reduction = 2 - cpu_output = self.op_exec_cpu(cpu_input1, cpu_target, margin, reduction).astype(np.int32) - npu_output = self.op_exec_npu(input1, target, margin, reduction) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestHingeEmbeddingLoss, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:1") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_index_fill_d.py b/pytorch1.8.1/test/test_npu/test_index_fill_d.py deleted file mode 100644 index dd832012e22..00000000000 --- a/pytorch1.8.1/test/test_npu/test_index_fill_d.py +++ /dev/null @@ -1,185 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestIndexFillD(TestCase): - - def generate_x_data(self, min_d, max_d, shape, dtype): - input1 = np.random.uniform(min_d, max_d, shape).astype(dtype) - npu_input1 = torch.from_numpy(input1) - return npu_input1 - - # cpu - def cpu_op_exec(self, x, dim, index, value): - output = torch.index_fill(x, dim, index, value) - output = output.numpy() - return output - - def cpu_op_exec_fp16(self, x, dim, index, value): - x = x.to(torch.float32) - output = torch.index_fill(x, dim, index, value) - output = output.numpy().astype(np.float16) - return output - - # torch.index_fill(input, dim, index, value) - # value is scalsr type or tensor type - def npu_op_exec_interface1(self, x, dim, index, value): - x = x.to("npu") - index = index.to("npu") - if type(value) == torch.Tensor: - value = value.to("npu") - output = torch.index_fill(x, dim, index, value) - output = output.to("cpu").numpy() - return output - # input.index_fill(dim, index, value) - # value is scalsr type or tensor type - def npu_op_exec_interface2(self, x, dim, index, value): - x = x.to("npu") - index = index.to("npu") - if type(value) == torch.Tensor: - value = value.to("npu") - output = x.index_fill(dim, index, value) - output = output.to("cpu").numpy() - return output - - # input.index_fill_(dim, index, value) - # value is scalsr type or tensor type - def npu_op_exec_interface3(self, x, dim, index, value): - x = x.to("npu") - index = index.to("npu") - if type(value) == torch.Tensor: - value = value.to("npu") - x.index_fill_(dim, index, value) - output = x.to("cpu").numpy() - return output - - def index_fill(self, testcases, value, dtype = "fp32"): - for i, item in enumerate(testcases): - index = torch.LongTensor(item[4]) - # testcase(s) for interface1 - npuinput_x1 = self.generate_x_data(item[0], item[1], item[2], item[5]) - if dtype == "fp16": - cpu_output1_fp16 = self.cpu_op_exec_fp16(npuinput_x1, item[3], index, value) - npu_output1 = self.npu_op_exec_interface1(npuinput_x1, item[3], index, value) - self.assertRtolEqual(cpu_output1_fp16, npu_output1) - else: - cpu_output1 = self.cpu_op_exec(npuinput_x1, item[3], index, value) - npu_output1 = self.npu_op_exec_interface1(npuinput_x1, item[3], index, value) - self.assertRtolEqual(cpu_output1, npu_output1) - - # testcase(s) for interface2 - npuinput_x2 = self.generate_x_data(item[0], item[1], item[2], item[5]) - if dtype == "fp16": - cpu_output2_fp16 = self.cpu_op_exec_fp16(npuinput_x2, item[3], index, value) - npu_output2 = self.npu_op_exec_interface2(npuinput_x2, item[3], index, value) - self.assertRtolEqual(cpu_output2_fp16, npu_output2) - else: - cpu_output2 = self.cpu_op_exec(npuinput_x2, item[3], index, value) - npu_output2 = self.npu_op_exec_interface2(npuinput_x2, item[3], index, value) - self.assertRtolEqual(cpu_output2, npu_output2) - - # testcase(s) for interface2 - npuinput_x3 = self.generate_x_data(item[0], item[1], item[2], item[5]) - if dtype == "fp16": - cpu_output3_fp16 = self.cpu_op_exec_fp16(npuinput_x3, item[3], index, value) - npu_output3 = self.npu_op_exec_interface3(npuinput_x3, item[3], index, value) - self.assertRtolEqual(cpu_output3_fp16, npu_output3) - else: - cpu_output3 = self.cpu_op_exec(npuinput_x3, item[3], index, value) - npu_output3 = self.npu_op_exec_interface3(npuinput_x3, item[3], index, value) - self.assertRtolEqual(cpu_output3, npu_output3) - - - #pylint: disable=unused-argument - def test_index_fill_d(self, device): - - testcases = [ #minV, maxV, shape, dim, index, dtype - # fp32 - [-10, 10, (2, 2, 3, 0), 1, [0, 1], np.float32], # spical case - [-10, 10, (2, 2, 3, 3), 1, [0, 1], np.float32], - [-10, 10, (2,), 0, [0, 1], np.float32], - [-100, 100, (2, 4, 6, 8, 10, 12), 0, [0, 1], np.float32], - [-0.000030517578125, 0.000030517578125, (2,32,149,31), 0, [0, 1], np.float32], - [-3402823500.0, 3402823500.0, (2,32,149,31), 0, [0, 1], np.float32], - [-100, 100, (65535, 2, 2, 2, 2, 2), 0, [0, 1, 10, 20], np.float32], - [-100, 100, (2, 65535, 2, 2, 2, 2), 0, [0, 1], np.float32], - [-100, 100, (2, 2, 65535, 2, 2, 2), 0, [0, 1], np.float32], - [-100, 100, (2, 2, 2, 65535, 2, 2), 0, [0, 1], np.float32], - [-100, 100, (2, 2, 2, 2, 65535, 2), 0, [0, 1], np.float32], - [-100, 100, (2, 2, 2, 2, 2, 65535), 0, [0, 1], np.float32], - # int32 - [-10, 10, (2, 2, 3, 0), 1, [0, 1], np.int32], # spical case - [-10, 10, (2, 2, 3, 3), 1, [0, 1], np.int32], - [-10, 10, (2,), 0, [0, 1], np.int32], - [-100, 100, (2, 4, 6, 8, 10, 12), 0, [0, 1], np.int32], - [-3402823500, 3402823500, (2,32,149,31), 0, [0, 1], np.int32], - [-100, 100, (65535, 2, 2, 2, 2, 2), 0, [0, 1, 10, 20], np.int32], - [-100, 100, (2, 65535, 2, 2, 2, 2), 0, [0, 1], np.int32], - [-100, 100, (2, 2, 65535, 2, 2, 2), 0, [0, 1], np.int32], - [-100, 100, (2, 2, 2, 65535, 2, 2), 0, [0, 1], np.int32], - [-100, 100, (2, 2, 2, 2, 65535, 2), 0, [0, 1], np.int32], - [-100, 100, (2, 2, 2, 2, 2, 65535), 0, [0, 1], np.int32], - ] - - testcases_fp16 = [ #minV, maxV, shape, dim, index, dtype - # fp16 - [-10, 10, (2, 2, 3, 3), 1, [0, 1], np.float16], - [-10, 10, (2,), 0, [0, 1], np.float16], - [-100, 100, (2, 4, 6, 8, 10, 12), 0, [0, 1], np.float16], - [-60000, 60000, (2,32,149,31), 0, [0, 1], np.float16], - [-100, 100, (65535, 2, 2, 2, 2, 2), 0, [0, 1, 10, 20], np.float16], - [-100, 100, (2, 65535, 2, 2, 2, 2), 0, [0, 1], np.float16], - [-100, 100, (2, 2, 65535, 2, 2, 2), 0, [0, 1], np.float16], - [-100, 100, (2, 2, 2, 65535, 2, 2), 0, [0, 1], np.float16], - [-100, 100, (2, 2, 2, 2, 65535, 2), 0, [0, 1], np.float16], - [-100, 100, (2, 2, 2, 2, 2, 65535), 0, [0, 1], np.float16], - ] - - # Test three interfaces for fp32, int32, fp16 with scalar value. - # Example. - # input = torch.randn(3, 3, 4) - # index = torch.LongTensor([1, 2]) - # value = 5 - # 1. output = torch.index_fill(input, dim, index, value) Out-Place - # 2. output = input.index_fill(dim, index, value) Out-Place - # 3. inpue.index_fill_(dim, index, value) In-Place - - value = np.random.uniform(-10000, 10000) - self.index_fill(testcases=testcases, value=value) - self.index_fill(testcases=testcases_fp16, value=value, dtype="fp16") - - # Test three interfaces for fp32, int32, fp16 with tensor value. - # Example. - # input = torch.randn(3, 3, 4) - # index = torch.LongTensor([1, 2]) - # value = torch.tensor(5) - # 1. output = torch.index_fill(input, dim, index, value) Out-Place - # 2. output = input.index_fill(dim, index, value) Out-Place - # 3. inpue.index_fill_(dim, index, value) In-Place - value_tensor = torch.tensor(value) - self.index_fill(testcases=testcases, value=value_tensor) - self.index_fill(testcases=testcases_fp16, value=value_tensor, dtype="fp16") - -instantiate_device_type_tests(TestIndexFillD, globals(), except_for='cpu') -if __name__ == "__main__": - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_index_select.py b/pytorch1.8.1/test/test_npu/test_index_select.py deleted file mode 100644 index 84b49d4594b..00000000000 --- a/pytorch1.8.1/test/test_npu/test_index_select.py +++ /dev/null @@ -1,127 +0,0 @@ -# Copyright (c) 2020 Huawei Technologies Co., Ltd -# Copyright (c) 2019, Facebook CORPORATION. -# All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestIndexSelect(TestCase): - - def cpu_op_exec(self, input, axis, indices): - '''the shape of input:float16, float32,int8,uint8,int32,uint32,int16,uint16,int64,uint64,''' - output = torch.index_select(input, dim=axis, index=indices) - output = output.numpy() - return output - - def npu_op_exec(self, input, axis, indices): - output = torch.index_select(input, dim=axis, index=indices) - output = output.to('cpu') - output = output.numpy() - return output - - def cpu_op_out_exec(self, input, axis, indices, output): - '''the shape of input:float16, float32,int8,uint8,int32,uint32,int16,uint16,int64,uint64,''' - torch.index_select(input, dim=axis, index=indices,out=output) - output = output.numpy() - return output - - def npu_op_out_exec(self, input, axis, indices, output): - torch.index_select(input, dim=axis, index=indices, out=output) - output = output.to('cpu') - output = output.numpy() - return output - - def test_index_select(self, device): - shape_format = [ - [[np.float32, 0, (3, )], torch.tensor([0, 1], dtype=torch.int64), 0], - [[np.float32, 0, (2, 4)], torch.tensor([0, 1, 2], dtype=torch.int64), 1], - [[np.float32, 0, (3, 4, 6)], torch.tensor([1, 2, 4], dtype=torch.int64), 2], - [[np.float32, 3, (4, 5, 6, 7)], torch.tensor([3, 5, 6], dtype=torch.int64), 3], - [[np.float32, -1, (3, 4, 8, 9, 12)], torch.tensor([2, 3, 5, 6], dtype=torch.int64), 4], - - [[np.int8, 0, (3,)], torch.tensor([0, 1], dtype=torch.int64), 0], - [[np.int8, 0, (2, 4)], torch.tensor([0, 1, 2], dtype=torch.int64), 1], - [[np.int8, 0, (3, 4, 6)], torch.tensor([1, 2, 4], dtype=torch.int64), 2], - [[np.int8, 0, (4, 5, 6, 7)], torch.tensor([3, 5, 6], dtype=torch.int64), 3], - [[np.int8, -1, (3, 4, 8, 9, 12)], torch.tensor([2, 3, 5, 6], dtype=torch.int64), 4], - - [[np.uint8, 0, (3,)], torch.tensor([0, 1], dtype=torch.int64), 0], - [[np.uint8, 0, (2, 4)], torch.tensor([0, 1, 2], dtype=torch.int64), 1], - [[np.uint8, 0, (3, 4, 6)], torch.tensor([1, 2, 4], dtype=torch.int64), 2], - [[np.uint8, 0, (4, 5, 6, 7)], torch.tensor([3, 5, 6], dtype=torch.int64), 3], - [[np.uint8, -1, (3, 4, 8, 9, 12)], torch.tensor([2, 3, 5, 6], dtype=torch.int64), 4], - - [[np.int32, 0, (3,)], torch.tensor([0, 1], dtype=torch.int64), 0], - [[np.int32, 0, (2, 4)], torch.tensor([0, 1, 2], dtype=torch.int64), 1], - [[np.int32, 0, (3, 4, 6)], torch.tensor([1, 2, 4], dtype=torch.int64), 2], - [[np.int32, 0, (4, 5, 6, 7)], torch.tensor([3, 5, 6], dtype=torch.int64), 3], - [[np.int32, -1, (3, 4, 8, 9, 12)], torch.tensor([2, 3, 5, 6], dtype=torch.int64), 4], - - [[np.uint8, 0, (3,)], torch.tensor([0, 1], dtype=torch.int64), 0], - [[np.uint8, 0, (2, 4)], torch.tensor([0, 1, 2], dtype=torch.int64), 1], - [[np.uint8, 0, (3, 4, 6)], torch.tensor([1, 2, 4], dtype=torch.int64), 2], - [[np.uint8, 0, (4, 5, 6, 7)], torch.tensor([3, 5, 6], dtype=torch.int64), 3], - [[np.uint8, -1, (3, 4, 8, 9, 12)], torch.tensor([2, 3, 5, 6], dtype=torch.int64), 4], - - [[np.uint8, 0, (3,)], torch.tensor([0, 1], dtype=torch.int64), 0], - [[np.uint8, 0, (2, 4)], torch.tensor([0, 1, 2], dtype=torch.int64), 1], - [[np.uint8, 0, (3, 4, 6)], torch.tensor([1, 2, 4], dtype=torch.int64), 2], - [[np.uint8, 0, (4, 5, 6, 7)], torch.tensor([3, 5, 6], dtype=torch.int64), 3], - [[np.uint8, -1, (3, 4, 8, 9, 12)], torch.tensor([2, 3, 5, 6], dtype=torch.int64), 4], - - [[np.int16, 0, (3,)], torch.tensor([0, 1], dtype=torch.int64), 0], - [[np.int16, 0, (2, 4)], torch.tensor([0, 1, 2], dtype=torch.int64), 1], - [[np.int16, 0, (3, 4, 6)], torch.tensor([1, 2, 4], dtype=torch.int64), 2], - [[np.int16, 0, (4, 5, 6, 7)], torch.tensor([3, 5, 6], dtype=torch.int64), 3], - [[np.int16, -1, (3, 4, 8, 9, 12)], torch.tensor([2, 3, 5, 6], dtype=torch.int64), 4], - - ] - for item in shape_format: - input1, npu_input = create_common_tensor(item[0], 1, 100) - - cpu_output = self.cpu_op_exec(input1, item[2], item[1]) - npu_output = self.npu_op_exec(npu_input, item[2], item[1].to('npu')) - self.assertRtolEqual(cpu_output, npu_output) - - - def test_index_select_fp16(self, device): - shape_format = [ - [[np.float16, 0, (3,)], torch.tensor([0, 1], dtype=torch.int64), 0], - [[np.float16, 0, (2, 4)], torch.tensor([0, 1, 2], dtype=torch.int64), 1], - [[np.float16, 0, (3, 4, 6)], torch.tensor([1, 2, 4], dtype=torch.int64), 2], - [[np.float16, 3, (4, 5, 6, 7)], torch.tensor([3, 5, 6], dtype=torch.int64), 3], - [[np.float16, -1, (3, 4, 8, 9, 12)], torch.tensor([2, 3, 5, 6], dtype=torch.int64), 4], - - ] - for item in shape_format: - input1, npu_input = create_common_tensor(item[0], 1, 100) - - input1 = input1.to(torch.float32) - - cpu_output = self.cpu_op_exec(input1, item[2], item[1]) - npu_output = self.npu_op_exec(npu_input, item[2], item[1].to('npu')) - - cpu_output = cpu_output.astype(np.float16) - - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestIndexSelect, globals(), except_for="cpu") -if __name__ == "__main__": - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_isclose.py b/pytorch1.8.1/test/test_npu/test_isclose.py deleted file mode 100644 index b210c1db2c5..00000000000 --- a/pytorch1.8.1/test/test_npu/test_isclose.py +++ /dev/null @@ -1,189 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestIsclose(TestCase): - - def generate_data(self, min_d, max_d, shape, dtype): - input1 = np.random.uniform(min_d, max_d, shape).astype(dtype) - input2 = np.random.uniform(min_d, max_d, shape).astype(dtype) - - #modify from numpy.ndarray to torch.tensor - npu_input1 = torch.from_numpy(input1) - npu_input2 = torch.from_numpy(input2) - - return npu_input1, npu_input2 - - def generate_nan(self, shape, dtype): - input1 = np.full(shape, np.nan).astype(dtype) - input2 = np.full(shape, np.nan).astype(dtype) - - #modify from numpy.ndarray to torch.tensor - npu_input1 = torch.from_numpy(input1) - npu_input2 = torch.from_numpy(input2) - - return npu_input1, npu_input2 - - def cpu_op_exec(self, input1, input2): - output = torch.isclose(input1, input2) - output = output.numpy() - return output - - def cpu_op_exec_rtol_atol(self, input1, input2, rtol, atol): - output = torch.isclose(input1, input2, rtol=rtol, atol=atol) - output = output.numpy() - return output - - def cpu_op_exec_equal_nan(self, input1, input2, equal_nan): - output = torch.isclose(input1, input2, equal_nan=equal_nan) - output = output.numpy() - return output - - def npu_op_exec_tensor_need_to_npu(self, input1, input2): - input1 = input1.to("npu") - input2 = input2.to("npu") - output = torch.isclose(input1, input2) - output = output.to("cpu") - output = output.numpy() - return output - - def npu_op_exec_tensor_need_to_npu_rtol_atol(self, input1, input2, rtol, atol): - input1 = input1.to("npu") - input2 = input2.to("npu") - output = torch.isclose(input1, input2, rtol=rtol, atol=atol) - output = output.to("cpu") - output = output.numpy() - return output - - def npu_op_exec_tensor_need_to_npu_equal_nan(self, input1, input2, equal_nan): - input1 = input1.to("npu") - input2 = input2.to("npu") - output = torch.isclose(input1, input2, equal_nan=equal_nan) - output = output.to("cpu") - output = output.numpy() - return output - - def test_isclose_int32_float32(self, device): - npu_input1, npu_input2 = self.generate_data(0, 100, (4,3), np.int32) - npu_input1 = npu_input1.to(torch.float32) - npu_input2 = npu_input2.to(torch.float32) - cpu_output = self.cpu_op_exec(npu_input1, npu_input2) - npu_output = self.npu_op_exec_tensor_need_to_npu(npu_input1, npu_input2) - self.assertRtolEqual(cpu_output,npu_output) - - def test_isclose_equal_nan_false(self, device): - npu_input1, npu_input2 = self.generate_nan((4,3), np.int32) - cpu_output = self.cpu_op_exec_equal_nan(npu_input1, npu_input2, False) - npu_output = self.npu_op_exec_tensor_need_to_npu_equal_nan(npu_input1, npu_input2, False) - self.assertRtolEqual(cpu_output,npu_output) - - def test_isclose_equal_nan_true(self, device): - npu_input1, npu_input2 = self.generate_nan((4,3), np.int32) - cpu_output = self.cpu_op_exec_equal_nan(npu_input1, npu_input2, True) - npu_output = self.npu_op_exec_tensor_need_to_npu_equal_nan(npu_input1, npu_input2, True) - self.assertRtolEqual(cpu_output,npu_output) - - def test_isclose_int32_001(self, device): - npu_input1, npu_input2 = self.generate_data(0, 100, (4,3), np.int32) - cpu_output = self.cpu_op_exec(npu_input1, npu_input2) - npu_output = self.npu_op_exec_tensor_need_to_npu(npu_input1, npu_input2) - self.assertRtolEqual(cpu_output,npu_output) - - def test_isclose_int32_002(self, device): - npu_input1, npu_input2 = self.generate_data(100, 100, (4,3,2), np.int32) - cpu_output = self.cpu_op_exec(npu_input1, npu_input2) - npu_output = self.npu_op_exec_tensor_need_to_npu(npu_input1, npu_input2) - self.assertRtolEqual(cpu_output,npu_output) - - def test_isclose_int32_003(self, device): - npu_input1, npu_input2 = self.generate_data(0, 100, (4,3,2), np.int32) - rtol=8e-05 - atol=8e-08 - cpu_output = self.cpu_op_exec_rtol_atol(npu_input1, npu_input2, rtol, atol) - npu_output = self.npu_op_exec_tensor_need_to_npu_rtol_atol(npu_input1, npu_input2, rtol, atol) - self.assertRtolEqual(cpu_output,npu_output) - - def test_isclose_float32_001(self, device): - npu_input1, npu_input2 = self.generate_data(100, 100, (4,3), np.float32) - cpu_output = self.cpu_op_exec(npu_input1, npu_input2) - npu_output = self.npu_op_exec_tensor_need_to_npu(npu_input1, npu_input2) - self.assertRtolEqual(cpu_output,npu_output) - - def test_isclose_float32_002(self, device): - npu_input1, npu_input2 = self.generate_data(0, 100, (4,3,2), np.float32) - cpu_output = self.cpu_op_exec(npu_input1, npu_input2) - npu_output = self.npu_op_exec_tensor_need_to_npu(npu_input1, npu_input2) - self.assertRtolEqual(cpu_output,npu_output) - - def test_isclose_float32_003(self, device): - npu_input1, npu_input2 = self.generate_data(0, 100, (4,3,2), np.float32) - rtol=8e-05 - atol=8e-08 - cpu_output = self.cpu_op_exec_rtol_atol(npu_input1, npu_input2, rtol, atol) - npu_output = self.npu_op_exec_tensor_need_to_npu_rtol_atol(npu_input1, npu_input2, rtol, atol) - self.assertRtolEqual(cpu_output,npu_output) - - def test_isclose_float16_001(self, device): - def cpu_op_exec_fp16(input1, input2): - input1 = input1.to(torch.float32) - input2 = input2.to(torch.float32) - output = torch.isclose(input1, input2) - output = output.numpy() - return output - - npu_input1, npu_input2 = self.generate_data(0, 100, (5,3), np.float16) - cpu_output = cpu_op_exec_fp16(npu_input1, npu_input2) - npu_output = self.npu_op_exec_tensor_need_to_npu(npu_input1, npu_input2) - cpu_output = cpu_output.astype(npu_output.dtype) - self.assertRtolEqual(cpu_output, npu_output) - - def test_isclose_float16_002(self, device): - def cpu_op_exec_fp16(input1, input2): - input1 = input1.to(torch.float32) - input2 = input2.to(torch.float32) - output = torch.isclose(input1, input2) - output = output.numpy() - return output - - npu_input1, npu_input2 = self.generate_data(100, 100, (5,3,2), np.float16) - cpu_output = cpu_op_exec_fp16(npu_input1, npu_input2) - npu_output = self.npu_op_exec_tensor_need_to_npu(npu_input1, npu_input2) - cpu_output = cpu_output.astype(npu_output.dtype) - self.assertRtolEqual(cpu_output, npu_output) - - def test_isclose_float16_003(self, device): - def cpu_op_exec_fp16_rtol_atol(input1, input2, rtol, atol): - input1 = input1.to(torch.float32) - input2 = input2.to(torch.float32) - output = torch.isclose(input1, input2, rtol=rtol, atol=atol) - output = output.numpy() - return output - npu_input1, npu_input2 = self.generate_data(0, 100, (4,3,2), np.float16) - rtol=8e-05 - atol=8e-08 - cpu_output = cpu_op_exec_fp16_rtol_atol(npu_input1, npu_input2, rtol, atol) - npu_output = self.npu_op_exec_tensor_need_to_npu_rtol_atol(npu_input1, npu_input2, rtol, atol) - cpu_output = cpu_output.astype(npu_output.dtype) - self.assertRtolEqual(cpu_output,npu_output) -instantiate_device_type_tests(TestIsclose, globals(), except_for='cpu') -if __name__ == "__main__": - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_kthvalue.py b/pytorch1.8.1/test/test_npu/test_kthvalue.py deleted file mode 100644 index 56841fc05d4..00000000000 --- a/pytorch1.8.1/test/test_npu/test_kthvalue.py +++ /dev/null @@ -1,183 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import random -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestKthvalues(TestCase): - def generate_data(self, min, max, shape, dtype): - if dtype == np.float32: - x = np.random.uniform(min, max, shape).astype(np.float16) - x = x.astype(np.float32) - npu_x = torch.from_numpy(x) - return npu_x - x = np.random.uniform(min, max, shape).astype(dtype) - npu_x = torch.from_numpy(x) - return npu_x - - def generate_int_k(self, max): - k = np.random.randint(1, max + 1) - return k - - def generate_int_dim(self, max): - dim = np.random.randint(-max, max) - return dim - - def generate_bool_keepdim(self): - keepdim = random.choice([True, False]) - return keepdim - - def cpu_op_exec(self, x, k, dim, keepdim): - y, indices = torch.kthvalue(x, k, dim, keepdim) - y = y.numpy() - indices = indices.numpy() - return y, indices - - def npu_op_exec(self, x, k, dim, keepdim): - x = x.to("npu") - y, indices = torch.kthvalue(x, k, dim, keepdim) - y = y.to("cpu") - y = y.numpy() - indices = indices.to("cpu") - indices = indices.numpy() - return y, indices - - def cpu_op_exec_without_dim(self, x, k, keepdim): - y, indices = torch.kthvalue(x, k, keepdim=keepdim) - y = y.numpy() - indices = indices.numpy() - return y, indices - - def npu_op_exec_without_dim(self, x, k, keepdim): - x = x.to("npu") - y, indices = torch.kthvalue(x, k, keepdim=keepdim) - y = y.to("cpu") - y = y.numpy() - indices = indices.to("cpu") - indices = indices.numpy() - return y, indices - - def cpu_op_exec_without_keepdim(self, x, k, dim): - y, indices = torch.kthvalue(x, k, dim=dim) - y = y.numpy() - indices = indices.numpy() - return y, indices - - def npu_op_exec_without_keepdim(self, x, k, dim): - x = x.to("npu") - y, indices = torch.kthvalue(x, k, dim=dim) - y = y.to("cpu") - y = y.numpy() - indices = indices.to("cpu") - indices = indices.numpy() - return y, indices - - def test_kthvalues(self, device): - x = self.generate_data(-100, 100, (3, 4, 5, 6), np.float32) - k = self.generate_int_k(3) - dim = self.generate_int_dim(4) - keepdim = self.generate_bool_keepdim() - cpu_y, cpu_indices = self.cpu_op_exec(x, k, dim, keepdim) - npu_y, npu_indices = self.npu_op_exec(x, k, dim, keepdim) - self.assertRtolEqual(cpu_y, npu_y) - self.assertRtolEqual(cpu_indices.astype(np.int32), npu_indices.astype(np.int32)) - - def test_kthvalues_without_dim(self, device): - x = self.generate_data(-100, 100, (3, 4, 5, 6), np.int32) - k = self.generate_int_k(3) - keepdim = self.generate_bool_keepdim() - cpu_y, cpu_indices = self.cpu_op_exec_without_dim(x, k, keepdim) - npu_y, npu_indices = self.npu_op_exec_without_dim(x, k, keepdim) - self.assertRtolEqual(cpu_y, npu_y) - self.assertRtolEqual(cpu_indices.astype(np.int32), npu_indices.astype(np.int32)) - - def test_kthvalues_without_keepdim(self, device): - x = self.generate_data(-100, 100, (3, 4, 5, 6), np.float16) - k = self.generate_int_k(3) - dim = self.generate_int_dim(4) - cpu_y, cpu_indices = self.cpu_op_exec_without_keepdim(x.float(), k, dim) - npu_y, npu_indices = self.npu_op_exec_without_keepdim(x, k, dim) - self.assertRtolEqual(cpu_y.astype(np.float16), npu_y) - self.assertRtolEqual(cpu_indices.astype(np.int32), npu_indices.astype(np.int32)) - - def test_kthvalues_out(self, device): - x = self.generate_data(-100, 100, (3, 4, 5, 6), np.float32) - k = self.generate_int_k(3) - dim = self.generate_int_dim(4) - keepdim = self.generate_bool_keepdim() - cpu_y = torch.tensor(0.).float() - cpu_indices = torch.tensor(0) - npu_y = torch.tensor(0.).float().to("npu") - npu_indices = torch.tensor(0).long().to("npu") - torch.kthvalue(x, k, dim, keepdim, out=(cpu_y, cpu_indices)) - torch.kthvalue(x.to("npu"), k, dim, keepdim, out=(npu_y, npu_indices)) - self.assertRtolEqual(cpu_y.numpy(), npu_y.to("cpu").numpy()) - self.assertRtolEqual(cpu_indices.numpy().astype(np.int32), npu_indices.to("cpu").numpy().astype(np.int32)) - - def test_kthvalues_dimname(self, device): - x = self.generate_data(-100, 100, (3, 4, 5, 6), np.float32) - x.names = ['A', 'B', 'C', 'D'] - k = self.generate_int_k(3) - keepdim = self.generate_bool_keepdim() - cpu_y, cpu_indices = self.cpu_op_exec(x, k, 'B', keepdim) - npu_y, npu_indices = self.npu_op_exec(x, k, 'B', keepdim) - self.assertRtolEqual(cpu_y, npu_y) - self.assertRtolEqual(cpu_indices.astype(np.int32), npu_indices.astype(np.int32)) - - def test_kthvalues_dimname_without_dim(self, device): - x = self.generate_data(-100, 100, (3, 4, 5, 6), np.int32) - x.names = ['A', 'B', 'C', 'D'] - k = self.generate_int_k(3) - keepdim = self.generate_bool_keepdim() - cpu_y, cpu_indices = self.cpu_op_exec_without_dim(x, k, keepdim) - npu_y, npu_indices = self.npu_op_exec_without_dim(x, k, keepdim) - self.assertRtolEqual(cpu_y, npu_y) - self.assertRtolEqual(cpu_indices.astype(np.int32), npu_indices.astype(np.int32)) - - def test_kthvalues_dimname_without_keepdim(self, device): - x = self.generate_data(-100, 100, (3, 4, 5, 6), np.float32) - x.names = ['A', 'B', 'C', 'D'] - k = self.generate_int_k(3) - cpu_y, cpu_indices = self.cpu_op_exec_without_keepdim(x, k, 'B') - npu_y, npu_indices = self.npu_op_exec_without_keepdim(x, k, 'B') - self.assertRtolEqual(cpu_y, npu_y) - self.assertRtolEqual(cpu_indices.astype(np.int32), npu_indices.astype(np.int32)) - - def test_kthvalues_dimname_out(self, device): - x = self.generate_data(-100, 100, (3, 4, 5, 6), np.int32) - x.names = ['A', 'B', 'C', 'D'] - k = self.generate_int_k(3) - dim = 'C' - keepdim = self.generate_bool_keepdim() - cpu_y = torch.tensor(0).int() - cpu_indices = torch.tensor(0) - npu_y = torch.tensor(0).int().to("npu") - npu_indices = torch.tensor(0).long().to("npu") - torch.kthvalue(x, k, dim, keepdim, out=(cpu_y, cpu_indices)) - torch.kthvalue(x.to("npu"), k, dim, keepdim, out=(npu_y, npu_indices)) - self.assertRtolEqual(cpu_y.numpy(), npu_y.to("cpu").numpy()) - self.assertRtolEqual(cpu_indices.numpy().astype(np.int32), npu_indices.to("cpu").numpy().astype(np.int32)) - -instantiate_device_type_tests(TestKthvalues, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:0") - run_tests() - - diff --git a/pytorch1.8.1/test/test_npu/test_lerp.py b/pytorch1.8.1/test/test_npu/test_lerp.py deleted file mode 100644 index fc577185b04..00000000000 --- a/pytorch1.8.1/test/test_npu/test_lerp.py +++ /dev/null @@ -1,225 +0,0 @@ -# Copyright 2020 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor -import random -import math - -class TestLerp(TestCase): -# pylint: disable=unused-variable,unused-argument - - def cpu_op_exec(self, input1, input2, input3): - output = torch.lerp(input1,input2,input3) - output = output.numpy() - return output - - def npu_op_exec(self, input1, input2, input3): - output = torch.lerp(input1, input2, input3) - output = output.to("cpu") - output = output.numpy() - return output - - def cpu_op_out_exec(self, input1, input2, input3): - output = torch.ones_like(input1) - torch.lerp(input1,input2,input3, out = output) - output = output.numpy() - return output - - def npu_op_out_exec(self, input1, input2, input3): - output = torch.ones_like(input1) - torch.lerp(input1, input2, input3, out = output) - output = output.to("cpu") - output = output.numpy() - return output - - def cpu_op_scalar_out_exec(self, input1, input2, input3): - output = torch.ones_like(input1) - torch.lerp(input1,input2,input3, out = output) - output = output.numpy() - return output - - def npu_op_scalar_out_exec(self, input1, input2, input3): - output = torch.ones_like(input1) - torch.lerp(input1, input2, input3, out = output) - output = output.to("cpu") - output = output.numpy() - return output - - - def test_lerp_common_shape_format(self, device): - shape_format = [ - [[np.float32, -1, (4, 2, 2, 3)]], - [[np.float32, -1, (2, 2, 3, 4)]], - [[np.float32, -1, (3, 3, 3)]], - [[np.float32, -1, (4, 4, 4)]] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100) - cpu_input2, npu_input2 = create_common_tensor(item[0], 1, 100) - cpu_input3, npu_input3 = create_common_tensor(item[0], 1, 100) - cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2, cpu_input3) - npu_output = self.npu_op_exec(npu_input1, npu_input2, npu_input3) - self.assertRtolEqual(cpu_output, npu_output) - - def test_lerp_float16_shape_format(self, device): - def cpu_op_exec_fp16(input1, input2, input3): - input1 = input1.to(torch.float32) - input2 = input2.to(torch.float32) - input3 = input3.to(torch.float32) - output = torch.lerp(input1,input2,input3) - output = output.numpy() - output = output.astype(np.float16) - return output - - shape_format = [ - [[np.float16, -1, (100, 4, 5, 5)]], - [[np.float16, -1, (100, 5, 5, 4)]], - ] - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 10, 100) - cpu_input2, npu_input2 = create_common_tensor(item[0], 10, 100) - cpu_input3, npu_input3 = create_common_tensor(item[0], 10, 100) - cpu_output = cpu_op_exec_fp16(cpu_input1, cpu_input2, cpu_input3) - npu_output = self.npu_op_exec(npu_input1, npu_input2, npu_input3) - self.assertRtolEqual(cpu_output, npu_output, prec=0.003, prec16=0.003) - - - def test_lerp_out_common_shape_format(self, device): - shape_format = [ - [[np.float32, -1, (4, 2, 2, 3)]], - [[np.float32, -1, (2, 2, 3, 4)]], - [[np.float32, -1, (3, 3, 3)]], - [[np.float32, -1, (4, 4, 4)]] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100) - cpu_input2, npu_input2 = create_common_tensor(item[0], 1, 100) - cpu_input3, npu_input3 = create_common_tensor(item[0], 1, 100) - cpu_output = self.cpu_op_out_exec(cpu_input1, cpu_input2, cpu_input3) - npu_output = self.npu_op_out_exec(npu_input1, npu_input2, npu_input3) - self.assertRtolEqual(cpu_output, npu_output) - - def test_lerp_out_float16_shape_format(self, device): - def cpu_op_out_exec_fp16(input1, input2, input3): - input1 = input1.to(torch.float32) - input2 = input2.to(torch.float32) - input3 = input3.to(torch.float32) - output = torch.ones_like(input1) - torch.lerp(input1,input2,input3, out = output) - output = output.numpy() - output = output.astype(np.float16) - return output - - shape_format = [ - [[np.float16, -1, (100, 4, 5, 5)]], - [[np.float16, -1, (100, 5, 5, 4)]], - ] - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 10, 100) - cpu_input2, npu_input2 = create_common_tensor(item[0], 10, 100) - cpu_input3, npu_input3 = create_common_tensor(item[0], 10, 100) - cpu_output = cpu_op_out_exec_fp16(cpu_input1, cpu_input2, cpu_input3) - npu_output = self.npu_op_out_exec(npu_input1, npu_input2, npu_input3) - self.assertRtolEqual(cpu_output, npu_output, prec=0.003, prec16=0.003) - - def test_lerp_scalar_common_shape_format(self, device): - shape_format = [ - [[np.float32, -1, (4, 2, 2, 3)], 1.0], - [[np.float32, -1, (2, 2, 3, 4)], 2.0], - [[np.float32, -1, (3, 3, 3)], 1.2], - [[np.float32, -1, (4, 4, 4)], 1.2] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100) - cpu_input2, npu_input2 = create_common_tensor(item[0], 1, 100) - cpu_input3 = item[1] - npu_input3 = item[1] - cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2, cpu_input3) - npu_output = self.npu_op_exec(npu_input1, npu_input2, npu_input3) - self.assertRtolEqual(cpu_output, npu_output) - - def test_lerp_scalar_float16_shape_format(self, device): - def cpu_op_scalar_exec_fp16(input1, input2, input3): - input1 = input1.to(torch.float32) - input2 = input2.to(torch.float32) - output = torch.lerp(input1,input2,input3) - output = output.numpy() - output = output.astype(np.float16) - return output - - shape_format = [ - [[np.float16, -1, (100, 4, 5, 5)], 1,2], - [[np.float16, -1, (100, 5, 5, 4)], 1.2], - ] - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 10, 100) - cpu_input2, npu_input2 = create_common_tensor(item[0], 10, 100) - cpu_input3 = item[1] - npu_input3 = item[1] - cpu_output = cpu_op_scalar_exec_fp16(cpu_input1, cpu_input2, cpu_input3) - npu_output = self.npu_op_exec(npu_input1, npu_input2, npu_input3) - self.assertRtolEqual(cpu_output, npu_output, prec=0.003, prec16=0.003) - - - def test_lerp_scalar_out_common_shape_format(self, device): - shape_format = [ - [[np.float32, -1, (4, 2, 2, 3)], 1.2], - [[np.float32, -1, (2, 2, 3, 4)],1.2], - [[np.float32, -1, (3, 3, 3)], 1.0], - [[np.float32, -1, (4, 4, 4)], 2.0] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100) - cpu_input2, npu_input2 = create_common_tensor(item[0], 1, 100) - cpu_input3, npu_input3 = create_common_tensor(item[0], 1, 100) - cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2, cpu_input3) - npu_output = self.npu_op_exec(npu_input1, npu_input2, npu_input3) - self.assertRtolEqual(cpu_output, npu_output) - - def test_lerp_scalar_out_float16_shape_format(self, device): - def cpu_op_scalar_out_exec_fp16(input1, input2, input3): - input1 = input1.to(torch.float32) - input2 = input2.to(torch.float32) - output = torch.ones_like(input1) - torch.lerp(input1,input2,input3, out = output) - output = output.numpy() - output = output.astype(np.float16) - return output - - shape_format = [ - [[np.float16, -1, (100, 4, 5, 5)], 1.2], - [[np.float16, -1, (100, 5, 5, 4)], 1.2], - ] - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 10, 100) - cpu_input2, npu_input2 = create_common_tensor(item[0], 10, 100) - cpu_input3 = item[1] - npu_input3 = item[1] - cpu_output = cpu_op_scalar_out_exec_fp16(cpu_input1, cpu_input2, cpu_input3) - npu_output = self.npu_op_scalar_out_exec(npu_input1, npu_input2, npu_input3) - self.assertRtolEqual(cpu_output, npu_output, prec=0.003, prec16=0.003) - -instantiate_device_type_tests(TestLerp, globals(), except_for='cpu') -if __name__ == '__main__': - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_linspace.py b/pytorch1.8.1/test/test_npu/test_linspace.py deleted file mode 100644 index 6568f5b9dff..00000000000 --- a/pytorch1.8.1/test/test_npu/test_linspace.py +++ /dev/null @@ -1,84 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestLinspace(TestCase): - def generate_scalar(self, dtype, min, max): - if dtype == "float32": - scalar = np.random.uniform(min, max) - if dtype == "int32": - scalar = np.random.randint(min, max) - return scalar - - def cpu_op_exec(self,start, end, steps): - output = torch.linspace(start, end, steps) - output = output.numpy() - return output - - def cpu_op_exec_out(self,start, end, steps, output): - torch.linspace(start, end, steps, out=output) - output = output.numpy() - return output - - def npu_op_exec(self, start, end, steps): - output = torch.linspace(start, end, steps=steps, device="npu") - output = output.to("cpu") - output = output.numpy() - return output - - def npu_op_exec_out(self,start, end, steps, output): - torch.linspace(start, end, steps=steps, out=output, device="npu") - output = output.to("cpu") - output = output.numpy() - return output - - def test_linspace_common_shape_format(self, device): - shape_format = [ - ["int32", 5], - ["float32", 3], - ["float32", 50], - ] - for item in shape_format: - cpu_start = npu_start = self.generate_scalar(item[0], 0, 10) - cpu_end = npu_end = self.generate_scalar(item[0], 70, 100) - steps = item[1] - cpu_output = self.cpu_op_exec(cpu_start, cpu_end, steps) - npu_output = self.npu_op_exec(cpu_start, cpu_end, steps) - self.assertRtolEqual(cpu_output, npu_output) - - def test_linspace_out_common_shape_format(self, device): - shape_format = [ - ["int32", 5, [np.float32, 0, (5)]], - ["float32", 3, [np.float32, 0, (3)]], - ] - for item in shape_format: - cpu_start = npu_start = self.generate_scalar(item[0], 0, 10) - cpu_end = npu_end = self.generate_scalar(item[0], 20, 30) - steps = item[1] - cpu_input2, npu_input2 = create_common_tensor(item[2], 0, 10) - cpu_output = self.cpu_op_exec_out(cpu_start, cpu_end, steps, cpu_input2) - npu_output = self.npu_op_exec_out(npu_start, npu_end, steps, npu_input2) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestLinspace, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:6") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_logical_not.py b/pytorch1.8.1/test/test_npu/test_logical_not.py deleted file mode 100644 index 865cdf073a6..00000000000 --- a/pytorch1.8.1/test/test_npu/test_logical_not.py +++ /dev/null @@ -1,68 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestLogicalNot(TestCase): - def cpu_op_exec(self, input): - output = torch.logical_not(input) - output = output.numpy() - return output - - def npu_op_exec(self, input): - output = torch.logical_not(input) - output = output.to("cpu") - output = output.numpy() - return output - - def test_logical_not_common_shape_format(self, device): - shape_format = [ - [[np.int8, -1, 1]], - [[np.int8, -1, (64, 10)]], - [[np.int8, -1, (256, 2048, 7, 7)]], - [[np.int8, -1, (32, 1, 3, 3)]], - [[np.int32, -1, (64, 10)]], - [[np.int32, -1, (256, 2048, 7, 7)]], - [[np.int32, -1, (32, 1, 3, 3)]], - [[np.uint8, -1, (64, 10)]], - [[np.uint8, -1, (256, 2048, 7, 7)]], - [[np.uint8, -1, (32, 1, 3, 3)]], - [[np.float16, -1, (64, 10)]], - [[np.float16, -1, (256, 2048, 7, 7)]], - [[np.float16, -1, (32, 1, 3, 3)]], - [[np.float32, -1, (64, 10)]], - [[np.float32, -1, (256, 2048, 7, 7)]], - [[np.float32, -1, (32, 1, 3, 3)]], - [[np.bool, -1, (64, 10)]], - [[np.bool, -1, (256, 2048, 7, 7)]] - ] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item[0], 1, 10) - cpu_output = self.cpu_op_exec(cpu_input) - npu_output = self.npu_op_exec(npu_input) - self.assertRtolEqual(cpu_output, npu_output) - - - -instantiate_device_type_tests(TestLogicalNot, globals(), except_for="cpu") -if __name__ == "__main__": - torch.npu.set_device("npu:5") - run_tests() - diff --git a/pytorch1.8.1/test/test_npu/test_logsumexp.py b/pytorch1.8.1/test/test_npu/test_logsumexp.py deleted file mode 100644 index daaacb619dd..00000000000 --- a/pytorch1.8.1/test/test_npu/test_logsumexp.py +++ /dev/null @@ -1,108 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import torch -import numpy as np -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestLogsumexp(TestCase): - - def generate_data(self, min, max, shape, dtype): - x = np.random.uniform(min, max, shape).astype(dtype) - npu_x = torch.from_numpy(x) - return npu_x - - def cpu_op_exec(self, input1, dim, keepdim): - output = torch.logsumexp(input1, dim, keepdim=keepdim) - return output - - def npu_op_exec(self, input1, dim, keepdim): - output = torch.logsumexp(input1, dim, keepdim=keepdim) - output = output.to("cpu") - return output - - def cpu_op_out_exec(self, input1, dim, out, keepdim): - torch.logsumexp(input1, dim, keepdim=keepdim, out=out) - return out - - def npu_op_out_exec(self, input1, dim, out, keepdim): - torch.logsumexp(input1, dim, keepdim=keepdim, out=out) - output = out.to("cpu") - return output - - - def test_logsumexp_shape_format(self, device): - shape_format = [ - [[np.float32, 0, (3, 4, 2)],[np.float32, 0, (3, 4, 1)], 2, True], - [[np.float32, 0, (3, 4, 2)],[np.float32, 0, (3, 4)], 2, False], - [[np.float32, 0, (3, 4, 2)],[np.float32, 0, (3,)], [1,2], False], - [[np.float32, 0, (2, 3, 4, 2)],[np.float32, 0, (2, 3, 1, 2)], 2, True], - [[np.float32, 0, (2, 3, 4, 2)],[np.float32, 0, (2,3,2)], 2, False], - [[np.float32, 0, (2, 3, 4, 2)],[np.float32, 0, (2,3)], [2,3], False], - [[np.float16, 0, (3, 4, 2)],[np.float16, 0, (3, 4, 1)], 2, True], - [[np.float16, 0, (3, 4, 2)],[np.float16, 0, (3, 4)], 2, False], - [[np.float16, 0, (3, 4, 2)],[np.float16, 0, (3,)], [1,2], False], - [[np.float16, 0, (2, 3, 4, 2)],[np.float16, 0, (2, 3, 1, 2)], 2, True], - [[np.float16, 0, (2, 3, 4, 2)],[np.float16, 0, (2,3,2)], 2, False], - [[np.float16, 0, (2, 3, 4, 2, 5)],[np.float16, 0, (2,3)], [2,3], False] - ] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item[0], 1, 100) - cpu_out, npu_out = create_common_tensor(item[1], 1, 10) - if cpu_input.dtype == torch.float16: - cpu_input = cpu_input.to(torch.float32) - if cpu_out.dtype == torch.float16: - cpu_out = cpu_out.to(torch.float32) - cpu_out_result = self.cpu_op_out_exec(cpu_input, item[2], cpu_out, item[3]) - npu_out_result = self.npu_op_out_exec(npu_input, item[2], npu_out, item[3]) - cpu_out_result = cpu_out_result.to(npu_out_result.dtype) - self.assertRtolEqual(cpu_out_result.numpy(), npu_out_result.numpy()) - - cpu_result = self.cpu_op_exec(cpu_input, item[2], item[3]) - npu_result = self.npu_op_exec(npu_input, item[2], item[3]) - cpu_result = cpu_result.to(npu_result.dtype) - self.assertRtolEqual(cpu_result.numpy(), npu_result.numpy()) - - def test_logsumexp_dimname1(self, device): - cpu_input = self.generate_data(-10, 10, (2, 14, 69, 96, 1824), np.float32) - cpu_input.names = ['A', 'B', 'C', 'D', 'E'] - dim = ['C'] - keepdim = True - cpu_out = self.cpu_op_exec(cpu_input, dim, keepdim) - npu_out = self.npu_op_exec(cpu_input.npu(), dim, keepdim) - self.assertRtolEqual(cpu_out.numpy(), npu_out.numpy()) - - def test_logsumexp_dimname2(self, device): - cpu_input = self.generate_data(-10, 10, (2, 14, 69, 96, 1824), np.float32) - cpu_input.names = ['A', 'B', 'C', 'D', 'E'] - dim = ['B', 'C'] - keepdim = False - cpu_out = self.cpu_op_exec(cpu_input, dim, keepdim) - npu_out = self.npu_op_exec(cpu_input.npu(), dim, keepdim) - self.assertRtolEqual(cpu_out.numpy(), npu_out.numpy()) - def test_logsumexp_dimname3(self, device): - cpu_input = self.generate_data(-10, 10, (2, 14, 69, 96, 1824), np.float32) - cpu_input.names = ['A', 'B', 'C', 'D', 'E'] - dim = ['B', 'C', 'D'] - keepdim = False - cpu_out = self.cpu_op_exec(cpu_input, dim, keepdim) - npu_out = self.npu_op_exec(cpu_input.npu(), dim, keepdim) - self.assertRtolEqual(cpu_out.numpy(), npu_out.numpy()) - -instantiate_device_type_tests(TestLogsumexp, globals(), except_for="cpu") -if __name__ == "__main__": - torch.npu.set_device("npu:6") - run_tests() - diff --git a/pytorch1.8.1/test/test_npu/test_max_pool2d_backward.py b/pytorch1.8.1/test/test_npu/test_max_pool2d_backward.py deleted file mode 100644 index 73f93f3a9ba..00000000000 --- a/pytorch1.8.1/test/test_npu/test_max_pool2d_backward.py +++ /dev/null @@ -1,68 +0,0 @@ -# Copyright (c) 2020 Huawei Technologies Co., Ltd -# Copyright (c) 2019, Facebook CORPORATION. -# All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import torch.nn.functional as F -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestMaxPool2dBackward(TestCase): - def cpu_op_exec(self, inputCpu, kernel_size, stride, padding): - inputCpu.requires_grad = True - dataCpu, argMaxCpu = F.max_pool2d_with_indices(inputCpu, kernel_size=kernel_size, stride=stride, padding=padding) - z1 = torch.sum(dataCpu) - z1.backward() - cpu_grad = inputCpu.grad - output1 = dataCpu.detach() - output1 = output1 - return output1, cpu_grad - - def npu_op_exec(self, inputNpu, kernel_size, stride, padding): - inputNpu.requires_grad = True - dataNpu, argMaxNpu = F.max_pool2d_with_indices(inputNpu, kernel_size=kernel_size, stride=stride, padding=padding) - z2 = torch.sum(dataNpu) - z2.backward() - npu_grad = inputNpu.grad - npu_grad = npu_grad.to("cpu") - output1 = dataNpu.to("cpu").detach() - return output1, npu_grad - - def test_max_pool2d_backward_shape_format(self, device): - shape_format = [ - [[np.float16, 3, [256, 64, 112, 112]], [3, 3], [2, 2], 1], - [[np.float16, 3, [1024, 24, 112, 112]], [3, 3], [2, 2], 1], - ] - - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item[0], 0, 100) - if cpu_input.dtype == torch.float16: - cpu_input = cpu_input.to(torch.float32) - cpu_output, cpu_grad = self.cpu_op_exec(cpu_input, item[1], item[2], item[3]) - npu_output, npu_grad = self.npu_op_exec(npu_input, item[1], item[2], item[3]) - cpu_output = cpu_output.to(npu_output.dtype) - cpu_grad = cpu_grad.to(npu_grad.dtype) - - self.assertRtolEqual(cpu_output.numpy(), npu_output.numpy()) - self.assertRtolEqual(cpu_grad.numpy(), npu_grad.numpy()) - - -instantiate_device_type_tests(TestMaxPool2dBackward, globals(), except_for='cpu') -if __name__ == "__main__": - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_miopen_batch_norm.py b/pytorch1.8.1/test/test_npu/test_miopen_batch_norm.py deleted file mode 100644 index 734577f5486..00000000000 --- a/pytorch1.8.1/test/test_npu/test_miopen_batch_norm.py +++ /dev/null @@ -1,103 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestBn(TestCase): - def cpu_op_exec(self, input1, grad_tensor, dim, fun): - input1.requires_grad_(True) - grad_tensor = grad_tensor.to("cpu") - if fun == "1d": - m = torch.nn.BatchNorm1d(dim) - elif fun == "2d": - m = torch.nn.BatchNorm2d(dim) - else: - m = torch.nn.BatchNorm3d(dim) - input_cpu = m(input1) - input_cpu = input_cpu.detach().numpy() - return input_cpu - - def npu_op_exec_new(self, input1, grad_tensor, dim, fun): - grad_tensor = grad_tensor.to("npu") - w = torch.ones_like(input1) - w = w.to("npu") - if fun == "1d": - m = torch.nn.BatchNorm1d(dim) - elif fun == "2d": - m = torch.nn.BatchNorm2d(dim) - else: - m = torch.nn.BatchNorm3d(dim) - m = m.to("npu") - input_npu = m(input1) - input_npu = input_npu.to("cpu") - input_npu = input_npu.detach().numpy() - return input_npu - - def do_test(self, item, prec, prec16, fun): - cpu_input1, npu_input1 = create_common_tensor(item[0], 0, 100) - if cpu_input1.dtype == torch.float16: - cpu_input1 = cpu_input1.to(torch.float32) - grad_tensor = torch.randn(item[0][2]) - cpu_output = self.cpu_op_exec(cpu_input1, grad_tensor, item[0][2][1], fun) - npu_output = self.npu_op_exec_new(npu_input1, grad_tensor, item[0][2][1], fun) - if (cpu_output.dtype != npu_output.dtype): - cpu_output = cpu_output.astype(npu_output.dtype) - self.assertRtolEqual(cpu_output, npu_output, prec, prec16) - - def test_batchnorm_shape_format(self, device): - #pylint:disable=unused-argument - shape_format_1d = [ - [[np.float32, 0, [25, 35, 40]]], - [[np.float32, 0, [256, 672, 7]]], - [[np.float32, 0, [256, 288, 14]]], - [[np.float16, 0, [1024, 58, 56]]], - [[np.float16, 0, [1024, 1024, 7]]], - [[np.float16, 0, [1024, 24, 28]]], - ] - shape_format_2d = [ - [[np.float32, 3, [2, 3, 2, 2]]], - [[np.float32, 3, [256, 672, 7, 7]]], - [[np.float32, 3, [256, 288, 14, 14]]], - [[np.float32, 3, [1024, 58, 28, 28]]], - [[np.float32, 3, [1024, 116, 14, 14]]], - [[np.float32, 3, [1024, 24, 112, 112]]], - [[np.float16, 3, [1024, 58, 56, 56]]], - [[np.float16, 3, [1024, 1024, 7, 7]]], - [[np.float16, 3, [1024, 24, 28, 28]]], - [[np.float16, 3, [1024, 116, 28, 28]]], - [[np.float16, 3, [1024, 232, 7, 7]]], - [[np.float16, 3, [1024, 232, 14, 14]]], - ] - shape_format_3d = [ - [[np.float32, -1, [2, 3, 2, 2, 5]]], - [[np.float16, -1, [1024, 232, 14, 14, 4]]], - ] - # BatchNorm1d ok - for item in shape_format_1d: - self.do_test(item, prec = 0.001, prec16 = 0.01, fun = "1d") - # BatchNorm2d ok - for item in shape_format_2d: - self.do_test(item, prec = 0.001, prec16 = 0.01, fun = "2d") - - -instantiate_device_type_tests(TestBn, globals(), except_for="cpu") -if __name__ == "__main__": - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_miopen_batch_norm_backward.py b/pytorch1.8.1/test/test_npu/test_miopen_batch_norm_backward.py deleted file mode 100644 index a2628526e9d..00000000000 --- a/pytorch1.8.1/test/test_npu/test_miopen_batch_norm_backward.py +++ /dev/null @@ -1,120 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestBnBackward(TestCase): - def cpu_op_exec(self, input1, grad_tensor, dim, fun): - input1.requires_grad_(True) - grad_tensor = grad_tensor.to("cpu") - if fun == "1d": - m = torch.nn.BatchNorm1d(dim) - elif fun == "2d": - m = torch.nn.BatchNorm2d(dim) - else: - m = torch.nn.BatchNorm3d(dim) - input_cpu = m(input1) - input_cpu = input_cpu.detach().numpy() - w = torch.ones_like(input1) - tmp = m(input1) - tmp.backward(grad_tensor) - output = input1.grad - output = output.detach().numpy() - return output, input_cpu - - def npu_op_exec_new(self, input1, grad_tensor, dim, fun): - grad_tensor = grad_tensor.to("npu") - w = torch.ones_like(input1) - w = w.to("npu") - if fun == "1d": - m = torch.nn.BatchNorm1d(dim) - elif fun == "2d": - m = torch.nn.BatchNorm2d(dim) - else: - m = torch.nn.BatchNorm3d(dim) - m = m.to("npu") - input_npu = m(input1) - input_npu = input_npu.to("cpu") - input_npu = input_npu.detach().numpy() - input1.requires_grad_(True) - tmp = m(input1) - tmp.backward(grad_tensor) - output = input1.grad.to("cpu") - output = output.detach().numpy() - return output, input_npu - - def do_test(self, item, prec, prec16, fun): - cpu_input1, npu_input1 = create_common_tensor(item[0], 0, 100) - if cpu_input1.dtype == torch.float16: - cpu_input1 = cpu_input1.to(torch.float32) - grad_tensor = torch.randn(item[0][2]) - cpu_output, cpu_input = self.cpu_op_exec(cpu_input1, grad_tensor, item[0][2][1], fun) - npu_output, npu_input = self.npu_op_exec_new(npu_input1, grad_tensor, item[0][2][1], fun) - - if (cpu_output.dtype != npu_output.dtype): - cpu_output = cpu_output.astype(npu_output.dtype) - self.assertRtolEqual(cpu_output, npu_output, prec, prec16) - - if (cpu_input.dtype != npu_input.dtype): - cpu_input = cpu_input.astype(npu_input.dtype) - self.assertRtolEqual(cpu_input, npu_input, prec, prec16) - - - def test_batchnorm_shape_format(self, device): - #pylint:disable=unused-argument - shape_format_1d = [ - [[np.float32, 0, [25, 35, 40]]], - [[np.float32, 0, [256, 672, 7]]], - [[np.float32, 0, [256, 288, 14]]], - [[np.float16, 0, [1024, 58, 56]]], - [[np.float16, 0, [1024, 1024, 7]]], - [[np.float16, 0, [1024, 24, 28]]], - ] - shape_format_2d = [ - [[np.float32, 3, [2, 3, 2, 2]]], - [[np.float32, 3, [256, 672, 7, 7]]], - [[np.float32, 3, [256, 288, 14, 14]]], - [[np.float32, 3, [1024, 58, 28, 28]]], - [[np.float32, 3, [1024, 116, 14, 14]]], - [[np.float32, 3, [1024, 24, 112, 112]]], - [[np.float16, 3, [1024, 58, 56, 56]]], - [[np.float16, 3, [1024, 1024, 7, 7]]], - [[np.float16, 3, [1024, 24, 28, 28]]], - [[np.float16, 3, [1024, 116, 28, 28]]], - [[np.float16, 3, [1024, 232, 7, 7]]], - [[np.float16, 3, [1024, 232, 14, 14]]], - ] - shape_format_3d = [ - [[np.float32, -1, [2, 3, 2, 2, 5]]], - [[np.float16, -1, [1024, 232, 14, 14, 4]]], - ] - - # BatchNorm1d ok - for item in shape_format_1d: - self.do_test(item, prec = 0.001, prec16 = 0.01, fun = "1d") - - # BatchNorm2d ok - for item in shape_format_2d: - self.do_test(item, prec = 0.001, prec16 = 0.001, fun = "2d") - - -instantiate_device_type_tests(TestBnBackward, globals(), except_for="cpu") -if __name__ == "__main__": - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_miopen_convolution.py b/pytorch1.8.1/test/test_npu/test_miopen_convolution.py deleted file mode 100644 index 8583f61d49f..00000000000 --- a/pytorch1.8.1/test/test_npu/test_miopen_convolution.py +++ /dev/null @@ -1,81 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -import torch.nn as nn -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestMiopenConvolution(TestCase): - - def op_exec_cpu(self, input, weight, bias, stride, padding, dilation, transposed, - output_padding, groups, benchmark, deterministic, cudnn_enabled): - - cpuOutput = torch._convolution(input, weight, bias, stride, padding, dilation, transposed=False, output_padding=[0, 0], - groups=1, benchmark=False, deterministic=False, cudnn_enabled=False) - - return cpuOutput - - def op_exec_npu(self, input, weight, bias, stride, padding, dilation, transposed, - output_padding, groups, benchmark, deterministic, cudnn_enabled): - - input = input.to("npu") - weight = weight.to("npu") - bias = bias.to("npu") - npuOutput = torch._convolution(input, weight, bias, stride, padding, dilation, transposed=False, output_padding=[0, 0], - groups=1, benchmark=False, deterministic=False, cudnn_enabled=False) - npuOutput = npuOutput.to("cpu") - - return npuOutput - - def test_miopen_convolution_float16_001(self, device): - - # input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, - # benchmark, deterministic, cudnn_enabled - item = [[np.float16, 3, [2, 1, 5, 5]], [np.float16, 3, (1, 1, 1, 1)], [np.float16, 3, (1)], - [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, False] - - input_cpu, input_npu = create_common_tensor(item[0], 0, 10) - if input_cpu.dtype == torch.float16: - input_cpu = input_cpu.to(torch.float32) - weight_cpu, weight_npu = create_common_tensor(item[1], 0, 10) - if weight_cpu.dtype == torch.float16: - weight_cpu = weight_cpu.to(torch.float32) - bias_cpu, bias_npu = create_common_tensor(item[2], 0, 10) - if bias_cpu.dtype == torch.float16: - bias_cpu = bias_cpu.to(torch.float32) - - cpu_output = self.op_exec_cpu(input_cpu, weight_cpu, bias_cpu, stride=item[3], padding=item[4], dilation=item[5], transposed=item[6], - output_padding=item[7], groups=item[8], benchmark=item[9], deterministic=item[10], cudnn_enabled=item[10]) - npu_output = self.op_exec_npu(input_npu, weight_npu, bias_npu, stride=item[3], padding=item[4], dilation=item[5], transposed=item[6], - output_padding=item[7], groups=item[8], benchmark=item[9], deterministic=item[10], cudnn_enabled=item[10]) - cpu_output = cpu_output.to(npu_output.dtype) - - print("======cpuOutput_float16_001======") - print(cpu_output) - print("======npuOutput_float16_001======") - print(npu_output) - - self.assertRtolEqual(cpu_output.detach().numpy(), npu_output.detach().numpy()) - - -instantiate_device_type_tests(TestMiopenConvolution, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:6") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_miopen_convolution_backward.py b/pytorch1.8.1/test/test_npu/test_miopen_convolution_backward.py deleted file mode 100644 index 0aaa54c061b..00000000000 --- a/pytorch1.8.1/test/test_npu/test_miopen_convolution_backward.py +++ /dev/null @@ -1,124 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -import torch.nn as nn -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestMiopenConvolutionBackward(TestCase): - weight_grad = [] - input_grad = [] - bias_grad = [] - - def getWeightGrad(self, grad): - self.weight_grad.append(grad.to("cpu")) - - def getInputGrad(self, grad): - self.input_grad.append(grad.to("cpu")) - - def getBiasGrad(self, grad): - self.bias_grad.append(grad.to("cpu")) - - def op_exec_cpu(self, input, weight, bias, stride, padding, dilation, transposed, - output_padding, groups, benchmark, deterministic, cudnn_enabled): - - input.requires_grad = True - input.register_hook(lambda grad: self.getInputGrad(grad)) - weight.requires_grad = True - weight.register_hook(lambda grad: self.getWeightGrad(grad)) - bias.requires_grad = True - bias.register_hook(lambda grad: self.getBiasGrad(grad)) - - cpu_res_forward = torch._convolution(input, weight, bias, stride, padding, dilation, transposed=False, output_padding=[0, 0], - groups=1, benchmark=False, deterministic=False, cudnn_enabled=False) - - tmp = torch.ones_like(cpu_res_forward).float() - cpu_res_forward.backward(tmp, retain_graph=True) - - return cpu_res_forward - - def op_exec_npu(self, input, weight, bias, stride, padding, dilation, transposed, - output_padding, groups, benchmark, deterministic, cudnn_enabled): - - input = input.to("npu") - input.requires_grad = True - input.register_hook(lambda grad: self.getInputGrad(grad)) - weight = weight.to("npu") - weight.requires_grad = True - weight.register_hook(lambda grad: self.getWeightGrad(grad)) - bias = bias.to("npu") - bias.requires_grad = True - bias.register_hook(lambda grad: self.getBiasGrad(grad)) - - npu_res_forward = torch._convolution(input, weight, bias, stride, padding, dilation, transposed=False, output_padding=[0, 0], - groups=1, benchmark=False, deterministic=False, cudnn_enabled=False) - - tmp = torch.ones_like(npu_res_forward).float() - tmp = tmp.to("npu") - npu_res_forward.backward(tmp, retain_graph=True) - - npu_res_forward = npu_res_forward.to("cpu") - return npu_res_forward - - def test_miopen_convolution_backward_float16_001(self, device): - - # input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, - # benchmark, deterministic, cudnn_enabled - item = [[np.float16, 3, [1, 2, 5, 5]], [np.float16, 3, (2, 2, 1, 1)], [np.float16, 3, (2)], - [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, False] - - self.weight_grad.clear() - self.input_grad.clear() - self.bias_grad.clear() - input_cpu, input_npu = create_common_tensor(item[0], -1,1) - if input_cpu.dtype == torch.float16: - input_cpu = input_cpu.to(torch.float32) - weight_cpu, weight_npu = create_common_tensor(item[1], -1,1) - if weight_cpu.dtype == torch.float16: - weight_cpu = weight_cpu.to(torch.float32) - bias_cpu, bias_npu = create_common_tensor(item[2], -1,1) - if bias_cpu.dtype == torch.float16: - bias_cpu = bias_cpu.to(torch.float32) - - cpu_output = self.op_exec_cpu(input_cpu, weight_cpu, bias_cpu, stride=item[3], padding=item[4], dilation=item[5], transposed=item[6], - output_padding=item[7], groups=item[8], benchmark=item[9], deterministic=item[10], cudnn_enabled=item[10]) - npu_output = self.op_exec_npu(input_npu, weight_npu, bias_npu, stride=item[3], padding=item[4], dilation=item[5], transposed=item[6], - output_padding=item[7], groups=item[8], benchmark=item[9], deterministic=item[10], cudnn_enabled=item[10]) - cpu_output = cpu_output.to(npu_output.dtype) - - self.input_grad[0] = self.input_grad[0].to(self.input_grad[1].dtype) - self.weight_grad[0] = self.weight_grad[0].to(self.weight_grad[1].dtype) - self.bias_grad[0] = self.bias_grad[0].to(self.bias_grad[1].dtype) - print("===input_grad_001===") - print(self.input_grad) - print("===weight_grad_001===") - print(self.weight_grad) - print("===bias_grad_001===") - print(self.bias_grad) - - self.assertRtolEqual(self.input_grad[0].numpy(), self.input_grad[1].numpy()) - self.assertRtolEqual(self.bias_grad[0].numpy(), self.bias_grad[1].numpy()) - self.assertRtolEqual(self.weight_grad[0].numpy(), self.weight_grad[1].numpy()) - - -instantiate_device_type_tests(TestMiopenConvolutionBackward, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:6") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_miopen_convolution_backward_bias.py b/pytorch1.8.1/test/test_npu/test_miopen_convolution_backward_bias.py deleted file mode 100644 index 00259de601a..00000000000 --- a/pytorch1.8.1/test/test_npu/test_miopen_convolution_backward_bias.py +++ /dev/null @@ -1,118 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -import torch.nn as nn -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestMiopenConvolutionBackwardBias(TestCase): - weight_grad = [] - input_grad = [] - bias_grad = [] - - def getWeightGrad(self, grad): - self.weight_grad.append(grad.to("cpu")) - - def getInputGrad(self, grad): - self.input_grad.append(grad.to("cpu")) - - def getBiasGrad(self, grad): - self.bias_grad.append(grad.to("cpu")) - - def op_exec_cpu(self, input, weight, bias, stride, padding, dilation, transposed, - output_padding, groups, benchmark, deterministic, cudnn_enabled): - - input.requires_grad = True - input.register_hook(lambda grad: self.getInputGrad(grad)) - weight.requires_grad = True - weight.register_hook(lambda grad: self.getWeightGrad(grad)) - bias.requires_grad = True - bias.register_hook(lambda grad: self.getBiasGrad(grad)) - - cpu_res_forward = torch._convolution(input, weight, bias, stride, padding, dilation, transposed=False, output_padding=[0, 0], - groups=1, benchmark=False, deterministic=False, cudnn_enabled=False) - - tmp = torch.ones_like(cpu_res_forward).float() - cpu_res_forward.backward(tmp, retain_graph=True) - - return cpu_res_forward - - def op_exec_npu(self, input, weight, bias, stride, padding, dilation, transposed, - output_padding, groups, benchmark, deterministic, cudnn_enabled): - - input = input.to("npu") - input.requires_grad = True - input.register_hook(lambda grad: self.getInputGrad(grad)) - weight = weight.to("npu") - weight.requires_grad = True - weight.register_hook(lambda grad: self.getWeightGrad(grad)) - bias = bias.to("npu") - bias.requires_grad = True - bias.register_hook(lambda grad: self.getBiasGrad(grad)) - - npu_res_forward = torch._convolution(input, weight, bias, stride, padding, dilation, transposed=False, output_padding=[0, 0], - groups=1, benchmark=False, deterministic=False, cudnn_enabled=False) - - tmp = torch.ones_like(npu_res_forward).float() - tmp = tmp.to("npu") - npu_res_forward.backward(tmp, retain_graph=True) - - npu_res_forward = npu_res_forward.to("cpu") - return npu_res_forward - - def test_miopen_convolution_backwrd_bias_float16_001(self, device): - - # input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, - # benchmark, deterministic, cudnn_enabled - item = [[np.float16, 3, [256,128,7,7]], [np.float16, 3, (16,128,3,3)], [np.float16, 3, (16)], - [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, False] - - self.weight_grad.clear() - self.input_grad.clear() - self.bias_grad.clear() - input_cpu, input_npu = create_common_tensor(item[0], -65500, 65500) - if input_cpu.dtype == torch.float16: - input_cpu = input_cpu.to(torch.float32) - weight_cpu, weight_npu = create_common_tensor(item[1], -65500, 65500) - if weight_cpu.dtype == torch.float16: - weight_cpu = weight_cpu.to(torch.float32) - bias_cpu, bias_npu = create_common_tensor(item[2], -65500, 65500) - if bias_cpu.dtype == torch.float16: - bias_cpu = bias_cpu.to(torch.float32) - - cpu_output = self.op_exec_cpu(input_cpu, weight_cpu, bias_cpu, stride=item[3], padding=item[4], dilation=item[5], transposed=item[6], - output_padding=item[7], groups=item[8], benchmark=item[9], deterministic=item[10], cudnn_enabled=item[10]) - npu_output = self.op_exec_npu(input_npu, weight_npu, bias_npu, stride=item[3], padding=item[4], dilation=item[5], transposed=item[6], - output_padding=item[7], groups=item[8], benchmark=item[9], deterministic=item[10], cudnn_enabled=item[10]) - cpu_output = cpu_output.to(npu_output.dtype) - - self.input_grad[0] = self.input_grad[0].to(self.input_grad[1].dtype) - self.weight_grad[0] = self.weight_grad[0].to(self.weight_grad[1].dtype) - self.bias_grad[0] = self.bias_grad[0].to(self.bias_grad[1].dtype) - print("===bias_grad_float16_001===") - print(self.bias_grad) - - self.assertRtolEqual(self.bias_grad[0].numpy(), self.bias_grad[1].numpy()) - - -instantiate_device_type_tests(TestMiopenConvolutionBackwardBias, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:6") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_miopen_convolution_backward_input.py b/pytorch1.8.1/test/test_npu/test_miopen_convolution_backward_input.py deleted file mode 100644 index 63e1282dc7d..00000000000 --- a/pytorch1.8.1/test/test_npu/test_miopen_convolution_backward_input.py +++ /dev/null @@ -1,118 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -import torch.nn as nn -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestMiopenConvolutionBackwardInput(TestCase): - weight_grad = [] - input_grad = [] - bias_grad = [] - - def getWeightGrad(self, grad): - self.weight_grad.append(grad.to("cpu")) - - def getInputGrad(self, grad): - self.input_grad.append(grad.to("cpu")) - - def getBiasGrad(self, grad): - self.bias_grad.append(grad.to("cpu")) - - def op_exec_cpu(self, input, weight, bias, stride, padding, dilation, transposed, - output_padding, groups, benchmark, deterministic, cudnn_enabled): - - input.requires_grad = True - input.register_hook(lambda grad: self.getInputGrad(grad)) - weight.requires_grad = True - weight.register_hook(lambda grad: self.getWeightGrad(grad)) - bias.requires_grad = True - bias.register_hook(lambda grad: self.getBiasGrad(grad)) - - cpu_res_forward = torch._convolution(input, weight, bias, stride, padding, dilation, transposed=False, output_padding=[0, 0], - groups=1, benchmark=False, deterministic=False, cudnn_enabled=False) - - tmp = torch.ones_like(cpu_res_forward).float() - cpu_res_forward.backward(tmp, retain_graph=True) - - return cpu_res_forward - - def op_exec_npu(self, input, weight, bias, stride, padding, dilation, transposed, - output_padding, groups, benchmark, deterministic, cudnn_enabled): - - input = input.to("npu") - input.requires_grad = True - input.register_hook(lambda grad: self.getInputGrad(grad)) - weight = weight.to("npu") - weight.requires_grad = True - weight.register_hook(lambda grad: self.getWeightGrad(grad)) - bias = bias.to("npu") - bias.requires_grad = True - bias.register_hook(lambda grad: self.getBiasGrad(grad)) - - npu_res_forward = torch._convolution(input, weight, bias, stride, padding, dilation, transposed=False, output_padding=[0, 0], - groups=1, benchmark=False, deterministic=False, cudnn_enabled=False) - - tmp = torch.ones_like(npu_res_forward).float() - tmp = tmp.to("npu") - npu_res_forward.backward(tmp, retain_graph=True) - - npu_res_forward = npu_res_forward.to("cpu") - return npu_res_forward - - def test_miopen_convolution_backward_input_float16_001(self, device): - - # input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, - # benchmark, deterministic, cudnn_enabled - item = [[np.float16, 3, [64, 8, 256, 256]], [np.float16, 3, (4, 8, 5, 5)], [np.float16, 3, (4)], - [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, False] - - self.weight_grad.clear() - self.input_grad.clear() - self.bias_grad.clear() - input_cpu, input_npu = create_common_tensor(item[0], 0,10) - if input_cpu.dtype == torch.float16: - input_cpu = input_cpu.to(torch.float32) - weight_cpu, weight_npu = create_common_tensor(item[1], 0,10) - if weight_cpu.dtype == torch.float16: - weight_cpu = weight_cpu.to(torch.float32) - bias_cpu, bias_npu = create_common_tensor(item[2], 0,10) - if bias_cpu.dtype == torch.float16: - bias_cpu = bias_cpu.to(torch.float32) - - cpu_output = self.op_exec_cpu(input_cpu, weight_cpu, bias_cpu, stride=item[3], padding=item[4], dilation=item[5], transposed=item[6], - output_padding=item[7], groups=item[8], benchmark=item[9], deterministic=item[10], cudnn_enabled=item[10]) - npu_output = self.op_exec_npu(input_npu, weight_npu, bias_npu, stride=item[3], padding=item[4], dilation=item[5], transposed=item[6], - output_padding=item[7], groups=item[8], benchmark=item[9], deterministic=item[10], cudnn_enabled=item[10]) - cpu_output = cpu_output.to(npu_output.dtype) - - self.input_grad[0] = self.input_grad[0].to(self.input_grad[1].dtype) - self.weight_grad[0] = self.weight_grad[0].to(self.weight_grad[1].dtype) - self.bias_grad[0] = self.bias_grad[0].to(self.bias_grad[1].dtype) - print("===input_grad_float32_001===") - print(self.input_grad) - - self.assertRtolEqual(self.input_grad[0].numpy(), self.input_grad[1].numpy()) - - -instantiate_device_type_tests(TestMiopenConvolutionBackwardInput, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:6") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_miopen_convolution_backward_weight.py b/pytorch1.8.1/test/test_npu/test_miopen_convolution_backward_weight.py deleted file mode 100644 index 64dca8cbff9..00000000000 --- a/pytorch1.8.1/test/test_npu/test_miopen_convolution_backward_weight.py +++ /dev/null @@ -1,120 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -import torch.nn as nn -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestMiopenConvolutionBackwardWeight(TestCase): - weight_grad = [] - input_grad = [] - bias_grad = [] - - def getWeightGrad(self, grad): - self.weight_grad.append(grad.to("cpu")) - - def getInputGrad(self, grad): - self.input_grad.append(grad.to("cpu")) - - def getBiasGrad(self, grad): - self.bias_grad.append(grad.to("cpu")) - - def op_exec_cpu(self, input, weight, bias, stride, padding, dilation, transposed, - output_padding, groups, benchmark, deterministic, cudnn_enabled): - - input.requires_grad = True - input.register_hook(lambda grad: self.getInputGrad(grad)) - weight.requires_grad = True - weight.register_hook(lambda grad: self.getWeightGrad(grad)) - bias.requires_grad = True - bias.register_hook(lambda grad: self.getBiasGrad(grad)) - - cpu_res_forward = torch._convolution(input, weight, bias, stride, padding, dilation, transposed=False, output_padding=[0, 0], - groups=1, benchmark=False, deterministic=False, cudnn_enabled=False) - - tmp = torch.ones_like(cpu_res_forward).float() - cpu_res_forward.backward(tmp, retain_graph=True) - - return cpu_res_forward - - def op_exec_npu(self, input, weight, bias, stride, padding, dilation, transposed, - output_padding, groups, benchmark, deterministic, cudnn_enabled): - - input = input.to("npu") - input.requires_grad = True - input.register_hook(lambda grad: self.getInputGrad(grad)) - weight = weight.to("npu") - weight.requires_grad = True - weight.register_hook(lambda grad: self.getWeightGrad(grad)) - bias = bias.to("npu") - bias.requires_grad = True - bias.register_hook(lambda grad: self.getBiasGrad(grad)) - - npu_res_forward = torch._convolution(input, weight, bias, stride, padding, dilation, transposed=False, output_padding=[0, 0], - groups=1, benchmark=False, deterministic=False, cudnn_enabled=False) - - tmp = torch.ones_like(npu_res_forward).float() - tmp = tmp.to("npu") - npu_res_forward.backward(tmp, retain_graph=True) - - npu_res_forward = npu_res_forward.to("cpu") - return npu_res_forward - - def test_miopen_convolution_backward_weight_float16_001(self, device): - - # input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, - # benchmark, deterministic, cudnn_enabled - item = [[np.float16, 3, [2,1,5,5]], [np.float16, 3, (1,1,1,1)], [np.float16, 3, (1)], - [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, False] - - self.weight_grad.clear() - self.input_grad.clear() - self.bias_grad.clear() - input_cpu, input_npu = create_common_tensor(item[0], -0.001, 0) - if input_cpu.dtype == torch.float16: - input_cpu = input_cpu.to(torch.float32) - weight_cpu, weight_npu = create_common_tensor(item[1], -0.001, 0) - if weight_cpu.dtype == torch.float16: - weight_cpu = weight_cpu.to(torch.float32) - bias_cpu, bias_npu = create_common_tensor(item[2], -0.001, 0) - if bias_cpu.dtype == torch.float16: - bias_cpu = bias_cpu.to(torch.float32) - - cpu_output = self.op_exec_cpu(input_cpu, weight_cpu, bias_cpu, stride=item[3], padding=item[4], dilation=item[5], transposed=item[6], - output_padding=item[7], groups=item[8], benchmark=item[9], deterministic=item[10], cudnn_enabled=item[10]) - npu_output = self.op_exec_npu(input_npu, weight_npu, bias_npu, stride=item[3], padding=item[4], dilation=item[5], transposed=item[6], - output_padding=item[7], groups=item[8], benchmark=item[9], deterministic=item[10], cudnn_enabled=item[10]) - cpu_output = cpu_output.to(npu_output.dtype) - - self.input_grad[0] = self.input_grad[0].to(self.input_grad[1].dtype) - self.weight_grad[0] = self.weight_grad[0].to(self.weight_grad[1].dtype) - self.bias_grad[0] = self.bias_grad[0].to(self.bias_grad[1].dtype) - - print("===weight_grad_001===") - print(self.weight_grad) - - self.assertRtolEqual(self.weight_grad[0].numpy(), self.weight_grad[1].numpy()) - - -instantiate_device_type_tests(TestMiopenConvolutionBackwardWeight, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:6") - run_tests() - diff --git a/pytorch1.8.1/test/test_npu/test_mkldnn_adaptive_avg_pool2d.py b/pytorch1.8.1/test/test_npu/test_mkldnn_adaptive_avg_pool2d.py deleted file mode 100644 index 554f8ba38c1..00000000000 --- a/pytorch1.8.1/test/test_npu/test_mkldnn_adaptive_avg_pool2d.py +++ /dev/null @@ -1,81 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import torch.nn as nn -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor -#pylint: disable=unused-argument - -class TestMkldnnAdaptiveAvgPool2d(TestCase): - - def generate_data(self, min_d, max_d, shape, dtype): - input1 = np.random.uniform(min_d, max_d, shape).astype(dtype) - npu_input1 = torch.from_numpy(input1) - return npu_input1 - - def cpu_op_exec(self, input1, output_size): - m = nn.AdaptiveAvgPool2d(output_size) - output= m(input1) - return output.numpy() - - def npu_op_exec(self, input1, output_size): - m = nn.AdaptiveAvgPool2d(output_size).npu() - output = m(input1) - return output.cpu().numpy() - - def test_mkldnn_adaptiveAvgPool2d_shape_format_fp32(self, device): - shape_list = [(32, 16, 16), - (16, 1024, 256), - (1024, 464, 11, 9), - (1, 2048, 15, 15)] - output_list = [(4, 4), (3, 5), (1), (1, None), (None, 2),(2,1)] - for item in shape_list: - input1= self.generate_data(0, 100, item, np.float32) - cpu_input1 = copy.deepcopy(input1) - for output_size in output_list: - cpu_output = self.cpu_op_exec(cpu_input1, output_size) - npu_output = self.npu_op_exec(input1, output_size) - self.assertRtolEqual(cpu_output, npu_output) - - def test_mkldnn_adaptiveAvgPool2d_shape_format_fp16(self, device): - def cpu_op_exec_fp16(input1, output_size): - input1 = input1.to(torch.float32) - m = nn.AdaptiveAvgPool2d(output_size) - output= m(input1) - output = output.numpy() - output = output.astype(np.float16) - return output - - def npu_op_exec_fp16(input1, output_size): - input1 = input1.to(torch.float32) - m = nn.AdaptiveAvgPool2d(output_size).npu() - output = m(input1) - output = output.to("cpu") - output = output.numpy().astype(np.float16) - return output - - npu_input1 = self.generate_data(0, 100, (5,3,4), np.float16) - cpu_input1 = copy.deepcopy(npu_input1) - cpu_output = cpu_op_exec_fp16(cpu_input1, (4, 4)) - npu_output = npu_op_exec_fp16(npu_input1, (4, 4)) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestMkldnnAdaptiveAvgPool2d, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:2") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_mkldnn_convolution_backward.py b/pytorch1.8.1/test/test_npu/test_mkldnn_convolution_backward.py deleted file mode 100644 index 8de7467f58f..00000000000 --- a/pytorch1.8.1/test/test_npu/test_mkldnn_convolution_backward.py +++ /dev/null @@ -1,163 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -import torch.nn as nn -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor -#pylint: disable=unused-argument - -class TestMkldnnConvolutionBackward(TestCase): - weight_grad = [] - input_grad = [] - bias_grad = [] - - def getWeightGrad(self, grad): - self.weight_grad.append(grad.to("cpu")) - - def getInputGrad(self, grad): - self.input_grad.append(grad.to("cpu")) - - def getBiasGrad(self, grad): - self.bias_grad.append(grad.to("cpu")) - - def op_exec_cpu(self, input, weight, bias, stride, padding, dilation, transposed, - output_padding, groups, benchmark, deterministic, cudnn_enabled): - - input.requires_grad = True - input.register_hook(lambda grad: self.getInputGrad(grad)) - weight.requires_grad = True - weight.register_hook(lambda grad: self.getWeightGrad(grad)) - bias.requires_grad = True - bias.register_hook(lambda grad: self.getBiasGrad(grad)) - - cpu_res_forward = torch._convolution(input, weight, bias, stride, padding, dilation, transposed=False, output_padding=[0, 0], - groups=1, benchmark=False, deterministic=False, cudnn_enabled=False) - - tmp = torch.ones_like(cpu_res_forward).float() - cpu_res_forward.backward(tmp, retain_graph=True) - - return cpu_res_forward - - def op_exec_npu(self, input, weight, bias, stride, padding, dilation, transposed, - output_padding, groups, benchmark, deterministic, cudnn_enabled): - - input = input.to("npu") - input.requires_grad = True - input.register_hook(lambda grad: self.getInputGrad(grad)) - weight = weight.to("npu") - weight.requires_grad = True - weight.register_hook(lambda grad: self.getWeightGrad(grad)) - bias = bias.to("npu") - bias.requires_grad = True - bias.register_hook(lambda grad: self.getBiasGrad(grad)) - - npu_res_forward = torch._convolution(input, weight, bias, stride, padding, dilation, transposed=False, output_padding=[0, 0], - groups=1, benchmark=False, deterministic=False, cudnn_enabled=False) - - tmp = torch.ones_like(npu_res_forward).float() - tmp = tmp.to("npu") - npu_res_forward.backward(tmp, retain_graph=True) - - npu_res_forward = npu_res_forward.to("cpu") - return npu_res_forward - - def test_mkldnn_convolution_backward_float16(self, device): - - # input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, - # benchmark, deterministic, cudnn_enabled - item = [[np.float16, 3, [1, 2, 5, 5]], [np.float16, 3, (2, 2, 1, 1)], [np.float16, 3, (2)], - [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, False] - - self.weight_grad.clear() - self.input_grad.clear() - self.bias_grad.clear() - input_cpu, input_npu = create_common_tensor(item[0], -1,1) - if input_cpu.dtype == torch.float16: - input_cpu = input_cpu.to(torch.float32) - weight_cpu, weight_npu = create_common_tensor(item[1], -1,1) - if weight_cpu.dtype == torch.float16: - weight_cpu = weight_cpu.to(torch.float32) - bias_cpu, bias_npu = create_common_tensor(item[2], -1,1) - if bias_cpu.dtype == torch.float16: - bias_cpu = bias_cpu.to(torch.float32) - - cpu_output = self.op_exec_cpu(input_cpu, weight_cpu, bias_cpu, stride=item[3], padding=item[4], dilation=item[5], transposed=item[6], - output_padding=item[7], groups=item[8], benchmark=item[9], deterministic=item[10], cudnn_enabled=item[10]) - npu_output = self.op_exec_npu(input_npu, weight_npu, bias_npu, stride=item[3], padding=item[4], dilation=item[5], transposed=item[6], - output_padding=item[7], groups=item[8], benchmark=item[9], deterministic=item[10], cudnn_enabled=item[10]) - cpu_output = cpu_output.to(npu_output.dtype) - - self.input_grad[0] = self.input_grad[0].to(self.input_grad[1].dtype) - self.weight_grad[0] = self.weight_grad[0].to(self.weight_grad[1].dtype) - self.bias_grad[0] = self.bias_grad[0].to(self.bias_grad[1].dtype) - print("===input_grad_001===") - print(self.input_grad) - print("===weight_grad_001===") - print(self.weight_grad) - print("===bias_grad_001===") - print(self.bias_grad) - - self.assertRtolEqual(self.input_grad[0].numpy(), self.input_grad[1].numpy()) - self.assertRtolEqual(self.bias_grad[0].numpy(), self.bias_grad[1].numpy()) - self.assertRtolEqual(self.weight_grad[0].numpy(), self.weight_grad[1].numpy()) - - def test_mkldnn_convolution_backward_float32(self, device): - - # input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, - # benchmark, deterministic, cudnn_enabled - item_2 = [[np.float16, 3, [1, 2, 5, 5]], [np.float16, 3, (2, 2, 1, 1)], [np.float16, 3, (2)], - [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, False] - - self.weight_grad.clear() - self.input_grad.clear() - self.bias_grad.clear() - input_cpu, input_npu = create_common_tensor(item_2[0], -1,1) - input_cpu = input_cpu.to(torch.float32) - input_npu = input_npu.to(torch.float32) - weight_cpu, weight_npu = create_common_tensor(item_2[1], -1,1) - weight_cpu = weight_cpu.to(torch.float32) - weight_npu = weight_npu.to(torch.float32) - bias_cpu, bias_npu = create_common_tensor(item_2[2], -1,1) - bias_cpu = bias_cpu.to(torch.float32) - bias_npu = bias_npu.to(torch.float32) - - cpu_output = self.op_exec_cpu(input_cpu, weight_cpu, bias_cpu, stride=item_2[3], padding=item_2[4], dilation=item_2[5], transposed=item_2[6], - output_padding=item_2[7], groups=item_2[8], benchmark=item_2[9], deterministic=item_2[10], cudnn_enabled=item_2[10]) - npu_output = self.op_exec_npu(input_npu, weight_npu, bias_npu, stride=item_2[3], padding=item_2[4], dilation=item_2[5], transposed=item_2[6], - output_padding=item_2[7], groups=item_2[8], benchmark=item_2[9], deterministic=item_2[10], cudnn_enabled=item_2[10]) - cpu_output = cpu_output.to(npu_output.dtype) - - self.input_grad[0] = self.input_grad[0].to(self.input_grad[1].dtype) - self.weight_grad[0] = self.weight_grad[0].to(self.weight_grad[1].dtype) - self.bias_grad[0] = self.bias_grad[0].to(self.bias_grad[1].dtype) - print("===input_grad_002===") - print(self.input_grad) - print("===weight_grad_002===") - print(self.weight_grad) - print("===bias_grad_002===") - print(self.bias_grad) - - self.assertRtolEqual(self.input_grad[0].numpy(), self.input_grad[1].numpy()) - self.assertRtolEqual(self.bias_grad[0].numpy(), self.bias_grad[1].numpy()) - self.assertRtolEqual(self.weight_grad[0].numpy(), self.weight_grad[1].numpy()) - -instantiate_device_type_tests(TestMkldnnConvolutionBackward, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:2") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_mkldnn_convolution_backward_input.py b/pytorch1.8.1/test/test_npu/test_mkldnn_convolution_backward_input.py deleted file mode 100644 index 7a90b52bc48..00000000000 --- a/pytorch1.8.1/test/test_npu/test_mkldnn_convolution_backward_input.py +++ /dev/null @@ -1,154 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -import torch.nn as nn -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor -#pylint: disable=unused-argument - -class TestMkldnnConvolutionBackwardInput(TestCase): - weight_grad = [] - input_grad = [] - bias_grad = [] - - def getWeightGrad(self, grad): - self.weight_grad.append(grad.to("cpu")) - - def getInputGrad(self, grad): - self.input_grad.append(grad.to("cpu")) - - def getBiasGrad(self, grad): - self.bias_grad.append(grad.to("cpu")) - - def op_exec_cpu(self, input1, weight, bias, stride, padding, dilation, transposed, - output_padding, groups, benchmark, deterministic, cudnn_enabled): - - input1.requires_grad = True - input1.register_hook(lambda grad: self.getInputGrad(grad)) - weight.requires_grad = True - weight.register_hook(lambda grad: self.getWeightGrad(grad)) - bias.requires_grad = True - bias.register_hook(lambda grad: self.getBiasGrad(grad)) - - cpu_res_forward = torch._convolution(input1, weight, bias, stride, padding, dilation, transposed=False, output_padding=[0, 0], - groups=1, benchmark=False, deterministic=False, cudnn_enabled=False) - - tmp = torch.ones_like(cpu_res_forward).float() - cpu_res_forward.backward(tmp, retain_graph=True) - - return cpu_res_forward - - def op_exec_npu(self, input1, weight, bias, stride, padding, dilation, transposed, - output_padding, groups, benchmark, deterministic, cudnn_enabled): - - input1 = input1.to("npu") - input1.requires_grad = True - input1.register_hook(lambda grad: self.getInputGrad(grad)) - weight = weight.to("npu") - weight.requires_grad = True - weight.register_hook(lambda grad: self.getWeightGrad(grad)) - bias = bias.to("npu") - bias.requires_grad = True - bias.register_hook(lambda grad: self.getBiasGrad(grad)) - - npu_res_forward = torch._convolution(input1, weight, bias, stride, padding, dilation, transposed=False, output_padding=[0, 0], - groups=1, benchmark=False, deterministic=False, cudnn_enabled=False) - - tmp = torch.ones_like(npu_res_forward).float() - tmp = tmp.to("npu") - npu_res_forward.backward(tmp, retain_graph=True) - - npu_res_forward = npu_res_forward.to("cpu") - return npu_res_forward - - def test_mkldnn_convolution_backward_float16(self, device): - - # input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, - # benchmark, deterministic, cudnn_enabled - item = [[np.float16, 3, [1, 2, 5, 5]], [np.float16, 3, (2, 2, 1, 1)], [np.float16, 3, (2)], - [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, False] - - self.weight_grad.clear() - self.input_grad.clear() - self.bias_grad.clear() - input_cpu, input_npu = create_common_tensor(item[0], -1,1) - if input_cpu.dtype == torch.float16: - input_cpu = input_cpu.to(torch.float32) - weight_cpu, weight_npu = create_common_tensor(item[1], -1,1) - if weight_cpu.dtype == torch.float16: - weight_cpu = weight_cpu.to(torch.float32) - bias_cpu, bias_npu = create_common_tensor(item[2], -1,1) - if bias_cpu.dtype == torch.float16: - bias_cpu = bias_cpu.to(torch.float32) - - cpu_output = self.op_exec_cpu(input_cpu, weight_cpu, bias_cpu, stride=item[3], padding=item[4], dilation=item[5], transposed=item[6], - output_padding=item[7], groups=item[8], benchmark=item[9], deterministic=item[10], cudnn_enabled=item[10]) - npu_output = self.op_exec_npu(input_npu, weight_npu, bias_npu, stride=item[3], padding=item[4], dilation=item[5], transposed=item[6], - output_padding=item[7], groups=item[8], benchmark=item[9], deterministic=item[10], cudnn_enabled=item[10]) - cpu_output = cpu_output.to(npu_output.dtype) - - self.input_grad[0] = self.input_grad[0].to(self.input_grad[1].dtype) - self.weight_grad[0] = self.weight_grad[0].to(self.weight_grad[1].dtype) - self.bias_grad[0] = self.bias_grad[0].to(self.bias_grad[1].dtype) - print("===input_grad_001===") - print(self.input_grad) - - - self.assertRtolEqual(self.input_grad[0].numpy(), self.input_grad[1].numpy()) - - def test_mkldnn_convolution_backward_float32(self, device): - - # input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, - # benchmark, deterministic, cudnn_enabled - item_2 = [[np.float16, 3, [1, 2, 5, 5]], [np.float16, 3, (2, 2, 1, 1)], [np.float16, 3, (2)], - [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, False] - - self.weight_grad.clear() - self.input_grad.clear() - self.bias_grad.clear() - input_cpu, input_npu = create_common_tensor(item_2[0], -1,1) - input_cpu = input_cpu.to(torch.float32) - input_npu = input_npu.to(torch.float32) - weight_cpu, weight_npu = create_common_tensor(item_2[1], -1,1) - weight_cpu = weight_cpu.to(torch.float32) - weight_npu = weight_npu.to(torch.float32) - bias_cpu, bias_npu = create_common_tensor(item_2[2], -1,1) - bias_cpu = bias_cpu.to(torch.float32) - bias_npu = bias_npu.to(torch.float32) - - cpu_output = self.op_exec_cpu(input_cpu, weight_cpu, bias_cpu, stride=item_2[3], padding=item_2[4], dilation=item_2[5], transposed=item_2[6], - output_padding=item_2[7], groups=item_2[8], benchmark=item_2[9], deterministic=item_2[10], cudnn_enabled=item_2[10]) - npu_output = self.op_exec_npu(input_npu, weight_npu, bias_npu, stride=item_2[3], padding=item_2[4], dilation=item_2[5], transposed=item_2[6], - output_padding=item_2[7], groups=item_2[8], benchmark=item_2[9], deterministic=item_2[10], cudnn_enabled=item_2[10]) - cpu_output = cpu_output.to(npu_output.dtype) - - self.input_grad[0] = self.input_grad[0].to(self.input_grad[1].dtype) - self.weight_grad[0] = self.weight_grad[0].to(self.weight_grad[1].dtype) - self.bias_grad[0] = self.bias_grad[0].to(self.bias_grad[1].dtype) - print("===input_grad_002===") - print(self.input_grad) - - - self.assertRtolEqual(self.input_grad[0].numpy(), self.input_grad[1].numpy()) - - -instantiate_device_type_tests(TestMkldnnConvolutionBackwardInput, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:2") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_mkldnn_convolution_backward_weights.py b/pytorch1.8.1/test/test_npu/test_mkldnn_convolution_backward_weights.py deleted file mode 100644 index 5bf471a52c5..00000000000 --- a/pytorch1.8.1/test/test_npu/test_mkldnn_convolution_backward_weights.py +++ /dev/null @@ -1,161 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -import torch.nn as nn -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor -#pylint: disable=unused-argument - -class TestMkldnnConvolutionBackwardWeights(TestCase): - weight_grad = [] - input_grad = [] - bias_grad = [] - - def getWeightGrad(self, grad): - self.weight_grad.append(grad.to("cpu")) - - def getInputGrad(self, grad): - self.input_grad.append(grad.to("cpu")) - - def getBiasGrad(self, grad): - self.bias_grad.append(grad.to("cpu")) - - def op_exec_cpu(self, input1, weight, bias, stride, padding, dilation, transposed, - output_padding, groups, benchmark, deterministic, cudnn_enabled): - - input1.requires_grad = True - input1.register_hook(lambda grad: self.getInputGrad(grad)) - weight.requires_grad = True - weight.register_hook(lambda grad: self.getWeightGrad(grad)) - bias.requires_grad = True - bias.register_hook(lambda grad: self.getBiasGrad(grad)) - - cpu_res_forward = torch._convolution(input1, weight, bias, stride, padding, dilation, transposed=False, output_padding=[0, 0], - groups=1, benchmark=False, deterministic=False, cudnn_enabled=False) - - tmp = torch.ones_like(cpu_res_forward).float() - cpu_res_forward.backward(tmp, retain_graph=True) - - return cpu_res_forward - - def op_exec_npu(self, input1, weight, bias, stride, padding, dilation, transposed, - output_padding, groups, benchmark, deterministic, cudnn_enabled): - - input1 = input1.to("npu") - input1.requires_grad = True - input1.register_hook(lambda grad: self.getInputGrad(grad)) - weight = weight.to("npu") - weight.requires_grad = True - weight.register_hook(lambda grad: self.getWeightGrad(grad)) - bias = bias.to("npu") - bias.requires_grad = True - bias.register_hook(lambda grad: self.getBiasGrad(grad)) - - npu_res_forward = torch._convolution(input1, weight, bias, stride, padding, dilation, transposed=False, output_padding=[0, 0], - groups=1, benchmark=False, deterministic=False, cudnn_enabled=False) - - tmp = torch.ones_like(npu_res_forward).float() - tmp = tmp.to("npu") - npu_res_forward.backward(tmp, retain_graph=True) - - npu_res_forward = npu_res_forward.to("cpu") - return npu_res_forward - - def test_mkldnn_convolution_backward_float16(self, device): - - # input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, - # benchmark, deterministic, cudnn_enabled - item = [[np.float16, 3, [1, 2, 5, 5]], [np.float16, 3, (2, 2, 1, 1)], [np.float16, 3, (2)], - [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, False] - - self.weight_grad.clear() - self.input_grad.clear() - self.bias_grad.clear() - input_cpu, input_npu = create_common_tensor(item[0], -1,1) - if input_cpu.dtype == torch.float16: - input_cpu = input_cpu.to(torch.float32) - weight_cpu, weight_npu = create_common_tensor(item[1], -1,1) - if weight_cpu.dtype == torch.float16: - weight_cpu = weight_cpu.to(torch.float32) - bias_cpu, bias_npu = create_common_tensor(item[2], -1,1) - if bias_cpu.dtype == torch.float16: - bias_cpu = bias_cpu.to(torch.float32) - - cpu_output = self.op_exec_cpu(input_cpu, weight_cpu, bias_cpu, stride=item[3], padding=item[4], dilation=item[5], transposed=item[6], - output_padding=item[7], groups=item[8], benchmark=item[9], deterministic=item[10], cudnn_enabled=item[10]) - npu_output = self.op_exec_npu(input_npu, weight_npu, bias_npu, stride=item[3], padding=item[4], dilation=item[5], transposed=item[6], - output_padding=item[7], groups=item[8], benchmark=item[9], deterministic=item[10], cudnn_enabled=item[10]) - cpu_output = cpu_output.to(npu_output.dtype) - - - self.weight_grad[0] = self.weight_grad[0].to(self.weight_grad[1].dtype) - self.bias_grad[0] = self.bias_grad[0].to(self.bias_grad[1].dtype) - - print("===weight_grad_001===") - print(self.weight_grad) - print("===bias_grad_001===") - print(self.bias_grad) - - - self.assertRtolEqual(self.bias_grad[0].numpy(), self.bias_grad[1].numpy()) - self.assertRtolEqual(self.weight_grad[0].numpy(), self.weight_grad[1].numpy()) - - def test_mkldnn_convolution_backward_float32(self, device): - - # input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, - # benchmark, deterministic, cudnn_enabled - item_2 = [[np.float16, 3, [1, 2, 5, 5]], [np.float16, 3, (2, 2, 1, 1)], [np.float16, 3, (2)], - [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, False] - - self.weight_grad.clear() - self.input_grad.clear() - self.bias_grad.clear() - input_cpu, input_npu = create_common_tensor(item_2[0], -1,1) - input_cpu = input_cpu.to(torch.float32) - input_npu = input_npu.to(torch.float32) - weight_cpu, weight_npu = create_common_tensor(item_2[1], -1,1) - weight_cpu = weight_cpu.to(torch.float32) - weight_npu = weight_npu.to(torch.float32) - bias_cpu, bias_npu = create_common_tensor(item_2[2], -1,1) - bias_cpu = bias_cpu.to(torch.float32) - bias_npu = bias_npu.to(torch.float32) - - cpu_output = self.op_exec_cpu(input_cpu, weight_cpu, bias_cpu, stride=item_2[3], padding=item_2[4], dilation=item_2[5], transposed=item_2[6], - output_padding=item_2[7], groups=item_2[8], benchmark=item_2[9], deterministic=item_2[10], cudnn_enabled=item_2[10]) - npu_output = self.op_exec_npu(input_npu, weight_npu, bias_npu, stride=item_2[3], padding=item_2[4], dilation=item_2[5], transposed=item_2[6], - output_padding=item_2[7], groups=item_2[8], benchmark=item_2[9], deterministic=item_2[10], cudnn_enabled=item_2[10]) - cpu_output = cpu_output.to(npu_output.dtype) - - - self.weight_grad[0] = self.weight_grad[0].to(self.weight_grad[1].dtype) - self.bias_grad[0] = self.bias_grad[0].to(self.bias_grad[1].dtype) - - print("===weight_grad_002===") - print(self.weight_grad) - print("===bias_grad_002===") - print(self.bias_grad) - - - self.assertRtolEqual(self.bias_grad[0].numpy(), self.bias_grad[1].numpy()) - self.assertRtolEqual(self.weight_grad[0].numpy(), self.weight_grad[1].numpy()) - -instantiate_device_type_tests(TestMkldnnConvolutionBackwardWeights, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:2") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_multinomial.py b/pytorch1.8.1/test/test_npu/test_multinomial.py deleted file mode 100644 index fd735267ee2..00000000000 --- a/pytorch1.8.1/test/test_npu/test_multinomial.py +++ /dev/null @@ -1,74 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# coding: utf-8 - -import torch -import numpy as np -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestMultinomial(TestCase): - - def sample_1d(self, weight, num_samples): - for replacement in [True, False]: - sample = torch.multinomial(weight, num_samples, replacement) - for index in sample: - self.assertNotEqual(weight[index], 0) - - def test_multinomial_1d_shape_format(self, device): - shape_format = [ - [[np.float32, 0, (5,)], 0, 100, 5], - [[np.float32, 0, (10,)], 0, 100, 10], - [[np.float32, 0, (20,)], 0, 100, 10], - [[np.float32, 0, (50,)], 0, 100, 5], - [[np.float16, 0, (5,)], 0, 100, 5], - [[np.float16, 0, (10,)], 0, 100, 10], - [[np.float16, 0, (20,)], 0, 100, 10], - [[np.float16, 0, (50,)], 0, 100, 5] - ] - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], item[1], item[2]) - self.sample_1d(npu_input1, item[3]) - - def sample_2d(self, weight, num_samples): - for replacement in [True, False]: - sample = torch.multinomial(weight, num_samples, replacement) - for i, row in enumerate(sample): - for j in row: - self.assertNotEqual(weight[i][j], 0) - - def test_multinomial_2d_shape_format(self, device): - shape_format = [ - [[np.float32, 0, (5,5)], 0, 100, 5], - [[np.float32, 0, (5,10)], 0, 100, 10], - [[np.float32, 0, (5,20)], 0, 100, 10], - [[np.float32, 0, (5,50)], 0, 100, 5], - [[np.float16, 0, (5,5)], 0, 100, 5], - [[np.float16, 0, (5,10)], 0, 100, 10], - [[np.float16, 0, (5,20)], 0, 100, 10], - [[np.float16, 0, (5,50)], 0, 100, 5] - ] - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], item[1], item[2]) - self.sample_2d(npu_input1, item[3]) - - -instantiate_device_type_tests(TestMultinomial, globals(), except_for="cpu") -if __name__ == "__main__": - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_narrow_copy.py b/pytorch1.8.1/test/test_npu/test_narrow_copy.py deleted file mode 100644 index 4b6018fb21a..00000000000 --- a/pytorch1.8.1/test/test_npu/test_narrow_copy.py +++ /dev/null @@ -1,72 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestNarrowCopy(TestCase): - def cpu_op_exec(self, data, dim, start, length): - output = data.narrow_copy(dim, start, length) - output = output.to("cpu") - output = output.detach().numpy().astype(np.int32) - return output - - def npu_op_exec(self, data, dim, start, length): - output = data.narrow_copy(dim, start, length) - output = output.to("cpu") - output = output.detach().numpy().astype(np.int32) - return output - - def test_narrow_copy_1(self, device): - data = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) - data_npu = data.to("npu") - - cpu_output = self.cpu_op_exec(data, 0, 0, 2) - npu_output = self.npu_op_exec(data_npu, 0, 0, 2) - - self.assertRtolEqual(cpu_output, npu_output) - - def test_narrow_copy_2(self, device): - data = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) - data_npu = data.to("npu") - - cpu_output = self.cpu_op_exec(data, 1, 1, 1) - npu_output = self.npu_op_exec(data_npu, 1, 1, 1) - - self.assertRtolEqual(cpu_output, npu_output) - - def test_narrow_copy_3(self, device): - data = torch.tensor([[[16,5,7,4],[16,5,7,4]],[[16,5,7,4],[16,5,7,4]],[[16,5,7,4],[16,5,7,4]]]) - data_npu = data.to("npu") - cpu_output = self.cpu_op_exec(data, 2, -2, 1) - npu_output = self.npu_op_exec(data_npu, 2, -2, 1) - - self.assertRtolEqual(cpu_output, npu_output) - - def test_narrow_copy_4(self, device): - data = torch.tensor([[[16,5,7,4],[16,5,7,4]],[[16,5,7,4],[16,5,7,4]],[[16,5,7,4],[16,5,7,4]]]) - data_npu = data.to("npu") - cpu_output = self.cpu_op_exec(data, -1, -2, 1) - npu_output = self.npu_op_exec(data_npu, -1, -2, 1) - - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestNarrowCopy, globals(), except_for="cpu") -if __name__ == "__main__": - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_network_ops/test_floatstatus.py b/pytorch1.8.1/test/test_npu/test_network_ops/test_floatstatus.py index 045f93c58aa..8c67f1e00fd 100644 --- a/pytorch1.8.1/test/test_npu/test_network_ops/test_floatstatus.py +++ b/pytorch1.8.1/test/test_npu/test_network_ops/test_floatstatus.py @@ -20,7 +20,7 @@ from common_utils import TestCase, run_tests class TestFloatStatus(TestCase): def test_float_status(self, device): float_tensor = torch.tensor([40000.0], dtype=torch.float16).npu() - float_tensor = float_tensor + float_tensor; + float_tensor = float_tensor + float_tensor input1 = torch.zeros(8).npu() float_status = torch.npu_alloc_float_status(input1) diff --git a/pytorch1.8.1/test/test_npu/test_norm_except_dim.py b/pytorch1.8.1/test/test_npu/test_norm_except_dim.py deleted file mode 100644 index c1555ee23a9..00000000000 --- a/pytorch1.8.1/test/test_npu/test_norm_except_dim.py +++ /dev/null @@ -1,77 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import math -import random -from torch._six import nan -from common_utils import TestCase, iter_indices, run_tests -from common_device_type import dtypes, instantiate_device_type_tests - - -class TestNormExceptDim(TestCase): - def generate_data(self, min, max, shape, dtype): - input1 = np.random.uniform(min, max, shape).astype(dtype) - input2 = np.random.uniform(min, max, shape).astype(dtype) - npu_input1 = torch.from_numpy(input1) - npu_input2 = torch.from_numpy(input2) - - return npu_input1, npu_input2 - - def generate_single_data(self, min, max, shape, dtype): - input = np.random.uniform(min, max, shape).astype(dtype) - npu_input = torch.from_numpy(input) - return npu_input - - def generate_int_dim(self, max): - dim = np.random.randint(0, max) - return dim - - def generate_bool_keepdim(self): - keepdim = random.choice([True, False]) - return keepdim - - def test_norm_except_dim_type(self, device): - def cpu_op_exec(input1, pow): - output = torch.norm_except_dim(input1, pow=pow, dim=0) - output = output.numpy() - return output - - def npu_op_exec(input1, pow): - print(input1.shape) - input1 = input1.to("npu") - output = torch.norm_except_dim(input1, pow=pow, dim=0) - output = output.to("cpu") - output = output.numpy() - print(output.shape) - return output - - def test_norm_except_dim_exec(input_type): - input1 = self.generate_single_data(0, 100, (5, 3), input_type) - pow = self.generate_int_dim(10) - cpu_output = cpu_op_exec(input1, pow) - npu_output = npu_op_exec(input1, pow) - return cpu_output, npu_output - - for dtype in [np.float32]: - cpu_output, npu_output = test_norm_except_dim_exec(dtype) - self.assertRtolEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(TestNormExceptDim, globals(), except_for="cpu") - -if __name__ == "__main__": - torch.npu.set_device("npu:0") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_norm_ext.py b/pytorch1.8.1/test/test_npu/test_norm_ext.py deleted file mode 100644 index bf3aac19f9f..00000000000 --- a/pytorch1.8.1/test/test_npu/test_norm_ext.py +++ /dev/null @@ -1,131 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestNorm(TestCase): - def norm_output_size(self, input, dim, keepdim): - output_size = list(input.size()) - for i in dim: - if i < 0: - i = i + input.dim() - if i < input.dim() and keepdim == True: - output_size[i] = 1 - if i < input.dim() and keepdim == False: - output_size.pop(i) - return output_size - - def cpu_out_exec(self, input, p1, dim1, keepdim1, dtype1): - output_size = self.norm_output_size(input, dim1, keepdim1) - cpu_out = torch.randn(output_size) - output = torch.norm(input, p = p1, dim = dim1 , keepdim = keepdim1, out = cpu_out, dtype = dtype1) - return output - - def npu_out_exec(self, input, p1, dim1, keepdim1, dtype1): - output_size = self.norm_output_size(input, dim1, keepdim1) - npu_out = torch.randn(output_size).npu() - output1 = torch.norm(input, p = p1, dim = dim1 , keepdim = keepdim1, out = npu_out, dtype = dtype1) - output = output1.to("cpu") - return output - - def test_norm_shape_format_0(self, device): - shape_format = [ - [[np.float16, 0, (1)]], - [[np.float32, 0, (1)]], - ] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item[0], 0, 100) - if cpu_input.dtype == torch.float16: - cpu_input = cpu_input.to(torch.float32) - cpu_output = self.cpu_out_exec(cpu_input, 0, [0], True, torch.float) - npu_output = self.npu_out_exec(npu_input, 0, [0], True, torch.float) - cpu_output = cpu_output.to(npu_output.dtype) - self.assertRtolEqual(cpu_output.numpy(), npu_output.numpy()) - - def test_norm_shape_format_1(self, device): - shape_format = [ - [[np.float16, 0, (12, 33)]], - [[np.float32, 0, (12, 33)]], - ] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item[0], 0, 100) - if cpu_input.dtype == torch.float16: - cpu_input = cpu_input.to(torch.float32) - cpu_output = self.cpu_out_exec(cpu_input, 1, [0,1], True, torch.float) - npu_output = self.npu_out_exec(npu_input, 1, [0,1], True, torch.float) - cpu_output = cpu_output.to(npu_output.dtype) - self.assertRtolEqual(cpu_output.numpy(), npu_output.numpy()) - - def test_norm_shape_format_2(self, device): - shape_format = [ - [[np.float16, 0, (12, 33)]], - [[np.float32, 0, (12, 33)]], - ] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item[0], 0, 100) - if cpu_input.dtype == torch.float16: - cpu_input = cpu_input.to(torch.float32) - cpu_output = self.cpu_out_exec(cpu_input, 2, [0], False, torch.float) - npu_output = self.npu_out_exec(npu_input, 2, [0], False, torch.float) - cpu_output = cpu_output.to(npu_output.dtype) - self.assertRtolEqual(cpu_output.numpy(), npu_output.numpy()) - - def test_norm_shape_format_3(self, device): - shape_format = [ - [[np.float16, 0, (10, 24, 56, 2048)]], - [[np.float32, 0, (10, 24, 56, 2048)]], - ] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item[0], 0, 100) - if cpu_input.dtype == torch.float16: - cpu_input = cpu_input.to(torch.float32) - cpu_output = self.cpu_out_exec(cpu_input, 3, [1,2], True, torch.float) - npu_output = self.npu_out_exec(npu_input, 3, [1,2], True, torch.float) - cpu_output = cpu_output.to(npu_output.dtype) - self.assertRtolEqual(cpu_output.numpy(), npu_output.numpy()) - - def test_norm_shape_format_inf(self, device): - shape_format = [ - [[np.float16, 0, (64, 64, 64, 64)]], - [[np.float32, 0, (64, 64, 64, 64)]], - ] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item[0], 0, 100) - if cpu_input.dtype == torch.float16: - cpu_input = cpu_input.to(torch.float32) - cpu_output = self.cpu_out_exec(cpu_input, float("inf"), [1,2], True, torch.float) - npu_output = self.npu_out_exec(npu_input, float("inf"), [1,2], True, torch.float) - cpu_output = cpu_output.to(npu_output.dtype) - self.assertRtolEqual(cpu_output.numpy(), npu_output.numpy()) - - def test_norm_shape_format_inf1(self, device): - shape_format = [ - [[np.float16, 0, (64, 64, 64, 64)]], - [[np.float32, 0, (64, 64, 64, 64)]], - ] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item[0], 0, 100) - cpu_output = self.cpu_out_exec(cpu_input, float("-inf"), [1,2], False, torch.float) - npu_output = self.npu_out_exec(npu_input, float("-inf"), [1,2], False, torch.float) - cpu_output = cpu_output.to(npu_output.dtype) - self.assertRtolEqual(cpu_output.numpy(), npu_output.numpy()) - -instantiate_device_type_tests(TestNorm, globals(), except_for="cpu") -if __name__ == "__main__": - torch.npu.set_device("npu:6") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_one_hot.py b/pytorch1.8.1/test/test_npu/test_one_hot.py deleted file mode 100644 index f9d69381841..00000000000 --- a/pytorch1.8.1/test/test_npu/test_one_hot.py +++ /dev/null @@ -1,94 +0,0 @@ -# Copyright (c) 2020 Huawei Technologies Co., Ltd -# Copyright (c) 2019, Facebook CORPORATION. -# All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import copy -import numpy as np -import sys -import torch -from common_device_type import dtypes, instantiate_device_type_tests -from common_utils import TestCase, run_tests -from util_test import create_common_tensor - - -class TestOneHot(TestCase): - def generate_single_data(self, low, high): - npu_input1 = torch.arange(low, high) - return npu_input1 - - def cpu_op_exec(self, input, num_classes): - output = torch.nn.functional.one_hot(input, num_classes=num_classes) - output = output.to(torch.int32) - output = output.numpy() - - return output - - def npu_op_exec(self, input, num_classes): - input = input.to(torch.int32) - input = input.to("npu") - output = torch.nn.functional.one_hot(input, num_classes=num_classes) - output = output.to("cpu") - output = output.numpy() - - return output - - def test_one_hot_1(self, device): - input = self.generate_single_data(0, 5) - cpu_output = self.cpu_op_exec(input, 5) - npu_output = self.npu_op_exec(input, 5) - - self.assertRtolEqual(cpu_output, npu_output) - - def test_one_hot_2(self, device): - input = self.generate_single_data(0, 5) - npu_output = self.npu_op_exec(input, -1) - cpu_output = self.cpu_op_exec(input, -1) - - self.assertRtolEqual(cpu_output, npu_output) - - def test_one_hot_3(self, device): - input = self.generate_single_data(0, 5) - npu_output = self.npu_op_exec(input, 6) - cpu_output = self.cpu_op_exec(input, 6) - - self.assertRtolEqual(cpu_output, npu_output) - - def test_one_hot_4(self, device): - input = self.generate_single_data(0, 10) - cpu_output = self.cpu_op_exec(input, 10) - npu_output = self.npu_op_exec(input, 10) - - self.assertRtolEqual(cpu_output, npu_output) - - def test_one_hot_5(self, device): - input = self.generate_single_data(0, 10) - cpu_output = self.cpu_op_exec(input, -1) - npu_output = self.npu_op_exec(input, -1) - - self.assertRtolEqual(cpu_output, npu_output) - - def test_one_hot_6(self, device): - input = self.generate_single_data(0, 10) - cpu_output = self.cpu_op_exec(input, 12) - npu_output = self.npu_op_exec(input, 12) - - self.assertRtolEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(TestOneHot, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:0") - run_tests() - diff --git a/pytorch1.8.1/test/test_npu/test_pixel_shuffle.py b/pytorch1.8.1/test/test_npu/test_pixel_shuffle.py deleted file mode 100644 index fa35bae0802..00000000000 --- a/pytorch1.8.1/test/test_npu/test_pixel_shuffle.py +++ /dev/null @@ -1,95 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestPixel_shuffle(TestCase): - - - def generate_single_data(self, min_d, max_d, shape, dtype): - input1 = np.random.uniform(min_d, max_d, shape).astype(dtype) - npu_input1 = torch.from_numpy(input1) - return npu_input1 - - - def cpu_op_exec(self, input1, block_size): - output = torch.nn.functional.pixel_shuffle(input1, block_size) - output = output.numpy() - return output - - def npu_op_exec_tensor_need_to_npu(self, input1, block_size): - input1 = input1.to("npu") - output = torch.nn.functional.pixel_shuffle(input1, block_size) - output = output.to("cpu") - output = output.numpy() - return output - - def test_pixel_shuffle_common_shape_format(self, device): - shape_format = [ - [np.float32, -1, (1, 16, 4, 4)], - [np.float32, -1, (1, 16, 2, 2)], - [np.float32, -1, (1, 16, 1, 1)], - [np.float32, -1, (1, 64, 1, 1)], - [np.float32, -1, (1, 256, 1, 1)], - [np.float32, -1, (1, 655360, 1, 1)], - #[np.int8, -1, (1, 786432, 1, 1)], - #[np.int64, -1, (1, 655360, 1, 1)], - #[np.uint8, -1, (1, 655360, 1, 1)], - [np.int32, -1, (1, 655360, 1, 1)] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item, 1, 100) - cpu_output = self.cpu_op_exec(cpu_input1, 4) - npu_output = self.npu_op_exec_tensor_need_to_npu(npu_input1, 4) - self.assertRtolEqual(cpu_output, npu_output) - - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item, 1, 100) - cpu_output = self.cpu_op_exec(cpu_input1, 1) - npu_output = self.npu_op_exec_tensor_need_to_npu(npu_input1, 1) - self.assertRtolEqual(cpu_output, npu_output) - - - def test_pixel_shuffle_float16_shape_format(self, device): - def cpu_op_exec_fp16(input1, block_size): - input1 = input1.to(torch.float32) - output = torch.pixel_shuffle(input1, block_size) - output = output.numpy() - output = output.astype(np.float16) - return output - - shape_format = [ - [np.float16, -1, (1, 16, 1, 1)], - [np.float16, -1, (1, 16, 4, 4)], - [np.float16, -1, (1, 655360, 1, 1)] - ] - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item, 1, 100) - cpu_output = cpu_op_exec_fp16(cpu_input1, 4) - npu_output = self.npu_op_exec_tensor_need_to_npu(npu_input1, 4) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestPixel_shuffle, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:7") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_prelu.py b/pytorch1.8.1/test/test_npu/test_prelu.py deleted file mode 100644 index 9b4079dd87e..00000000000 --- a/pytorch1.8.1/test/test_npu/test_prelu.py +++ /dev/null @@ -1,61 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestPrelu(TestCase): - - def cpu_op_exec(self, input1, input2): - output = input1.prelu(input2) - return output.numpy() - - def npu_op_exec(self, input1, input2): - output = input1.prelu(input2) - output = output.to("cpu") - if output.dtype != torch.float32: - output = output.to(torch.float32) - return output.numpy() - - def test_prelu_shape_format(self, device): - shape_format = [ - [[np.float32, 0, [1, 1]], [np.float32, 0, 1]], - [[np.float32, 0, [2, 2]], [np.float32, 0, 1]], - [[np.float16, 0, [1, 1]], [np.float16, 0, 1]], - [[np.float16, 0, [2, 2]], [np.float16, 0, 1]] - ] - - for item in shape_format: - - cpu_input1, npu_input1 = create_common_tensor(item[0], 0, 10) - cpu_input2, npu_input2 = create_common_tensor(item[1], 0, 10) - if cpu_input1.dtype == torch.float16: - cpu_input1 = cpu_input1.to(torch.float32) - if cpu_input2.dtype == torch.float16: - cpu_input2 = cpu_input2.to(torch.float32) - cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2) - cpu_output = cpu_output.astype(npu_output.dtype) - self.assertRtolEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(TestPrelu, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:5") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_prelu_backward.py b/pytorch1.8.1/test/test_npu/test_prelu_backward.py deleted file mode 100644 index d058a061658..00000000000 --- a/pytorch1.8.1/test/test_npu/test_prelu_backward.py +++ /dev/null @@ -1,92 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestPreluBackward(TestCase): - def cpu_op_back_exec_ext(self,input1,weight): - w = torch.ones_like(input1) - input1.requires_grad_(True) - m = torch.nn.PReLU(weight) - tmp = m(input1) - tmp.backward(w) - output = input1.grad - output = output.numpy() - return output - - def npu_op_back_exec_ext(self,input1,weight): - w = torch.ones_like(input1) - w = w.to("npu") - m = torch.nn.PReLU(weight) - m = m.to("npu") - input1.requires_grad_(True) - input1 = input1.to("npu") - tmp = m(input1) - tmp.backward(w) - output = input1.grad.to("cpu") - output = output.numpy() - return output - - def test_PreluBackward_shape_format_fp32(self, device): - shape_format = [ - [np.float32, 0, (17, 12, 38, 15)], - [np.float32, 0, (1, 12, 38, 5)], - [np.float32, 0, (124, 12, 38, 25)], - [np.float32, 0, (4, 12, 38, 5)], - [np.float32, 0, (10, 12, 38, 45)], - ] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item, -2, 2) - cpu_weight = npu_weight = torch.randn(12) - cpu_output = self.cpu_op_back_exec_ext(cpu_input, cpu_weight) - npu_output = self.npu_op_back_exec_ext(npu_input, npu_weight) - self.assertRtolEqual(cpu_output, npu_output) - - def test_PreluBackward_shape_format_fp16(self, device): - def cpu_op_back_exec_fp16_ext(input1,weight): - input1 = input1.to(torch.float32) - weight = weight.to(torch.float32) - w = torch.ones_like(input1) - input1.requires_grad_(True) - m = torch.nn.PReLU(weight) - tmp = m(input1) - tmp.backward(w) - output = input1.grad - output = output.detach().numpy() - output = output.astype(np.float16) - return output - shape_format = [ - [np.float16, 0, (3, 5, 4)], - [np.float16, 0, (32, 1, 1)], - [np.float16, 0, (3, 224, 224)], - [np.float16, 0, (5, 32, 112)], - [np.float16, 0, (2, 672, 7)], - [np.float16, 0, (6, 288, 14)], - [np.float16, 0, (4, 58, 28)], - ] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item, -2, 2) - cpu_weight = npu_weight = torch.randn(1) - cpu_output = cpu_op_back_exec_fp16_ext(cpu_input, cpu_weight) - npu_output = self.npu_op_back_exec_ext(npu_input, npu_weight) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestPreluBackward, globals(), except_for="cpu") -if __name__ == "__main__": - torch.npu.set_device("npu:6") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_qr.py b/pytorch1.8.1/test/test_npu/test_qr.py deleted file mode 100644 index b35adf216a4..00000000000 --- a/pytorch1.8.1/test/test_npu/test_qr.py +++ /dev/null @@ -1,148 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class Testqr(TestCase): - - def cpu_op_exec(self, input1, some): - out = torch.qr(input1, some) - output_q = out.Q - output_r = out.R - output_q = output_q.numpy() - output_r = output_r.numpy() - return output_q, output_r, out - - def npu_op_exec(self, input1, some): - out = torch.qr(input1.to("npu"), some) - output_q = out.Q - output_r = out.R - output_q = output_q.to("cpu") - output_r = output_r.to("cpu") - output_q = output_q.numpy() - output_r = output_r.numpy() - return output_q, output_r, out -# pylint: disable=W0613 - def test_qr_common_shape_format(self, device): - - shape_format = [ - [np.float32, -1, (5, 3)], - [np.float32, -1, (1, 64, 147, 147)], - [np.float32, -1, (65536, 14, 7, 1)], - [np.int32, -1, (1000000, 3, 3, 1)], - [np.int32, -1, (1024, 107, 31, 2)], - [np.int32, -1, (1, 128, 1, 1)] - ] - for item in shape_format: - some = True - cpu_input1, npu_input1 = create_common_tensor(item, -0.001, 0.001) - if cpu_input1.dtype == torch.int32: - cpu_input1 = cpu_input1.to(torch.float32) - if npu_input1.dtype == torch.int32: - npu_input1 = npu_input1.to(torch.float32) - cpu_output_q, cpu_output_r, cpu_out = self.cpu_op_exec(cpu_input1, some) - npu_output_q, npu_output_r, npu_out = self.npu_op_exec(npu_input1, some) - npu_output = np.matmul(npu_output_q, npu_output_r) - - self.assertRtolEqual(cpu_output_q, npu_output_q) - self.assertRtolEqual(cpu_output_r, npu_output_r) - self.assertRtolEqual(cpu_input1.numpy(), npu_output) - self.assertRtolEqual(cpu_out, npu_out) - - def test_qr_float16_shape_format(self, device): - shape_format = [ - [np.float16, -1, (5, 3)], - [np.float16, -1, (1, 64, 147, 147)], - [np.float16, -1, (65536, 14, 7, 1)], - [np.float16, -1, (1000000, 3, 3, 1)], - [np.float16, -1, (1024, 107, 31, 2)], - [np.float16, -1, (1, 128, 1, 1)] - ] - for item in shape_format: - some = True - cpu_input1, npu_input1 = create_common_tensor(item, -0.001, 0.001) - if cpu_input1.dtype == torch.float16: - cpu_input1 = cpu_input1.to(torch.float32) - if npu_input1.dtype == torch.float16: - npu_input1 = npu_input1.to(torch.float32) - cpu_output_q, cpu_output_r, cpu_out = self.cpu_op_exec(cpu_input1, some) - npu_output_q, npu_output_r, npu_out = self.npu_op_exec(npu_input1, some) - npu_output = np.matmul(npu_output_q, npu_output_r) - - self.assertRtolEqual(cpu_output_q, npu_output_q) - self.assertRtolEqual(cpu_output_r, npu_output_r) - self.assertRtolEqual(cpu_input1.numpy(), npu_output) - self.assertRtolEqual(cpu_out, npu_out) - - def test_qr_common_False_shape_format(self, device): - - shape_format = [ - [np.float32, -1, (5, 3)], - [np.float32, -1, (1, 64, 147, 147)], - [np.float32, -1, (65536, 14, 7, 1)], - [np.int32, -1, (1000000, 3, 3, 1)], - [np.int32, -1, (1024, 107, 31, 2)], - [np.int32, -1, (1, 128, 1, 1)] - ] - for item in shape_format: - some = False - cpu_input1, npu_input1 = create_common_tensor(item, -0.001, 0.001) - if cpu_input1.dtype == torch.int32: - cpu_input1 = cpu_input1.to(torch.float32) - if npu_input1.dtype == torch.int32: - npu_input1 = npu_input1.to(torch.float32) - cpu_output_q, cpu_output_r, cpu_out = self.cpu_op_exec(cpu_input1, some) - npu_output_q, npu_output_r, npu_out = self.npu_op_exec(npu_input1, some) - npu_output = np.matmul(npu_output_q, npu_output_r) - - self.assertRtolEqual(cpu_output_q, npu_output_q) - self.assertRtolEqual(cpu_output_r, npu_output_r) - self.assertRtolEqual(cpu_input1.numpy(), npu_output) - self.assertRtolEqual(cpu_out, npu_out) - - def test_qr_float16_False_shape_format(self, device): - shape_format = [ - [np.float16, -1, (5, 3)], - [np.float16, -1, (1, 64, 147, 147)], - [np.float16, -1, (65536, 14, 7, 1)], - [np.float16, -1, (1000000, 3, 3, 1)], - [np.float16, -1, (1024, 107, 31, 2)], - [np.float16, -1, (1, 128, 1, 1)] - ] - for item in shape_format: - some = False - cpu_input1, npu_input1 = create_common_tensor(item, -0.001, 0.001) - if cpu_input1.dtype == torch.float16: - cpu_input1 = cpu_input1.to(torch.float32) - if npu_input1.dtype == torch.float16: - npu_input1 = npu_input1.to(torch.float32) - cpu_output_q, cpu_output_r, cpu_out = self.cpu_op_exec(cpu_input1, some) - npu_output_q, npu_output_r, npu_out = self.npu_op_exec(npu_input1, some) - npu_output = np.matmul(npu_output_q, npu_output_r) - - self.assertRtolEqual(cpu_output_q, npu_output_q) - self.assertRtolEqual(cpu_output_r, npu_output_r) - self.assertRtolEqual(cpu_input1.numpy(), npu_output) - self.assertRtolEqual(cpu_out, npu_out) - -instantiate_device_type_tests(Testqr, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:1") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_quantize_per_channel.py b/pytorch1.8.1/test/test_npu/test_quantize_per_channel.py deleted file mode 100644 index f4fdc118125..00000000000 --- a/pytorch1.8.1/test/test_npu/test_quantize_per_channel.py +++ /dev/null @@ -1,73 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestQuantizePerChannel(TestCase): - def generate_data_per_channel(self, min_d, max_d, shape_x, shape_scale, shape_zp, dtype_x, dtype_scale, dtype_zp): - input_x = np.random.uniform(min_d, max_d, shape_x).astype(dtype_x) - scales = np.random.uniform(min_d, max_d, shape_scale).astype(dtype_scale) - zero_points = np.random.uniform(min_d, max_d, shape_zp).astype(dtype_zp) - npu_input_x = torch.from_numpy(input_x) - npu_input_scales = torch.from_numpy(scales) - npu_input_zero_points = torch.from_numpy(zero_points) - return npu_input_x, npu_input_scales, npu_input_zero_points - - def cpu_op_exec_per_channel(self, input_x, input_scales, input_zero_points, axis, dtype): - output = torch.quantize_per_channel(input_x, input_scales, input_zero_points, axis, dtype).int_repr() - output = output.numpy() - return output - - def npu_op_exec_per_channel(self, input_x, input_scales, input_zero_points, axis, dtype): - input_x = input_x.to("npu") - input_scales = input_scales.to("npu") - input_zero_points = input_zero_points.to("npu") - output = torch.quantize_per_channel(input_x, input_scales, input_zero_points, axis, dtype) - output = output.to("cpu") - output = output.numpy() - return output - - def test_per_channel_3_3_0_int32(self, device): - input_x1, scales, zero_points = self.generate_data_per_channel(-1, 1, (3, 3), (3,), (3,), np.float32, np.float32, np.int32) - cpu_output1 = self.cpu_op_exec_per_channel(input_x1, scales, zero_points, 0, torch.qint32) - npu_output1 = self.npu_op_exec_per_channel(input_x1, scales, zero_points, 0, torch.qint32) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_per_channel_3_3_3_3_1_int8(self, device): - input_x1, scales, zero_points = self.generate_data_per_channel(-1, 1, (3, 3), (3,), (3,), np.float32, np.float32, np.int8) - cpu_output1 = self.cpu_op_exec_per_channel(input_x1, scales, zero_points, 1, torch.qint8).astype(np.int32) - npu_output1 = self.npu_op_exec_per_channel(input_x1, scales, zero_points, 1, torch.qint8).astype(np.int32) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_per_channel_3_3_3_3_3_3_3_3_4_uint8(self, device): - input_x1, scales, zero_points = self.generate_data_per_channel(-1, 1, (3, 3, 3, 3, 3, 3, 3, 3), (3,), (3,), np.float32, np.float32, np.int32) - cpu_output1 = self.cpu_op_exec_per_channel(input_x1, scales, zero_points, 4, torch.quint8) - npu_output1 = self.npu_op_exec_per_channel(input_x1, scales, zero_points, 4, torch.quint8) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_per_channel_30_30_30_30_30_2_uint8(self, device): - input_x1, scales, zero_points = self.generate_data_per_channel(-1, 1, (30, 30, 30, 30), (30,), (30,), np.float16, np.float32, np.uint8) - input_x1_cpu = input_x1.float() - cpu_output1 = self.cpu_op_exec_per_channel(input_x1_cpu, scales, zero_points, 2, torch.quint8) - npu_output1 = self.npu_op_exec_per_channel(input_x1, scales, zero_points, 2, torch.quint8) - self.assertRtolEqual(cpu_output1, npu_output1) - -instantiate_device_type_tests(TestQuantizePerChannel, globals(), except_for='cpu') -if __name__ == "__main__": - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_quantize_per_tensor.py b/pytorch1.8.1/test/test_npu/test_quantize_per_tensor.py deleted file mode 100644 index a0612614e29..00000000000 --- a/pytorch1.8.1/test/test_npu/test_quantize_per_tensor.py +++ /dev/null @@ -1,69 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestQuantizePerTensor(TestCase): - - def generate_data_per_tensor(self, min_d, max_d, shape_x, dtype_x): - input_x = np.random.uniform(min_d, max_d, shape_x).astype(dtype_x) - npu_input_x = torch.from_numpy(input_x) - return npu_input_x - - def cpu_op_exec_per_tensor(self, input_x, input_scale, input_zero_point, dtype): - output = torch.quantize_per_tensor(input_x, input_scale, input_zero_point, dtype).int_repr() - output = output.numpy() - return output - - def npu_op_exec_per_tensor(self, input_x, input_scale, input_zero_point, dtype): - input_x = input_x.to("npu") - output = torch.quantize_per_tensor(input_x, input_scale, input_zero_point, dtype) - output = output.to("cpu") - output = output.numpy() - return output - - def test_per_tensor_3_3_0p1_10_int32(self, device): - input_x1 = self.generate_data_per_tensor(-1, 1, (3, 3), np.float32) - cpu_output1 = self.cpu_op_exec_per_tensor(input_x1, 0.1, 10, torch.qint32) - npu_output1 = self.npu_op_exec_per_tensor(input_x1, 0.1, 10, torch.qint32) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_per_tensor_3_3_0p1_10_int8(self, device): - input_x1 = self.generate_data_per_tensor(-1, 1, (3, 3), np.float16) - input_cpu = input_x1.float() - cpu_output1 = self.cpu_op_exec_per_tensor(input_cpu, 0.1, 10, torch.qint8) - npu_output1 = self.npu_op_exec_per_tensor(input_x1, 0.1, 10, torch.qint8) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_per_tensor_3_3_3_3_3_3_0p1_10_uint8(self, device): - input_x1 = self.generate_data_per_tensor(-1, 1, (3, 3, 3, 3, 3, 3), np.float32) - cpu_output1 = self.cpu_op_exec_per_tensor(input_x1, 0.1, 10, torch.quint8) - npu_output1 = self.npu_op_exec_per_tensor(input_x1, 0.1, 10, torch.quint8) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_per_tensor_30_30_30_30_30_30_0p01_5_uint8(self, device): - input_x1 = self.generate_data_per_tensor(-1, 1, (30, 30, 30, 30, 30, 30), np.float32) - cpu_output1 = self.cpu_op_exec_per_tensor(input_x1, 0.01, 5, torch.quint8) - npu_output1 = self.npu_op_exec_per_tensor(input_x1, 0.01, 5, torch.quint8) - self.assertRtolEqual(cpu_output1, npu_output1) - - -instantiate_device_type_tests(TestQuantizePerTensor, globals(), except_for='cpu') -if __name__ == "__main__": - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_real.py b/pytorch1.8.1/test/test_npu/test_real.py deleted file mode 100644 index 75ab28b3ba7..00000000000 --- a/pytorch1.8.1/test/test_npu/test_real.py +++ /dev/null @@ -1,61 +0,0 @@ -import torch -import numpy as np -import sys -import random -import copy -from torch.autograd import Variable -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestReal(TestCase): - def generate_data(self, min, max, shape, dtype): - input1 = np.random.uniform(min, max, shape).astype(dtype) - npu_input1 = torch.from_numpy(input1) - return npu_input1 - - def cpu_op_exec(self, input1): - output = torch.real(input1) - print(torch.real(input1)) - return output - - def npu_op_exec(self, input1): - input1 = input1.to("npu") - output = torch.real(input1) - output = output.to("cpu") - return output - - def test_real_float32_1(self, device): - npu_input1 = self.generate_data(0, 100, (4, ), np.float32) - cpu_output = self.npu_op_exec(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_real_float32_2(self, device): - npu_input1 = self.generate_data(0, 100, (5, 1), np.float32) - cpu_output = self.cpu_op_exec(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_real_int32_1(self, device): - npu_input1 = self.generate_data(0, 100, (4, ), np.int32) - cpu_output = self.cpu_op_exec(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_real_float32_1_1(self, device): - npu_input1 = self.generate_data(0, 100, (5, 1, 1), np.int32) - cpu_output = self.cpu_op_exec(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_real_float32_2_2(self, device): - npu_input1 = self.generate_data(0, 100, (5, 1, 1), np.float32) - cpu_output = self.cpu_op_exec(npu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(TestReal, globals(), except_for='cpu') -if __name__ == '__main__': - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_reflection_pad2d.py b/pytorch1.8.1/test/test_npu/test_reflection_pad2d.py deleted file mode 100644 index d150c4c955b..00000000000 --- a/pytorch1.8.1/test/test_npu/test_reflection_pad2d.py +++ /dev/null @@ -1,238 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestReflectionPad2d(TestCase): - def cpu_op_out_exec(self, input1, pad, output): - m = torch._C._nn.reflection_pad2d(input1, pad, out=output) - m = m.numpy() - return m - - def npu_op_out_exec(self, input1, pad, output): - m_n = torch._C._nn.reflection_pad2d(input1, pad, out=output) - m_n = m_n.to("cpu") - m_n = m_n.numpy() - return m_n - - def cpu_op_exec(self, input1, pad): - m = torch.nn.ReflectionPad2d(pad) - output = m(input1) - output = output.numpy() - return output - - def npu_op_exec(self, input1, pad): - m = torch.nn.ReflectionPad2d(pad).to("npu") - output = m(input1) - output = output.to("cpu") - output = output.numpy() - return output - - def test_reflectionPad2d_out_shape_format(self, device): - shape_format = [ - [[np.float32, 0, (1, 1, 3, 3)], [2, 2, 2, 2]], - [[np.float32, 3, (1, 1, 4, 3)], 2] - ] - for item in shape_format: - cpuout = torch.randn(1, 1, 3, 3) - npuout = cpuout.npu() - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100) - cpu_output = self.cpu_op_out_exec(cpu_input1, item[1], cpuout) - npu_output = self.npu_op_out_exec(npu_input1, item[1], npuout) - self.assertRtolEqual(cpu_output, npu_output) - - def test_reflectionPad2d_out_shape_format_fp16(self, device): - shape_format = [ - [[np.float16, 0, (1, 1, 4, 3)], [2, 2, 2, 2]], - [[np.float16, 3, (1, 1, 4, 3)], 2] - ] - - def cpu_op_out_exec_fp16(input1, pad, output): - input1 = input1.to(torch.float32) - m = torch._C._nn.reflection_pad2d(input1, pad, out=output) - m = m.numpy() - m = m.astype(np.float16) - return m - - for item in shape_format: - cpuout = torch.randn(1, 1, 3, 3) - npuout = cpuout.npu() - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100) - cpu_output = cpu_op_out_exec_fp16(cpu_input1, item[1], cpuout) - npu_output = self.npu_op_out_exec(npu_input1, item[1], npuout) - self.assertRtolEqual(cpu_output, npu_output) - - def test_reflectionPad2d_out_shape_format_int8(self, device): - shape_format = [ - [[np.int8, 0, (1, 1, 4, 3)], [2, 2, 2, 2]], - [[np.int8, 0, (1, 1, 5, 3)], 2] - ] - - def cpu_op_out_exec_int8(input1, pad, output): - input1 = input1.to(torch.float32) - m = torch._C._nn.reflection_pad2d(input1, pad, out=output) - m = m.numpy() - m = m.astype(np.int8) - return m - - for item in shape_format: - cpuout = torch.randn(1, 1, 3, 3) - npuout = cpuout.npu() - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100) - cpu_output = cpu_op_out_exec_int8(cpu_input1, item[1], cpuout) - npu_output = self.npu_op_out_exec(npu_input1, item[1], npuout) - self.assertRtolEqual(cpu_output, npu_output) - - def test_reflectionPad2d_out_shape_format_uint8(self, device): - shape_format = [ - [[np.uint8, 0, (1, 1, 4, 3)], [2, 2, 2, 2]], - [[np.uint8, 0, (1, 1, 4, 9)], 3] - ] - - def cpu_op_out_exec_uint8(input1, pad, output): - input1 = input1.to(torch.float32) - m = torch._C._nn.reflection_pad2d(input1, pad, out=output) - m = m.numpy() - m = m.astype(np.uint8) - return m - - for item in shape_format: - cpuout = torch.randn(1, 1, 3, 3) - npuout = cpuout.npu() - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100) - cpu_output = cpu_op_out_exec_uint8(cpu_input1, item[1], cpuout) - npu_output = self.npu_op_out_exec(npu_input1, item[1], npuout) - self.assertRtolEqual(cpu_output, npu_output) - - def test_reflectionPad2d_out_shape_format_int32(self, device): - shape_format = [ - [[np.int32, 0, (1, 1, 4, 3)], [2, 2, 2, 2]], - [[np.int32, 0, (1, 1, 4, 9)], 2] - ] - - def cpu_op_out_exec_int32(input1, pad, output): - input1 = input1.to(torch.float32) - m = torch._C._nn.reflection_pad2d(input1, pad, out=output) - m = m.numpy() - m = m.astype(np.int32) - return m - - for item in shape_format: - cpuout = torch.randn(1, 1, 3, 3) - npuout = cpuout.npu() - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100) - cpu_output = cpu_op_out_exec_int32(cpu_input1, item[1], cpuout) - npu_output = self.npu_op_out_exec(npu_input1, item[1], npuout) - self.assertRtolEqual(cpu_output, npu_output) - - def test_reflectionPad2d_shape_format(self, device): - shape_format = [ - [[np.float32, 0, (1, 1, 3, 3)], [2, 2, 2, 2]], - [[np.float32, 3, (1, 1, 4, 3)], 2] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100) - cpu_output = self.cpu_op_exec(cpu_input1, item[1]) - npu_output = self.npu_op_exec(npu_input1, item[1]) - self.assertRtolEqual(cpu_output, npu_output) - - def test_reflectionPad2d_shape_format_fp16(self, device): - shape_format = [ - [[np.float16, 0, (1, 1, 4, 3)], [2, 2, 2, 2]], - [[np.float16, 3, (1, 1, 4, 3)], 2] - ] - - def cpu_op_exec_fp16(input1, pad): - input1 = input1.to(torch.float32) - m = torch.nn.ReflectionPad2d(pad) - output = m(input1) - output = output.numpy() - output = output.astype(np.float16) - return output - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100) - cpu_output = cpu_op_exec_fp16(cpu_input1, item[1]) - npu_output = self.npu_op_exec(npu_input1, item[1]) - self.assertRtolEqual(cpu_output, npu_output) - - def test_reflectionPad2d_shape_format_int8(self, device): - shape_format = [ - [[np.int8, 0, (1, 1, 4, 3)], [2, 2, 2, 2]], - [[np.int8, 0, (1, 1, 5, 3)], 2] - ] - - def cpu_op_exec_int8(input1, pad): - input1 = input1.to(torch.float32) - m = torch.nn.ReflectionPad2d(pad) - output = m(input1) - output = output.numpy() - output = output.astype(np.int8) - return output - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100) - cpu_output = cpu_op_exec_int8(cpu_input1, item[1]) - npu_output = self.npu_op_exec(npu_input1, item[1]) - self.assertRtolEqual(cpu_output, npu_output) - - def test_reflectionPad2d_shape_format_uint8(self, device): - shape_format = [ - [[np.uint8, 0, (1, 1, 4, 3)], [2, 2, 2, 2]], - [[np.uint8, 0, (1, 1, 4, 9)], 3] - ] - - def cpu_op_exec_uint8(input1, pad): - input1 = input1.to(torch.float32) - m = torch.nn.ReflectionPad2d(pad) - output = m(input1) - output = output.numpy() - output = output.astype(np.uint8) - return output - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100) - cpu_output = cpu_op_exec_uint8(cpu_input1, item[1]) - npu_output = self.npu_op_exec(npu_input1, item[1]) - self.assertRtolEqual(cpu_output, npu_output) - - def test_reflectionPad2d_shape_format_int32(self, device): - shape_format = [ - [[np.int32, 0, (1, 1, 4, 3)], [2, 2, 2, 2]], - [[np.int32, 0, (1, 1, 4, 9)], 2] - ] - - def cpu_op_exec_int32(input1, pad): - input1 = input1.to(torch.float32) - m = torch.nn.ReflectionPad2d(pad) - output = m(input1) - output = output.numpy() - output = output.astype(np.int32) - return output - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100) - cpu_output = cpu_op_exec_int32(cpu_input1, item[1]) - npu_output = self.npu_op_exec(npu_input1, item[1]) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestReflectionPad2d, globals(), except_for="cpu") -if __name__ == "__main__": - torch.npu.set_device("npu:2") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_renorm.py b/pytorch1.8.1/test/test_npu/test_renorm.py deleted file mode 100644 index a1c258f913a..00000000000 --- a/pytorch1.8.1/test/test_npu/test_renorm.py +++ /dev/null @@ -1,273 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestRenorm(TestCase): - def generate_data(self, min_d, max_d, shape, dtype): - input_x = np.random.uniform(min_d, max_d, shape).astype(dtype) - npu_input = torch.from_numpy(input_x) - return npu_input - - def get_p0_result_cpu(self, input_x, dim, maxnorm=1.0): - input_x = input_x.numpy() - dims = len(input_x.shape) - shape_list = [] - for i in range(dims): - if(i != dim): - shape_list = shape_list + [i] - shape_list = tuple(shape_list) - tmp = (input_x!=0) - N = np.sum(tmp, shape_list, keepdims=True) - N = np.where(N > maxnorm, maxnorm/(N+1e-7), 1.0) - output = input_x * N - return output - - def cpu_op_exec(self, input_x, p, dim, maxnorm): - if(p==0): - output = self.get_p0_result_cpu(input_x, dim, maxnorm) - else: - output = torch.renorm(input_x, p, dim, maxnorm) - output = output.numpy() - return output.astype(np.float32) - - def npu_op_exec(self, input_x, p, dim, maxnorm): - input1 = input_x.to("npu") - output = torch.renorm(input1, p, dim, maxnorm) - output = output.to("cpu") - output = output.numpy() - return output - - def npu_op_exec_out(self, input_x, p, dim, maxnorm, output_y): - input_x = input_x.to("npu") - output_y = output_y.to("npu") - torch.renorm(input_x, p, dim, maxnorm, out=output_y) - output_y = output_y.to("cpu") - output_y = output_y.numpy() - return output_y - - def npu_op_exec_inplace(self, input_x, p, dim, maxnorm): - input_x = input_x.to("npu") - input_x.renorm_(p, dim, maxnorm) - output = input_x.to("cpu") - output = output.numpy() - return output - - def test_renorm_3_3_4_0_1(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 4, 0, 1) - npu_output1 = self.npu_op_exec(input_x1, 4, 0, 1) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_1_1_1(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 1, 1, 1) - npu_output1 = self.npu_op_exec(input_x1, 1, 1, 1) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_0_0_1_float16(self, device): - input_x1 = self.generate_data(-10, 10, (3, 3), np.float16) - input_x1_cpu = input_x1.float() - cpu_output1 = self.cpu_op_exec(input_x1_cpu, 0, 0, 1).astype(np.float16) - npu_output1 = self.npu_op_exec(input_x1, 0, 0, 1) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_0_0_1(self, device): - input_x1 = self.generate_data(-10, 10, (3, 3), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 0, 0, 1) - npu_output1 = self.npu_op_exec(input_x1, 0, 0, 1) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_4_0_1_float16(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3), np.float16) - input_x1_cpu = input_x1.float() - cpu_output1 = self.cpu_op_exec(input_x1_cpu, 4, 0, 1).astype(np.float16) - npu_output1 = self.npu_op_exec(input_x1, 4, 0, 1) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_1_1_1_float16(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3), np.float16) - input_x1_cpu = input_x1.float() - cpu_output1 = self.cpu_op_exec(input_x1_cpu, 1, 1, 1).astype(np.float16) - npu_output1 = self.npu_op_exec(input_x1, 1, 1, 1) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_1_0_1(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 1, 0, 1) - npu_output1 = self.npu_op_exec(input_x1, 1, 0, 1) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_3_3_1_1(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3, 3), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 3, 1, 1) - npu_output1 = self.npu_op_exec(input_x1, 3, 1, 1) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_3_2_2_1(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3, 3), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 2, 2, 1) - npu_output1 = self.npu_op_exec(input_x1, 2, 2, 1) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_3_3_2_0_1(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3, 3, 3), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 2, 0, 1) - npu_output1 = self.npu_op_exec(input_x1, 2, 0, 1) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_3_3_3_3_1(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3, 3, 3), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 3, 3, 1) - npu_output1 = self.npu_op_exec(input_x1, 3, 3, 1) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_3_3_3_4_4_1(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3, 3, 3, 3), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 4, 4, 1) - npu_output1 = self.npu_op_exec(input_x1, 4, 4, 1) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_4_0_1_out(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3), np.float32) - output_y = self.generate_data(-1, 1, (3, 3), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 4, 0, 1) - npu_output1 = self.npu_op_exec_out(input_x1, 4, 0, 1, output_y) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_1_1_1_out(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3), np.float32) - output_y = self.generate_data(-1, 1, (3, 3), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 1, 1, 1) - npu_output1 = self.npu_op_exec_out(input_x1, 1, 1, 1, output_y) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_1_0_1_out(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3), np.float32) - output_y = self.generate_data(-1, 1, (3, 3), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 1, 0, 1) - npu_output1 = self.npu_op_exec_out(input_x1, 1, 0, 1, output_y) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_3_3_1_1_out(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3, 3), np.float32) - output_y = self.generate_data(-1, 1, (3, 3, 3), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 3, 1, 1) - npu_output1 = self.npu_op_exec_out(input_x1, 3, 1, 1, output_y) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_30_40_50_2_1_1_out_fp16(self, device): - input_x1 = self.generate_data(-1, 1, (30, 40, 50), np.float16) - output_y = self.generate_data(-1, 1, (30, 40, 50), np.float16) - input_cpu = input_x1.float() - cpu_output1 = self.cpu_op_exec(input_cpu, 2, 1, 1) - cpu_output1 = cpu_output1.astype(np.float16) - npu_output1 = self.npu_op_exec_out(input_x1, 2, 1, 1, output_y) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_30_40_50_2_0_2_out_fp16(self, device): - input_x1 = self.generate_data(-1, 1, (30, 40, 50), np.float16) - output_y = self.generate_data(-1, 1, (30, 40, 50), np.float16) - input_cpu = input_x1.float() - cpu_output1 = self.cpu_op_exec(input_cpu, 2, 0, 2) - cpu_output1 = cpu_output1.astype(np.float16) - npu_output1 = self.npu_op_exec_out(input_x1, 2, 0, 2, output_y) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_3_2_2_1_out(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3, 3), np.float32) - output_y = self.generate_data(-1, 1, (3, 3, 3), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 2, 2, 1) - npu_output1 = self.npu_op_exec_out(input_x1, 2, 2, 1, output_y) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_3_3_2_0_1_out(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3, 3, 3), np.float32) - output_y = self.generate_data(-1, 1, (3, 3, 3, 3), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 2, 0, 1) - npu_output1 = self.npu_op_exec_out(input_x1, 2, 0, 1, output_y) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_3_3_3_3_1_out(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3, 3, 3), np.float32) - output_y = self.generate_data(-1, 1, (3, 3, 3, 3), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 3, 3, 1) - npu_output1 = self.npu_op_exec_out(input_x1, 3, 3, 1, output_y) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_3_3_3_4_4_1_out(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3, 3, 3, 3), np.float32) - output_y = self.generate_data(-1, 1, (3, 3, 3, 3, 3), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 4, 4, 1) - npu_output1 = self.npu_op_exec_out(input_x1, 4, 4, 1, output_y) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_4_0_1_inplace(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 4, 0, 1) - npu_output1 = self.npu_op_exec_inplace(input_x1, 4, 0, 1) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_1_1_1_inplace(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 1, 1, 1) - npu_output1 = self.npu_op_exec_inplace(input_x1, 1, 1, 1) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_1_0_1_inplace(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 1, 0, 1) - npu_output1 = self.npu_op_exec_inplace(input_x1, 1, 0, 1) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_3_3_1_1_inplace(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3, 3), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 3, 1, 1) - npu_output1 = self.npu_op_exec_inplace(input_x1, 3, 1, 1) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_3_2_2_1_inplace(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3, 3), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 2, 2, 1) - npu_output1 = self.npu_op_exec_inplace(input_x1, 2, 2, 1) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_3_3_2_0_1_inplace(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3, 3, 3), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 2, 0, 1) - npu_output1 = self.npu_op_exec_inplace(input_x1, 2, 0, 1) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_3_3_3_3_1_inplace(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3, 3, 3), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 3, 3, 1) - npu_output1 = self.npu_op_exec_inplace(input_x1, 3, 3, 1) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_renorm_3_3_3_3_3_4_4_1_inplace(self, device): - input_x1 = self.generate_data(-1, 1, (3, 3, 3, 3, 3), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, 4, 4, 1) - npu_output1 = self.npu_op_exec_inplace(input_x1, 4, 4, 1) - self.assertRtolEqual(cpu_output1, npu_output1) - - -instantiate_device_type_tests(TestRenorm, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:0") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_repeat_interleave.py b/pytorch1.8.1/test/test_npu/test_repeat_interleave.py deleted file mode 100644 index 1ca4e3f4da7..00000000000 --- a/pytorch1.8.1/test/test_npu/test_repeat_interleave.py +++ /dev/null @@ -1,89 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestRepeatInterleave(TestCase): - - def generate_data(self, min_d, max_d, shape, dtype): - input1 = np.random.uniform(min_d, max_d, shape).astype(dtype) - #modify from numpy.ndarray to torch.tensor - npu_input1 = torch.from_numpy(input1) - - return npu_input1 - - def cpu_op_exec(self, input1, input2, input3): - output = torch.repeat_interleave(input1, input2, dim=input3) - output = output.numpy() - return output - - def npu_op_exec(self, input1, input2, input3): - output = torch.repeat_interleave(input1, input2, dim=input3) - output = output.to("cpu") - output = output.numpy() - return output - - def cpu_op_exec_without_dim(self, input1, input2): - output = torch.repeat_interleave(input1, input2) - output = output.numpy() - return output - - def npu_op_exec_without_dim(self, input1, input2): - output = torch.repeat_interleave(input1, input2) - output = output.to("cpu") - output = output.numpy() - return output - - def test_repeat_interleave_float16(self, device): - npu_input1 = self.generate_data(0, 100, (3,3,3), np.float16) - npu_input2 = np.random.randint(1, 100) - npu_input3 = np.random.randint(0, 2) - cpu_output = self.cpu_op_exec(npu_input1, npu_input2, npu_input3) - npu_output = self.npu_op_exec(npu_input1, npu_input2, npu_input3) - self.assertRtolEqual(cpu_output, npu_output) - - def test_repeat_interleave_float32(self, device): - npu_input1 = self.generate_data(0, 100, (3,3,3), np.float32) - npu_input2 = np.random.randint(1, 100) - npu_input3 = np.random.randint(0, 2) - cpu_output = self.cpu_op_exec(npu_input1, npu_input2, npu_input3) - npu_output = self.npu_op_exec(npu_input1, npu_input2, npu_input3) - self.assertRtolEqual(cpu_output, npu_output) - - def test_repeat_interleave_int32(self, device): - npu_input1 = self.generate_data(0, 100, (3,3,3), np.int32) - npu_input2 = np.random.randint(1, 100) - npu_input3 = np.random.randint(0, 2) - cpu_output = self.cpu_op_exec(npu_input1, npu_input2, npu_input3) - npu_output = self.npu_op_exec(npu_input1, npu_input2, npu_input3) - self.assertRtolEqual(cpu_output, npu_output) - - def test_repeat_interleave_int32_without_dim(self, device): - npu_input1 = self.generate_data(0, 100, (3,3,3), np.int32) - npu_input2 = np.random.randint(1, 100) - cpu_output = self.cpu_op_exec_without_dim(npu_input1, npu_input2) - npu_output = self.npu_op_exec_without_dim(npu_input1, npu_input2) - self.assertRtolEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(TestRepeatInterleave, globals(), except_for='cpu') -if __name__ == '__main__': - torch.npu.set_device("npu:3") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_replication_pad2d.py b/pytorch1.8.1/test/test_npu/test_replication_pad2d.py deleted file mode 100644 index 8a27c86f8d9..00000000000 --- a/pytorch1.8.1/test/test_npu/test_replication_pad2d.py +++ /dev/null @@ -1,124 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestReplicationPad2d(TestCase): - def cpu_op_exec(self, input1, pad): - m = torch.nn.ReplicationPad2d(pad) - output = m(input1) - output = output.numpy() - return output - - def npu_op_exec(self, input1, pad): - m = torch.nn.ReplicationPad2d(pad).to("npu") - output = m(input1) - output = output.to("cpu") - output = output.numpy() - return output - - def test_replicationPad2d_shape_format(self, device): - shape_format = [ - [[np.float32, 0, (1, 1, 2,3 )],[2,2,2,2]], - [[np.float32, 3, (1, 1, 4,3 )],2] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100) - cpu_output = self.cpu_op_exec(cpu_input1, item[1]) - npu_output = self.npu_op_exec(npu_input1, item[1]) - self.assertRtolEqual(cpu_output, npu_output) - - def test_replicationPad2d_shape_format_fp16(self, device): - shape_format = [ - [[np.float16, 0, (1, 1, 4,3 )],[2,2,2,2]], - [[np.float16, 3, (1, 1, 4,3 )],3] - ] - def cpu_op_exec_fp16(input1, pad): - input1 = input1.to(torch.float32) - m = torch.nn.ReplicationPad2d(pad) - output = m(input1) - output = output.numpy() - output = output.astype(np.float16) - return output - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100) - cpu_output = cpu_op_exec_fp16(cpu_input1, item[1]) - npu_output = self.npu_op_exec(npu_input1, item[1]) - self.assertRtolEqual(cpu_output, npu_output) - - def test_replicationPad2d_shape_format_int8(self, device): - shape_format = [ - [[np.int8, 0, (1, 1, 4,3 )],[2,2,2,2]], - [[np.int8, 0, (1, 1, 5,3 )],6] - ] - def cpu_op_exec_int8(input1, pad): - input1 = input1.to(torch.float32) - m = torch.nn.ReplicationPad2d(pad) - output = m(input1) - output = output.numpy() - output = output.astype(np.int8) - return output - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100) - cpu_output = cpu_op_exec_int8(cpu_input1, item[1]) - npu_output = self.npu_op_exec(npu_input1, item[1]) - self.assertRtolEqual(cpu_output, npu_output) - - def test_replicationPad2d_shape_format_uint8(self, device): - shape_format = [ - [[np.uint8, 0, (1, 1, 4,3 )],[2,2,2,2]], - [[np.uint8, 0, (1, 1, 4, 9 )],2] - ] - def cpu_op_exec_uint8(input1, pad): - input1 = input1.to(torch.float32) - m = torch.nn.ReplicationPad2d(pad) - output = m(input1) - output = output.numpy() - output = output.astype(np.uint8) - return output - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100) - cpu_output = cpu_op_exec_uint8(cpu_input1, item[1]) - npu_output = self.npu_op_exec(npu_input1, item[1]) - self.assertRtolEqual(cpu_output, npu_output) - - def test_replicationPad2d_shape_format_int32(self, device): - shape_format = [ - [[np.int32, 0, (1, 1, 4,3 )],[2,2,2,2]], - [[np.int32, 0, (1, 1, 4, 9 )],2] - ] - def cpu_op_exec_int32(input1, pad): - input1 = input1.to(torch.float32) - m = torch.nn.ReplicationPad2d(pad) - output = m(input1) - output = output.numpy() - output = output.astype(np.int32) - return output - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100) - cpu_output = cpu_op_exec_int32(cpu_input1, item[1]) - npu_output = self.npu_op_exec(npu_input1, item[1]) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestReplicationPad2d, globals(), except_for="cpu") -if __name__ == "__main__": - torch.npu.set_device("npu:6") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_roll.py b/pytorch1.8.1/test/test_npu/test_roll.py deleted file mode 100644 index f53c7d796c2..00000000000 --- a/pytorch1.8.1/test/test_npu/test_roll.py +++ /dev/null @@ -1,79 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestRoll(TestCase): - def generate_data(self, min_d, max_d, shape, dtype): - input_x = np.random.uniform(min_d, max_d, shape).astype(dtype) - npu_input = torch.from_numpy(input_x) - return npu_input - - def cpu_op_exec(self, input_x, shifts, dims): - output = torch.roll(input_x, shifts, dims).numpy() - return output - - def npu_op_exec(self, input_x, shifts, dims): - input1 = input_x.to("npu") - output = torch.roll(input1, shifts, dims) - output = output.to("cpu") - output = output.numpy() - return output - - def test_roll_3_4_5_float32(self, device): - input_x1 = self.generate_data(-1, 1, (3, 4, 5), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, [2, 1], [0, 1]) - npu_output1 = self.npu_op_exec(input_x1, [2, 1], [0, 1]) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_roll_3_4_5_float16(self, device): - input_x1 = self.generate_data(-1, 1, (3, 4, 5), np.float16) - input_cpu = input_x1.float() - cpu_output1 = self.cpu_op_exec(input_cpu, [2, 1], [0, 1]).astype(np.float16) - npu_output1 = self.npu_op_exec(input_x1, [2, 1], [0, 1]) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_roll_30_40_50_int32(self, device): - input_x1 = self.generate_data(-1, 1, (30, 40, 50), np.int32) - cpu_output1 = self.cpu_op_exec(input_x1, [20], []) - npu_output1 = self.npu_op_exec(input_x1, [20], []) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_roll_10_10_10_10_10_10_int8(self, device): - input_x1 = self.generate_data(-1, 1, (10, 10, 10, 10, 10, 10), np.int8) - cpu_output1 = self.cpu_op_exec(input_x1, [-20, 30, 5], [-3, -4, -5]) - npu_output1 = self.npu_op_exec(input_x1, [-20, 30, 5], [-3, -4, -5]) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_roll_20_30_40_50_uint8(self, device): - input_x1 = self.generate_data(-1, 1, (20, 30, 40, 50), np.uint8) - cpu_output1 = self.cpu_op_exec(input_x1, [-20, 30], [-1, 0]) - npu_output1 = self.npu_op_exec(input_x1, [-20, 30], [-1, 0]) - self.assertRtolEqual(cpu_output1, npu_output1) - - def test_roll_20_30_40_50_flaot32(self, device): - input_x1 = self.generate_data(-1, 1, (20, 30, 40, 50), np.float32) - cpu_output1 = self.cpu_op_exec(input_x1, [30], [3]) - npu_output1 = self.npu_op_exec(input_x1, [30], [3]) - self.assertRtolEqual(cpu_output1, npu_output1) - - -instantiate_device_type_tests(TestRoll, globals(), except_for='cpu') -if __name__ == "__main__": - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_scatter_dim_update.py b/pytorch1.8.1/test/test_npu/test_scatter_dim_update.py deleted file mode 100644 index 630dbe48e41..00000000000 --- a/pytorch1.8.1/test/test_npu/test_scatter_dim_update.py +++ /dev/null @@ -1,96 +0,0 @@ -# Copyright 2020 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestScatterDimUpdate(TestCase): - - def generate_data(self, min, max, shape_var, shape_indices, shape_updates, dtype_var, - dtype_indices, dtype_updates, dim): - var = np.random.uniform(min, max, shape_var).astype(dtype_var) - updates = np.random.uniform(min, max, shape_updates).astype(dtype_updates) - indices = np.random.randint(0, shape_var[dim], shape_indices).astype(dtype_indices) - - #modify from numpy.ndarray to torch.tensor - var = torch.from_numpy(var) - indices = torch.from_numpy(indices) - updates = torch.from_numpy(updates) - - return var, indices, updates, dim - - def cpu_op_exec(self, var, indices, updates, dim): - output = var.scatter(dim=dim, index=indices.long(), src=updates) - return output.numpy() - - def npu_op_exec(self, var, indices, updates, dim): - var = var.to("npu") - indices = indices.to("npu") - updates = updates.to("npu") - output = torch.scatter(var, dim, indices, updates) - output = output.to("cpu") - output = output.numpy() - return output - - def test_scatter_dim_update_32_float32(self, device): - var, indices, updates, dim = self.generate_data(-2, 2, (32, ), (32, ), (32, ), - "float32", "int32", "float32", 0) - cpu_output = self.cpu_op_exec(var, indices, updates, dim) - npu_output = self.npu_op_exec(var, indices, updates, dim) - self.assertRtolEqual(cpu_output, npu_output) - - def test_scatter_dim_update_32_32_float16(self, device): - var, indices, updates, dim = self.generate_data(-2, 2, (32, 32), (32, 32), (32, 32), - "float16", "int32", "float16", 0) - cpu_output = self.cpu_op_exec(var, indices, updates, dim) - npu_output = self.npu_op_exec(var, indices, updates, dim) - self.assertRtolEqual(cpu_output, npu_output) - - def test_scatter_dim_update_32_32_float32(self, device): - var, indices, updates, dim = self.generate_data(-2, 2, (32, 32), (24, 24), (24, 24), - "float32", "int32", "float32", 1) - cpu_output = self.cpu_op_exec(var, indices, updates, dim) - npu_output = self.npu_op_exec(var, indices, updates, dim) - self.assertRtolEqual(cpu_output, npu_output) - - def test_scatter_dim_update_32_32_32_int8(self, device): - var, indices, updates, dim = self.generate_data(-2, 2, (32, 32, 32), (24, 24, 24), (32, 32, 32), - "int8", "int32", "int8", 1) - cpu_output = self.cpu_op_exec(var, indices, updates, dim) - npu_output = self.npu_op_exec(var, indices, updates, dim) - self.assertRtolEqual(cpu_output, npu_output) - - def test_scatter_dim_update_16_16_16_16_float16(self, device): - var, indices, updates, dim = self.generate_data(-2, 2, (16, 16, 16, 16), (8, 8, 8, 8), (12, 12, 12, 12), - "float16", "int32", "float16", 2) - cpu_output = self.cpu_op_exec(var, indices, updates, dim) - npu_output = self.npu_op_exec(var, indices, updates, dim) - self.assertRtolEqual(cpu_output, npu_output) - - def test_scatter_dim_update_8_8_8_8_8_floa32(self, device): - var, indices, updates, dim = self.generate_data(-2, 2, (8, 8, 8, 8, 8), (3, 3, 3, 3, 3), (8, 8, 8, 8, 8), - "float32", "int32", "float32", 3) - cpu_output = self.cpu_op_exec(var, indices, updates, dim) - npu_output = self.npu_op_exec(var, indices, updates, dim) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestScatterDimUpdate, globals(), except_for='cpu') -if __name__ == '__main__': - torch.npu.set_device("npu:2") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_slow_conv_transpose3d.py b/pytorch1.8.1/test/test_npu/test_slow_conv_transpose3d.py deleted file mode 100644 index ca8bf35b8c3..00000000000 --- a/pytorch1.8.1/test/test_npu/test_slow_conv_transpose3d.py +++ /dev/null @@ -1,60 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -import torch.nn as nn -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestSlowConvTranspose3d(TestCase): - def cpu_op_exec(self, input_x, in_channels, out_channels, kernel_size): - m = nn.ConvTranspose3d(in_channels, out_channels, kernel_size) - output = m(input_x) - return output.detach().numpy() - - def cpu_op_exec_fp16(self, input_x, in_channels, out_channels, kernel_size): - input_x = input_x.to(torch.float32) - m = nn.ConvTranspose3d(in_channels, out_channels, kernel_size) - output = m(input_x) - return output.detach().numpy() - - def npu_op_exec(self, input_x, in_channels, out_channels, kernel_size): - m = nn.ConvTranspose3d(in_channels, out_channels, kernel_size) - output = m(input_x) - output = output.to("cpu") - return output.detach().numpy() - - def test_slow_conv_transpose3d(self, device): - - shape_format = [ - [[np.float16, -1, [20, 16, 10, 50, 100]], 16, 33, 3], - [[np.float32, -1, [20, 16, 10, 50, 100]], 16, 33, 3], - [[np.float16, -1, [6, 12, 12, 60, 120]], 12, 25, 3], - [[np.float32, -1, [10, 8, 6, 30, 60]], 8, 17, 2], - ] - for item in shape_format: - input_x_cpu, input_x_npu = create_common_tensor(item[0], 0, 1) - if input_x_cpu.dtype == torch.float16: - cpu_output = self.cpu_op_exec_fp16(input_x_cpu, item[1], item[2], item[3]) - else: - cpu_output = self.cpu_op_exec(input_x_cpu, item[1], item[2], item[3]) - -instantiate_device_type_tests(TestSlowConvTranspose3d, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:5") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_softmax_backward.py b/pytorch1.8.1/test/test_npu/test_softmax_backward.py deleted file mode 100644 index b20fcd8d88d..00000000000 --- a/pytorch1.8.1/test/test_npu/test_softmax_backward.py +++ /dev/null @@ -1,104 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -def input_grad_hook(grad): - global input_grad - input_grad = grad - - -def npu_input_grad_hook(grad): - global npu_input_grad - npu_input_grad = grad.to("cpu") - - -class TestSoftmaxBackward(TestCase): - - def cpu_op_exec(self, input, is_contiguous=True, dim=-1): - if is_contiguous is False: - input = input.as_strided([2, 2], [1, 2], 1) - input.requires_grad = True - input.register_hook(input_grad_hook) - - output = torch.softmax(input, dim=dim) - z = output.sum() - z.backward() - - def npu_op_exec(self, input, is_contiguous=True, dim=-1): - if is_contiguous is False: - input = input.as_strided([2, 2], [1, 2], 1) - input.requires_grad = True - input.register_hook(npu_input_grad_hook) - - output = torch.softmax(input, dim=dim) - z = output.sum() - z.backward() - input = input.cpu() - - def test_softmax_backward_shape_format(self, device): - shape_format = [ - [np.float32, 0, 5], - [np.float32, 3, (64, 10)], - [np.float32, 3, (256, 2048, 7, 7)], - [np.float32, 3, (32, 1, 3, 3)], - [np.float32, 0, (10, 128)] - ] - for item in shape_format: - input1, npu_input1 = create_common_tensor(item, 10, 100) - input2, npu_input2 = create_common_tensor(item, 10, 100) - - self.cpu_op_exec(input1) - self.npu_op_exec(npu_input1) - self.assertRtolEqual(input_grad.numpy(), npu_input_grad.numpy()) - - self.cpu_op_exec(input2, False) - self.npu_op_exec(npu_input2, False) - self.assertRtolEqual(input_grad.numpy(), npu_input_grad.numpy()) - - def test_softmax_backward_shape_format_fp16(self, device): - shape_format = [ - [np.float16, 0, 5], - [np.float16, 3, (64, 10)], - [np.float16, 3, (256, 2048, 7, 7)], - [np.float16, 3, (32, 1, 3, 3)], - [np.float16, 0, (10, 128)] - ] - for item in shape_format: - input1, npu_input1 = create_common_tensor(item, 10, 100) - input2, npu_input2 = create_common_tensor(item, 10, 100) - - input1 = input1.to(torch.float32) - input2 = input2.to(torch.float32) - - self.cpu_op_exec(input1) - self.npu_op_exec(npu_input1) - - self.assertRtolEqual(input_grad.numpy().astype(np.float16), npu_input_grad.numpy()) - - self.cpu_op_exec(input2, False) - self.npu_op_exec(npu_input2, False) - self.assertRtolEqual(input_grad.numpy().astype(np.float16), npu_input_grad.numpy()) - - -instantiate_device_type_tests(TestSoftmaxBackward, globals(), except_for="cpu") -if __name__ == "__main__": - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_split_with_sizes.py b/pytorch1.8.1/test/test_npu/test_split_with_sizes.py deleted file mode 100644 index 6cae3f107c8..00000000000 --- a/pytorch1.8.1/test/test_npu/test_split_with_sizes.py +++ /dev/null @@ -1,83 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class Test_split_with_sizes(TestCase): - def cpu_op_exec(self, input1, split_sizes, dim): - outputs = torch.split_with_sizes(input1, split_sizes, dim) - outputs_np = [] - for output in outputs: - outputs_np.append(output.numpy()) - return outputs_np - - def npu_op_exec(self, input1, split_sizes, dim): - input1 = input1.to("npu") - outputs = torch.split_with_sizes(input1, split_sizes, dim) - outputs = list(outputs) - output_cpu = [] - output_np = [] - for i in outputs: - output_cpu.append(i.to("cpu")) - for i in output_cpu: - output_np.append(i.numpy()) - return output_np - - def test_add_common_shape_format1(self, device): - shape_format = [ # input, split_sizes, dim - [[np.int32, -1, (2, 3)], [1, 1], 0], - [[np.int32, -1, (2, 3)], [1, 1, 1], 1], - [[np.int32, -1, (2, 3, 10)], [2, 3, 5], 2], - [[np.int32, -1, (2, 3, 10, 4, 5)], [1, 1, 1, 1], 3], - [[np.int32, -1, (2, 3, 10, 4, 5)], [1, 1, 1, 1, 1], 4] - ] - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100) - - split_sizes = item[1] - dim = item[2] - cpu_outputs = self.cpu_op_exec(cpu_input1, split_sizes, dim) - npu_outputs = self.npu_op_exec(npu_input1, split_sizes, dim) - for i in range(0, len(cpu_outputs)): - self.assertRtolEqual(cpu_outputs[i], npu_outputs[i]) - - def test_add_common_shape_format2(self, device): - shape_format = [ # input, split_sizes, dim - [[np.float32, -1, (10, 31, 149, 2)], [2, 3, 5], 0], - [[np.float32, -1, (10, 31, 149, 2)], [2, 3, 5, 10, 11], 1], - [[np.float32, -1, (10, 31, 149, 2)], [50, 50, 20, 29], 2], - [[np.float32, -1, (10, 31, 149, 2)], [25, 25, 25, 25, 20, 29], 2], - [[np.float32, -1, (10, 31, 149, 2)], [1, 1], 3] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], -1.1754943508e-38, -1.1754943508e-38) - split_sizes = item[1] - dim = item[2] - cpu_outputs = self.cpu_op_exec(cpu_input1, split_sizes, dim) - npu_outputs = self.npu_op_exec(npu_input1, split_sizes, dim) - for i in range(0, len(cpu_outputs)): - self.assertRtolEqual(cpu_outputs[i], npu_outputs[i]) - - -instantiate_device_type_tests(Test_split_with_sizes, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:5") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_square.py b/pytorch1.8.1/test/test_npu/test_square.py deleted file mode 100644 index 6cf7d9fdf67..00000000000 --- a/pytorch1.8.1/test/test_npu/test_square.py +++ /dev/null @@ -1,110 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestSquare(TestCase): -# pylint: disable=unused-variable,unused-argument - def cpu_op_exec(self, input1): - flag = 0 - if input1.dtype == torch.float16: - input1 = input1.to(torch.float32) - flag = 1 - output = torch.square(input1) - if flag == 1: - output = output.to(torch.float16) - output = output.numpy() - return output - - def npu_op_exec(self, input1): - input1 = input1.to("npu") - output = torch.square(input1) - output = output.to("cpu") - output = output.numpy() - return output - - def cpu_op_inplace_exec(self, input1): - flag = 0 - if input1.dtype == torch.float16: - input1 = input1.to(torch.float32) - flag = 1 - input1.square_() - if flag == 1: - input1 = input1.to(torch.float16) - output = input1.numpy() - return output - - def npu_op_inplace_exec(self, input1): - input1 = input1.to("npu") - input1.square_() - output = input1.to("cpu") - output = output.numpy() - return output - - def test_square_common_shape_format(self, device): - shape_format = [ - [[np.float32, -1, (4, 3, 3)]], - [[np.float32, -1, (4, 5, 5)]], - [[np.float32, -1, (3, 3, 3)]], - [[np.float32, -1, (4, 4, 4)]] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], -100, 10) - cpu_output = self.cpu_op_exec(cpu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - cpu_output = self.cpu_op_inplace_exec(cpu_input1) - npu_output = self.npu_op_inplace_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_square_int32_shape_format(self, device): - shape_format = [ - [[np.int32, -1, (4, 2)]], - [[np.int32, -1, (4, 2)]], - ] - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], -10, 10) - cpu_output = self.cpu_op_exec(cpu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - cpu_output = self.cpu_op_inplace_exec(cpu_input1) - npu_output = self.npu_op_inplace_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - def test_square_float16_shape_format(self, device): - shape_format = [ - [[np.float16, -1, (4, 2, 6, 6)]], - [[np.float16, -1, (4, 2, 8, 8)]], - ] - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100) - cpu_output = self.cpu_op_exec(cpu_input1) - npu_output = self.npu_op_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - cpu_output = self.cpu_op_inplace_exec(cpu_input1) - npu_output = self.npu_op_inplace_exec(npu_input1) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestSquare, globals(), except_for='cpu') -if __name__ == '__main__': - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_sum_to_size.py b/pytorch1.8.1/test/test_npu/test_sum_to_size.py deleted file mode 100644 index 1820b9d9596..00000000000 --- a/pytorch1.8.1/test/test_npu/test_sum_to_size.py +++ /dev/null @@ -1,68 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestSumToSize(TestCase): - - def generate_single_data(self, min_d, max_d, shape, dtype): - input1 = np.random.uniform(min_d, max_d, shape).astype(dtype) - input1 = torch.from_numpy(input1) - return input1 - - def cpu_op_exec(self, input1, shape): - output = input1.sum_to_size(shape) - output = output.numpy() - return output - - def npu_op_exec(self, input1, shape): - input1 = input1.to("npu") - output = input1.sum_to_size(shape) - output = output.to("cpu") - output = output.numpy() - return output - - def test_sum_to_size_float16(self, device): - def cpu_op_exec_fp16(input1, shape): - input1 = input1.to(torch.float32) - output = input1.sum_to_size(shape) - output = output.numpy() - output = output.astype(np.float16) - return output - input1 = self.generate_single_data(0, 100, (5,3), np.float16) - cpu_output = cpu_op_exec_fp16(input1, (5,1)) - npu_output = self.npu_op_exec(input1, (5,1)) - self.assertRtolEqual(cpu_output, npu_output) - - def test_sum_to_size_float32_two(self, device): - input1 = self.generate_single_data(0, 100, (4,3), np.float32) - cpu_output = self.cpu_op_exec(input1, (4,1)) - npu_output = self.npu_op_exec(input1, (4,1)) - self.assertRtolEqual(cpu_output, npu_output) - - def test_sum_to_size_float32_three(self, device): - input1 = self.generate_single_data(0, 100, (4,3,6), np.float32) - cpu_output = self.cpu_op_exec(input1, (4,3,1)) - npu_output = self.npu_op_exec(input1, (4,3,1)) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestSumToSize, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:3") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_take.py b/pytorch1.8.1/test/test_npu/test_take.py deleted file mode 100644 index 85c378758cf..00000000000 --- a/pytorch1.8.1/test/test_npu/test_take.py +++ /dev/null @@ -1,89 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# coding: utf-8 -import torch -import numpy as np -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestTake(TestCase): - def cpu_op_out_exec(self, input1,input2, out): - torch.take(input1,input2, out=out) - output = out.numpy() - return output - - def npu_op_out_exec(self, input1,input2, out): - torch.take(input1,input2, out=out) - output = out.to("cpu") - output = output.numpy() - return output - - def cpu_op_exec(self, input1, input2): - output = torch.take(input1, input2) - output = output.numpy() - return output - - def npu_op_exec(self, input1, input2): - output = torch.take(input1, input2) - output = output.to("cpu").numpy() - return output - - def test_take_shape_format(self, device): - shape_format = [ - [[np.float32, 0, (5,3)], [np.int64, 0, (3)],8], - [[np.int8, 0, (64, 10)], [np.int64,0, (10)],74], - [[np.uint8, -1, (256, 2048, 7, 7)], [np.int64, -1, (30)],2748 ], - [[np.int16, -1, (32,1, 3, 3)], [np.int64, -1, (32)], 39], - [[np.int64, -1, (10, 128)], [np.int64, -1, (128)], 138], - [[np.float16, 0, (64, 10)], [np.int64, 0, (10)], 74], - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100) - cpu_input2, npu_input2 = create_common_tensor(item[1], 1, item[2]) - if item[0][0] == np.float16: - cpu_input1 = cpu_input1.to(torch.float32) - cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2) - npu_output = self.npu_op_exec(npu_input1, npu_input2) - if npu_input1.dtype == torch.float16: - cpu_output = cpu_output.astype(np.float16) - self.assertRtolEqual(cpu_output, npu_output) - - def test_take_out_shape_format(self, device): - shape_format = [ - [[np.float32, 0, (5,3)], [np.int64, 0, (3)],8, [np.float32, 0, (3)]], - [[np.int8, 0, (64, 10)], [np.int64,0, (10)],74, [np.int8, 0, (10)]], - [[np.uint8, -1, (256, 2048, 7, 7)], [np.int64, -1, (30)], 2748 , [np.uint8, -1, (30)] ], - [[np.int16, -1, (32,1, 3, 3)], [np.int64, -1, (32)], 39, [np.int16, -1, (32)]], - [[np.int64, -1, (10, 128)], [np.int64, -1, (128)], 138, [np.int64, -1, (128)]], - [[np.float16, 0, (64, 10)], [np.int64, 0, (10)], 74,[np.float16, 0, (10)]], - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100) - cpu_output1, npu_output1 = create_common_tensor(item[3], 1, 100) - cpu_input2, npu_input2 = create_common_tensor(item[1], 1, item[2]) - if item[0][0] == np.float16: - cpu_input1 = cpu_input1.to(torch.float32) - cpu_output1 = cpu_output1.to(torch.float32) - cpu_output = self.cpu_op_out_exec(cpu_input1, cpu_input2, cpu_output1) - npu_output = self.npu_op_out_exec(npu_input1, npu_input2, npu_output1) - if npu_input1.dtype == torch.float16: - cpu_output = cpu_output.astype(np.float16) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestTake, globals(), except_for="cpu") -if __name__ == "__main__": - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_tensor_npu.py b/pytorch1.8.1/test/test_npu/test_tensor_npu.py deleted file mode 100644 index abe6ce9b020..00000000000 --- a/pytorch1.8.1/test/test_npu/test_tensor_npu.py +++ /dev/null @@ -1,74 +0,0 @@ -# Copyright (c) 2020 Huawei Technologies Co., Ltd -# Copyright (c) 2019, Facebook CORPORATION. -# All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -from torch.testing._internal.common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestTensorNpu(TestCase): - - def cpu_op_exec(self, input): - output = input.to("cpu") - return output - - def npu_op_exec(self, input): - output = torch.npu() - output = output.to("cpu") - return output - - def cpu_type_exec(self, input): - output = input.to("cpu") - output = output.is_npu - return output - - def npu_type_exec(self, input): - output = torch.npu() - output = output.is_npu - return output - - def test_tensor_npu_shape_format(self): - shape_format = [ - [np.float32, 0, 1], - [np.float32, 0, (64, 10)], - [np.float32, 3, (256, 2048, 7, 7)], - [np.float32, 4, (32, 1, 3, 3)], - [np.float32, 29, (10, 128)] - ] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item, 1, 100) - cpu_output = self.cpu_op_exec(cpu_input) - npu_output = self.npu_op_exec(npu_input) - self.assertRtolEqual(cpu_output, npu_output) - - def test_is_npu_shape_format(self): - shape_format = [ - [np.float32, 0, 1], - [np.float32, 0, (64, 10)], - [np.float32, 3, (256, 2048, 7, 7)], - [np.float32, 4, (32, 1, 3, 3)], - [np.float32, 29, (10, 128)] - ] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item, 1, 100) - cpu_output = self.cpu_type_exec(cpu_input) - npu_output = self.npu_type_exec(npu_input) - self.assertEqual(cpu_output, False) - self.assertEqual(npu_output, True) - -if __name__ == "__main__": - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_threshold_grad_v2_d.py b/pytorch1.8.1/test/test_npu/test_threshold_grad_v2_d.py deleted file mode 100644 index f4307075bb0..00000000000 --- a/pytorch1.8.1/test/test_npu/test_threshold_grad_v2_d.py +++ /dev/null @@ -1,83 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import torch -import torch.nn.functional as F -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestThresholdGradV2DBackward(TestCase): - - def cpu_op_exec(self, input1, val_0, val_1): - input1.requires_grad = True - input1_res = F.threshold(input1, val_0, val_1) - input1_res.backward(torch.ones_like(input1_res)) - output = input1.grad.numpy() - return output - - def npu_op_exec(self, input1, val_0, val_1): - input1.requires_grad = True - input1_res = F.threshold(input1, val_0, val_1) - input1_res.backward(torch.ones_like(input1_res)) - output = input1.grad - output = output.to("cpu") - output = output.numpy() - return output - - def test_threshold_grad_v2_d_common_shape_format(self, device): - shape_format = [ - [[np.float32, -1, (4, 3)], 1, 100, 0.1, 1.0], - [[np.float32, -1, (7, 5, 5)], 21474836, 21474837, -0.001, 1.001], - [[np.float32, -1, (4, 44, 44)], 3450,34020, 3154, -2200], - [[np.float32, -1, (65500,3,3)], -214748, -214746, -134, 0.001], - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], item[1], item[2]) - cpu_output = self.cpu_op_exec(cpu_input1, item[3], item[4]) - npu_output = self.npu_op_exec(npu_input1, item[3], item[4]) - self.assertRtolEqual(cpu_output, npu_output) - - def test_threshold_grad_v2_d_float16_shape_format(self, device): - def cpu_op_exec_fp16(input1, val_0, val_1): - input1 = input1.to(torch.float32) - input1.requires_grad = True - input1_res = F.threshold(input1, val_0, val_1) - input1_res.backward(torch.ones_like(input1_res)) - output = input1.grad.numpy() - output = output.astype(np.float16) - return output - - shape_format = [ - [[np.float16, -1, (4, 3)], 1, 100, 0.1, 1.0], - [[np.float16, -1, (7, 5, 5)], 21474836, 21474837, -0.001, 1.001], - [[np.float16, -1, (4, 44, 44)], 3450,34020, 3154, -2200], - [[np.float16, -1, (65500,3,3)], -214748, -214746, -134, 0.001], - ] - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], item[1], item[2]) - cpu_output = cpu_op_exec_fp16(cpu_input1, item[3], item[4]) - npu_output = self.npu_op_exec(npu_input1, item[3], item[4]) - self.assertRtolEqual(cpu_output, npu_output) - - -instantiate_device_type_tests(TestThresholdGradV2DBackward, globals(), except_for='cpu') - -if __name__ == "__main__": - torch.npu.set_device("npu:6") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_threshold_v2_d.py b/pytorch1.8.1/test/test_npu/test_threshold_v2_d.py deleted file mode 100644 index 723946b0876..00000000000 --- a/pytorch1.8.1/test/test_npu/test_threshold_v2_d.py +++ /dev/null @@ -1,56 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies. -# Copyright (c) 2019, Facebook CORPORATION. -# All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestThreshold(TestCase): - - def cpu_op_exec(self, input1, threshold, value, inplace): - output = torch.nn.functional.threshold(input1, threshold, value, inplace) - output = output.numpy() - return output - - def npu_op_exec(self, input1, threshold, value, inplace): - output = torch.nn.functional.threshold(input1, threshold, value, inplace) - output = output.to("cpu") - output = output.numpy() - return output - - def test_threshold_common_shape_format(self, device): - shape_format = [ - [[np.float32, 0, (1,5)], [1.0], [20.0], True], - [[np.int32, 0, (1,5)], [2], [20], False], - [[np.int8, 0, (4, 16)], [1], [2], True], - [[np.uint8, 0, (2, 20)], [1], [2], False] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 0, 3) - cpu_threshold = npu_threshold = item[1][0] - cpu_value = npu_value = item[2][0] - inplace = item[3] - cpu_output = self.cpu_op_exec(cpu_input1, cpu_threshold, cpu_value, inplace) - npu_output = self.npu_op_exec(npu_input1, npu_threshold, npu_value, inplace) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestThreshold, globals(), except_for='cpu') -if __name__ == "__main__": - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_trapz_dx.py b/pytorch1.8.1/test/test_npu/test_trapz_dx.py deleted file mode 100644 index 900d890c4c4..00000000000 --- a/pytorch1.8.1/test/test_npu/test_trapz_dx.py +++ /dev/null @@ -1,80 +0,0 @@ -# Copyright (c) 2020 Huawei Technologies Co., Ltd -# Copyright (c) 2019, Facebook CORPORATION. -# All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor,compare_res_new - - -class TestTrapzDx(TestCase): - - def generate_data(self, minValue, maxValue, shape, dtype): - input1 = np.random.uniform(minValue, maxValue, shape).astype(dtype) - # modify from numpy.ndarray to torch.tensor - npu_input1 = torch.from_numpy(input1) - return npu_input1 - - - def cpu_op_exec(self, input1, dx=1, dim=-1): - output = torch.trapz(input1,dim=dim) - output = output.numpy() - return output - - - def npu_op_exec(self, input1, dx=1, dim=-1): - input1 = input1.to("npu") - output = torch.trapz(input1,dim=dim) - output = output.to("cpu") - output = output.numpy() - return output - - - def test_trapz_dx_default_attr(self, device): - shape_format = [ - [[np.float32, -1, (5, 5, 5)]], - [[np.float32, -1, (4, 3, 3)]], - [[np.float32, -1, (5, 5, 5, 5)]] - ] - - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], -100, 100) - npu_output = self.npu_op_exec(npu_input1) - cpu_output = self.cpu_op_exec(cpu_input1) - self.assertRtolEqual(cpu_output, npu_output) - - - def test_trapz_dx_given_attr(self, device): - shape_format = [ - [[np.float32, -1, (5, 5, 5)]], - [[np.float32, -1, (4, 1, 3)]], - [[np.float32, -1, (5, 1, 5, 1)]] - ] - - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], -128, 128) - npu_output = self.npu_op_exec(npu_input1,1,0) - cpu_output = self.cpu_op_exec(cpu_input1,1,0) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestTrapzDx, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:3") - run_tests() \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_trapz_x.py b/pytorch1.8.1/test/test_npu/test_trapz_x.py deleted file mode 100644 index 2be857a74fe..00000000000 --- a/pytorch1.8.1/test/test_npu/test_trapz_x.py +++ /dev/null @@ -1,92 +0,0 @@ -# Copyright (c) 2020 Huawei Technologies Co., Ltd -# Copyright (c) 2019, Facebook CORPORATION. -# All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestTrapzX(TestCase): - def generate_data(self, min, max, shape, dtype): - input1 = np.random.uniform(min, max, shape).astype(dtype) - npu_input1 = torch.from_numpy(input1) - return npu_input1 - - def cpu_op_exec(self, input1, input2, dim): - output = torch.trapz(input1, input2, dim=dim) - output = output.numpy() - return output - - def npu_op_exec(self, input1, input2, dim = -1): - input1 = input1.to("npu") - input2 = input2.to("npu") - output = torch.trapz(input1, input2, dim=dim) - output = output.to("cpu") - output = output.numpy() - return output - - def cpu_op_exec_float16(self, input1, input2): - input1 = input1.to(torch.float32) - input2 = input2.to(torch.float32) - output = torch.trapz(input1, input2) - output = output.numpy() - output = output.astype(np.float16) - return output - - def cpu_op_exec_trapz_dx(self, input1, dx, dim): - output = torch.trapz(input1, dx=dx, dim=dim) - output = output.numpy() - return output - - def npu_op_exec_trapz_dx(self, input1, dx, dim): - output = torch.trapz(input1, dx=dx, dim=dim) - output = output.to("cpu") - output = output.numpy() - return output - - def test_trapz_x(self, device): - shape_format = [ - [[np.float32, -1, (2,3)]], - [[np.float32, -1, (2,2,3)]], - [[np.float32, -1, (7,2,4,5)]] - ] - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item[0], 0, 100) - cpu_input2, npu_input2 = create_common_tensor(item[0], 0, 100) - cpu_output1 = self.cpu_op_exec(cpu_input1, cpu_input2, -1) - npu_output1 = self.npu_op_exec(npu_input1, npu_input2, -1) - cpu_output2 = self.cpu_op_exec(cpu_input1, cpu_input2, 1) - npu_output2 = self.npu_op_exec(npu_input1, npu_input2, 1) - cpu_output3 = self.cpu_op_exec_trapz_dx(cpu_input1,2,1) - npu_output3 = self.npu_op_exec_trapz_dx(npu_input1,2,1) - self.assertRtolEqual(cpu_output1, npu_output1) - self.assertRtolEqual(cpu_output2, npu_output2) - self.assertRtolEqual(cpu_output3, npu_output3) - - def test_trapz_x_float16(self, device): - cpu_input1 = self.generate_data(0, 100, (2,2,3), np.float16) - cpu_input2 = self.generate_data(0, 100, (2,2,3), np.float16) - cpu_output = self.cpu_op_exec_float16(cpu_input1, cpu_input2) - npu_output = self.npu_op_exec(cpu_input1, cpu_input2) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestTrapzX, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:2") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_triangular_solve.py b/pytorch1.8.1/test/test_npu/test_triangular_solve.py deleted file mode 100644 index 19c29815db5..00000000000 --- a/pytorch1.8.1/test/test_npu/test_triangular_solve.py +++ /dev/null @@ -1,84 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestTriangularSolve(TestCase): - def generate_data(self, min, max, shape, dtype): - input1 = np.random.uniform(min, max, shape).astype(dtype) - npu_input1 = torch.from_numpy(input1) - return npu_input1 - - def cpu_op_exec(self, input1, input2, input3, input4, input5): - output = input1.triangular_solve(input2,upper=input3,transpose=input4,unitriangular=input5) - return output - - def cpu_op_exec_float16(self, input1, input2, input3, input4, input5): - input1 = input1.to(torch.float32) - input2 = input2.to(torch.float32) - output = input1.triangular_solve(input2,upper=input3,transpose=input4,unitriangular=input5) - return output - - def npu_op_exec(self, input1, input2, input3, input4, input5): - input1 = input1.to("npu") - input2 = input2.to("npu") - output = input1.triangular_solve(input2,upper=input3,transpose=input4,unitriangular=input5) - output = output.to("cpu") - return output - - def test_triangular_solve_float32(self, device): - npu_input1 = self.generate_data(0, 100, (2,3) , np.float32) - npu_input2 = self.generate_data(0, 100, (2,2) , np.float32) - npu_true = True - npu_false = False - cpu_output = self.cpu_op_exec(npu_input1, npu_input2, npu_true, npu_false, npu_false) - #npu_output = self.npu_op_exec(npu_input1, npu_input2, npu_true, npu_false, npu_false) - #self.assertRtolEqual(cpu_output, npu_output) - - def test_triangular_solve_float32_zhuanzhi(self, device): - npu_input1 = self.generate_data(0, 100, (2,3) , np.float32) - npu_input2 = self.generate_data(0, 100, (2,2) , np.float32) - npu_true = True - npu_false = False - cpu_output = self.cpu_op_exec(npu_input1, npu_input2, npu_true, npu_true, npu_false) - #npu_output = self.npu_op_exec(npu_input1, npu_input2, npu_true, npu_true, npu_false) - #self.assertRtolEqual(cpu_output, npu_output) - - def test_triangular_solve_float32_danwei(self, device): - npu_input1 = self.generate_data(0, 100, (2,3) , np.float32) - npu_input2 = self.generate_data(0, 100, (2,2) , np.float32) - npu_true = True - npu_false = False - cpu_output = self.cpu_op_exec(npu_input1, npu_input2, npu_true, npu_false, npu_true) - #npu_output = self.npu_op_exec(npu_input1, npu_input2, npu_true, npu_false, npu_true) - #self.assertRtolEqual(cpu_output, npu_output) - - def test_triangular_solve_float16(self, device): - npu_input1 = self.generate_data(0, 100, (2,3) , np.float16) - npu_input2 = self.generate_data(0, 100, (2,2) , np.float16) - npu_true = True - npu_false = False - cpu_output = self.cpu_op_exec_float16(npu_input1, npu_input2, npu_true, npu_false, npu_true) - #npu_output = self.npu_op_exec(npu_input1, npu_input2, npu_true, npu_false, npu_true) - #self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestTriangularSolve, globals(), except_for='cpu') -if __name__ == '__main__': - torch.npu.set_device("npu:2") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_upsample_bicubic2d_backward.py b/pytorch1.8.1/test/test_npu/test_upsample_bicubic2d_backward.py deleted file mode 100644 index 76ffbef3017..00000000000 --- a/pytorch1.8.1/test/test_npu/test_upsample_bicubic2d_backward.py +++ /dev/null @@ -1,120 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - - -class TestResizeGradD(TestCase): - - def generate_grads_data(self, min_d, max_d, shape, dtype): - input1 = np.random.uniform(min_d, max_d, shape).astype(dtype) - npu_input1 = torch.from_numpy(input1) - return npu_input1 - - def cpu_op_exec(self, grads, shape_x, output_size, align_corners, scale_h, scale_w): - input1 = torch.ones(shape_x) - flag = 0 - if input1.dtype != torch.float32: - input1 = input1.to(torch.float32) - flag = 1 - input_data = input1.clone().detach().requires_grad_(True) - y = torch._C._nn.upsample_bicubic2d(input_data, output_size, align_corners, scale_h, scale_w) - y.backward(grads) - output = input_data.grad - if flag == 1: - output = output.to(torch.float16) - output = output.numpy() - return output - - - def npu_op_exec(self, grads, shape_x, output_size, align_corners, scale_h, scale_w): - input1 = torch.ones(shape_x) - input1 = input1.to("npu") - grads = grads.to("npu") - input_data = input1.clone().detach().requires_grad_(True) - y = torch._C._nn.upsample_bicubic2d(input_data, output_size, align_corners, scale_h, scale_w) - y.backward(grads) - output = input_data.grad - output = output.to("cpu") - output = output.numpy() - return output - - #pylint: disable=too-many-arguments - def resize_grad_d(self, shape_x, output_size, scales, align_corners, minVal, maxVal, dtype): - grads = self.generate_grads_data(minVal, maxVal, (shape_x[0], shape_x[1], output_size[0], output_size[1]), dtype) - scale_h = scales[0] - scale_w = scales[1] - cpu_output = self.cpu_op_exec(grads, shape_x, output_size, align_corners, scale_h, scale_w) - npu_output = self.npu_op_exec(grads, shape_x, output_size, align_corners, scale_h, scale_w) - self.assertRtolEqual(cpu_output, npu_output) - - #pylint: disable=unused-argument - def test_resize_grad_d(self, device): - testcases = \ - [ - # special case : same size fp32 - [[4, 3, 128, 64], [128, 64], [0, 0], True, -3.4028235E-14, 3.4028235E-14, np.float32], # case 1 - [[128, 3, 128, 64], [128, 64], [0, 0], False, -3.4028235E14, 3.4028235E14, np.float32], # case 2 - [[65535, 2, 4, 8], [4, 8], [0, 0], True, -10, 10, np.float32], # case 3 - [[2, 65535, 4, 8], [4, 8], [0, 0], True, -10, 10, np.float32], # case 4 - [[2, 4, 65535, 8], [65535, 8], [0, 0], True, -10, 10, np.float32], # case 5 - [[2, 4, 8, 65535], [8, 65535], [0, 0], True, -10, 10, np.float32], # case 6 - [[2, 4, 8, 786432], [8, 786432], [0, 0], True, -10, 10, np.float32], # case 7 - - # special case : same size fp16 - [[4, 3, 128, 64], [128, 64], [0, 0], True, -3.4028235E-6, 3.4028235E-6, np.float16], # case 8 - [[128, 3, 128, 64], [128, 64], [0, 0], False, -3.4028235E3, 3.4028235E4, np.float16], # case 9 - [[65535, 2, 4, 8], [4, 8], [0, 0], True, -10, 10, np.float16], # case 10 - [[2, 65535, 4, 8], [4, 8], [0, 0], True, -10, 10, np.float16], # case 11 - [[2, 4, 65535, 8], [65535, 8], [0, 0], True, -10, 10, np.float16], # case 12 - [[2, 4, 8, 65535], [8, 65535], [0, 0], True, -10, 10, np.float16], # case 13 - [[2, 4, 8, 786432], [8, 786432], [0, 0], True, -10, 10, np.float16], # case 14 - - # common case fp32 - [[4, 3, 128, 64], [128, 128], [0, 0], True, -3.4028235E-14, 3.4028235E-14, np.float32], # case 15 - [[128, 3, 128, 64], [128, 128], [0, 0], False, -3.4028235E14, 3.4028235E14, np.float32], # case 16 - [[65535, 2, 4, 8], [16, 32], [0, 0], True, -10, 10, np.float32], # case 17 - [[2, 65535, 4, 8], [8, 16], [0, 0], True, -10, 10, np.float32], # case 18 - [[2, 4, 65535, 8], [65535, 16], [0, 0], False, -10, 10, np.float32], # case 19 - [[2, 4, 8, 65535], [16, 65535], [0, 0], True, -10, 10, np.float32], # case 20 - [[2, 4, 8, 786432], [16, 786432], [0, 0], True, -10, 10, np.float32], # case 21 - - # common case fp16 - [[4, 3, 128, 64], [128, 128], [0, 0], False, -3.4028235E-6, 3.4028235E-5, np.float16], # case 22 - [[128, 3, 128, 64], [128, 128], [0, 0], True, -3.4028235E3, 3.4028235E3, np.float16], # case 23 - [[65535, 2, 4, 8], [16, 32], [0, 0], True, -10, 10, np.float16], # case 24 - [[2, 65535, 4, 8], [8, 16], [0, 0], True, -10, 10, np.float16], # case 25 - [[2, 4, 65535, 8], [65535, 16], [0, 0], False, -10, 10, np.float16], # case 26 - [[2, 4, 8, 65535], [16, 65535], [0, 0], True, -10, 10, np.float16], # case 27 - [[2, 4, 8, 786432], [16, 786432], [0, 0], True, -10, 10, np.float16] # case 28 - - ] - case = 1 - for item in testcases: - print("==========\nrunning case:{}...".format(case)) - self.resize_grad_d(item[0], item[1], item[2], item[3], item[4], item[5], item[6]) - print("case:{} cmp success\n".format(case)) - case += 1 - - -instantiate_device_type_tests(TestResizeGradD, globals(), except_for='cpu') -if __name__ == "__main__": - torch.npu.set_device("npu:1") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_upsample_linear1d.py b/pytorch1.8.1/test/test_npu/test_upsample_linear1d.py deleted file mode 100644 index 982b1a6eeb5..00000000000 --- a/pytorch1.8.1/test/test_npu/test_upsample_linear1d.py +++ /dev/null @@ -1,110 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies.All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor -import time - - -class TestUpsampleLinear1D(TestCase): - def cpu_op_exec(self, input, size, align_corners): - out_result = torch.ones(input.shape[0], input.shape[1], size[0], dtype=input.dtype) - output = torch._C._nn.upsample_linear1d(input=input, output_size=size, align_corners=align_corners) - torch._C._nn.upsample_linear1d(input=input, output_size=size, align_corners=align_corners, out=out_result) - return output.numpy(), out_result.numpy() - - def npu_op_exec(self, input, size, align_corners): - out_result = torch.ones(input.shape[0], input.shape[1], size[0], dtype=input.dtype) - out_result = out_result.to("npu") - output = torch._C._nn.upsample_linear1d(input=input, output_size=size, align_corners=align_corners) - torch._C._nn.upsample_linear1d(input=input, output_size=size, align_corners=align_corners, out=out_result) - output = output.to("cpu") - out_result = out_result.to("cpu") - return output.numpy(), out_result.numpy() - - def test_upsample_linear1d_shape_format(self, device): - test_cases = [ - [[np.float16, 0, (1, 1, 1, 2)], [4, ], True], - [[np.float16, 0, (2, 1, 1, 4)], [8, ], True], - [[np.float16, 0, (2, 2, 1, 3)], [1, ], True], - [[np.float16, 0, (2, 1, 1, 1)], [4, ], False], - [[np.float16, 0, (4, 1, 1, 2)], [4, ], False], - [[np.float16, 0, (1, 1, 1, 1)], [1, ], False], - - [[np.float32, 0, (1, 1, 1, 2)], [4, ], True], - [[np.float32, 0, (2, 1, 1, 2)], [4, ], True], - [[np.float32, 0, (2, 2, 1, 3)], [1, ], True], - [[np.float32, 0, (3, 1, 1, 1)], [2, ], False], - [[np.float32, 0, (4, 1, 1, 1)], [2, ], False], - [[np.float32, 0, (1, 1, 1, 1)], [1, ], False], - - [[np.float16, 0, (9, 7, 1, 2)], [15, ], True], - [[np.float16, 0, (8, 7, 1, 1)], [2, ], True], - [[np.float16, 0, (17, 2, 1, 3)], [1, ], True], - [[np.float16, 0, (6, 4, 1, 1)], [3, ], False], - [[np.float16, 0, (8, 7, 1, 2)], [4, ], False], - [[np.float16, 0, (2, 7, 1, 7)], [1, ], False], - - [[np.float32, 0, (9, 7, 1, 2)], [7, ], True], - [[np.float32, 0, (8, 3, 1, 1)], [2, ], True], - [[np.float32, 0, (8, 3, 1, 1)], [2, ], True], - [[np.float32, 0, (17, 2, 1, 3)], [1, ], True], - [[np.float32, 0, (9, 7, 1, 2)], [7, ], False], - [[np.float32, 0, (8, 3, 1, 3)], [2, ], False], - [[np.float32, 0, (2, 7, 1, 7)], [1, ], False], - - [[np.float16, 0, (9, 7, 1, 2)], [17, ], True], - [[np.float16, 0, (17, 13, 1, 15)], [16, ], True], - [[np.float16, 0, (61, 41, 1, 1)], [7, ], False], - [[np.float16, 0, (38, 7, 1, 7)], [16, ], False], - [[np.float32, 0, (997, 3, 1, 1)], [32, ], True], - [[np.float32, 0, (627, 2, 1, 3)], [17, ], False], - [[np.float32, 0, (78, 73, 1, 1)], [48, ], False], - [[np.float32, 0, (65535, 2, 1, 4)], [8, ], False], - [[np.float16, 0, (65535, 2, 1, 4)], [8, ], False], - [[np.float32, 0, (10086, 3, 1, 17)], [57, ], False], - [[np.float16, 0, (10086, 3, 1, 17)], [57, ], False] - ] - for item in test_cases: - cpu_input, npu_input = create_common_tensor(item[0], 0, 100) - - if cpu_input.dtype == torch.float16: - cpu_input = cpu_input.to(torch.float32) - - if cpu_input.dim() == 4: - cpu_input = cpu_input.squeeze(2) - - if npu_input.dim() == 4: - npu_input = npu_input.squeeze(2) - - size = item[1] - align_corners = item[2] - - npu_output ,npu_out_result= self.npu_op_exec(npu_input, size, align_corners) - cpu_output ,cpu_out_result= self.cpu_op_exec(cpu_input, size, align_corners) - - cpu_output = cpu_output.astype(npu_output.dtype) - cpu_out_result = cpu_out_result.astype(npu_out_result.dtype) - - self.assertRtolEqual(cpu_output, npu_output) - self.assertRtolEqual(cpu_out_result, npu_out_result) - - -instantiate_device_type_tests(TestUpsampleLinear1D, globals(), except_for="cpu") -if __name__ == "__main__": - torch.npu.set_device("npu:3") - run_tests() diff --git a/pytorch1.8.1/test/test_npu/test_zeros_like.py b/pytorch1.8.1/test/test_npu/test_zeros_like.py deleted file mode 100644 index 057987e4cd6..00000000000 --- a/pytorch1.8.1/test/test_npu/test_zeros_like.py +++ /dev/null @@ -1,68 +0,0 @@ -# Copyright (c) 2020, Huawei Technologies. -# Copyright (c) 2019, Facebook CORPORATION. -# All rights reserved. -# -# Licensed under the BSD 3-Clause License (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://opensource.org/licenses/BSD-3-Clause -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import numpy as np -import sys -import copy -from common_utils import TestCase, run_tests -from common_device_type import dtypes, instantiate_device_type_tests -from util_test import create_common_tensor - -class TestZerosLike(TestCase): - def cpu_op_exec(self, input1): - output = torch.zeros_like(input1) - return output - - def npu_op_exec(self, input1): - output = torch.zeros_like(input1) - output = output.to("cpu") - return output - - def cpu_op_dtype_exec(self, input1, dtype): - output = torch.zeros_like(input1, dtype=dtype) - return output - - def npu_op_dtype_exec(self, input1, dtype): - output = torch.zeros_like(input1, dtype=dtype) - output = output.to("cpu") - return output - - def test_zeros_like_shape_format(self, device): - shape_format = [ - [ [np.float32, 0, (1, 6, 4)] ], - [ [np.float32, 3, (2, 4, 5)] ] - ] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item[0], 1, 100) - cpu_output = self.cpu_op_exec(cpu_input) - npu_output = self.npu_op_exec(npu_input) - self.assertRtolEqual(cpu_output, npu_output) - - def test_zeros_like_dtype_shape_format(self, device): - shape_format = [ - [ [np.float32, 0, (1, 6, 4)], torch.float32], - [ [np.float32, 3, (2, 4, 5)], torch.float16 ], - ] - for item in shape_format: - cpu_input, npu_input = create_common_tensor(item[0], 1, 100) - cpu_output = self.cpu_op_dtype_exec(cpu_input, item[1]) - npu_output = self.npu_op_dtype_exec(npu_input, item[1]) - self.assertRtolEqual(cpu_output, npu_output) - -instantiate_device_type_tests(TestZerosLike, globals(), except_for="cpu") -if __name__ == "__main__": - run_tests() -- Gitee From 6b4748d7e6f11ed7a0c338cca18869b581a936a3 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Tue, 14 Dec 2021 23:02:11 +0800 Subject: [PATCH 03/55] =?UTF-8?q?=E5=90=8C=E6=AD=A51.5.0=20f1e3ae1004dd8b6?= =?UTF-8?q?09c2c1ab4062a4769d31202c2=20=20=20=20=20!2855=20=E3=80=90Optim&?= =?UTF-8?q?reFactor=E3=80=91addcdivfp16=E8=BD=ACfp32=E6=8F=90=E9=AB=98?= =?UTF-8?q?=E7=B2=BE=E5=BA=A6=E4=BC=98=E5=8C=96=20=20=20=20=20Merge=20pull?= =?UTF-8?q?=20request=20!2855=20from=20wuxiankun/addcdiv16=20--=20=20=20?= =?UTF-8?q?=20=20!2856=20=E3=80=90FixBug=E3=80=91BatchNormBackwardReduce?= =?UTF-8?q?=E9=92=88=E5=AF=B9=E6=9C=AA=E5=AE=9A=E4=B9=89=E6=97=B6tensor?= =?UTF-8?q?=E7=9A=84=E9=97=AE=E9=A2=98=E4=BF=AE=E5=A4=8D=20=20=20=20=20Mer?= =?UTF-8?q?ge=20pull=20request=20!2856=20from=20wuxiankun/bnwcbug=20--=20?= =?UTF-8?q?=20=20=20=20!2878=20=E3=80=90fixbug=E3=80=91=E5=8A=A0=E5=85=A5?= =?UTF-8?q?=E7=A9=BA=E5=AE=9E=E7=8E=B0=E4=BF=AE=E5=A4=8Dcpu=E7=BC=96?= =?UTF-8?q?=E8=AF=91=20=20=20=20=20Merge=20pull=20request=20!2878=20from?= =?UTF-8?q?=20wuxiankun/bertadam=20--=20=20=20=20=20!2852=20bug:=20?= =?UTF-8?q?=E4=BF=AE=E6=94=B9batchnorm=E6=8E=A8=E7=90=86=E5=A4=B1=E8=B4=A5?= =?UTF-8?q?=E9=94=99=E8=AF=AF=E9=97=AE=E9=A2=98=20=20=20=20=20Merge=20pull?= =?UTF-8?q?=20request=20!2852=20from=20=E9=83=AD=E5=A4=8F/bntrainingupdate?= =?UTF-8?q?=20=E5=90=8C=E6=AD=A51.8.1=20db9b820f4aba41894f26e187a67450e4b2?= =?UTF-8?q?064a4c=20=20=20=20=20!2863=20=E3=80=90fixbug=E3=80=91=E4=BF=AE?= =?UTF-8?q?=E5=A4=8D1.8.1=20ne=E9=87=8D=E5=A4=8D=E5=88=86=E5=8F=91?= =?UTF-8?q?=E7=9A=84=E9=97=AE=E9=A2=98=20=20=20=20=20Merge=20pull=20reques?= =?UTF-8?q?t=20!2863=20from=20=E7=8E=8B=E6=99=93/pytorch1.8.1=20--=20=20?= =?UTF-8?q?=20=20=20!2879=20clean=20codecheck=20warnings=20=20=20=20=20Mer?= =?UTF-8?q?ge=20pull=20request=20!2879=20from=20=E9=BE=9A=E4=BF=8A?= =?UTF-8?q?=E5=BC=BA/pytorch1.8.1?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- patch/pytorch1.5.0_npu.patch | 224 +++++++++--------- patch/pytorch1.8.1_npu.patch | 197 ++++++++------- .../src/ATen/native/native_functions.yaml | 4 + .../src/ATen/native/npu/AddcdivKernelNpu.cpp | 56 +++-- .../BatchNormBackwardReduceKernelNpu.cpp | 18 +- .../npu/normalization/BatchNormKernelNpu.cpp | 15 +- .../test_npu/test_network_ops/test_addcdiv.py | 195 +++++++++------ pytorch1.8.1/access_control_test.py | 8 +- .../src/ATen/native/native_functions.yaml | 2 - 9 files changed, 400 insertions(+), 319 deletions(-) diff --git a/patch/pytorch1.5.0_npu.patch b/patch/pytorch1.5.0_npu.patch index bcf87a32433..edc9e2b8eab 100644 --- a/patch/pytorch1.5.0_npu.patch +++ b/patch/pytorch1.5.0_npu.patch @@ -1,6 +1,6 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/CMakeLists.txt pytorch-develop-150/aten/CMakeLists.txt --- pytorch-v1.5.0/aten/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/CMakeLists.txt 2021-12-14 12:00:40.705545026 +0800 ++++ pytorch-develop-150/aten/CMakeLists.txt 2021-12-14 23:02:04.911162503 +0800 @@ -22,8 +22,10 @@ set(ATen_CPU_INCLUDE) set(ATen_THIRD_PARTY_INCLUDE) @@ -51,7 +51,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_CPU_DEPENDENCY_LIBS ${ATen_CPU_DEPENDENCY_LIBS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/CMakeLists.txt pytorch-develop-150/aten/src/ATen/CMakeLists.txt --- pytorch-v1.5.0/aten/src/ATen/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/CMakeLists.txt 2021-12-14 12:00:40.705545026 +0800 ++++ pytorch-develop-150/aten/src/ATen/CMakeLists.txt 2021-12-14 23:02:04.911162503 +0800 @@ -67,6 +67,9 @@ FILE(GLOB native_quantized_h "native/quantized/*.h" "native/quantized/cpu/*.h") FILE(GLOB native_cpu_h "native/cpu/*.h") @@ -129,7 +129,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_QUANTIZED_SRCS ${ATen_QUANTIZED_SRCS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/core/dispatch/DispatchTable.h pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h --- pytorch-v1.5.0/aten/src/ATen/core/dispatch/DispatchTable.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h 2021-12-14 12:00:40.709545051 +0800 ++++ pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h 2021-12-14 23:02:04.915162445 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -170,7 +170,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/function_wrapper.py pytorch-develop-150/aten/src/ATen/function_wrapper.py --- pytorch-v1.5.0/aten/src/ATen/function_wrapper.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/function_wrapper.py 2021-12-14 12:00:40.709545051 +0800 ++++ pytorch-develop-150/aten/src/ATen/function_wrapper.py 2021-12-14 23:02:04.919162386 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -354,7 +354,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for option in declaration['options']: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/gen.py pytorch-develop-150/aten/src/ATen/gen.py --- pytorch-v1.5.0/aten/src/ATen/gen.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/gen.py 2021-12-14 12:00:40.709545051 +0800 ++++ pytorch-develop-150/aten/src/ATen/gen.py 2021-12-14 23:02:04.919162386 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -512,7 +512,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= generate_outputs() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/BatchLinearAlgebra.cpp pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp --- pytorch-v1.5.0/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-12-14 12:00:40.713545077 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-12-14 23:02:04.919162386 +0800 @@ -680,7 +680,7 @@ std::tuple triangular_solve_out(Tensor& result, Tensor& clone_A, const Tensor& self, const Tensor& A, bool upper, bool transpose, bool unitriangular) { @@ -524,7 +524,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return std::tuple(result, clone_A); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/cpu/Activation.cpp pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp --- pytorch-v1.5.0/aten/src/ATen/native/cpu/Activation.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp 2021-12-14 12:00:40.713545077 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp 2021-12-14 23:02:04.923162327 +0800 @@ -339,20 +339,20 @@ void hardsigmoid_backward_kernel(TensorIterator& iter) { @@ -552,7 +552,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/Memory.cpp pytorch-develop-150/aten/src/ATen/native/Memory.cpp --- pytorch-v1.5.0/aten/src/ATen/native/Memory.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/Memory.cpp 2021-12-14 12:00:40.713545077 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/Memory.cpp 2021-12-14 23:02:04.919162386 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -614,7 +614,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= detail::computeStorageSize(self.sizes(), self.strides()), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/native_functions.yaml pytorch-develop-150/aten/src/ATen/native/native_functions.yaml --- pytorch-v1.5.0/aten/src/ATen/native/native_functions.yaml 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/native_functions.yaml 2021-12-14 12:00:40.721545127 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/native_functions.yaml 2021-12-14 23:02:04.927162269 +0800 @@ -1,6 +1,5 @@ # See README.md in this directory for more guidance @@ -6679,7 +6679,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: slow_conv_dilated3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1) -> Tensor python_module: nn -@@ -6577,57 +8245,462 @@ +@@ -6577,57 +8245,466 @@ dispatch: CPU: col2im_out_cpu CUDA: col2im_out_cuda @@ -7002,6 +7002,8 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + NPU: apply_adam_npu + +- func: npu_apply_adam(Scalar beta1_power, Scalar beta2_power, Scalar lr, Scalar beta1, Scalar beta2, Scalar epsilon, Tensor grad, bool? use_locking, bool? use_nesterov) -> (Tensor var, Tensor m, Tensor v) ++ npu_dispatch_only: ++ NPU: npu_apply_adam + +- func: npu_apply_adam.out(Scalar beta1_power, Scalar beta2_power, Scalar lr, Scalar beta1, Scalar beta2, Scalar epsilon, Tensor grad, bool? use_locking, bool? use_nesterov, *, Tensor(a!) var, Tensor(b!) m, Tensor(c!) v) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + npu_dispatch_only: @@ -7110,6 +7112,8 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + NPU: bert_apply_adam_npu + +- func: npu_bert_apply_adam(Scalar lr, Scalar beta1, Scalar beta2, Scalar epsilon, Tensor grad, Scalar max_grad_norm, Scalar global_grad_norm, Scalar weight_decay, Scalar? step_size=None, int adam_mode=0) -> (Tensor var, Tensor m, Tensor v) ++ npu_dispatch_only: ++ NPU: npu_bert_apply_adam + +- func: npu_bert_apply_adam.out(Scalar lr, Scalar beta1, Scalar beta2, Scalar epsilon, Tensor grad, Scalar max_grad_norm, Scalar global_grad_norm, Scalar weight_decay, Scalar? step_size=None, int adam_mode=0, *, Tensor(a!) var, Tensor(b!) m, Tensor(c!) v) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + npu_dispatch_only: @@ -7144,7 +7148,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + NPU: rotated_overlaps_npu diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S --- pytorch-v1.5.0/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-12-14 12:00:40.733545202 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-12-14 23:02:04.947161976 +0800 @@ -659,14 +659,14 @@ SUB x1, x1, 4 @@ -7170,7 +7174,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CMP x1, 2 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorCompare.cpp pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorCompare.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp 2021-12-14 12:00:40.713545077 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp 2021-12-14 23:02:04.923162327 +0800 @@ -64,7 +64,7 @@ Tensor isinf(const Tensor &self) { @@ -7182,7 +7186,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return AT_DISPATCH_FLOATING_TYPES_AND_HALF(self.scalar_type(), "isinf", [&]() { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorFactories.cpp pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorFactories.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp 2021-12-14 12:00:40.713545077 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp 2021-12-14 23:02:04.923162327 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7227,7 +7231,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorProperties.cpp pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorProperties.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp 2021-12-14 12:00:40.713545077 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp 2021-12-14 23:02:04.923162327 +0800 @@ -87,6 +87,7 @@ if (self.is_contiguous(memory_format)) { return self; @@ -7238,7 +7242,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= "preserve memory format is unsupported by the contiguous operator"); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/UpSampleBicubic2d.cpp pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp --- pytorch-v1.5.0/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-12-14 12:00:40.713545077 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-12-14 23:02:04.923162327 +0800 @@ -26,7 +26,7 @@ const scalar_t* in = &idata[output_y * input_width + output_x]; scalar_t* out = &odata[output_y * output_width + output_x]; @@ -7250,7 +7254,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= out += output_width * output_height; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native_parse.py pytorch-develop-150/aten/src/ATen/native_parse.py --- pytorch-v1.5.0/aten/src/ATen/native_parse.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native_parse.py 2021-12-14 12:00:40.709545051 +0800 ++++ pytorch-develop-150/aten/src/ATen/native_parse.py 2021-12-14 23:02:04.955161858 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -7288,7 +7292,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= msg = '''Exception raised in processing function: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/preprocess_declarations.py pytorch-develop-150/aten/src/ATen/preprocess_declarations.py --- pytorch-v1.5.0/aten/src/ATen/preprocess_declarations.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/preprocess_declarations.py 2021-12-14 12:00:40.709545051 +0800 ++++ pytorch-develop-150/aten/src/ATen/preprocess_declarations.py 2021-12-14 23:02:04.955161858 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -7320,7 +7324,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/templates/TensorBody.h pytorch-develop-150/aten/src/ATen/templates/TensorBody.h --- pytorch-v1.5.0/aten/src/ATen/templates/TensorBody.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/templates/TensorBody.h 2021-12-14 12:00:40.741545253 +0800 ++++ pytorch-develop-150/aten/src/ATen/templates/TensorBody.h 2021-12-14 23:02:04.959161799 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7353,7 +7357,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/templates/TensorMethods.h pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h --- pytorch-v1.5.0/aten/src/ATen/templates/TensorMethods.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h 2021-12-14 12:00:40.741545253 +0800 ++++ pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h 2021-12-14 23:02:04.959161799 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7387,7 +7391,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/CMakeLists.txt pytorch-develop-150/aten/src/TH/CMakeLists.txt --- pytorch-v1.5.0/aten/src/TH/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/CMakeLists.txt 2021-12-14 12:00:40.741545253 +0800 ++++ pytorch-develop-150/aten/src/TH/CMakeLists.txt 2021-12-14 23:02:04.959161799 +0800 @@ -48,6 +48,11 @@ ${CMAKE_CURRENT_SOURCE_DIR} PARENT_SCOPE) @@ -7402,7 +7406,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/generic/THStorage.cpp pytorch-develop-150/aten/src/TH/generic/THStorage.cpp --- pytorch-v1.5.0/aten/src/TH/generic/THStorage.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/generic/THStorage.cpp 2021-12-14 12:00:40.741545253 +0800 ++++ pytorch-develop-150/aten/src/TH/generic/THStorage.cpp 2021-12-14 23:02:04.959161799 +0800 @@ -1,9 +1,32 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7511,7 +7515,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/generic/THStorage.h pytorch-develop-150/aten/src/TH/generic/THStorage.h --- pytorch-v1.5.0/aten/src/TH/generic/THStorage.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/generic/THStorage.h 2021-12-14 12:00:40.741545253 +0800 ++++ pytorch-develop-150/aten/src/TH/generic/THStorage.h 2021-12-14 23:02:04.959161799 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7550,7 +7554,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/CMakeLists.txt pytorch-develop-150/c10/CMakeLists.txt --- pytorch-v1.5.0/c10/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/CMakeLists.txt 2021-12-14 12:00:40.749545303 +0800 ++++ pytorch-develop-150/c10/CMakeLists.txt 2021-12-14 23:02:04.971161624 +0800 @@ -63,6 +63,14 @@ message(STATUS "don't use NUMA") endif() @@ -7579,7 +7583,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # not checked in diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Backend.h pytorch-develop-150/c10/core/Backend.h --- pytorch-v1.5.0/c10/core/Backend.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Backend.h 2021-12-14 12:00:40.749545303 +0800 ++++ pytorch-develop-150/c10/core/Backend.h 2021-12-14 23:02:04.971161624 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7676,7 +7680,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case Backend::CUDA: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Device.cpp pytorch-develop-150/c10/core/Device.cpp --- pytorch-v1.5.0/c10/core/Device.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Device.cpp 2021-12-14 12:00:40.749545303 +0800 ++++ pytorch-develop-150/c10/core/Device.cpp 2021-12-14 23:02:04.971161624 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7716,7 +7720,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= types.begin(), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Device.h pytorch-develop-150/c10/core/Device.h --- pytorch-v1.5.0/c10/core/Device.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Device.h 2021-12-14 12:00:40.749545303 +0800 ++++ pytorch-develop-150/c10/core/Device.h 2021-12-14 23:02:04.971161624 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7751,7 +7755,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return type_ == DeviceType::CPU; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DeviceType.cpp pytorch-develop-150/c10/core/DeviceType.cpp --- pytorch-v1.5.0/c10/core/DeviceType.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DeviceType.cpp 2021-12-14 12:00:40.749545303 +0800 ++++ pytorch-develop-150/c10/core/DeviceType.cpp 2021-12-14 23:02:04.971161624 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7791,7 +7795,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return false; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DeviceType.h pytorch-develop-150/c10/core/DeviceType.h --- pytorch-v1.5.0/c10/core/DeviceType.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DeviceType.h 2021-12-14 12:00:40.749545303 +0800 ++++ pytorch-develop-150/c10/core/DeviceType.h 2021-12-14 23:02:04.971161624 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7834,7 +7838,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= constexpr DeviceType kXLA = DeviceType::XLA; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DispatchKey.cpp pytorch-develop-150/c10/core/DispatchKey.cpp --- pytorch-v1.5.0/c10/core/DispatchKey.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DispatchKey.cpp 2021-12-14 12:00:40.749545303 +0800 ++++ pytorch-develop-150/c10/core/DispatchKey.cpp 2021-12-14 23:02:04.971161624 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7866,7 +7870,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::SparseCPUTensorId: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DispatchKey.h pytorch-develop-150/c10/core/DispatchKey.h --- pytorch-v1.5.0/c10/core/DispatchKey.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DispatchKey.h 2021-12-14 12:00:40.749545303 +0800 ++++ pytorch-develop-150/c10/core/DispatchKey.h 2021-12-14 23:02:04.971161624 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7898,7 +7902,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Storage.h pytorch-develop-150/c10/core/Storage.h --- pytorch-v1.5.0/c10/core/Storage.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Storage.h 2021-12-14 12:00:40.749545303 +0800 ++++ pytorch-develop-150/c10/core/Storage.h 2021-12-14 23:02:04.971161624 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7932,7 +7936,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/StorageImpl.cpp pytorch-develop-150/c10/core/StorageImpl.cpp --- pytorch-v1.5.0/c10/core/StorageImpl.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/StorageImpl.cpp 2021-12-14 12:00:40.749545303 +0800 ++++ pytorch-develop-150/c10/core/StorageImpl.cpp 2021-12-14 23:02:04.971161624 +0800 @@ -1 +1,18 @@ #include + @@ -7954,7 +7958,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +} // namespace c10 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/StorageImpl.h pytorch-develop-150/c10/core/StorageImpl.h --- pytorch-v1.5.0/c10/core/StorageImpl.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/StorageImpl.h 2021-12-14 12:00:40.749545303 +0800 ++++ pytorch-develop-150/c10/core/StorageImpl.h 2021-12-14 23:02:04.971161624 +0800 @@ -1,11 +1,55 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8081,7 +8085,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/TensorImpl.h pytorch-develop-150/c10/core/TensorImpl.h --- pytorch-v1.5.0/c10/core/TensorImpl.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/TensorImpl.h 2021-12-14 12:00:40.749545303 +0800 ++++ pytorch-develop-150/c10/core/TensorImpl.h 2021-12-14 23:02:04.971161624 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8149,7 +8153,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/TensorOptions.h pytorch-develop-150/c10/core/TensorOptions.h --- pytorch-v1.5.0/c10/core/TensorOptions.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/TensorOptions.h 2021-12-14 12:00:40.749545303 +0800 ++++ pytorch-develop-150/c10/core/TensorOptions.h 2021-12-14 23:02:04.971161624 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8190,7 +8194,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } else if (tid == DispatchKey::HIPTensorId) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/cuda/CMakeLists.txt pytorch-develop-150/c10/cuda/CMakeLists.txt --- pytorch-v1.5.0/c10/cuda/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/cuda/CMakeLists.txt 2021-12-14 12:00:40.753545329 +0800 ++++ pytorch-develop-150/c10/cuda/CMakeLists.txt 2021-12-14 23:02:04.971161624 +0800 @@ -24,6 +24,7 @@ CUDACachingAllocator.cpp impl/CUDAGuardImpl.cpp @@ -8209,7 +8213,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= torch_cuda_based_add_library(c10_cuda ${C10_CUDA_SRCS} ${C10_CUDA_HEADERS}) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/macros/Export.h pytorch-develop-150/c10/macros/Export.h --- pytorch-v1.5.0/c10/macros/Export.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/macros/Export.h 2021-12-14 12:00:40.753545329 +0800 ++++ pytorch-develop-150/c10/macros/Export.h 2021-12-14 23:02:04.971161624 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8336,7 +8340,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/caffe2/CMakeLists.txt pytorch-develop-150/caffe2/CMakeLists.txt --- pytorch-v1.5.0/caffe2/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/caffe2/CMakeLists.txt 2021-12-14 12:00:40.753545329 +0800 ++++ pytorch-develop-150/caffe2/CMakeLists.txt 2021-12-14 23:02:04.975161565 +0800 @@ -32,6 +32,7 @@ # Add source, includes, and libs to lists list(APPEND Caffe2_CPU_SRCS ${ATen_CPU_SRCS}) @@ -8491,7 +8495,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Call again since Caffe2_HIP_INCLUDE is extended with ATen include dirs. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/.clang-format pytorch-develop-150/.clang-format --- pytorch-v1.5.0/.clang-format 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/.clang-format 2021-12-14 12:00:40.701545000 +0800 ++++ pytorch-develop-150/.clang-format 2021-12-14 23:02:04.907162562 +0800 @@ -84,5 +84,4 @@ SpacesInSquareBrackets: false Standard: Cpp11 @@ -8502,7 +8506,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/BuildVariables.cmake pytorch-develop-150/cmake/BuildVariables.cmake --- pytorch-v1.5.0/cmake/BuildVariables.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/BuildVariables.cmake 2021-12-14 12:00:40.809545683 +0800 ++++ pytorch-develop-150/cmake/BuildVariables.cmake 2021-12-14 23:02:05.043160568 +0800 @@ -11,6 +11,7 @@ # CMakeLists.txt files under each folder respectively. set(Caffe2_CPU_SRCS) @@ -8529,7 +8533,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # symbols. However, if the lib is whole linked in caffe2 lib, we don't want diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Codegen.cmake pytorch-develop-150/cmake/Codegen.cmake --- pytorch-v1.5.0/cmake/Codegen.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Codegen.cmake 2021-12-14 12:00:40.809545683 +0800 ++++ pytorch-develop-150/cmake/Codegen.cmake 2021-12-14 23:02:05.043160568 +0800 @@ -191,13 +191,14 @@ file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt generated_cpp) file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt-cuda cuda_generated_cpp) @@ -8560,7 +8564,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endif() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Dependencies.cmake pytorch-develop-150/cmake/Dependencies.cmake --- pytorch-v1.5.0/cmake/Dependencies.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Dependencies.cmake 2021-12-14 12:00:40.809545683 +0800 ++++ pytorch-develop-150/cmake/Dependencies.cmake 2021-12-14 23:02:05.043160568 +0800 @@ -1509,6 +1509,13 @@ ENDIF(NOT C_HAS_THREAD) endif() @@ -8577,7 +8581,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Summary.cmake pytorch-develop-150/cmake/Summary.cmake --- pytorch-v1.5.0/cmake/Summary.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Summary.cmake 2021-12-14 12:00:40.809545683 +0800 ++++ pytorch-develop-150/cmake/Summary.cmake 2021-12-14 23:02:05.043160568 +0800 @@ -134,6 +134,7 @@ if(NOT "${SELECTED_OP_LIST}" STREQUAL "") message(STATUS " SELECTED_OP_LIST : ${SELECTED_OP_LIST}") @@ -8588,7 +8592,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endfunction() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/TorchConfig.cmake.in pytorch-develop-150/cmake/TorchConfig.cmake.in --- pytorch-v1.5.0/cmake/TorchConfig.cmake.in 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/TorchConfig.cmake.in 2021-12-14 12:00:40.809545683 +0800 ++++ pytorch-develop-150/cmake/TorchConfig.cmake.in 2021-12-14 23:02:05.043160568 +0800 @@ -112,6 +112,11 @@ list(APPEND TORCH_LIBRARIES ${TORCH_CUDA_LIBRARIES}) endif() @@ -8603,7 +8607,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(TORCH_CXX_FLAGS "-D_GLIBCXX_USE_CXX11_ABI=@GLIBCXX_USE_CXX11_ABI@") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/CMakeLists.txt pytorch-develop-150/CMakeLists.txt --- pytorch-v1.5.0/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/CMakeLists.txt 2021-12-14 12:00:40.701545000 +0800 ++++ pytorch-develop-150/CMakeLists.txt 2021-12-14 23:02:04.907162562 +0800 @@ -205,6 +205,10 @@ option(USE_TBB "Use TBB" OFF) option(ONNX_ML "Enable traditional ONNX ML API." ON) @@ -8670,7 +8674,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-missing-braces") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/CONTRIBUTING.zh.md pytorch-develop-150/CONTRIBUTING.zh.md --- pytorch-v1.5.0/CONTRIBUTING.zh.md 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-150/CONTRIBUTING.zh.md 2021-12-14 12:00:40.701545000 +0800 ++++ pytorch-develop-150/CONTRIBUTING.zh.md 2021-12-14 23:02:04.907162562 +0800 @@ -0,0 +1,228 @@ +# PyTorch贡献指南 +- [贡献者许可协议](#贡献者许可协议.md) @@ -8902,7 +8906,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/.dockerignore pytorch-develop-150/.dockerignore --- pytorch-v1.5.0/.dockerignore 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/.dockerignore 2021-12-14 12:00:40.701545000 +0800 ++++ pytorch-develop-150/.dockerignore 2021-12-14 23:02:04.907162562 +0800 @@ -1,257 +1 @@ -# READ THIS BEFORE YOU REFACTOR ME -# @@ -9178,7 +9182,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/requirements.txt pytorch-develop-150/requirements.txt --- pytorch-v1.5.0/requirements.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/requirements.txt 2021-12-14 12:00:40.701545000 +0800 ++++ pytorch-develop-150/requirements.txt 2021-12-14 23:02:04.907162562 +0800 @@ -4,4 +4,13 @@ requests setuptools @@ -9198,7 +9202,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/setup.py pytorch-develop-150/setup.py --- pytorch-v1.5.0/setup.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/setup.py 2021-12-14 12:00:40.701545000 +0800 ++++ pytorch-develop-150/setup.py 2021-12-14 23:02:04.907162562 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -9301,7 +9305,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= 'python/serialized_test/data/operator_test/*.zip', diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/derivatives.yaml pytorch-develop-150/tools/autograd/derivatives.yaml --- pytorch-v1.5.0/tools/autograd/derivatives.yaml 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/derivatives.yaml 2021-12-14 12:00:41.445549703 +0800 ++++ pytorch-develop-150/tools/autograd/derivatives.yaml 2021-12-14 23:02:05.639151835 +0800 @@ -107,6 +107,10 @@ # # NB: The parameter names here MUST be consistent with the parameter names @@ -9426,7 +9430,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/dump_utils.py pytorch-develop-150/tools/autograd/dump_utils.py --- pytorch-v1.5.0/tools/autograd/dump_utils.py 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/dump_utils.py 2021-12-14 12:00:41.445549703 +0800 ++++ pytorch-develop-150/tools/autograd/dump_utils.py 2021-12-14 23:02:05.639151835 +0800 @@ -0,0 +1,312 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# All rights reserved. @@ -9742,7 +9746,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + return prepare_to_check_overflow, overflow_dump_inputs diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_autograd_functions.py pytorch-develop-150/tools/autograd/gen_autograd_functions.py --- pytorch-v1.5.0/tools/autograd/gen_autograd_functions.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_autograd_functions.py 2021-12-14 12:00:41.445549703 +0800 ++++ pytorch-develop-150/tools/autograd/gen_autograd_functions.py 2021-12-14 23:02:05.639151835 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -9948,7 +9952,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_python_functions.py pytorch-develop-150/tools/autograd/gen_python_functions.py --- pytorch-v1.5.0/tools/autograd/gen_python_functions.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_python_functions.py 2021-12-14 12:00:41.445549703 +0800 ++++ pytorch-develop-150/tools/autograd/gen_python_functions.py 2021-12-14 23:02:05.643151776 +0800 @@ -1,3 +1,20 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -9990,7 +9994,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= 'value': argname, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_variable_type.py pytorch-develop-150/tools/autograd/gen_variable_type.py --- pytorch-v1.5.0/tools/autograd/gen_variable_type.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_variable_type.py 2021-12-14 12:00:41.445549703 +0800 ++++ pytorch-develop-150/tools/autograd/gen_variable_type.py 2021-12-14 23:02:05.643151776 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -10171,7 +10175,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/Functions.cpp pytorch-develop-150/tools/autograd/templates/Functions.cpp --- pytorch-v1.5.0/tools/autograd/templates/Functions.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/Functions.cpp 2021-12-14 12:00:41.445549703 +0800 ++++ pytorch-develop-150/tools/autograd/templates/Functions.cpp 2021-12-14 23:02:05.643151776 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2021 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10252,7 +10256,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto sparse = sparse_.coalesce(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/python_torch_functions.cpp pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp --- pytorch-v1.5.0/tools/autograd/templates/python_torch_functions.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp 2021-12-14 12:00:41.445549703 +0800 ++++ pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp 2021-12-14 23:02:05.643151776 +0800 @@ -22,7 +22,7 @@ #include "torch/csrc/autograd/generated/variable_factories.h" #include "torch/csrc/utils/structseq.h" @@ -10336,7 +10340,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/python_variable_methods.cpp pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp --- pytorch-v1.5.0/tools/autograd/templates/python_variable_methods.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp 2021-12-14 12:00:41.445549703 +0800 ++++ pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp 2021-12-14 23:02:05.643151776 +0800 @@ -15,7 +15,13 @@ #include "torch/csrc/cuda/Stream.h" #include "torch/csrc/cuda/Event.h" @@ -10431,7 +10435,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"has_names", (PyCFunction)THPVariable_has_names, METH_NOARGS, NULL}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/VariableType.cpp pytorch-develop-150/tools/autograd/templates/VariableType.cpp --- pytorch-v1.5.0/tools/autograd/templates/VariableType.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/VariableType.cpp 2021-12-14 12:00:41.445549703 +0800 ++++ pytorch-develop-150/tools/autograd/templates/VariableType.cpp 2021-12-14 23:02:05.643151776 +0800 @@ -1,7 +1,29 @@ +// Copyright (c) 2021 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10464,7 +10468,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/VariableType.h pytorch-develop-150/tools/autograd/templates/VariableType.h --- pytorch-v1.5.0/tools/autograd/templates/VariableType.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/VariableType.h 2021-12-14 12:00:41.445549703 +0800 ++++ pytorch-develop-150/tools/autograd/templates/VariableType.h 2021-12-14 23:02:05.643151776 +0800 @@ -1,3 +1,20 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10496,7 +10500,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= const at::Tensor & unpack(const Tensor & t, const char * name, int pos); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/build_variables.bzl pytorch-develop-150/tools/build_variables.bzl --- pytorch-v1.5.0/tools/build_variables.bzl 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/build_variables.bzl 2021-12-14 12:00:41.441549678 +0800 ++++ pytorch-develop-150/tools/build_variables.bzl 2021-12-14 23:02:05.639151835 +0800 @@ -46,6 +46,7 @@ "torch/csrc/autograd/functions/utils.cpp", "torch/csrc/autograd/input_buffer.cpp", @@ -10582,7 +10586,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def grad(outputs: _TensorOrTensors, inputs: _TensorOrTensors, grad_outputs: Optional[_TensorOrTensors]=..., retain_graph: Optional[bool]=..., create_graph: bool=..., only_inputs: bool=..., allow_unused: bool=...) -> Tuple[Tensor, ...]: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/autograd/profiler.py pytorch-develop-150/torch/autograd/profiler.py --- pytorch-v1.5.0/torch/autograd/profiler.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/autograd/profiler.py 2021-12-14 12:00:41.445549703 +0800 ++++ pytorch-develop-150/torch/autograd/profiler.py 2021-12-14 23:02:05.643151776 +0800 @@ -1,8 +1,25 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -11059,7 +11063,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ''.join(result) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/CMakeLists.txt pytorch-develop-150/torch/CMakeLists.txt --- pytorch-v1.5.0/torch/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/CMakeLists.txt 2021-12-14 12:00:41.445549703 +0800 ++++ pytorch-develop-150/torch/CMakeLists.txt 2021-12-14 23:02:05.643151776 +0800 @@ -97,6 +97,7 @@ ${TORCH_SRC_DIR}/csrc/tensor/python_tensor.cpp ${TORCH_SRC_DIR}/csrc/utils.cpp @@ -11091,7 +11095,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endif() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/engine.cpp pytorch-develop-150/torch/csrc/autograd/engine.cpp --- pytorch-v1.5.0/torch/csrc/autograd/engine.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/engine.cpp 2021-12-14 12:00:41.453549753 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/engine.cpp 2021-12-14 23:02:05.651151659 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11243,7 +11247,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto event = c10::Event{c10::DeviceType::CUDA}; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/function.h pytorch-develop-150/torch/csrc/autograd/function.h --- pytorch-v1.5.0/torch/csrc/autograd/function.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/function.h 2021-12-14 12:00:41.453549753 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/function.h 2021-12-14 23:02:05.651151659 +0800 @@ -11,6 +11,7 @@ #include @@ -11265,7 +11269,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // probably operate with names. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/functions/tensor.cpp pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp --- pytorch-v1.5.0/torch/csrc/autograd/functions/tensor.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp 2021-12-14 12:00:41.453549753 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp 2021-12-14 23:02:05.651151659 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11297,7 +11301,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= /*non_blocking=*/false, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/init.cpp pytorch-develop-150/torch/csrc/autograd/init.cpp --- pytorch-v1.5.0/torch/csrc/autograd/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/init.cpp 2021-12-14 12:00:41.457549779 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/init.cpp 2021-12-14 23:02:05.651151659 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11340,7 +11344,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= m.def("_enable_profiler", enableProfiler); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/input_buffer.cpp pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp --- pytorch-v1.5.0/torch/csrc/autograd/input_buffer.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp 2021-12-14 12:00:41.457549779 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp 2021-12-14 23:02:05.651151659 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11392,7 +11396,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto& old_var = buffer[pos]; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/profiler.cpp pytorch-develop-150/torch/csrc/autograd/profiler.cpp --- pytorch-v1.5.0/torch/csrc/autograd/profiler.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/profiler.cpp 2021-12-14 12:00:41.457549779 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/profiler.cpp 2021-12-14 23:02:05.651151659 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11632,7 +11636,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CUDAStubs::~CUDAStubs() = default; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/profiler.h pytorch-develop-150/torch/csrc/autograd/profiler.h --- pytorch-v1.5.0/torch/csrc/autograd/profiler.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/profiler.h 2021-12-14 12:00:41.457549779 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/profiler.h 2021-12-14 23:02:05.651151659 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11768,7 +11772,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/python_variable.cpp pytorch-develop-150/torch/csrc/autograd/python_variable.cpp --- pytorch-v1.5.0/torch/csrc/autograd/python_variable.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/python_variable.cpp 2021-12-14 12:00:41.457549779 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/python_variable.cpp 2021-12-14 23:02:05.651151659 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11822,7 +11826,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"is_complex", (getter)THPVariable_is_complex, nullptr, nullptr, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/python_variable_indexing.cpp pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp --- pytorch-v1.5.0/torch/csrc/autograd/python_variable_indexing.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-14 12:00:41.457549779 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-14 23:02:05.651151659 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11853,7 +11857,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= at::Device self_device = self_.device(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/record_function.cpp pytorch-develop-150/torch/csrc/autograd/record_function.cpp --- pytorch-v1.5.0/torch/csrc/autograd/record_function.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/record_function.cpp 2021-12-14 12:00:41.457549779 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/record_function.cpp 2021-12-14 23:02:05.651151659 +0800 @@ -154,6 +154,12 @@ } } @@ -11887,7 +11891,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/record_function.h pytorch-develop-150/torch/csrc/autograd/record_function.h --- pytorch-v1.5.0/torch/csrc/autograd/record_function.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/record_function.h 2021-12-14 12:00:41.457549779 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/record_function.h 2021-12-14 23:02:05.651151659 +0800 @@ -44,6 +44,9 @@ // Default constructor is used with before function called afterwards RecordFunction() {} @@ -11951,7 +11955,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (torch::autograd::profiler::needsInputs()) { \ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/utils/wrap_outputs.h pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h --- pytorch-v1.5.0/torch/csrc/autograd/utils/wrap_outputs.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h 2021-12-14 12:00:41.457549779 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h 2021-12-14 23:02:05.651151659 +0800 @@ -168,6 +168,45 @@ return r.release(); } @@ -12000,7 +12004,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (!r) throw python_error(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/VariableTypeManual.cpp pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp --- pytorch-v1.5.0/torch/csrc/autograd/VariableTypeManual.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp 2021-12-14 12:00:41.453549753 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp 2021-12-14 23:02:05.651151659 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12034,7 +12038,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (!t.defined()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/comm.cpp pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/comm.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp 2021-12-14 12:00:41.457549779 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp 2021-12-14 23:02:05.651151659 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12140,7 +12144,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= while (!in_flight.empty()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/init.cpp pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp 2021-12-14 12:00:41.457549779 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp 2021-12-14 23:02:05.651151659 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12197,7 +12201,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= .def("is_success", &::c10d::ProcessGroup::Work::isSuccess) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/reducer.cpp pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/reducer.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp 2021-12-14 12:00:41.457549779 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp 2021-12-14 23:02:05.651151659 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12332,7 +12336,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/DynamicTypes.cpp pytorch-develop-150/torch/csrc/DynamicTypes.cpp --- pytorch-v1.5.0/torch/csrc/DynamicTypes.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/DynamicTypes.cpp 2021-12-14 12:00:41.449549729 +0800 ++++ pytorch-develop-150/torch/csrc/DynamicTypes.cpp 2021-12-14 23:02:05.643151776 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12381,7 +12385,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return it->second; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/Generator.cpp pytorch-develop-150/torch/csrc/Generator.cpp --- pytorch-v1.5.0/torch/csrc/Generator.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/Generator.cpp 2021-12-14 12:00:41.449549729 +0800 ++++ pytorch-develop-150/torch/csrc/Generator.cpp 2021-12-14 23:02:05.643151776 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12449,7 +12453,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #endif diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/serialization.cpp pytorch-develop-150/torch/csrc/generic/serialization.cpp --- pytorch-v1.5.0/torch/csrc/generic/serialization.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/serialization.cpp 2021-12-14 12:00:41.461549804 +0800 ++++ pytorch-develop-150/torch/csrc/generic/serialization.cpp 2021-12-14 23:02:05.655151601 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12549,7 +12553,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/Storage.cpp pytorch-develop-150/torch/csrc/generic/Storage.cpp --- pytorch-v1.5.0/torch/csrc/generic/Storage.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/Storage.cpp 2021-12-14 12:00:41.461549804 +0800 ++++ pytorch-develop-150/torch/csrc/generic/Storage.cpp 2021-12-14 23:02:05.655151601 +0800 @@ -1,7 +1,25 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12628,7 +12632,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for (Py_ssize_t i = 0; i < length; i++) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/StorageMethods.cpp pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp --- pytorch-v1.5.0/torch/csrc/generic/StorageMethods.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp 2021-12-14 12:00:41.461549804 +0800 ++++ pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp 2021-12-14 23:02:05.655151601 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12676,7 +12680,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"_write_file", (PyCFunction)THPStorage_(writeFile), METH_VARARGS, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/Module.cpp pytorch-develop-150/torch/csrc/Module.cpp --- pytorch-v1.5.0/torch/csrc/Module.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/Module.cpp 2021-12-14 12:00:41.449549729 +0800 ++++ pytorch-develop-150/torch/csrc/Module.cpp 2021-12-14 23:02:05.643151776 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12837,7 +12841,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto set_module_attr = [&](const char* name, PyObject* v, bool incref = true) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/tensor/python_tensor.cpp pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp --- pytorch-v1.5.0/torch/csrc/tensor/python_tensor.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp 2021-12-14 12:00:41.469549855 +0800 ++++ pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp 2021-12-14 23:02:05.663151483 +0800 @@ -1,18 +1,35 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13214,7 +13218,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +} // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/init.cpp pytorch-develop-150/torch/csrc/utils/init.cpp --- pytorch-v1.5.0/torch/csrc/utils/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/init.cpp 2021-12-14 12:00:41.469549855 +0800 ++++ pytorch-develop-150/torch/csrc/utils/init.cpp 2021-12-14 23:02:05.663151483 +0800 @@ -1,7 +1,13 @@ #include #include @@ -13379,7 +13383,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/init.h pytorch-develop-150/torch/csrc/utils/init.h --- pytorch-v1.5.0/torch/csrc/utils/init.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/init.h 2021-12-14 12:00:41.469549855 +0800 ++++ pytorch-develop-150/torch/csrc/utils/init.h 2021-12-14 23:02:05.663151483 +0800 @@ -8,4 +8,7 @@ void initThroughputBenchmarkBindings(PyObject* module); @@ -13390,7 +13394,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/python_arg_parser.h pytorch-develop-150/torch/csrc/utils/python_arg_parser.h --- pytorch-v1.5.0/torch/csrc/utils/python_arg_parser.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/python_arg_parser.h 2021-12-14 12:00:41.473549881 +0800 ++++ pytorch-develop-150/torch/csrc/utils/python_arg_parser.h 2021-12-14 23:02:05.663151483 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13425,7 +13429,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return at::Device(device_str); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_layouts.cpp pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_layouts.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp 2021-12-14 12:00:41.473549881 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp 2021-12-14 23:02:05.663151483 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13456,7 +13460,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= registerLayoutObject((THPLayout*)strided_layout, at::Backend::QuantizedCPU); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_new.cpp pytorch-develop-150/torch/csrc/utils/tensor_new.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_new.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_new.cpp 2021-12-14 12:00:41.473549881 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_new.cpp 2021-12-14 23:02:05.663151483 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13592,7 +13596,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= " or ", c10::DispatchKey::XLATensorId, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_types.cpp pytorch-develop-150/torch/csrc/utils/tensor_types.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_types.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_types.cpp 2021-12-14 12:00:41.473549881 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_types.cpp 2021-12-14 23:02:05.663151483 +0800 @@ -1,58 +1,91 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13805,7 +13809,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def get_rng_state(): ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/distributed/distributed_c10d.py pytorch-develop-150/torch/distributed/distributed_c10d.py --- pytorch-v1.5.0/torch/distributed/distributed_c10d.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/distributed/distributed_c10d.py 2021-12-14 12:00:41.473549881 +0800 ++++ pytorch-develop-150/torch/distributed/distributed_c10d.py 2021-12-14 23:02:05.663151483 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -13886,7 +13890,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/__init__.py pytorch-develop-150/torch/__init__.py --- pytorch-v1.5.0/torch/__init__.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/__init__.py 2021-12-14 12:00:41.445549703 +0800 ++++ pytorch-develop-150/torch/__init__.py 2021-12-14 23:02:05.643151776 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -13929,7 +13933,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/lib/c10d/CMakeLists.txt pytorch-develop-150/torch/lib/c10d/CMakeLists.txt --- pytorch-v1.5.0/torch/lib/c10d/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/lib/c10d/CMakeLists.txt 2021-12-14 12:00:41.473549881 +0800 ++++ pytorch-develop-150/torch/lib/c10d/CMakeLists.txt 2021-12-14 23:02:05.667151425 +0800 @@ -28,6 +28,10 @@ option(USE_C10D_NCCL "USE C10D NCCL" ON) endif() @@ -13982,7 +13986,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= copy_header(ProcessGroupMPI.hpp) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/lib/libshm/CMakeLists.txt pytorch-develop-150/torch/lib/libshm/CMakeLists.txt --- pytorch-v1.5.0/torch/lib/libshm/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/lib/libshm/CMakeLists.txt 2021-12-14 12:00:41.477549905 +0800 ++++ pytorch-develop-150/torch/lib/libshm/CMakeLists.txt 2021-12-14 23:02:05.667151425 +0800 @@ -37,8 +37,11 @@ SET_TARGET_PROPERTIES(shm PROPERTIES PREFIX "lib" @@ -14039,7 +14043,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -_maybe_indices_t = _scalar_or_tuple_2_t[Tensor] diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/functional.py pytorch-develop-150/torch/nn/functional.py --- pytorch-v1.5.0/torch/nn/functional.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/functional.py 2021-12-14 12:00:41.477549905 +0800 ++++ pytorch-develop-150/torch/nn/functional.py 2021-12-14 23:02:05.667151425 +0800 @@ -1611,7 +1611,7 @@ else: output = input.matmul(weight.t()) @@ -14062,7 +14066,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -from . import parallel as parallel diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/batchnorm.py pytorch-develop-150/torch/nn/modules/batchnorm.py --- pytorch-v1.5.0/torch/nn/modules/batchnorm.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/batchnorm.py 2021-12-14 12:00:41.477549905 +0800 ++++ pytorch-develop-150/torch/nn/modules/batchnorm.py 2021-12-14 23:02:05.667151425 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14094,7 +14098,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= self.register_parameter('running_var', None) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/module.py pytorch-develop-150/torch/nn/modules/module.py --- pytorch-v1.5.0/torch/nn/modules/module.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/module.py 2021-12-14 12:00:41.477549905 +0800 ++++ pytorch-develop-150/torch/nn/modules/module.py 2021-12-14 23:02:05.671151366 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14254,7 +14258,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return t.to(device, dtype if t.is_floating_point() else None, non_blocking, memory_format=convert_to_format) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/normalization.py pytorch-develop-150/torch/nn/modules/normalization.py --- pytorch-v1.5.0/torch/nn/modules/normalization.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/normalization.py 2021-12-14 12:00:41.477549905 +0800 ++++ pytorch-develop-150/torch/nn/modules/normalization.py 2021-12-14 23:02:05.671151366 +0800 @@ -128,13 +128,14 @@ """ __constants__ = ['normalized_shape', 'eps', 'elementwise_affine'] @@ -14323,7 +14327,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - module_kwargs: Optional[Any] = ...) -> Tensor: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/parallel/distributed.py pytorch-develop-150/torch/nn/parallel/distributed.py --- pytorch-v1.5.0/torch/nn/parallel/distributed.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/parallel/distributed.py 2021-12-14 12:00:41.477549905 +0800 ++++ pytorch-develop-150/torch/nn/parallel/distributed.py 2021-12-14 23:02:05.671151366 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14678,7 +14682,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def remove_weight_norm(module: T_module, name: str = ...) -> T_module: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/onnx/symbolic_opset9.py pytorch-develop-150/torch/onnx/symbolic_opset9.py --- pytorch-v1.5.0/torch/onnx/symbolic_opset9.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/onnx/symbolic_opset9.py 2021-12-14 12:00:41.481549931 +0800 ++++ pytorch-develop-150/torch/onnx/symbolic_opset9.py 2021-12-14 23:02:05.671151366 +0800 @@ -1621,14 +1621,23 @@ slices = [sym_help._slice_helper(g, w, axes=[0], starts=[x * n], ends=[y * n]) for x, y in intervals] return g.op('Concat', *slices, axis_i=0) @@ -14756,7 +14760,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, params: _params_t, lr: float=..., lr_decay: float=..., weight_decay: float=..., initial_accumulator_value: float=..., eps: float=...) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/optim/adamax.py pytorch-develop-150/torch/optim/adamax.py --- pytorch-v1.5.0/torch/optim/adamax.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/optim/adamax.py 2021-12-14 12:00:41.481549931 +0800 ++++ pytorch-develop-150/torch/optim/adamax.py 2021-12-14 23:02:05.671151366 +0800 @@ -80,8 +80,8 @@ exp_inf.mul_(beta2).unsqueeze(0), grad.abs().add_(eps).unsqueeze_(0) @@ -14933,7 +14937,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, params: _params_t, lr: float=..., betas: Tuple[float, float]=..., eps: float=...) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/serialization.py pytorch-develop-150/torch/serialization.py --- pytorch-v1.5.0/torch/serialization.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/serialization.py 2021-12-14 12:00:41.445549703 +0800 ++++ pytorch-develop-150/torch/serialization.py 2021-12-14 23:02:05.643151776 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15017,7 +15021,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def location_tag(storage): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/storage.py pytorch-develop-150/torch/storage.py --- pytorch-v1.5.0/torch/storage.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/storage.py 2021-12-14 12:00:41.445549703 +0800 ++++ pytorch-develop-150/torch/storage.py 2021-12-14 23:02:05.643151776 +0800 @@ -7,6 +7,7 @@ class _StorageBase(object): @@ -15037,7 +15041,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= else: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/tensor.py pytorch-develop-150/torch/tensor.py --- pytorch-v1.5.0/torch/tensor.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/tensor.py 2021-12-14 12:00:41.445549703 +0800 ++++ pytorch-develop-150/torch/tensor.py 2021-12-14 23:02:05.643151776 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15099,7 +15103,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def __reversed__(self): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/_tensor_str.py pytorch-develop-150/torch/_tensor_str.py --- pytorch-v1.5.0/torch/_tensor_str.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/_tensor_str.py 2021-12-14 12:00:41.445549703 +0800 ++++ pytorch-develop-150/torch/_tensor_str.py 2021-12-14 23:02:05.643151776 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15153,7 +15157,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= has_default_dtype = self.dtype in (torch.get_default_dtype(), torch.int64, torch.bool) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/data/dataloader.py pytorch-develop-150/torch/utils/data/dataloader.py --- pytorch-v1.5.0/torch/utils/data/dataloader.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/data/dataloader.py 2021-12-14 12:00:41.481549931 +0800 ++++ pytorch-develop-150/torch/utils/data/dataloader.py 2021-12-14 23:02:05.675151307 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15362,7 +15366,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, sampler: Sampler[int], batch_size: int, drop_last: bool) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/data/_utils/pin_memory.py pytorch-develop-150/torch/utils/data/_utils/pin_memory.py --- pytorch-v1.5.0/torch/utils/data/_utils/pin_memory.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/data/_utils/pin_memory.py 2021-12-14 12:00:41.481549931 +0800 ++++ pytorch-develop-150/torch/utils/data/_utils/pin_memory.py 2021-12-14 23:02:05.675151307 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15423,7 +15427,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/__init__.py pytorch-develop-150/torch/utils/__init__.py --- pytorch-v1.5.0/torch/utils/__init__.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/__init__.py 2021-12-14 12:00:41.481549931 +0800 ++++ pytorch-develop-150/torch/utils/__init__.py 2021-12-14 23:02:05.671151366 +0800 @@ -1,6 +1,9 @@ from __future__ import absolute_import, division, print_function, unicode_literals @@ -15436,7 +15440,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def set_module(obj, mod): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/_utils.py pytorch-develop-150/torch/_utils.py --- pytorch-v1.5.0/torch/_utils.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/_utils.py 2021-12-14 12:00:41.445549703 +0800 ++++ pytorch-develop-150/torch/_utils.py 2021-12-14 23:02:05.643151776 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. diff --git a/patch/pytorch1.8.1_npu.patch b/patch/pytorch1.8.1_npu.patch index 46bd2390c14..3624e016d7b 100644 --- a/patch/pytorch1.8.1_npu.patch +++ b/patch/pytorch1.8.1_npu.patch @@ -1,6 +1,6 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/CMakeLists.txt pytorch-develop-181/aten/CMakeLists.txt --- pytorch-v1.8.1/aten/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/CMakeLists.txt 2021-12-14 12:00:45.657576320 +0800 ++++ pytorch-develop-181/aten/CMakeLists.txt 2021-12-14 23:02:09.715092161 +0800 @@ -22,9 +22,11 @@ set(ATen_CPU_INCLUDE) set(ATen_THIRD_PARTY_INCLUDE) @@ -52,7 +52,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_CPU_DEPENDENCY_LIBS ${ATen_CPU_DEPENDENCY_LIBS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/autocast_mode.h pytorch-develop-181/aten/src/ATen/autocast_mode.h --- pytorch-v1.8.1/aten/src/ATen/autocast_mode.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/autocast_mode.h 2021-12-14 12:00:45.661576346 +0800 ++++ pytorch-develop-181/aten/src/ATen/autocast_mode.h 2021-12-14 23:02:09.723092044 +0800 @@ -5,7 +5,7 @@ namespace { @@ -64,7 +64,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/CMakeLists.txt pytorch-develop-181/aten/src/ATen/CMakeLists.txt --- pytorch-v1.8.1/aten/src/ATen/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/CMakeLists.txt 2021-12-14 12:00:45.657576320 +0800 ++++ pytorch-develop-181/aten/src/ATen/CMakeLists.txt 2021-12-14 23:02:09.719092103 +0800 @@ -85,6 +85,10 @@ file(GLOB native_quantized_h "native/quantized/*.h" "native/quantized/cpu/*.h") file(GLOB native_cpu_h "native/cpu/*.h") @@ -115,7 +115,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_HIP_SRCS ${ATen_HIP_SRCS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/Dispatcher.h pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/Dispatcher.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h 2021-12-14 12:00:45.665576370 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h 2021-12-14 23:02:09.727091986 +0800 @@ -417,6 +417,11 @@ } } @@ -130,7 +130,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.cpp pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-12-14 12:00:45.665576370 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-12-14 23:02:09.727091986 +0800 @@ -6,6 +6,7 @@ namespace c10 { @@ -141,7 +141,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= std::unordered_set not_observed_ops = { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.h pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-12-14 12:00:45.665576370 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-12-14 23:02:09.727091986 +0800 @@ -1,12 +1,16 @@ #pragma once @@ -161,7 +161,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/OperatorEntry.h pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-12-14 12:00:45.665576370 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-12-14 23:02:09.727091986 +0800 @@ -10,6 +10,7 @@ #include #include @@ -172,7 +172,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #include diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/VariableFallbackKernel.cpp pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp --- pytorch-v1.8.1/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-12-14 12:00:45.661576346 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-12-14 23:02:09.723092044 +0800 @@ -48,4 +48,8 @@ m.fallback(torch::CppFunction::makeFallthrough()); } @@ -184,7 +184,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/Convolution.cpp pytorch-develop-181/aten/src/ATen/native/Convolution.cpp --- pytorch-v1.8.1/aten/src/ATen/native/Convolution.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/Convolution.cpp 2021-12-14 12:00:45.677576447 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/Convolution.cpp 2021-12-14 23:02:09.735091868 +0800 @@ -603,7 +603,9 @@ const Tensor& input, const Tensor& weight, const Tensor& bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, @@ -198,7 +198,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= at::Tensor _convolution( diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/Memory.cpp pytorch-develop-181/aten/src/ATen/native/Memory.cpp --- pytorch-v1.8.1/aten/src/ATen/native/Memory.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/Memory.cpp 2021-12-14 12:00:45.681576471 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/Memory.cpp 2021-12-14 23:02:09.739091810 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -249,7 +249,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= detail::computeStorageNbytes( diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/native_functions.yaml pytorch-develop-181/aten/src/ATen/native/native_functions.yaml --- pytorch-v1.8.1/aten/src/ATen/native/native_functions.yaml 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/native_functions.yaml 2021-12-14 12:00:45.713576674 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/native_functions.yaml 2021-12-14 23:02:09.775091283 +0800 @@ -2073,6 +2073,8 @@ dispatch: CPU, CUDA: isnan @@ -306,16 +306,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: tile(Tensor self, int[] dims) -> Tensor variants: function, method -@@ -5446,6 +5459,8 @@ - dispatch: - CPU, CUDA: ne - QuantizedCPU: ne_quantized_cpu -+ npu_dispatch: -+ NPU: ne_npu - - - func: ne_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) - variants: method -@@ -5611,18 +5626,24 @@ +@@ -5611,18 +5624,24 @@ dispatch: CPU, CUDA: gt_out QuantizedCPU: gt_out_quantized_cpu @@ -340,7 +331,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: gt.Tensor(Tensor self, Tensor other) -> Tensor variants: method, function -@@ -9233,3 +9254,146 @@ +@@ -9233,3 +9252,146 @@ - func: _test_ambiguous_defaults.b(Tensor dummy, int a=2, str b="2") -> Tensor cpp_no_default_args: ['a', 'b'] python_module: nn @@ -489,7 +480,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +- func: npu_apply_adam.out(Scalar beta1_power, Scalar beta2_power, Scalar lr, Scalar beta1, Scalar beta2, Scalar epsilon, Tensor grad, bool? use_locking, bool? use_nesterov, *, Tensor(a!) var, Tensor(b!) m, Tensor(c!) v) -> (Tensor(a!), Tensor(b!), Tensor(c!)) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/NPUVariableMethodStubs.cpp pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp --- pytorch-v1.8.1/aten/src/ATen/native/NPUVariableMethodStubs.cpp 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp 2021-12-14 12:00:45.681576471 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp 2021-12-14 23:02:09.739091810 +0800 @@ -0,0 +1,464 @@ +#include "ATen/native/npu/common/FormatCastHelper.h" +#include "ATen/native/npu/frame/FormatHelper.h" @@ -1873,7 +1864,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -} diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/TensorFactories.cpp pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp --- pytorch-v1.8.1/aten/src/ATen/native/TensorFactories.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp 2021-12-14 12:00:45.693576548 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp 2021-12-14 23:02:09.755091576 +0800 @@ -1,3 +1,20 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -1906,7 +1897,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= namespace { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h --- pytorch-v1.8.1/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-12-14 12:00:45.749576901 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-12-14 23:02:09.807090815 +0800 @@ -1,19229 +1,19229 @@ -// -// Copyright (c) 2017-2020 Advanced Micro Devices, Inc. All rights reserved. @@ -40368,7 +40359,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +#endif // #ifdef VMA_IMPLEMENTATION diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/record_function.cpp pytorch-develop-181/aten/src/ATen/record_function.cpp --- pytorch-v1.8.1/aten/src/ATen/record_function.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/record_function.cpp 2021-12-14 12:00:45.753576927 +0800 ++++ pytorch-develop-181/aten/src/ATen/record_function.cpp 2021-12-14 23:02:09.811090757 +0800 @@ -400,6 +400,9 @@ rf_tls_.tls_record_function_enabled_ = enable; } @@ -40381,7 +40372,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (rf_tls_ptr->tls_record_function_enabled_) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/record_function.h pytorch-develop-181/aten/src/ATen/record_function.h --- pytorch-v1.8.1/aten/src/ATen/record_function.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/record_function.h 2021-12-14 12:00:45.753576927 +0800 ++++ pytorch-develop-181/aten/src/ATen/record_function.h 2021-12-14 23:02:09.811090757 +0800 @@ -2,6 +2,7 @@ #include @@ -40417,7 +40408,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // scope - record scope that this function tracks diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/templates/TensorBody.h pytorch-develop-181/aten/src/ATen/templates/TensorBody.h --- pytorch-v1.8.1/aten/src/ATen/templates/TensorBody.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/templates/TensorBody.h 2021-12-14 12:00:45.753576927 +0800 ++++ pytorch-develop-181/aten/src/ATen/templates/TensorBody.h 2021-12-14 23:02:09.811090757 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40450,7 +40441,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= bool is_xpu() const; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/templates/TensorMethods.cpp pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp --- pytorch-v1.8.1/aten/src/ATen/templates/TensorMethods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp 2021-12-14 12:00:45.753576927 +0800 ++++ pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp 2021-12-14 23:02:09.811090757 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40495,7 +40486,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/Utils.cpp pytorch-develop-181/aten/src/ATen/Utils.cpp --- pytorch-v1.8.1/aten/src/ATen/Utils.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/Utils.cpp 2021-12-14 12:00:45.661576346 +0800 ++++ pytorch-develop-181/aten/src/ATen/Utils.cpp 2021-12-14 23:02:09.719092103 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40539,7 +40530,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/TH/generic/THStorage.cpp pytorch-develop-181/aten/src/TH/generic/THStorage.cpp --- pytorch-v1.8.1/aten/src/TH/generic/THStorage.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/TH/generic/THStorage.cpp 2021-12-14 12:00:45.757576951 +0800 ++++ pytorch-develop-181/aten/src/TH/generic/THStorage.cpp 2021-12-14 23:02:09.815090698 +0800 @@ -1,9 +1,32 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40652,7 +40643,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/TH/generic/THStorage.h pytorch-develop-181/aten/src/TH/generic/THStorage.h --- pytorch-v1.8.1/aten/src/TH/generic/THStorage.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/TH/generic/THStorage.h 2021-12-14 12:00:45.757576951 +0800 ++++ pytorch-develop-181/aten/src/TH/generic/THStorage.h 2021-12-14 23:02:09.815090698 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40691,7 +40682,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/CMakeLists.txt pytorch-develop-181/c10/CMakeLists.txt --- pytorch-v1.8.1/c10/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/CMakeLists.txt 2021-12-14 12:00:45.769577028 +0800 ++++ pytorch-develop-181/c10/CMakeLists.txt 2021-12-14 23:02:09.827090523 +0800 @@ -79,6 +79,14 @@ message(STATUS "don't use NUMA") endif() @@ -40720,7 +40711,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # not checked in diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Backend.h pytorch-develop-181/c10/core/Backend.h --- pytorch-v1.8.1/c10/core/Backend.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Backend.h 2021-12-14 12:00:45.769577028 +0800 ++++ pytorch-develop-181/c10/core/Backend.h 2021-12-14 23:02:09.827090523 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40814,7 +40805,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Device.cpp pytorch-develop-181/c10/core/Device.cpp --- pytorch-v1.8.1/c10/core/Device.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Device.cpp 2021-12-14 12:00:45.769577028 +0800 ++++ pytorch-develop-181/c10/core/Device.cpp 2021-12-14 23:02:09.827090523 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40845,7 +40836,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= types.begin(), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Device.h pytorch-develop-181/c10/core/Device.h --- pytorch-v1.8.1/c10/core/Device.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Device.h 2021-12-14 12:00:45.769577028 +0800 ++++ pytorch-develop-181/c10/core/Device.h 2021-12-14 23:02:09.827090523 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40880,7 +40871,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= bool is_cpu() const noexcept { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DeviceType.cpp pytorch-develop-181/c10/core/DeviceType.cpp --- pytorch-v1.8.1/c10/core/DeviceType.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DeviceType.cpp 2021-12-14 12:00:45.769577028 +0800 ++++ pytorch-develop-181/c10/core/DeviceType.cpp 2021-12-14 23:02:09.827090523 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40920,7 +40911,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return false; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DeviceType.h pytorch-develop-181/c10/core/DeviceType.h --- pytorch-v1.8.1/c10/core/DeviceType.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DeviceType.h 2021-12-14 12:00:45.769577028 +0800 ++++ pytorch-develop-181/c10/core/DeviceType.h 2021-12-14 23:02:09.827090523 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40962,7 +40953,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= constexpr DeviceType kMSNPU = DeviceType::MSNPU; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKey.cpp pytorch-develop-181/c10/core/DispatchKey.cpp --- pytorch-v1.8.1/c10/core/DispatchKey.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKey.cpp 2021-12-14 12:00:45.769577028 +0800 ++++ pytorch-develop-181/c10/core/DispatchKey.cpp 2021-12-14 23:02:09.827090523 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41011,7 +41002,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::PrivateUse1: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKey.h pytorch-develop-181/c10/core/DispatchKey.h --- pytorch-v1.8.1/c10/core/DispatchKey.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKey.h 2021-12-14 12:00:45.769577028 +0800 ++++ pytorch-develop-181/c10/core/DispatchKey.h 2021-12-14 23:02:09.827090523 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41058,7 +41049,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= PrivateUse3_PreAutograd = AutogradPrivateUse3, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKeySet.cpp pytorch-develop-181/c10/core/DispatchKeySet.cpp --- pytorch-v1.8.1/c10/core/DispatchKeySet.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKeySet.cpp 2021-12-14 12:00:45.769577028 +0800 ++++ pytorch-develop-181/c10/core/DispatchKeySet.cpp 2021-12-14 23:02:09.827090523 +0800 @@ -11,6 +11,7 @@ DispatchKey::XLA, DispatchKey::NestedTensor, @@ -41078,7 +41069,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::AutogradXPU: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKeySet.h pytorch-develop-181/c10/core/DispatchKeySet.h --- pytorch-v1.8.1/c10/core/DispatchKeySet.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKeySet.h 2021-12-14 12:00:45.769577028 +0800 ++++ pytorch-develop-181/c10/core/DispatchKeySet.h 2021-12-14 23:02:09.827090523 +0800 @@ -193,6 +193,7 @@ DispatchKey::AutogradCPU, DispatchKey::AutogradCUDA, @@ -41089,7 +41080,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= DispatchKey::AutogradPrivateUse1, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Storage.h pytorch-develop-181/c10/core/Storage.h --- pytorch-v1.8.1/c10/core/Storage.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Storage.h 2021-12-14 12:00:45.769577028 +0800 ++++ pytorch-develop-181/c10/core/Storage.h 2021-12-14 23:02:09.831090464 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41123,7 +41114,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/StorageImpl.h pytorch-develop-181/c10/core/StorageImpl.h --- pytorch-v1.8.1/c10/core/StorageImpl.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/StorageImpl.h 2021-12-14 12:00:45.769577028 +0800 ++++ pytorch-develop-181/c10/core/StorageImpl.h 2021-12-14 23:02:09.831090464 +0800 @@ -1,12 +1,42 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41183,7 +41174,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/TensorImpl.h pytorch-develop-181/c10/core/TensorImpl.h --- pytorch-v1.8.1/c10/core/TensorImpl.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/TensorImpl.h 2021-12-14 12:00:45.769577028 +0800 ++++ pytorch-develop-181/c10/core/TensorImpl.h 2021-12-14 23:02:09.831090464 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41226,7 +41217,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ts.has(DispatchKey::SparseCPU) || diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/TensorOptions.h pytorch-develop-181/c10/core/TensorOptions.h --- pytorch-v1.8.1/c10/core/TensorOptions.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/TensorOptions.h 2021-12-14 12:00:45.769577028 +0800 ++++ pytorch-develop-181/c10/core/TensorOptions.h 2021-12-14 23:02:09.831090464 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41267,7 +41258,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } else if (tid == DispatchKey::QuantizedXPU) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/macros/Export.h pytorch-develop-181/c10/macros/Export.h --- pytorch-v1.8.1/c10/macros/Export.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/macros/Export.h 2021-12-14 12:00:45.773577053 +0800 ++++ pytorch-develop-181/c10/macros/Export.h 2021-12-14 23:02:09.831090464 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41336,7 +41327,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #define C10_API_ENUM C10_API diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/caffe2/CMakeLists.txt pytorch-develop-181/caffe2/CMakeLists.txt --- pytorch-v1.8.1/caffe2/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/caffe2/CMakeLists.txt 2021-12-14 12:00:45.781577104 +0800 ++++ pytorch-develop-181/caffe2/CMakeLists.txt 2021-12-14 23:02:09.839090347 +0800 @@ -76,6 +76,7 @@ # Add source, includes, and libs to lists list(APPEND Caffe2_CPU_SRCS ${ATen_CPU_SRCS}) @@ -41461,7 +41452,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Call again since Caffe2_HIP_INCLUDE is extended with ATen include dirs. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/BuildVariables.cmake pytorch-develop-181/cmake/BuildVariables.cmake --- pytorch-v1.8.1/cmake/BuildVariables.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/BuildVariables.cmake 2021-12-14 12:00:45.853577558 +0800 ++++ pytorch-develop-181/cmake/BuildVariables.cmake 2021-12-14 23:02:09.911089294 +0800 @@ -11,6 +11,7 @@ # CMakeLists.txt files under each folder respectively. set(Caffe2_CPU_SRCS) @@ -41485,7 +41476,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # symbols. However, if the lib is whole linked in caffe2 lib, we don't want diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Codegen.cmake pytorch-develop-181/cmake/Codegen.cmake --- pytorch-v1.8.1/cmake/Codegen.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Codegen.cmake 2021-12-14 12:00:45.853577558 +0800 ++++ pytorch-develop-181/cmake/Codegen.cmake 2021-12-14 23:02:09.911089294 +0800 @@ -208,13 +208,14 @@ file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt generated_cpp) file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt-cuda cuda_generated_cpp) @@ -41518,7 +41509,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= function(append_filelist name outputvar) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Dependencies.cmake pytorch-develop-181/cmake/Dependencies.cmake --- pytorch-v1.8.1/cmake/Dependencies.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Dependencies.cmake 2021-12-14 12:00:45.853577558 +0800 ++++ pytorch-develop-181/cmake/Dependencies.cmake 2021-12-14 23:02:09.911089294 +0800 @@ -1771,6 +1771,13 @@ endif(NOT C_HAS_THREAD) endif() @@ -41535,7 +41526,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Summary.cmake pytorch-develop-181/cmake/Summary.cmake --- pytorch-v1.8.1/cmake/Summary.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Summary.cmake 2021-12-14 12:00:45.853577558 +0800 ++++ pytorch-develop-181/cmake/Summary.cmake 2021-12-14 23:02:09.915089235 +0800 @@ -127,6 +127,7 @@ message(STATUS " USE_MKLDNN_CBLAS : ${USE_MKLDNN_CBLAS}") endif() @@ -41554,7 +41545,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= message(STATUS " Private Dependencies : ${Caffe2_DEPENDENCY_LIBS}") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/TorchConfig.cmake.in pytorch-develop-181/cmake/TorchConfig.cmake.in --- pytorch-v1.8.1/cmake/TorchConfig.cmake.in 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/TorchConfig.cmake.in 2021-12-14 12:00:45.853577558 +0800 ++++ pytorch-develop-181/cmake/TorchConfig.cmake.in 2021-12-14 23:02:09.915089235 +0800 @@ -158,6 +158,11 @@ list(APPEND TORCH_LIBRARIES ${TORCH_CUDA_LIBRARIES}) endif() @@ -41569,7 +41560,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(TORCH_CXX_FLAGS "-D_GLIBCXX_USE_CXX11_ABI=@GLIBCXX_USE_CXX11_ABI@") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/CMakeLists.txt pytorch-develop-181/CMakeLists.txt --- pytorch-v1.8.1/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/CMakeLists.txt 2021-12-14 12:00:45.649576269 +0800 ++++ pytorch-develop-181/CMakeLists.txt 2021-12-14 23:02:09.707092278 +0800 @@ -261,6 +261,10 @@ "USE_DISTRIBUTED" OFF) option(USE_TBB "Use TBB" OFF) @@ -41613,7 +41604,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if(APPLE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/setup.py pytorch-develop-181/setup.py --- pytorch-v1.8.1/setup.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/setup.py 2021-12-14 12:00:45.869577659 +0800 ++++ pytorch-develop-181/setup.py 2021-12-14 23:02:09.927089060 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -41682,7 +41673,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= 'utils/benchmark/utils/valgrind_wrapper/*.h', diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/derivatives.yaml pytorch-develop-181/tools/autograd/derivatives.yaml --- pytorch-v1.8.1/tools/autograd/derivatives.yaml 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/derivatives.yaml 2021-12-14 12:00:46.353580717 +0800 ++++ pytorch-develop-181/tools/autograd/derivatives.yaml 2021-12-14 23:02:10.379082449 +0800 @@ -1976,3 +1976,7 @@ - name: nonzero(Tensor self) -> Tensor @@ -41693,7 +41684,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + mat2: npu_bmm_v2_mat2_backward(grad, self, mat2, mat2.sizes()) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/gen_python_functions.py pytorch-develop-181/tools/autograd/gen_python_functions.py --- pytorch-v1.8.1/tools/autograd/gen_python_functions.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/gen_python_functions.py 2021-12-14 12:00:46.353580717 +0800 ++++ pytorch-develop-181/tools/autograd/gen_python_functions.py 2021-12-14 23:02:10.379082449 +0800 @@ -1,3 +1,20 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -41717,7 +41708,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # The bindings are generated as methods on python_variable or functions on the diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/Functions.cpp pytorch-develop-181/tools/autograd/templates/Functions.cpp --- pytorch-v1.8.1/tools/autograd/templates/Functions.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/Functions.cpp 2021-12-14 12:00:46.353580717 +0800 ++++ pytorch-develop-181/tools/autograd/templates/Functions.cpp 2021-12-14 23:02:10.379082449 +0800 @@ -14,6 +14,35 @@ namespace torch { namespace autograd { namespace generated { @@ -41756,7 +41747,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }}} // namespace torch::autograd::generated diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/python_torch_functions.cpp pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp --- pytorch-v1.8.1/tools/autograd/templates/python_torch_functions.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp 2021-12-14 12:00:46.353580717 +0800 ++++ pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp 2021-12-14 23:02:10.379082449 +0800 @@ -30,7 +30,7 @@ #include "torch/csrc/autograd/generated/variable_factories.h" #include "torch/csrc/utils/structseq.h" @@ -41792,7 +41783,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return torch::range(start, end, step, options); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/python_variable_methods.cpp pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp --- pytorch-v1.8.1/tools/autograd/templates/python_variable_methods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp 2021-12-14 12:00:46.353580717 +0800 ++++ pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp 2021-12-14 23:02:10.379082449 +0800 @@ -20,7 +20,13 @@ #ifdef USE_CUDA #include "torch/csrc/cuda/Event.h" @@ -41860,7 +41851,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"has_names", THPVariable_has_names, METH_NOARGS, NULL}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/build_variables.bzl pytorch-develop-181/tools/build_variables.bzl --- pytorch-v1.8.1/tools/build_variables.bzl 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/build_variables.bzl 2021-12-14 12:00:46.353580717 +0800 ++++ pytorch-develop-181/tools/build_variables.bzl 2021-12-14 23:02:10.379082449 +0800 @@ -362,6 +362,7 @@ libtorch_cuda_core_sources = [ "torch/csrc/CudaIPCTypes.cpp", @@ -41879,7 +41870,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= "torch/csrc/utils/python_arg_parser.cpp", diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/codegen/gen.py pytorch-develop-181/tools/codegen/gen.py --- pytorch-v1.8.1/tools/codegen/gen.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/codegen/gen.py 2021-12-14 12:00:46.357580743 +0800 ++++ pytorch-develop-181/tools/codegen/gen.py 2021-12-14 23:02:10.383082391 +0800 @@ -815,7 +815,7 @@ core_fm = make_file_manager(core_install_dir) cpu_fm = make_file_manager(options.install_dir) @@ -41927,7 +41918,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= main() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/codegen/model.py pytorch-develop-181/tools/codegen/model.py --- pytorch-v1.8.1/tools/codegen/model.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/codegen/model.py 2021-12-14 12:00:46.357580743 +0800 ++++ pytorch-develop-181/tools/codegen/model.py 2021-12-14 23:02:10.383082391 +0800 @@ -79,6 +79,7 @@ SparseHIP = auto() SparseXPU = auto() @@ -42069,7 +42060,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/autograd/profiler.py pytorch-develop-181/torch/autograd/profiler.py --- pytorch-v1.8.1/torch/autograd/profiler.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/autograd/profiler.py 2021-12-14 12:00:46.361580767 +0800 ++++ pytorch-develop-181/torch/autograd/profiler.py 2021-12-14 23:02:10.387082332 +0800 @@ -37,14 +37,17 @@ class EventList(list): """A list of Events (for pretty printing)""" @@ -42748,7 +42739,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ''.join(result) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_C/_autograd.pyi pytorch-develop-181/torch/_C/_autograd.pyi --- pytorch-v1.8.1/torch/_C/_autograd.pyi 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_C/_autograd.pyi 2021-12-14 12:00:46.357580743 +0800 ++++ pytorch-develop-181/torch/_C/_autograd.pyi 2021-12-14 23:02:10.387082332 +0800 @@ -9,14 +9,17 @@ CUDA = ... NVTX = ... @@ -42769,7 +42760,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= class ProfilerConfig: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/CMakeLists.txt pytorch-develop-181/torch/CMakeLists.txt --- pytorch-v1.8.1/torch/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/CMakeLists.txt 2021-12-14 12:00:46.357580743 +0800 ++++ pytorch-develop-181/torch/CMakeLists.txt 2021-12-14 23:02:10.387082332 +0800 @@ -131,6 +131,20 @@ endif() @@ -42793,7 +42784,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= list(APPEND TORCH_PYTHON_SRCS ${GENERATED_THNN_CXX_CUDA}) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/engine.cpp pytorch-develop-181/torch/csrc/autograd/engine.cpp --- pytorch-v1.8.1/torch/csrc/autograd/engine.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/engine.cpp 2021-12-14 12:00:46.365580793 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/engine.cpp 2021-12-14 23:02:10.395082215 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -42890,7 +42881,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto outputs = call_function(graph_task, func, inputs); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/functions/tensor.cpp pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp --- pytorch-v1.8.1/torch/csrc/autograd/functions/tensor.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp 2021-12-14 12:00:46.365580793 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp 2021-12-14 23:02:10.395082215 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -42922,7 +42913,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= /*non_blocking=*/false, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/init.cpp pytorch-develop-181/torch/csrc/autograd/init.cpp --- pytorch-v1.8.1/torch/csrc/autograd/init.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/init.cpp 2021-12-14 12:00:46.365580793 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/init.cpp 2021-12-14 23:02:10.395082215 +0800 @@ -52,6 +52,7 @@ .value("Disabled", ProfilerState::Disabled) .value("CPU", ProfilerState::CPU) @@ -42965,7 +42956,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= .value("OPENCL", c10::DeviceType::OPENCL) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/input_buffer.cpp pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp --- pytorch-v1.8.1/torch/csrc/autograd/input_buffer.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp 2021-12-14 12:00:46.365580793 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp 2021-12-14 23:02:10.395082215 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43017,7 +43008,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto& old_var = buffer[pos]; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.cpp pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp --- pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp 2021-12-14 12:00:46.365580793 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp 2021-12-14 23:02:10.395082215 +0800 @@ -147,7 +147,7 @@ constexpr const CUDAStubs* default_stubs_addr = &default_stubs; // Constant initialization, so it is guaranteed to be initialized before @@ -43260,7 +43251,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CUDAStubs::~CUDAStubs() = default; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.h pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h --- pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h 2021-12-14 12:00:46.365580793 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h 2021-12-14 23:02:10.395082215 +0800 @@ -19,6 +19,8 @@ #include // for gettimeofday() #endif @@ -43449,7 +43440,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= std::vector&& remoteProfiledEvents); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/python_variable.cpp pytorch-develop-181/torch/csrc/autograd/python_variable.cpp --- pytorch-v1.8.1/torch/csrc/autograd/python_variable.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/python_variable.cpp 2021-12-14 12:00:46.369580818 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/python_variable.cpp 2021-12-14 23:02:10.395082215 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43503,7 +43494,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"is_mkldnn", (getter)THPVariable_is_mkldnn, nullptr, nullptr, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/python_variable_indexing.cpp pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp --- pytorch-v1.8.1/torch/csrc/autograd/python_variable_indexing.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-14 12:00:46.369580818 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-14 23:02:10.395082215 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43536,7 +43527,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/distributed/c10d/init.cpp pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp --- pytorch-v1.8.1/torch/csrc/distributed/c10d/init.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp 2021-12-14 12:00:46.369580818 +0800 ++++ pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp 2021-12-14 23:02:10.399082157 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43703,7 +43694,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= torch::class_<::c10d::DistributedC10d>("dist_c10d", "frontend") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/DynamicTypes.cpp pytorch-develop-181/torch/csrc/DynamicTypes.cpp --- pytorch-v1.8.1/torch/csrc/DynamicTypes.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/DynamicTypes.cpp 2021-12-14 12:00:46.361580767 +0800 ++++ pytorch-develop-181/torch/csrc/DynamicTypes.cpp 2021-12-14 23:02:10.387082332 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43752,7 +43743,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return it->second; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/Generator.cpp pytorch-develop-181/torch/csrc/Generator.cpp --- pytorch-v1.8.1/torch/csrc/Generator.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/Generator.cpp 2021-12-14 12:00:46.361580767 +0800 ++++ pytorch-develop-181/torch/csrc/Generator.cpp 2021-12-14 23:02:10.387082332 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43799,7 +43790,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= " is not supported for torch.Generator() api."); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/serialization.cpp pytorch-develop-181/torch/csrc/generic/serialization.cpp --- pytorch-v1.8.1/torch/csrc/generic/serialization.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/serialization.cpp 2021-12-14 12:00:46.373580844 +0800 ++++ pytorch-develop-181/torch/csrc/generic/serialization.cpp 2021-12-14 23:02:10.399082157 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43897,7 +43888,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/Storage.cpp pytorch-develop-181/torch/csrc/generic/Storage.cpp --- pytorch-v1.8.1/torch/csrc/generic/Storage.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/Storage.cpp 2021-12-14 12:00:46.373580844 +0800 ++++ pytorch-develop-181/torch/csrc/generic/Storage.cpp 2021-12-14 23:02:10.399082157 +0800 @@ -1,7 +1,25 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43977,7 +43968,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for (Py_ssize_t i = 0; i < length; i++) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/StorageMethods.cpp pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp --- pytorch-v1.8.1/torch/csrc/generic/StorageMethods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp 2021-12-14 12:00:46.373580844 +0800 ++++ pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp 2021-12-14 23:02:10.399082157 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44057,7 +44048,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/Module.cpp pytorch-develop-181/torch/csrc/Module.cpp --- pytorch-v1.8.1/torch/csrc/Module.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/Module.cpp 2021-12-14 12:00:46.361580767 +0800 ++++ pytorch-develop-181/torch/csrc/Module.cpp 2021-12-14 23:02:10.387082332 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44183,7 +44174,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (incref) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/tensor/python_tensor.cpp pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp --- pytorch-v1.8.1/torch/csrc/tensor/python_tensor.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp 2021-12-14 12:00:46.397580995 +0800 ++++ pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp 2021-12-14 23:02:10.427081747 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44221,7 +44212,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/python_arg_parser.h pytorch-develop-181/torch/csrc/utils/python_arg_parser.h --- pytorch-v1.8.1/torch/csrc/utils/python_arg_parser.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/python_arg_parser.h 2021-12-14 12:00:46.401581021 +0800 ++++ pytorch-develop-181/torch/csrc/utils/python_arg_parser.h 2021-12-14 23:02:10.427081747 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44256,7 +44247,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return at::Device(device_str); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_layouts.cpp pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_layouts.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp 2021-12-14 12:00:46.401581021 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp 2021-12-14 23:02:10.427081747 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44287,7 +44278,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= PyObject *sparse_coo_layout = THPLayout_New(at::Layout::Sparse, "torch.sparse_coo"); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_new.cpp pytorch-develop-181/torch/csrc/utils/tensor_new.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_new.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_new.cpp 2021-12-14 12:00:46.401581021 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_new.cpp 2021-12-14 23:02:10.427081747 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44419,7 +44410,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= dispatch_key); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_types.cpp pytorch-develop-181/torch/csrc/utils/tensor_types.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_types.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_types.cpp 2021-12-14 12:00:46.401581021 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_types.cpp 2021-12-14 23:02:10.427081747 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44469,7 +44460,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/distributed/distributed_c10d.py pytorch-develop-181/torch/distributed/distributed_c10d.py --- pytorch-v1.8.1/torch/distributed/distributed_c10d.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/distributed/distributed_c10d.py 2021-12-14 12:00:46.401581021 +0800 ++++ pytorch-develop-181/torch/distributed/distributed_c10d.py 2021-12-14 23:02:10.427081747 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -44560,7 +44551,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= prefix_store, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/__init__.py pytorch-develop-181/torch/__init__.py --- pytorch-v1.8.1/torch/__init__.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/__init__.py 2021-12-14 12:00:46.357580743 +0800 ++++ pytorch-develop-181/torch/__init__.py 2021-12-14 23:02:10.387082332 +0800 @@ -675,3 +675,11 @@ # class usage. We add these lines here to preserve backward compatibility. quantized_lstm = torch.ops.aten.quantized_lstm @@ -44576,7 +44567,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/CMakeLists.txt pytorch-develop-181/torch/lib/c10d/CMakeLists.txt --- pytorch-v1.8.1/torch/lib/c10d/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/CMakeLists.txt 2021-12-14 12:00:46.409581071 +0800 ++++ pytorch-develop-181/torch/lib/c10d/CMakeLists.txt 2021-12-14 23:02:10.435081630 +0800 @@ -27,6 +27,10 @@ option(USE_C10D_NCCL "USE C10D NCCL" ON) endif() @@ -44629,7 +44620,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= copy_header(ProcessGroupMPI.hpp) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/comm.cpp pytorch-develop-181/torch/lib/c10d/comm.cpp --- pytorch-v1.8.1/torch/lib/c10d/comm.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/comm.cpp 2021-12-14 12:00:46.409581071 +0800 ++++ pytorch-develop-181/torch/lib/c10d/comm.cpp 2021-12-14 23:02:10.435081630 +0800 @@ -12,6 +12,26 @@ class BroadcastWork { @@ -44718,7 +44709,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= while (!in_flight.empty()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/reducer.cpp pytorch-develop-181/torch/lib/c10d/reducer.cpp --- pytorch-v1.8.1/torch/lib/c10d/reducer.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/reducer.cpp 2021-12-14 12:00:46.409581071 +0800 ++++ pytorch-develop-181/torch/lib/c10d/reducer.cpp 2021-12-14 23:02:10.435081630 +0800 @@ -18,6 +18,18 @@ namespace c10d { namespace { @@ -44910,7 +44901,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // A bucket with one or more dense tensors needs to be unflattened. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/libshm/CMakeLists.txt pytorch-develop-181/torch/lib/libshm/CMakeLists.txt --- pytorch-v1.8.1/torch/lib/libshm/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/libshm/CMakeLists.txt 2021-12-14 12:00:46.409581071 +0800 ++++ pytorch-develop-181/torch/lib/libshm/CMakeLists.txt 2021-12-14 23:02:10.439081572 +0800 @@ -41,8 +41,11 @@ set_target_properties(shm PROPERTIES PREFIX "lib" @@ -44926,7 +44917,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # https://github.com/libgit2/libgit2/issues/2128#issuecomment-35649830 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/batchnorm.py pytorch-develop-181/torch/nn/modules/batchnorm.py --- pytorch-v1.8.1/torch/nn/modules/batchnorm.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/batchnorm.py 2021-12-14 12:00:46.413581096 +0800 ++++ pytorch-develop-181/torch/nn/modules/batchnorm.py 2021-12-14 23:02:10.439081572 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -44948,7 +44939,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= from ._functions import SyncBatchNorm as sync_batch_norm diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/module.py pytorch-develop-181/torch/nn/modules/module.py --- pytorch-v1.8.1/torch/nn/modules/module.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/module.py 2021-12-14 12:00:46.413581096 +0800 ++++ pytorch-develop-181/torch/nn/modules/module.py 2021-12-14 23:02:10.439081572 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45074,7 +45065,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if convert_to_format is not None and t.dim() == 4: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/normalization.py pytorch-develop-181/torch/nn/modules/normalization.py --- pytorch-v1.8.1/torch/nn/modules/normalization.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/normalization.py 2021-12-14 12:00:46.413581096 +0800 ++++ pytorch-develop-181/torch/nn/modules/normalization.py 2021-12-14 23:02:10.439081572 +0800 @@ -167,8 +167,11 @@ init.zeros_(self.bias) @@ -45091,7 +45082,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return '{normalized_shape}, eps={eps}, ' \ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/parallel/distributed.py pytorch-develop-181/torch/nn/parallel/distributed.py --- pytorch-v1.8.1/torch/nn/parallel/distributed.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/parallel/distributed.py 2021-12-14 12:00:46.413581096 +0800 ++++ pytorch-develop-181/torch/nn/parallel/distributed.py 2021-12-14 23:02:10.439081572 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45123,7 +45114,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= output = self.module(*inputs[0], **kwargs[0]) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/serialization.py pytorch-develop-181/torch/serialization.py --- pytorch-v1.8.1/torch/serialization.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/serialization.py 2021-12-14 12:00:46.421581146 +0800 ++++ pytorch-develop-181/torch/serialization.py 2021-12-14 23:02:10.447081454 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45212,7 +45203,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= See also: `saving-loading-tensors` diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/storage.py pytorch-develop-181/torch/storage.py --- pytorch-v1.8.1/torch/storage.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/storage.py 2021-12-14 12:00:46.421581146 +0800 ++++ pytorch-develop-181/torch/storage.py 2021-12-14 23:02:10.447081454 +0800 @@ -8,6 +8,7 @@ class _StorageBase(object): _cdata: Any @@ -45232,7 +45223,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= else: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/tensor.py pytorch-develop-181/torch/tensor.py --- pytorch-v1.8.1/torch/tensor.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/tensor.py 2021-12-14 12:00:46.421581146 +0800 ++++ pytorch-develop-181/torch/tensor.py 2021-12-14 23:02:10.447081454 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45294,7 +45285,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def __reversed__(self): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_tensor_str.py pytorch-develop-181/torch/_tensor_str.py --- pytorch-v1.8.1/torch/_tensor_str.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_tensor_str.py 2021-12-14 12:00:46.361580767 +0800 ++++ pytorch-develop-181/torch/_tensor_str.py 2021-12-14 23:02:10.387082332 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45338,7 +45329,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # TODO: add an API to map real -> complex dtypes diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/utils/data/dataloader.py pytorch-develop-181/torch/utils/data/dataloader.py --- pytorch-v1.8.1/torch/utils/data/dataloader.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/utils/data/dataloader.py 2021-12-14 12:00:46.429581197 +0800 ++++ pytorch-develop-181/torch/utils/data/dataloader.py 2021-12-14 23:02:10.455081338 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45399,7 +45390,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= pin_memory_thread.start() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/utils/data/_utils/pin_memory.py pytorch-develop-181/torch/utils/data/_utils/pin_memory.py --- pytorch-v1.8.1/torch/utils/data/_utils/pin_memory.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/utils/data/_utils/pin_memory.py 2021-12-14 12:00:42.445556024 +0800 ++++ pytorch-develop-181/torch/utils/data/_utils/pin_memory.py 2021-12-14 23:02:06.643137127 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45445,7 +45436,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # logic of this function. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_utils.py pytorch-develop-181/torch/_utils.py --- pytorch-v1.8.1/torch/_utils.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_utils.py 2021-12-14 12:00:46.361580767 +0800 ++++ pytorch-develop-181/torch/_utils.py 2021-12-14 23:02:10.387082332 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. diff --git a/pytorch1.5.0/src/aten/src/ATen/native/native_functions.yaml b/pytorch1.5.0/src/aten/src/ATen/native/native_functions.yaml index df964967d06..32a9ed26ba1 100644 --- a/pytorch1.5.0/src/aten/src/ATen/native/native_functions.yaml +++ b/pytorch1.5.0/src/aten/src/ATen/native/native_functions.yaml @@ -8564,6 +8564,8 @@ NPU: apply_adam_npu - func: npu_apply_adam(Scalar beta1_power, Scalar beta2_power, Scalar lr, Scalar beta1, Scalar beta2, Scalar epsilon, Tensor grad, bool? use_locking, bool? use_nesterov) -> (Tensor var, Tensor m, Tensor v) + npu_dispatch_only: + NPU: npu_apply_adam - func: npu_apply_adam.out(Scalar beta1_power, Scalar beta2_power, Scalar lr, Scalar beta1, Scalar beta2, Scalar epsilon, Tensor grad, bool? use_locking, bool? use_nesterov, *, Tensor(a!) var, Tensor(b!) m, Tensor(c!) v) -> (Tensor(a!), Tensor(b!), Tensor(c!)) npu_dispatch_only: @@ -8672,6 +8674,8 @@ NPU: bert_apply_adam_npu - func: npu_bert_apply_adam(Scalar lr, Scalar beta1, Scalar beta2, Scalar epsilon, Tensor grad, Scalar max_grad_norm, Scalar global_grad_norm, Scalar weight_decay, Scalar? step_size=None, int adam_mode=0) -> (Tensor var, Tensor m, Tensor v) + npu_dispatch_only: + NPU: npu_bert_apply_adam - func: npu_bert_apply_adam.out(Scalar lr, Scalar beta1, Scalar beta2, Scalar epsilon, Tensor grad, Scalar max_grad_norm, Scalar global_grad_norm, Scalar weight_decay, Scalar? step_size=None, int adam_mode=0, *, Tensor(a!) var, Tensor(b!) m, Tensor(c!) v) -> (Tensor(a!), Tensor(b!), Tensor(c!)) npu_dispatch_only: diff --git a/pytorch1.5.0/src/aten/src/ATen/native/npu/AddcdivKernelNpu.cpp b/pytorch1.5.0/src/aten/src/ATen/native/npu/AddcdivKernelNpu.cpp index 5789038f690..665badb227e 100644 --- a/pytorch1.5.0/src/aten/src/ATen/native/npu/AddcdivKernelNpu.cpp +++ b/pytorch1.5.0/src/aten/src/ATen/native/npu/AddcdivKernelNpu.cpp @@ -1,5 +1,5 @@ // Copyright (c) 2020 Huawei Technologies Co., Ltd -// Copyright (c) 2019, Facebook CORPORATION. +// Copyright (c) 2019, Facebook CORPORATION. // All rights reserved. // // Licensed under the BSD 3-Clause License (the "License"); @@ -15,27 +15,51 @@ // limitations under the License. #include "ATen/native/npu/utils/OpAdapter.h" +#include "ATen/native/npu/utils/CalcuOpUtil.h" namespace at { namespace native { using namespace at::native::npu; -Tensor& addcdiv_out_npu( +Tensor& addcdiv_npu_nocheck( Tensor& result, const Tensor& self, const Tensor& tensor1, const Tensor& tensor2, Scalar value) { - + bool isFp32 = self.scalar_type() == at::kFloat && tensor1.scalar_type() == at::kFloat && tensor2.scalar_type() == at::kFloat; + Tensor selfCp = isFp32 ? self : self.npu_dtype_cast(at::kFloat); + Tensor tensor1Cp = isFp32 ? tensor1 : tensor1.npu_dtype_cast(at::kFloat); + Tensor tensor2Cp = isFp32 ? tensor2 : tensor2.npu_dtype_cast(at::kFloat); OpCommand cmd; cmd.Name("Addcdiv") - .Input(self) - .Input(tensor1) - .Input(tensor2) - .Input(value, self.scalar_type()) + .Input(selfCp) + .Input(tensor1Cp) + .Input(tensor2Cp) + .Input(value, selfCp.scalar_type()) .Output(result) .Run(); + return result; +} +Tensor& addcdiv_out_npu( + Tensor& result, + const Tensor& self, + const Tensor& tensor1, + const Tensor& tensor2, + Scalar value) { + auto divOutputSize = broadcast_ops_npu_output_size(tensor1, tensor2); + auto outputSize = broadcast_ops_npu_output_size(self.sizes(), divOutputSize); + bool isFp32 = self.scalar_type() == at::kFloat && tensor1.scalar_type() == at::kFloat && tensor2.scalar_type() == at::kFloat; + Tensor temp = isFp32 ? OpPreparation::ApplyTensor(self, outputSize) + : OpPreparation::ApplyTensor(outputSize, self.options().dtype(at::kFloat), self); + addcdiv_npu_nocheck(temp, self, tensor1, tensor2, value); + temp = isFp32 ? temp : temp.npu_dtype_cast(self.scalar_type()); + OpPreparation::CheckOut( + {temp}, + result, + temp); + result.copy_(temp); return result; } @@ -44,11 +68,14 @@ Tensor addcdiv_npu( const Tensor& tensor1, const Tensor& tensor2, Scalar value) { + auto divOutputSize = broadcast_ops_npu_output_size(tensor1, tensor2); auto outputSize = broadcast_ops_npu_output_size(self.sizes(), divOutputSize); - Tensor result = OpPreparation::ApplyTensor(self, outputSize); - addcdiv_out_npu(result, self, tensor1, tensor2, value); - + bool isFp32 = self.scalar_type() == at::kFloat && tensor1.scalar_type() == at::kFloat && tensor2.scalar_type() == at::kFloat; + Tensor result = isFp32 ? OpPreparation::ApplyTensor(self, outputSize) + : OpPreparation::ApplyTensor(outputSize, self.options().dtype(at::kFloat), self); + addcdiv_npu_nocheck(result, self, tensor1, tensor2, value); + result = isFp32 ? result : result.npu_dtype_cast(self.scalar_type()); return result; } @@ -57,14 +84,7 @@ Tensor& addcdiv_npu_( const Tensor& tensor1, const Tensor& tensor2, Scalar value) { - OpPreparation::CheckMemory({self, tensor1, tensor2}, {self}); - if (!NpuUtils::check_match(&self)) { - Tensor contiguousSelf = NpuUtils::format_contiguous(self); - Tensor result = addcdiv_out_npu(contiguousSelf, contiguousSelf, tensor1, tensor2, value); - NpuUtils::format_fresh_view(self, result); - } else { - addcdiv_out_npu(self, self, tensor1, tensor2, value); - } + addcdiv_out_npu(self, self, tensor1, tensor2, value); return self; } diff --git a/pytorch1.5.0/src/aten/src/ATen/native/npu/normalization/BatchNormBackwardReduceKernelNpu.cpp b/pytorch1.5.0/src/aten/src/ATen/native/npu/normalization/BatchNormBackwardReduceKernelNpu.cpp index fd14d290a24..bdf0db4b678 100644 --- a/pytorch1.5.0/src/aten/src/ATen/native/npu/normalization/BatchNormBackwardReduceKernelNpu.cpp +++ b/pytorch1.5.0/src/aten/src/ATen/native/npu/normalization/BatchNormBackwardReduceKernelNpu.cpp @@ -39,11 +39,11 @@ std::tuple batch_norm_backward_reduce_npu_im Tensor grad_bias_; auto origin_dtype = self.scalar_type(); - Tensor grad_out_ = grad_out.npu_dtype_cast(at::kFloat); - Tensor self_ = self.npu_dtype_cast(at::kFloat); - Tensor mean_ = mean.npu_dtype_cast(at::kFloat); - Tensor invstd_ = invstd.npu_dtype_cast(at::kFloat); - Tensor weight_ = weight.npu_dtype_cast(at::kFloat); + Tensor grad_out_ = grad_out.scalar_type() == at::kFloat ? grad_out : grad_out.npu_dtype_cast(at::kFloat); + Tensor self_ = self.scalar_type() == at::kFloat ? self : self.npu_dtype_cast(at::kFloat); + Tensor mean_ = mean.scalar_type() == at::kFloat ? mean : mean.npu_dtype_cast(at::kFloat); + Tensor invstd_ = invstd.scalar_type() == at::kFloat ? invstd : invstd.npu_dtype_cast(at::kFloat); + Tensor weight_ = weight.scalar_type() == at::kFloat ? weight : weight.npu_dtype_cast(at::kFloat); SmallVector axes; int dimN = self_.ndimension(); @@ -74,17 +74,17 @@ std::tuple batch_norm_backward_reduce_npu_im if (input_g){ sum_dy_xmu.copy_(sum_dy_xmu_out); sum_dy.copy_(sum_dy_); + sum_dy = sum_dy.scalar_type() == origin_dtype ? sum_dy : sum_dy.npu_dtype_cast(origin_dtype); + sum_dy_xmu = sum_dy_xmu.scalar_type() == origin_dtype ? sum_dy_xmu : sum_dy_xmu.npu_dtype_cast(origin_dtype); } if (weight_g) { grad_weight.copy_(grad_weight_res); + grad_weight = grad_weight.scalar_type() == origin_dtype ? grad_weight : grad_weight.npu_dtype_cast(origin_dtype); } if (bias_g) { grad_bias.copy_(grad_bias_); + grad_bias = grad_bias.scalar_type() == origin_dtype ? grad_bias : grad_bias.npu_dtype_cast(origin_dtype); } - sum_dy = sum_dy.npu_dtype_cast(origin_dtype); - sum_dy_xmu = sum_dy_xmu.npu_dtype_cast(origin_dtype); - grad_weight = grad_weight.npu_dtype_cast(origin_dtype); - grad_bias = grad_bias.npu_dtype_cast(origin_dtype); return std::tie(sum_dy, sum_dy_xmu, grad_weight, grad_bias); } diff --git a/pytorch1.5.0/src/aten/src/ATen/native/npu/normalization/BatchNormKernelNpu.cpp b/pytorch1.5.0/src/aten/src/ATen/native/npu/normalization/BatchNormKernelNpu.cpp index 78c3737805d..f6dbedf696a 100644 --- a/pytorch1.5.0/src/aten/src/ATen/native/npu/normalization/BatchNormKernelNpu.cpp +++ b/pytorch1.5.0/src/aten/src/ATen/native/npu/normalization/BatchNormKernelNpu.cpp @@ -179,10 +179,17 @@ tuple batch_norm_impl( eps); // BNTrainingUpdate can only support FP32 for mean and var - auto running_mean_fp32 = (running_mean.scalar_type() == at::kFloat) ? - running_mean : running_mean.npu_dtype_cast(at::kFloat); - auto running_var_fp32 = (running_var.scalar_type() == at::kFloat) ? - running_var : running_var.npu_dtype_cast(at::kFloat); + auto running_mean_fp32 = running_mean; + auto running_var_fp32 = running_var; + + if (train && (running_mean.scalar_type() != at::kFloat)) { + running_mean_fp32 = running_mean.npu_dtype_cast(at::kFloat); + } + + if (train && (running_var.scalar_type() != at::kFloat)) { + running_var_fp32 = running_var.npu_dtype_cast(at::kFloat); + } + batch_norm_training_update_nocheck( result, save_mean, diff --git a/pytorch1.5.0/test/test_npu/test_network_ops/test_addcdiv.py b/pytorch1.5.0/test/test_npu/test_network_ops/test_addcdiv.py index ae33fda7514..19c5166c152 100644 --- a/pytorch1.5.0/test/test_npu/test_network_ops/test_addcdiv.py +++ b/pytorch1.5.0/test/test_npu/test_network_ops/test_addcdiv.py @@ -1,5 +1,5 @@ # Copyright (c) 2020 Huawei Technologies Co., Ltd -# Copyright (c) 2019, Facebook CORPORATION. +# Copyright (c) 2019, Facebook CORPORATION. # All rights reserved. # # Licensed under the BSD 3-Clause License (the "License"); @@ -86,7 +86,14 @@ class TestAddcdiv(TestCase): def test_addcdiv_float32(self, device): def cpu_op_exec(input1, input2, input3, scalar): + ori_dtype = input1.dtype + if ori_dtype == torch.float16: + input1 = input1.to(torch.float32) + input2 = input2.to(torch.float32) + input3 = input3.to(torch.float32) output = torch.addcdiv(input1, input2, input3, value=scalar) + if ori_dtype == torch.float16: + output = output.to(ori_dtype) return output def npu_op_exec(input1, input2, input3, scalar): @@ -96,18 +103,27 @@ class TestAddcdiv(TestCase): output = torch.addcdiv(input1, input2, input3, value=scalar) output = output.to("cpu") return output - - npu_input1, npu_input2, npu_input3 = self.generate_data(1, 100, (5, 3), np.float32) - scalar = self.generate_scalar(1, 10) - cpu_output = cpu_op_exec(npu_input1, npu_input2, npu_input3, scalar) - npu_output = npu_op_exec(npu_input1, npu_input2, npu_input3, scalar) - self.assertEqual(cpu_output, npu_output) + dtype_list = [np.float32, np.float16] + for dtype in dtype_list: + npu_input1, npu_input2, npu_input3 = self.generate_data(1, 100, (5, 3), dtype) + scalar = self.generate_scalar(1, 10) + cpu_output = cpu_op_exec(npu_input1, npu_input2, npu_input3, scalar) + npu_output = npu_op_exec(npu_input1, npu_input2, npu_input3, scalar) + self.assertEqual(cpu_output, npu_output) def test_addcdiv_float32_out(self, device): def cpu_op_exec_out(input1, input2, input3, scalar, input4): + ori_dtype = input1.dtype + if ori_dtype == torch.float16: + input1 = input1.to(torch.float32) + input2 = input2.to(torch.float32) + input3 = input3.to(torch.float32) + input4 = input4.to(torch.float32) output = input4 torch.addcdiv(input1, input2, input3, value=scalar, out=output) + if ori_dtype == torch.float16: + output = output.to(ori_dtype) output = output.numpy() return output @@ -120,17 +136,25 @@ class TestAddcdiv(TestCase): output = output.to("cpu") output = output.numpy() return output - - npu_input1, npu_input2, npu_input3 = self.generate_data(1, 100, (5, 3), np.float32) - scalar = self.generate_scalar(1, 10) - npu_input4 = self.generate_single_data(1, 100, (5, 3), np.float32) - cpu_output = cpu_op_exec_out(npu_input1, npu_input2, npu_input3, scalar, npu_input4) - npu_output = npu_op_exec_out(npu_input1, npu_input2, npu_input3, scalar, npu_input4) - self.assertEqual(cpu_output, npu_output) + dtype_list = [np.float32, np.float16] + for dtype in dtype_list: + npu_input1, npu_input2, npu_input3 = self.generate_data(1, 100, (5, 3), dtype) + scalar = self.generate_scalar(1, 10) + npu_input4 = self.generate_single_data(1, 100, (5, 3), dtype) + cpu_output = cpu_op_exec_out(npu_input1, npu_input2, npu_input3, scalar, npu_input4) + npu_output = npu_op_exec_out(npu_input1, npu_input2, npu_input3, scalar, npu_input4) + self.assertEqual(cpu_output, npu_output) def test_addcdiv_float32_broadcast(self, device): def cpu_op_exec(input1, input2, input3, scalar): + ori_dtype = input1.dtype + if ori_dtype == torch.float16: + input1 = input1.to(torch.float32) + input2 = input2.to(torch.float32) + input3 = input3.to(torch.float32) output = torch.addcdiv(input1, input2, input3, value=scalar) + if ori_dtype == torch.float16: + output = output.to(ori_dtype) return output def npu_op_exec(input1, input2, input3, scalar): @@ -140,19 +164,27 @@ class TestAddcdiv(TestCase): output = torch.addcdiv(input1, input2, input3, value=scalar) output = output.to("cpu") return output - - npu_input1 = self.generate_single_data(1, 100, (5, 3, 1), np.float32) - npu_input2 = self.generate_single_data(1, 100, (5, 1, 5), np.float32) - npu_input3 = self.generate_single_data(1, 100, (1, 1, 5), np.float32) - scalar = self.generate_scalar(1, 10) - cpu_output = cpu_op_exec(npu_input1, npu_input2, npu_input3, scalar) - npu_output = npu_op_exec(npu_input1, npu_input2, npu_input3, scalar) - # self.assertEqual(cpu_output, npu_output) - self.assertRtolEqual(cpu_output, npu_output) + dtype_list = [np.float32, np.float16] + for dtype in dtype_list: + npu_input1 = self.generate_single_data(1, 100, (5, 3, 1), dtype) + npu_input2 = self.generate_single_data(1, 100, (5, 1, 5), dtype) + npu_input3 = self.generate_single_data(1, 100, (1, 1, 5), dtype) + scalar = self.generate_scalar(1, 10) + cpu_output = cpu_op_exec(npu_input1, npu_input2, npu_input3, scalar) + npu_output = npu_op_exec(npu_input1, npu_input2, npu_input3, scalar) + # self.assertEqual(cpu_output, npu_output) + self.assertRtolEqual(cpu_output, npu_output) def test_addcdiv_inp_contiguous_float32(self, device): def cpu_op_inp_contiguous_exec(input1, input2, input3, scalar): + ori_dtype = input1.dtype + if ori_dtype == torch.float16: + input1 = input1.to(torch.float32) + input2 = input2.to(torch.float32) + input3 = input3.to(torch.float32) input1.addcdiv_(input2, input3, value=scalar) + if ori_dtype == torch.float16: + input1 = input1.to(ori_dtype) output = input1.numpy() return output @@ -164,20 +196,28 @@ class TestAddcdiv(TestCase): output = input1.to("cpu") output = output.numpy() return output - - npu_input1, npu_input2, npu_input3 = self.generate_data(1, 100, (5, 3), np.float32) - cpu_input1 = copy.deepcopy(npu_input1) - cpu_input2 = copy.deepcopy(npu_input2) - cpu_input3 = copy.deepcopy(npu_input3) - scalar = self.generate_int_scalar(1, 10) - cpu_output = cpu_op_inp_contiguous_exec(cpu_input1, cpu_input2, cpu_input3, scalar) - npu_output = npu_op_inp_contiguous_exec(npu_input1, npu_input2, npu_input3, scalar) - self.assertEqual(cpu_output, npu_output) + dtype_list = [np.float32, np.float16] + for dtype in dtype_list: + npu_input1, npu_input2, npu_input3 = self.generate_data(1, 100, (5, 3), dtype) + cpu_input1 = copy.deepcopy(npu_input1) + cpu_input2 = copy.deepcopy(npu_input2) + cpu_input3 = copy.deepcopy(npu_input3) + scalar = self.generate_int_scalar(1, 10) + cpu_output = cpu_op_inp_contiguous_exec(cpu_input1, cpu_input2, cpu_input3, scalar) + npu_output = npu_op_inp_contiguous_exec(npu_input1, npu_input2, npu_input3, scalar) + self.assertEqual(cpu_output, npu_output) def test_addcdiv_inp_input1_noncontiguous_float32(self, device): def cpu_op_inp_input1_noncontiguous_exec(input1, input2, input3, scalar): + ori_dtype = input1.dtype + if ori_dtype == torch.float16: + input1 = input1.to(torch.float32) + input2 = input2.to(torch.float32) + input3 = input3.to(torch.float32) input1_strided = input1.as_strided([2, 2], [1, 2], 2) input1_strided.addcdiv_(input2, input3, value=scalar) + if ori_dtype == torch.float16: + input1 = input1.to(ori_dtype) output = input1.numpy() return output @@ -190,22 +230,30 @@ class TestAddcdiv(TestCase): output = input1.to("cpu") output = output.numpy() return output - - npu_input1 = self.generate_single_data(1, 100, (4, 3), np.float32) - npu_input2 = self.generate_single_data(1, 100, (2, 2), np.float32) - npu_input3 = self.generate_single_data(1, 100, (2, 2), np.float32) - cpu_input1 = copy.deepcopy(npu_input1) - cpu_input2 = copy.deepcopy(npu_input2) - cpu_input3 = copy.deepcopy(npu_input3) - scalar = self.generate_int_scalar(1, 10) - cpu_output = cpu_op_inp_input1_noncontiguous_exec(cpu_input1, cpu_input2, cpu_input3, scalar) - npu_output = npu_op_inp_input1_noncontiguous_exec(npu_input1, npu_input2, npu_input3, scalar) - self.assertEqual(cpu_output, npu_output) + dtype_list = [np.float32, np.float16] + for dtype in dtype_list: + npu_input1 = self.generate_single_data(1, 100, (4, 3), dtype) + npu_input2 = self.generate_single_data(1, 100, (2, 2), dtype) + npu_input3 = self.generate_single_data(1, 100, (2, 2), dtype) + cpu_input1 = copy.deepcopy(npu_input1) + cpu_input2 = copy.deepcopy(npu_input2) + cpu_input3 = copy.deepcopy(npu_input3) + scalar = self.generate_int_scalar(1, 10) + cpu_output = cpu_op_inp_input1_noncontiguous_exec(cpu_input1, cpu_input2, cpu_input3, scalar) + npu_output = npu_op_inp_input1_noncontiguous_exec(npu_input1, npu_input2, npu_input3, scalar) + self.assertEqual(cpu_output, npu_output) def test_addcdiv_inp_input2_noncontiguous_float32(self, device): def cpu_op_inp_input2_noncontiguous_exec(input1, input2, input3, scalar): + ori_dtype = input1.dtype + if ori_dtype == torch.float16: + input1 = input1.to(torch.float32) + input2 = input2.to(torch.float32) + input3 = input3.to(torch.float32) input2_strided = input2.as_strided([2, 2], [1, 2], 2) input1.addcdiv_(input2_strided, input3, value=scalar) + if ori_dtype == torch.float16: + input1 = input1.to(ori_dtype) output = input1.numpy() return output @@ -219,21 +267,30 @@ class TestAddcdiv(TestCase): output = output.numpy() return output - npu_input1 = self.generate_single_data(1, 100, (2, 2), np.float32) - npu_input2 = self.generate_single_data(1, 100, (4, 3), np.float32) - npu_input3 = self.generate_single_data(1, 100, (2, 2), np.float32) - cpu_input1 = copy.deepcopy(npu_input1) - cpu_input2 = copy.deepcopy(npu_input2) - cpu_input3 = copy.deepcopy(npu_input3) - scalar = self.generate_int_scalar(1, 10) - cpu_output = cpu_op_inp_input2_noncontiguous_exec(cpu_input1, cpu_input2, cpu_input3, scalar) - npu_output = npu_op_inp_input2_noncontiguous_exec(npu_input1, npu_input2, npu_input3, scalar) - self.assertEqual(cpu_output, npu_output) - - def test_addcdiv_inp_input3_noncontiguous_float32(self, device): + dtype_list = [np.float32, np.float16] + for dtype in dtype_list: + npu_input1 = self.generate_single_data(1, 100, (2, 2), dtype) + npu_input2 = self.generate_single_data(1, 100, (4, 3), dtype) + npu_input3 = self.generate_single_data(1, 100, (2, 2), dtype) + cpu_input1 = copy.deepcopy(npu_input1) + cpu_input2 = copy.deepcopy(npu_input2) + cpu_input3 = copy.deepcopy(npu_input3) + scalar = self.generate_int_scalar(1, 10) + cpu_output = cpu_op_inp_input2_noncontiguous_exec(cpu_input1, cpu_input2, cpu_input3, scalar) + npu_output = npu_op_inp_input2_noncontiguous_exec(npu_input1, npu_input2, npu_input3, scalar) + self.assertEqual(cpu_output, npu_output) + + def test_addcdiv_inp_input3_noncontiguous_fp32_fp16(self, device): def cpu_op_inp_input3_noncontiguous_exec(input1, input2, input3, scalar): + ori_dtype = input1.dtype + if ori_dtype == torch.float16: + input1 = input1.to(torch.float32) + input2 = input2.to(torch.float32) + input3 = input3.to(torch.float32) input3_strided = input3.as_strided([2, 2], [1, 2], 2) input1.addcdiv_(input2, input3_strided, value=scalar) + if ori_dtype == torch.float16: + input1 = input1.to(ori_dtype) output = input1.numpy() return output @@ -247,23 +304,19 @@ class TestAddcdiv(TestCase): output = output.numpy() return output - npu_input1 = self.generate_single_data(1, 100, (2, 2), np.float32) - npu_input2 = self.generate_single_data(1, 100, (2, 2), np.float32) - npu_input3 = self.generate_single_data(1, 100, (4, 3), np.float32) - cpu_input1 = copy.deepcopy(npu_input1) - cpu_input2 = copy.deepcopy(npu_input2) - cpu_input3 = copy.deepcopy(npu_input3) - scalar = self.generate_int_scalar(1, 10) - cpu_output = cpu_op_inp_input3_noncontiguous_exec(cpu_input1, cpu_input2, cpu_input3, scalar) - npu_output = npu_op_inp_input3_noncontiguous_exec(npu_input1, npu_input2, npu_input3, scalar) - self.assertEqual(cpu_output, npu_output) - - - - - + dtype_list = [np.float32, np.float16] + for dtype in dtype_list: + npu_input1 = self.generate_single_data(1, 100, (2, 2), dtype) + npu_input2 = self.generate_single_data(1, 100, (2, 2), dtype) + npu_input3 = self.generate_single_data(1, 100, (4, 3), dtype) + cpu_input1 = copy.deepcopy(npu_input1) + cpu_input2 = copy.deepcopy(npu_input2) + cpu_input3 = copy.deepcopy(npu_input3) + scalar = self.generate_int_scalar(1, 10) + cpu_output = cpu_op_inp_input3_noncontiguous_exec(cpu_input1, cpu_input2, cpu_input3, scalar) + npu_output = npu_op_inp_input3_noncontiguous_exec(npu_input1, npu_input2, npu_input3, scalar) + self.assertEqual(cpu_output, npu_output) instantiate_device_type_tests(TestAddcdiv, globals(), except_for="cpu") - if __name__ == "__main__": run_tests() diff --git a/pytorch1.8.1/access_control_test.py b/pytorch1.8.1/access_control_test.py index b3c122263b9..dd397cf0db8 100644 --- a/pytorch1.8.1/access_control_test.py +++ b/pytorch1.8.1/access_control_test.py @@ -176,7 +176,7 @@ def exec_ut(ut_files): return ret_status -if __name__ == "__main__": +def main(): cur_dir = os.path.abspath(os.path.dirname(__file__)) modify_files = os.path.join(cur_dir, 'modify_files.txt') test_mgr = TestMgr() @@ -188,4 +188,8 @@ if __name__ == "__main__": test_mgr.print_ut_files() ret = exec_ut(ut_files) - sys.exit(ret) \ No newline at end of file + sys.exit(ret) + + +if __name__ == "__main__": + main() diff --git a/pytorch1.8.1/src/aten/src/ATen/native/native_functions.yaml b/pytorch1.8.1/src/aten/src/ATen/native/native_functions.yaml index 99be57775d9..47f51012c5d 100644 --- a/pytorch1.8.1/src/aten/src/ATen/native/native_functions.yaml +++ b/pytorch1.8.1/src/aten/src/ATen/native/native_functions.yaml @@ -5459,8 +5459,6 @@ dispatch: CPU, CUDA: ne QuantizedCPU: ne_quantized_cpu - npu_dispatch: - NPU: ne_npu - func: ne_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) variants: method -- Gitee From db461d865c715fa6326b6f40bf7a6a2a8f03c3f3 Mon Sep 17 00:00:00 2001 From: AtlasAccount Date: Wed, 15 Dec 2021 23:03:23 +0800 Subject: [PATCH 04/55] =?UTF-8?q?=E5=90=8C=E6=AD=A51.5.0=20d3724a9f1132ddb?= =?UTF-8?q?957773d154b801584903caa18=20=20=20=20=20!2865=20[Requirement]?= =?UTF-8?q?=20=E6=A8=A1=E5=9E=8B=E9=9C=80=E6=B1=82=20rotated=5Fiou?= =?UTF-8?q?=E7=AE=97=E5=AD=90adapt=20=20=20=20=20Merge=20pull=20request=20?= =?UTF-8?q?!2865=20from=20=E6=B2=88=E9=B9=8F=E7=A8=8B/adaptIR=20--=20=20?= =?UTF-8?q?=20=20=20!2882=20Task:=20clean=20codecheck=20warnings=20=20=20?= =?UTF-8?q?=20=20Merge=20pull=20request=20!2882=20from=20=E8=B5=B5?= =?UTF-8?q?=E5=85=89=E7=81=BF/master=20=E5=90=8C=E6=AD=A51.8.1=20db9b820f4?= =?UTF-8?q?aba41894f26e187a67450e4b2064a4c?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- patch/pytorch1.5.0_npu.patch | 226 +++++++++--------- patch/pytorch1.8.1_npu.patch | 184 +++++++------- .../src/ATen/native/native_functions.yaml | 5 + .../aten/src/ATen/native/npu/MulKernelNpu.cpp | 10 +- .../aten/src/ATen/native/npu/NeKernelNpu.cpp | 6 +- .../ATen/native/npu/RotatedIouKernelNpu.cpp | 76 ++++++ .../test_network_ops/test_rotated_iou.py | 68 ++++++ 7 files changed, 365 insertions(+), 210 deletions(-) create mode 100644 pytorch1.5.0/src/aten/src/ATen/native/npu/RotatedIouKernelNpu.cpp create mode 100644 pytorch1.5.0/test/test_npu/test_network_ops/test_rotated_iou.py diff --git a/patch/pytorch1.5.0_npu.patch b/patch/pytorch1.5.0_npu.patch index edc9e2b8eab..6d7343eaa6b 100644 --- a/patch/pytorch1.5.0_npu.patch +++ b/patch/pytorch1.5.0_npu.patch @@ -1,6 +1,6 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/CMakeLists.txt pytorch-develop-150/aten/CMakeLists.txt --- pytorch-v1.5.0/aten/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/CMakeLists.txt 2021-12-14 23:02:04.911162503 +0800 ++++ pytorch-develop-150/aten/CMakeLists.txt 2021-12-15 23:03:15.997226187 +0800 @@ -22,8 +22,10 @@ set(ATen_CPU_INCLUDE) set(ATen_THIRD_PARTY_INCLUDE) @@ -51,7 +51,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_CPU_DEPENDENCY_LIBS ${ATen_CPU_DEPENDENCY_LIBS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/CMakeLists.txt pytorch-develop-150/aten/src/ATen/CMakeLists.txt --- pytorch-v1.5.0/aten/src/ATen/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/CMakeLists.txt 2021-12-14 23:02:04.911162503 +0800 ++++ pytorch-develop-150/aten/src/ATen/CMakeLists.txt 2021-12-15 23:03:15.997226187 +0800 @@ -67,6 +67,9 @@ FILE(GLOB native_quantized_h "native/quantized/*.h" "native/quantized/cpu/*.h") FILE(GLOB native_cpu_h "native/cpu/*.h") @@ -129,7 +129,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_QUANTIZED_SRCS ${ATen_QUANTIZED_SRCS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/core/dispatch/DispatchTable.h pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h --- pytorch-v1.5.0/aten/src/ATen/core/dispatch/DispatchTable.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h 2021-12-14 23:02:04.915162445 +0800 ++++ pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h 2021-12-15 23:03:16.001226201 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -170,7 +170,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/function_wrapper.py pytorch-develop-150/aten/src/ATen/function_wrapper.py --- pytorch-v1.5.0/aten/src/ATen/function_wrapper.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/function_wrapper.py 2021-12-14 23:02:04.919162386 +0800 ++++ pytorch-develop-150/aten/src/ATen/function_wrapper.py 2021-12-15 23:03:16.005226215 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -354,7 +354,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for option in declaration['options']: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/gen.py pytorch-develop-150/aten/src/ATen/gen.py --- pytorch-v1.5.0/aten/src/ATen/gen.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/gen.py 2021-12-14 23:02:04.919162386 +0800 ++++ pytorch-develop-150/aten/src/ATen/gen.py 2021-12-15 23:03:16.005226215 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -512,7 +512,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= generate_outputs() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/BatchLinearAlgebra.cpp pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp --- pytorch-v1.5.0/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-12-14 23:02:04.919162386 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-12-15 23:03:16.005226215 +0800 @@ -680,7 +680,7 @@ std::tuple triangular_solve_out(Tensor& result, Tensor& clone_A, const Tensor& self, const Tensor& A, bool upper, bool transpose, bool unitriangular) { @@ -524,7 +524,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return std::tuple(result, clone_A); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/cpu/Activation.cpp pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp --- pytorch-v1.5.0/aten/src/ATen/native/cpu/Activation.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp 2021-12-14 23:02:04.923162327 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp 2021-12-15 23:03:16.009226227 +0800 @@ -339,20 +339,20 @@ void hardsigmoid_backward_kernel(TensorIterator& iter) { @@ -552,7 +552,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/Memory.cpp pytorch-develop-150/aten/src/ATen/native/Memory.cpp --- pytorch-v1.5.0/aten/src/ATen/native/Memory.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/Memory.cpp 2021-12-14 23:02:04.919162386 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/Memory.cpp 2021-12-15 23:03:16.005226215 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -614,7 +614,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= detail::computeStorageSize(self.sizes(), self.strides()), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/native_functions.yaml pytorch-develop-150/aten/src/ATen/native/native_functions.yaml --- pytorch-v1.5.0/aten/src/ATen/native/native_functions.yaml 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/native_functions.yaml 2021-12-14 23:02:04.927162269 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/native_functions.yaml 2021-12-15 23:03:16.013226241 +0800 @@ -1,6 +1,5 @@ # See README.md in this directory for more guidance @@ -6679,7 +6679,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: slow_conv_dilated3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1) -> Tensor python_module: nn -@@ -6577,57 +8245,466 @@ +@@ -6577,57 +8245,471 @@ dispatch: CPU: col2im_out_cpu CUDA: col2im_out_cuda @@ -7143,12 +7143,18 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +- func: npu_reshape.out(Tensor self, int[] shape, bool can_refresh=False, *, Tensor(a!) out) -> Tensor(a!) + npu_dispatch_only: + NPU: reshape_out_npu ++ +- func: npu_rotated_overlaps(Tensor self, Tensor query_boxes, bool trans=False) -> Tensor + npu_dispatch_only: + NPU: rotated_overlaps_npu ++ ++- func: npu_rotated_iou(Tensor self, Tensor query_boxes, bool trans=False, int mode=0, bool is_cross=True) -> Tensor ++ npu_dispatch_only: ++ NPU: rotated_iou_npu +\ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S --- pytorch-v1.5.0/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-12-14 23:02:04.947161976 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-12-15 23:03:16.029226295 +0800 @@ -659,14 +659,14 @@ SUB x1, x1, 4 @@ -7174,7 +7180,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CMP x1, 2 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorCompare.cpp pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorCompare.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp 2021-12-14 23:02:04.923162327 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp 2021-12-15 23:03:16.009226227 +0800 @@ -64,7 +64,7 @@ Tensor isinf(const Tensor &self) { @@ -7186,7 +7192,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return AT_DISPATCH_FLOATING_TYPES_AND_HALF(self.scalar_type(), "isinf", [&]() { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorFactories.cpp pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorFactories.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp 2021-12-14 23:02:04.923162327 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp 2021-12-15 23:03:16.009226227 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7231,7 +7237,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorProperties.cpp pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorProperties.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp 2021-12-14 23:02:04.923162327 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp 2021-12-15 23:03:16.009226227 +0800 @@ -87,6 +87,7 @@ if (self.is_contiguous(memory_format)) { return self; @@ -7242,7 +7248,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= "preserve memory format is unsupported by the contiguous operator"); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/UpSampleBicubic2d.cpp pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp --- pytorch-v1.5.0/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-12-14 23:02:04.923162327 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-12-15 23:03:16.009226227 +0800 @@ -26,7 +26,7 @@ const scalar_t* in = &idata[output_y * input_width + output_x]; scalar_t* out = &odata[output_y * output_width + output_x]; @@ -7254,7 +7260,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= out += output_width * output_height; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native_parse.py pytorch-develop-150/aten/src/ATen/native_parse.py --- pytorch-v1.5.0/aten/src/ATen/native_parse.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native_parse.py 2021-12-14 23:02:04.955161858 +0800 ++++ pytorch-develop-150/aten/src/ATen/native_parse.py 2021-12-15 23:03:16.033226307 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -7292,7 +7298,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= msg = '''Exception raised in processing function: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/preprocess_declarations.py pytorch-develop-150/aten/src/ATen/preprocess_declarations.py --- pytorch-v1.5.0/aten/src/ATen/preprocess_declarations.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/preprocess_declarations.py 2021-12-14 23:02:04.955161858 +0800 ++++ pytorch-develop-150/aten/src/ATen/preprocess_declarations.py 2021-12-15 23:03:16.033226307 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -7324,7 +7330,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/templates/TensorBody.h pytorch-develop-150/aten/src/ATen/templates/TensorBody.h --- pytorch-v1.5.0/aten/src/ATen/templates/TensorBody.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/templates/TensorBody.h 2021-12-14 23:02:04.959161799 +0800 ++++ pytorch-develop-150/aten/src/ATen/templates/TensorBody.h 2021-12-15 23:03:16.033226307 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7357,7 +7363,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/templates/TensorMethods.h pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h --- pytorch-v1.5.0/aten/src/ATen/templates/TensorMethods.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h 2021-12-14 23:02:04.959161799 +0800 ++++ pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h 2021-12-15 23:03:16.033226307 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7391,7 +7397,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/CMakeLists.txt pytorch-develop-150/aten/src/TH/CMakeLists.txt --- pytorch-v1.5.0/aten/src/TH/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/CMakeLists.txt 2021-12-14 23:02:04.959161799 +0800 ++++ pytorch-develop-150/aten/src/TH/CMakeLists.txt 2021-12-15 23:03:16.037226321 +0800 @@ -48,6 +48,11 @@ ${CMAKE_CURRENT_SOURCE_DIR} PARENT_SCOPE) @@ -7406,7 +7412,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/generic/THStorage.cpp pytorch-develop-150/aten/src/TH/generic/THStorage.cpp --- pytorch-v1.5.0/aten/src/TH/generic/THStorage.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/generic/THStorage.cpp 2021-12-14 23:02:04.959161799 +0800 ++++ pytorch-develop-150/aten/src/TH/generic/THStorage.cpp 2021-12-15 23:03:16.037226321 +0800 @@ -1,9 +1,32 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7515,7 +7521,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/generic/THStorage.h pytorch-develop-150/aten/src/TH/generic/THStorage.h --- pytorch-v1.5.0/aten/src/TH/generic/THStorage.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/generic/THStorage.h 2021-12-14 23:02:04.959161799 +0800 ++++ pytorch-develop-150/aten/src/TH/generic/THStorage.h 2021-12-15 23:03:16.037226321 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7554,7 +7560,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/CMakeLists.txt pytorch-develop-150/c10/CMakeLists.txt --- pytorch-v1.5.0/c10/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/CMakeLists.txt 2021-12-14 23:02:04.971161624 +0800 ++++ pytorch-develop-150/c10/CMakeLists.txt 2021-12-15 23:03:16.045226348 +0800 @@ -63,6 +63,14 @@ message(STATUS "don't use NUMA") endif() @@ -7583,7 +7589,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # not checked in diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Backend.h pytorch-develop-150/c10/core/Backend.h --- pytorch-v1.5.0/c10/core/Backend.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Backend.h 2021-12-14 23:02:04.971161624 +0800 ++++ pytorch-develop-150/c10/core/Backend.h 2021-12-15 23:03:16.045226348 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7680,7 +7686,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case Backend::CUDA: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Device.cpp pytorch-develop-150/c10/core/Device.cpp --- pytorch-v1.5.0/c10/core/Device.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Device.cpp 2021-12-14 23:02:04.971161624 +0800 ++++ pytorch-develop-150/c10/core/Device.cpp 2021-12-15 23:03:16.045226348 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7720,7 +7726,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= types.begin(), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Device.h pytorch-develop-150/c10/core/Device.h --- pytorch-v1.5.0/c10/core/Device.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Device.h 2021-12-14 23:02:04.971161624 +0800 ++++ pytorch-develop-150/c10/core/Device.h 2021-12-15 23:03:16.045226348 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7755,7 +7761,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return type_ == DeviceType::CPU; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DeviceType.cpp pytorch-develop-150/c10/core/DeviceType.cpp --- pytorch-v1.5.0/c10/core/DeviceType.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DeviceType.cpp 2021-12-14 23:02:04.971161624 +0800 ++++ pytorch-develop-150/c10/core/DeviceType.cpp 2021-12-15 23:03:16.045226348 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7795,7 +7801,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return false; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DeviceType.h pytorch-develop-150/c10/core/DeviceType.h --- pytorch-v1.5.0/c10/core/DeviceType.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DeviceType.h 2021-12-14 23:02:04.971161624 +0800 ++++ pytorch-develop-150/c10/core/DeviceType.h 2021-12-15 23:03:16.045226348 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7838,7 +7844,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= constexpr DeviceType kXLA = DeviceType::XLA; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DispatchKey.cpp pytorch-develop-150/c10/core/DispatchKey.cpp --- pytorch-v1.5.0/c10/core/DispatchKey.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DispatchKey.cpp 2021-12-14 23:02:04.971161624 +0800 ++++ pytorch-develop-150/c10/core/DispatchKey.cpp 2021-12-15 23:03:16.045226348 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7870,7 +7876,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::SparseCPUTensorId: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DispatchKey.h pytorch-develop-150/c10/core/DispatchKey.h --- pytorch-v1.5.0/c10/core/DispatchKey.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DispatchKey.h 2021-12-14 23:02:04.971161624 +0800 ++++ pytorch-develop-150/c10/core/DispatchKey.h 2021-12-15 23:03:16.045226348 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7902,7 +7908,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Storage.h pytorch-develop-150/c10/core/Storage.h --- pytorch-v1.5.0/c10/core/Storage.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Storage.h 2021-12-14 23:02:04.971161624 +0800 ++++ pytorch-develop-150/c10/core/Storage.h 2021-12-15 23:03:16.045226348 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7936,7 +7942,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/StorageImpl.cpp pytorch-develop-150/c10/core/StorageImpl.cpp --- pytorch-v1.5.0/c10/core/StorageImpl.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/StorageImpl.cpp 2021-12-14 23:02:04.971161624 +0800 ++++ pytorch-develop-150/c10/core/StorageImpl.cpp 2021-12-15 23:03:16.045226348 +0800 @@ -1 +1,18 @@ #include + @@ -7958,7 +7964,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +} // namespace c10 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/StorageImpl.h pytorch-develop-150/c10/core/StorageImpl.h --- pytorch-v1.5.0/c10/core/StorageImpl.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/StorageImpl.h 2021-12-14 23:02:04.971161624 +0800 ++++ pytorch-develop-150/c10/core/StorageImpl.h 2021-12-15 23:03:16.045226348 +0800 @@ -1,11 +1,55 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8085,7 +8091,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/TensorImpl.h pytorch-develop-150/c10/core/TensorImpl.h --- pytorch-v1.5.0/c10/core/TensorImpl.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/TensorImpl.h 2021-12-14 23:02:04.971161624 +0800 ++++ pytorch-develop-150/c10/core/TensorImpl.h 2021-12-15 23:03:16.045226348 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8153,7 +8159,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/TensorOptions.h pytorch-develop-150/c10/core/TensorOptions.h --- pytorch-v1.5.0/c10/core/TensorOptions.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/TensorOptions.h 2021-12-14 23:02:04.971161624 +0800 ++++ pytorch-develop-150/c10/core/TensorOptions.h 2021-12-15 23:03:16.045226348 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8194,7 +8200,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } else if (tid == DispatchKey::HIPTensorId) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/cuda/CMakeLists.txt pytorch-develop-150/c10/cuda/CMakeLists.txt --- pytorch-v1.5.0/c10/cuda/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/cuda/CMakeLists.txt 2021-12-14 23:02:04.971161624 +0800 ++++ pytorch-develop-150/c10/cuda/CMakeLists.txt 2021-12-15 23:03:16.045226348 +0800 @@ -24,6 +24,7 @@ CUDACachingAllocator.cpp impl/CUDAGuardImpl.cpp @@ -8213,7 +8219,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= torch_cuda_based_add_library(c10_cuda ${C10_CUDA_SRCS} ${C10_CUDA_HEADERS}) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/macros/Export.h pytorch-develop-150/c10/macros/Export.h --- pytorch-v1.5.0/c10/macros/Export.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/macros/Export.h 2021-12-14 23:02:04.971161624 +0800 ++++ pytorch-develop-150/c10/macros/Export.h 2021-12-15 23:03:16.045226348 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8340,7 +8346,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/caffe2/CMakeLists.txt pytorch-develop-150/caffe2/CMakeLists.txt --- pytorch-v1.5.0/caffe2/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/caffe2/CMakeLists.txt 2021-12-14 23:02:04.975161565 +0800 ++++ pytorch-develop-150/caffe2/CMakeLists.txt 2021-12-15 23:03:16.049226361 +0800 @@ -32,6 +32,7 @@ # Add source, includes, and libs to lists list(APPEND Caffe2_CPU_SRCS ${ATen_CPU_SRCS}) @@ -8495,7 +8501,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Call again since Caffe2_HIP_INCLUDE is extended with ATen include dirs. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/.clang-format pytorch-develop-150/.clang-format --- pytorch-v1.5.0/.clang-format 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/.clang-format 2021-12-14 23:02:04.907162562 +0800 ++++ pytorch-develop-150/.clang-format 2021-12-15 23:03:15.993226174 +0800 @@ -84,5 +84,4 @@ SpacesInSquareBrackets: false Standard: Cpp11 @@ -8506,7 +8512,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/BuildVariables.cmake pytorch-develop-150/cmake/BuildVariables.cmake --- pytorch-v1.5.0/cmake/BuildVariables.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/BuildVariables.cmake 2021-12-14 23:02:05.043160568 +0800 ++++ pytorch-develop-150/cmake/BuildVariables.cmake 2021-12-15 23:03:16.109226563 +0800 @@ -11,6 +11,7 @@ # CMakeLists.txt files under each folder respectively. set(Caffe2_CPU_SRCS) @@ -8533,7 +8539,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # symbols. However, if the lib is whole linked in caffe2 lib, we don't want diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Codegen.cmake pytorch-develop-150/cmake/Codegen.cmake --- pytorch-v1.5.0/cmake/Codegen.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Codegen.cmake 2021-12-14 23:02:05.043160568 +0800 ++++ pytorch-develop-150/cmake/Codegen.cmake 2021-12-15 23:03:16.109226563 +0800 @@ -191,13 +191,14 @@ file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt generated_cpp) file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt-cuda cuda_generated_cpp) @@ -8564,7 +8570,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endif() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Dependencies.cmake pytorch-develop-150/cmake/Dependencies.cmake --- pytorch-v1.5.0/cmake/Dependencies.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Dependencies.cmake 2021-12-14 23:02:05.043160568 +0800 ++++ pytorch-develop-150/cmake/Dependencies.cmake 2021-12-15 23:03:16.109226563 +0800 @@ -1509,6 +1509,13 @@ ENDIF(NOT C_HAS_THREAD) endif() @@ -8581,7 +8587,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Summary.cmake pytorch-develop-150/cmake/Summary.cmake --- pytorch-v1.5.0/cmake/Summary.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Summary.cmake 2021-12-14 23:02:05.043160568 +0800 ++++ pytorch-develop-150/cmake/Summary.cmake 2021-12-15 23:03:16.109226563 +0800 @@ -134,6 +134,7 @@ if(NOT "${SELECTED_OP_LIST}" STREQUAL "") message(STATUS " SELECTED_OP_LIST : ${SELECTED_OP_LIST}") @@ -8592,7 +8598,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endfunction() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/TorchConfig.cmake.in pytorch-develop-150/cmake/TorchConfig.cmake.in --- pytorch-v1.5.0/cmake/TorchConfig.cmake.in 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/TorchConfig.cmake.in 2021-12-14 23:02:05.043160568 +0800 ++++ pytorch-develop-150/cmake/TorchConfig.cmake.in 2021-12-15 23:03:16.109226563 +0800 @@ -112,6 +112,11 @@ list(APPEND TORCH_LIBRARIES ${TORCH_CUDA_LIBRARIES}) endif() @@ -8607,7 +8613,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(TORCH_CXX_FLAGS "-D_GLIBCXX_USE_CXX11_ABI=@GLIBCXX_USE_CXX11_ABI@") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/CMakeLists.txt pytorch-develop-150/CMakeLists.txt --- pytorch-v1.5.0/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/CMakeLists.txt 2021-12-14 23:02:04.907162562 +0800 ++++ pytorch-develop-150/CMakeLists.txt 2021-12-15 23:03:15.993226174 +0800 @@ -205,6 +205,10 @@ option(USE_TBB "Use TBB" OFF) option(ONNX_ML "Enable traditional ONNX ML API." ON) @@ -8674,7 +8680,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-missing-braces") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/CONTRIBUTING.zh.md pytorch-develop-150/CONTRIBUTING.zh.md --- pytorch-v1.5.0/CONTRIBUTING.zh.md 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-150/CONTRIBUTING.zh.md 2021-12-14 23:02:04.907162562 +0800 ++++ pytorch-develop-150/CONTRIBUTING.zh.md 2021-12-15 23:03:15.993226174 +0800 @@ -0,0 +1,228 @@ +# PyTorch贡献指南 +- [贡献者许可协议](#贡献者许可协议.md) @@ -8906,7 +8912,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/.dockerignore pytorch-develop-150/.dockerignore --- pytorch-v1.5.0/.dockerignore 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/.dockerignore 2021-12-14 23:02:04.907162562 +0800 ++++ pytorch-develop-150/.dockerignore 2021-12-15 23:03:15.993226174 +0800 @@ -1,257 +1 @@ -# READ THIS BEFORE YOU REFACTOR ME -# @@ -9182,7 +9188,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/requirements.txt pytorch-develop-150/requirements.txt --- pytorch-v1.5.0/requirements.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/requirements.txt 2021-12-14 23:02:04.907162562 +0800 ++++ pytorch-develop-150/requirements.txt 2021-12-15 23:03:15.993226174 +0800 @@ -4,4 +4,13 @@ requests setuptools @@ -9202,7 +9208,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/setup.py pytorch-develop-150/setup.py --- pytorch-v1.5.0/setup.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/setup.py 2021-12-14 23:02:04.907162562 +0800 ++++ pytorch-develop-150/setup.py 2021-12-15 23:03:15.993226174 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -9305,7 +9311,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= 'python/serialized_test/data/operator_test/*.zip', diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/derivatives.yaml pytorch-develop-150/tools/autograd/derivatives.yaml --- pytorch-v1.5.0/tools/autograd/derivatives.yaml 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/derivatives.yaml 2021-12-14 23:02:05.639151835 +0800 ++++ pytorch-develop-150/tools/autograd/derivatives.yaml 2021-12-15 23:03:16.709228570 +0800 @@ -107,6 +107,10 @@ # # NB: The parameter names here MUST be consistent with the parameter names @@ -9430,7 +9436,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/dump_utils.py pytorch-develop-150/tools/autograd/dump_utils.py --- pytorch-v1.5.0/tools/autograd/dump_utils.py 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/dump_utils.py 2021-12-14 23:02:05.639151835 +0800 ++++ pytorch-develop-150/tools/autograd/dump_utils.py 2021-12-15 23:03:16.709228570 +0800 @@ -0,0 +1,312 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# All rights reserved. @@ -9746,7 +9752,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + return prepare_to_check_overflow, overflow_dump_inputs diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_autograd_functions.py pytorch-develop-150/tools/autograd/gen_autograd_functions.py --- pytorch-v1.5.0/tools/autograd/gen_autograd_functions.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_autograd_functions.py 2021-12-14 23:02:05.639151835 +0800 ++++ pytorch-develop-150/tools/autograd/gen_autograd_functions.py 2021-12-15 23:03:16.709228570 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -9952,7 +9958,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_python_functions.py pytorch-develop-150/tools/autograd/gen_python_functions.py --- pytorch-v1.5.0/tools/autograd/gen_python_functions.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_python_functions.py 2021-12-14 23:02:05.643151776 +0800 ++++ pytorch-develop-150/tools/autograd/gen_python_functions.py 2021-12-15 23:03:16.709228570 +0800 @@ -1,3 +1,20 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -9994,7 +10000,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= 'value': argname, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_variable_type.py pytorch-develop-150/tools/autograd/gen_variable_type.py --- pytorch-v1.5.0/tools/autograd/gen_variable_type.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_variable_type.py 2021-12-14 23:02:05.643151776 +0800 ++++ pytorch-develop-150/tools/autograd/gen_variable_type.py 2021-12-15 23:03:16.709228570 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -10175,7 +10181,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/Functions.cpp pytorch-develop-150/tools/autograd/templates/Functions.cpp --- pytorch-v1.5.0/tools/autograd/templates/Functions.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/Functions.cpp 2021-12-14 23:02:05.643151776 +0800 ++++ pytorch-develop-150/tools/autograd/templates/Functions.cpp 2021-12-15 23:03:16.709228570 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2021 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10256,7 +10262,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto sparse = sparse_.coalesce(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/python_torch_functions.cpp pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp --- pytorch-v1.5.0/tools/autograd/templates/python_torch_functions.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp 2021-12-14 23:02:05.643151776 +0800 ++++ pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp 2021-12-15 23:03:16.709228570 +0800 @@ -22,7 +22,7 @@ #include "torch/csrc/autograd/generated/variable_factories.h" #include "torch/csrc/utils/structseq.h" @@ -10340,7 +10346,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/python_variable_methods.cpp pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp --- pytorch-v1.5.0/tools/autograd/templates/python_variable_methods.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp 2021-12-14 23:02:05.643151776 +0800 ++++ pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp 2021-12-15 23:03:16.709228570 +0800 @@ -15,7 +15,13 @@ #include "torch/csrc/cuda/Stream.h" #include "torch/csrc/cuda/Event.h" @@ -10435,7 +10441,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"has_names", (PyCFunction)THPVariable_has_names, METH_NOARGS, NULL}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/VariableType.cpp pytorch-develop-150/tools/autograd/templates/VariableType.cpp --- pytorch-v1.5.0/tools/autograd/templates/VariableType.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/VariableType.cpp 2021-12-14 23:02:05.643151776 +0800 ++++ pytorch-develop-150/tools/autograd/templates/VariableType.cpp 2021-12-15 23:03:16.709228570 +0800 @@ -1,7 +1,29 @@ +// Copyright (c) 2021 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10468,7 +10474,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/VariableType.h pytorch-develop-150/tools/autograd/templates/VariableType.h --- pytorch-v1.5.0/tools/autograd/templates/VariableType.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/VariableType.h 2021-12-14 23:02:05.643151776 +0800 ++++ pytorch-develop-150/tools/autograd/templates/VariableType.h 2021-12-15 23:03:16.709228570 +0800 @@ -1,3 +1,20 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10500,7 +10506,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= const at::Tensor & unpack(const Tensor & t, const char * name, int pos); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/build_variables.bzl pytorch-develop-150/tools/build_variables.bzl --- pytorch-v1.5.0/tools/build_variables.bzl 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/build_variables.bzl 2021-12-14 23:02:05.639151835 +0800 ++++ pytorch-develop-150/tools/build_variables.bzl 2021-12-15 23:03:16.709228570 +0800 @@ -46,6 +46,7 @@ "torch/csrc/autograd/functions/utils.cpp", "torch/csrc/autograd/input_buffer.cpp", @@ -10586,7 +10592,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def grad(outputs: _TensorOrTensors, inputs: _TensorOrTensors, grad_outputs: Optional[_TensorOrTensors]=..., retain_graph: Optional[bool]=..., create_graph: bool=..., only_inputs: bool=..., allow_unused: bool=...) -> Tuple[Tensor, ...]: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/autograd/profiler.py pytorch-develop-150/torch/autograd/profiler.py --- pytorch-v1.5.0/torch/autograd/profiler.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/autograd/profiler.py 2021-12-14 23:02:05.643151776 +0800 ++++ pytorch-develop-150/torch/autograd/profiler.py 2021-12-15 23:03:16.709228570 +0800 @@ -1,8 +1,25 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -11063,7 +11069,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ''.join(result) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/CMakeLists.txt pytorch-develop-150/torch/CMakeLists.txt --- pytorch-v1.5.0/torch/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/CMakeLists.txt 2021-12-14 23:02:05.643151776 +0800 ++++ pytorch-develop-150/torch/CMakeLists.txt 2021-12-15 23:03:16.709228570 +0800 @@ -97,6 +97,7 @@ ${TORCH_SRC_DIR}/csrc/tensor/python_tensor.cpp ${TORCH_SRC_DIR}/csrc/utils.cpp @@ -11095,7 +11101,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endif() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/engine.cpp pytorch-develop-150/torch/csrc/autograd/engine.cpp --- pytorch-v1.5.0/torch/csrc/autograd/engine.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/engine.cpp 2021-12-14 23:02:05.651151659 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/engine.cpp 2021-12-15 23:03:16.717228596 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11247,7 +11253,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto event = c10::Event{c10::DeviceType::CUDA}; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/function.h pytorch-develop-150/torch/csrc/autograd/function.h --- pytorch-v1.5.0/torch/csrc/autograd/function.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/function.h 2021-12-14 23:02:05.651151659 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/function.h 2021-12-15 23:03:16.717228596 +0800 @@ -11,6 +11,7 @@ #include @@ -11269,7 +11275,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // probably operate with names. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/functions/tensor.cpp pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp --- pytorch-v1.5.0/torch/csrc/autograd/functions/tensor.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp 2021-12-14 23:02:05.651151659 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp 2021-12-15 23:03:16.717228596 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11301,7 +11307,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= /*non_blocking=*/false, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/init.cpp pytorch-develop-150/torch/csrc/autograd/init.cpp --- pytorch-v1.5.0/torch/csrc/autograd/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/init.cpp 2021-12-14 23:02:05.651151659 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/init.cpp 2021-12-15 23:03:16.717228596 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11344,7 +11350,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= m.def("_enable_profiler", enableProfiler); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/input_buffer.cpp pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp --- pytorch-v1.5.0/torch/csrc/autograd/input_buffer.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp 2021-12-14 23:02:05.651151659 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp 2021-12-15 23:03:16.717228596 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11396,7 +11402,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto& old_var = buffer[pos]; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/profiler.cpp pytorch-develop-150/torch/csrc/autograd/profiler.cpp --- pytorch-v1.5.0/torch/csrc/autograd/profiler.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/profiler.cpp 2021-12-14 23:02:05.651151659 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/profiler.cpp 2021-12-15 23:03:16.717228596 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11636,7 +11642,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CUDAStubs::~CUDAStubs() = default; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/profiler.h pytorch-develop-150/torch/csrc/autograd/profiler.h --- pytorch-v1.5.0/torch/csrc/autograd/profiler.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/profiler.h 2021-12-14 23:02:05.651151659 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/profiler.h 2021-12-15 23:03:16.717228596 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11772,7 +11778,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/python_variable.cpp pytorch-develop-150/torch/csrc/autograd/python_variable.cpp --- pytorch-v1.5.0/torch/csrc/autograd/python_variable.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/python_variable.cpp 2021-12-14 23:02:05.651151659 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/python_variable.cpp 2021-12-15 23:03:16.717228596 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11826,7 +11832,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"is_complex", (getter)THPVariable_is_complex, nullptr, nullptr, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/python_variable_indexing.cpp pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp --- pytorch-v1.5.0/torch/csrc/autograd/python_variable_indexing.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-14 23:02:05.651151659 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-15 23:03:16.717228596 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11857,7 +11863,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= at::Device self_device = self_.device(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/record_function.cpp pytorch-develop-150/torch/csrc/autograd/record_function.cpp --- pytorch-v1.5.0/torch/csrc/autograd/record_function.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/record_function.cpp 2021-12-14 23:02:05.651151659 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/record_function.cpp 2021-12-15 23:03:16.717228596 +0800 @@ -154,6 +154,12 @@ } } @@ -11891,7 +11897,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/record_function.h pytorch-develop-150/torch/csrc/autograd/record_function.h --- pytorch-v1.5.0/torch/csrc/autograd/record_function.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/record_function.h 2021-12-14 23:02:05.651151659 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/record_function.h 2021-12-15 23:03:16.717228596 +0800 @@ -44,6 +44,9 @@ // Default constructor is used with before function called afterwards RecordFunction() {} @@ -11955,7 +11961,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (torch::autograd::profiler::needsInputs()) { \ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/utils/wrap_outputs.h pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h --- pytorch-v1.5.0/torch/csrc/autograd/utils/wrap_outputs.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h 2021-12-14 23:02:05.651151659 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h 2021-12-15 23:03:16.717228596 +0800 @@ -168,6 +168,45 @@ return r.release(); } @@ -12004,7 +12010,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (!r) throw python_error(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/VariableTypeManual.cpp pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp --- pytorch-v1.5.0/torch/csrc/autograd/VariableTypeManual.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp 2021-12-14 23:02:05.651151659 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp 2021-12-15 23:03:16.717228596 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12038,7 +12044,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (!t.defined()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/comm.cpp pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/comm.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp 2021-12-14 23:02:05.651151659 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp 2021-12-15 23:03:16.721228610 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12144,7 +12150,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= while (!in_flight.empty()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/init.cpp pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp 2021-12-14 23:02:05.651151659 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp 2021-12-15 23:03:16.721228610 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12201,7 +12207,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= .def("is_success", &::c10d::ProcessGroup::Work::isSuccess) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/reducer.cpp pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/reducer.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp 2021-12-14 23:02:05.651151659 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp 2021-12-15 23:03:16.721228610 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12336,7 +12342,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/DynamicTypes.cpp pytorch-develop-150/torch/csrc/DynamicTypes.cpp --- pytorch-v1.5.0/torch/csrc/DynamicTypes.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/DynamicTypes.cpp 2021-12-14 23:02:05.643151776 +0800 ++++ pytorch-develop-150/torch/csrc/DynamicTypes.cpp 2021-12-15 23:03:16.713228583 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12385,7 +12391,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return it->second; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/Generator.cpp pytorch-develop-150/torch/csrc/Generator.cpp --- pytorch-v1.5.0/torch/csrc/Generator.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/Generator.cpp 2021-12-14 23:02:05.643151776 +0800 ++++ pytorch-develop-150/torch/csrc/Generator.cpp 2021-12-15 23:03:16.713228583 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12453,7 +12459,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #endif diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/serialization.cpp pytorch-develop-150/torch/csrc/generic/serialization.cpp --- pytorch-v1.5.0/torch/csrc/generic/serialization.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/serialization.cpp 2021-12-14 23:02:05.655151601 +0800 ++++ pytorch-develop-150/torch/csrc/generic/serialization.cpp 2021-12-15 23:03:16.721228610 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12553,7 +12559,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/Storage.cpp pytorch-develop-150/torch/csrc/generic/Storage.cpp --- pytorch-v1.5.0/torch/csrc/generic/Storage.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/Storage.cpp 2021-12-14 23:02:05.655151601 +0800 ++++ pytorch-develop-150/torch/csrc/generic/Storage.cpp 2021-12-15 23:03:16.721228610 +0800 @@ -1,7 +1,25 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12632,7 +12638,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for (Py_ssize_t i = 0; i < length; i++) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/StorageMethods.cpp pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp --- pytorch-v1.5.0/torch/csrc/generic/StorageMethods.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp 2021-12-14 23:02:05.655151601 +0800 ++++ pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp 2021-12-15 23:03:16.721228610 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12680,7 +12686,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"_write_file", (PyCFunction)THPStorage_(writeFile), METH_VARARGS, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/Module.cpp pytorch-develop-150/torch/csrc/Module.cpp --- pytorch-v1.5.0/torch/csrc/Module.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/Module.cpp 2021-12-14 23:02:05.643151776 +0800 ++++ pytorch-develop-150/torch/csrc/Module.cpp 2021-12-15 23:03:16.713228583 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12841,7 +12847,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto set_module_attr = [&](const char* name, PyObject* v, bool incref = true) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/tensor/python_tensor.cpp pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp --- pytorch-v1.5.0/torch/csrc/tensor/python_tensor.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp 2021-12-14 23:02:05.663151483 +0800 ++++ pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp 2021-12-15 23:03:16.729228637 +0800 @@ -1,18 +1,35 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13218,7 +13224,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +} // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/init.cpp pytorch-develop-150/torch/csrc/utils/init.cpp --- pytorch-v1.5.0/torch/csrc/utils/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/init.cpp 2021-12-14 23:02:05.663151483 +0800 ++++ pytorch-develop-150/torch/csrc/utils/init.cpp 2021-12-15 23:03:16.729228637 +0800 @@ -1,7 +1,13 @@ #include #include @@ -13383,7 +13389,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/init.h pytorch-develop-150/torch/csrc/utils/init.h --- pytorch-v1.5.0/torch/csrc/utils/init.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/init.h 2021-12-14 23:02:05.663151483 +0800 ++++ pytorch-develop-150/torch/csrc/utils/init.h 2021-12-15 23:03:16.729228637 +0800 @@ -8,4 +8,7 @@ void initThroughputBenchmarkBindings(PyObject* module); @@ -13394,7 +13400,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/python_arg_parser.h pytorch-develop-150/torch/csrc/utils/python_arg_parser.h --- pytorch-v1.5.0/torch/csrc/utils/python_arg_parser.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/python_arg_parser.h 2021-12-14 23:02:05.663151483 +0800 ++++ pytorch-develop-150/torch/csrc/utils/python_arg_parser.h 2021-12-15 23:03:16.729228637 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13429,7 +13435,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return at::Device(device_str); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_layouts.cpp pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_layouts.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp 2021-12-14 23:02:05.663151483 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp 2021-12-15 23:03:16.729228637 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13460,7 +13466,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= registerLayoutObject((THPLayout*)strided_layout, at::Backend::QuantizedCPU); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_new.cpp pytorch-develop-150/torch/csrc/utils/tensor_new.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_new.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_new.cpp 2021-12-14 23:02:05.663151483 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_new.cpp 2021-12-15 23:03:16.729228637 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13596,7 +13602,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= " or ", c10::DispatchKey::XLATensorId, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_types.cpp pytorch-develop-150/torch/csrc/utils/tensor_types.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_types.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_types.cpp 2021-12-14 23:02:05.663151483 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_types.cpp 2021-12-15 23:03:16.733228650 +0800 @@ -1,58 +1,91 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13809,7 +13815,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def get_rng_state(): ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/distributed/distributed_c10d.py pytorch-develop-150/torch/distributed/distributed_c10d.py --- pytorch-v1.5.0/torch/distributed/distributed_c10d.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/distributed/distributed_c10d.py 2021-12-14 23:02:05.663151483 +0800 ++++ pytorch-develop-150/torch/distributed/distributed_c10d.py 2021-12-15 23:03:16.733228650 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -13890,7 +13896,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/__init__.py pytorch-develop-150/torch/__init__.py --- pytorch-v1.5.0/torch/__init__.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/__init__.py 2021-12-14 23:02:05.643151776 +0800 ++++ pytorch-develop-150/torch/__init__.py 2021-12-15 23:03:16.709228570 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -13933,7 +13939,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/lib/c10d/CMakeLists.txt pytorch-develop-150/torch/lib/c10d/CMakeLists.txt --- pytorch-v1.5.0/torch/lib/c10d/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/lib/c10d/CMakeLists.txt 2021-12-14 23:02:05.667151425 +0800 ++++ pytorch-develop-150/torch/lib/c10d/CMakeLists.txt 2021-12-15 23:03:16.733228650 +0800 @@ -28,6 +28,10 @@ option(USE_C10D_NCCL "USE C10D NCCL" ON) endif() @@ -13986,7 +13992,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= copy_header(ProcessGroupMPI.hpp) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/lib/libshm/CMakeLists.txt pytorch-develop-150/torch/lib/libshm/CMakeLists.txt --- pytorch-v1.5.0/torch/lib/libshm/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/lib/libshm/CMakeLists.txt 2021-12-14 23:02:05.667151425 +0800 ++++ pytorch-develop-150/torch/lib/libshm/CMakeLists.txt 2021-12-15 23:03:16.733228650 +0800 @@ -37,8 +37,11 @@ SET_TARGET_PROPERTIES(shm PROPERTIES PREFIX "lib" @@ -14043,7 +14049,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -_maybe_indices_t = _scalar_or_tuple_2_t[Tensor] diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/functional.py pytorch-develop-150/torch/nn/functional.py --- pytorch-v1.5.0/torch/nn/functional.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/functional.py 2021-12-14 23:02:05.667151425 +0800 ++++ pytorch-develop-150/torch/nn/functional.py 2021-12-15 23:03:16.733228650 +0800 @@ -1611,7 +1611,7 @@ else: output = input.matmul(weight.t()) @@ -14066,7 +14072,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -from . import parallel as parallel diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/batchnorm.py pytorch-develop-150/torch/nn/modules/batchnorm.py --- pytorch-v1.5.0/torch/nn/modules/batchnorm.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/batchnorm.py 2021-12-14 23:02:05.667151425 +0800 ++++ pytorch-develop-150/torch/nn/modules/batchnorm.py 2021-12-15 23:03:16.737228664 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14098,7 +14104,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= self.register_parameter('running_var', None) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/module.py pytorch-develop-150/torch/nn/modules/module.py --- pytorch-v1.5.0/torch/nn/modules/module.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/module.py 2021-12-14 23:02:05.671151366 +0800 ++++ pytorch-develop-150/torch/nn/modules/module.py 2021-12-15 23:03:16.737228664 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14258,7 +14264,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return t.to(device, dtype if t.is_floating_point() else None, non_blocking, memory_format=convert_to_format) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/normalization.py pytorch-develop-150/torch/nn/modules/normalization.py --- pytorch-v1.5.0/torch/nn/modules/normalization.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/normalization.py 2021-12-14 23:02:05.671151366 +0800 ++++ pytorch-develop-150/torch/nn/modules/normalization.py 2021-12-15 23:03:16.737228664 +0800 @@ -128,13 +128,14 @@ """ __constants__ = ['normalized_shape', 'eps', 'elementwise_affine'] @@ -14327,7 +14333,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - module_kwargs: Optional[Any] = ...) -> Tensor: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/parallel/distributed.py pytorch-develop-150/torch/nn/parallel/distributed.py --- pytorch-v1.5.0/torch/nn/parallel/distributed.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/parallel/distributed.py 2021-12-14 23:02:05.671151366 +0800 ++++ pytorch-develop-150/torch/nn/parallel/distributed.py 2021-12-15 23:03:16.737228664 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14682,7 +14688,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def remove_weight_norm(module: T_module, name: str = ...) -> T_module: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/onnx/symbolic_opset9.py pytorch-develop-150/torch/onnx/symbolic_opset9.py --- pytorch-v1.5.0/torch/onnx/symbolic_opset9.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/onnx/symbolic_opset9.py 2021-12-14 23:02:05.671151366 +0800 ++++ pytorch-develop-150/torch/onnx/symbolic_opset9.py 2021-12-15 23:03:16.737228664 +0800 @@ -1621,14 +1621,23 @@ slices = [sym_help._slice_helper(g, w, axes=[0], starts=[x * n], ends=[y * n]) for x, y in intervals] return g.op('Concat', *slices, axis_i=0) @@ -14760,7 +14766,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, params: _params_t, lr: float=..., lr_decay: float=..., weight_decay: float=..., initial_accumulator_value: float=..., eps: float=...) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/optim/adamax.py pytorch-develop-150/torch/optim/adamax.py --- pytorch-v1.5.0/torch/optim/adamax.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/optim/adamax.py 2021-12-14 23:02:05.671151366 +0800 ++++ pytorch-develop-150/torch/optim/adamax.py 2021-12-15 23:03:16.737228664 +0800 @@ -80,8 +80,8 @@ exp_inf.mul_(beta2).unsqueeze(0), grad.abs().add_(eps).unsqueeze_(0) @@ -14937,7 +14943,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, params: _params_t, lr: float=..., betas: Tuple[float, float]=..., eps: float=...) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/serialization.py pytorch-develop-150/torch/serialization.py --- pytorch-v1.5.0/torch/serialization.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/serialization.py 2021-12-14 23:02:05.643151776 +0800 ++++ pytorch-develop-150/torch/serialization.py 2021-12-15 23:03:16.709228570 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15021,7 +15027,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def location_tag(storage): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/storage.py pytorch-develop-150/torch/storage.py --- pytorch-v1.5.0/torch/storage.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/storage.py 2021-12-14 23:02:05.643151776 +0800 ++++ pytorch-develop-150/torch/storage.py 2021-12-15 23:03:16.709228570 +0800 @@ -7,6 +7,7 @@ class _StorageBase(object): @@ -15041,7 +15047,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= else: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/tensor.py pytorch-develop-150/torch/tensor.py --- pytorch-v1.5.0/torch/tensor.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/tensor.py 2021-12-14 23:02:05.643151776 +0800 ++++ pytorch-develop-150/torch/tensor.py 2021-12-15 23:03:16.709228570 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15103,7 +15109,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def __reversed__(self): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/_tensor_str.py pytorch-develop-150/torch/_tensor_str.py --- pytorch-v1.5.0/torch/_tensor_str.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/_tensor_str.py 2021-12-14 23:02:05.643151776 +0800 ++++ pytorch-develop-150/torch/_tensor_str.py 2021-12-15 23:03:16.709228570 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15157,7 +15163,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= has_default_dtype = self.dtype in (torch.get_default_dtype(), torch.int64, torch.bool) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/data/dataloader.py pytorch-develop-150/torch/utils/data/dataloader.py --- pytorch-v1.5.0/torch/utils/data/dataloader.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/data/dataloader.py 2021-12-14 23:02:05.675151307 +0800 ++++ pytorch-develop-150/torch/utils/data/dataloader.py 2021-12-15 23:03:16.741228677 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15366,7 +15372,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, sampler: Sampler[int], batch_size: int, drop_last: bool) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/data/_utils/pin_memory.py pytorch-develop-150/torch/utils/data/_utils/pin_memory.py --- pytorch-v1.5.0/torch/utils/data/_utils/pin_memory.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/data/_utils/pin_memory.py 2021-12-14 23:02:05.675151307 +0800 ++++ pytorch-develop-150/torch/utils/data/_utils/pin_memory.py 2021-12-15 23:03:16.741228677 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15427,7 +15433,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/__init__.py pytorch-develop-150/torch/utils/__init__.py --- pytorch-v1.5.0/torch/utils/__init__.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/__init__.py 2021-12-14 23:02:05.671151366 +0800 ++++ pytorch-develop-150/torch/utils/__init__.py 2021-12-15 23:03:16.741228677 +0800 @@ -1,6 +1,9 @@ from __future__ import absolute_import, division, print_function, unicode_literals @@ -15440,7 +15446,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def set_module(obj, mod): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/_utils.py pytorch-develop-150/torch/_utils.py --- pytorch-v1.5.0/torch/_utils.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/_utils.py 2021-12-14 23:02:05.643151776 +0800 ++++ pytorch-develop-150/torch/_utils.py 2021-12-15 23:03:16.709228570 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. diff --git a/patch/pytorch1.8.1_npu.patch b/patch/pytorch1.8.1_npu.patch index 3624e016d7b..e67c0d5e237 100644 --- a/patch/pytorch1.8.1_npu.patch +++ b/patch/pytorch1.8.1_npu.patch @@ -1,6 +1,6 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/CMakeLists.txt pytorch-develop-181/aten/CMakeLists.txt --- pytorch-v1.8.1/aten/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/CMakeLists.txt 2021-12-14 23:02:09.715092161 +0800 ++++ pytorch-develop-181/aten/CMakeLists.txt 2021-12-15 23:03:20.809242288 +0800 @@ -22,9 +22,11 @@ set(ATen_CPU_INCLUDE) set(ATen_THIRD_PARTY_INCLUDE) @@ -52,7 +52,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_CPU_DEPENDENCY_LIBS ${ATen_CPU_DEPENDENCY_LIBS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/autocast_mode.h pytorch-develop-181/aten/src/ATen/autocast_mode.h --- pytorch-v1.8.1/aten/src/ATen/autocast_mode.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/autocast_mode.h 2021-12-14 23:02:09.723092044 +0800 ++++ pytorch-develop-181/aten/src/ATen/autocast_mode.h 2021-12-15 23:03:20.813242300 +0800 @@ -5,7 +5,7 @@ namespace { @@ -64,7 +64,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/CMakeLists.txt pytorch-develop-181/aten/src/ATen/CMakeLists.txt --- pytorch-v1.8.1/aten/src/ATen/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/CMakeLists.txt 2021-12-14 23:02:09.719092103 +0800 ++++ pytorch-develop-181/aten/src/ATen/CMakeLists.txt 2021-12-15 23:03:20.809242288 +0800 @@ -85,6 +85,10 @@ file(GLOB native_quantized_h "native/quantized/*.h" "native/quantized/cpu/*.h") file(GLOB native_cpu_h "native/cpu/*.h") @@ -115,7 +115,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_HIP_SRCS ${ATen_HIP_SRCS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/Dispatcher.h pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/Dispatcher.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h 2021-12-14 23:02:09.727091986 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h 2021-12-15 23:03:20.817242314 +0800 @@ -417,6 +417,11 @@ } } @@ -130,7 +130,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.cpp pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-12-14 23:02:09.727091986 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-12-15 23:03:20.817242314 +0800 @@ -6,6 +6,7 @@ namespace c10 { @@ -141,7 +141,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= std::unordered_set not_observed_ops = { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.h pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-12-14 23:02:09.727091986 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-12-15 23:03:20.817242314 +0800 @@ -1,12 +1,16 @@ #pragma once @@ -161,7 +161,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/OperatorEntry.h pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-12-14 23:02:09.727091986 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-12-15 23:03:20.817242314 +0800 @@ -10,6 +10,7 @@ #include #include @@ -172,7 +172,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #include diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/VariableFallbackKernel.cpp pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp --- pytorch-v1.8.1/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-12-14 23:02:09.723092044 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-12-15 23:03:20.813242300 +0800 @@ -48,4 +48,8 @@ m.fallback(torch::CppFunction::makeFallthrough()); } @@ -184,7 +184,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/Convolution.cpp pytorch-develop-181/aten/src/ATen/native/Convolution.cpp --- pytorch-v1.8.1/aten/src/ATen/native/Convolution.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/Convolution.cpp 2021-12-14 23:02:09.735091868 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/Convolution.cpp 2021-12-15 23:03:20.829242354 +0800 @@ -603,7 +603,9 @@ const Tensor& input, const Tensor& weight, const Tensor& bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, @@ -198,7 +198,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= at::Tensor _convolution( diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/Memory.cpp pytorch-develop-181/aten/src/ATen/native/Memory.cpp --- pytorch-v1.8.1/aten/src/ATen/native/Memory.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/Memory.cpp 2021-12-14 23:02:09.739091810 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/Memory.cpp 2021-12-15 23:03:20.829242354 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -249,7 +249,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= detail::computeStorageNbytes( diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/native_functions.yaml pytorch-develop-181/aten/src/ATen/native/native_functions.yaml --- pytorch-v1.8.1/aten/src/ATen/native/native_functions.yaml 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/native_functions.yaml 2021-12-14 23:02:09.775091283 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/native_functions.yaml 2021-12-15 23:03:20.865242474 +0800 @@ -2073,6 +2073,8 @@ dispatch: CPU, CUDA: isnan @@ -480,7 +480,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +- func: npu_apply_adam.out(Scalar beta1_power, Scalar beta2_power, Scalar lr, Scalar beta1, Scalar beta2, Scalar epsilon, Tensor grad, bool? use_locking, bool? use_nesterov, *, Tensor(a!) var, Tensor(b!) m, Tensor(c!) v) -> (Tensor(a!), Tensor(b!), Tensor(c!)) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/NPUVariableMethodStubs.cpp pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp --- pytorch-v1.8.1/aten/src/ATen/native/NPUVariableMethodStubs.cpp 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp 2021-12-14 23:02:09.739091810 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp 2021-12-15 23:03:20.833242367 +0800 @@ -0,0 +1,464 @@ +#include "ATen/native/npu/common/FormatCastHelper.h" +#include "ATen/native/npu/frame/FormatHelper.h" @@ -1864,7 +1864,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -} diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/TensorFactories.cpp pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp --- pytorch-v1.8.1/aten/src/ATen/native/TensorFactories.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp 2021-12-14 23:02:09.755091576 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp 2021-12-15 23:03:20.845242407 +0800 @@ -1,3 +1,20 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -1897,7 +1897,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= namespace { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h --- pytorch-v1.8.1/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-12-14 23:02:09.807090815 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-12-15 23:03:20.901242595 +0800 @@ -1,19229 +1,19229 @@ -// -// Copyright (c) 2017-2020 Advanced Micro Devices, Inc. All rights reserved. @@ -40359,7 +40359,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +#endif // #ifdef VMA_IMPLEMENTATION diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/record_function.cpp pytorch-develop-181/aten/src/ATen/record_function.cpp --- pytorch-v1.8.1/aten/src/ATen/record_function.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/record_function.cpp 2021-12-14 23:02:09.811090757 +0800 ++++ pytorch-develop-181/aten/src/ATen/record_function.cpp 2021-12-15 23:03:20.905242608 +0800 @@ -400,6 +400,9 @@ rf_tls_.tls_record_function_enabled_ = enable; } @@ -40372,7 +40372,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (rf_tls_ptr->tls_record_function_enabled_) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/record_function.h pytorch-develop-181/aten/src/ATen/record_function.h --- pytorch-v1.8.1/aten/src/ATen/record_function.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/record_function.h 2021-12-14 23:02:09.811090757 +0800 ++++ pytorch-develop-181/aten/src/ATen/record_function.h 2021-12-15 23:03:20.905242608 +0800 @@ -2,6 +2,7 @@ #include @@ -40408,7 +40408,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // scope - record scope that this function tracks diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/templates/TensorBody.h pytorch-develop-181/aten/src/ATen/templates/TensorBody.h --- pytorch-v1.8.1/aten/src/ATen/templates/TensorBody.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/templates/TensorBody.h 2021-12-14 23:02:09.811090757 +0800 ++++ pytorch-develop-181/aten/src/ATen/templates/TensorBody.h 2021-12-15 23:03:20.905242608 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40441,7 +40441,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= bool is_xpu() const; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/templates/TensorMethods.cpp pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp --- pytorch-v1.8.1/aten/src/ATen/templates/TensorMethods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp 2021-12-14 23:02:09.811090757 +0800 ++++ pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp 2021-12-15 23:03:20.905242608 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40486,7 +40486,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/Utils.cpp pytorch-develop-181/aten/src/ATen/Utils.cpp --- pytorch-v1.8.1/aten/src/ATen/Utils.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/Utils.cpp 2021-12-14 23:02:09.719092103 +0800 ++++ pytorch-develop-181/aten/src/ATen/Utils.cpp 2021-12-15 23:03:20.813242300 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40530,7 +40530,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/TH/generic/THStorage.cpp pytorch-develop-181/aten/src/TH/generic/THStorage.cpp --- pytorch-v1.8.1/aten/src/TH/generic/THStorage.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/TH/generic/THStorage.cpp 2021-12-14 23:02:09.815090698 +0800 ++++ pytorch-develop-181/aten/src/TH/generic/THStorage.cpp 2021-12-15 23:03:20.909242622 +0800 @@ -1,9 +1,32 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40643,7 +40643,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/TH/generic/THStorage.h pytorch-develop-181/aten/src/TH/generic/THStorage.h --- pytorch-v1.8.1/aten/src/TH/generic/THStorage.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/TH/generic/THStorage.h 2021-12-14 23:02:09.815090698 +0800 ++++ pytorch-develop-181/aten/src/TH/generic/THStorage.h 2021-12-15 23:03:20.909242622 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40682,7 +40682,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/CMakeLists.txt pytorch-develop-181/c10/CMakeLists.txt --- pytorch-v1.8.1/c10/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/CMakeLists.txt 2021-12-14 23:02:09.827090523 +0800 ++++ pytorch-develop-181/c10/CMakeLists.txt 2021-12-15 23:03:20.921242662 +0800 @@ -79,6 +79,14 @@ message(STATUS "don't use NUMA") endif() @@ -40711,7 +40711,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # not checked in diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Backend.h pytorch-develop-181/c10/core/Backend.h --- pytorch-v1.8.1/c10/core/Backend.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Backend.h 2021-12-14 23:02:09.827090523 +0800 ++++ pytorch-develop-181/c10/core/Backend.h 2021-12-15 23:03:20.925242675 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40805,7 +40805,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Device.cpp pytorch-develop-181/c10/core/Device.cpp --- pytorch-v1.8.1/c10/core/Device.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Device.cpp 2021-12-14 23:02:09.827090523 +0800 ++++ pytorch-develop-181/c10/core/Device.cpp 2021-12-15 23:03:20.925242675 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40836,7 +40836,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= types.begin(), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Device.h pytorch-develop-181/c10/core/Device.h --- pytorch-v1.8.1/c10/core/Device.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Device.h 2021-12-14 23:02:09.827090523 +0800 ++++ pytorch-develop-181/c10/core/Device.h 2021-12-15 23:03:20.925242675 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40871,7 +40871,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= bool is_cpu() const noexcept { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DeviceType.cpp pytorch-develop-181/c10/core/DeviceType.cpp --- pytorch-v1.8.1/c10/core/DeviceType.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DeviceType.cpp 2021-12-14 23:02:09.827090523 +0800 ++++ pytorch-develop-181/c10/core/DeviceType.cpp 2021-12-15 23:03:20.925242675 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40911,7 +40911,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return false; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DeviceType.h pytorch-develop-181/c10/core/DeviceType.h --- pytorch-v1.8.1/c10/core/DeviceType.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DeviceType.h 2021-12-14 23:02:09.827090523 +0800 ++++ pytorch-develop-181/c10/core/DeviceType.h 2021-12-15 23:03:20.925242675 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40953,7 +40953,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= constexpr DeviceType kMSNPU = DeviceType::MSNPU; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKey.cpp pytorch-develop-181/c10/core/DispatchKey.cpp --- pytorch-v1.8.1/c10/core/DispatchKey.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKey.cpp 2021-12-14 23:02:09.827090523 +0800 ++++ pytorch-develop-181/c10/core/DispatchKey.cpp 2021-12-15 23:03:20.925242675 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41002,7 +41002,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::PrivateUse1: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKey.h pytorch-develop-181/c10/core/DispatchKey.h --- pytorch-v1.8.1/c10/core/DispatchKey.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKey.h 2021-12-14 23:02:09.827090523 +0800 ++++ pytorch-develop-181/c10/core/DispatchKey.h 2021-12-15 23:03:20.925242675 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41049,7 +41049,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= PrivateUse3_PreAutograd = AutogradPrivateUse3, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKeySet.cpp pytorch-develop-181/c10/core/DispatchKeySet.cpp --- pytorch-v1.8.1/c10/core/DispatchKeySet.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKeySet.cpp 2021-12-14 23:02:09.827090523 +0800 ++++ pytorch-develop-181/c10/core/DispatchKeySet.cpp 2021-12-15 23:03:20.925242675 +0800 @@ -11,6 +11,7 @@ DispatchKey::XLA, DispatchKey::NestedTensor, @@ -41069,7 +41069,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::AutogradXPU: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKeySet.h pytorch-develop-181/c10/core/DispatchKeySet.h --- pytorch-v1.8.1/c10/core/DispatchKeySet.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKeySet.h 2021-12-14 23:02:09.827090523 +0800 ++++ pytorch-develop-181/c10/core/DispatchKeySet.h 2021-12-15 23:03:20.925242675 +0800 @@ -193,6 +193,7 @@ DispatchKey::AutogradCPU, DispatchKey::AutogradCUDA, @@ -41080,7 +41080,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= DispatchKey::AutogradPrivateUse1, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Storage.h pytorch-develop-181/c10/core/Storage.h --- pytorch-v1.8.1/c10/core/Storage.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Storage.h 2021-12-14 23:02:09.831090464 +0800 ++++ pytorch-develop-181/c10/core/Storage.h 2021-12-15 23:03:20.925242675 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41114,7 +41114,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/StorageImpl.h pytorch-develop-181/c10/core/StorageImpl.h --- pytorch-v1.8.1/c10/core/StorageImpl.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/StorageImpl.h 2021-12-14 23:02:09.831090464 +0800 ++++ pytorch-develop-181/c10/core/StorageImpl.h 2021-12-15 23:03:20.925242675 +0800 @@ -1,12 +1,42 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41174,7 +41174,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/TensorImpl.h pytorch-develop-181/c10/core/TensorImpl.h --- pytorch-v1.8.1/c10/core/TensorImpl.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/TensorImpl.h 2021-12-14 23:02:09.831090464 +0800 ++++ pytorch-develop-181/c10/core/TensorImpl.h 2021-12-15 23:03:20.925242675 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41217,7 +41217,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ts.has(DispatchKey::SparseCPU) || diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/TensorOptions.h pytorch-develop-181/c10/core/TensorOptions.h --- pytorch-v1.8.1/c10/core/TensorOptions.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/TensorOptions.h 2021-12-14 23:02:09.831090464 +0800 ++++ pytorch-develop-181/c10/core/TensorOptions.h 2021-12-15 23:03:20.925242675 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41258,7 +41258,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } else if (tid == DispatchKey::QuantizedXPU) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/macros/Export.h pytorch-develop-181/c10/macros/Export.h --- pytorch-v1.8.1/c10/macros/Export.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/macros/Export.h 2021-12-14 23:02:09.831090464 +0800 ++++ pytorch-develop-181/c10/macros/Export.h 2021-12-15 23:03:20.929242689 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41327,7 +41327,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #define C10_API_ENUM C10_API diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/caffe2/CMakeLists.txt pytorch-develop-181/caffe2/CMakeLists.txt --- pytorch-v1.8.1/caffe2/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/caffe2/CMakeLists.txt 2021-12-14 23:02:09.839090347 +0800 ++++ pytorch-develop-181/caffe2/CMakeLists.txt 2021-12-15 23:03:20.933242702 +0800 @@ -76,6 +76,7 @@ # Add source, includes, and libs to lists list(APPEND Caffe2_CPU_SRCS ${ATen_CPU_SRCS}) @@ -41452,7 +41452,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Call again since Caffe2_HIP_INCLUDE is extended with ATen include dirs. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/BuildVariables.cmake pytorch-develop-181/cmake/BuildVariables.cmake --- pytorch-v1.8.1/cmake/BuildVariables.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/BuildVariables.cmake 2021-12-14 23:02:09.911089294 +0800 ++++ pytorch-develop-181/cmake/BuildVariables.cmake 2021-12-15 23:03:21.013242969 +0800 @@ -11,6 +11,7 @@ # CMakeLists.txt files under each folder respectively. set(Caffe2_CPU_SRCS) @@ -41476,7 +41476,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # symbols. However, if the lib is whole linked in caffe2 lib, we don't want diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Codegen.cmake pytorch-develop-181/cmake/Codegen.cmake --- pytorch-v1.8.1/cmake/Codegen.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Codegen.cmake 2021-12-14 23:02:09.911089294 +0800 ++++ pytorch-develop-181/cmake/Codegen.cmake 2021-12-15 23:03:21.013242969 +0800 @@ -208,13 +208,14 @@ file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt generated_cpp) file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt-cuda cuda_generated_cpp) @@ -41509,7 +41509,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= function(append_filelist name outputvar) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Dependencies.cmake pytorch-develop-181/cmake/Dependencies.cmake --- pytorch-v1.8.1/cmake/Dependencies.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Dependencies.cmake 2021-12-14 23:02:09.911089294 +0800 ++++ pytorch-develop-181/cmake/Dependencies.cmake 2021-12-15 23:03:21.013242969 +0800 @@ -1771,6 +1771,13 @@ endif(NOT C_HAS_THREAD) endif() @@ -41526,7 +41526,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Summary.cmake pytorch-develop-181/cmake/Summary.cmake --- pytorch-v1.8.1/cmake/Summary.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Summary.cmake 2021-12-14 23:02:09.915089235 +0800 ++++ pytorch-develop-181/cmake/Summary.cmake 2021-12-15 23:03:21.013242969 +0800 @@ -127,6 +127,7 @@ message(STATUS " USE_MKLDNN_CBLAS : ${USE_MKLDNN_CBLAS}") endif() @@ -41545,7 +41545,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= message(STATUS " Private Dependencies : ${Caffe2_DEPENDENCY_LIBS}") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/TorchConfig.cmake.in pytorch-develop-181/cmake/TorchConfig.cmake.in --- pytorch-v1.8.1/cmake/TorchConfig.cmake.in 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/TorchConfig.cmake.in 2021-12-14 23:02:09.915089235 +0800 ++++ pytorch-develop-181/cmake/TorchConfig.cmake.in 2021-12-15 23:03:21.013242969 +0800 @@ -158,6 +158,11 @@ list(APPEND TORCH_LIBRARIES ${TORCH_CUDA_LIBRARIES}) endif() @@ -41560,7 +41560,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(TORCH_CXX_FLAGS "-D_GLIBCXX_USE_CXX11_ABI=@GLIBCXX_USE_CXX11_ABI@") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/CMakeLists.txt pytorch-develop-181/CMakeLists.txt --- pytorch-v1.8.1/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/CMakeLists.txt 2021-12-14 23:02:09.707092278 +0800 ++++ pytorch-develop-181/CMakeLists.txt 2021-12-15 23:03:20.797242248 +0800 @@ -261,6 +261,10 @@ "USE_DISTRIBUTED" OFF) option(USE_TBB "Use TBB" OFF) @@ -41604,7 +41604,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if(APPLE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/setup.py pytorch-develop-181/setup.py --- pytorch-v1.8.1/setup.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/setup.py 2021-12-14 23:02:09.927089060 +0800 ++++ pytorch-develop-181/setup.py 2021-12-15 23:03:21.025243010 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -41673,7 +41673,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= 'utils/benchmark/utils/valgrind_wrapper/*.h', diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/derivatives.yaml pytorch-develop-181/tools/autograd/derivatives.yaml --- pytorch-v1.8.1/tools/autograd/derivatives.yaml 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/derivatives.yaml 2021-12-14 23:02:10.379082449 +0800 ++++ pytorch-develop-181/tools/autograd/derivatives.yaml 2021-12-15 23:03:21.489244562 +0800 @@ -1976,3 +1976,7 @@ - name: nonzero(Tensor self) -> Tensor @@ -41684,7 +41684,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + mat2: npu_bmm_v2_mat2_backward(grad, self, mat2, mat2.sizes()) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/gen_python_functions.py pytorch-develop-181/tools/autograd/gen_python_functions.py --- pytorch-v1.8.1/tools/autograd/gen_python_functions.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/gen_python_functions.py 2021-12-14 23:02:10.379082449 +0800 ++++ pytorch-develop-181/tools/autograd/gen_python_functions.py 2021-12-15 23:03:21.489244562 +0800 @@ -1,3 +1,20 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -41708,7 +41708,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # The bindings are generated as methods on python_variable or functions on the diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/Functions.cpp pytorch-develop-181/tools/autograd/templates/Functions.cpp --- pytorch-v1.8.1/tools/autograd/templates/Functions.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/Functions.cpp 2021-12-14 23:02:10.379082449 +0800 ++++ pytorch-develop-181/tools/autograd/templates/Functions.cpp 2021-12-15 23:03:21.489244562 +0800 @@ -14,6 +14,35 @@ namespace torch { namespace autograd { namespace generated { @@ -41747,7 +41747,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }}} // namespace torch::autograd::generated diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/python_torch_functions.cpp pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp --- pytorch-v1.8.1/tools/autograd/templates/python_torch_functions.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp 2021-12-14 23:02:10.379082449 +0800 ++++ pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp 2021-12-15 23:03:21.489244562 +0800 @@ -30,7 +30,7 @@ #include "torch/csrc/autograd/generated/variable_factories.h" #include "torch/csrc/utils/structseq.h" @@ -41783,7 +41783,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return torch::range(start, end, step, options); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/python_variable_methods.cpp pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp --- pytorch-v1.8.1/tools/autograd/templates/python_variable_methods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp 2021-12-14 23:02:10.379082449 +0800 ++++ pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp 2021-12-15 23:03:21.489244562 +0800 @@ -20,7 +20,13 @@ #ifdef USE_CUDA #include "torch/csrc/cuda/Event.h" @@ -41851,7 +41851,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"has_names", THPVariable_has_names, METH_NOARGS, NULL}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/build_variables.bzl pytorch-develop-181/tools/build_variables.bzl --- pytorch-v1.8.1/tools/build_variables.bzl 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/build_variables.bzl 2021-12-14 23:02:10.379082449 +0800 ++++ pytorch-develop-181/tools/build_variables.bzl 2021-12-15 23:03:21.493244575 +0800 @@ -362,6 +362,7 @@ libtorch_cuda_core_sources = [ "torch/csrc/CudaIPCTypes.cpp", @@ -41870,7 +41870,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= "torch/csrc/utils/python_arg_parser.cpp", diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/codegen/gen.py pytorch-develop-181/tools/codegen/gen.py --- pytorch-v1.8.1/tools/codegen/gen.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/codegen/gen.py 2021-12-14 23:02:10.383082391 +0800 ++++ pytorch-develop-181/tools/codegen/gen.py 2021-12-15 23:03:21.493244575 +0800 @@ -815,7 +815,7 @@ core_fm = make_file_manager(core_install_dir) cpu_fm = make_file_manager(options.install_dir) @@ -41918,7 +41918,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= main() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/codegen/model.py pytorch-develop-181/tools/codegen/model.py --- pytorch-v1.8.1/tools/codegen/model.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/codegen/model.py 2021-12-14 23:02:10.383082391 +0800 ++++ pytorch-develop-181/tools/codegen/model.py 2021-12-15 23:03:21.493244575 +0800 @@ -79,6 +79,7 @@ SparseHIP = auto() SparseXPU = auto() @@ -42060,7 +42060,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/autograd/profiler.py pytorch-develop-181/torch/autograd/profiler.py --- pytorch-v1.8.1/torch/autograd/profiler.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/autograd/profiler.py 2021-12-14 23:02:10.387082332 +0800 ++++ pytorch-develop-181/torch/autograd/profiler.py 2021-12-15 23:03:21.497244589 +0800 @@ -37,14 +37,17 @@ class EventList(list): """A list of Events (for pretty printing)""" @@ -42739,7 +42739,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ''.join(result) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_C/_autograd.pyi pytorch-develop-181/torch/_C/_autograd.pyi --- pytorch-v1.8.1/torch/_C/_autograd.pyi 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_C/_autograd.pyi 2021-12-14 23:02:10.387082332 +0800 ++++ pytorch-develop-181/torch/_C/_autograd.pyi 2021-12-15 23:03:21.497244589 +0800 @@ -9,14 +9,17 @@ CUDA = ... NVTX = ... @@ -42760,7 +42760,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= class ProfilerConfig: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/CMakeLists.txt pytorch-develop-181/torch/CMakeLists.txt --- pytorch-v1.8.1/torch/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/CMakeLists.txt 2021-12-14 23:02:10.387082332 +0800 ++++ pytorch-develop-181/torch/CMakeLists.txt 2021-12-15 23:03:21.497244589 +0800 @@ -131,6 +131,20 @@ endif() @@ -42784,7 +42784,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= list(APPEND TORCH_PYTHON_SRCS ${GENERATED_THNN_CXX_CUDA}) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/engine.cpp pytorch-develop-181/torch/csrc/autograd/engine.cpp --- pytorch-v1.8.1/torch/csrc/autograd/engine.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/engine.cpp 2021-12-14 23:02:10.395082215 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/engine.cpp 2021-12-15 23:03:21.505244615 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -42881,7 +42881,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto outputs = call_function(graph_task, func, inputs); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/functions/tensor.cpp pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp --- pytorch-v1.8.1/torch/csrc/autograd/functions/tensor.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp 2021-12-14 23:02:10.395082215 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp 2021-12-15 23:03:21.505244615 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -42913,7 +42913,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= /*non_blocking=*/false, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/init.cpp pytorch-develop-181/torch/csrc/autograd/init.cpp --- pytorch-v1.8.1/torch/csrc/autograd/init.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/init.cpp 2021-12-14 23:02:10.395082215 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/init.cpp 2021-12-15 23:03:21.505244615 +0800 @@ -52,6 +52,7 @@ .value("Disabled", ProfilerState::Disabled) .value("CPU", ProfilerState::CPU) @@ -42956,7 +42956,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= .value("OPENCL", c10::DeviceType::OPENCL) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/input_buffer.cpp pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp --- pytorch-v1.8.1/torch/csrc/autograd/input_buffer.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp 2021-12-14 23:02:10.395082215 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp 2021-12-15 23:03:21.505244615 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43008,7 +43008,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto& old_var = buffer[pos]; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.cpp pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp --- pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp 2021-12-14 23:02:10.395082215 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp 2021-12-15 23:03:21.505244615 +0800 @@ -147,7 +147,7 @@ constexpr const CUDAStubs* default_stubs_addr = &default_stubs; // Constant initialization, so it is guaranteed to be initialized before @@ -43251,7 +43251,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CUDAStubs::~CUDAStubs() = default; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.h pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h --- pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h 2021-12-14 23:02:10.395082215 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h 2021-12-15 23:03:21.505244615 +0800 @@ -19,6 +19,8 @@ #include // for gettimeofday() #endif @@ -43440,7 +43440,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= std::vector&& remoteProfiledEvents); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/python_variable.cpp pytorch-develop-181/torch/csrc/autograd/python_variable.cpp --- pytorch-v1.8.1/torch/csrc/autograd/python_variable.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/python_variable.cpp 2021-12-14 23:02:10.395082215 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/python_variable.cpp 2021-12-15 23:03:21.505244615 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43494,7 +43494,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"is_mkldnn", (getter)THPVariable_is_mkldnn, nullptr, nullptr, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/python_variable_indexing.cpp pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp --- pytorch-v1.8.1/torch/csrc/autograd/python_variable_indexing.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-14 23:02:10.395082215 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-15 23:03:21.505244615 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43527,7 +43527,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/distributed/c10d/init.cpp pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp --- pytorch-v1.8.1/torch/csrc/distributed/c10d/init.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp 2021-12-14 23:02:10.399082157 +0800 ++++ pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp 2021-12-15 23:03:21.509244629 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43694,7 +43694,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= torch::class_<::c10d::DistributedC10d>("dist_c10d", "frontend") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/DynamicTypes.cpp pytorch-develop-181/torch/csrc/DynamicTypes.cpp --- pytorch-v1.8.1/torch/csrc/DynamicTypes.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/DynamicTypes.cpp 2021-12-14 23:02:10.387082332 +0800 ++++ pytorch-develop-181/torch/csrc/DynamicTypes.cpp 2021-12-15 23:03:21.501244602 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43743,7 +43743,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return it->second; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/Generator.cpp pytorch-develop-181/torch/csrc/Generator.cpp --- pytorch-v1.8.1/torch/csrc/Generator.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/Generator.cpp 2021-12-14 23:02:10.387082332 +0800 ++++ pytorch-develop-181/torch/csrc/Generator.cpp 2021-12-15 23:03:21.501244602 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43790,7 +43790,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= " is not supported for torch.Generator() api."); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/serialization.cpp pytorch-develop-181/torch/csrc/generic/serialization.cpp --- pytorch-v1.8.1/torch/csrc/generic/serialization.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/serialization.cpp 2021-12-14 23:02:10.399082157 +0800 ++++ pytorch-develop-181/torch/csrc/generic/serialization.cpp 2021-12-15 23:03:21.513244642 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43888,7 +43888,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/Storage.cpp pytorch-develop-181/torch/csrc/generic/Storage.cpp --- pytorch-v1.8.1/torch/csrc/generic/Storage.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/Storage.cpp 2021-12-14 23:02:10.399082157 +0800 ++++ pytorch-develop-181/torch/csrc/generic/Storage.cpp 2021-12-15 23:03:21.513244642 +0800 @@ -1,7 +1,25 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43968,7 +43968,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for (Py_ssize_t i = 0; i < length; i++) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/StorageMethods.cpp pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp --- pytorch-v1.8.1/torch/csrc/generic/StorageMethods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp 2021-12-14 23:02:10.399082157 +0800 ++++ pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp 2021-12-15 23:03:21.513244642 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44048,7 +44048,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/Module.cpp pytorch-develop-181/torch/csrc/Module.cpp --- pytorch-v1.8.1/torch/csrc/Module.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/Module.cpp 2021-12-14 23:02:10.387082332 +0800 ++++ pytorch-develop-181/torch/csrc/Module.cpp 2021-12-15 23:03:21.501244602 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44174,7 +44174,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (incref) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/tensor/python_tensor.cpp pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp --- pytorch-v1.8.1/torch/csrc/tensor/python_tensor.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp 2021-12-14 23:02:10.427081747 +0800 ++++ pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp 2021-12-15 23:03:21.537244723 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44212,7 +44212,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/python_arg_parser.h pytorch-develop-181/torch/csrc/utils/python_arg_parser.h --- pytorch-v1.8.1/torch/csrc/utils/python_arg_parser.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/python_arg_parser.h 2021-12-14 23:02:10.427081747 +0800 ++++ pytorch-develop-181/torch/csrc/utils/python_arg_parser.h 2021-12-15 23:03:21.537244723 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44247,7 +44247,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return at::Device(device_str); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_layouts.cpp pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_layouts.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp 2021-12-14 23:02:10.427081747 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp 2021-12-15 23:03:21.537244723 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44278,7 +44278,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= PyObject *sparse_coo_layout = THPLayout_New(at::Layout::Sparse, "torch.sparse_coo"); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_new.cpp pytorch-develop-181/torch/csrc/utils/tensor_new.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_new.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_new.cpp 2021-12-14 23:02:10.427081747 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_new.cpp 2021-12-15 23:03:21.541244735 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44410,7 +44410,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= dispatch_key); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_types.cpp pytorch-develop-181/torch/csrc/utils/tensor_types.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_types.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_types.cpp 2021-12-14 23:02:10.427081747 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_types.cpp 2021-12-15 23:03:21.541244735 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44460,7 +44460,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/distributed/distributed_c10d.py pytorch-develop-181/torch/distributed/distributed_c10d.py --- pytorch-v1.8.1/torch/distributed/distributed_c10d.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/distributed/distributed_c10d.py 2021-12-14 23:02:10.427081747 +0800 ++++ pytorch-develop-181/torch/distributed/distributed_c10d.py 2021-12-15 23:03:21.541244735 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -44551,7 +44551,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= prefix_store, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/__init__.py pytorch-develop-181/torch/__init__.py --- pytorch-v1.8.1/torch/__init__.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/__init__.py 2021-12-14 23:02:10.387082332 +0800 ++++ pytorch-develop-181/torch/__init__.py 2021-12-15 23:03:21.497244589 +0800 @@ -675,3 +675,11 @@ # class usage. We add these lines here to preserve backward compatibility. quantized_lstm = torch.ops.aten.quantized_lstm @@ -44567,7 +44567,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/CMakeLists.txt pytorch-develop-181/torch/lib/c10d/CMakeLists.txt --- pytorch-v1.8.1/torch/lib/c10d/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/CMakeLists.txt 2021-12-14 23:02:10.435081630 +0800 ++++ pytorch-develop-181/torch/lib/c10d/CMakeLists.txt 2021-12-15 23:03:21.549244763 +0800 @@ -27,6 +27,10 @@ option(USE_C10D_NCCL "USE C10D NCCL" ON) endif() @@ -44620,7 +44620,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= copy_header(ProcessGroupMPI.hpp) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/comm.cpp pytorch-develop-181/torch/lib/c10d/comm.cpp --- pytorch-v1.8.1/torch/lib/c10d/comm.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/comm.cpp 2021-12-14 23:02:10.435081630 +0800 ++++ pytorch-develop-181/torch/lib/c10d/comm.cpp 2021-12-15 23:03:21.549244763 +0800 @@ -12,6 +12,26 @@ class BroadcastWork { @@ -44709,7 +44709,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= while (!in_flight.empty()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/reducer.cpp pytorch-develop-181/torch/lib/c10d/reducer.cpp --- pytorch-v1.8.1/torch/lib/c10d/reducer.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/reducer.cpp 2021-12-14 23:02:10.435081630 +0800 ++++ pytorch-develop-181/torch/lib/c10d/reducer.cpp 2021-12-15 23:03:21.549244763 +0800 @@ -18,6 +18,18 @@ namespace c10d { namespace { @@ -44901,7 +44901,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // A bucket with one or more dense tensors needs to be unflattened. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/libshm/CMakeLists.txt pytorch-develop-181/torch/lib/libshm/CMakeLists.txt --- pytorch-v1.8.1/torch/lib/libshm/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/libshm/CMakeLists.txt 2021-12-14 23:02:10.439081572 +0800 ++++ pytorch-develop-181/torch/lib/libshm/CMakeLists.txt 2021-12-15 23:03:21.549244763 +0800 @@ -41,8 +41,11 @@ set_target_properties(shm PROPERTIES PREFIX "lib" @@ -44917,7 +44917,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # https://github.com/libgit2/libgit2/issues/2128#issuecomment-35649830 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/batchnorm.py pytorch-develop-181/torch/nn/modules/batchnorm.py --- pytorch-v1.8.1/torch/nn/modules/batchnorm.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/batchnorm.py 2021-12-14 23:02:10.439081572 +0800 ++++ pytorch-develop-181/torch/nn/modules/batchnorm.py 2021-12-15 23:03:21.553244776 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -44939,7 +44939,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= from ._functions import SyncBatchNorm as sync_batch_norm diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/module.py pytorch-develop-181/torch/nn/modules/module.py --- pytorch-v1.8.1/torch/nn/modules/module.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/module.py 2021-12-14 23:02:10.439081572 +0800 ++++ pytorch-develop-181/torch/nn/modules/module.py 2021-12-15 23:03:21.553244776 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45065,7 +45065,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if convert_to_format is not None and t.dim() == 4: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/normalization.py pytorch-develop-181/torch/nn/modules/normalization.py --- pytorch-v1.8.1/torch/nn/modules/normalization.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/normalization.py 2021-12-14 23:02:10.439081572 +0800 ++++ pytorch-develop-181/torch/nn/modules/normalization.py 2021-12-15 23:03:21.553244776 +0800 @@ -167,8 +167,11 @@ init.zeros_(self.bias) @@ -45082,7 +45082,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return '{normalized_shape}, eps={eps}, ' \ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/parallel/distributed.py pytorch-develop-181/torch/nn/parallel/distributed.py --- pytorch-v1.8.1/torch/nn/parallel/distributed.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/parallel/distributed.py 2021-12-14 23:02:10.439081572 +0800 ++++ pytorch-develop-181/torch/nn/parallel/distributed.py 2021-12-15 23:03:21.553244776 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45114,7 +45114,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= output = self.module(*inputs[0], **kwargs[0]) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/serialization.py pytorch-develop-181/torch/serialization.py --- pytorch-v1.8.1/torch/serialization.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/serialization.py 2021-12-14 23:02:10.447081454 +0800 ++++ pytorch-develop-181/torch/serialization.py 2021-12-15 23:03:21.561244803 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45203,7 +45203,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= See also: `saving-loading-tensors` diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/storage.py pytorch-develop-181/torch/storage.py --- pytorch-v1.8.1/torch/storage.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/storage.py 2021-12-14 23:02:10.447081454 +0800 ++++ pytorch-develop-181/torch/storage.py 2021-12-15 23:03:21.561244803 +0800 @@ -8,6 +8,7 @@ class _StorageBase(object): _cdata: Any @@ -45223,7 +45223,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= else: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/tensor.py pytorch-develop-181/torch/tensor.py --- pytorch-v1.8.1/torch/tensor.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/tensor.py 2021-12-14 23:02:10.447081454 +0800 ++++ pytorch-develop-181/torch/tensor.py 2021-12-15 23:03:21.561244803 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45285,7 +45285,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def __reversed__(self): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_tensor_str.py pytorch-develop-181/torch/_tensor_str.py --- pytorch-v1.8.1/torch/_tensor_str.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_tensor_str.py 2021-12-14 23:02:10.387082332 +0800 ++++ pytorch-develop-181/torch/_tensor_str.py 2021-12-15 23:03:21.497244589 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45329,7 +45329,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # TODO: add an API to map real -> complex dtypes diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/utils/data/dataloader.py pytorch-develop-181/torch/utils/data/dataloader.py --- pytorch-v1.8.1/torch/utils/data/dataloader.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/utils/data/dataloader.py 2021-12-14 23:02:10.455081338 +0800 ++++ pytorch-develop-181/torch/utils/data/dataloader.py 2021-12-15 23:03:21.569244829 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45390,7 +45390,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= pin_memory_thread.start() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/utils/data/_utils/pin_memory.py pytorch-develop-181/torch/utils/data/_utils/pin_memory.py --- pytorch-v1.8.1/torch/utils/data/_utils/pin_memory.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/utils/data/_utils/pin_memory.py 2021-12-14 23:02:06.643137127 +0800 ++++ pytorch-develop-181/torch/utils/data/_utils/pin_memory.py 2021-12-15 23:03:17.677231809 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45436,7 +45436,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # logic of this function. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_utils.py pytorch-develop-181/torch/_utils.py --- pytorch-v1.8.1/torch/_utils.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_utils.py 2021-12-14 23:02:10.387082332 +0800 ++++ pytorch-develop-181/torch/_utils.py 2021-12-15 23:03:21.497244589 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. diff --git a/pytorch1.5.0/src/aten/src/ATen/native/native_functions.yaml b/pytorch1.5.0/src/aten/src/ATen/native/native_functions.yaml index 32a9ed26ba1..52db5b162e1 100644 --- a/pytorch1.5.0/src/aten/src/ATen/native/native_functions.yaml +++ b/pytorch1.5.0/src/aten/src/ATen/native/native_functions.yaml @@ -8705,6 +8705,11 @@ - func: npu_reshape.out(Tensor self, int[] shape, bool can_refresh=False, *, Tensor(a!) out) -> Tensor(a!) npu_dispatch_only: NPU: reshape_out_npu + - func: npu_rotated_overlaps(Tensor self, Tensor query_boxes, bool trans=False) -> Tensor npu_dispatch_only: NPU: rotated_overlaps_npu + +- func: npu_rotated_iou(Tensor self, Tensor query_boxes, bool trans=False, int mode=0, bool is_cross=True) -> Tensor + npu_dispatch_only: + NPU: rotated_iou_npu \ No newline at end of file diff --git a/pytorch1.5.0/src/aten/src/ATen/native/npu/MulKernelNpu.cpp b/pytorch1.5.0/src/aten/src/ATen/native/npu/MulKernelNpu.cpp index c009de29f81..0dbdeb22953 100644 --- a/pytorch1.5.0/src/aten/src/ATen/native/npu/MulKernelNpu.cpp +++ b/pytorch1.5.0/src/aten/src/ATen/native/npu/MulKernelNpu.cpp @@ -74,11 +74,11 @@ Tensor& mul_out_npu(Tensor& result, const Tensor& self, const Tensor& other) { Tensor outputTensor = mul_dest_output(self, other); auto outputSize = broadcast_ops_npu_output_size(self, other); OpPreparation::CheckOut( - {self}, - result, - CalcuOpUtil::get_tensor_npu_format(outputTensor), - self.scalar_type(), - outputSize); + {self}, + result, + CalcuOpUtil::get_tensor_npu_format(outputTensor), + self.scalar_type(), + outputSize); mul_out_npu_nocheck(result, self, other); return result; diff --git a/pytorch1.5.0/src/aten/src/ATen/native/npu/NeKernelNpu.cpp b/pytorch1.5.0/src/aten/src/ATen/native/npu/NeKernelNpu.cpp index fb5ed271eac..2ffb9ddf7c2 100644 --- a/pytorch1.5.0/src/aten/src/ATen/native/npu/NeKernelNpu.cpp +++ b/pytorch1.5.0/src/aten/src/ATen/native/npu/NeKernelNpu.cpp @@ -97,9 +97,9 @@ Tensor ne_npu(const Tensor& self, const Tensor& other) { // construct the output tensor of the NPU Tensor result = at::empty_with_format( - outputSize, - formatCastOfSelf.options().dtype(kBool), - ACL_FORMAT_ND); + outputSize, + formatCastOfSelf.options().dtype(kBool), + ACL_FORMAT_ND); // calculate the output result of the NPU ne_out_npu_nocheck(result, formatCastOfSelf, formatCastOfOther); diff --git a/pytorch1.5.0/src/aten/src/ATen/native/npu/RotatedIouKernelNpu.cpp b/pytorch1.5.0/src/aten/src/ATen/native/npu/RotatedIouKernelNpu.cpp new file mode 100644 index 00000000000..3086b4f891d --- /dev/null +++ b/pytorch1.5.0/src/aten/src/ATen/native/npu/RotatedIouKernelNpu.cpp @@ -0,0 +1,76 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. +// All rights reserved. +// +// Licensed under the BSD 3-Clause License (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// https://opensource.org/licenses/BSD-3-Clause +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "ATen/native/npu/utils/OpAdapter.h" + +namespace at { +namespace native { +using namespace at::native::npu; + +Tensor& rotated_iou_npu_nocheck( + Tensor& iou, + const Tensor& boxes, + const Tensor& query_boxes, + bool trans, + int64_t mode, + bool is_cross) { + string mode_str = (mode == 0) ? "iou" : "iof"; + + OpCommand cmd; + cmd.Name("RotatedIou") + .Input(boxes) + .Input(query_boxes) + .Output(iou) + .Attr("trans", trans) + .Attr("mode_str", mode_str) + .Attr("is_cross", is_cross) + .Run(); + return iou; +} + +Tensor rotated_iou_npu( + const Tensor& boxes, + const Tensor& query_boxes, + bool trans, + int64_t mode, + bool is_cross) { + TORCH_CHECK(boxes.ndimension() == 3 && query_boxes.ndimension() == 3); + + auto origin_dtype = boxes.scalar_type(); + + Tensor boxesOk = boxes.permute({0, 2, 1}); + if (boxesOk.scalar_type() == at::kHalf){ + Tensor boxesOk = boxesOk.npu_dtype_cast(at::kFloat).permute({0, 2, 1}); + } + Tensor queryBoxesOk = query_boxes.permute({0, 2, 1}); + if (queryBoxesOk.scalar_type() == at::kHalf){ + Tensor queryBoxes = queryBoxesOk.npu_dtype_cast(at::kFloat).permute({0, 2, 1}); + } + + int64_t B = boxesOk.size(0); + int64_t N = boxesOk.size(-1); + int64_t K = queryBoxesOk.size(-1); + + SmallVector output_size({B, N, K}); + Tensor iou = OpPreparation::ApplyTensor(boxesOk, output_size); + + rotated_iou_npu_nocheck(iou, boxesOk, queryBoxesOk, trans, mode, is_cross); + iou = iou.npu_dtype_cast(origin_dtype); + return iou; +} + +} // namespace native +} // namespace at diff --git a/pytorch1.5.0/test/test_npu/test_network_ops/test_rotated_iou.py b/pytorch1.5.0/test/test_npu/test_network_ops/test_rotated_iou.py new file mode 100644 index 00000000000..10934ff6332 --- /dev/null +++ b/pytorch1.5.0/test/test_npu/test_network_ops/test_rotated_iou.py @@ -0,0 +1,68 @@ +import math +import sys +import torch +import numpy as np +from common_utils import TestCase, run_tests +from common_device_type import dtypes, instantiate_device_type_tests + +class TestRotatedIou(TestCase): + def generate_rto_data(self, item): + minValue, maxValue = 20, 60 + scope = 20 + dtype = item[0][0] + shape_one = item[0][-1] + shape_two = item[1][-1] + trans = item[-1] + + boxes_array1 = np.random.uniform(minValue, maxValue, shape_one[:2]+[2]).astype(dtype) + boxes_wh = np.random.randint(1, scope, size=shape_one[:2]+[2]) + boxes_angle = np.random.randint(-180, 180, size=shape_one[:2]+[1]) + boxes = np.concatenate([boxes_array1, boxes_wh, boxes_angle], dtype=dtype, axis=-1) + #query_boxes + query_boxes_array1 = np.random.uniform(minValue, maxValue, shape_two[:2]+[2]).astype(dtype) + query_boxes_wh = np.random.randint(1, scope, size=shape_two[:2]+[2] ) + query_boxes_angle = np.random.randint(-180, 180, size=shape_two[:2]+[1]) + query_boxes = np.concatenate([query_boxes_array1, query_boxes_wh, query_boxes_angle], dtype=dtype, axis=-1) + + cpu_input1 = torch.from_numpy(boxes) + cpu_input2 = torch.from_numpy(query_boxes) + npu_input1 = cpu_input1.npu() + npu_input2 = cpu_input2.npu() + return boxes, query_boxes, npu_input1, npu_input2 + + def cpu_expect_result(self, dtype): + if dtype == np.float32: + output = np.array([[[0., 0.00045966, 0.],[0., 0., 0.]], + [[0., 0., 0.],[0., 0., 0.]], + [[0., 0., 0.],[0.00600622, 0.10504241, 0.]], + [[0., 0., 0.],[0., 0., 0.]]], dtype=np.float32) + else: + output = np.array([[[0., 0.00045966, 0.],[0., 0., 0.]], + [[0., 0., 0.],[0., 0., 0.]], + [[0., 0., 0.],[0.00600622, 0.10504241, 0.]], + [[0., 0., 0.],[0., 0., 0.]]], dtype=np.float16) + return output + + def npu_op_exec(self, box1, box2, trans=False): + output = torch.npu_rotated_iou(box1, box2, trans, 0, True) + output = output.detach().cpu().numpy() + return output + + def test_rotated_iou_shape_format_fp32(self, device): + dtype = np.float32 + shape_format = [[dtype, -1, [4,2,5]],[dtype, -1, [4,3,5]], False] + cpu_input1, cpu_input2, npu_input1, npu_input2 = self.generate_rto_data(shape_format) + cpu_output = self.cpu_expect_result(dtype) + npu_output = self.npu_op_exec(npu_input1, npu_input2, shape_format[-1]) + self.assertRtolEqual(cpu_output, npu_output) + + def test_rotated_iou_shape_format_fp16(self, device): + dtype = np.float16 + shape_format = [[dtype, -1, [4,2,5]],[dtype, -1, [4,3,5]], False] + cpu_input1, cpu_input2, npu_input1, npu_input2 = self.generate_rto_data(shape_format) + cpu_output = self.cpu_expect_result(dtype) + npu_output = self.npu_op_exec(npu_input1, npu_input2, shape_format[-1]) + self.assertRtolEqual(cpu_output, npu_output) +instantiate_device_type_tests(TestRotatedIou, globals(), except_for="cpu") +if __name__ == "__main__": + run_tests() \ No newline at end of file -- Gitee From 91aec5cee9425b5d51bf517f3ba00d463ae04e7f Mon Sep 17 00:00:00 2001 From: pengzirong Date: Thu, 16 Dec 2021 12:02:46 +0800 Subject: [PATCH 05/55] =?UTF-8?q?=E5=90=8C=E6=AD=A51.5.0=20704b98e1c093599?= =?UTF-8?q?b549fc69004270956af73b6b9=20=20=20=20=20!2749=20=E3=80=90Requir?= =?UTF-8?q?ement=E3=80=91SyncBatchnorm=E5=86=85=E9=83=A8=E6=8E=A5=E5=8F=A3?= =?UTF-8?q?=E7=AE=97=E5=AD=90batch=5Fnorm=5Fgather=5Fstats=5Fwith=5Fcounts?= =?UTF-8?q?=E9=80=82=E9=85=8D=20=20=20=20=20Merge=20pull=20request=20!2749?= =?UTF-8?q?=20from=20wuxiankun/bngswc=20--=20=20=20=20=20!2874=20prof?= =?UTF-8?q?=E5=A2=9E=E5=8A=A0UT=E7=94=A8=E4=BE=8B=20=20=20=20=20Merge=20pu?= =?UTF-8?q?ll=20request=20!2874=20from=20shibo/e2e=5Fut=20--=20=20=20=20?= =?UTF-8?q?=20!2876=20bugfix=EF=BC=9A=E4=BF=AE=E5=A4=8Dprofiler=E5=8F=AF?= =?UTF-8?q?=E8=83=BD=E5=A4=9A=E6=AC=A1=E5=88=9D=E5=A7=8B=E5=8C=96=E9=94=99?= =?UTF-8?q?=E8=AF=AF=20=20=20=20=20Merge=20pull=20request=20!2876=20from?= =?UTF-8?q?=20shibo/master=20=E5=90=8C=E6=AD=A51.8.1=20db9b820f4aba41894f2?= =?UTF-8?q?6e187a67450e4b2064a4c?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- patch/pytorch1.5.0_npu.patch | 609 +++++++++--------- patch/pytorch1.8.1_npu.patch | 184 +++--- .../src/ATen/native/native_functions.yaml | 2 + ...atchNormGatherStatsWithCountsKernelNpu.cpp | 120 ++++ .../src/torch/npu/npu_frontend_enhance.py | 4 + .../test_batchnorm_gatherstats_withcounts.py | 69 ++ .../test_npu/test_npu_tools/common_utils.py | 28 + .../test_npu_tools/test_npu_profiler.py | 111 ++++ 8 files changed, 734 insertions(+), 393 deletions(-) create mode 100644 pytorch1.5.0/src/aten/src/ATen/native/npu/normalization/BatchNormGatherStatsWithCountsKernelNpu.cpp create mode 100644 pytorch1.5.0/test/test_npu/test_network_ops/test_batchnorm_gatherstats_withcounts.py create mode 100644 pytorch1.5.0/test/test_npu/test_npu_tools/common_utils.py create mode 100644 pytorch1.5.0/test/test_npu/test_npu_tools/test_npu_profiler.py diff --git a/patch/pytorch1.5.0_npu.patch b/patch/pytorch1.5.0_npu.patch index 6d7343eaa6b..3685f920ffd 100644 --- a/patch/pytorch1.5.0_npu.patch +++ b/patch/pytorch1.5.0_npu.patch @@ -1,6 +1,6 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/CMakeLists.txt pytorch-develop-150/aten/CMakeLists.txt --- pytorch-v1.5.0/aten/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/CMakeLists.txt 2021-12-15 23:03:15.997226187 +0800 ++++ pytorch-develop-150/aten/CMakeLists.txt 2021-12-16 12:02:40.299263155 +0800 @@ -22,8 +22,10 @@ set(ATen_CPU_INCLUDE) set(ATen_THIRD_PARTY_INCLUDE) @@ -51,7 +51,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_CPU_DEPENDENCY_LIBS ${ATen_CPU_DEPENDENCY_LIBS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/CMakeLists.txt pytorch-develop-150/aten/src/ATen/CMakeLists.txt --- pytorch-v1.5.0/aten/src/ATen/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/CMakeLists.txt 2021-12-15 23:03:15.997226187 +0800 ++++ pytorch-develop-150/aten/src/ATen/CMakeLists.txt 2021-12-16 12:02:40.299263155 +0800 @@ -67,6 +67,9 @@ FILE(GLOB native_quantized_h "native/quantized/*.h" "native/quantized/cpu/*.h") FILE(GLOB native_cpu_h "native/cpu/*.h") @@ -129,7 +129,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_QUANTIZED_SRCS ${ATen_QUANTIZED_SRCS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/core/dispatch/DispatchTable.h pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h --- pytorch-v1.5.0/aten/src/ATen/core/dispatch/DispatchTable.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h 2021-12-15 23:03:16.001226201 +0800 ++++ pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h 2021-12-16 12:02:40.303263139 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -170,7 +170,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/function_wrapper.py pytorch-develop-150/aten/src/ATen/function_wrapper.py --- pytorch-v1.5.0/aten/src/ATen/function_wrapper.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/function_wrapper.py 2021-12-15 23:03:16.005226215 +0800 ++++ pytorch-develop-150/aten/src/ATen/function_wrapper.py 2021-12-16 12:02:40.307263122 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -354,7 +354,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for option in declaration['options']: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/gen.py pytorch-develop-150/aten/src/ATen/gen.py --- pytorch-v1.5.0/aten/src/ATen/gen.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/gen.py 2021-12-15 23:03:16.005226215 +0800 ++++ pytorch-develop-150/aten/src/ATen/gen.py 2021-12-16 12:02:40.307263122 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -512,7 +512,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= generate_outputs() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/BatchLinearAlgebra.cpp pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp --- pytorch-v1.5.0/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-12-15 23:03:16.005226215 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-12-16 12:02:40.307263122 +0800 @@ -680,7 +680,7 @@ std::tuple triangular_solve_out(Tensor& result, Tensor& clone_A, const Tensor& self, const Tensor& A, bool upper, bool transpose, bool unitriangular) { @@ -524,7 +524,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return std::tuple(result, clone_A); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/cpu/Activation.cpp pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp --- pytorch-v1.5.0/aten/src/ATen/native/cpu/Activation.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp 2021-12-15 23:03:16.009226227 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp 2021-12-16 12:02:40.311263106 +0800 @@ -339,20 +339,20 @@ void hardsigmoid_backward_kernel(TensorIterator& iter) { @@ -552,7 +552,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/Memory.cpp pytorch-develop-150/aten/src/ATen/native/Memory.cpp --- pytorch-v1.5.0/aten/src/ATen/native/Memory.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/Memory.cpp 2021-12-15 23:03:16.005226215 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/Memory.cpp 2021-12-16 12:02:40.307263122 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -614,7 +614,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= detail::computeStorageSize(self.sizes(), self.strides()), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/native_functions.yaml pytorch-develop-150/aten/src/ATen/native/native_functions.yaml --- pytorch-v1.5.0/aten/src/ATen/native/native_functions.yaml 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/native_functions.yaml 2021-12-15 23:03:16.013226241 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/native_functions.yaml 2021-12-16 12:02:40.315263089 +0800 @@ -1,6 +1,5 @@ # See README.md in this directory for more guidance @@ -2589,7 +2589,14 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # for backward compatibility - func: batch_norm_gather_stats(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int count) -> (Tensor, Tensor) -@@ -2098,14 +2677,20 @@ +@@ -2093,19 +2672,27 @@ + - func: batch_norm_gather_stats_with_counts(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int[] counts) -> (Tensor, Tensor) + dispatch: + CUDA: batch_norm_gather_stats_with_counts_cuda ++ npu_dispatch: ++ NPU: batch_norm_gather_stats_with_counts_npu + + - func: native_batch_norm_backward(Tensor grad_out, Tensor input, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_invstd, bool train, float eps, bool[3] output_mask) -> (Tensor, Tensor, Tensor) dispatch: CPU: batch_norm_backward_cpu CUDA: batch_norm_backward_cuda @@ -2610,7 +2617,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: batch_norm_update_stats(Tensor input, Tensor? running_mean, Tensor? running_var, float momentum) -> (Tensor, Tensor) dispatch: -@@ -2117,6 +2702,8 @@ +@@ -2117,6 +2704,8 @@ - func: _nnpack_spatial_convolution(Tensor input, Tensor weight, Tensor? bias, int[2] padding, int[2] stride=1) -> Tensor variants: function @@ -2619,7 +2626,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _nnpack_spatial_convolution_backward(Tensor input, Tensor grad_output, Tensor weight, int[2] padding, bool[3] output_mask) -> (Tensor, Tensor, Tensor) variants: function -@@ -2129,42 +2716,60 @@ +@@ -2129,42 +2718,60 @@ - func: ones.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor device_guard: False @@ -2682,7 +2689,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Only exposed from C++ -- in Python, # we expose it as an attribute `T`, not a function. -@@ -2253,54 +2858,82 @@ +@@ -2253,54 +2860,82 @@ supports_named_tensor: True - func: randperm(int n, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor @@ -2766,7 +2773,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: repeat_interleave.Tensor(Tensor repeats) -> Tensor use_c10_dispatcher: full -@@ -2316,6 +2949,8 @@ +@@ -2316,6 +2951,8 @@ - func: repeat_interleave.self_int(Tensor self, int repeats, int? dim=None) -> Tensor use_c10_dispatcher: full variants: function, method @@ -2775,7 +2782,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: reshape(Tensor self, int[] shape) -> Tensor variants: function, method -@@ -2337,16 +2972,22 @@ +@@ -2337,16 +2974,22 @@ use_c10_dispatcher: full supports_named_tensor: True variants: function, method @@ -2798,7 +2805,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: rrelu(Tensor self, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor -@@ -2360,6 +3001,8 @@ +@@ -2360,6 +3003,8 @@ CUDA: relu MkldnnCPU: mkldnn_relu QuantizedCPU: quantized_relu @@ -2807,7 +2814,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: relu_(Tensor(a!) self) -> Tensor(a!) -@@ -2370,6 +3013,8 @@ +@@ -2370,6 +3015,8 @@ CUDA: relu_ MkldnnCPU: mkldnn_relu_ QuantizedCPU: quantized_relu_ @@ -2816,7 +2823,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: prelu(Tensor self, Tensor weight) -> Tensor use_c10_dispatcher: full -@@ -2377,12 +3022,16 @@ +@@ -2377,12 +3024,16 @@ dispatch: CPU: prelu_cpu CUDA: prelu_cuda @@ -2833,7 +2840,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: gelu(Tensor self) -> Tensor use_c10_dispatcher: full -@@ -2390,6 +3039,8 @@ +@@ -2390,6 +3041,8 @@ dispatch: CPU: gelu_cpu CUDA: gelu_cuda @@ -2842,7 +2849,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: gelu_backward(Tensor grad, Tensor self) -> Tensor use_c10_dispatcher: full -@@ -2397,29 +3048,41 @@ +@@ -2397,29 +3050,41 @@ dispatch: CPU: gelu_backward_cpu CUDA: gelu_backward_cuda @@ -2884,7 +2891,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: select.Dimname(Tensor(a) self, Dimname dim, int index) -> Tensor(a) variants: function, method -@@ -2433,14 +3096,21 @@ +@@ -2433,14 +3098,21 @@ - func: selu(Tensor self) -> Tensor use_c10_dispatcher: full @@ -2907,7 +2914,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: sigmoid(Tensor self) -> Tensor use_c10_dispatcher: full -@@ -2451,6 +3121,8 @@ +@@ -2451,6 +3123,8 @@ CUDA: sigmoid QuantizedCPU: quantized_sigmoid MkldnnCPU: mkldnn_sigmoid @@ -2916,7 +2923,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: sigmoid_(Tensor(a!) self) -> Tensor(a!) supports_named_tensor: True -@@ -2459,36 +3131,52 @@ +@@ -2459,36 +3133,52 @@ CPU: sigmoid_ CUDA: sigmoid_ MkldnnCPU: mkldnn_sigmoid_ @@ -2969,7 +2976,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Returns a copy of this `Variable` that is detached from its autograd graph. # This method is OK to call if the `Variable` is a view. -@@ -2533,6 +3221,8 @@ +@@ -2533,6 +3223,8 @@ - func: slogdet(Tensor self) -> (Tensor sign, Tensor logabsdet) variants: function, method @@ -2978,7 +2985,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: smm(Tensor self, Tensor mat2) -> Tensor use_c10_dispatcher: full -@@ -2542,10 +3232,14 @@ +@@ -2542,10 +3234,14 @@ - func: softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor variants: function, method supports_named_tensor: True @@ -2993,7 +3000,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _softmax(Tensor self, int dim, bool half_to_float) -> Tensor use_c10_dispatcher: full -@@ -2553,12 +3247,16 @@ +@@ -2553,12 +3249,16 @@ CPU: softmax_cpu CUDA: softmax_cuda MkldnnCPU: mkldnn_softmax @@ -3010,7 +3017,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: split.Tensor(Tensor(a) self, int split_size, int dim=0) -> Tensor(a)[] variants: function, method -@@ -2609,8 +3307,12 @@ +@@ -2609,8 +3309,12 @@ SparseCUDA: _sspaddmm_out_cuda - func: stack(Tensor[] tensors, int dim=0) -> Tensor @@ -3023,7 +3030,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # The signature is designed to be consistent with librosa except that it is # missing the `pad_mode` and `center` arguments, which are taken care of at -@@ -2633,20 +3335,30 @@ +@@ -2633,20 +3337,30 @@ - func: sum(Tensor self, *, ScalarType? dtype=None) -> Tensor variants: function, method supports_named_tensor: True @@ -3054,7 +3061,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: sum_to_size(Tensor self, int[] size) -> Tensor variants: method -@@ -2656,13 +3368,19 @@ +@@ -2656,13 +3370,19 @@ use_c10_dispatcher: full supports_named_tensor: True variants: function, method @@ -3074,7 +3081,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: square(Tensor self) -> Tensor use_c10_dispatcher: full -@@ -2677,51 +3395,81 @@ +@@ -2677,51 +3397,81 @@ use_c10_dispatcher: full variants: function, method supports_named_tensor: True @@ -3157,7 +3164,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: t(Tensor(a) self) -> Tensor(a) device_guard: False -@@ -2736,6 +3484,8 @@ +@@ -2736,6 +3486,8 @@ use_c10_dispatcher: full supports_named_tensor: True variants: function, method @@ -3166,7 +3173,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: tan_(Tensor(a!) self) -> Tensor(a!) supports_named_tensor: True -@@ -2743,12 +3493,16 @@ +@@ -2743,12 +3495,16 @@ dispatch: CPU: _tan__cpu CUDA: _tan__cuda @@ -3183,7 +3190,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: tanh(Tensor self) -> Tensor use_c10_dispatcher: full -@@ -2758,6 +3512,8 @@ +@@ -2758,6 +3514,8 @@ CPU: tanh CUDA: tanh QuantizedCPU: quantized_tanh @@ -3192,7 +3199,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: tanh_(Tensor(a!) self) -> Tensor(a!) supports_named_tensor: True -@@ -2765,12 +3521,16 @@ +@@ -2765,12 +3523,16 @@ dispatch: CPU: _tanh__cpu CUDA: _tanh__cuda @@ -3209,7 +3216,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: tensordot(Tensor self, Tensor other, int[] dims_self, int[] dims_other) -> Tensor variants: function -@@ -2783,6 +3543,8 @@ +@@ -2783,6 +3545,8 @@ dispatch: CPU: threshold CUDA: threshold_cuda @@ -3218,7 +3225,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: threshold_(Tensor(a!) self, Scalar threshold, Scalar value) -> Tensor(a!) variants: function -@@ -2790,12 +3552,16 @@ +@@ -2790,12 +3554,16 @@ dispatch: CPU: threshold_ CUDA: threshold__cuda @@ -3235,7 +3242,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: threshold_backward(Tensor grad_output, Tensor self, Scalar threshold) -> Tensor use_c10_dispatcher: full -@@ -2803,6 +3569,8 @@ +@@ -2803,6 +3571,8 @@ dispatch: CPU: threshold_backward CUDA: threshold_backward_cuda @@ -3244,7 +3251,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: transpose.int(Tensor(a) self, int dim0, int dim1) -> Tensor(a) variants: function, method -@@ -2835,18 +3603,24 @@ +@@ -2835,18 +3605,24 @@ use_c10_dispatcher: full python_module: nn variants: function @@ -3269,7 +3276,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # default int[] value [0,1] should not add space after comma, since native_parse.py uses ', ' to split args -@@ -2872,6 +3646,8 @@ +@@ -2872,6 +3648,8 @@ CUDA: true_divide SparseCPU: true_divide_sparse SparseCUDA: true_divide_sparse @@ -3278,7 +3285,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: true_divide_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) -@@ -2881,6 +3657,8 @@ +@@ -2881,6 +3659,8 @@ CUDA: true_divide_ SparseCPU: true_divide_sparse_ SparseCUDA: true_divide_sparse_ @@ -3287,7 +3294,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: true_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) -@@ -2889,31 +3667,43 @@ +@@ -2889,31 +3669,43 @@ CUDA: true_divide_out SparseCPU: true_divide_out_sparse_zerodim SparseCUDA: true_divide_out_sparse_zerodim @@ -3331,7 +3338,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: type_as(Tensor self, Tensor other) -> Tensor use_c10_dispatcher: full -@@ -2956,6 +3746,8 @@ +@@ -2956,6 +3748,8 @@ dispatch: CPU: _unique2_cpu CUDA: _unique2_cuda @@ -3340,7 +3347,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _unsafe_view(Tensor self, int[] size) -> Tensor -@@ -2971,32 +3763,48 @@ +@@ -2971,32 +3765,48 @@ use_c10_dispatcher: full variants: function, method supports_named_tensor: True @@ -3389,7 +3396,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: view_as(Tensor self, Tensor other) -> Tensor use_c10_dispatcher: full -@@ -3009,13 +3817,19 @@ +@@ -3009,13 +3819,19 @@ - func: where.self(Tensor condition, Tensor self, Tensor other) -> Tensor use_c10_dispatcher: full variants: function, method @@ -3409,7 +3416,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: norm_except_dim(Tensor v, int pow=2, int dim=0) -> Tensor variants: function -@@ -3041,13 +3855,21 @@ +@@ -3041,13 +3857,21 @@ - func: zeros.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor device_guard: False @@ -3431,7 +3438,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _standard_gamma_grad(Tensor self, Tensor output) -> Tensor use_c10_dispatcher: full -@@ -3100,25 +3922,37 @@ +@@ -3100,25 +3924,37 @@ - func: _sparse_sum_backward(Tensor grad, Tensor self, int[] dim) -> Tensor dispatch: @@ -3471,7 +3478,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: norm.names_ScalarOpt_dim_dtype(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim, *, ScalarType dtype) -> Tensor variants: function, method -@@ -3162,12 +3996,16 @@ +@@ -3162,12 +3998,16 @@ SparseCUDA: clone_sparse MkldnnCPU: mkldnn_clone QuantizedCPU: quantized_clone @@ -3488,7 +3495,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: pow.Tensor_Scalar_out(Tensor self, Scalar exponent, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True -@@ -3176,6 +4014,8 @@ +@@ -3176,6 +4016,8 @@ CUDA: pow_out SparseCPU: pow_out_sparse_scalar SparseCUDA: pow_out_sparse_scalar @@ -3497,7 +3504,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: pow.Tensor_Scalar(Tensor self, Scalar exponent) -> Tensor use_c10_dispatcher: full -@@ -3186,6 +4026,8 @@ +@@ -3186,6 +4028,8 @@ CUDA: pow SparseCPU: pow_sparse_scalar SparseCUDA: pow_sparse_scalar @@ -3506,7 +3513,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: zero_(Tensor(a!) self) -> Tensor(a!) supports_named_tensor: True -@@ -3196,6 +4038,14 @@ +@@ -3196,6 +4040,14 @@ SparseCPU: zero_sparse_ SparseCUDA: zero_sparse_ MkldnnCPU: mkldnn_zero_ @@ -3521,7 +3528,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: sub.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) dispatch: -@@ -3204,6 +4054,8 @@ +@@ -3204,6 +4056,8 @@ SparseCPU: sub_out_sparse SparseCUDA: sub_out_sparse supports_named_tensor: True @@ -3530,7 +3537,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: sub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor use_c10_dispatcher: full -@@ -3213,6 +4065,8 @@ +@@ -3213,6 +4067,8 @@ CUDA: sub SparseCPU: sub_sparse SparseCUDA: sub_sparse @@ -3539,7 +3546,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: sub_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!) -@@ -3222,6 +4076,8 @@ +@@ -3222,6 +4078,8 @@ CUDA: sub_ SparseCPU: sub_sparse_ SparseCUDA: sub_sparse_ @@ -3548,7 +3555,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True # For C++ only, until we have conversion from C++ numbers to Tensor -@@ -3229,21 +4085,29 @@ +@@ -3229,21 +4087,29 @@ use_c10_dispatcher: full variants: function, method supports_named_tensor: True @@ -3578,7 +3585,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Functionally the same as addmm, but we give it a different derivative formula # that doesn't propagate gradients to non-present entries on sparse. -@@ -3257,6 +4121,8 @@ +@@ -3257,6 +4123,8 @@ CUDA: legacy::cuda::_th_addmm_out SparseCPU: addmm_out_sparse_dense_cpu SparseCUDA: addmm_out_sparse_dense_cuda @@ -3587,7 +3594,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor -@@ -3267,6 +4133,8 @@ +@@ -3267,6 +4135,8 @@ CUDA: legacy::cuda::_th_addmm SparseCPU: addmm_sparse_dense_cpu SparseCUDA: addmm_sparse_dense_cuda @@ -3596,7 +3603,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: addmm_(Tensor(a!) self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!) -@@ -3278,9 +4146,10 @@ +@@ -3278,9 +4148,10 @@ # broadcasting SparseCPU: s_addmm_sparse_dense_cpu_ SparseCUDA: s_addmm_sparse_dense_cuda_ @@ -3608,7 +3615,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # NOTE [ Sparse: autograd and API ] # # -@@ -3396,7 +4265,6 @@ +@@ -3396,7 +4267,6 @@ # shared. In other words, their outputs are non-differentiable views of the # sparse tensor. @@ -3616,7 +3623,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # FIXME: would be nicer if TensorOptions was optional based; not adding default arguments for options given # the default would never make sense. - func: sparse_coo_tensor.size(int[] size, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False) -> Tensor -@@ -3433,7 +4301,6 @@ +@@ -3433,7 +4303,6 @@ SparseCUDA: sparse_resize_and_clear_ requires_tensor: True @@ -3624,7 +3631,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: sparse_mask(Tensor self, Tensor mask) -> Tensor use_c10_dispatcher: full variants: method -@@ -3442,7 +4309,6 @@ +@@ -3442,7 +4311,6 @@ SparseCUDA: sparse_mask_cuda requires_tensor: True @@ -3632,7 +3639,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: to_dense(Tensor self) -> Tensor use_c10_dispatcher: full variants: method -@@ -3474,7 +4340,6 @@ +@@ -3474,7 +4342,6 @@ requires_tensor: True device_guard: False @@ -3640,7 +3647,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: dense_dim(Tensor self) -> int use_c10_dispatcher: full variants: method -@@ -3494,7 +4359,6 @@ +@@ -3494,7 +4361,6 @@ requires_tensor: True device_guard: False @@ -3648,7 +3655,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _nnz(Tensor self) -> int use_c10_dispatcher: full variants: method -@@ -3504,7 +4368,6 @@ +@@ -3504,7 +4370,6 @@ requires_tensor: True device_guard: False @@ -3656,7 +3663,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: coalesce(Tensor self) -> Tensor use_c10_dispatcher: full variants: method -@@ -3513,7 +4376,6 @@ +@@ -3513,7 +4378,6 @@ SparseCUDA: coalesce_sparse_cuda requires_tensor: True @@ -3664,7 +3671,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: is_coalesced(Tensor self) -> bool use_c10_dispatcher: full variants: method -@@ -3524,7 +4386,6 @@ +@@ -3524,7 +4388,6 @@ device_guard: False supports_named_tensor: True @@ -3672,7 +3679,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _indices(Tensor(a) self) -> Tensor(a) variants: method dispatch: -@@ -3568,7 +4429,6 @@ +@@ -3568,7 +4431,6 @@ requires_tensor: True device_guard: False @@ -3680,7 +3687,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: hspmm.out(Tensor mat1, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) dispatch: SparseCPU: hspmm_out_sparse_cpu -@@ -3630,11 +4490,15 @@ +@@ -3630,11 +4492,15 @@ variants: function dispatch: CPU: quantize_per_tensor_cpu @@ -3696,7 +3703,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: dequantize(Tensor self) -> Tensor use_c10_dispatcher: full -@@ -3713,20 +4577,28 @@ +@@ -3713,20 +4579,28 @@ variants: method device_guard: False supports_named_tensor: True @@ -3725,7 +3732,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: meshgrid(Tensor[] tensors) -> Tensor[] -@@ -3765,6 +4637,8 @@ +@@ -3765,6 +4639,8 @@ dispatch: CPU: _local_scalar_dense_cpu CUDA: _local_scalar_dense_cuda @@ -3734,7 +3741,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= variants: function supports_named_tensor: True -@@ -3791,10 +4665,16 @@ +@@ -3791,10 +4667,16 @@ # RNN cells and layers - func: lstm.input(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor, Tensor) @@ -3751,7 +3758,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: gru.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor) -@@ -3839,10 +4719,14 @@ +@@ -3839,10 +4721,14 @@ # PackedSequence utilities - func: _pack_padded_sequence(Tensor input, Tensor lengths, bool batch_first) -> (Tensor, Tensor) @@ -3766,7 +3773,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # wrappers for legacy TH methods -@@ -3852,6 +4736,8 @@ +@@ -3852,6 +4738,8 @@ dispatch: CPU: set_ CUDA: set_ @@ -3775,7 +3782,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: set_.source_Storage_storage_offset(Tensor(a!) self, Storage source, int storage_offset, int[] size, int[] stride=[]) -> Tensor(a!) variants: method -@@ -3860,6 +4746,8 @@ +@@ -3860,6 +4748,8 @@ CPU: legacy::cpu::_th_set_ CUDA: legacy::cuda::_th_set_ QuantizedCPU: set_storage @@ -3784,7 +3791,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: set_.source_Tensor(Tensor(a!) self, Tensor source) -> Tensor(a!) variants: method -@@ -3867,12 +4755,16 @@ +@@ -3867,12 +4757,16 @@ dispatch: CPU: set_tensor_ CUDA: set_tensor_ @@ -3801,7 +3808,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: set_quantizer_(Tensor(a!) self, ConstQuantizerPtr quantizer) -> Tensor(a!) variants: method -@@ -3892,6 +4784,8 @@ +@@ -3892,6 +4786,8 @@ dispatch: CPU: masked_fill__cpu CUDA: masked_fill__cuda @@ -3810,7 +3817,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: masked_fill.Scalar(Tensor self, Tensor mask, Scalar value) -> Tensor -@@ -3904,6 +4798,8 @@ +@@ -3904,6 +4800,8 @@ dispatch: CPU: masked_fill__cpu CUDA: masked_fill__cuda @@ -3819,7 +3826,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: masked_fill.Tensor(Tensor self, Tensor mask, Tensor value) -> Tensor -@@ -3916,6 +4812,8 @@ +@@ -3916,6 +4814,8 @@ dispatch: CPU: masked_scatter__cpu CUDA: masked_scatter__cuda @@ -3828,7 +3835,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: masked_scatter(Tensor self, Tensor mask, Tensor source) -> Tensor use_c10_dispatcher: full -@@ -3929,25 +4827,35 @@ +@@ -3929,25 +4829,35 @@ CUDA: view MkldnnCPU: mkldnn_view QuantizedCPU: view @@ -3864,7 +3871,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: index_fill_.int_Scalar(Tensor(a!) self, int dim, Tensor index, Scalar value) -> Tensor(a!) variants: method -@@ -3955,11 +4863,15 @@ +@@ -3955,11 +4865,15 @@ dispatch: CPU: legacy::cpu::_th_index_fill_ CUDA: legacy::cuda::_th_index_fill_ @@ -3880,7 +3887,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: index_fill_.int_Tensor(Tensor(a!) self, int dim, Tensor index, Tensor value) -> Tensor(a!) variants: method -@@ -3967,11 +4879,15 @@ +@@ -3967,11 +4881,15 @@ CPU: index_fill_ CUDA: index_fill_ supports_named_tensor: True @@ -3896,7 +3903,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: index_fill_.Dimname_Scalar(Tensor(a!) self, Dimname dim, Tensor index, Scalar value) -> Tensor(a!) variants: method -@@ -3994,6 +4910,8 @@ +@@ -3994,6 +4912,8 @@ dispatch: CPU: scatter_cpu_ CUDA: legacy::cuda::_th_scatter_ @@ -3905,7 +3912,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: scatter.src(Tensor self, int dim, Tensor index, Tensor src) -> Tensor use_c10_dispatcher: full -@@ -4004,6 +4922,8 @@ +@@ -4004,6 +4924,8 @@ dispatch: CPU: scatter_fill_cpu_ CUDA: legacy::cuda::_th_scatter_ @@ -3914,7 +3921,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: scatter.value(Tensor self, int dim, Tensor index, Scalar value) -> Tensor use_c10_dispatcher: full -@@ -4020,81 +4940,127 @@ +@@ -4020,81 +4942,127 @@ dispatch: CPU: scatter_add_cpu_ CUDA: legacy::cuda::_th_scatter_add_ @@ -4042,7 +4049,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: __iand__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) variants: method -@@ -4107,70 +5073,106 @@ +@@ -4107,70 +5075,106 @@ dispatch: CPU: bitwise_or_out CUDA: bitwise_or_out @@ -4149,7 +4156,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: __ixor__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) variants: method -@@ -4184,6 +5186,8 @@ +@@ -4184,6 +5188,8 @@ dispatch: CPU: __lshift__ CUDA: __lshift__ @@ -4158,7 +4165,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: __lshift__.Tensor(Tensor self, Tensor other) -> Tensor use_c10_dispatcher: full -@@ -4191,18 +5195,24 @@ +@@ -4191,18 +5197,24 @@ dispatch: CPU: __lshift__ CUDA: __lshift__ @@ -4183,7 +4190,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: __rshift__.Scalar(Tensor self, Scalar other) -> Tensor use_c10_dispatcher: full -@@ -4210,6 +5220,8 @@ +@@ -4210,6 +5222,8 @@ dispatch: CPU: __rshift__ CUDA: __rshift__ @@ -4192,7 +4199,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: __rshift__.Tensor(Tensor self, Tensor other) -> Tensor use_c10_dispatcher: full -@@ -4217,18 +5229,24 @@ +@@ -4217,18 +5231,24 @@ dispatch: CPU: __rshift__ CUDA: __rshift__ @@ -4217,7 +4224,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: lgamma_(Tensor(a!) self) -> Tensor(a!) supports_named_tensor: True -@@ -4240,18 +5258,24 @@ +@@ -4240,18 +5260,24 @@ - func: atan2_(Tensor(a!) self, Tensor other) -> Tensor(a!) supports_named_tensor: True variants: method @@ -4242,7 +4249,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: digamma_(Tensor(a!) self) -> Tensor(a!) supports_named_tensor: True -@@ -4266,6 +5290,8 @@ +@@ -4266,6 +5292,8 @@ dispatch: CPU: legacy::cpu::_th_renorm_ CUDA: legacy::cuda::_th_renorm_ @@ -4251,7 +4258,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: pow_.Scalar(Tensor(a!) self, Scalar exponent) -> Tensor(a!) supports_named_tensor: True -@@ -4273,6 +5299,8 @@ +@@ -4273,6 +5301,8 @@ dispatch: CPU: pow_ CUDA: pow_ @@ -4260,7 +4267,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: pow_.Tensor(Tensor(a!) self, Tensor exponent) -> Tensor(a!) supports_named_tensor: True -@@ -4280,53 +5308,71 @@ +@@ -4280,53 +5310,71 @@ dispatch: CPU: pow_ CUDA: pow_ @@ -4332,7 +4339,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: addbmm(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor use_c10_dispatcher: full -@@ -4334,28 +5380,40 @@ +@@ -4334,28 +5382,40 @@ dispatch: CPU: legacy::cpu::_th_addbmm CUDA: legacy::cuda::_th_addbmm @@ -4373,7 +4380,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: cauchy_(Tensor(a!) self, float median=0, float sigma=1, *, Generator? generator=None) -> Tensor(a!) -@@ -4380,6 +5438,8 @@ +@@ -4380,6 +5440,8 @@ dispatch: CPU: legacy::cpu::_th_diag_out CUDA: legacy::cuda::_th_diag_out @@ -4382,7 +4389,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: diag(Tensor self, int diagonal=0) -> Tensor use_c10_dispatcher: full -@@ -4387,40 +5447,58 @@ +@@ -4387,40 +5449,58 @@ dispatch: CPU: legacy::cpu::_th_diag CUDA: legacy::cuda::_th_diag @@ -4441,7 +4448,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: trace(Tensor self) -> Tensor use_c10_dispatcher: full -@@ -4435,6 +5513,8 @@ +@@ -4435,6 +5515,8 @@ CPU: ne_out CUDA: ne_out QuantizedCPU: ne_out_quantized_cpu @@ -4450,7 +4457,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: ne.Scalar(Tensor self, Scalar other) -> Tensor supports_named_tensor: True -@@ -4444,6 +5524,8 @@ +@@ -4444,6 +5526,8 @@ CPU: ne CUDA: ne QuantizedCPU: ne_quantized_cpu @@ -4459,7 +4466,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: ne.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True -@@ -4451,6 +5533,8 @@ +@@ -4451,6 +5535,8 @@ CPU: ne_out CUDA: ne_out QuantizedCPU: ne_out_quantized_cpu @@ -4468,7 +4475,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: ne.Tensor(Tensor self, Tensor other) -> Tensor supports_named_tensor: True -@@ -4460,6 +5544,8 @@ +@@ -4460,6 +5546,8 @@ CPU: ne CUDA: ne QuantizedCPU: ne_quantized_cpu @@ -4477,7 +4484,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: eq.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True -@@ -4467,6 +5553,8 @@ +@@ -4467,6 +5555,8 @@ CPU: eq_out CUDA: eq_out QuantizedCPU: eq_out_quantized_cpu @@ -4486,7 +4493,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: eq.Scalar(Tensor self, Scalar other) -> Tensor supports_named_tensor: True -@@ -4476,6 +5564,8 @@ +@@ -4476,6 +5566,8 @@ CPU: eq CUDA: eq QuantizedCPU: eq_quantized_cpu @@ -4495,7 +4502,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: eq.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True -@@ -4483,6 +5573,8 @@ +@@ -4483,6 +5575,8 @@ CPU: eq_out CUDA: eq_out QuantizedCPU: eq_out_quantized_cpu @@ -4504,7 +4511,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: eq.Tensor(Tensor self, Tensor other) -> Tensor supports_named_tensor: True -@@ -4492,6 +5584,8 @@ +@@ -4492,6 +5586,8 @@ CPU: eq CUDA: eq QuantizedCPU: eq_quantized_cpu @@ -4513,7 +4520,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: ge.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True -@@ -4499,6 +5593,8 @@ +@@ -4499,6 +5595,8 @@ CPU: ge_out CUDA: ge_out QuantizedCPU: ge_out_quantized_cpu @@ -4522,7 +4529,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: ge.Scalar(Tensor self, Scalar other) -> Tensor supports_named_tensor: True -@@ -4508,6 +5604,8 @@ +@@ -4508,6 +5606,8 @@ CPU: ge CUDA: ge QuantizedCPU: ge_quantized_cpu @@ -4531,7 +4538,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: ge.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True -@@ -4515,6 +5613,8 @@ +@@ -4515,6 +5615,8 @@ CPU: ge_out CUDA: ge_out QuantizedCPU: ge_out_quantized_cpu @@ -4540,7 +4547,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: ge.Tensor(Tensor self, Tensor other) -> Tensor supports_named_tensor: True -@@ -4524,6 +5624,8 @@ +@@ -4524,6 +5626,8 @@ CPU: ge CUDA: ge QuantizedCPU: ge_quantized_cpu @@ -4549,7 +4556,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: le.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True -@@ -4531,6 +5633,8 @@ +@@ -4531,6 +5635,8 @@ CPU: le_out CUDA: le_out QuantizedCPU: le_out_quantized_cpu @@ -4558,7 +4565,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: le.Scalar(Tensor self, Scalar other) -> Tensor supports_named_tensor: True -@@ -4540,6 +5644,8 @@ +@@ -4540,6 +5646,8 @@ CPU: le CUDA: le QuantizedCPU: le_quantized_cpu @@ -4567,7 +4574,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: le.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True -@@ -4547,6 +5653,8 @@ +@@ -4547,6 +5655,8 @@ CPU: le_out CUDA: le_out QuantizedCPU: le_out_quantized_cpu @@ -4576,7 +4583,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: le.Tensor(Tensor self, Tensor other) -> Tensor supports_named_tensor: True -@@ -4556,6 +5664,8 @@ +@@ -4556,6 +5666,8 @@ CPU: le CUDA: le QuantizedCPU: le_quantized_cpu @@ -4585,7 +4592,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: gt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True -@@ -4563,6 +5673,8 @@ +@@ -4563,6 +5675,8 @@ CPU: gt_out CUDA: gt_out QuantizedCPU: gt_out_quantized_cpu @@ -4594,7 +4601,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: gt.Scalar(Tensor self, Scalar other) -> Tensor supports_named_tensor: True -@@ -4572,6 +5684,8 @@ +@@ -4572,6 +5686,8 @@ CPU: gt CUDA: gt QuantizedCPU: gt_quantized_cpu @@ -4603,7 +4610,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: gt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True -@@ -4579,6 +5693,8 @@ +@@ -4579,6 +5695,8 @@ CPU: gt_out CUDA: gt_out QuantizedCPU: gt_out_quantized_cpu @@ -4612,7 +4619,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: gt.Tensor(Tensor self, Tensor other) -> Tensor supports_named_tensor: True -@@ -4588,6 +5704,8 @@ +@@ -4588,6 +5706,8 @@ CPU: gt CUDA: gt QuantizedCPU: gt_quantized_cpu @@ -4621,7 +4628,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: lt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True -@@ -4595,6 +5713,8 @@ +@@ -4595,6 +5715,8 @@ CPU: lt_out CUDA: lt_out QuantizedCPU: lt_out_quantized_cpu @@ -4630,7 +4637,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: lt.Scalar(Tensor self, Scalar other) -> Tensor supports_named_tensor: True -@@ -4604,6 +5724,8 @@ +@@ -4604,6 +5726,8 @@ CPU: lt CUDA: lt QuantizedCPU: lt_quantized_cpu @@ -4639,7 +4646,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: lt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True -@@ -4611,6 +5733,8 @@ +@@ -4611,6 +5735,8 @@ CPU: lt_out CUDA: lt_out QuantizedCPU: lt_out_quantized_cpu @@ -4648,7 +4655,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: lt.Tensor(Tensor self, Tensor other) -> Tensor supports_named_tensor: True -@@ -4620,11 +5744,16 @@ +@@ -4620,11 +5746,16 @@ CPU: lt CUDA: lt QuantizedCPU: lt_quantized_cpu @@ -4665,7 +4672,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: take(Tensor self, Tensor index) -> Tensor use_c10_dispatcher: full -@@ -4632,11 +5761,16 @@ +@@ -4632,11 +5763,16 @@ dispatch: CPU: legacy::cpu::_th_take CUDA: legacy::cuda::_th_take @@ -4682,7 +4689,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: index_select(Tensor self, int dim, Tensor index) -> Tensor use_c10_dispatcher: full -@@ -4646,17 +5780,25 @@ +@@ -4646,17 +5782,25 @@ CUDA: legacy::cuda::_th_index_select SparseCPU: index_select_sparse SparseCUDA: index_select_sparse @@ -4708,7 +4715,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: masked_select(Tensor self, Tensor mask) -> Tensor use_c10_dispatcher: full -@@ -4665,11 +5807,15 @@ +@@ -4665,11 +5809,15 @@ CPU: masked_select_cpu CUDA: masked_select_cuda supports_named_tensor: True @@ -4724,7 +4731,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: nonzero(Tensor self) -> Tensor use_c10_dispatcher: full -@@ -4677,6 +5823,8 @@ +@@ -4677,6 +5825,8 @@ dispatch: CPU: legacy::cpu::_th_nonzero CUDA: legacy::cuda::_th_nonzero @@ -4733,7 +4740,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: nonzero_numpy(Tensor self) -> Tensor[] variants: method, function -@@ -4685,6 +5833,8 @@ +@@ -4685,6 +5835,8 @@ dispatch: CPU: gather_out_cpu CUDA: gather_out_cuda @@ -4742,7 +4749,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: gather(Tensor self, int dim, Tensor index, *, bool sparse_grad=False) -> Tensor use_c10_dispatcher: full -@@ -4692,34 +5842,50 @@ +@@ -4692,34 +5844,50 @@ dispatch: CPU: gather_cpu CUDA: gather_cuda @@ -4793,7 +4800,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: lstsq.X(Tensor self, Tensor A, *, Tensor(a!) X, Tensor(b!) qr) -> (Tensor(a!) solution, Tensor(b!) QR) dispatch: -@@ -4742,6 +5908,8 @@ +@@ -4742,6 +5910,8 @@ dispatch: CPU: _triangular_solve_helper_cpu CUDA: _triangular_solve_helper_cuda @@ -4802,7 +4809,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: symeig.e(Tensor self, bool eigenvectors=False, bool upper=True, *, Tensor(a!) e, Tensor(b!) V) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) -@@ -4753,6 +5921,8 @@ +@@ -4753,6 +5923,8 @@ dispatch: CPU: _symeig_helper_cpu CUDA: _symeig_helper_cuda @@ -4811,7 +4818,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: eig.e(Tensor self, bool eigenvectors=False, *, Tensor(a!) e, Tensor(b!) v) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) dispatch: -@@ -4775,6 +5945,8 @@ +@@ -4775,6 +5947,8 @@ dispatch: CPU: _svd_helper_cpu CUDA: _svd_helper_cuda @@ -4820,7 +4827,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: cholesky.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!) -@@ -4826,9 +5998,13 @@ +@@ -4826,9 +6000,13 @@ CUDA: legacy::cuda::_th_potri - func: qr.Q(Tensor self, bool some=True, *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R) @@ -4834,7 +4841,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _qr_helper(Tensor self, bool some) -> (Tensor, Tensor) variants: function -@@ -4891,12 +6067,16 @@ +@@ -4891,12 +6069,16 @@ dispatch: CPU: multinomial_out CUDA: multinomial_out @@ -4851,7 +4858,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _multinomial_alias_setup(Tensor probs) -> (Tensor, Tensor) variants: function -@@ -4947,6 +6127,8 @@ +@@ -4947,6 +6129,8 @@ dispatch: CPU: erfinv CUDA: erfinv @@ -4860,7 +4867,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: erfinv_(Tensor(a!) self) -> Tensor(a!) supports_named_tensor: True -@@ -4954,26 +6136,36 @@ +@@ -4954,26 +6138,36 @@ dispatch: CPU: _erfinv__cpu CUDA: _erfinv__cuda @@ -4897,7 +4904,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: dist(Tensor self, Tensor other, Scalar p=2) -> Tensor use_c10_dispatcher: full -@@ -4981,21 +6173,29 @@ +@@ -4981,21 +6175,29 @@ - func: atan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True @@ -4927,7 +4934,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: lerp.Scalar(Tensor self, Tensor end, Scalar weight) -> Tensor use_c10_dispatcher: full -@@ -5003,6 +6203,8 @@ +@@ -5003,6 +6205,8 @@ dispatch: CPU: lerp_cpu_scalar CUDA: lerp_cuda_scalar @@ -4936,7 +4943,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: lerp.Tensor(Tensor self, Tensor end, Tensor weight) -> Tensor use_c10_dispatcher: full -@@ -5010,6 +6212,8 @@ +@@ -5010,6 +6214,8 @@ dispatch: CPU: lerp_cpu_tensor CUDA: lerp_cuda_tensor @@ -4945,7 +4952,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: histc.out(Tensor self, int bins=100, Scalar min=0, Scalar max=0, *, Tensor(a!) out) -> Tensor(a!) dispatch: -@@ -5027,6 +6231,8 @@ +@@ -5027,6 +6233,8 @@ dispatch: CPU: fmod_out CUDA: legacy::cuda::_th_fmod_out @@ -4954,7 +4961,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: fmod.Scalar(Tensor self, Scalar other) -> Tensor use_c10_dispatcher: full -@@ -5034,11 +6240,15 @@ +@@ -5034,11 +6242,15 @@ dispatch: CPU: fmod CUDA: legacy::cuda::_th_fmod @@ -4970,7 +4977,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: fmod.Tensor(Tensor self, Tensor other) -> Tensor use_c10_dispatcher: full -@@ -5046,11 +6256,15 @@ +@@ -5046,11 +6258,15 @@ dispatch: CPU: fmod CUDA: legacy::cuda::_th_fmod @@ -4986,7 +4993,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: remainder.Scalar(Tensor self, Scalar other) -> Tensor use_c10_dispatcher: full -@@ -5058,11 +6272,15 @@ +@@ -5058,11 +6274,15 @@ dispatch: CPU: remainder CUDA: remainder @@ -5002,7 +5009,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: remainder.Tensor(Tensor self, Tensor other) -> Tensor use_c10_dispatcher: full -@@ -5070,12 +6288,18 @@ +@@ -5070,12 +6290,18 @@ dispatch: CPU: remainder CUDA: remainder @@ -5021,7 +5028,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: min(Tensor self) -> Tensor use_c10_dispatcher: full -@@ -5084,13 +6308,19 @@ +@@ -5084,13 +6310,19 @@ CPU: min CUDA: legacy::cuda::_th_min QuantizedCPU: min_quant @@ -5041,7 +5048,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: max(Tensor self) -> Tensor use_c10_dispatcher: full -@@ -5099,6 +6329,8 @@ +@@ -5099,6 +6331,8 @@ CPU: max CUDA: legacy::cuda::_th_max QuantizedCPU: max_quant @@ -5050,7 +5057,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: median(Tensor self) -> Tensor -@@ -5107,12 +6339,16 @@ +@@ -5107,12 +6341,16 @@ dispatch: CPU: median_cpu CUDA: median_cuda @@ -5067,7 +5074,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: sort(Tensor self, int dim=-1, bool descending=False) -> (Tensor values, Tensor indices) variants: method, function -@@ -5120,23 +6356,45 @@ +@@ -5120,23 +6358,45 @@ CPU: legacy::cpu::_th_sort CUDA: legacy::cuda::_th_sort QuantizedCPU: sort_quant @@ -5113,7 +5120,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: topk(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices) variants: method, function -@@ -5144,11 +6402,15 @@ +@@ -5144,11 +6404,15 @@ CPU: topk CUDA: topk QuantizedCPU: quantized_topk_cpu @@ -5129,7 +5136,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: any(Tensor self) -> Tensor use_c10_dispatcher: full -@@ -5159,11 +6421,15 @@ +@@ -5159,11 +6423,15 @@ CUDA: any SparseCPU: any_sparse SparseCUDA: any_sparse @@ -5145,7 +5152,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: renorm(Tensor self, Scalar p, int dim, Scalar maxnorm) -> Tensor use_c10_dispatcher: full -@@ -5171,6 +6437,8 @@ +@@ -5171,6 +6439,8 @@ dispatch: CPU: legacy::cpu::_th_renorm CUDA: legacy::cuda::_th_renorm @@ -5154,7 +5161,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: unfold(Tensor(a) self, int dimension, int size, int step) -> Tensor(a) variants: method -@@ -5178,6 +6446,8 @@ +@@ -5178,6 +6448,8 @@ dispatch: CPU: unfold CUDA: unfold @@ -5163,7 +5170,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: equal(Tensor self, Tensor other) -> bool use_c10_dispatcher: full -@@ -5186,6 +6456,8 @@ +@@ -5186,6 +6458,8 @@ CPU: legacy::cpu::_th_equal CUDA: legacy::cuda::_th_equal QuantizedCPU: quantized_equal @@ -5172,7 +5179,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: pow.Tensor_Tensor_out(Tensor self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!) -@@ -5193,6 +6465,8 @@ +@@ -5193,6 +6467,8 @@ dispatch: CPU: pow_out CUDA: pow_out @@ -5181,7 +5188,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: pow.Tensor_Tensor(Tensor self, Tensor exponent) -> Tensor use_c10_dispatcher: full -@@ -5201,12 +6475,16 @@ +@@ -5201,12 +6477,16 @@ dispatch: CPU: pow CUDA: pow @@ -5198,7 +5205,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: pow.Scalar(Scalar self, Tensor exponent) -> Tensor use_c10_dispatcher: full -@@ -5214,6 +6492,8 @@ +@@ -5214,6 +6494,8 @@ dispatch: CPU: pow CUDA: pow @@ -5207,7 +5214,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: normal_(Tensor(a!) self, float mean=0, float std=1, *, Generator? generator=None) -> Tensor(a!) variants: method -@@ -5221,40 +6501,58 @@ +@@ -5221,40 +6503,58 @@ CPU: normal_cpu_ CUDA: normal_cuda_ supports_named_tensor: True @@ -5266,7 +5273,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: alias(Tensor(a) self) -> Tensor(a) variants: method, function -@@ -5265,43 +6563,59 @@ +@@ -5265,43 +6565,59 @@ dispatch: CPU: legacy::cpu::_th_addr CUDA: legacy::cuda::_th_addr @@ -5326,7 +5333,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _var(Tensor self, bool unbiased=True) -> Tensor use_c10_dispatcher: full -@@ -5309,6 +6623,8 @@ +@@ -5309,6 +6625,8 @@ CPU: legacy::cpu::_th_var CUDA: legacy::cuda::_th_var supports_named_tensor: True @@ -5335,7 +5342,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _std(Tensor self, bool unbiased=True) -> Tensor use_c10_dispatcher: full -@@ -5321,6 +6637,8 @@ +@@ -5321,6 +6639,8 @@ variants: function dispatch: CUDA: _amp_non_finite_check_and_unscale_cuda_ @@ -5344,7 +5351,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _amp_update_scale(Tensor(a!) growth_tracker, Tensor current_scale, Tensor found_inf, float scale_growth_factor, float scale_backoff_factor, int growth_interval) -> Tensor variants: function -@@ -5332,12 +6650,16 @@ +@@ -5332,12 +6652,16 @@ CPU: _cat_cpu CUDA: cat_cuda QuantizedCPU: quantized_cat @@ -5361,7 +5368,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _mode(Tensor self, int dim=-1, bool keepdim=False) -> (Tensor, Tensor) dispatch: -@@ -5353,36 +6675,50 @@ +@@ -5353,36 +6677,50 @@ dispatch: CPU: legacy::cpu::_th_max CUDA: legacy::cuda::_th_max @@ -5412,7 +5419,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: mse_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction) -> Tensor use_c10_dispatcher: full -@@ -5390,23 +6726,33 @@ +@@ -5390,23 +6728,33 @@ dispatch: CPU: mse_loss_backward CUDA: mse_loss_backward @@ -5446,7 +5453,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: multi_margin_loss.out(Tensor self, Tensor target, Scalar p=1, Scalar margin=1, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) python_module: nn -@@ -5434,22 +6780,30 @@ +@@ -5434,22 +6782,30 @@ - func: multilabel_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) python_module: nn @@ -5477,7 +5484,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: multilabel_margin_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, Tensor is_target, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn -@@ -5466,97 +6820,137 @@ +@@ -5466,97 +6822,137 @@ - func: nll_loss.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!) python_module: nn @@ -5615,7 +5622,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: elu.out(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1, *, Tensor(a!) out) -> Tensor(a!) python_module: nn -@@ -5564,6 +6958,8 @@ +@@ -5564,6 +6960,8 @@ CPU: elu_out CUDA: elu_out QuantizedCPU: quantized_elu_out @@ -5624,7 +5631,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: elu(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor use_c10_dispatcher: full -@@ -5572,16 +6968,22 @@ +@@ -5572,16 +6970,22 @@ CPU: elu CUDA: elu QuantizedCPU: quantized_elu @@ -5647,7 +5654,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: elu_(Tensor(a!) self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor(a!) python_module: nn -@@ -5589,12 +6991,16 @@ +@@ -5589,12 +6993,16 @@ CPU: elu_ CUDA: elu_ QuantizedCPU: quantized_elu_ @@ -5664,7 +5671,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: glu(Tensor self, int dim=-1) -> Tensor use_c10_dispatcher: full -@@ -5602,12 +7008,16 @@ +@@ -5602,12 +7010,16 @@ dispatch: CPU: glu CUDA: legacy::cuda::_thnn_glu_forward @@ -5681,7 +5688,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: glu_backward(Tensor grad_output, Tensor self, int dim) -> Tensor use_c10_dispatcher: full -@@ -5615,20 +7025,30 @@ +@@ -5615,20 +7027,30 @@ dispatch: CPU: glu_backward CUDA: legacy::cuda::_thnn_glu_backward @@ -5712,7 +5719,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: hardtanh.out(Tensor self, Scalar min_val=-1, Scalar max_val=1, *, Tensor(a!) out) -> Tensor(a!) python_module: nn -@@ -5636,6 +7056,8 @@ +@@ -5636,6 +7058,8 @@ CPU: hardtanh_out CUDA: hardtanh_out QuantizedCPU: quantized_hardtanh_out @@ -5721,7 +5728,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -> Tensor use_c10_dispatcher: full -@@ -5644,16 +7066,22 @@ +@@ -5644,16 +7068,22 @@ CPU: hardtanh CUDA: hardtanh QuantizedCPU: quantized_hardtanh @@ -5744,7 +5751,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: hardtanh_(Tensor(a!) self, Scalar min_val=-1, Scalar max_val=1) -> Tensor(a!) python_module: nn -@@ -5661,6 +7089,8 @@ +@@ -5661,6 +7091,8 @@ CPU: hardtanh_ CUDA: hardtanh_ QuantizedCPU: quantized_hardtanh_ @@ -5753,7 +5760,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: leaky_relu.out(Tensor self, Scalar negative_slope=0.01, *, Tensor(a!) out) -> Tensor(a!) python_module: nn -@@ -5668,6 +7098,8 @@ +@@ -5668,6 +7100,8 @@ CPU: leaky_relu_out CUDA: leaky_relu_out QuantizedCPU: quantized_leaky_relu_out @@ -5762,7 +5769,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: leaky_relu(Tensor self, Scalar negative_slope=0.01) -> Tensor use_c10_dispatcher: full -@@ -5676,10 +7108,14 @@ +@@ -5676,10 +7110,14 @@ CPU: leaky_relu CUDA: leaky_relu QuantizedCPU: quantized_leaky_relu @@ -5777,7 +5784,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: leaky_relu_(Tensor(a!) self, Scalar negative_slope=0.01) -> Tensor(a!) python_module: nn -@@ -5687,31 +7123,44 @@ +@@ -5687,31 +7125,44 @@ CPU: leaky_relu_ CUDA: leaky_relu_ QuantizedCPU: quantized_leaky_relu_ @@ -5822,7 +5829,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: log_sigmoid_backward(Tensor grad_output, Tensor self, Tensor buffer) -> Tensor use_c10_dispatcher: full -@@ -5719,62 +7168,88 @@ +@@ -5719,62 +7170,88 @@ dispatch: CPU: log_sigmoid_backward_cpu CUDA: legacy::cuda::_thnn_log_sigmoid_backward @@ -5911,7 +5918,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: adaptive_avg_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!) python_module: nn -@@ -5782,9 +7257,13 @@ +@@ -5782,9 +7259,13 @@ CPU: adaptive_avg_pool2d_out_cpu CUDA: adaptive_avg_pool2d_out_cuda MkldnnCPU: mkldnn_adaptive_avg_pool2d_out @@ -5925,7 +5932,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: mkldnn_adaptive_avg_pool2d(Tensor self, int[2] output_size) -> Tensor dispatch: -@@ -5796,6 +7275,8 @@ +@@ -5796,6 +7277,8 @@ CPU: adaptive_avg_pool2d_cpu CUDA: adaptive_avg_pool2d_cuda QuantizedCPU: quantized_adaptive_avg_pool2d @@ -5934,7 +5941,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _adaptive_avg_pool2d_backward(Tensor grad_output, Tensor self) -> Tensor use_c10_dispatcher: full -@@ -5803,24 +7284,32 @@ +@@ -5803,24 +7286,32 @@ dispatch: CPU: adaptive_avg_pool2d_backward_cpu CUDA: adaptive_avg_pool2d_backward_cuda @@ -5967,7 +5974,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: adaptive_avg_pool3d_backward(Tensor grad_output, Tensor self) -> Tensor use_c10_dispatcher: full -@@ -5828,6 +7317,8 @@ +@@ -5828,6 +7319,8 @@ dispatch: CPU: adaptive_avg_pool3d_backward_cpu CUDA: adaptive_avg_pool3d_backward_cuda @@ -5976,7 +5983,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Return: (Tensor output, Tensor indices) - func: adaptive_max_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) -@@ -5835,6 +7326,8 @@ +@@ -5835,6 +7328,8 @@ dispatch: CPU: adaptive_max_pool2d_out_cpu CUDA: adaptive_max_pool2d_out_cuda @@ -5985,7 +5992,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Return: (Tensor output, Tensor indices) - func: adaptive_max_pool2d(Tensor self, int[2] output_size) -> (Tensor, Tensor) -@@ -5842,12 +7335,16 @@ +@@ -5842,12 +7337,16 @@ dispatch: CPU: adaptive_max_pool2d_cpu CUDA: adaptive_max_pool2d_cuda @@ -6002,7 +6009,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: adaptive_max_pool2d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor use_c10_dispatcher: full -@@ -5855,6 +7352,8 @@ +@@ -5855,6 +7354,8 @@ dispatch: CPU: adaptive_max_pool2d_backward_cpu CUDA: adaptive_max_pool2d_backward_cuda @@ -6011,7 +6018,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Return: (Tensor output, Tensor indices) - func: adaptive_max_pool3d.out(Tensor self, int[3] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) -@@ -5889,6 +7388,8 @@ +@@ -5889,6 +7390,8 @@ CPU: avg_pool2d_out_cpu CUDA: avg_pool2d_out_cuda MkldnnCPU: mkldnn_avg_pool2d_out @@ -6020,7 +6027,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: avg_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> Tensor python_module: nn -@@ -5897,24 +7398,32 @@ +@@ -5897,24 +7400,32 @@ CUDA: avg_pool2d_cuda MkldnnCPU: mkldnn_avg_pool2d QuantizedCPU: quantized_avg_pool2d @@ -6053,7 +6060,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: avg_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> Tensor python_module: nn -@@ -5922,18 +7431,24 @@ +@@ -5922,18 +7433,24 @@ CPU: avg_pool3d_cpu CUDA: avg_pool3d_cuda QuantizedCPU: quantized_avg_pool3d @@ -6078,7 +6085,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Return: (Tensor output, Tensor indices) - func: fractional_max_pool2d.output(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) -@@ -5993,6 +7508,8 @@ +@@ -5993,6 +7510,8 @@ dispatch: CPU: max_pool2d_with_indices_out_cpu CUDA: max_pool2d_with_indices_out_cuda @@ -6087,7 +6094,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Return: (Tensor output, Tensor indices) - func: max_pool2d_with_indices(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor) -@@ -6000,6 +7517,8 @@ +@@ -6000,6 +7519,8 @@ dispatch: CPU: max_pool2d_with_indices_cpu CUDA: max_pool2d_with_indices_cuda @@ -6096,7 +6103,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: max_pool2d_with_indices_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, int[2] dilation, bool ceil_mode, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) -@@ -6007,12 +7526,16 @@ +@@ -6007,12 +7528,16 @@ dispatch: CPU: max_pool2d_with_indices_backward_out_cpu CUDA: max_pool2d_with_indices_backward_out_cuda @@ -6113,7 +6120,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Return: (Tensor output, Tensor indices) - func: max_pool3d_with_indices.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) -@@ -6020,6 +7543,8 @@ +@@ -6020,6 +7545,8 @@ dispatch: CPU: max_pool3d_with_indices_out_cpu CUDA: max_pool3d_with_indices_out_cuda @@ -6122,7 +6129,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Return: (Tensor output, Tensor indices) - func: max_pool3d_with_indices(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor) -@@ -6027,6 +7552,8 @@ +@@ -6027,6 +7554,8 @@ dispatch: CPU: max_pool3d_with_indices_cpu CUDA: max_pool3d_with_indices_cuda @@ -6131,7 +6138,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: max_pool3d_with_indices_backward.grad_input(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, int[3] dilation, bool ceil_mode, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) -@@ -6034,60 +7561,81 @@ +@@ -6034,60 +7563,81 @@ dispatch: CPU: max_pool3d_with_indices_backward_out_cpu CUDA: max_pool3d_with_indices_backward_out_cuda @@ -6213,7 +6220,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: reflection_pad1d.out(Tensor self, int[2] padding, *, Tensor(a!) out) -> Tensor(a!) python_module: nn -@@ -6118,24 +7666,32 @@ +@@ -6118,24 +7668,32 @@ dispatch: CPU: reflection_pad2d_out_cpu CUDA: reflection_pad2d_out_cuda @@ -6246,7 +6253,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: replication_pad1d.out(Tensor self, int[2] padding, *, Tensor(a!) out) -> Tensor(a!) python_module: nn -@@ -6166,24 +7722,32 @@ +@@ -6166,24 +7724,32 @@ dispatch: CPU: replication_pad2d_out_cpu CUDA: replication_pad2d_out_cuda @@ -6279,7 +6286,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: replication_pad3d.out(Tensor self, int[6] padding, *, Tensor(a!) out) -> Tensor(a!) python_module: nn -@@ -6214,12 +7778,16 @@ +@@ -6214,12 +7780,16 @@ dispatch: CPU: upsample_linear1d_out_cpu CUDA: upsample_linear1d_out_cuda @@ -6296,7 +6303,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: upsample_linear1d_backward.grad_input(Tensor grad_output, int[1] output_size, int[3] input_size, bool align_corners, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn -@@ -6232,12 +7800,16 @@ +@@ -6232,12 +7802,16 @@ dispatch: CPU: upsample_linear1d_backward_cpu CUDA: upsample_linear1d_backward_cuda @@ -6313,7 +6320,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: upsample_bilinear2d(Tensor self, int[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor python_module: nn -@@ -6245,96 +7817,128 @@ +@@ -6245,96 +7819,128 @@ CPU: upsample_bilinear2d_cpu CUDA: upsample_bilinear2d_cuda QuantizedCPU: quantized_upsample_bilinear2d_cpu @@ -6442,7 +6449,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: upsample_nearest2d(Tensor self, int[2] output_size, float? scales_h=None, float? scales_w=None) -> Tensor python_module: nn -@@ -6342,24 +7946,32 @@ +@@ -6342,24 +7948,32 @@ CPU: upsample_nearest2d_cpu CUDA: upsample_nearest2d_cuda QuantizedCPU: quantized_upsample_nearest2d_cpu @@ -6475,7 +6482,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: upsample_nearest3d(Tensor self, int[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor python_module: nn -@@ -6367,38 +7979,52 @@ +@@ -6367,38 +7981,52 @@ CPU: upsample_nearest3d_cpu CUDA: upsample_nearest3d_cuda QuantizedCPU: quantized_upsample_nearest3d_cpu @@ -6528,7 +6535,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # What's a thnn_conv_ versus a slow_conv_? # -@@ -6423,24 +8049,32 @@ +@@ -6423,24 +8051,32 @@ dispatch: CPU: slow_conv_transpose2d_out_cpu CUDA: slow_conv_transpose2d_out_cuda @@ -6561,7 +6568,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: slow_conv_transpose3d.out(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] output_padding=0, int[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!) python_module: nn -@@ -6468,21 +8102,29 @@ +@@ -6468,21 +8104,29 @@ - func: thnn_conv2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, *, Tensor(a!) out) -> Tensor(a!) python_module: nn @@ -6591,7 +6598,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: thnn_conv2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor weight, int[2] kernel_size, int[2] stride, int[2] padding, Tensor finput, Tensor fgrad_input, *, Tensor(a!)? grad_input, Tensor(b!)? grad_weight, Tensor(c!)? grad_bias) -> (Tensor(a!), Tensor(b!), Tensor(c!)) python_module: nn -@@ -6495,48 +8137,70 @@ +@@ -6495,48 +8139,70 @@ dispatch: CPU: slow_conv2d_backward_cpu CUDA: legacy::cuda::_thnn_conv2d_backward @@ -6662,7 +6669,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: slow_conv3d_backward.grad_input(Tensor grad_output, Tensor self, Tensor weight, int[3] kernel_size, int[3] stride, int[3] padding, Tensor finput, Tensor fgrad_input, *, Tensor(a!)? grad_input, Tensor(b!)? grad_weight, Tensor(c!)? grad_bias) -> (Tensor(a!), Tensor(b!), Tensor(c!)) python_module: nn -@@ -6553,12 +8217,16 @@ +@@ -6553,12 +8219,16 @@ dispatch: CPU: slow_conv_dilated2d_cpu CUDA: slow_conv_dilated2d_cuda @@ -6679,7 +6686,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: slow_conv_dilated3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1) -> Tensor python_module: nn -@@ -6577,57 +8245,471 @@ +@@ -6577,57 +8247,471 @@ dispatch: CPU: col2im_out_cpu CUDA: col2im_out_cuda @@ -7154,7 +7161,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S --- pytorch-v1.5.0/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-12-15 23:03:16.029226295 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-12-16 12:02:40.327263040 +0800 @@ -659,14 +659,14 @@ SUB x1, x1, 4 @@ -7180,7 +7187,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CMP x1, 2 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorCompare.cpp pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorCompare.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp 2021-12-15 23:03:16.009226227 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp 2021-12-16 12:02:40.307263122 +0800 @@ -64,7 +64,7 @@ Tensor isinf(const Tensor &self) { @@ -7192,7 +7199,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return AT_DISPATCH_FLOATING_TYPES_AND_HALF(self.scalar_type(), "isinf", [&]() { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorFactories.cpp pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorFactories.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp 2021-12-15 23:03:16.009226227 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp 2021-12-16 12:02:40.307263122 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7237,7 +7244,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorProperties.cpp pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorProperties.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp 2021-12-15 23:03:16.009226227 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp 2021-12-16 12:02:40.307263122 +0800 @@ -87,6 +87,7 @@ if (self.is_contiguous(memory_format)) { return self; @@ -7248,7 +7255,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= "preserve memory format is unsupported by the contiguous operator"); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/UpSampleBicubic2d.cpp pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp --- pytorch-v1.5.0/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-12-15 23:03:16.009226227 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-12-16 12:02:40.311263106 +0800 @@ -26,7 +26,7 @@ const scalar_t* in = &idata[output_y * input_width + output_x]; scalar_t* out = &odata[output_y * output_width + output_x]; @@ -7260,7 +7267,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= out += output_width * output_height; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native_parse.py pytorch-develop-150/aten/src/ATen/native_parse.py --- pytorch-v1.5.0/aten/src/ATen/native_parse.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native_parse.py 2021-12-15 23:03:16.033226307 +0800 ++++ pytorch-develop-150/aten/src/ATen/native_parse.py 2021-12-16 12:02:40.335263006 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -7298,7 +7305,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= msg = '''Exception raised in processing function: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/preprocess_declarations.py pytorch-develop-150/aten/src/ATen/preprocess_declarations.py --- pytorch-v1.5.0/aten/src/ATen/preprocess_declarations.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/preprocess_declarations.py 2021-12-15 23:03:16.033226307 +0800 ++++ pytorch-develop-150/aten/src/ATen/preprocess_declarations.py 2021-12-16 12:02:40.335263006 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -7330,7 +7337,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/templates/TensorBody.h pytorch-develop-150/aten/src/ATen/templates/TensorBody.h --- pytorch-v1.5.0/aten/src/ATen/templates/TensorBody.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/templates/TensorBody.h 2021-12-15 23:03:16.033226307 +0800 ++++ pytorch-develop-150/aten/src/ATen/templates/TensorBody.h 2021-12-16 12:02:40.335263006 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7363,7 +7370,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/templates/TensorMethods.h pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h --- pytorch-v1.5.0/aten/src/ATen/templates/TensorMethods.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h 2021-12-15 23:03:16.033226307 +0800 ++++ pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h 2021-12-16 12:02:40.335263006 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7397,7 +7404,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/CMakeLists.txt pytorch-develop-150/aten/src/TH/CMakeLists.txt --- pytorch-v1.5.0/aten/src/TH/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/CMakeLists.txt 2021-12-15 23:03:16.037226321 +0800 ++++ pytorch-develop-150/aten/src/TH/CMakeLists.txt 2021-12-16 12:02:40.335263006 +0800 @@ -48,6 +48,11 @@ ${CMAKE_CURRENT_SOURCE_DIR} PARENT_SCOPE) @@ -7412,7 +7419,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/generic/THStorage.cpp pytorch-develop-150/aten/src/TH/generic/THStorage.cpp --- pytorch-v1.5.0/aten/src/TH/generic/THStorage.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/generic/THStorage.cpp 2021-12-15 23:03:16.037226321 +0800 ++++ pytorch-develop-150/aten/src/TH/generic/THStorage.cpp 2021-12-16 12:02:40.339262990 +0800 @@ -1,9 +1,32 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7521,7 +7528,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/generic/THStorage.h pytorch-develop-150/aten/src/TH/generic/THStorage.h --- pytorch-v1.5.0/aten/src/TH/generic/THStorage.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/generic/THStorage.h 2021-12-15 23:03:16.037226321 +0800 ++++ pytorch-develop-150/aten/src/TH/generic/THStorage.h 2021-12-16 12:02:40.339262990 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7560,7 +7567,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/CMakeLists.txt pytorch-develop-150/c10/CMakeLists.txt --- pytorch-v1.5.0/c10/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/CMakeLists.txt 2021-12-15 23:03:16.045226348 +0800 ++++ pytorch-develop-150/c10/CMakeLists.txt 2021-12-16 12:02:40.343262973 +0800 @@ -63,6 +63,14 @@ message(STATUS "don't use NUMA") endif() @@ -7589,7 +7596,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # not checked in diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Backend.h pytorch-develop-150/c10/core/Backend.h --- pytorch-v1.5.0/c10/core/Backend.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Backend.h 2021-12-15 23:03:16.045226348 +0800 ++++ pytorch-develop-150/c10/core/Backend.h 2021-12-16 12:02:40.343262973 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7686,7 +7693,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case Backend::CUDA: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Device.cpp pytorch-develop-150/c10/core/Device.cpp --- pytorch-v1.5.0/c10/core/Device.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Device.cpp 2021-12-15 23:03:16.045226348 +0800 ++++ pytorch-develop-150/c10/core/Device.cpp 2021-12-16 12:02:40.343262973 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7726,7 +7733,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= types.begin(), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Device.h pytorch-develop-150/c10/core/Device.h --- pytorch-v1.5.0/c10/core/Device.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Device.h 2021-12-15 23:03:16.045226348 +0800 ++++ pytorch-develop-150/c10/core/Device.h 2021-12-16 12:02:40.343262973 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7761,7 +7768,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return type_ == DeviceType::CPU; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DeviceType.cpp pytorch-develop-150/c10/core/DeviceType.cpp --- pytorch-v1.5.0/c10/core/DeviceType.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DeviceType.cpp 2021-12-15 23:03:16.045226348 +0800 ++++ pytorch-develop-150/c10/core/DeviceType.cpp 2021-12-16 12:02:40.343262973 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7801,7 +7808,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return false; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DeviceType.h pytorch-develop-150/c10/core/DeviceType.h --- pytorch-v1.5.0/c10/core/DeviceType.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DeviceType.h 2021-12-15 23:03:16.045226348 +0800 ++++ pytorch-develop-150/c10/core/DeviceType.h 2021-12-16 12:02:40.343262973 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7844,7 +7851,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= constexpr DeviceType kXLA = DeviceType::XLA; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DispatchKey.cpp pytorch-develop-150/c10/core/DispatchKey.cpp --- pytorch-v1.5.0/c10/core/DispatchKey.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DispatchKey.cpp 2021-12-15 23:03:16.045226348 +0800 ++++ pytorch-develop-150/c10/core/DispatchKey.cpp 2021-12-16 12:02:40.343262973 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7876,7 +7883,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::SparseCPUTensorId: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DispatchKey.h pytorch-develop-150/c10/core/DispatchKey.h --- pytorch-v1.5.0/c10/core/DispatchKey.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DispatchKey.h 2021-12-15 23:03:16.045226348 +0800 ++++ pytorch-develop-150/c10/core/DispatchKey.h 2021-12-16 12:02:40.343262973 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7908,7 +7915,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Storage.h pytorch-develop-150/c10/core/Storage.h --- pytorch-v1.5.0/c10/core/Storage.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Storage.h 2021-12-15 23:03:16.045226348 +0800 ++++ pytorch-develop-150/c10/core/Storage.h 2021-12-16 12:02:40.343262973 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7942,7 +7949,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/StorageImpl.cpp pytorch-develop-150/c10/core/StorageImpl.cpp --- pytorch-v1.5.0/c10/core/StorageImpl.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/StorageImpl.cpp 2021-12-15 23:03:16.045226348 +0800 ++++ pytorch-develop-150/c10/core/StorageImpl.cpp 2021-12-16 12:02:40.343262973 +0800 @@ -1 +1,18 @@ #include + @@ -7964,7 +7971,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +} // namespace c10 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/StorageImpl.h pytorch-develop-150/c10/core/StorageImpl.h --- pytorch-v1.5.0/c10/core/StorageImpl.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/StorageImpl.h 2021-12-15 23:03:16.045226348 +0800 ++++ pytorch-develop-150/c10/core/StorageImpl.h 2021-12-16 12:02:40.347262957 +0800 @@ -1,11 +1,55 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8091,7 +8098,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/TensorImpl.h pytorch-develop-150/c10/core/TensorImpl.h --- pytorch-v1.5.0/c10/core/TensorImpl.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/TensorImpl.h 2021-12-15 23:03:16.045226348 +0800 ++++ pytorch-develop-150/c10/core/TensorImpl.h 2021-12-16 12:02:40.347262957 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8159,7 +8166,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/TensorOptions.h pytorch-develop-150/c10/core/TensorOptions.h --- pytorch-v1.5.0/c10/core/TensorOptions.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/TensorOptions.h 2021-12-15 23:03:16.045226348 +0800 ++++ pytorch-develop-150/c10/core/TensorOptions.h 2021-12-16 12:02:40.347262957 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8200,7 +8207,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } else if (tid == DispatchKey::HIPTensorId) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/cuda/CMakeLists.txt pytorch-develop-150/c10/cuda/CMakeLists.txt --- pytorch-v1.5.0/c10/cuda/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/cuda/CMakeLists.txt 2021-12-15 23:03:16.045226348 +0800 ++++ pytorch-develop-150/c10/cuda/CMakeLists.txt 2021-12-16 12:02:40.347262957 +0800 @@ -24,6 +24,7 @@ CUDACachingAllocator.cpp impl/CUDAGuardImpl.cpp @@ -8219,7 +8226,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= torch_cuda_based_add_library(c10_cuda ${C10_CUDA_SRCS} ${C10_CUDA_HEADERS}) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/macros/Export.h pytorch-develop-150/c10/macros/Export.h --- pytorch-v1.5.0/c10/macros/Export.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/macros/Export.h 2021-12-15 23:03:16.045226348 +0800 ++++ pytorch-develop-150/c10/macros/Export.h 2021-12-16 12:02:40.347262957 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8346,7 +8353,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/caffe2/CMakeLists.txt pytorch-develop-150/caffe2/CMakeLists.txt --- pytorch-v1.5.0/caffe2/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/caffe2/CMakeLists.txt 2021-12-15 23:03:16.049226361 +0800 ++++ pytorch-develop-150/caffe2/CMakeLists.txt 2021-12-16 12:02:40.351262940 +0800 @@ -32,6 +32,7 @@ # Add source, includes, and libs to lists list(APPEND Caffe2_CPU_SRCS ${ATen_CPU_SRCS}) @@ -8501,7 +8508,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Call again since Caffe2_HIP_INCLUDE is extended with ATen include dirs. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/.clang-format pytorch-develop-150/.clang-format --- pytorch-v1.5.0/.clang-format 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/.clang-format 2021-12-15 23:03:15.993226174 +0800 ++++ pytorch-develop-150/.clang-format 2021-12-16 12:02:40.295263171 +0800 @@ -84,5 +84,4 @@ SpacesInSquareBrackets: false Standard: Cpp11 @@ -8512,7 +8519,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/BuildVariables.cmake pytorch-develop-150/cmake/BuildVariables.cmake --- pytorch-v1.5.0/cmake/BuildVariables.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/BuildVariables.cmake 2021-12-15 23:03:16.109226563 +0800 ++++ pytorch-develop-150/cmake/BuildVariables.cmake 2021-12-16 12:02:40.403262726 +0800 @@ -11,6 +11,7 @@ # CMakeLists.txt files under each folder respectively. set(Caffe2_CPU_SRCS) @@ -8539,7 +8546,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # symbols. However, if the lib is whole linked in caffe2 lib, we don't want diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Codegen.cmake pytorch-develop-150/cmake/Codegen.cmake --- pytorch-v1.5.0/cmake/Codegen.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Codegen.cmake 2021-12-15 23:03:16.109226563 +0800 ++++ pytorch-develop-150/cmake/Codegen.cmake 2021-12-16 12:02:40.403262726 +0800 @@ -191,13 +191,14 @@ file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt generated_cpp) file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt-cuda cuda_generated_cpp) @@ -8570,7 +8577,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endif() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Dependencies.cmake pytorch-develop-150/cmake/Dependencies.cmake --- pytorch-v1.5.0/cmake/Dependencies.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Dependencies.cmake 2021-12-15 23:03:16.109226563 +0800 ++++ pytorch-develop-150/cmake/Dependencies.cmake 2021-12-16 12:02:40.403262726 +0800 @@ -1509,6 +1509,13 @@ ENDIF(NOT C_HAS_THREAD) endif() @@ -8587,7 +8594,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Summary.cmake pytorch-develop-150/cmake/Summary.cmake --- pytorch-v1.5.0/cmake/Summary.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Summary.cmake 2021-12-15 23:03:16.109226563 +0800 ++++ pytorch-develop-150/cmake/Summary.cmake 2021-12-16 12:02:40.407262709 +0800 @@ -134,6 +134,7 @@ if(NOT "${SELECTED_OP_LIST}" STREQUAL "") message(STATUS " SELECTED_OP_LIST : ${SELECTED_OP_LIST}") @@ -8598,7 +8605,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endfunction() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/TorchConfig.cmake.in pytorch-develop-150/cmake/TorchConfig.cmake.in --- pytorch-v1.5.0/cmake/TorchConfig.cmake.in 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/TorchConfig.cmake.in 2021-12-15 23:03:16.109226563 +0800 ++++ pytorch-develop-150/cmake/TorchConfig.cmake.in 2021-12-16 12:02:40.407262709 +0800 @@ -112,6 +112,11 @@ list(APPEND TORCH_LIBRARIES ${TORCH_CUDA_LIBRARIES}) endif() @@ -8613,7 +8620,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(TORCH_CXX_FLAGS "-D_GLIBCXX_USE_CXX11_ABI=@GLIBCXX_USE_CXX11_ABI@") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/CMakeLists.txt pytorch-develop-150/CMakeLists.txt --- pytorch-v1.5.0/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/CMakeLists.txt 2021-12-15 23:03:15.993226174 +0800 ++++ pytorch-develop-150/CMakeLists.txt 2021-12-16 12:02:40.295263171 +0800 @@ -205,6 +205,10 @@ option(USE_TBB "Use TBB" OFF) option(ONNX_ML "Enable traditional ONNX ML API." ON) @@ -8680,7 +8687,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-missing-braces") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/CONTRIBUTING.zh.md pytorch-develop-150/CONTRIBUTING.zh.md --- pytorch-v1.5.0/CONTRIBUTING.zh.md 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-150/CONTRIBUTING.zh.md 2021-12-15 23:03:15.993226174 +0800 ++++ pytorch-develop-150/CONTRIBUTING.zh.md 2021-12-16 12:02:40.295263171 +0800 @@ -0,0 +1,228 @@ +# PyTorch贡献指南 +- [贡献者许可协议](#贡献者许可协议.md) @@ -8912,7 +8919,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/.dockerignore pytorch-develop-150/.dockerignore --- pytorch-v1.5.0/.dockerignore 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/.dockerignore 2021-12-15 23:03:15.993226174 +0800 ++++ pytorch-develop-150/.dockerignore 2021-12-16 12:02:40.295263171 +0800 @@ -1,257 +1 @@ -# READ THIS BEFORE YOU REFACTOR ME -# @@ -9188,7 +9195,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/requirements.txt pytorch-develop-150/requirements.txt --- pytorch-v1.5.0/requirements.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/requirements.txt 2021-12-15 23:03:15.993226174 +0800 ++++ pytorch-develop-150/requirements.txt 2021-12-16 12:02:40.295263171 +0800 @@ -4,4 +4,13 @@ requests setuptools @@ -9208,7 +9215,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/setup.py pytorch-develop-150/setup.py --- pytorch-v1.5.0/setup.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/setup.py 2021-12-15 23:03:15.993226174 +0800 ++++ pytorch-develop-150/setup.py 2021-12-16 12:02:40.295263171 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -9311,7 +9318,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= 'python/serialized_test/data/operator_test/*.zip', diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/derivatives.yaml pytorch-develop-150/tools/autograd/derivatives.yaml --- pytorch-v1.5.0/tools/autograd/derivatives.yaml 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/derivatives.yaml 2021-12-15 23:03:16.709228570 +0800 ++++ pytorch-develop-150/tools/autograd/derivatives.yaml 2021-12-16 12:02:40.975260362 +0800 @@ -107,6 +107,10 @@ # # NB: The parameter names here MUST be consistent with the parameter names @@ -9436,7 +9443,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/dump_utils.py pytorch-develop-150/tools/autograd/dump_utils.py --- pytorch-v1.5.0/tools/autograd/dump_utils.py 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/dump_utils.py 2021-12-15 23:03:16.709228570 +0800 ++++ pytorch-develop-150/tools/autograd/dump_utils.py 2021-12-16 12:02:40.975260362 +0800 @@ -0,0 +1,312 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# All rights reserved. @@ -9752,7 +9759,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + return prepare_to_check_overflow, overflow_dump_inputs diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_autograd_functions.py pytorch-develop-150/tools/autograd/gen_autograd_functions.py --- pytorch-v1.5.0/tools/autograd/gen_autograd_functions.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_autograd_functions.py 2021-12-15 23:03:16.709228570 +0800 ++++ pytorch-develop-150/tools/autograd/gen_autograd_functions.py 2021-12-16 12:02:40.975260362 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -9958,7 +9965,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_python_functions.py pytorch-develop-150/tools/autograd/gen_python_functions.py --- pytorch-v1.5.0/tools/autograd/gen_python_functions.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_python_functions.py 2021-12-15 23:03:16.709228570 +0800 ++++ pytorch-develop-150/tools/autograd/gen_python_functions.py 2021-12-16 12:02:40.975260362 +0800 @@ -1,3 +1,20 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -10000,7 +10007,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= 'value': argname, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_variable_type.py pytorch-develop-150/tools/autograd/gen_variable_type.py --- pytorch-v1.5.0/tools/autograd/gen_variable_type.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_variable_type.py 2021-12-15 23:03:16.709228570 +0800 ++++ pytorch-develop-150/tools/autograd/gen_variable_type.py 2021-12-16 12:02:40.975260362 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -10181,7 +10188,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/Functions.cpp pytorch-develop-150/tools/autograd/templates/Functions.cpp --- pytorch-v1.5.0/tools/autograd/templates/Functions.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/Functions.cpp 2021-12-15 23:03:16.709228570 +0800 ++++ pytorch-develop-150/tools/autograd/templates/Functions.cpp 2021-12-16 12:02:40.975260362 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2021 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10262,7 +10269,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto sparse = sparse_.coalesce(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/python_torch_functions.cpp pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp --- pytorch-v1.5.0/tools/autograd/templates/python_torch_functions.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp 2021-12-15 23:03:16.709228570 +0800 ++++ pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp 2021-12-16 12:02:40.975260362 +0800 @@ -22,7 +22,7 @@ #include "torch/csrc/autograd/generated/variable_factories.h" #include "torch/csrc/utils/structseq.h" @@ -10346,7 +10353,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/python_variable_methods.cpp pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp --- pytorch-v1.5.0/tools/autograd/templates/python_variable_methods.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp 2021-12-15 23:03:16.709228570 +0800 ++++ pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp 2021-12-16 12:02:40.975260362 +0800 @@ -15,7 +15,13 @@ #include "torch/csrc/cuda/Stream.h" #include "torch/csrc/cuda/Event.h" @@ -10441,7 +10448,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"has_names", (PyCFunction)THPVariable_has_names, METH_NOARGS, NULL}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/VariableType.cpp pytorch-develop-150/tools/autograd/templates/VariableType.cpp --- pytorch-v1.5.0/tools/autograd/templates/VariableType.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/VariableType.cpp 2021-12-15 23:03:16.709228570 +0800 ++++ pytorch-develop-150/tools/autograd/templates/VariableType.cpp 2021-12-16 12:02:40.975260362 +0800 @@ -1,7 +1,29 @@ +// Copyright (c) 2021 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10474,7 +10481,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/VariableType.h pytorch-develop-150/tools/autograd/templates/VariableType.h --- pytorch-v1.5.0/tools/autograd/templates/VariableType.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/VariableType.h 2021-12-15 23:03:16.709228570 +0800 ++++ pytorch-develop-150/tools/autograd/templates/VariableType.h 2021-12-16 12:02:40.975260362 +0800 @@ -1,3 +1,20 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10506,7 +10513,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= const at::Tensor & unpack(const Tensor & t, const char * name, int pos); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/build_variables.bzl pytorch-develop-150/tools/build_variables.bzl --- pytorch-v1.5.0/tools/build_variables.bzl 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/build_variables.bzl 2021-12-15 23:03:16.709228570 +0800 ++++ pytorch-develop-150/tools/build_variables.bzl 2021-12-16 12:02:40.975260362 +0800 @@ -46,6 +46,7 @@ "torch/csrc/autograd/functions/utils.cpp", "torch/csrc/autograd/input_buffer.cpp", @@ -10592,7 +10599,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def grad(outputs: _TensorOrTensors, inputs: _TensorOrTensors, grad_outputs: Optional[_TensorOrTensors]=..., retain_graph: Optional[bool]=..., create_graph: bool=..., only_inputs: bool=..., allow_unused: bool=...) -> Tuple[Tensor, ...]: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/autograd/profiler.py pytorch-develop-150/torch/autograd/profiler.py --- pytorch-v1.5.0/torch/autograd/profiler.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/autograd/profiler.py 2021-12-15 23:03:16.709228570 +0800 ++++ pytorch-develop-150/torch/autograd/profiler.py 2021-12-16 12:02:40.979260346 +0800 @@ -1,8 +1,25 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -11069,7 +11076,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ''.join(result) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/CMakeLists.txt pytorch-develop-150/torch/CMakeLists.txt --- pytorch-v1.5.0/torch/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/CMakeLists.txt 2021-12-15 23:03:16.709228570 +0800 ++++ pytorch-develop-150/torch/CMakeLists.txt 2021-12-16 12:02:40.975260362 +0800 @@ -97,6 +97,7 @@ ${TORCH_SRC_DIR}/csrc/tensor/python_tensor.cpp ${TORCH_SRC_DIR}/csrc/utils.cpp @@ -11101,7 +11108,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endif() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/engine.cpp pytorch-develop-150/torch/csrc/autograd/engine.cpp --- pytorch-v1.5.0/torch/csrc/autograd/engine.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/engine.cpp 2021-12-15 23:03:16.717228596 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/engine.cpp 2021-12-16 12:02:40.983260329 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11253,7 +11260,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto event = c10::Event{c10::DeviceType::CUDA}; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/function.h pytorch-develop-150/torch/csrc/autograd/function.h --- pytorch-v1.5.0/torch/csrc/autograd/function.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/function.h 2021-12-15 23:03:16.717228596 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/function.h 2021-12-16 12:02:40.983260329 +0800 @@ -11,6 +11,7 @@ #include @@ -11275,7 +11282,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // probably operate with names. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/functions/tensor.cpp pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp --- pytorch-v1.5.0/torch/csrc/autograd/functions/tensor.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp 2021-12-15 23:03:16.717228596 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp 2021-12-16 12:02:40.983260329 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11307,7 +11314,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= /*non_blocking=*/false, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/init.cpp pytorch-develop-150/torch/csrc/autograd/init.cpp --- pytorch-v1.5.0/torch/csrc/autograd/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/init.cpp 2021-12-15 23:03:16.717228596 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/init.cpp 2021-12-16 12:02:40.983260329 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11350,7 +11357,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= m.def("_enable_profiler", enableProfiler); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/input_buffer.cpp pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp --- pytorch-v1.5.0/torch/csrc/autograd/input_buffer.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp 2021-12-15 23:03:16.717228596 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp 2021-12-16 12:02:40.983260329 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11402,7 +11409,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto& old_var = buffer[pos]; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/profiler.cpp pytorch-develop-150/torch/csrc/autograd/profiler.cpp --- pytorch-v1.5.0/torch/csrc/autograd/profiler.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/profiler.cpp 2021-12-15 23:03:16.717228596 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/profiler.cpp 2021-12-16 12:02:40.983260329 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11642,7 +11649,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CUDAStubs::~CUDAStubs() = default; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/profiler.h pytorch-develop-150/torch/csrc/autograd/profiler.h --- pytorch-v1.5.0/torch/csrc/autograd/profiler.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/profiler.h 2021-12-15 23:03:16.717228596 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/profiler.h 2021-12-16 12:02:40.983260329 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11778,7 +11785,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/python_variable.cpp pytorch-develop-150/torch/csrc/autograd/python_variable.cpp --- pytorch-v1.5.0/torch/csrc/autograd/python_variable.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/python_variable.cpp 2021-12-15 23:03:16.717228596 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/python_variable.cpp 2021-12-16 12:02:40.983260329 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11832,7 +11839,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"is_complex", (getter)THPVariable_is_complex, nullptr, nullptr, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/python_variable_indexing.cpp pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp --- pytorch-v1.5.0/torch/csrc/autograd/python_variable_indexing.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-15 23:03:16.717228596 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-16 12:02:40.983260329 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11863,7 +11870,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= at::Device self_device = self_.device(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/record_function.cpp pytorch-develop-150/torch/csrc/autograd/record_function.cpp --- pytorch-v1.5.0/torch/csrc/autograd/record_function.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/record_function.cpp 2021-12-15 23:03:16.717228596 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/record_function.cpp 2021-12-16 12:02:40.983260329 +0800 @@ -154,6 +154,12 @@ } } @@ -11897,7 +11904,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/record_function.h pytorch-develop-150/torch/csrc/autograd/record_function.h --- pytorch-v1.5.0/torch/csrc/autograd/record_function.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/record_function.h 2021-12-15 23:03:16.717228596 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/record_function.h 2021-12-16 12:02:40.987260313 +0800 @@ -44,6 +44,9 @@ // Default constructor is used with before function called afterwards RecordFunction() {} @@ -11961,7 +11968,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (torch::autograd::profiler::needsInputs()) { \ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/utils/wrap_outputs.h pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h --- pytorch-v1.5.0/torch/csrc/autograd/utils/wrap_outputs.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h 2021-12-15 23:03:16.717228596 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h 2021-12-16 12:02:40.987260313 +0800 @@ -168,6 +168,45 @@ return r.release(); } @@ -12010,7 +12017,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (!r) throw python_error(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/VariableTypeManual.cpp pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp --- pytorch-v1.5.0/torch/csrc/autograd/VariableTypeManual.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp 2021-12-15 23:03:16.717228596 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp 2021-12-16 12:02:40.983260329 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12044,7 +12051,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (!t.defined()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/comm.cpp pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/comm.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp 2021-12-15 23:03:16.721228610 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp 2021-12-16 12:02:40.987260313 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12150,7 +12157,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= while (!in_flight.empty()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/init.cpp pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp 2021-12-15 23:03:16.721228610 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp 2021-12-16 12:02:40.987260313 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12207,7 +12214,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= .def("is_success", &::c10d::ProcessGroup::Work::isSuccess) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/reducer.cpp pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/reducer.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp 2021-12-15 23:03:16.721228610 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp 2021-12-16 12:02:40.987260313 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12342,7 +12349,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/DynamicTypes.cpp pytorch-develop-150/torch/csrc/DynamicTypes.cpp --- pytorch-v1.5.0/torch/csrc/DynamicTypes.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/DynamicTypes.cpp 2021-12-15 23:03:16.713228583 +0800 ++++ pytorch-develop-150/torch/csrc/DynamicTypes.cpp 2021-12-16 12:02:40.979260346 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12391,7 +12398,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return it->second; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/Generator.cpp pytorch-develop-150/torch/csrc/Generator.cpp --- pytorch-v1.5.0/torch/csrc/Generator.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/Generator.cpp 2021-12-15 23:03:16.713228583 +0800 ++++ pytorch-develop-150/torch/csrc/Generator.cpp 2021-12-16 12:02:40.979260346 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12459,7 +12466,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #endif diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/serialization.cpp pytorch-develop-150/torch/csrc/generic/serialization.cpp --- pytorch-v1.5.0/torch/csrc/generic/serialization.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/serialization.cpp 2021-12-15 23:03:16.721228610 +0800 ++++ pytorch-develop-150/torch/csrc/generic/serialization.cpp 2021-12-16 12:02:40.987260313 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12559,7 +12566,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/Storage.cpp pytorch-develop-150/torch/csrc/generic/Storage.cpp --- pytorch-v1.5.0/torch/csrc/generic/Storage.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/Storage.cpp 2021-12-15 23:03:16.721228610 +0800 ++++ pytorch-develop-150/torch/csrc/generic/Storage.cpp 2021-12-16 12:02:40.987260313 +0800 @@ -1,7 +1,25 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12638,7 +12645,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for (Py_ssize_t i = 0; i < length; i++) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/StorageMethods.cpp pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp --- pytorch-v1.5.0/torch/csrc/generic/StorageMethods.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp 2021-12-15 23:03:16.721228610 +0800 ++++ pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp 2021-12-16 12:02:40.987260313 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12686,7 +12693,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"_write_file", (PyCFunction)THPStorage_(writeFile), METH_VARARGS, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/Module.cpp pytorch-develop-150/torch/csrc/Module.cpp --- pytorch-v1.5.0/torch/csrc/Module.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/Module.cpp 2021-12-15 23:03:16.713228583 +0800 ++++ pytorch-develop-150/torch/csrc/Module.cpp 2021-12-16 12:02:40.979260346 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12847,7 +12854,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto set_module_attr = [&](const char* name, PyObject* v, bool incref = true) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/tensor/python_tensor.cpp pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp --- pytorch-v1.5.0/torch/csrc/tensor/python_tensor.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp 2021-12-15 23:03:16.729228637 +0800 ++++ pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp 2021-12-16 12:02:40.995260280 +0800 @@ -1,18 +1,35 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13224,7 +13231,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +} // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/init.cpp pytorch-develop-150/torch/csrc/utils/init.cpp --- pytorch-v1.5.0/torch/csrc/utils/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/init.cpp 2021-12-15 23:03:16.729228637 +0800 ++++ pytorch-develop-150/torch/csrc/utils/init.cpp 2021-12-16 12:02:40.995260280 +0800 @@ -1,7 +1,13 @@ #include #include @@ -13389,7 +13396,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/init.h pytorch-develop-150/torch/csrc/utils/init.h --- pytorch-v1.5.0/torch/csrc/utils/init.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/init.h 2021-12-15 23:03:16.729228637 +0800 ++++ pytorch-develop-150/torch/csrc/utils/init.h 2021-12-16 12:02:40.999260264 +0800 @@ -8,4 +8,7 @@ void initThroughputBenchmarkBindings(PyObject* module); @@ -13400,7 +13407,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/python_arg_parser.h pytorch-develop-150/torch/csrc/utils/python_arg_parser.h --- pytorch-v1.5.0/torch/csrc/utils/python_arg_parser.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/python_arg_parser.h 2021-12-15 23:03:16.729228637 +0800 ++++ pytorch-develop-150/torch/csrc/utils/python_arg_parser.h 2021-12-16 12:02:40.999260264 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13435,7 +13442,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return at::Device(device_str); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_layouts.cpp pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_layouts.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp 2021-12-15 23:03:16.729228637 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp 2021-12-16 12:02:40.999260264 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13466,7 +13473,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= registerLayoutObject((THPLayout*)strided_layout, at::Backend::QuantizedCPU); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_new.cpp pytorch-develop-150/torch/csrc/utils/tensor_new.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_new.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_new.cpp 2021-12-15 23:03:16.729228637 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_new.cpp 2021-12-16 12:02:40.999260264 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13602,7 +13609,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= " or ", c10::DispatchKey::XLATensorId, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_types.cpp pytorch-develop-150/torch/csrc/utils/tensor_types.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_types.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_types.cpp 2021-12-15 23:03:16.733228650 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_types.cpp 2021-12-16 12:02:40.999260264 +0800 @@ -1,58 +1,91 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13815,7 +13822,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def get_rng_state(): ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/distributed/distributed_c10d.py pytorch-develop-150/torch/distributed/distributed_c10d.py --- pytorch-v1.5.0/torch/distributed/distributed_c10d.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/distributed/distributed_c10d.py 2021-12-15 23:03:16.733228650 +0800 ++++ pytorch-develop-150/torch/distributed/distributed_c10d.py 2021-12-16 12:02:40.999260264 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -13896,7 +13903,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/__init__.py pytorch-develop-150/torch/__init__.py --- pytorch-v1.5.0/torch/__init__.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/__init__.py 2021-12-15 23:03:16.709228570 +0800 ++++ pytorch-develop-150/torch/__init__.py 2021-12-16 12:02:40.975260362 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -13939,7 +13946,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/lib/c10d/CMakeLists.txt pytorch-develop-150/torch/lib/c10d/CMakeLists.txt --- pytorch-v1.5.0/torch/lib/c10d/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/lib/c10d/CMakeLists.txt 2021-12-15 23:03:16.733228650 +0800 ++++ pytorch-develop-150/torch/lib/c10d/CMakeLists.txt 2021-12-16 12:02:40.999260264 +0800 @@ -28,6 +28,10 @@ option(USE_C10D_NCCL "USE C10D NCCL" ON) endif() @@ -13992,7 +13999,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= copy_header(ProcessGroupMPI.hpp) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/lib/libshm/CMakeLists.txt pytorch-develop-150/torch/lib/libshm/CMakeLists.txt --- pytorch-v1.5.0/torch/lib/libshm/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/lib/libshm/CMakeLists.txt 2021-12-15 23:03:16.733228650 +0800 ++++ pytorch-develop-150/torch/lib/libshm/CMakeLists.txt 2021-12-16 12:02:40.999260264 +0800 @@ -37,8 +37,11 @@ SET_TARGET_PROPERTIES(shm PROPERTIES PREFIX "lib" @@ -14049,7 +14056,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -_maybe_indices_t = _scalar_or_tuple_2_t[Tensor] diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/functional.py pytorch-develop-150/torch/nn/functional.py --- pytorch-v1.5.0/torch/nn/functional.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/functional.py 2021-12-15 23:03:16.733228650 +0800 ++++ pytorch-develop-150/torch/nn/functional.py 2021-12-16 12:02:41.003260247 +0800 @@ -1611,7 +1611,7 @@ else: output = input.matmul(weight.t()) @@ -14072,7 +14079,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -from . import parallel as parallel diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/batchnorm.py pytorch-develop-150/torch/nn/modules/batchnorm.py --- pytorch-v1.5.0/torch/nn/modules/batchnorm.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/batchnorm.py 2021-12-15 23:03:16.737228664 +0800 ++++ pytorch-develop-150/torch/nn/modules/batchnorm.py 2021-12-16 12:02:41.003260247 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14104,7 +14111,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= self.register_parameter('running_var', None) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/module.py pytorch-develop-150/torch/nn/modules/module.py --- pytorch-v1.5.0/torch/nn/modules/module.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/module.py 2021-12-15 23:03:16.737228664 +0800 ++++ pytorch-develop-150/torch/nn/modules/module.py 2021-12-16 12:02:41.003260247 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14264,7 +14271,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return t.to(device, dtype if t.is_floating_point() else None, non_blocking, memory_format=convert_to_format) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/normalization.py pytorch-develop-150/torch/nn/modules/normalization.py --- pytorch-v1.5.0/torch/nn/modules/normalization.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/normalization.py 2021-12-15 23:03:16.737228664 +0800 ++++ pytorch-develop-150/torch/nn/modules/normalization.py 2021-12-16 12:02:41.003260247 +0800 @@ -128,13 +128,14 @@ """ __constants__ = ['normalized_shape', 'eps', 'elementwise_affine'] @@ -14333,7 +14340,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - module_kwargs: Optional[Any] = ...) -> Tensor: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/parallel/distributed.py pytorch-develop-150/torch/nn/parallel/distributed.py --- pytorch-v1.5.0/torch/nn/parallel/distributed.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/parallel/distributed.py 2021-12-15 23:03:16.737228664 +0800 ++++ pytorch-develop-150/torch/nn/parallel/distributed.py 2021-12-16 12:02:41.003260247 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14688,7 +14695,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def remove_weight_norm(module: T_module, name: str = ...) -> T_module: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/onnx/symbolic_opset9.py pytorch-develop-150/torch/onnx/symbolic_opset9.py --- pytorch-v1.5.0/torch/onnx/symbolic_opset9.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/onnx/symbolic_opset9.py 2021-12-15 23:03:16.737228664 +0800 ++++ pytorch-develop-150/torch/onnx/symbolic_opset9.py 2021-12-16 12:02:41.003260247 +0800 @@ -1621,14 +1621,23 @@ slices = [sym_help._slice_helper(g, w, axes=[0], starts=[x * n], ends=[y * n]) for x, y in intervals] return g.op('Concat', *slices, axis_i=0) @@ -14766,7 +14773,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, params: _params_t, lr: float=..., lr_decay: float=..., weight_decay: float=..., initial_accumulator_value: float=..., eps: float=...) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/optim/adamax.py pytorch-develop-150/torch/optim/adamax.py --- pytorch-v1.5.0/torch/optim/adamax.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/optim/adamax.py 2021-12-15 23:03:16.737228664 +0800 ++++ pytorch-develop-150/torch/optim/adamax.py 2021-12-16 12:02:41.003260247 +0800 @@ -80,8 +80,8 @@ exp_inf.mul_(beta2).unsqueeze(0), grad.abs().add_(eps).unsqueeze_(0) @@ -14943,7 +14950,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, params: _params_t, lr: float=..., betas: Tuple[float, float]=..., eps: float=...) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/serialization.py pytorch-develop-150/torch/serialization.py --- pytorch-v1.5.0/torch/serialization.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/serialization.py 2021-12-15 23:03:16.709228570 +0800 ++++ pytorch-develop-150/torch/serialization.py 2021-12-16 12:02:40.979260346 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15027,7 +15034,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def location_tag(storage): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/storage.py pytorch-develop-150/torch/storage.py --- pytorch-v1.5.0/torch/storage.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/storage.py 2021-12-15 23:03:16.709228570 +0800 ++++ pytorch-develop-150/torch/storage.py 2021-12-16 12:02:40.979260346 +0800 @@ -7,6 +7,7 @@ class _StorageBase(object): @@ -15047,7 +15054,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= else: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/tensor.py pytorch-develop-150/torch/tensor.py --- pytorch-v1.5.0/torch/tensor.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/tensor.py 2021-12-15 23:03:16.709228570 +0800 ++++ pytorch-develop-150/torch/tensor.py 2021-12-16 12:02:40.979260346 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15109,7 +15116,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def __reversed__(self): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/_tensor_str.py pytorch-develop-150/torch/_tensor_str.py --- pytorch-v1.5.0/torch/_tensor_str.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/_tensor_str.py 2021-12-15 23:03:16.709228570 +0800 ++++ pytorch-develop-150/torch/_tensor_str.py 2021-12-16 12:02:40.979260346 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15163,7 +15170,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= has_default_dtype = self.dtype in (torch.get_default_dtype(), torch.int64, torch.bool) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/data/dataloader.py pytorch-develop-150/torch/utils/data/dataloader.py --- pytorch-v1.5.0/torch/utils/data/dataloader.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/data/dataloader.py 2021-12-15 23:03:16.741228677 +0800 ++++ pytorch-develop-150/torch/utils/data/dataloader.py 2021-12-16 12:02:41.007260231 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15372,7 +15379,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, sampler: Sampler[int], batch_size: int, drop_last: bool) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/data/_utils/pin_memory.py pytorch-develop-150/torch/utils/data/_utils/pin_memory.py --- pytorch-v1.5.0/torch/utils/data/_utils/pin_memory.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/data/_utils/pin_memory.py 2021-12-15 23:03:16.741228677 +0800 ++++ pytorch-develop-150/torch/utils/data/_utils/pin_memory.py 2021-12-16 12:02:41.007260231 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15433,7 +15440,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/__init__.py pytorch-develop-150/torch/utils/__init__.py --- pytorch-v1.5.0/torch/utils/__init__.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/__init__.py 2021-12-15 23:03:16.741228677 +0800 ++++ pytorch-develop-150/torch/utils/__init__.py 2021-12-16 12:02:41.007260231 +0800 @@ -1,6 +1,9 @@ from __future__ import absolute_import, division, print_function, unicode_literals @@ -15446,7 +15453,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def set_module(obj, mod): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/_utils.py pytorch-develop-150/torch/_utils.py --- pytorch-v1.5.0/torch/_utils.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/_utils.py 2021-12-15 23:03:16.709228570 +0800 ++++ pytorch-develop-150/torch/_utils.py 2021-12-16 12:02:40.979260346 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. diff --git a/patch/pytorch1.8.1_npu.patch b/patch/pytorch1.8.1_npu.patch index e67c0d5e237..d250d9b3811 100644 --- a/patch/pytorch1.8.1_npu.patch +++ b/patch/pytorch1.8.1_npu.patch @@ -1,6 +1,6 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/CMakeLists.txt pytorch-develop-181/aten/CMakeLists.txt --- pytorch-v1.8.1/aten/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/CMakeLists.txt 2021-12-15 23:03:20.809242288 +0800 ++++ pytorch-develop-181/aten/CMakeLists.txt 2021-12-16 12:02:44.883244252 +0800 @@ -22,9 +22,11 @@ set(ATen_CPU_INCLUDE) set(ATen_THIRD_PARTY_INCLUDE) @@ -52,7 +52,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_CPU_DEPENDENCY_LIBS ${ATen_CPU_DEPENDENCY_LIBS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/autocast_mode.h pytorch-develop-181/aten/src/ATen/autocast_mode.h --- pytorch-v1.8.1/aten/src/ATen/autocast_mode.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/autocast_mode.h 2021-12-15 23:03:20.813242300 +0800 ++++ pytorch-develop-181/aten/src/ATen/autocast_mode.h 2021-12-16 12:02:44.891244219 +0800 @@ -5,7 +5,7 @@ namespace { @@ -64,7 +64,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/CMakeLists.txt pytorch-develop-181/aten/src/ATen/CMakeLists.txt --- pytorch-v1.8.1/aten/src/ATen/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/CMakeLists.txt 2021-12-15 23:03:20.809242288 +0800 ++++ pytorch-develop-181/aten/src/ATen/CMakeLists.txt 2021-12-16 12:02:44.887244235 +0800 @@ -85,6 +85,10 @@ file(GLOB native_quantized_h "native/quantized/*.h" "native/quantized/cpu/*.h") file(GLOB native_cpu_h "native/cpu/*.h") @@ -115,7 +115,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_HIP_SRCS ${ATen_HIP_SRCS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/Dispatcher.h pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/Dispatcher.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h 2021-12-15 23:03:20.817242314 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h 2021-12-16 12:02:44.895244202 +0800 @@ -417,6 +417,11 @@ } } @@ -130,7 +130,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.cpp pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-12-15 23:03:20.817242314 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-12-16 12:02:44.895244202 +0800 @@ -6,6 +6,7 @@ namespace c10 { @@ -141,7 +141,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= std::unordered_set not_observed_ops = { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.h pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-12-15 23:03:20.817242314 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-12-16 12:02:44.895244202 +0800 @@ -1,12 +1,16 @@ #pragma once @@ -161,7 +161,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/OperatorEntry.h pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-12-15 23:03:20.817242314 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-12-16 12:02:44.895244202 +0800 @@ -10,6 +10,7 @@ #include #include @@ -172,7 +172,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #include diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/VariableFallbackKernel.cpp pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp --- pytorch-v1.8.1/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-12-15 23:03:20.813242300 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-12-16 12:02:44.891244219 +0800 @@ -48,4 +48,8 @@ m.fallback(torch::CppFunction::makeFallthrough()); } @@ -184,7 +184,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/Convolution.cpp pytorch-develop-181/aten/src/ATen/native/Convolution.cpp --- pytorch-v1.8.1/aten/src/ATen/native/Convolution.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/Convolution.cpp 2021-12-15 23:03:20.829242354 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/Convolution.cpp 2021-12-16 12:02:44.903244169 +0800 @@ -603,7 +603,9 @@ const Tensor& input, const Tensor& weight, const Tensor& bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, @@ -198,7 +198,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= at::Tensor _convolution( diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/Memory.cpp pytorch-develop-181/aten/src/ATen/native/Memory.cpp --- pytorch-v1.8.1/aten/src/ATen/native/Memory.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/Memory.cpp 2021-12-15 23:03:20.829242354 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/Memory.cpp 2021-12-16 12:02:44.907244153 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -249,7 +249,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= detail::computeStorageNbytes( diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/native_functions.yaml pytorch-develop-181/aten/src/ATen/native/native_functions.yaml --- pytorch-v1.8.1/aten/src/ATen/native/native_functions.yaml 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/native_functions.yaml 2021-12-15 23:03:20.865242474 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/native_functions.yaml 2021-12-16 12:02:44.943244005 +0800 @@ -2073,6 +2073,8 @@ dispatch: CPU, CUDA: isnan @@ -480,7 +480,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +- func: npu_apply_adam.out(Scalar beta1_power, Scalar beta2_power, Scalar lr, Scalar beta1, Scalar beta2, Scalar epsilon, Tensor grad, bool? use_locking, bool? use_nesterov, *, Tensor(a!) var, Tensor(b!) m, Tensor(c!) v) -> (Tensor(a!), Tensor(b!), Tensor(c!)) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/NPUVariableMethodStubs.cpp pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp --- pytorch-v1.8.1/aten/src/ATen/native/NPUVariableMethodStubs.cpp 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp 2021-12-15 23:03:20.833242367 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp 2021-12-16 12:02:44.907244153 +0800 @@ -0,0 +1,464 @@ +#include "ATen/native/npu/common/FormatCastHelper.h" +#include "ATen/native/npu/frame/FormatHelper.h" @@ -1864,7 +1864,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -} diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/TensorFactories.cpp pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp --- pytorch-v1.8.1/aten/src/ATen/native/TensorFactories.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp 2021-12-15 23:03:20.845242407 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp 2021-12-16 12:02:44.923244087 +0800 @@ -1,3 +1,20 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -1897,7 +1897,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= namespace { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h --- pytorch-v1.8.1/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-12-15 23:03:20.901242595 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-12-16 12:02:44.975243873 +0800 @@ -1,19229 +1,19229 @@ -// -// Copyright (c) 2017-2020 Advanced Micro Devices, Inc. All rights reserved. @@ -40359,7 +40359,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +#endif // #ifdef VMA_IMPLEMENTATION diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/record_function.cpp pytorch-develop-181/aten/src/ATen/record_function.cpp --- pytorch-v1.8.1/aten/src/ATen/record_function.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/record_function.cpp 2021-12-15 23:03:20.905242608 +0800 ++++ pytorch-develop-181/aten/src/ATen/record_function.cpp 2021-12-16 12:02:44.979243857 +0800 @@ -400,6 +400,9 @@ rf_tls_.tls_record_function_enabled_ = enable; } @@ -40372,7 +40372,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (rf_tls_ptr->tls_record_function_enabled_) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/record_function.h pytorch-develop-181/aten/src/ATen/record_function.h --- pytorch-v1.8.1/aten/src/ATen/record_function.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/record_function.h 2021-12-15 23:03:20.905242608 +0800 ++++ pytorch-develop-181/aten/src/ATen/record_function.h 2021-12-16 12:02:44.979243857 +0800 @@ -2,6 +2,7 @@ #include @@ -40408,7 +40408,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // scope - record scope that this function tracks diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/templates/TensorBody.h pytorch-develop-181/aten/src/ATen/templates/TensorBody.h --- pytorch-v1.8.1/aten/src/ATen/templates/TensorBody.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/templates/TensorBody.h 2021-12-15 23:03:20.905242608 +0800 ++++ pytorch-develop-181/aten/src/ATen/templates/TensorBody.h 2021-12-16 12:02:44.979243857 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40441,7 +40441,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= bool is_xpu() const; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/templates/TensorMethods.cpp pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp --- pytorch-v1.8.1/aten/src/ATen/templates/TensorMethods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp 2021-12-15 23:03:20.905242608 +0800 ++++ pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp 2021-12-16 12:02:44.979243857 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40486,7 +40486,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/Utils.cpp pytorch-develop-181/aten/src/ATen/Utils.cpp --- pytorch-v1.8.1/aten/src/ATen/Utils.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/Utils.cpp 2021-12-15 23:03:20.813242300 +0800 ++++ pytorch-develop-181/aten/src/ATen/Utils.cpp 2021-12-16 12:02:44.887244235 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40530,7 +40530,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/TH/generic/THStorage.cpp pytorch-develop-181/aten/src/TH/generic/THStorage.cpp --- pytorch-v1.8.1/aten/src/TH/generic/THStorage.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/TH/generic/THStorage.cpp 2021-12-15 23:03:20.909242622 +0800 ++++ pytorch-develop-181/aten/src/TH/generic/THStorage.cpp 2021-12-16 12:02:44.983243841 +0800 @@ -1,9 +1,32 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40643,7 +40643,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/TH/generic/THStorage.h pytorch-develop-181/aten/src/TH/generic/THStorage.h --- pytorch-v1.8.1/aten/src/TH/generic/THStorage.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/TH/generic/THStorage.h 2021-12-15 23:03:20.909242622 +0800 ++++ pytorch-develop-181/aten/src/TH/generic/THStorage.h 2021-12-16 12:02:44.983243841 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40682,7 +40682,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/CMakeLists.txt pytorch-develop-181/c10/CMakeLists.txt --- pytorch-v1.8.1/c10/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/CMakeLists.txt 2021-12-15 23:03:20.921242662 +0800 ++++ pytorch-develop-181/c10/CMakeLists.txt 2021-12-16 12:02:44.999243775 +0800 @@ -79,6 +79,14 @@ message(STATUS "don't use NUMA") endif() @@ -40711,7 +40711,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # not checked in diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Backend.h pytorch-develop-181/c10/core/Backend.h --- pytorch-v1.8.1/c10/core/Backend.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Backend.h 2021-12-15 23:03:20.925242675 +0800 ++++ pytorch-develop-181/c10/core/Backend.h 2021-12-16 12:02:44.999243775 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40805,7 +40805,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Device.cpp pytorch-develop-181/c10/core/Device.cpp --- pytorch-v1.8.1/c10/core/Device.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Device.cpp 2021-12-15 23:03:20.925242675 +0800 ++++ pytorch-develop-181/c10/core/Device.cpp 2021-12-16 12:02:44.999243775 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40836,7 +40836,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= types.begin(), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Device.h pytorch-develop-181/c10/core/Device.h --- pytorch-v1.8.1/c10/core/Device.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Device.h 2021-12-15 23:03:20.925242675 +0800 ++++ pytorch-develop-181/c10/core/Device.h 2021-12-16 12:02:44.999243775 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40871,7 +40871,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= bool is_cpu() const noexcept { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DeviceType.cpp pytorch-develop-181/c10/core/DeviceType.cpp --- pytorch-v1.8.1/c10/core/DeviceType.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DeviceType.cpp 2021-12-15 23:03:20.925242675 +0800 ++++ pytorch-develop-181/c10/core/DeviceType.cpp 2021-12-16 12:02:44.999243775 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40911,7 +40911,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return false; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DeviceType.h pytorch-develop-181/c10/core/DeviceType.h --- pytorch-v1.8.1/c10/core/DeviceType.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DeviceType.h 2021-12-15 23:03:20.925242675 +0800 ++++ pytorch-develop-181/c10/core/DeviceType.h 2021-12-16 12:02:44.999243775 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40953,7 +40953,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= constexpr DeviceType kMSNPU = DeviceType::MSNPU; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKey.cpp pytorch-develop-181/c10/core/DispatchKey.cpp --- pytorch-v1.8.1/c10/core/DispatchKey.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKey.cpp 2021-12-15 23:03:20.925242675 +0800 ++++ pytorch-develop-181/c10/core/DispatchKey.cpp 2021-12-16 12:02:44.999243775 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41002,7 +41002,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::PrivateUse1: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKey.h pytorch-develop-181/c10/core/DispatchKey.h --- pytorch-v1.8.1/c10/core/DispatchKey.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKey.h 2021-12-15 23:03:20.925242675 +0800 ++++ pytorch-develop-181/c10/core/DispatchKey.h 2021-12-16 12:02:44.999243775 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41049,7 +41049,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= PrivateUse3_PreAutograd = AutogradPrivateUse3, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKeySet.cpp pytorch-develop-181/c10/core/DispatchKeySet.cpp --- pytorch-v1.8.1/c10/core/DispatchKeySet.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKeySet.cpp 2021-12-15 23:03:20.925242675 +0800 ++++ pytorch-develop-181/c10/core/DispatchKeySet.cpp 2021-12-16 12:02:44.999243775 +0800 @@ -11,6 +11,7 @@ DispatchKey::XLA, DispatchKey::NestedTensor, @@ -41069,7 +41069,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::AutogradXPU: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKeySet.h pytorch-develop-181/c10/core/DispatchKeySet.h --- pytorch-v1.8.1/c10/core/DispatchKeySet.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKeySet.h 2021-12-15 23:03:20.925242675 +0800 ++++ pytorch-develop-181/c10/core/DispatchKeySet.h 2021-12-16 12:02:44.999243775 +0800 @@ -193,6 +193,7 @@ DispatchKey::AutogradCPU, DispatchKey::AutogradCUDA, @@ -41080,7 +41080,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= DispatchKey::AutogradPrivateUse1, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Storage.h pytorch-develop-181/c10/core/Storage.h --- pytorch-v1.8.1/c10/core/Storage.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Storage.h 2021-12-15 23:03:20.925242675 +0800 ++++ pytorch-develop-181/c10/core/Storage.h 2021-12-16 12:02:44.999243775 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41114,7 +41114,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/StorageImpl.h pytorch-develop-181/c10/core/StorageImpl.h --- pytorch-v1.8.1/c10/core/StorageImpl.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/StorageImpl.h 2021-12-15 23:03:20.925242675 +0800 ++++ pytorch-develop-181/c10/core/StorageImpl.h 2021-12-16 12:02:44.999243775 +0800 @@ -1,12 +1,42 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41174,7 +41174,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/TensorImpl.h pytorch-develop-181/c10/core/TensorImpl.h --- pytorch-v1.8.1/c10/core/TensorImpl.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/TensorImpl.h 2021-12-15 23:03:20.925242675 +0800 ++++ pytorch-develop-181/c10/core/TensorImpl.h 2021-12-16 12:02:44.999243775 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41217,7 +41217,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ts.has(DispatchKey::SparseCPU) || diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/TensorOptions.h pytorch-develop-181/c10/core/TensorOptions.h --- pytorch-v1.8.1/c10/core/TensorOptions.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/TensorOptions.h 2021-12-15 23:03:20.925242675 +0800 ++++ pytorch-develop-181/c10/core/TensorOptions.h 2021-12-16 12:02:44.999243775 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41258,7 +41258,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } else if (tid == DispatchKey::QuantizedXPU) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/macros/Export.h pytorch-develop-181/c10/macros/Export.h --- pytorch-v1.8.1/c10/macros/Export.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/macros/Export.h 2021-12-15 23:03:20.929242689 +0800 ++++ pytorch-develop-181/c10/macros/Export.h 2021-12-16 12:02:45.003243758 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41327,7 +41327,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #define C10_API_ENUM C10_API diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/caffe2/CMakeLists.txt pytorch-develop-181/caffe2/CMakeLists.txt --- pytorch-v1.8.1/caffe2/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/caffe2/CMakeLists.txt 2021-12-15 23:03:20.933242702 +0800 ++++ pytorch-develop-181/caffe2/CMakeLists.txt 2021-12-16 12:02:45.007243742 +0800 @@ -76,6 +76,7 @@ # Add source, includes, and libs to lists list(APPEND Caffe2_CPU_SRCS ${ATen_CPU_SRCS}) @@ -41452,7 +41452,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Call again since Caffe2_HIP_INCLUDE is extended with ATen include dirs. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/BuildVariables.cmake pytorch-develop-181/cmake/BuildVariables.cmake --- pytorch-v1.8.1/cmake/BuildVariables.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/BuildVariables.cmake 2021-12-15 23:03:21.013242969 +0800 ++++ pytorch-develop-181/cmake/BuildVariables.cmake 2021-12-16 12:02:45.083243429 +0800 @@ -11,6 +11,7 @@ # CMakeLists.txt files under each folder respectively. set(Caffe2_CPU_SRCS) @@ -41476,7 +41476,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # symbols. However, if the lib is whole linked in caffe2 lib, we don't want diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Codegen.cmake pytorch-develop-181/cmake/Codegen.cmake --- pytorch-v1.8.1/cmake/Codegen.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Codegen.cmake 2021-12-15 23:03:21.013242969 +0800 ++++ pytorch-develop-181/cmake/Codegen.cmake 2021-12-16 12:02:45.083243429 +0800 @@ -208,13 +208,14 @@ file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt generated_cpp) file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt-cuda cuda_generated_cpp) @@ -41509,7 +41509,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= function(append_filelist name outputvar) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Dependencies.cmake pytorch-develop-181/cmake/Dependencies.cmake --- pytorch-v1.8.1/cmake/Dependencies.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Dependencies.cmake 2021-12-15 23:03:21.013242969 +0800 ++++ pytorch-develop-181/cmake/Dependencies.cmake 2021-12-16 12:02:45.083243429 +0800 @@ -1771,6 +1771,13 @@ endif(NOT C_HAS_THREAD) endif() @@ -41526,7 +41526,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Summary.cmake pytorch-develop-181/cmake/Summary.cmake --- pytorch-v1.8.1/cmake/Summary.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Summary.cmake 2021-12-15 23:03:21.013242969 +0800 ++++ pytorch-develop-181/cmake/Summary.cmake 2021-12-16 12:02:45.083243429 +0800 @@ -127,6 +127,7 @@ message(STATUS " USE_MKLDNN_CBLAS : ${USE_MKLDNN_CBLAS}") endif() @@ -41545,7 +41545,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= message(STATUS " Private Dependencies : ${Caffe2_DEPENDENCY_LIBS}") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/TorchConfig.cmake.in pytorch-develop-181/cmake/TorchConfig.cmake.in --- pytorch-v1.8.1/cmake/TorchConfig.cmake.in 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/TorchConfig.cmake.in 2021-12-15 23:03:21.013242969 +0800 ++++ pytorch-develop-181/cmake/TorchConfig.cmake.in 2021-12-16 12:02:45.083243429 +0800 @@ -158,6 +158,11 @@ list(APPEND TORCH_LIBRARIES ${TORCH_CUDA_LIBRARIES}) endif() @@ -41560,7 +41560,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(TORCH_CXX_FLAGS "-D_GLIBCXX_USE_CXX11_ABI=@GLIBCXX_USE_CXX11_ABI@") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/CMakeLists.txt pytorch-develop-181/CMakeLists.txt --- pytorch-v1.8.1/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/CMakeLists.txt 2021-12-15 23:03:20.797242248 +0800 ++++ pytorch-develop-181/CMakeLists.txt 2021-12-16 12:02:44.875244285 +0800 @@ -261,6 +261,10 @@ "USE_DISTRIBUTED" OFF) option(USE_TBB "Use TBB" OFF) @@ -41604,7 +41604,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if(APPLE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/setup.py pytorch-develop-181/setup.py --- pytorch-v1.8.1/setup.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/setup.py 2021-12-15 23:03:21.025243010 +0800 ++++ pytorch-develop-181/setup.py 2021-12-16 12:02:45.095243379 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -41673,7 +41673,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= 'utils/benchmark/utils/valgrind_wrapper/*.h', diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/derivatives.yaml pytorch-develop-181/tools/autograd/derivatives.yaml --- pytorch-v1.8.1/tools/autograd/derivatives.yaml 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/derivatives.yaml 2021-12-15 23:03:21.489244562 +0800 ++++ pytorch-develop-181/tools/autograd/derivatives.yaml 2021-12-16 12:02:45.543241537 +0800 @@ -1976,3 +1976,7 @@ - name: nonzero(Tensor self) -> Tensor @@ -41684,7 +41684,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + mat2: npu_bmm_v2_mat2_backward(grad, self, mat2, mat2.sizes()) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/gen_python_functions.py pytorch-develop-181/tools/autograd/gen_python_functions.py --- pytorch-v1.8.1/tools/autograd/gen_python_functions.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/gen_python_functions.py 2021-12-15 23:03:21.489244562 +0800 ++++ pytorch-develop-181/tools/autograd/gen_python_functions.py 2021-12-16 12:02:45.543241537 +0800 @@ -1,3 +1,20 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -41708,7 +41708,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # The bindings are generated as methods on python_variable or functions on the diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/Functions.cpp pytorch-develop-181/tools/autograd/templates/Functions.cpp --- pytorch-v1.8.1/tools/autograd/templates/Functions.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/Functions.cpp 2021-12-15 23:03:21.489244562 +0800 ++++ pytorch-develop-181/tools/autograd/templates/Functions.cpp 2021-12-16 12:02:45.547241520 +0800 @@ -14,6 +14,35 @@ namespace torch { namespace autograd { namespace generated { @@ -41747,7 +41747,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }}} // namespace torch::autograd::generated diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/python_torch_functions.cpp pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp --- pytorch-v1.8.1/tools/autograd/templates/python_torch_functions.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp 2021-12-15 23:03:21.489244562 +0800 ++++ pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp 2021-12-16 12:02:45.547241520 +0800 @@ -30,7 +30,7 @@ #include "torch/csrc/autograd/generated/variable_factories.h" #include "torch/csrc/utils/structseq.h" @@ -41783,7 +41783,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return torch::range(start, end, step, options); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/python_variable_methods.cpp pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp --- pytorch-v1.8.1/tools/autograd/templates/python_variable_methods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp 2021-12-15 23:03:21.489244562 +0800 ++++ pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp 2021-12-16 12:02:45.547241520 +0800 @@ -20,7 +20,13 @@ #ifdef USE_CUDA #include "torch/csrc/cuda/Event.h" @@ -41851,7 +41851,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"has_names", THPVariable_has_names, METH_NOARGS, NULL}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/build_variables.bzl pytorch-develop-181/tools/build_variables.bzl --- pytorch-v1.8.1/tools/build_variables.bzl 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/build_variables.bzl 2021-12-15 23:03:21.493244575 +0800 ++++ pytorch-develop-181/tools/build_variables.bzl 2021-12-16 12:02:45.547241520 +0800 @@ -362,6 +362,7 @@ libtorch_cuda_core_sources = [ "torch/csrc/CudaIPCTypes.cpp", @@ -41870,7 +41870,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= "torch/csrc/utils/python_arg_parser.cpp", diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/codegen/gen.py pytorch-develop-181/tools/codegen/gen.py --- pytorch-v1.8.1/tools/codegen/gen.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/codegen/gen.py 2021-12-15 23:03:21.493244575 +0800 ++++ pytorch-develop-181/tools/codegen/gen.py 2021-12-16 12:02:45.551241504 +0800 @@ -815,7 +815,7 @@ core_fm = make_file_manager(core_install_dir) cpu_fm = make_file_manager(options.install_dir) @@ -41918,7 +41918,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= main() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/codegen/model.py pytorch-develop-181/tools/codegen/model.py --- pytorch-v1.8.1/tools/codegen/model.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/codegen/model.py 2021-12-15 23:03:21.493244575 +0800 ++++ pytorch-develop-181/tools/codegen/model.py 2021-12-16 12:02:45.551241504 +0800 @@ -79,6 +79,7 @@ SparseHIP = auto() SparseXPU = auto() @@ -42060,7 +42060,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/autograd/profiler.py pytorch-develop-181/torch/autograd/profiler.py --- pytorch-v1.8.1/torch/autograd/profiler.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/autograd/profiler.py 2021-12-15 23:03:21.497244589 +0800 ++++ pytorch-develop-181/torch/autograd/profiler.py 2021-12-16 12:02:45.555241488 +0800 @@ -37,14 +37,17 @@ class EventList(list): """A list of Events (for pretty printing)""" @@ -42739,7 +42739,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ''.join(result) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_C/_autograd.pyi pytorch-develop-181/torch/_C/_autograd.pyi --- pytorch-v1.8.1/torch/_C/_autograd.pyi 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_C/_autograd.pyi 2021-12-15 23:03:21.497244589 +0800 ++++ pytorch-develop-181/torch/_C/_autograd.pyi 2021-12-16 12:02:45.551241504 +0800 @@ -9,14 +9,17 @@ CUDA = ... NVTX = ... @@ -42760,7 +42760,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= class ProfilerConfig: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/CMakeLists.txt pytorch-develop-181/torch/CMakeLists.txt --- pytorch-v1.8.1/torch/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/CMakeLists.txt 2021-12-15 23:03:21.497244589 +0800 ++++ pytorch-develop-181/torch/CMakeLists.txt 2021-12-16 12:02:45.551241504 +0800 @@ -131,6 +131,20 @@ endif() @@ -42784,7 +42784,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= list(APPEND TORCH_PYTHON_SRCS ${GENERATED_THNN_CXX_CUDA}) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/engine.cpp pytorch-develop-181/torch/csrc/autograd/engine.cpp --- pytorch-v1.8.1/torch/csrc/autograd/engine.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/engine.cpp 2021-12-15 23:03:21.505244615 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/engine.cpp 2021-12-16 12:02:45.559241471 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -42881,7 +42881,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto outputs = call_function(graph_task, func, inputs); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/functions/tensor.cpp pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp --- pytorch-v1.8.1/torch/csrc/autograd/functions/tensor.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp 2021-12-15 23:03:21.505244615 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp 2021-12-16 12:02:45.559241471 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -42913,7 +42913,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= /*non_blocking=*/false, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/init.cpp pytorch-develop-181/torch/csrc/autograd/init.cpp --- pytorch-v1.8.1/torch/csrc/autograd/init.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/init.cpp 2021-12-15 23:03:21.505244615 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/init.cpp 2021-12-16 12:02:45.559241471 +0800 @@ -52,6 +52,7 @@ .value("Disabled", ProfilerState::Disabled) .value("CPU", ProfilerState::CPU) @@ -42956,7 +42956,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= .value("OPENCL", c10::DeviceType::OPENCL) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/input_buffer.cpp pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp --- pytorch-v1.8.1/torch/csrc/autograd/input_buffer.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp 2021-12-15 23:03:21.505244615 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp 2021-12-16 12:02:45.559241471 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43008,7 +43008,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto& old_var = buffer[pos]; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.cpp pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp --- pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp 2021-12-15 23:03:21.505244615 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp 2021-12-16 12:02:45.559241471 +0800 @@ -147,7 +147,7 @@ constexpr const CUDAStubs* default_stubs_addr = &default_stubs; // Constant initialization, so it is guaranteed to be initialized before @@ -43251,7 +43251,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CUDAStubs::~CUDAStubs() = default; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.h pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h --- pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h 2021-12-15 23:03:21.505244615 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h 2021-12-16 12:02:45.559241471 +0800 @@ -19,6 +19,8 @@ #include // for gettimeofday() #endif @@ -43440,7 +43440,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= std::vector&& remoteProfiledEvents); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/python_variable.cpp pytorch-develop-181/torch/csrc/autograd/python_variable.cpp --- pytorch-v1.8.1/torch/csrc/autograd/python_variable.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/python_variable.cpp 2021-12-15 23:03:21.505244615 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/python_variable.cpp 2021-12-16 12:02:45.559241471 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43494,7 +43494,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"is_mkldnn", (getter)THPVariable_is_mkldnn, nullptr, nullptr, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/python_variable_indexing.cpp pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp --- pytorch-v1.8.1/torch/csrc/autograd/python_variable_indexing.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-15 23:03:21.505244615 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-16 12:02:45.559241471 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43527,7 +43527,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/distributed/c10d/init.cpp pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp --- pytorch-v1.8.1/torch/csrc/distributed/c10d/init.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp 2021-12-15 23:03:21.509244629 +0800 ++++ pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp 2021-12-16 12:02:45.563241455 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43694,7 +43694,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= torch::class_<::c10d::DistributedC10d>("dist_c10d", "frontend") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/DynamicTypes.cpp pytorch-develop-181/torch/csrc/DynamicTypes.cpp --- pytorch-v1.8.1/torch/csrc/DynamicTypes.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/DynamicTypes.cpp 2021-12-15 23:03:21.501244602 +0800 ++++ pytorch-develop-181/torch/csrc/DynamicTypes.cpp 2021-12-16 12:02:45.555241488 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43743,7 +43743,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return it->second; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/Generator.cpp pytorch-develop-181/torch/csrc/Generator.cpp --- pytorch-v1.8.1/torch/csrc/Generator.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/Generator.cpp 2021-12-15 23:03:21.501244602 +0800 ++++ pytorch-develop-181/torch/csrc/Generator.cpp 2021-12-16 12:02:45.555241488 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43790,7 +43790,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= " is not supported for torch.Generator() api."); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/serialization.cpp pytorch-develop-181/torch/csrc/generic/serialization.cpp --- pytorch-v1.8.1/torch/csrc/generic/serialization.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/serialization.cpp 2021-12-15 23:03:21.513244642 +0800 ++++ pytorch-develop-181/torch/csrc/generic/serialization.cpp 2021-12-16 12:02:45.567241438 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43888,7 +43888,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/Storage.cpp pytorch-develop-181/torch/csrc/generic/Storage.cpp --- pytorch-v1.8.1/torch/csrc/generic/Storage.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/Storage.cpp 2021-12-15 23:03:21.513244642 +0800 ++++ pytorch-develop-181/torch/csrc/generic/Storage.cpp 2021-12-16 12:02:45.567241438 +0800 @@ -1,7 +1,25 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43968,7 +43968,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for (Py_ssize_t i = 0; i < length; i++) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/StorageMethods.cpp pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp --- pytorch-v1.8.1/torch/csrc/generic/StorageMethods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp 2021-12-15 23:03:21.513244642 +0800 ++++ pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp 2021-12-16 12:02:45.567241438 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44048,7 +44048,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/Module.cpp pytorch-develop-181/torch/csrc/Module.cpp --- pytorch-v1.8.1/torch/csrc/Module.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/Module.cpp 2021-12-15 23:03:21.501244602 +0800 ++++ pytorch-develop-181/torch/csrc/Module.cpp 2021-12-16 12:02:45.555241488 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44174,7 +44174,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (incref) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/tensor/python_tensor.cpp pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp --- pytorch-v1.8.1/torch/csrc/tensor/python_tensor.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp 2021-12-15 23:03:21.537244723 +0800 ++++ pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp 2021-12-16 12:02:45.591241340 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44212,7 +44212,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/python_arg_parser.h pytorch-develop-181/torch/csrc/utils/python_arg_parser.h --- pytorch-v1.8.1/torch/csrc/utils/python_arg_parser.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/python_arg_parser.h 2021-12-15 23:03:21.537244723 +0800 ++++ pytorch-develop-181/torch/csrc/utils/python_arg_parser.h 2021-12-16 12:02:45.591241340 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44247,7 +44247,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return at::Device(device_str); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_layouts.cpp pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_layouts.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp 2021-12-15 23:03:21.537244723 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp 2021-12-16 12:02:45.591241340 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44278,7 +44278,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= PyObject *sparse_coo_layout = THPLayout_New(at::Layout::Sparse, "torch.sparse_coo"); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_new.cpp pytorch-develop-181/torch/csrc/utils/tensor_new.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_new.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_new.cpp 2021-12-15 23:03:21.541244735 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_new.cpp 2021-12-16 12:02:45.591241340 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44410,7 +44410,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= dispatch_key); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_types.cpp pytorch-develop-181/torch/csrc/utils/tensor_types.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_types.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_types.cpp 2021-12-15 23:03:21.541244735 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_types.cpp 2021-12-16 12:02:45.591241340 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44460,7 +44460,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/distributed/distributed_c10d.py pytorch-develop-181/torch/distributed/distributed_c10d.py --- pytorch-v1.8.1/torch/distributed/distributed_c10d.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/distributed/distributed_c10d.py 2021-12-15 23:03:21.541244735 +0800 ++++ pytorch-develop-181/torch/distributed/distributed_c10d.py 2021-12-16 12:02:45.595241323 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -44551,7 +44551,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= prefix_store, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/__init__.py pytorch-develop-181/torch/__init__.py --- pytorch-v1.8.1/torch/__init__.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/__init__.py 2021-12-15 23:03:21.497244589 +0800 ++++ pytorch-develop-181/torch/__init__.py 2021-12-16 12:02:45.551241504 +0800 @@ -675,3 +675,11 @@ # class usage. We add these lines here to preserve backward compatibility. quantized_lstm = torch.ops.aten.quantized_lstm @@ -44567,7 +44567,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/CMakeLists.txt pytorch-develop-181/torch/lib/c10d/CMakeLists.txt --- pytorch-v1.8.1/torch/lib/c10d/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/CMakeLists.txt 2021-12-15 23:03:21.549244763 +0800 ++++ pytorch-develop-181/torch/lib/c10d/CMakeLists.txt 2021-12-16 12:02:45.599241307 +0800 @@ -27,6 +27,10 @@ option(USE_C10D_NCCL "USE C10D NCCL" ON) endif() @@ -44620,7 +44620,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= copy_header(ProcessGroupMPI.hpp) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/comm.cpp pytorch-develop-181/torch/lib/c10d/comm.cpp --- pytorch-v1.8.1/torch/lib/c10d/comm.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/comm.cpp 2021-12-15 23:03:21.549244763 +0800 ++++ pytorch-develop-181/torch/lib/c10d/comm.cpp 2021-12-16 12:02:45.603241290 +0800 @@ -12,6 +12,26 @@ class BroadcastWork { @@ -44709,7 +44709,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= while (!in_flight.empty()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/reducer.cpp pytorch-develop-181/torch/lib/c10d/reducer.cpp --- pytorch-v1.8.1/torch/lib/c10d/reducer.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/reducer.cpp 2021-12-15 23:03:21.549244763 +0800 ++++ pytorch-develop-181/torch/lib/c10d/reducer.cpp 2021-12-16 12:02:45.603241290 +0800 @@ -18,6 +18,18 @@ namespace c10d { namespace { @@ -44901,7 +44901,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // A bucket with one or more dense tensors needs to be unflattened. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/libshm/CMakeLists.txt pytorch-develop-181/torch/lib/libshm/CMakeLists.txt --- pytorch-v1.8.1/torch/lib/libshm/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/libshm/CMakeLists.txt 2021-12-15 23:03:21.549244763 +0800 ++++ pytorch-develop-181/torch/lib/libshm/CMakeLists.txt 2021-12-16 12:02:45.603241290 +0800 @@ -41,8 +41,11 @@ set_target_properties(shm PROPERTIES PREFIX "lib" @@ -44917,7 +44917,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # https://github.com/libgit2/libgit2/issues/2128#issuecomment-35649830 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/batchnorm.py pytorch-develop-181/torch/nn/modules/batchnorm.py --- pytorch-v1.8.1/torch/nn/modules/batchnorm.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/batchnorm.py 2021-12-15 23:03:21.553244776 +0800 ++++ pytorch-develop-181/torch/nn/modules/batchnorm.py 2021-12-16 12:02:45.607241274 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -44939,7 +44939,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= from ._functions import SyncBatchNorm as sync_batch_norm diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/module.py pytorch-develop-181/torch/nn/modules/module.py --- pytorch-v1.8.1/torch/nn/modules/module.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/module.py 2021-12-15 23:03:21.553244776 +0800 ++++ pytorch-develop-181/torch/nn/modules/module.py 2021-12-16 12:02:45.607241274 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45065,7 +45065,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if convert_to_format is not None and t.dim() == 4: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/normalization.py pytorch-develop-181/torch/nn/modules/normalization.py --- pytorch-v1.8.1/torch/nn/modules/normalization.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/normalization.py 2021-12-15 23:03:21.553244776 +0800 ++++ pytorch-develop-181/torch/nn/modules/normalization.py 2021-12-16 12:02:45.607241274 +0800 @@ -167,8 +167,11 @@ init.zeros_(self.bias) @@ -45082,7 +45082,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return '{normalized_shape}, eps={eps}, ' \ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/parallel/distributed.py pytorch-develop-181/torch/nn/parallel/distributed.py --- pytorch-v1.8.1/torch/nn/parallel/distributed.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/parallel/distributed.py 2021-12-15 23:03:21.553244776 +0800 ++++ pytorch-develop-181/torch/nn/parallel/distributed.py 2021-12-16 12:02:45.607241274 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45114,7 +45114,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= output = self.module(*inputs[0], **kwargs[0]) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/serialization.py pytorch-develop-181/torch/serialization.py --- pytorch-v1.8.1/torch/serialization.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/serialization.py 2021-12-15 23:03:21.561244803 +0800 ++++ pytorch-develop-181/torch/serialization.py 2021-12-16 12:02:45.615241241 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45203,7 +45203,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= See also: `saving-loading-tensors` diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/storage.py pytorch-develop-181/torch/storage.py --- pytorch-v1.8.1/torch/storage.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/storage.py 2021-12-15 23:03:21.561244803 +0800 ++++ pytorch-develop-181/torch/storage.py 2021-12-16 12:02:45.615241241 +0800 @@ -8,6 +8,7 @@ class _StorageBase(object): _cdata: Any @@ -45223,7 +45223,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= else: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/tensor.py pytorch-develop-181/torch/tensor.py --- pytorch-v1.8.1/torch/tensor.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/tensor.py 2021-12-15 23:03:21.561244803 +0800 ++++ pytorch-develop-181/torch/tensor.py 2021-12-16 12:02:45.615241241 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45285,7 +45285,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def __reversed__(self): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_tensor_str.py pytorch-develop-181/torch/_tensor_str.py --- pytorch-v1.8.1/torch/_tensor_str.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_tensor_str.py 2021-12-15 23:03:21.497244589 +0800 ++++ pytorch-develop-181/torch/_tensor_str.py 2021-12-16 12:02:45.551241504 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45329,7 +45329,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # TODO: add an API to map real -> complex dtypes diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/utils/data/dataloader.py pytorch-develop-181/torch/utils/data/dataloader.py --- pytorch-v1.8.1/torch/utils/data/dataloader.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/utils/data/dataloader.py 2021-12-15 23:03:21.569244829 +0800 ++++ pytorch-develop-181/torch/utils/data/dataloader.py 2021-12-16 12:02:45.619241224 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45390,7 +45390,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= pin_memory_thread.start() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/utils/data/_utils/pin_memory.py pytorch-develop-181/torch/utils/data/_utils/pin_memory.py --- pytorch-v1.8.1/torch/utils/data/_utils/pin_memory.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/utils/data/_utils/pin_memory.py 2021-12-15 23:03:17.677231809 +0800 ++++ pytorch-develop-181/torch/utils/data/_utils/pin_memory.py 2021-12-16 12:02:41.899256549 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45436,7 +45436,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # logic of this function. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_utils.py pytorch-develop-181/torch/_utils.py --- pytorch-v1.8.1/torch/_utils.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_utils.py 2021-12-15 23:03:21.497244589 +0800 ++++ pytorch-develop-181/torch/_utils.py 2021-12-16 12:02:45.551241504 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. diff --git a/pytorch1.5.0/src/aten/src/ATen/native/native_functions.yaml b/pytorch1.5.0/src/aten/src/ATen/native/native_functions.yaml index 52db5b162e1..ff12e661dd6 100644 --- a/pytorch1.5.0/src/aten/src/ATen/native/native_functions.yaml +++ b/pytorch1.5.0/src/aten/src/ATen/native/native_functions.yaml @@ -2672,6 +2672,8 @@ - func: batch_norm_gather_stats_with_counts(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int[] counts) -> (Tensor, Tensor) dispatch: CUDA: batch_norm_gather_stats_with_counts_cuda + npu_dispatch: + NPU: batch_norm_gather_stats_with_counts_npu - func: native_batch_norm_backward(Tensor grad_out, Tensor input, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_invstd, bool train, float eps, bool[3] output_mask) -> (Tensor, Tensor, Tensor) dispatch: diff --git a/pytorch1.5.0/src/aten/src/ATen/native/npu/normalization/BatchNormGatherStatsWithCountsKernelNpu.cpp b/pytorch1.5.0/src/aten/src/ATen/native/npu/normalization/BatchNormGatherStatsWithCountsKernelNpu.cpp new file mode 100644 index 00000000000..14ce281556d --- /dev/null +++ b/pytorch1.5.0/src/aten/src/ATen/native/npu/normalization/BatchNormGatherStatsWithCountsKernelNpu.cpp @@ -0,0 +1,120 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. +// All rights reserved. +// +// Licensed under the BSD 3-Clause License (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// https://opensource.org/licenses/BSD-3-Clause +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "ATen/native/npu/utils/OpAdapter.h" + +namespace at { +namespace native { +using namespace at::native::npu; + +std::tuple batch_norm_gather_stats_with_counts_npu_impl( + Tensor& mean_all, + Tensor& invstd_all, + const Tensor& self, + const Tensor& mean, + const Tensor& invstd, + const Tensor& running_mean, + const Tensor& running_var, + double momentum, + double eps, + IntArrayRef counts) { + auto options = self.options(); + auto dimC = self.size(1); + + Tensor running_mean_ = running_mean.defined() ? running_mean.unsqueeze(0) : zeros_npu({1, dimC}, options); + Tensor running_var_ = running_var.defined() ? running_var.unsqueeze(0) : ones_npu({1, dimC}, options); + IntArrayRef axes({0}); + Tensor countsTensor; + // create countsTensor + { + SmallVector countList = array_to_small_vector(counts); + auto cpuTensor = at::empty(countList.size(), TensorOptions(kCPU).dtype(at::kLong)); + std::memcpy(cpuTensor.data_ptr(), (void*)countList.data(), sizeof(int64_t) * cpuTensor.numel()); + countsTensor = cpuTensor.to(at::kNPU).npu_dtype_cast(mean.scalar_type()); + } + Tensor countsTensorT = transpose_npu(countsTensor.unsqueeze(-1), {0, 1}); + Tensor countsTensorBroadcast = npu_broadcast(countsTensorT, invstd.sizes()); + + Tensor countsAllSum = OpPreparation::ApplyTensorWithSizes({1, dimC}, mean.options()); + OpCommand cmd1; + cmd1.Name("ReduceSum") + .Input(countsTensorBroadcast) + .Input(axes, at::kInt) + .Attr("keep_dims", true) + .Output(countsAllSum) + .Run(); + + Tensor countsAllSumBroadcast = countsAllSum.expand(countsTensorBroadcast.sizes()); + OpCommand cmd2; + cmd2.Name("ReduceMeanWithCount") + .Input(mean) + .Input(countsTensorBroadcast) + .Input(countsAllSumBroadcast) + .Output(mean_all) + .Attr("axes", axes) + .Attr("keep_dims", true) + .Run(); + + Tensor meanBroadcast = mean_all.expand(mean.sizes()); + OpCommand cmd3; + cmd3.Name("SyncBatchNormGatherStatsWithCounts") + .Input(mean) + .Input(invstd) + .Input(countsTensorBroadcast) + .Input(meanBroadcast) + .Input(countsAllSum) + .Input(running_var_) + .Output(invstd_all) + .Output(running_var_) + .Attr("momentum", static_cast(momentum)) + .Attr("epsilon", static_cast(eps)) + .Run(); + + if (running_mean.defined()){ + OpCommand cmd4; + cmd4.Name("SyncBNTrainingUpdate") + .Input(mean_all) + .Input(running_mean_) + .Output(running_mean_) + .Attr("momentum", static_cast(momentum)) + .Run(); + running_mean.copy_(running_mean_.squeeze(0)); + running_var.copy_(running_var_.squeeze(0)); + } + + return std::tie(mean_all, invstd_all); +} + +std::tuple batch_norm_gather_stats_with_counts_npu( + const Tensor& self, + const Tensor& mean, + const Tensor& invstd, + const Tensor& running_mean, + const Tensor& running_var, + double momentum, + double eps, + IntArrayRef counts) { + Tensor mean_all = OpPreparation::ApplyTensor(self, {1, self.size(1)}); + Tensor invstd_all = OpPreparation::ApplyTensor(self, {1, self.size(1)}); + batch_norm_gather_stats_with_counts_npu_impl(mean_all, invstd_all, self, + mean, invstd, running_mean, running_var, + momentum, eps, counts); + + return std::make_tuple(mean_all.squeeze(0), invstd_all.squeeze(0)); +} + +} // namespace native +} // namespace at diff --git a/pytorch1.5.0/src/torch/npu/npu_frontend_enhance.py b/pytorch1.5.0/src/torch/npu/npu_frontend_enhance.py index 607621238ca..6b41e5b559c 100644 --- a/pytorch1.5.0/src/torch/npu/npu_frontend_enhance.py +++ b/pytorch1.5.0/src/torch/npu/npu_frontend_enhance.py @@ -153,8 +153,12 @@ class profile(object): self.use_e2e_profiler = use_e2e_profiler self.npu_event = config.NpuEventConfig self.aicore_metrics = config.AiCoreMetricsConfig + self.entered = False def __enter__(self): + if self.entered: + raise RuntimeError("npu profiler traces are not reentrant") + self.entered = True if self.use_e2e_profiler: torch._C._enable_e2e_profiler(self.result_path, self.npu_event | npuEvent().ACL_PROF_MSPROFTX, self.aicore_metrics) diff --git a/pytorch1.5.0/test/test_npu/test_network_ops/test_batchnorm_gatherstats_withcounts.py b/pytorch1.5.0/test/test_npu/test_network_ops/test_batchnorm_gatherstats_withcounts.py new file mode 100644 index 00000000000..4ca2799e85b --- /dev/null +++ b/pytorch1.5.0/test/test_npu/test_network_ops/test_batchnorm_gatherstats_withcounts.py @@ -0,0 +1,69 @@ +# Copyright (c) 2020, Huawei Technologies.All rights reserved. +# +# Licensed under the BSD 3-Clause License (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://opensource.org/licenses/BSD-3-Clause +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import sys +import copy +import torch +import numpy as np +from common_utils import TestCase, run_tests +from common_device_type import dtypes, instantiate_device_type_tests +from util_test import create_common_tensor + +class TestBatchNormGatherStatsWithCounts(TestCase): + def expect_cuda_out_fp16(self): + return [np.array([0.5757, 0.4543, 0.3857], dtype=np.float16), + np.array([0.139, 0.1124, 0.2357], dtype=np.float16), + np.array([0.0842, 0.9673, 0.75], dtype=np.float16), + np.array([0.681, 1.668, 1.11], dtype=np.float16)] + + def expect_cuda_out_fp32(self): + return [np.array([0.46471214, 0.6849079, 0.83278275], dtype=np.float32), + np.array([0.3682663, 0.46639538, 0.23710594], dtype=np.float32), + np.array([0.41927528, 0.56878287, 0.04250176], dtype=np.float32), + np.array([1.0024216, 0.6232378, 0.7974624], dtype=np.float32)] + + def npu_op_exec(self, *args): + npu_mean, npu_invstd = torch.batch_norm_gather_stats_with_counts(*args) + out_mean = npu_mean.cpu().numpy() + out_invstd = npu_invstd.cpu().numpy() + return out_mean, out_invstd + + def test_batch_norm_gather_stats_with_counts(self, device): + shape_format = [ + [[np.float32, -1, [2, 3, 12, 12]], [np.float32, -1, [4, 3]], [np.float32, -1, [4, 3]], \ + [np.float32, -1, [3]], [np.float32, -1, [3]], 1e-3, 1e-5, [4, 5, 6, 4]], + [[np.float16, -1, [16, 3, 12, 12]], [np.float16, -1, [4, 3]], [np.float16, -1, [4, 3]], \ + [np.float16, -1, [3]], [np.float16, -1, [3]], 1e-2, 1e-4, [4, 5, 3, 2]], + ] + for item in shape_format: + assert len(item[-1]) == item[1][-1][0] + cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 10) + cpu_mean, npu_mean = create_common_tensor(item[1], 0, 1) + cpu_invstd, npu_invstd = create_common_tensor(item[2], 0, 1) + cpu_running_mean, npu_running_mean = create_common_tensor(item[3], 0, 1) + cpu_running_invstd, npu_running_invstd = create_common_tensor(item[4], 0, 1) + npu_output = self.npu_op_exec(npu_input1, npu_mean, npu_invstd, npu_running_mean, npu_running_invstd, item[-3], item[-2], item[-1]) + + if item[0][0] == np.float16: + cuda_output = self.expect_cuda_out_fp16() + else: + cuda_output = self.expect_cuda_out_fp32() + self.assertRtolEqual(npu_output[0], cuda_output[0]) + self.assertRtolEqual(npu_output[1], cuda_output[1]) + self.assertRtolEqual(npu_running_mean.cpu().numpy(), cuda_output[2]) + self.assertRtolEqual(npu_running_invstd.cpu().numpy(), cuda_output[3]) + +instantiate_device_type_tests(TestBatchNormGatherStatsWithCounts, globals(), except_for='cpu') +if __name__ == "__main__": + # NB: Op support static && dynamic, but static is more faster, so use static ut here! + run_tests() diff --git a/pytorch1.5.0/test/test_npu/test_npu_tools/common_utils.py b/pytorch1.5.0/test/test_npu/test_npu_tools/common_utils.py new file mode 100644 index 00000000000..6df722dc300 --- /dev/null +++ b/pytorch1.5.0/test/test_npu/test_npu_tools/common_utils.py @@ -0,0 +1,28 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. +# All rights reserved. +# +# Licensed under the BSD 3-Clause License (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://opensource.org/licenses/BSD-3-Clause +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +r"""Importing this file must **not** initialize CUDA context. test_distributed +relies on this assumption to properly run. This means that when this is imported +no CUDA calls shall be made, including torch.cuda.device_count(), etc. + +torch.testing._internal.common_cuda.py can freely initialize CUDA context when imported. +""" +import os +import sys +common_path = os.path.dirname("../common/") +if common_path not in sys.path: + sys.path.append(common_path) +from common_utils_new import TestCase, run_tests diff --git a/pytorch1.5.0/test/test_npu/test_npu_tools/test_npu_profiler.py b/pytorch1.5.0/test/test_npu/test_npu_tools/test_npu_profiler.py new file mode 100644 index 00000000000..d4f18687068 --- /dev/null +++ b/pytorch1.5.0/test/test_npu/test_npu_tools/test_npu_profiler.py @@ -0,0 +1,111 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. +# All rights reserved. +# +# Licensed under the BSD 3-Clause License (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://opensource.org/licenses/BSD-3-Clause +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import shutil +from itertools import combinations +import torch +from common_utils import TestCase, run_tests + +class SmallModel(torch.nn.Module): + def __init__(self, in_channel=3, out_channel=12): + super(SmallModel, self).__init__() + self.conv1 = torch.nn.Conv2d(in_channel, in_channel, 3, padding=1) + self.relu1 = torch.nn.ReLU() + self.conv2 = torch.nn.Conv2d(in_channel, out_channel, 3, padding=1) + + def forward(self, input_1): + input_1 = self.conv1(input_1) + input_1 = self.relu1(input_1) + input_1 = self.conv2(input_1) + return input_1.reshape(input_1.shape[0], -1) + +class TestCannProfiler(TestCase): + enevtTypeResults = [] + results_path = "./results" + + @classmethod + def setUpClass(cls): + if not os.path.exists(TestCannProfiler.results_path): + os.makedirs(TestCannProfiler.results_path) + torch.npu.prof_init(TestCannProfiler.results_path) + tensor = torch.rand(2,3).npu() + + enevtTypes = [{"ACL_PROF_ACL_API":False}, {"ACL_PROF_TASK_TIME":False}, + {"ACL_PROF_AICORE_METRICS":False}, {"ACL_PROF_AICPU":False}, + {"ACL_PROF_L2CACHE":False}, {"ACL_PROF_HCCL_TRACE":False}, + {"ACL_PROF_TRAINING_TRACE":False}] + + enevtTypeCombinations = list(combinations(enevtTypes, 1)) + list(combinations(enevtTypes, 2)) + \ + list(combinations(enevtTypes, 3)) + list(combinations(enevtTypes, 4)) + \ + list(combinations(enevtTypes, 5)) + list(combinations(enevtTypes, 6)) + for events in enevtTypeCombinations: + temp_events = {} + for event in events: + temp_events.update(event) + TestCannProfiler.enevtTypeResults.append(temp_events) + + @classmethod + def tearDownClass(cls): + if os.path.exists(TestCannProfiler.results_path): + shutil.rmtree(TestCannProfiler.results_path) + torch.npu.prof_finalize() + + def _run_ops(self): + input_1 = torch.rand(10, 10).npu() + input_2 = torch.rand(10, 10).npu() + out = input_1*input_2 + + def _run_small_model(self): + input_shape = (4, 3, 24, 24) + out_shape = (4, 12, 24, 24) + device = "npu" + model = SmallModel(input_shape[1], out_shape[1]).to(device) + criterion = torch.nn.MSELoss() + optimizer = torch.optim.SGD(model.parameters(), lr=0.0001) + for i in range(10): + inputs = torch.rand(input_shape).to(device) + target = torch.rand(out_shape).reshape(out_shape[0], -1).to(device) + output = model(inputs) + loss = criterion(output, target) + loss.backward() + optimizer.zero_grad() + optimizer.step() + + def _test_e2e_ops(self, *args, **kwargs): + config = torch.npu.profileConfig(**kwargs) + torch.npu.prof_start(config.NpuEventConfig, config.AiCoreMetricsConfig) + self._run_ops() + torch.npu.prof_stop() + + def _test_e2e_model(self, *args, **kwargs): + config = torch.npu.profileConfig(**kwargs) + torch.npu.prof_start(config.NpuEventConfig, config.AiCoreMetricsConfig) + self._run_small_model() + torch.npu.prof_stop() + + def test_with_ops(self): + for events in TestCannProfiler.enevtTypeResults: + for i in range(5): + self._test_e2e_ops(**events, aiCoreMetricsType=i) + + def test_with_small_model(self): + for events in TestCannProfiler.enevtTypeResults: + for i in range(5): + self._test_e2e_model(**events, aiCoreMetricsType=i) + +if __name__ == "__main__": + run_tests() -- Gitee From a1430f09b80dcbbbaecd40dd9c050e8188286ad3 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Thu, 16 Dec 2021 23:02:06 +0800 Subject: [PATCH 06/55] =?UTF-8?q?=E5=90=8C=E6=AD=A51.5.0=203caf6d087849ae9?= =?UTF-8?q?9931af5393a5c0c747e1330a6=20=20=20=20=20!2794=20=E3=80=90Issue?= =?UTF-8?q?=E3=80=91=E3=80=91Silu=E6=94=AF=E6=8C=81inplace=20=20=20=20=20M?= =?UTF-8?q?erge=20pull=20request=20!2794=20from=20wuxiankun/issue1.0=20--?= =?UTF-8?q?=20=20=20=20=20!2775=20=E8=A1=A5=E5=85=85ut=E6=B5=8B=E8=AF=95?= =?UTF-8?q?=E7=94=A8=E4=BE=8B=20=20=20=20=20Merge=20pull=20request=20!2775?= =?UTF-8?q?=20from=20=E7=8E=8B=E5=A4=8F=E5=A4=8F/master=20=E5=90=8C?= =?UTF-8?q?=E6=AD=A51.8.1=20db9b820f4aba41894f26e187a67450e4b2064a4c?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- patch/pytorch1.5.0_npu.patch | 224 +++++++++--------- patch/pytorch1.8.1_npu.patch | 184 +++++++------- .../src/ATen/native/native_functions.yaml | 4 + .../src/ATen/native/npu/SiluKernelNpu.cpp | 18 +- .../test_network_ops/test_log_sigmoid.py | 37 +++ .../test_log_sigmoid_backward.py | 10 +- .../test_poisson_nll_loss.py | 199 ++++++++++------ .../test_npu/test_network_ops/test_silu.py | 32 +++ 8 files changed, 434 insertions(+), 274 deletions(-) rename pytorch1.5.0/test/test_npu/{ => test_network_ops}/test_poisson_nll_loss.py (74%) diff --git a/patch/pytorch1.5.0_npu.patch b/patch/pytorch1.5.0_npu.patch index 3685f920ffd..80a56ee7224 100644 --- a/patch/pytorch1.5.0_npu.patch +++ b/patch/pytorch1.5.0_npu.patch @@ -1,6 +1,6 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/CMakeLists.txt pytorch-develop-150/aten/CMakeLists.txt --- pytorch-v1.5.0/aten/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/CMakeLists.txt 2021-12-16 12:02:40.299263155 +0800 ++++ pytorch-develop-150/aten/CMakeLists.txt 2021-12-16 23:01:59.612640545 +0800 @@ -22,8 +22,10 @@ set(ATen_CPU_INCLUDE) set(ATen_THIRD_PARTY_INCLUDE) @@ -51,7 +51,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_CPU_DEPENDENCY_LIBS ${ATen_CPU_DEPENDENCY_LIBS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/CMakeLists.txt pytorch-develop-150/aten/src/ATen/CMakeLists.txt --- pytorch-v1.5.0/aten/src/ATen/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/CMakeLists.txt 2021-12-16 12:02:40.299263155 +0800 ++++ pytorch-develop-150/aten/src/ATen/CMakeLists.txt 2021-12-16 23:01:59.612640545 +0800 @@ -67,6 +67,9 @@ FILE(GLOB native_quantized_h "native/quantized/*.h" "native/quantized/cpu/*.h") FILE(GLOB native_cpu_h "native/cpu/*.h") @@ -129,7 +129,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_QUANTIZED_SRCS ${ATen_QUANTIZED_SRCS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/core/dispatch/DispatchTable.h pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h --- pytorch-v1.5.0/aten/src/ATen/core/dispatch/DispatchTable.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h 2021-12-16 12:02:40.303263139 +0800 ++++ pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h 2021-12-16 23:01:59.616640791 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -170,7 +170,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/function_wrapper.py pytorch-develop-150/aten/src/ATen/function_wrapper.py --- pytorch-v1.5.0/aten/src/ATen/function_wrapper.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/function_wrapper.py 2021-12-16 12:02:40.307263122 +0800 ++++ pytorch-develop-150/aten/src/ATen/function_wrapper.py 2021-12-16 23:01:59.616640791 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -354,7 +354,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for option in declaration['options']: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/gen.py pytorch-develop-150/aten/src/ATen/gen.py --- pytorch-v1.5.0/aten/src/ATen/gen.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/gen.py 2021-12-16 12:02:40.307263122 +0800 ++++ pytorch-develop-150/aten/src/ATen/gen.py 2021-12-16 23:01:59.616640791 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -512,7 +512,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= generate_outputs() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/BatchLinearAlgebra.cpp pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp --- pytorch-v1.5.0/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-12-16 12:02:40.307263122 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-12-16 23:01:59.620641037 +0800 @@ -680,7 +680,7 @@ std::tuple triangular_solve_out(Tensor& result, Tensor& clone_A, const Tensor& self, const Tensor& A, bool upper, bool transpose, bool unitriangular) { @@ -524,7 +524,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return std::tuple(result, clone_A); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/cpu/Activation.cpp pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp --- pytorch-v1.5.0/aten/src/ATen/native/cpu/Activation.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp 2021-12-16 12:02:40.311263106 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp 2021-12-16 23:01:59.620641037 +0800 @@ -339,20 +339,20 @@ void hardsigmoid_backward_kernel(TensorIterator& iter) { @@ -552,7 +552,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/Memory.cpp pytorch-develop-150/aten/src/ATen/native/Memory.cpp --- pytorch-v1.5.0/aten/src/ATen/native/Memory.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/Memory.cpp 2021-12-16 12:02:40.307263122 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/Memory.cpp 2021-12-16 23:01:59.620641037 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -614,7 +614,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= detail::computeStorageSize(self.sizes(), self.strides()), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/native_functions.yaml pytorch-develop-150/aten/src/ATen/native/native_functions.yaml --- pytorch-v1.5.0/aten/src/ATen/native/native_functions.yaml 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/native_functions.yaml 2021-12-16 12:02:40.315263089 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/native_functions.yaml 2021-12-16 23:01:59.628641528 +0800 @@ -1,6 +1,5 @@ # See README.md in this directory for more guidance @@ -6686,7 +6686,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: slow_conv_dilated3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1) -> Tensor python_module: nn -@@ -6577,57 +8247,471 @@ +@@ -6577,57 +8247,475 @@ dispatch: CPU: col2im_out_cpu CUDA: col2im_out_cuda @@ -7138,6 +7138,10 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + npu_dispatch_only: + NPU: silu_npu + ++- func: npu_silu_(Tensor(a!) self) -> Tensor(a!) ++ npu_dispatch_only: ++ NPU: silu_npu_ ++ +- func: npu_silu_backward(Tensor grad_output, Tensor x0, Tensor x1) -> Tensor + npu_dispatch_only: + NPU: silu_backward_npu @@ -7161,7 +7165,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S --- pytorch-v1.5.0/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-12-16 12:02:40.327263040 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-12-16 23:01:59.640642265 +0800 @@ -659,14 +659,14 @@ SUB x1, x1, 4 @@ -7187,7 +7191,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CMP x1, 2 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorCompare.cpp pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorCompare.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp 2021-12-16 12:02:40.307263122 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp 2021-12-16 23:01:59.620641037 +0800 @@ -64,7 +64,7 @@ Tensor isinf(const Tensor &self) { @@ -7199,7 +7203,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return AT_DISPATCH_FLOATING_TYPES_AND_HALF(self.scalar_type(), "isinf", [&]() { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorFactories.cpp pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorFactories.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp 2021-12-16 12:02:40.307263122 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp 2021-12-16 23:01:59.620641037 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7244,7 +7248,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorProperties.cpp pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorProperties.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp 2021-12-16 12:02:40.307263122 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp 2021-12-16 23:01:59.620641037 +0800 @@ -87,6 +87,7 @@ if (self.is_contiguous(memory_format)) { return self; @@ -7255,7 +7259,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= "preserve memory format is unsupported by the contiguous operator"); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/UpSampleBicubic2d.cpp pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp --- pytorch-v1.5.0/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-12-16 12:02:40.311263106 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-12-16 23:01:59.620641037 +0800 @@ -26,7 +26,7 @@ const scalar_t* in = &idata[output_y * input_width + output_x]; scalar_t* out = &odata[output_y * output_width + output_x]; @@ -7267,7 +7271,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= out += output_width * output_height; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native_parse.py pytorch-develop-150/aten/src/ATen/native_parse.py --- pytorch-v1.5.0/aten/src/ATen/native_parse.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native_parse.py 2021-12-16 12:02:40.335263006 +0800 ++++ pytorch-develop-150/aten/src/ATen/native_parse.py 2021-12-16 23:01:59.648642757 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -7305,7 +7309,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= msg = '''Exception raised in processing function: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/preprocess_declarations.py pytorch-develop-150/aten/src/ATen/preprocess_declarations.py --- pytorch-v1.5.0/aten/src/ATen/preprocess_declarations.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/preprocess_declarations.py 2021-12-16 12:02:40.335263006 +0800 ++++ pytorch-develop-150/aten/src/ATen/preprocess_declarations.py 2021-12-16 23:01:59.648642757 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -7337,7 +7341,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/templates/TensorBody.h pytorch-develop-150/aten/src/ATen/templates/TensorBody.h --- pytorch-v1.5.0/aten/src/ATen/templates/TensorBody.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/templates/TensorBody.h 2021-12-16 12:02:40.335263006 +0800 ++++ pytorch-develop-150/aten/src/ATen/templates/TensorBody.h 2021-12-16 23:01:59.648642757 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7370,7 +7374,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/templates/TensorMethods.h pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h --- pytorch-v1.5.0/aten/src/ATen/templates/TensorMethods.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h 2021-12-16 12:02:40.335263006 +0800 ++++ pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h 2021-12-16 23:01:59.648642757 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7404,7 +7408,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/CMakeLists.txt pytorch-develop-150/aten/src/TH/CMakeLists.txt --- pytorch-v1.5.0/aten/src/TH/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/CMakeLists.txt 2021-12-16 12:02:40.335263006 +0800 ++++ pytorch-develop-150/aten/src/TH/CMakeLists.txt 2021-12-16 23:01:59.648642757 +0800 @@ -48,6 +48,11 @@ ${CMAKE_CURRENT_SOURCE_DIR} PARENT_SCOPE) @@ -7419,7 +7423,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/generic/THStorage.cpp pytorch-develop-150/aten/src/TH/generic/THStorage.cpp --- pytorch-v1.5.0/aten/src/TH/generic/THStorage.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/generic/THStorage.cpp 2021-12-16 12:02:40.339262990 +0800 ++++ pytorch-develop-150/aten/src/TH/generic/THStorage.cpp 2021-12-16 23:01:59.652643002 +0800 @@ -1,9 +1,32 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7528,7 +7532,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/generic/THStorage.h pytorch-develop-150/aten/src/TH/generic/THStorage.h --- pytorch-v1.5.0/aten/src/TH/generic/THStorage.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/generic/THStorage.h 2021-12-16 12:02:40.339262990 +0800 ++++ pytorch-develop-150/aten/src/TH/generic/THStorage.h 2021-12-16 23:01:59.652643002 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7567,7 +7571,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/CMakeLists.txt pytorch-develop-150/c10/CMakeLists.txt --- pytorch-v1.5.0/c10/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/CMakeLists.txt 2021-12-16 12:02:40.343262973 +0800 ++++ pytorch-develop-150/c10/CMakeLists.txt 2021-12-16 23:01:59.656643248 +0800 @@ -63,6 +63,14 @@ message(STATUS "don't use NUMA") endif() @@ -7596,7 +7600,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # not checked in diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Backend.h pytorch-develop-150/c10/core/Backend.h --- pytorch-v1.5.0/c10/core/Backend.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Backend.h 2021-12-16 12:02:40.343262973 +0800 ++++ pytorch-develop-150/c10/core/Backend.h 2021-12-16 23:01:59.660643494 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7693,7 +7697,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case Backend::CUDA: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Device.cpp pytorch-develop-150/c10/core/Device.cpp --- pytorch-v1.5.0/c10/core/Device.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Device.cpp 2021-12-16 12:02:40.343262973 +0800 ++++ pytorch-develop-150/c10/core/Device.cpp 2021-12-16 23:01:59.660643494 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7733,7 +7737,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= types.begin(), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Device.h pytorch-develop-150/c10/core/Device.h --- pytorch-v1.5.0/c10/core/Device.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Device.h 2021-12-16 12:02:40.343262973 +0800 ++++ pytorch-develop-150/c10/core/Device.h 2021-12-16 23:01:59.660643494 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7768,7 +7772,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return type_ == DeviceType::CPU; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DeviceType.cpp pytorch-develop-150/c10/core/DeviceType.cpp --- pytorch-v1.5.0/c10/core/DeviceType.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DeviceType.cpp 2021-12-16 12:02:40.343262973 +0800 ++++ pytorch-develop-150/c10/core/DeviceType.cpp 2021-12-16 23:01:59.660643494 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7808,7 +7812,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return false; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DeviceType.h pytorch-develop-150/c10/core/DeviceType.h --- pytorch-v1.5.0/c10/core/DeviceType.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DeviceType.h 2021-12-16 12:02:40.343262973 +0800 ++++ pytorch-develop-150/c10/core/DeviceType.h 2021-12-16 23:01:59.660643494 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7851,7 +7855,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= constexpr DeviceType kXLA = DeviceType::XLA; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DispatchKey.cpp pytorch-develop-150/c10/core/DispatchKey.cpp --- pytorch-v1.5.0/c10/core/DispatchKey.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DispatchKey.cpp 2021-12-16 12:02:40.343262973 +0800 ++++ pytorch-develop-150/c10/core/DispatchKey.cpp 2021-12-16 23:01:59.660643494 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7883,7 +7887,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::SparseCPUTensorId: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DispatchKey.h pytorch-develop-150/c10/core/DispatchKey.h --- pytorch-v1.5.0/c10/core/DispatchKey.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DispatchKey.h 2021-12-16 12:02:40.343262973 +0800 ++++ pytorch-develop-150/c10/core/DispatchKey.h 2021-12-16 23:01:59.660643494 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7915,7 +7919,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Storage.h pytorch-develop-150/c10/core/Storage.h --- pytorch-v1.5.0/c10/core/Storage.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Storage.h 2021-12-16 12:02:40.343262973 +0800 ++++ pytorch-develop-150/c10/core/Storage.h 2021-12-16 23:01:59.660643494 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7949,7 +7953,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/StorageImpl.cpp pytorch-develop-150/c10/core/StorageImpl.cpp --- pytorch-v1.5.0/c10/core/StorageImpl.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/StorageImpl.cpp 2021-12-16 12:02:40.343262973 +0800 ++++ pytorch-develop-150/c10/core/StorageImpl.cpp 2021-12-16 23:01:59.660643494 +0800 @@ -1 +1,18 @@ #include + @@ -7971,7 +7975,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +} // namespace c10 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/StorageImpl.h pytorch-develop-150/c10/core/StorageImpl.h --- pytorch-v1.5.0/c10/core/StorageImpl.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/StorageImpl.h 2021-12-16 12:02:40.347262957 +0800 ++++ pytorch-develop-150/c10/core/StorageImpl.h 2021-12-16 23:01:59.660643494 +0800 @@ -1,11 +1,55 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8098,7 +8102,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/TensorImpl.h pytorch-develop-150/c10/core/TensorImpl.h --- pytorch-v1.5.0/c10/core/TensorImpl.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/TensorImpl.h 2021-12-16 12:02:40.347262957 +0800 ++++ pytorch-develop-150/c10/core/TensorImpl.h 2021-12-16 23:01:59.660643494 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8166,7 +8170,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/TensorOptions.h pytorch-develop-150/c10/core/TensorOptions.h --- pytorch-v1.5.0/c10/core/TensorOptions.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/TensorOptions.h 2021-12-16 12:02:40.347262957 +0800 ++++ pytorch-develop-150/c10/core/TensorOptions.h 2021-12-16 23:01:59.660643494 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8207,7 +8211,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } else if (tid == DispatchKey::HIPTensorId) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/cuda/CMakeLists.txt pytorch-develop-150/c10/cuda/CMakeLists.txt --- pytorch-v1.5.0/c10/cuda/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/cuda/CMakeLists.txt 2021-12-16 12:02:40.347262957 +0800 ++++ pytorch-develop-150/c10/cuda/CMakeLists.txt 2021-12-16 23:01:59.660643494 +0800 @@ -24,6 +24,7 @@ CUDACachingAllocator.cpp impl/CUDAGuardImpl.cpp @@ -8226,7 +8230,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= torch_cuda_based_add_library(c10_cuda ${C10_CUDA_SRCS} ${C10_CUDA_HEADERS}) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/macros/Export.h pytorch-develop-150/c10/macros/Export.h --- pytorch-v1.5.0/c10/macros/Export.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/macros/Export.h 2021-12-16 12:02:40.347262957 +0800 ++++ pytorch-develop-150/c10/macros/Export.h 2021-12-16 23:01:59.660643494 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8353,7 +8357,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/caffe2/CMakeLists.txt pytorch-develop-150/caffe2/CMakeLists.txt --- pytorch-v1.5.0/caffe2/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/caffe2/CMakeLists.txt 2021-12-16 12:02:40.351262940 +0800 ++++ pytorch-develop-150/caffe2/CMakeLists.txt 2021-12-16 23:01:59.664643741 +0800 @@ -32,6 +32,7 @@ # Add source, includes, and libs to lists list(APPEND Caffe2_CPU_SRCS ${ATen_CPU_SRCS}) @@ -8508,7 +8512,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Call again since Caffe2_HIP_INCLUDE is extended with ATen include dirs. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/.clang-format pytorch-develop-150/.clang-format --- pytorch-v1.5.0/.clang-format 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/.clang-format 2021-12-16 12:02:40.295263171 +0800 ++++ pytorch-develop-150/.clang-format 2021-12-16 23:01:59.608640299 +0800 @@ -84,5 +84,4 @@ SpacesInSquareBrackets: false Standard: Cpp11 @@ -8519,7 +8523,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/BuildVariables.cmake pytorch-develop-150/cmake/BuildVariables.cmake --- pytorch-v1.5.0/cmake/BuildVariables.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/BuildVariables.cmake 2021-12-16 12:02:40.403262726 +0800 ++++ pytorch-develop-150/cmake/BuildVariables.cmake 2021-12-16 23:01:59.720647181 +0800 @@ -11,6 +11,7 @@ # CMakeLists.txt files under each folder respectively. set(Caffe2_CPU_SRCS) @@ -8546,7 +8550,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # symbols. However, if the lib is whole linked in caffe2 lib, we don't want diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Codegen.cmake pytorch-develop-150/cmake/Codegen.cmake --- pytorch-v1.5.0/cmake/Codegen.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Codegen.cmake 2021-12-16 12:02:40.403262726 +0800 ++++ pytorch-develop-150/cmake/Codegen.cmake 2021-12-16 23:01:59.720647181 +0800 @@ -191,13 +191,14 @@ file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt generated_cpp) file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt-cuda cuda_generated_cpp) @@ -8577,7 +8581,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endif() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Dependencies.cmake pytorch-develop-150/cmake/Dependencies.cmake --- pytorch-v1.5.0/cmake/Dependencies.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Dependencies.cmake 2021-12-16 12:02:40.403262726 +0800 ++++ pytorch-develop-150/cmake/Dependencies.cmake 2021-12-16 23:01:59.720647181 +0800 @@ -1509,6 +1509,13 @@ ENDIF(NOT C_HAS_THREAD) endif() @@ -8594,7 +8598,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Summary.cmake pytorch-develop-150/cmake/Summary.cmake --- pytorch-v1.5.0/cmake/Summary.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Summary.cmake 2021-12-16 12:02:40.407262709 +0800 ++++ pytorch-develop-150/cmake/Summary.cmake 2021-12-16 23:01:59.720647181 +0800 @@ -134,6 +134,7 @@ if(NOT "${SELECTED_OP_LIST}" STREQUAL "") message(STATUS " SELECTED_OP_LIST : ${SELECTED_OP_LIST}") @@ -8605,7 +8609,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endfunction() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/TorchConfig.cmake.in pytorch-develop-150/cmake/TorchConfig.cmake.in --- pytorch-v1.5.0/cmake/TorchConfig.cmake.in 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/TorchConfig.cmake.in 2021-12-16 12:02:40.407262709 +0800 ++++ pytorch-develop-150/cmake/TorchConfig.cmake.in 2021-12-16 23:01:59.720647181 +0800 @@ -112,6 +112,11 @@ list(APPEND TORCH_LIBRARIES ${TORCH_CUDA_LIBRARIES}) endif() @@ -8620,7 +8624,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(TORCH_CXX_FLAGS "-D_GLIBCXX_USE_CXX11_ABI=@GLIBCXX_USE_CXX11_ABI@") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/CMakeLists.txt pytorch-develop-150/CMakeLists.txt --- pytorch-v1.5.0/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/CMakeLists.txt 2021-12-16 12:02:40.295263171 +0800 ++++ pytorch-develop-150/CMakeLists.txt 2021-12-16 23:01:59.608640299 +0800 @@ -205,6 +205,10 @@ option(USE_TBB "Use TBB" OFF) option(ONNX_ML "Enable traditional ONNX ML API." ON) @@ -8687,7 +8691,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-missing-braces") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/CONTRIBUTING.zh.md pytorch-develop-150/CONTRIBUTING.zh.md --- pytorch-v1.5.0/CONTRIBUTING.zh.md 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-150/CONTRIBUTING.zh.md 2021-12-16 12:02:40.295263171 +0800 ++++ pytorch-develop-150/CONTRIBUTING.zh.md 2021-12-16 23:01:59.608640299 +0800 @@ -0,0 +1,228 @@ +# PyTorch贡献指南 +- [贡献者许可协议](#贡献者许可协议.md) @@ -8919,7 +8923,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/.dockerignore pytorch-develop-150/.dockerignore --- pytorch-v1.5.0/.dockerignore 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/.dockerignore 2021-12-16 12:02:40.295263171 +0800 ++++ pytorch-develop-150/.dockerignore 2021-12-16 23:01:59.608640299 +0800 @@ -1,257 +1 @@ -# READ THIS BEFORE YOU REFACTOR ME -# @@ -9195,7 +9199,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/requirements.txt pytorch-develop-150/requirements.txt --- pytorch-v1.5.0/requirements.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/requirements.txt 2021-12-16 12:02:40.295263171 +0800 ++++ pytorch-develop-150/requirements.txt 2021-12-16 23:01:59.728647672 +0800 @@ -4,4 +4,13 @@ requests setuptools @@ -9215,7 +9219,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/setup.py pytorch-develop-150/setup.py --- pytorch-v1.5.0/setup.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/setup.py 2021-12-16 12:02:40.295263171 +0800 ++++ pytorch-develop-150/setup.py 2021-12-16 23:01:59.732647918 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -9318,7 +9322,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= 'python/serialized_test/data/operator_test/*.zip', diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/derivatives.yaml pytorch-develop-150/tools/autograd/derivatives.yaml --- pytorch-v1.5.0/tools/autograd/derivatives.yaml 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/derivatives.yaml 2021-12-16 12:02:40.975260362 +0800 ++++ pytorch-develop-150/tools/autograd/derivatives.yaml 2021-12-16 23:02:00.360686464 +0800 @@ -107,6 +107,10 @@ # # NB: The parameter names here MUST be consistent with the parameter names @@ -9443,7 +9447,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/dump_utils.py pytorch-develop-150/tools/autograd/dump_utils.py --- pytorch-v1.5.0/tools/autograd/dump_utils.py 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/dump_utils.py 2021-12-16 12:02:40.975260362 +0800 ++++ pytorch-develop-150/tools/autograd/dump_utils.py 2021-12-16 23:02:00.360686464 +0800 @@ -0,0 +1,312 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# All rights reserved. @@ -9759,7 +9763,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + return prepare_to_check_overflow, overflow_dump_inputs diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_autograd_functions.py pytorch-develop-150/tools/autograd/gen_autograd_functions.py --- pytorch-v1.5.0/tools/autograd/gen_autograd_functions.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_autograd_functions.py 2021-12-16 12:02:40.975260362 +0800 ++++ pytorch-develop-150/tools/autograd/gen_autograd_functions.py 2021-12-16 23:02:00.360686464 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -9965,7 +9969,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_python_functions.py pytorch-develop-150/tools/autograd/gen_python_functions.py --- pytorch-v1.5.0/tools/autograd/gen_python_functions.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_python_functions.py 2021-12-16 12:02:40.975260362 +0800 ++++ pytorch-develop-150/tools/autograd/gen_python_functions.py 2021-12-16 23:02:00.360686464 +0800 @@ -1,3 +1,20 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -10007,7 +10011,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= 'value': argname, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_variable_type.py pytorch-develop-150/tools/autograd/gen_variable_type.py --- pytorch-v1.5.0/tools/autograd/gen_variable_type.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_variable_type.py 2021-12-16 12:02:40.975260362 +0800 ++++ pytorch-develop-150/tools/autograd/gen_variable_type.py 2021-12-16 23:02:00.360686464 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -10188,7 +10192,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/Functions.cpp pytorch-develop-150/tools/autograd/templates/Functions.cpp --- pytorch-v1.5.0/tools/autograd/templates/Functions.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/Functions.cpp 2021-12-16 12:02:40.975260362 +0800 ++++ pytorch-develop-150/tools/autograd/templates/Functions.cpp 2021-12-16 23:02:00.360686464 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2021 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10269,7 +10273,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto sparse = sparse_.coalesce(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/python_torch_functions.cpp pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp --- pytorch-v1.5.0/tools/autograd/templates/python_torch_functions.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp 2021-12-16 12:02:40.975260362 +0800 ++++ pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp 2021-12-16 23:02:00.360686464 +0800 @@ -22,7 +22,7 @@ #include "torch/csrc/autograd/generated/variable_factories.h" #include "torch/csrc/utils/structseq.h" @@ -10353,7 +10357,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/python_variable_methods.cpp pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp --- pytorch-v1.5.0/tools/autograd/templates/python_variable_methods.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp 2021-12-16 12:02:40.975260362 +0800 ++++ pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp 2021-12-16 23:02:00.360686464 +0800 @@ -15,7 +15,13 @@ #include "torch/csrc/cuda/Stream.h" #include "torch/csrc/cuda/Event.h" @@ -10448,7 +10452,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"has_names", (PyCFunction)THPVariable_has_names, METH_NOARGS, NULL}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/VariableType.cpp pytorch-develop-150/tools/autograd/templates/VariableType.cpp --- pytorch-v1.5.0/tools/autograd/templates/VariableType.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/VariableType.cpp 2021-12-16 12:02:40.975260362 +0800 ++++ pytorch-develop-150/tools/autograd/templates/VariableType.cpp 2021-12-16 23:02:00.360686464 +0800 @@ -1,7 +1,29 @@ +// Copyright (c) 2021 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10481,7 +10485,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/VariableType.h pytorch-develop-150/tools/autograd/templates/VariableType.h --- pytorch-v1.5.0/tools/autograd/templates/VariableType.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/VariableType.h 2021-12-16 12:02:40.975260362 +0800 ++++ pytorch-develop-150/tools/autograd/templates/VariableType.h 2021-12-16 23:02:00.360686464 +0800 @@ -1,3 +1,20 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10513,7 +10517,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= const at::Tensor & unpack(const Tensor & t, const char * name, int pos); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/build_variables.bzl pytorch-develop-150/tools/build_variables.bzl --- pytorch-v1.5.0/tools/build_variables.bzl 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/build_variables.bzl 2021-12-16 12:02:40.975260362 +0800 ++++ pytorch-develop-150/tools/build_variables.bzl 2021-12-16 23:02:00.356686219 +0800 @@ -46,6 +46,7 @@ "torch/csrc/autograd/functions/utils.cpp", "torch/csrc/autograd/input_buffer.cpp", @@ -10599,7 +10603,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def grad(outputs: _TensorOrTensors, inputs: _TensorOrTensors, grad_outputs: Optional[_TensorOrTensors]=..., retain_graph: Optional[bool]=..., create_graph: bool=..., only_inputs: bool=..., allow_unused: bool=...) -> Tuple[Tensor, ...]: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/autograd/profiler.py pytorch-develop-150/torch/autograd/profiler.py --- pytorch-v1.5.0/torch/autograd/profiler.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/autograd/profiler.py 2021-12-16 12:02:40.979260346 +0800 ++++ pytorch-develop-150/torch/autograd/profiler.py 2021-12-16 23:02:00.360686464 +0800 @@ -1,8 +1,25 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -11076,7 +11080,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ''.join(result) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/CMakeLists.txt pytorch-develop-150/torch/CMakeLists.txt --- pytorch-v1.5.0/torch/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/CMakeLists.txt 2021-12-16 12:02:40.975260362 +0800 ++++ pytorch-develop-150/torch/CMakeLists.txt 2021-12-16 23:02:00.360686464 +0800 @@ -97,6 +97,7 @@ ${TORCH_SRC_DIR}/csrc/tensor/python_tensor.cpp ${TORCH_SRC_DIR}/csrc/utils.cpp @@ -11108,7 +11112,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endif() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/engine.cpp pytorch-develop-150/torch/csrc/autograd/engine.cpp --- pytorch-v1.5.0/torch/csrc/autograd/engine.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/engine.cpp 2021-12-16 12:02:40.983260329 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/engine.cpp 2021-12-16 23:02:00.368686955 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11260,7 +11264,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto event = c10::Event{c10::DeviceType::CUDA}; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/function.h pytorch-develop-150/torch/csrc/autograd/function.h --- pytorch-v1.5.0/torch/csrc/autograd/function.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/function.h 2021-12-16 12:02:40.983260329 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/function.h 2021-12-16 23:02:00.368686955 +0800 @@ -11,6 +11,7 @@ #include @@ -11282,7 +11286,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // probably operate with names. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/functions/tensor.cpp pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp --- pytorch-v1.5.0/torch/csrc/autograd/functions/tensor.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp 2021-12-16 12:02:40.983260329 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp 2021-12-16 23:02:00.372687200 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11314,7 +11318,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= /*non_blocking=*/false, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/init.cpp pytorch-develop-150/torch/csrc/autograd/init.cpp --- pytorch-v1.5.0/torch/csrc/autograd/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/init.cpp 2021-12-16 12:02:40.983260329 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/init.cpp 2021-12-16 23:02:00.372687200 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11357,7 +11361,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= m.def("_enable_profiler", enableProfiler); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/input_buffer.cpp pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp --- pytorch-v1.5.0/torch/csrc/autograd/input_buffer.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp 2021-12-16 12:02:40.983260329 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp 2021-12-16 23:02:00.372687200 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11409,7 +11413,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto& old_var = buffer[pos]; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/profiler.cpp pytorch-develop-150/torch/csrc/autograd/profiler.cpp --- pytorch-v1.5.0/torch/csrc/autograd/profiler.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/profiler.cpp 2021-12-16 12:02:40.983260329 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/profiler.cpp 2021-12-16 23:02:00.372687200 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11649,7 +11653,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CUDAStubs::~CUDAStubs() = default; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/profiler.h pytorch-develop-150/torch/csrc/autograd/profiler.h --- pytorch-v1.5.0/torch/csrc/autograd/profiler.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/profiler.h 2021-12-16 12:02:40.983260329 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/profiler.h 2021-12-16 23:02:00.372687200 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11785,7 +11789,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/python_variable.cpp pytorch-develop-150/torch/csrc/autograd/python_variable.cpp --- pytorch-v1.5.0/torch/csrc/autograd/python_variable.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/python_variable.cpp 2021-12-16 12:02:40.983260329 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/python_variable.cpp 2021-12-16 23:02:00.372687200 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11839,7 +11843,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"is_complex", (getter)THPVariable_is_complex, nullptr, nullptr, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/python_variable_indexing.cpp pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp --- pytorch-v1.5.0/torch/csrc/autograd/python_variable_indexing.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-16 12:02:40.983260329 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-16 23:02:00.372687200 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11870,7 +11874,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= at::Device self_device = self_.device(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/record_function.cpp pytorch-develop-150/torch/csrc/autograd/record_function.cpp --- pytorch-v1.5.0/torch/csrc/autograd/record_function.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/record_function.cpp 2021-12-16 12:02:40.983260329 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/record_function.cpp 2021-12-16 23:02:00.372687200 +0800 @@ -154,6 +154,12 @@ } } @@ -11904,7 +11908,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/record_function.h pytorch-develop-150/torch/csrc/autograd/record_function.h --- pytorch-v1.5.0/torch/csrc/autograd/record_function.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/record_function.h 2021-12-16 12:02:40.987260313 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/record_function.h 2021-12-16 23:02:00.372687200 +0800 @@ -44,6 +44,9 @@ // Default constructor is used with before function called afterwards RecordFunction() {} @@ -11968,7 +11972,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (torch::autograd::profiler::needsInputs()) { \ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/utils/wrap_outputs.h pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h --- pytorch-v1.5.0/torch/csrc/autograd/utils/wrap_outputs.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h 2021-12-16 12:02:40.987260313 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h 2021-12-16 23:02:00.372687200 +0800 @@ -168,6 +168,45 @@ return r.release(); } @@ -12017,7 +12021,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (!r) throw python_error(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/VariableTypeManual.cpp pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp --- pytorch-v1.5.0/torch/csrc/autograd/VariableTypeManual.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp 2021-12-16 12:02:40.983260329 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp 2021-12-16 23:02:00.368686955 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12051,7 +12055,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (!t.defined()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/comm.cpp pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/comm.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp 2021-12-16 12:02:40.987260313 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp 2021-12-16 23:02:00.372687200 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12157,7 +12161,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= while (!in_flight.empty()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/init.cpp pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp 2021-12-16 12:02:40.987260313 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp 2021-12-16 23:02:00.372687200 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12214,7 +12218,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= .def("is_success", &::c10d::ProcessGroup::Work::isSuccess) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/reducer.cpp pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/reducer.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp 2021-12-16 12:02:40.987260313 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp 2021-12-16 23:02:00.372687200 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12349,7 +12353,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/DynamicTypes.cpp pytorch-develop-150/torch/csrc/DynamicTypes.cpp --- pytorch-v1.5.0/torch/csrc/DynamicTypes.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/DynamicTypes.cpp 2021-12-16 12:02:40.979260346 +0800 ++++ pytorch-develop-150/torch/csrc/DynamicTypes.cpp 2021-12-16 23:02:00.364686709 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12398,7 +12402,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return it->second; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/Generator.cpp pytorch-develop-150/torch/csrc/Generator.cpp --- pytorch-v1.5.0/torch/csrc/Generator.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/Generator.cpp 2021-12-16 12:02:40.979260346 +0800 ++++ pytorch-develop-150/torch/csrc/Generator.cpp 2021-12-16 23:02:00.364686709 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12466,7 +12470,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #endif diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/serialization.cpp pytorch-develop-150/torch/csrc/generic/serialization.cpp --- pytorch-v1.5.0/torch/csrc/generic/serialization.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/serialization.cpp 2021-12-16 12:02:40.987260313 +0800 ++++ pytorch-develop-150/torch/csrc/generic/serialization.cpp 2021-12-16 23:02:00.376687446 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12566,7 +12570,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/Storage.cpp pytorch-develop-150/torch/csrc/generic/Storage.cpp --- pytorch-v1.5.0/torch/csrc/generic/Storage.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/Storage.cpp 2021-12-16 12:02:40.987260313 +0800 ++++ pytorch-develop-150/torch/csrc/generic/Storage.cpp 2021-12-16 23:02:00.376687446 +0800 @@ -1,7 +1,25 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12645,7 +12649,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for (Py_ssize_t i = 0; i < length; i++) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/StorageMethods.cpp pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp --- pytorch-v1.5.0/torch/csrc/generic/StorageMethods.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp 2021-12-16 12:02:40.987260313 +0800 ++++ pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp 2021-12-16 23:02:00.376687446 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12693,7 +12697,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"_write_file", (PyCFunction)THPStorage_(writeFile), METH_VARARGS, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/Module.cpp pytorch-develop-150/torch/csrc/Module.cpp --- pytorch-v1.5.0/torch/csrc/Module.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/Module.cpp 2021-12-16 12:02:40.979260346 +0800 ++++ pytorch-develop-150/torch/csrc/Module.cpp 2021-12-16 23:02:00.364686709 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12854,7 +12858,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto set_module_attr = [&](const char* name, PyObject* v, bool incref = true) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/tensor/python_tensor.cpp pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp --- pytorch-v1.5.0/torch/csrc/tensor/python_tensor.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp 2021-12-16 12:02:40.995260280 +0800 ++++ pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp 2021-12-16 23:02:00.384687936 +0800 @@ -1,18 +1,35 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13231,7 +13235,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +} // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/init.cpp pytorch-develop-150/torch/csrc/utils/init.cpp --- pytorch-v1.5.0/torch/csrc/utils/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/init.cpp 2021-12-16 12:02:40.995260280 +0800 ++++ pytorch-develop-150/torch/csrc/utils/init.cpp 2021-12-16 23:02:00.388688182 +0800 @@ -1,7 +1,13 @@ #include #include @@ -13396,7 +13400,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/init.h pytorch-develop-150/torch/csrc/utils/init.h --- pytorch-v1.5.0/torch/csrc/utils/init.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/init.h 2021-12-16 12:02:40.999260264 +0800 ++++ pytorch-develop-150/torch/csrc/utils/init.h 2021-12-16 23:02:00.388688182 +0800 @@ -8,4 +8,7 @@ void initThroughputBenchmarkBindings(PyObject* module); @@ -13407,7 +13411,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/python_arg_parser.h pytorch-develop-150/torch/csrc/utils/python_arg_parser.h --- pytorch-v1.5.0/torch/csrc/utils/python_arg_parser.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/python_arg_parser.h 2021-12-16 12:02:40.999260264 +0800 ++++ pytorch-develop-150/torch/csrc/utils/python_arg_parser.h 2021-12-16 23:02:00.388688182 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13442,7 +13446,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return at::Device(device_str); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_layouts.cpp pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_layouts.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp 2021-12-16 12:02:40.999260264 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp 2021-12-16 23:02:00.388688182 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13473,7 +13477,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= registerLayoutObject((THPLayout*)strided_layout, at::Backend::QuantizedCPU); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_new.cpp pytorch-develop-150/torch/csrc/utils/tensor_new.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_new.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_new.cpp 2021-12-16 12:02:40.999260264 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_new.cpp 2021-12-16 23:02:00.388688182 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13609,7 +13613,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= " or ", c10::DispatchKey::XLATensorId, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_types.cpp pytorch-develop-150/torch/csrc/utils/tensor_types.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_types.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_types.cpp 2021-12-16 12:02:40.999260264 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_types.cpp 2021-12-16 23:02:00.388688182 +0800 @@ -1,58 +1,91 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13822,7 +13826,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def get_rng_state(): ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/distributed/distributed_c10d.py pytorch-develop-150/torch/distributed/distributed_c10d.py --- pytorch-v1.5.0/torch/distributed/distributed_c10d.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/distributed/distributed_c10d.py 2021-12-16 12:02:40.999260264 +0800 ++++ pytorch-develop-150/torch/distributed/distributed_c10d.py 2021-12-16 23:02:00.388688182 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -13903,7 +13907,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/__init__.py pytorch-develop-150/torch/__init__.py --- pytorch-v1.5.0/torch/__init__.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/__init__.py 2021-12-16 12:02:40.975260362 +0800 ++++ pytorch-develop-150/torch/__init__.py 2021-12-16 23:02:00.360686464 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -13946,7 +13950,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/lib/c10d/CMakeLists.txt pytorch-develop-150/torch/lib/c10d/CMakeLists.txt --- pytorch-v1.5.0/torch/lib/c10d/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/lib/c10d/CMakeLists.txt 2021-12-16 12:02:40.999260264 +0800 ++++ pytorch-develop-150/torch/lib/c10d/CMakeLists.txt 2021-12-16 23:02:00.388688182 +0800 @@ -28,6 +28,10 @@ option(USE_C10D_NCCL "USE C10D NCCL" ON) endif() @@ -13999,7 +14003,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= copy_header(ProcessGroupMPI.hpp) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/lib/libshm/CMakeLists.txt pytorch-develop-150/torch/lib/libshm/CMakeLists.txt --- pytorch-v1.5.0/torch/lib/libshm/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/lib/libshm/CMakeLists.txt 2021-12-16 12:02:40.999260264 +0800 ++++ pytorch-develop-150/torch/lib/libshm/CMakeLists.txt 2021-12-16 23:02:00.392688427 +0800 @@ -37,8 +37,11 @@ SET_TARGET_PROPERTIES(shm PROPERTIES PREFIX "lib" @@ -14056,7 +14060,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -_maybe_indices_t = _scalar_or_tuple_2_t[Tensor] diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/functional.py pytorch-develop-150/torch/nn/functional.py --- pytorch-v1.5.0/torch/nn/functional.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/functional.py 2021-12-16 12:02:41.003260247 +0800 ++++ pytorch-develop-150/torch/nn/functional.py 2021-12-16 23:02:00.392688427 +0800 @@ -1611,7 +1611,7 @@ else: output = input.matmul(weight.t()) @@ -14079,7 +14083,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -from . import parallel as parallel diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/batchnorm.py pytorch-develop-150/torch/nn/modules/batchnorm.py --- pytorch-v1.5.0/torch/nn/modules/batchnorm.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/batchnorm.py 2021-12-16 12:02:41.003260247 +0800 ++++ pytorch-develop-150/torch/nn/modules/batchnorm.py 2021-12-16 23:02:00.392688427 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14111,7 +14115,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= self.register_parameter('running_var', None) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/module.py pytorch-develop-150/torch/nn/modules/module.py --- pytorch-v1.5.0/torch/nn/modules/module.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/module.py 2021-12-16 12:02:41.003260247 +0800 ++++ pytorch-develop-150/torch/nn/modules/module.py 2021-12-16 23:02:00.392688427 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14271,7 +14275,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return t.to(device, dtype if t.is_floating_point() else None, non_blocking, memory_format=convert_to_format) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/normalization.py pytorch-develop-150/torch/nn/modules/normalization.py --- pytorch-v1.5.0/torch/nn/modules/normalization.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/normalization.py 2021-12-16 12:02:41.003260247 +0800 ++++ pytorch-develop-150/torch/nn/modules/normalization.py 2021-12-16 23:02:00.392688427 +0800 @@ -128,13 +128,14 @@ """ __constants__ = ['normalized_shape', 'eps', 'elementwise_affine'] @@ -14340,7 +14344,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - module_kwargs: Optional[Any] = ...) -> Tensor: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/parallel/distributed.py pytorch-develop-150/torch/nn/parallel/distributed.py --- pytorch-v1.5.0/torch/nn/parallel/distributed.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/parallel/distributed.py 2021-12-16 12:02:41.003260247 +0800 ++++ pytorch-develop-150/torch/nn/parallel/distributed.py 2021-12-16 23:02:00.392688427 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14695,7 +14699,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def remove_weight_norm(module: T_module, name: str = ...) -> T_module: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/onnx/symbolic_opset9.py pytorch-develop-150/torch/onnx/symbolic_opset9.py --- pytorch-v1.5.0/torch/onnx/symbolic_opset9.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/onnx/symbolic_opset9.py 2021-12-16 12:02:41.003260247 +0800 ++++ pytorch-develop-150/torch/onnx/symbolic_opset9.py 2021-12-16 23:02:00.396688672 +0800 @@ -1621,14 +1621,23 @@ slices = [sym_help._slice_helper(g, w, axes=[0], starts=[x * n], ends=[y * n]) for x, y in intervals] return g.op('Concat', *slices, axis_i=0) @@ -14773,7 +14777,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, params: _params_t, lr: float=..., lr_decay: float=..., weight_decay: float=..., initial_accumulator_value: float=..., eps: float=...) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/optim/adamax.py pytorch-develop-150/torch/optim/adamax.py --- pytorch-v1.5.0/torch/optim/adamax.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/optim/adamax.py 2021-12-16 12:02:41.003260247 +0800 ++++ pytorch-develop-150/torch/optim/adamax.py 2021-12-16 23:02:00.396688672 +0800 @@ -80,8 +80,8 @@ exp_inf.mul_(beta2).unsqueeze(0), grad.abs().add_(eps).unsqueeze_(0) @@ -14950,7 +14954,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, params: _params_t, lr: float=..., betas: Tuple[float, float]=..., eps: float=...) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/serialization.py pytorch-develop-150/torch/serialization.py --- pytorch-v1.5.0/torch/serialization.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/serialization.py 2021-12-16 12:02:40.979260346 +0800 ++++ pytorch-develop-150/torch/serialization.py 2021-12-16 23:02:00.360686464 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15034,7 +15038,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def location_tag(storage): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/storage.py pytorch-develop-150/torch/storage.py --- pytorch-v1.5.0/torch/storage.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/storage.py 2021-12-16 12:02:40.979260346 +0800 ++++ pytorch-develop-150/torch/storage.py 2021-12-16 23:02:00.360686464 +0800 @@ -7,6 +7,7 @@ class _StorageBase(object): @@ -15054,7 +15058,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= else: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/tensor.py pytorch-develop-150/torch/tensor.py --- pytorch-v1.5.0/torch/tensor.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/tensor.py 2021-12-16 12:02:40.979260346 +0800 ++++ pytorch-develop-150/torch/tensor.py 2021-12-16 23:02:00.360686464 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15116,7 +15120,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def __reversed__(self): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/_tensor_str.py pytorch-develop-150/torch/_tensor_str.py --- pytorch-v1.5.0/torch/_tensor_str.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/_tensor_str.py 2021-12-16 12:02:40.979260346 +0800 ++++ pytorch-develop-150/torch/_tensor_str.py 2021-12-16 23:02:00.360686464 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15170,7 +15174,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= has_default_dtype = self.dtype in (torch.get_default_dtype(), torch.int64, torch.bool) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/data/dataloader.py pytorch-develop-150/torch/utils/data/dataloader.py --- pytorch-v1.5.0/torch/utils/data/dataloader.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/data/dataloader.py 2021-12-16 12:02:41.007260231 +0800 ++++ pytorch-develop-150/torch/utils/data/dataloader.py 2021-12-16 23:02:00.400688917 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15379,7 +15383,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, sampler: Sampler[int], batch_size: int, drop_last: bool) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/data/_utils/pin_memory.py pytorch-develop-150/torch/utils/data/_utils/pin_memory.py --- pytorch-v1.5.0/torch/utils/data/_utils/pin_memory.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/data/_utils/pin_memory.py 2021-12-16 12:02:41.007260231 +0800 ++++ pytorch-develop-150/torch/utils/data/_utils/pin_memory.py 2021-12-16 23:02:00.400688917 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15440,7 +15444,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/__init__.py pytorch-develop-150/torch/utils/__init__.py --- pytorch-v1.5.0/torch/utils/__init__.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/__init__.py 2021-12-16 12:02:41.007260231 +0800 ++++ pytorch-develop-150/torch/utils/__init__.py 2021-12-16 23:02:00.396688672 +0800 @@ -1,6 +1,9 @@ from __future__ import absolute_import, division, print_function, unicode_literals @@ -15453,7 +15457,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def set_module(obj, mod): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/_utils.py pytorch-develop-150/torch/_utils.py --- pytorch-v1.5.0/torch/_utils.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/_utils.py 2021-12-16 12:02:40.979260346 +0800 ++++ pytorch-develop-150/torch/_utils.py 2021-12-16 23:02:00.360686464 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. diff --git a/patch/pytorch1.8.1_npu.patch b/patch/pytorch1.8.1_npu.patch index d250d9b3811..acd30dcadbd 100644 --- a/patch/pytorch1.8.1_npu.patch +++ b/patch/pytorch1.8.1_npu.patch @@ -1,6 +1,6 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/CMakeLists.txt pytorch-develop-181/aten/CMakeLists.txt --- pytorch-v1.8.1/aten/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/CMakeLists.txt 2021-12-16 12:02:44.883244252 +0800 ++++ pytorch-develop-181/aten/CMakeLists.txt 2021-12-16 23:02:04.448936374 +0800 @@ -22,9 +22,11 @@ set(ATen_CPU_INCLUDE) set(ATen_THIRD_PARTY_INCLUDE) @@ -52,7 +52,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_CPU_DEPENDENCY_LIBS ${ATen_CPU_DEPENDENCY_LIBS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/autocast_mode.h pytorch-develop-181/aten/src/ATen/autocast_mode.h --- pytorch-v1.8.1/aten/src/ATen/autocast_mode.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/autocast_mode.h 2021-12-16 12:02:44.891244219 +0800 ++++ pytorch-develop-181/aten/src/ATen/autocast_mode.h 2021-12-16 23:02:04.452936617 +0800 @@ -5,7 +5,7 @@ namespace { @@ -64,7 +64,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/CMakeLists.txt pytorch-develop-181/aten/src/ATen/CMakeLists.txt --- pytorch-v1.8.1/aten/src/ATen/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/CMakeLists.txt 2021-12-16 12:02:44.887244235 +0800 ++++ pytorch-develop-181/aten/src/ATen/CMakeLists.txt 2021-12-16 23:02:04.448936374 +0800 @@ -85,6 +85,10 @@ file(GLOB native_quantized_h "native/quantized/*.h" "native/quantized/cpu/*.h") file(GLOB native_cpu_h "native/cpu/*.h") @@ -115,7 +115,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_HIP_SRCS ${ATen_HIP_SRCS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/Dispatcher.h pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/Dispatcher.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h 2021-12-16 12:02:44.895244202 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h 2021-12-16 23:02:04.456936862 +0800 @@ -417,6 +417,11 @@ } } @@ -130,7 +130,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.cpp pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-12-16 12:02:44.895244202 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-12-16 23:02:04.456936862 +0800 @@ -6,6 +6,7 @@ namespace c10 { @@ -141,7 +141,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= std::unordered_set not_observed_ops = { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.h pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-12-16 12:02:44.895244202 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-12-16 23:02:04.456936862 +0800 @@ -1,12 +1,16 @@ #pragma once @@ -161,7 +161,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/OperatorEntry.h pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-12-16 12:02:44.895244202 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-12-16 23:02:04.456936862 +0800 @@ -10,6 +10,7 @@ #include #include @@ -172,7 +172,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #include diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/VariableFallbackKernel.cpp pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp --- pytorch-v1.8.1/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-12-16 12:02:44.891244219 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-12-16 23:02:04.456936862 +0800 @@ -48,4 +48,8 @@ m.fallback(torch::CppFunction::makeFallthrough()); } @@ -184,7 +184,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/Convolution.cpp pytorch-develop-181/aten/src/ATen/native/Convolution.cpp --- pytorch-v1.8.1/aten/src/ATen/native/Convolution.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/Convolution.cpp 2021-12-16 12:02:44.903244169 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/Convolution.cpp 2021-12-16 23:02:04.468937593 +0800 @@ -603,7 +603,9 @@ const Tensor& input, const Tensor& weight, const Tensor& bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, @@ -198,7 +198,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= at::Tensor _convolution( diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/Memory.cpp pytorch-develop-181/aten/src/ATen/native/Memory.cpp --- pytorch-v1.8.1/aten/src/ATen/native/Memory.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/Memory.cpp 2021-12-16 12:02:44.907244153 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/Memory.cpp 2021-12-16 23:02:04.472937836 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -249,7 +249,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= detail::computeStorageNbytes( diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/native_functions.yaml pytorch-develop-181/aten/src/ATen/native/native_functions.yaml --- pytorch-v1.8.1/aten/src/ATen/native/native_functions.yaml 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/native_functions.yaml 2021-12-16 12:02:44.943244005 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/native_functions.yaml 2021-12-16 23:02:04.504939786 +0800 @@ -2073,6 +2073,8 @@ dispatch: CPU, CUDA: isnan @@ -480,7 +480,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +- func: npu_apply_adam.out(Scalar beta1_power, Scalar beta2_power, Scalar lr, Scalar beta1, Scalar beta2, Scalar epsilon, Tensor grad, bool? use_locking, bool? use_nesterov, *, Tensor(a!) var, Tensor(b!) m, Tensor(c!) v) -> (Tensor(a!), Tensor(b!), Tensor(c!)) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/NPUVariableMethodStubs.cpp pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp --- pytorch-v1.8.1/aten/src/ATen/native/NPUVariableMethodStubs.cpp 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp 2021-12-16 12:02:44.907244153 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp 2021-12-16 23:02:04.472937836 +0800 @@ -0,0 +1,464 @@ +#include "ATen/native/npu/common/FormatCastHelper.h" +#include "ATen/native/npu/frame/FormatHelper.h" @@ -1864,7 +1864,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -} diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/TensorFactories.cpp pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp --- pytorch-v1.8.1/aten/src/ATen/native/TensorFactories.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp 2021-12-16 12:02:44.923244087 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp 2021-12-16 23:02:04.484938567 +0800 @@ -1,3 +1,20 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -1897,7 +1897,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= namespace { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h --- pytorch-v1.8.1/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-12-16 12:02:44.975243873 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-12-16 23:02:04.540941978 +0800 @@ -1,19229 +1,19229 @@ -// -// Copyright (c) 2017-2020 Advanced Micro Devices, Inc. All rights reserved. @@ -40359,7 +40359,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +#endif // #ifdef VMA_IMPLEMENTATION diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/record_function.cpp pytorch-develop-181/aten/src/ATen/record_function.cpp --- pytorch-v1.8.1/aten/src/ATen/record_function.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/record_function.cpp 2021-12-16 12:02:44.979243857 +0800 ++++ pytorch-develop-181/aten/src/ATen/record_function.cpp 2021-12-16 23:02:04.544942222 +0800 @@ -400,6 +400,9 @@ rf_tls_.tls_record_function_enabled_ = enable; } @@ -40372,7 +40372,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (rf_tls_ptr->tls_record_function_enabled_) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/record_function.h pytorch-develop-181/aten/src/ATen/record_function.h --- pytorch-v1.8.1/aten/src/ATen/record_function.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/record_function.h 2021-12-16 12:02:44.979243857 +0800 ++++ pytorch-develop-181/aten/src/ATen/record_function.h 2021-12-16 23:02:04.544942222 +0800 @@ -2,6 +2,7 @@ #include @@ -40408,7 +40408,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // scope - record scope that this function tracks diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/templates/TensorBody.h pytorch-develop-181/aten/src/ATen/templates/TensorBody.h --- pytorch-v1.8.1/aten/src/ATen/templates/TensorBody.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/templates/TensorBody.h 2021-12-16 12:02:44.979243857 +0800 ++++ pytorch-develop-181/aten/src/ATen/templates/TensorBody.h 2021-12-16 23:02:04.544942222 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40441,7 +40441,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= bool is_xpu() const; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/templates/TensorMethods.cpp pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp --- pytorch-v1.8.1/aten/src/ATen/templates/TensorMethods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp 2021-12-16 12:02:44.979243857 +0800 ++++ pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp 2021-12-16 23:02:04.544942222 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40486,7 +40486,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/Utils.cpp pytorch-develop-181/aten/src/ATen/Utils.cpp --- pytorch-v1.8.1/aten/src/ATen/Utils.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/Utils.cpp 2021-12-16 12:02:44.887244235 +0800 ++++ pytorch-develop-181/aten/src/ATen/Utils.cpp 2021-12-16 23:02:04.452936617 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40530,7 +40530,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/TH/generic/THStorage.cpp pytorch-develop-181/aten/src/TH/generic/THStorage.cpp --- pytorch-v1.8.1/aten/src/TH/generic/THStorage.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/TH/generic/THStorage.cpp 2021-12-16 12:02:44.983243841 +0800 ++++ pytorch-develop-181/aten/src/TH/generic/THStorage.cpp 2021-12-16 23:02:04.548942466 +0800 @@ -1,9 +1,32 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40643,7 +40643,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/TH/generic/THStorage.h pytorch-develop-181/aten/src/TH/generic/THStorage.h --- pytorch-v1.8.1/aten/src/TH/generic/THStorage.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/TH/generic/THStorage.h 2021-12-16 12:02:44.983243841 +0800 ++++ pytorch-develop-181/aten/src/TH/generic/THStorage.h 2021-12-16 23:02:04.548942466 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40682,7 +40682,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/CMakeLists.txt pytorch-develop-181/c10/CMakeLists.txt --- pytorch-v1.8.1/c10/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/CMakeLists.txt 2021-12-16 12:02:44.999243775 +0800 ++++ pytorch-develop-181/c10/CMakeLists.txt 2021-12-16 23:02:04.560943197 +0800 @@ -79,6 +79,14 @@ message(STATUS "don't use NUMA") endif() @@ -40711,7 +40711,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # not checked in diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Backend.h pytorch-develop-181/c10/core/Backend.h --- pytorch-v1.8.1/c10/core/Backend.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Backend.h 2021-12-16 12:02:44.999243775 +0800 ++++ pytorch-develop-181/c10/core/Backend.h 2021-12-16 23:02:04.560943197 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40805,7 +40805,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Device.cpp pytorch-develop-181/c10/core/Device.cpp --- pytorch-v1.8.1/c10/core/Device.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Device.cpp 2021-12-16 12:02:44.999243775 +0800 ++++ pytorch-develop-181/c10/core/Device.cpp 2021-12-16 23:02:04.560943197 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40836,7 +40836,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= types.begin(), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Device.h pytorch-develop-181/c10/core/Device.h --- pytorch-v1.8.1/c10/core/Device.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Device.h 2021-12-16 12:02:44.999243775 +0800 ++++ pytorch-develop-181/c10/core/Device.h 2021-12-16 23:02:04.560943197 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40871,7 +40871,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= bool is_cpu() const noexcept { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DeviceType.cpp pytorch-develop-181/c10/core/DeviceType.cpp --- pytorch-v1.8.1/c10/core/DeviceType.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DeviceType.cpp 2021-12-16 12:02:44.999243775 +0800 ++++ pytorch-develop-181/c10/core/DeviceType.cpp 2021-12-16 23:02:04.560943197 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40911,7 +40911,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return false; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DeviceType.h pytorch-develop-181/c10/core/DeviceType.h --- pytorch-v1.8.1/c10/core/DeviceType.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DeviceType.h 2021-12-16 12:02:44.999243775 +0800 ++++ pytorch-develop-181/c10/core/DeviceType.h 2021-12-16 23:02:04.560943197 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40953,7 +40953,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= constexpr DeviceType kMSNPU = DeviceType::MSNPU; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKey.cpp pytorch-develop-181/c10/core/DispatchKey.cpp --- pytorch-v1.8.1/c10/core/DispatchKey.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKey.cpp 2021-12-16 12:02:44.999243775 +0800 ++++ pytorch-develop-181/c10/core/DispatchKey.cpp 2021-12-16 23:02:04.560943197 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41002,7 +41002,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::PrivateUse1: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKey.h pytorch-develop-181/c10/core/DispatchKey.h --- pytorch-v1.8.1/c10/core/DispatchKey.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKey.h 2021-12-16 12:02:44.999243775 +0800 ++++ pytorch-develop-181/c10/core/DispatchKey.h 2021-12-16 23:02:04.560943197 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41049,7 +41049,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= PrivateUse3_PreAutograd = AutogradPrivateUse3, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKeySet.cpp pytorch-develop-181/c10/core/DispatchKeySet.cpp --- pytorch-v1.8.1/c10/core/DispatchKeySet.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKeySet.cpp 2021-12-16 12:02:44.999243775 +0800 ++++ pytorch-develop-181/c10/core/DispatchKeySet.cpp 2021-12-16 23:02:04.560943197 +0800 @@ -11,6 +11,7 @@ DispatchKey::XLA, DispatchKey::NestedTensor, @@ -41069,7 +41069,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::AutogradXPU: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKeySet.h pytorch-develop-181/c10/core/DispatchKeySet.h --- pytorch-v1.8.1/c10/core/DispatchKeySet.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKeySet.h 2021-12-16 12:02:44.999243775 +0800 ++++ pytorch-develop-181/c10/core/DispatchKeySet.h 2021-12-16 23:02:04.560943197 +0800 @@ -193,6 +193,7 @@ DispatchKey::AutogradCPU, DispatchKey::AutogradCUDA, @@ -41080,7 +41080,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= DispatchKey::AutogradPrivateUse1, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Storage.h pytorch-develop-181/c10/core/Storage.h --- pytorch-v1.8.1/c10/core/Storage.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Storage.h 2021-12-16 12:02:44.999243775 +0800 ++++ pytorch-develop-181/c10/core/Storage.h 2021-12-16 23:02:04.560943197 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41114,7 +41114,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/StorageImpl.h pytorch-develop-181/c10/core/StorageImpl.h --- pytorch-v1.8.1/c10/core/StorageImpl.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/StorageImpl.h 2021-12-16 12:02:44.999243775 +0800 ++++ pytorch-develop-181/c10/core/StorageImpl.h 2021-12-16 23:02:04.560943197 +0800 @@ -1,12 +1,42 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41174,7 +41174,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/TensorImpl.h pytorch-develop-181/c10/core/TensorImpl.h --- pytorch-v1.8.1/c10/core/TensorImpl.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/TensorImpl.h 2021-12-16 12:02:44.999243775 +0800 ++++ pytorch-develop-181/c10/core/TensorImpl.h 2021-12-16 23:02:04.564943440 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41217,7 +41217,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ts.has(DispatchKey::SparseCPU) || diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/TensorOptions.h pytorch-develop-181/c10/core/TensorOptions.h --- pytorch-v1.8.1/c10/core/TensorOptions.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/TensorOptions.h 2021-12-16 12:02:44.999243775 +0800 ++++ pytorch-develop-181/c10/core/TensorOptions.h 2021-12-16 23:02:04.564943440 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41258,7 +41258,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } else if (tid == DispatchKey::QuantizedXPU) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/macros/Export.h pytorch-develop-181/c10/macros/Export.h --- pytorch-v1.8.1/c10/macros/Export.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/macros/Export.h 2021-12-16 12:02:45.003243758 +0800 ++++ pytorch-develop-181/c10/macros/Export.h 2021-12-16 23:02:04.564943440 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41327,7 +41327,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #define C10_API_ENUM C10_API diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/caffe2/CMakeLists.txt pytorch-develop-181/caffe2/CMakeLists.txt --- pytorch-v1.8.1/caffe2/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/caffe2/CMakeLists.txt 2021-12-16 12:02:45.007243742 +0800 ++++ pytorch-develop-181/caffe2/CMakeLists.txt 2021-12-16 23:02:04.572943928 +0800 @@ -76,6 +76,7 @@ # Add source, includes, and libs to lists list(APPEND Caffe2_CPU_SRCS ${ATen_CPU_SRCS}) @@ -41452,7 +41452,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Call again since Caffe2_HIP_INCLUDE is extended with ATen include dirs. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/BuildVariables.cmake pytorch-develop-181/cmake/BuildVariables.cmake --- pytorch-v1.8.1/cmake/BuildVariables.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/BuildVariables.cmake 2021-12-16 12:02:45.083243429 +0800 ++++ pytorch-develop-181/cmake/BuildVariables.cmake 2021-12-16 23:02:04.644948313 +0800 @@ -11,6 +11,7 @@ # CMakeLists.txt files under each folder respectively. set(Caffe2_CPU_SRCS) @@ -41476,7 +41476,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # symbols. However, if the lib is whole linked in caffe2 lib, we don't want diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Codegen.cmake pytorch-develop-181/cmake/Codegen.cmake --- pytorch-v1.8.1/cmake/Codegen.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Codegen.cmake 2021-12-16 12:02:45.083243429 +0800 ++++ pytorch-develop-181/cmake/Codegen.cmake 2021-12-16 23:02:04.644948313 +0800 @@ -208,13 +208,14 @@ file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt generated_cpp) file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt-cuda cuda_generated_cpp) @@ -41509,7 +41509,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= function(append_filelist name outputvar) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Dependencies.cmake pytorch-develop-181/cmake/Dependencies.cmake --- pytorch-v1.8.1/cmake/Dependencies.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Dependencies.cmake 2021-12-16 12:02:45.083243429 +0800 ++++ pytorch-develop-181/cmake/Dependencies.cmake 2021-12-16 23:02:04.644948313 +0800 @@ -1771,6 +1771,13 @@ endif(NOT C_HAS_THREAD) endif() @@ -41526,7 +41526,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Summary.cmake pytorch-develop-181/cmake/Summary.cmake --- pytorch-v1.8.1/cmake/Summary.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Summary.cmake 2021-12-16 12:02:45.083243429 +0800 ++++ pytorch-develop-181/cmake/Summary.cmake 2021-12-16 23:02:04.648948557 +0800 @@ -127,6 +127,7 @@ message(STATUS " USE_MKLDNN_CBLAS : ${USE_MKLDNN_CBLAS}") endif() @@ -41545,7 +41545,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= message(STATUS " Private Dependencies : ${Caffe2_DEPENDENCY_LIBS}") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/TorchConfig.cmake.in pytorch-develop-181/cmake/TorchConfig.cmake.in --- pytorch-v1.8.1/cmake/TorchConfig.cmake.in 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/TorchConfig.cmake.in 2021-12-16 12:02:45.083243429 +0800 ++++ pytorch-develop-181/cmake/TorchConfig.cmake.in 2021-12-16 23:02:04.648948557 +0800 @@ -158,6 +158,11 @@ list(APPEND TORCH_LIBRARIES ${TORCH_CUDA_LIBRARIES}) endif() @@ -41560,7 +41560,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(TORCH_CXX_FLAGS "-D_GLIBCXX_USE_CXX11_ABI=@GLIBCXX_USE_CXX11_ABI@") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/CMakeLists.txt pytorch-develop-181/CMakeLists.txt --- pytorch-v1.8.1/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/CMakeLists.txt 2021-12-16 12:02:44.875244285 +0800 ++++ pytorch-develop-181/CMakeLists.txt 2021-12-16 23:02:04.440935887 +0800 @@ -261,6 +261,10 @@ "USE_DISTRIBUTED" OFF) option(USE_TBB "Use TBB" OFF) @@ -41604,7 +41604,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if(APPLE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/setup.py pytorch-develop-181/setup.py --- pytorch-v1.8.1/setup.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/setup.py 2021-12-16 12:02:45.095243379 +0800 ++++ pytorch-develop-181/setup.py 2021-12-16 23:02:04.660949287 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -41673,7 +41673,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= 'utils/benchmark/utils/valgrind_wrapper/*.h', diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/derivatives.yaml pytorch-develop-181/tools/autograd/derivatives.yaml --- pytorch-v1.8.1/tools/autograd/derivatives.yaml 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/derivatives.yaml 2021-12-16 12:02:45.543241537 +0800 ++++ pytorch-develop-181/tools/autograd/derivatives.yaml 2021-12-16 23:02:05.112976809 +0800 @@ -1976,3 +1976,7 @@ - name: nonzero(Tensor self) -> Tensor @@ -41684,7 +41684,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + mat2: npu_bmm_v2_mat2_backward(grad, self, mat2, mat2.sizes()) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/gen_python_functions.py pytorch-develop-181/tools/autograd/gen_python_functions.py --- pytorch-v1.8.1/tools/autograd/gen_python_functions.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/gen_python_functions.py 2021-12-16 12:02:45.543241537 +0800 ++++ pytorch-develop-181/tools/autograd/gen_python_functions.py 2021-12-16 23:02:05.112976809 +0800 @@ -1,3 +1,20 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -41708,7 +41708,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # The bindings are generated as methods on python_variable or functions on the diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/Functions.cpp pytorch-develop-181/tools/autograd/templates/Functions.cpp --- pytorch-v1.8.1/tools/autograd/templates/Functions.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/Functions.cpp 2021-12-16 12:02:45.547241520 +0800 ++++ pytorch-develop-181/tools/autograd/templates/Functions.cpp 2021-12-16 23:02:05.112976809 +0800 @@ -14,6 +14,35 @@ namespace torch { namespace autograd { namespace generated { @@ -41747,7 +41747,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }}} // namespace torch::autograd::generated diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/python_torch_functions.cpp pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp --- pytorch-v1.8.1/tools/autograd/templates/python_torch_functions.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp 2021-12-16 12:02:45.547241520 +0800 ++++ pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp 2021-12-16 23:02:05.116977051 +0800 @@ -30,7 +30,7 @@ #include "torch/csrc/autograd/generated/variable_factories.h" #include "torch/csrc/utils/structseq.h" @@ -41783,7 +41783,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return torch::range(start, end, step, options); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/python_variable_methods.cpp pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp --- pytorch-v1.8.1/tools/autograd/templates/python_variable_methods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp 2021-12-16 12:02:45.547241520 +0800 ++++ pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp 2021-12-16 23:02:05.116977051 +0800 @@ -20,7 +20,13 @@ #ifdef USE_CUDA #include "torch/csrc/cuda/Event.h" @@ -41851,7 +41851,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"has_names", THPVariable_has_names, METH_NOARGS, NULL}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/build_variables.bzl pytorch-develop-181/tools/build_variables.bzl --- pytorch-v1.8.1/tools/build_variables.bzl 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/build_variables.bzl 2021-12-16 12:02:45.547241520 +0800 ++++ pytorch-develop-181/tools/build_variables.bzl 2021-12-16 23:02:05.116977051 +0800 @@ -362,6 +362,7 @@ libtorch_cuda_core_sources = [ "torch/csrc/CudaIPCTypes.cpp", @@ -41870,7 +41870,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= "torch/csrc/utils/python_arg_parser.cpp", diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/codegen/gen.py pytorch-develop-181/tools/codegen/gen.py --- pytorch-v1.8.1/tools/codegen/gen.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/codegen/gen.py 2021-12-16 12:02:45.551241504 +0800 ++++ pytorch-develop-181/tools/codegen/gen.py 2021-12-16 23:02:05.116977051 +0800 @@ -815,7 +815,7 @@ core_fm = make_file_manager(core_install_dir) cpu_fm = make_file_manager(options.install_dir) @@ -41918,7 +41918,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= main() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/codegen/model.py pytorch-develop-181/tools/codegen/model.py --- pytorch-v1.8.1/tools/codegen/model.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/codegen/model.py 2021-12-16 12:02:45.551241504 +0800 ++++ pytorch-develop-181/tools/codegen/model.py 2021-12-16 23:02:05.120977296 +0800 @@ -79,6 +79,7 @@ SparseHIP = auto() SparseXPU = auto() @@ -42060,7 +42060,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/autograd/profiler.py pytorch-develop-181/torch/autograd/profiler.py --- pytorch-v1.8.1/torch/autograd/profiler.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/autograd/profiler.py 2021-12-16 12:02:45.555241488 +0800 ++++ pytorch-develop-181/torch/autograd/profiler.py 2021-12-16 23:02:05.120977296 +0800 @@ -37,14 +37,17 @@ class EventList(list): """A list of Events (for pretty printing)""" @@ -42739,7 +42739,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ''.join(result) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_C/_autograd.pyi pytorch-develop-181/torch/_C/_autograd.pyi --- pytorch-v1.8.1/torch/_C/_autograd.pyi 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_C/_autograd.pyi 2021-12-16 12:02:45.551241504 +0800 ++++ pytorch-develop-181/torch/_C/_autograd.pyi 2021-12-16 23:02:05.120977296 +0800 @@ -9,14 +9,17 @@ CUDA = ... NVTX = ... @@ -42760,7 +42760,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= class ProfilerConfig: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/CMakeLists.txt pytorch-develop-181/torch/CMakeLists.txt --- pytorch-v1.8.1/torch/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/CMakeLists.txt 2021-12-16 12:02:45.551241504 +0800 ++++ pytorch-develop-181/torch/CMakeLists.txt 2021-12-16 23:02:05.120977296 +0800 @@ -131,6 +131,20 @@ endif() @@ -42784,7 +42784,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= list(APPEND TORCH_PYTHON_SRCS ${GENERATED_THNN_CXX_CUDA}) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/engine.cpp pytorch-develop-181/torch/csrc/autograd/engine.cpp --- pytorch-v1.8.1/torch/csrc/autograd/engine.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/engine.cpp 2021-12-16 12:02:45.559241471 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/engine.cpp 2021-12-16 23:02:05.128977781 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -42881,7 +42881,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto outputs = call_function(graph_task, func, inputs); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/functions/tensor.cpp pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp --- pytorch-v1.8.1/torch/csrc/autograd/functions/tensor.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp 2021-12-16 12:02:45.559241471 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp 2021-12-16 23:02:05.128977781 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -42913,7 +42913,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= /*non_blocking=*/false, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/init.cpp pytorch-develop-181/torch/csrc/autograd/init.cpp --- pytorch-v1.8.1/torch/csrc/autograd/init.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/init.cpp 2021-12-16 12:02:45.559241471 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/init.cpp 2021-12-16 23:02:05.128977781 +0800 @@ -52,6 +52,7 @@ .value("Disabled", ProfilerState::Disabled) .value("CPU", ProfilerState::CPU) @@ -42956,7 +42956,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= .value("OPENCL", c10::DeviceType::OPENCL) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/input_buffer.cpp pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp --- pytorch-v1.8.1/torch/csrc/autograd/input_buffer.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp 2021-12-16 12:02:45.559241471 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp 2021-12-16 23:02:05.128977781 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43008,7 +43008,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto& old_var = buffer[pos]; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.cpp pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp --- pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp 2021-12-16 12:02:45.559241471 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp 2021-12-16 23:02:05.128977781 +0800 @@ -147,7 +147,7 @@ constexpr const CUDAStubs* default_stubs_addr = &default_stubs; // Constant initialization, so it is guaranteed to be initialized before @@ -43251,7 +43251,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CUDAStubs::~CUDAStubs() = default; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.h pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h --- pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h 2021-12-16 12:02:45.559241471 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h 2021-12-16 23:02:05.128977781 +0800 @@ -19,6 +19,8 @@ #include // for gettimeofday() #endif @@ -43440,7 +43440,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= std::vector&& remoteProfiledEvents); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/python_variable.cpp pytorch-develop-181/torch/csrc/autograd/python_variable.cpp --- pytorch-v1.8.1/torch/csrc/autograd/python_variable.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/python_variable.cpp 2021-12-16 12:02:45.559241471 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/python_variable.cpp 2021-12-16 23:02:05.128977781 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43494,7 +43494,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"is_mkldnn", (getter)THPVariable_is_mkldnn, nullptr, nullptr, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/python_variable_indexing.cpp pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp --- pytorch-v1.8.1/torch/csrc/autograd/python_variable_indexing.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-16 12:02:45.559241471 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-16 23:02:05.128977781 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43527,7 +43527,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/distributed/c10d/init.cpp pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp --- pytorch-v1.8.1/torch/csrc/distributed/c10d/init.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp 2021-12-16 12:02:45.563241455 +0800 ++++ pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp 2021-12-16 23:02:05.132978025 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43694,7 +43694,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= torch::class_<::c10d::DistributedC10d>("dist_c10d", "frontend") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/DynamicTypes.cpp pytorch-develop-181/torch/csrc/DynamicTypes.cpp --- pytorch-v1.8.1/torch/csrc/DynamicTypes.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/DynamicTypes.cpp 2021-12-16 12:02:45.555241488 +0800 ++++ pytorch-develop-181/torch/csrc/DynamicTypes.cpp 2021-12-16 23:02:05.124977539 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43743,7 +43743,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return it->second; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/Generator.cpp pytorch-develop-181/torch/csrc/Generator.cpp --- pytorch-v1.8.1/torch/csrc/Generator.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/Generator.cpp 2021-12-16 12:02:45.555241488 +0800 ++++ pytorch-develop-181/torch/csrc/Generator.cpp 2021-12-16 23:02:05.124977539 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43790,7 +43790,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= " is not supported for torch.Generator() api."); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/serialization.cpp pytorch-develop-181/torch/csrc/generic/serialization.cpp --- pytorch-v1.8.1/torch/csrc/generic/serialization.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/serialization.cpp 2021-12-16 12:02:45.567241438 +0800 ++++ pytorch-develop-181/torch/csrc/generic/serialization.cpp 2021-12-16 23:02:05.136978268 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43888,7 +43888,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/Storage.cpp pytorch-develop-181/torch/csrc/generic/Storage.cpp --- pytorch-v1.8.1/torch/csrc/generic/Storage.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/Storage.cpp 2021-12-16 12:02:45.567241438 +0800 ++++ pytorch-develop-181/torch/csrc/generic/Storage.cpp 2021-12-16 23:02:05.136978268 +0800 @@ -1,7 +1,25 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43968,7 +43968,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for (Py_ssize_t i = 0; i < length; i++) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/StorageMethods.cpp pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp --- pytorch-v1.8.1/torch/csrc/generic/StorageMethods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp 2021-12-16 12:02:45.567241438 +0800 ++++ pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp 2021-12-16 23:02:05.136978268 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44048,7 +44048,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/Module.cpp pytorch-develop-181/torch/csrc/Module.cpp --- pytorch-v1.8.1/torch/csrc/Module.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/Module.cpp 2021-12-16 12:02:45.555241488 +0800 ++++ pytorch-develop-181/torch/csrc/Module.cpp 2021-12-16 23:02:05.124977539 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44174,7 +44174,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (incref) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/tensor/python_tensor.cpp pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp --- pytorch-v1.8.1/torch/csrc/tensor/python_tensor.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp 2021-12-16 12:02:45.591241340 +0800 ++++ pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp 2021-12-16 23:02:05.160979727 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44212,7 +44212,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/python_arg_parser.h pytorch-develop-181/torch/csrc/utils/python_arg_parser.h --- pytorch-v1.8.1/torch/csrc/utils/python_arg_parser.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/python_arg_parser.h 2021-12-16 12:02:45.591241340 +0800 ++++ pytorch-develop-181/torch/csrc/utils/python_arg_parser.h 2021-12-16 23:02:05.160979727 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44247,7 +44247,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return at::Device(device_str); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_layouts.cpp pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_layouts.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp 2021-12-16 12:02:45.591241340 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp 2021-12-16 23:02:05.160979727 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44278,7 +44278,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= PyObject *sparse_coo_layout = THPLayout_New(at::Layout::Sparse, "torch.sparse_coo"); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_new.cpp pytorch-develop-181/torch/csrc/utils/tensor_new.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_new.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_new.cpp 2021-12-16 12:02:45.591241340 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_new.cpp 2021-12-16 23:02:05.160979727 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44410,7 +44410,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= dispatch_key); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_types.cpp pytorch-develop-181/torch/csrc/utils/tensor_types.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_types.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_types.cpp 2021-12-16 12:02:45.591241340 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_types.cpp 2021-12-16 23:02:05.160979727 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44460,7 +44460,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/distributed/distributed_c10d.py pytorch-develop-181/torch/distributed/distributed_c10d.py --- pytorch-v1.8.1/torch/distributed/distributed_c10d.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/distributed/distributed_c10d.py 2021-12-16 12:02:45.595241323 +0800 ++++ pytorch-develop-181/torch/distributed/distributed_c10d.py 2021-12-16 23:02:05.164979971 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -44551,7 +44551,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= prefix_store, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/__init__.py pytorch-develop-181/torch/__init__.py --- pytorch-v1.8.1/torch/__init__.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/__init__.py 2021-12-16 12:02:45.551241504 +0800 ++++ pytorch-develop-181/torch/__init__.py 2021-12-16 23:02:05.120977296 +0800 @@ -675,3 +675,11 @@ # class usage. We add these lines here to preserve backward compatibility. quantized_lstm = torch.ops.aten.quantized_lstm @@ -44567,7 +44567,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/CMakeLists.txt pytorch-develop-181/torch/lib/c10d/CMakeLists.txt --- pytorch-v1.8.1/torch/lib/c10d/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/CMakeLists.txt 2021-12-16 12:02:45.599241307 +0800 ++++ pytorch-develop-181/torch/lib/c10d/CMakeLists.txt 2021-12-16 23:02:05.168980214 +0800 @@ -27,6 +27,10 @@ option(USE_C10D_NCCL "USE C10D NCCL" ON) endif() @@ -44620,7 +44620,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= copy_header(ProcessGroupMPI.hpp) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/comm.cpp pytorch-develop-181/torch/lib/c10d/comm.cpp --- pytorch-v1.8.1/torch/lib/c10d/comm.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/comm.cpp 2021-12-16 12:02:45.603241290 +0800 ++++ pytorch-develop-181/torch/lib/c10d/comm.cpp 2021-12-16 23:02:05.172980456 +0800 @@ -12,6 +12,26 @@ class BroadcastWork { @@ -44709,7 +44709,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= while (!in_flight.empty()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/reducer.cpp pytorch-develop-181/torch/lib/c10d/reducer.cpp --- pytorch-v1.8.1/torch/lib/c10d/reducer.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/reducer.cpp 2021-12-16 12:02:45.603241290 +0800 ++++ pytorch-develop-181/torch/lib/c10d/reducer.cpp 2021-12-16 23:02:05.172980456 +0800 @@ -18,6 +18,18 @@ namespace c10d { namespace { @@ -44901,7 +44901,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // A bucket with one or more dense tensors needs to be unflattened. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/libshm/CMakeLists.txt pytorch-develop-181/torch/lib/libshm/CMakeLists.txt --- pytorch-v1.8.1/torch/lib/libshm/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/libshm/CMakeLists.txt 2021-12-16 12:02:45.603241290 +0800 ++++ pytorch-develop-181/torch/lib/libshm/CMakeLists.txt 2021-12-16 23:02:05.172980456 +0800 @@ -41,8 +41,11 @@ set_target_properties(shm PROPERTIES PREFIX "lib" @@ -44917,7 +44917,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # https://github.com/libgit2/libgit2/issues/2128#issuecomment-35649830 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/batchnorm.py pytorch-develop-181/torch/nn/modules/batchnorm.py --- pytorch-v1.8.1/torch/nn/modules/batchnorm.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/batchnorm.py 2021-12-16 12:02:45.607241274 +0800 ++++ pytorch-develop-181/torch/nn/modules/batchnorm.py 2021-12-16 23:02:05.176980701 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -44939,7 +44939,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= from ._functions import SyncBatchNorm as sync_batch_norm diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/module.py pytorch-develop-181/torch/nn/modules/module.py --- pytorch-v1.8.1/torch/nn/modules/module.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/module.py 2021-12-16 12:02:45.607241274 +0800 ++++ pytorch-develop-181/torch/nn/modules/module.py 2021-12-16 23:02:05.176980701 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45065,7 +45065,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if convert_to_format is not None and t.dim() == 4: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/normalization.py pytorch-develop-181/torch/nn/modules/normalization.py --- pytorch-v1.8.1/torch/nn/modules/normalization.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/normalization.py 2021-12-16 12:02:45.607241274 +0800 ++++ pytorch-develop-181/torch/nn/modules/normalization.py 2021-12-16 23:02:05.176980701 +0800 @@ -167,8 +167,11 @@ init.zeros_(self.bias) @@ -45082,7 +45082,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return '{normalized_shape}, eps={eps}, ' \ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/parallel/distributed.py pytorch-develop-181/torch/nn/parallel/distributed.py --- pytorch-v1.8.1/torch/nn/parallel/distributed.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/parallel/distributed.py 2021-12-16 12:02:45.607241274 +0800 ++++ pytorch-develop-181/torch/nn/parallel/distributed.py 2021-12-16 23:02:05.176980701 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45114,7 +45114,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= output = self.module(*inputs[0], **kwargs[0]) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/serialization.py pytorch-develop-181/torch/serialization.py --- pytorch-v1.8.1/torch/serialization.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/serialization.py 2021-12-16 12:02:45.615241241 +0800 ++++ pytorch-develop-181/torch/serialization.py 2021-12-16 23:02:05.184981187 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45203,7 +45203,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= See also: `saving-loading-tensors` diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/storage.py pytorch-develop-181/torch/storage.py --- pytorch-v1.8.1/torch/storage.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/storage.py 2021-12-16 12:02:45.615241241 +0800 ++++ pytorch-develop-181/torch/storage.py 2021-12-16 23:02:05.184981187 +0800 @@ -8,6 +8,7 @@ class _StorageBase(object): _cdata: Any @@ -45223,7 +45223,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= else: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/tensor.py pytorch-develop-181/torch/tensor.py --- pytorch-v1.8.1/torch/tensor.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/tensor.py 2021-12-16 12:02:45.615241241 +0800 ++++ pytorch-develop-181/torch/tensor.py 2021-12-16 23:02:05.184981187 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45285,7 +45285,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def __reversed__(self): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_tensor_str.py pytorch-develop-181/torch/_tensor_str.py --- pytorch-v1.8.1/torch/_tensor_str.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_tensor_str.py 2021-12-16 12:02:45.551241504 +0800 ++++ pytorch-develop-181/torch/_tensor_str.py 2021-12-16 23:02:05.120977296 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45329,7 +45329,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # TODO: add an API to map real -> complex dtypes diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/utils/data/dataloader.py pytorch-develop-181/torch/utils/data/dataloader.py --- pytorch-v1.8.1/torch/utils/data/dataloader.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/utils/data/dataloader.py 2021-12-16 12:02:45.619241224 +0800 ++++ pytorch-develop-181/torch/utils/data/dataloader.py 2021-12-16 23:02:05.188981430 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45390,7 +45390,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= pin_memory_thread.start() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/utils/data/_utils/pin_memory.py pytorch-develop-181/torch/utils/data/_utils/pin_memory.py --- pytorch-v1.8.1/torch/utils/data/_utils/pin_memory.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/utils/data/_utils/pin_memory.py 2021-12-16 12:02:41.899256549 +0800 ++++ pytorch-develop-181/torch/utils/data/_utils/pin_memory.py 2021-12-16 23:02:01.372748494 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45436,7 +45436,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # logic of this function. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_utils.py pytorch-develop-181/torch/_utils.py --- pytorch-v1.8.1/torch/_utils.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_utils.py 2021-12-16 12:02:45.551241504 +0800 ++++ pytorch-develop-181/torch/_utils.py 2021-12-16 23:02:05.120977296 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. diff --git a/pytorch1.5.0/src/aten/src/ATen/native/native_functions.yaml b/pytorch1.5.0/src/aten/src/ATen/native/native_functions.yaml index ff12e661dd6..7f9eaf7216f 100644 --- a/pytorch1.5.0/src/aten/src/ATen/native/native_functions.yaml +++ b/pytorch1.5.0/src/aten/src/ATen/native/native_functions.yaml @@ -8695,6 +8695,10 @@ npu_dispatch_only: NPU: silu_npu +- func: npu_silu_(Tensor(a!) self) -> Tensor(a!) + npu_dispatch_only: + NPU: silu_npu_ + - func: npu_silu_backward(Tensor grad_output, Tensor x0, Tensor x1) -> Tensor npu_dispatch_only: NPU: silu_backward_npu diff --git a/pytorch1.5.0/src/aten/src/ATen/native/npu/SiluKernelNpu.cpp b/pytorch1.5.0/src/aten/src/ATen/native/npu/SiluKernelNpu.cpp index a9542a0cd3d..3620e0fd90d 100644 --- a/pytorch1.5.0/src/aten/src/ATen/native/npu/SiluKernelNpu.cpp +++ b/pytorch1.5.0/src/aten/src/ATen/native/npu/SiluKernelNpu.cpp @@ -1,5 +1,5 @@ // Copyright (c) 2020 Huawei Technologies Co., Ltd -// Copyright (c) 2019, Facebook CORPORATION. +// Copyright (c) 2019, Facebook CORPORATION. // All rights reserved. // // Licensed under the BSD 3-Clause License (the "License"); @@ -29,6 +29,17 @@ Tensor& silu_out_npu_nocheck(Tensor& result, const Tensor& self) { return result; } +Tensor& silu_out_npu(const Tensor& self, Tensor& out){ + OpPreparation::CheckOut( + {self}, + out, + self); + OpPipeWithDefinedOut pipe; + return pipe.CheckMemory({self}, {out}) + .Func([&self](Tensor& out){silu_out_npu_nocheck(out, self);}) + .Call(out); +} + Tensor silu_npu(const Tensor& self) { OpPipeWithApplyOut pipe; return pipe.ApplyOutputSameAs(self) @@ -36,5 +47,10 @@ Tensor silu_npu(const Tensor& self) { .Call(); } +Tensor& silu_npu_(Tensor& self) { + silu_out_npu(self, self); + return self; +} + } // namespace native } // namespace at diff --git a/pytorch1.5.0/test/test_npu/test_network_ops/test_log_sigmoid.py b/pytorch1.5.0/test/test_npu/test_network_ops/test_log_sigmoid.py index e2c0ffdfeac..5d9018f8333 100644 --- a/pytorch1.5.0/test/test_npu/test_network_ops/test_log_sigmoid.py +++ b/pytorch1.5.0/test/test_npu/test_network_ops/test_log_sigmoid.py @@ -33,12 +33,27 @@ class TestLogsigmoid(TestCase): output = output.numpy() return output + def cpu_op_exec_out(self, input1, out): + output = torch.nn.functional.logsigmoid(input1, out=out) + output = output.numpy() + return output + + def npu_op_exec_out(self, input1, out): + output = torch.nn.functional.logsigmoid(input1, out=out) + output = output.to("cpu") + output = output.numpy() + return output + def test_log_sigmoid_shape_format(self, device): shape_format = [ [[np.float32, 0, (6, 4)]], [[np.float32, 3, (2, 4, 5)]], [[np.float32, 4, (1, 2, 3, 3)]], [[np.float32, 29, (11, 22, 33, 43)]], + [[np.float32, 2, (2, 11, 51, 8, 3)]], + [[np.float32, 2, (2, 11, 51, 8, 3, 8)]], + [[np.float32, 2, (2, 11, 51, 8, 20, 12, 6)]], + [[np.float32, 2, (2, 11, 51, 8, 3, 2, 4, 7)]] ] for item in shape_format: cpu_input, npu_input = create_common_tensor(item[0], -50, 50) @@ -46,6 +61,11 @@ class TestLogsigmoid(TestCase): npu_output = self.npu_op_exec(npu_input) self.assertRtolEqual(cpu_output, npu_output) + cpu_out, npu_out = create_common_tensor(item[0], -50, 50) + cpu_output = self.cpu_op_exec_out(cpu_input, cpu_out) + cpu_output = self.npu_op_exec_out(npu_input, npu_out) + self.assertRtolEqual(cpu_output, cpu_output) + def test_log_sigmoid_float16_shape_format(self, device): def cpu_op_exec_fp16(input1): input1 = input1.to(torch.float32) @@ -54,11 +74,23 @@ class TestLogsigmoid(TestCase): output = output.astype(np.float16) return output + def cpu_op_exec_fp16_out(input1, out): + input1 = input1.to(torch.float32) + out = out.to(torch.float32) + output = torch.nn.functional.logsigmoid(input1, out=out) + output = output.numpy() + output = output.astype(np.float16) + return output + shape_format = [ [[np.float16, 0, (6, 4)]], [[np.float16, 3, (2, 4, 5)]], [[np.float16, 4, (1, 2, 3, 3)]], [[np.float16, 29, (10, 22, 33, 33)]], + [[np.float16, 2, (2, 11, 51, 8, 3)]], + [[np.float16, 2, (2, 11, 51, 8, 3, 8)]], + [[np.float16, 2, (2, 11, 51, 8, 20, 12, 6)]], + [[np.float16, 2, (2, 11, 51, 8, 3, 2, 4, 7)]] ] for item in shape_format: @@ -67,6 +99,11 @@ class TestLogsigmoid(TestCase): npu_output = self.npu_op_exec(npu_input1) self.assertRtolEqual(cpu_output, npu_output) + cpu_out, npu_out = create_common_tensor(item[0], -50, 50) + cpu_out = cpu_op_exec_fp16_out(cpu_input1, cpu_out) + npu_out = self.npu_op_exec_out(npu_input1, npu_out) + self.assertRtolEqual(cpu_out, npu_out) + instantiate_device_type_tests(TestLogsigmoid, globals(), except_for="cpu") if __name__ == "__main__": diff --git a/pytorch1.5.0/test/test_npu/test_network_ops/test_log_sigmoid_backward.py b/pytorch1.5.0/test/test_npu/test_network_ops/test_log_sigmoid_backward.py index 63ae01b4ff0..1c418ff7de9 100644 --- a/pytorch1.5.0/test/test_npu/test_network_ops/test_log_sigmoid_backward.py +++ b/pytorch1.5.0/test/test_npu/test_network_ops/test_log_sigmoid_backward.py @@ -58,7 +58,11 @@ class TestLogSigmoidBackward(TestCase): [[np.float32, 0, (6, 4)]], [[np.float32, 3, (2, 4, 5)]], [[np.float32, 4, (1, 2, 3, 3)]], - [[np.float32, 29, (10, 3, 5, 3)]] + [[np.float32, 29, (10, 3, 5, 3)]], + [[np.float32, 2, (2, 11, 51, 8, 3)]], + [[np.float32, 2, (2, 11, 51, 8, 3, 8)]], + [[np.float32, 2, (2, 11, 51, 8, 20, 12, 6)]], + [[np.float32, 2, (2, 11, 51, 8, 3, 2, 4, 7)]] ] for item in shape_format: cpu_input, npu_input = create_common_tensor(item[0], -50, 50) @@ -80,6 +84,10 @@ class TestLogSigmoidBackward(TestCase): [[np.float16, 3, (2, 4, 5)]], [[np.float16, 4, (1, 2, 3, 3)]], [[np.float16, 29, (10, 3, 5, 3)]], + [[np.float16, 2, (2, 11, 51, 8, 3)]], + [[np.float16, 2, (2, 11, 51, 8, 3, 8)]], + [[np.float16, 2, (2, 11, 51, 8, 20, 12, 6)]], + [[np.float16, 2, (2, 11, 51, 8, 3, 2, 4, 7)]] ] for item in shape_format: diff --git a/pytorch1.5.0/test/test_npu/test_poisson_nll_loss.py b/pytorch1.5.0/test/test_npu/test_network_ops/test_poisson_nll_loss.py similarity index 74% rename from pytorch1.5.0/test/test_npu/test_poisson_nll_loss.py rename to pytorch1.5.0/test/test_npu/test_network_ops/test_poisson_nll_loss.py index cf1e389f377..c2b4159d444 100644 --- a/pytorch1.5.0/test/test_npu/test_poisson_nll_loss.py +++ b/pytorch1.5.0/test/test_npu/test_network_ops/test_poisson_nll_loss.py @@ -12,20 +12,19 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - -import torch -import numpy as np import sys import copy +import torch +import numpy as np from common_utils import TestCase, run_tests from common_device_type import dtypes, instantiate_device_type_tests from util_test import create_common_tensor class TestPoissonNllLoss(TestCase): - def generate_data(self, min, max, shape, dtype): - input1 = np.random.uniform(min, max, shape).astype(dtype) - input2 = np.random.uniform(min, max, shape).astype(dtype) + def generate_data(self, min_num, max_num, shape, dtype): + input1 = np.random.uniform(min_num, max_num, shape).astype(dtype) + input2 = np.random.uniform(min_num, max_num, shape).astype(dtype) #modify from numpy.ndarray to torch.tensor npu_input1 = torch.from_numpy(input1) @@ -58,326 +57,386 @@ class TestPoissonNllLoss(TestCase): return output - def test_poisson_nll_loss_float16_0(self, device): + def test_poisson_nll_loss_float16_0_none(self, device): eps = 1e-8 log_input = True full = False - reduction = 0 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 0 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 2, (2, 2), np.float16) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float16_1(self, device): + def test_poisson_nll_loss_float16_1_mean(self, device): eps = 1e-8 log_input = True full = False - reduction = 1 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 1 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 2, (2, 2), np.float16) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float16_2(self, device): + def test_poisson_nll_loss_float16_2_sum(self, device): eps = 1e-8 log_input = True full = False - reduction = 2 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 2 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 2, (2, 2), np.float16) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float16_3(self, device): + def test_poisson_nll_loss_float16_3_sum(self, device): eps = 1e-8 log_input = False full = False - reduction = 2 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 2 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 2, (8, 16), np.float16) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float16_4(self, device): + def test_poisson_nll_loss_float16_4_mean(self, device): eps = 1e-8 log_input = False full = False - reduction = 1 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 1 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 2, (8, 16), np.float16) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float16_5(self, device): + def test_poisson_nll_loss_float16_5_mean(self, device): eps = 1e-8 log_input = False full = False - reduction = 1 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 1 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 2, (8, 16), np.float16) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float16_6(self, device): + def test_poisson_nll_loss_float16_6_mean(self, device): eps = 1e-8 log_input = True full = True - reduction = 1 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 1 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 16, (8, 16, 32), np.float16) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float16_7(self, device): + def test_poisson_nll_loss_float16_7_none(self, device): eps = 1e-8 log_input = True full = True - reduction = 0 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 0 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 32, (8, 16, 32), np.float16) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float16_8(self, device): + def test_poisson_nll_loss_float16_8_sum(self, device): eps = 1e-8 log_input = True full = True - reduction = 2 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 2 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 32, (8, 16, 32), np.float16) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float16_9(self, device): + def test_poisson_nll_loss_float16_9_none(self, device): eps = 1e-8 log_input = False full = True - reduction = 0 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 0 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 64, (2, 4, 8, 16), np.float16) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float16_10(self, device): + def test_poisson_nll_loss_float16_10_sum(self, device): eps = 1e-8 log_input = False full = True - reduction = 2 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 2 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 64, (2, 4, 8, 16), np.float16) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float16_11(self, device): + def test_poisson_nll_loss_float16_11_mean(self, device): eps = 1e-8 log_input = False full = True - reduction = 1 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 1 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 64, (2, 4, 8, 16), np.float16) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float16_12(self, device): + def test_poisson_nll_loss_float16_12_sum(self, device): eps = 1.0 log_input = True full = False - reduction = 2 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 2 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 2, (65500, 1, 1, 1), np.float16) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float16_13(self, device): + def test_poisson_nll_loss_float16_13_none(self, device): eps = 1.0 log_input = True full = True - reduction = 0 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 0 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 2, (8192, 1, 1, 1), np.float16) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float16_14(self, device): + def test_poisson_nll_loss_float16_14_mean(self, device): eps = 1.0 log_input = False full = True - reduction = 1 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 1 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 2, (16384, 1, 1, 1), np.float16) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float16_15(self, device): + def test_poisson_nll_loss_float16_15_sum(self, device): eps = 1.0 log_input = False full = False - reduction = 2 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 2 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 2, (32768, 1, 1, 1), np.float16) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float32_0(self, device): + def test_poisson_nll_loss_float16_16_none(self, device): + eps = 1.0 + log_input = False + full = False + reduction = 0 # 可选值 0/1/2 分别表示 'none','mean','sum' + input_x, target = self.generate_data(-2.0, 2.0, (7,1,11,160,1088), np.float16) + cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) + npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) + self.assertRtolEqual(cpu_output, npu_output) + + def test_poisson_nll_loss_float16_17_mean(self, device): + eps = 1.0 + log_input = False + full = False + reduction = 1 # 可选值 0/1/2 分别表示 'none','mean','sum' + input_x, target = self.generate_data(-2.0, 2.0, (7,1,11,160,1088), np.float16) + cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) + npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) + self.assertRtolEqual(cpu_output, npu_output) + + def test_poisson_nll_loss_float16_18_sum(self, device): + eps = 1.0 + log_input = False + full = False + reduction = 2 # 可选值 0/1/2 分别表示 'none','mean','sum' + input_x, target = self.generate_data(-2.0, 2.0, (7,1,11,160,1088), np.float16) + cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) + npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) + self.assertRtolEqual(cpu_output, npu_output) + + def test_poisson_nll_loss_float32_0_none(self, device): eps = 1e-8 log_input = True full = False - reduction = 0 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 0 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 2, (1, 31, 149, 2), np.float32) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float32_1(self, device): + def test_poisson_nll_loss_float32_1_mean(self, device): eps = 1e-8 log_input = True full = False - reduction = 1 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 1 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 2, (1, 31, 149, 2), np.float32) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float32_2(self, device): + def test_poisson_nll_loss_float32_2_sum(self, device): eps = 1e-8 log_input = True full = False - reduction = 2 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 2 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 2, (1, 31, 149, 2), np.float32) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float32_3(self, device): + def test_poisson_nll_loss_float32_3_sum(self, device): eps = 1e-8 log_input = False full = False - reduction = 2 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 2 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 3402823500.0, (1, 32, 31, 1), np.float32) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float32_4(self, device): + def test_poisson_nll_loss_float32_4_mean(self, device): eps = 1e-8 log_input = False full = False - reduction = 1 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 1 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 0.000030517578125, (2, 32, 149, 31), np.float32) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float32_5(self, device): + def test_poisson_nll_loss_float32_5_none(self, device): eps = 1e-8 log_input = False full = False - reduction = 0 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 0 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 3402800000, (128), np.float32) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float32_6(self, device): + def test_poisson_nll_loss_float32_6_mean(self, device): eps = 1e-8 log_input = True full = True - reduction = 1 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 1 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 9.313225746154785e-10,(128, 1), np.float32) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float32_7(self, device): + def test_poisson_nll_loss_float32_7_none(self, device): eps = 1e-8 log_input = True full = True - reduction = 0 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 0 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 9.313225746154785e-10, (1, 31, 149, 2), np.float32) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float32_8(self, device): + def test_poisson_nll_loss_float32_8_sum(self, device): eps = 1e-8 log_input = True full = True - reduction = 2 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 2 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 16, (1, 1, 1, 16384), np.float32) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float32_9(self, device): + def test_poisson_nll_loss_float32_9_none(self, device): eps = 1e-8 log_input = False full = True - reduction = 0 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 0 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0,0.000000000000000000000000000000000000011754943508, (2, 31, 149, 2), np.float32) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float32_10(self, device): + def test_poisson_nll_loss_float32_10_sum(self, device): eps = 1e-8 log_input = False full = True - reduction = 2 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 2 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0,0.000000000000000000000000000000000000011754943508, (2, 31, 149, 2), np.float32) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float32_11(self, device): + def test_poisson_nll_loss_float32_11_mean(self, device): eps = 1e-8 log_input = False full = True - reduction = 1 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 1 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0,0.000000000000000000000000000000000000011754943508, (2, 31, 149, 2), np.float32) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float32_12(self, device): + def test_poisson_nll_loss_float32_12_sum(self, device): eps = 1.0 log_input = True full = False - reduction = 2 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 2 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 2, (65535, 1, 1, 1), np.float32) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float32_13(self, device): + def test_poisson_nll_loss_float32_13_none(self, device): eps = 1.0 log_input = True full = True - reduction = 0 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 0 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 3402823500.0, (1, 32, 31, 1), np.float32) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float32_14(self, device): + def test_poisson_nll_loss_float32_14_mean(self, device): eps = 1.0 log_input = False full = True - reduction = 1 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 1 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 3402823500.0, (1, 32, 31, 1), np.float32) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) - def test_poisson_nll_loss_float32_15(self, device): + def test_poisson_nll_loss_float32_15_sum(self, device): eps = 1.0 log_input = False full = False - reduction = 2 # 可选值 0/1/2 分别表示 'none','mean','add' + reduction = 2 # 可选值 0/1/2 分别表示 'none','mean','sum' input_x, target = self.generate_data(0, 3402823500.0, (1, 32, 31, 1), np.float32) cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) self.assertRtolEqual(cpu_output, npu_output) + def test_poisson_nll_loss_float32_16_none(self, device): + eps = 1.0 + log_input = True + full = False + reduction = 0 # 可选值 0/1/2 分别表示 'none','mean','sum' + input_x, target = self.generate_data(-2.0, 2.0, (7,1,11,160,1088), np.float32) + cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) + npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) + self.assertRtolEqual(cpu_output, npu_output) + + def test_poisson_nll_loss_float32_17_mean(self, device): + eps = 1.0 + log_input = True + full = False + reduction = 1 # 可选值 0/1/2 分别表示 'none','mean','sum' + input_x, target = self.generate_data(-2.0, 2.0, (7,1,11,160,1088), np.float32) + cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) + npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) + self.assertRtolEqual(cpu_output, npu_output) + + def test_poisson_nll_loss_float32_18_sum(self, device): + eps = 1.0 + log_input = True + full = False + reduction = 2 # 可选值 0/1/2 分别表示 'none','mean','sum' + input_x, target = self.generate_data(-2.0, 2.0, (7,1,11,160,1088), np.float32) + cpu_output = self.cpu_op_exec(input_x, target, log_input, full, eps, reduction) + npu_output = self.npu_op_exec(input_x, target, log_input, full, eps, reduction) + self.assertRtolEqual(cpu_output, npu_output) + instantiate_device_type_tests(TestPoissonNllLoss, globals(), except_for='cpu') if __name__ == '__main__': # 当前版本需要调用如下代码 diff --git a/pytorch1.5.0/test/test_npu/test_network_ops/test_silu.py b/pytorch1.5.0/test/test_npu/test_network_ops/test_silu.py index ef63be75130..f9ab74df217 100644 --- a/pytorch1.5.0/test/test_npu/test_network_ops/test_silu.py +++ b/pytorch1.5.0/test/test_npu/test_network_ops/test_silu.py @@ -32,6 +32,12 @@ class TestSilu(TestCase): output = output.numpy() return output + def npu_op_exec_inplace(self, input1): + torch.npu_silu_(input1) + output = input1.to("cpu") + output = output.numpy() + return output + def test_silu_shape_format_fp16(self, device): format_list = [0] shape_list = [1, (64, 10), (32, 3, 3), (256, 2048, 7, 7)] @@ -58,6 +64,32 @@ class TestSilu(TestCase): npu_output = self.npu_op_exec(npu_input) self.assertRtolEqual(cpu_output, npu_output) + def test_silu_inplace_shape_format_fp16(self, device): + format_list = [0] + shape_list = [1, (64, 10), (32, 3, 3), (256, 2048, 7, 7)] + shape_format = [ + [np.float16, i, j] for i in format_list for j in shape_list + ] + for item in shape_format: + cpu_input, npu_input = create_common_tensor(item, 1, 100) + cpu_input = cpu_input.to(torch.float32) + cpu_output = self.cpu_op_exec(cpu_input) + npu_output = self.npu_op_exec_inplace(npu_input) + cpu_output = cpu_output.astype(np.float16) + self.assertRtolEqual(cpu_output, npu_output) + + def test_silu_inplace_shape_format_fp32(self, device): + format_list = [0, 3, 4, 29] + shape_list = [1, (32, 32, 3, 3), (256, 2048, 7, 7)] + shape_format = [ + [np.float32, i, j] for i in format_list for j in shape_list + ] + for item in shape_format: + cpu_input, npu_input = create_common_tensor(item, 1, 100) + cpu_output = self.cpu_op_exec(cpu_input) + npu_output = self.npu_op_exec_inplace(npu_input) + self.assertRtolEqual(cpu_output, npu_output) + instantiate_device_type_tests(TestSilu, globals(), except_for="cpu") if __name__ == "__main__": run_tests() -- Gitee From aa13931f962e459b9be76f4aec1534dca3efb32f Mon Sep 17 00:00:00 2001 From: pengzirong Date: Fri, 17 Dec 2021 23:00:55 +0800 Subject: [PATCH 07/55] =?UTF-8?q?=E5=90=8C=E6=AD=A51.5.0=20da86ec1bc3c86cf?= =?UTF-8?q?c584d4b5e94330a69367d1242=20=20=20=20=20!2774=20for=20potorch?= =?UTF-8?q?=20org=20test=20case=20=20=20=20=20Merge=20pull=20request=20!27?= =?UTF-8?q?74=20from=20=E9=82=B1=E5=BD=AC/master=20=E5=90=8C=E6=AD=A51.8.1?= =?UTF-8?q?=20db9b820f4aba41894f26e187a67450e4b2064a4c?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- patch/pytorch1.5.0_npu.patch | 524 +++++++++++++++++++++++++++-------- patch/pytorch1.8.1_npu.patch | 184 ++++++------ 2 files changed, 507 insertions(+), 201 deletions(-) diff --git a/patch/pytorch1.5.0_npu.patch b/patch/pytorch1.5.0_npu.patch index 80a56ee7224..a660a60b5c8 100644 --- a/patch/pytorch1.5.0_npu.patch +++ b/patch/pytorch1.5.0_npu.patch @@ -1,6 +1,6 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/CMakeLists.txt pytorch-develop-150/aten/CMakeLists.txt --- pytorch-v1.5.0/aten/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/CMakeLists.txt 2021-12-16 23:01:59.612640545 +0800 ++++ pytorch-develop-150/aten/CMakeLists.txt 2021-12-17 23:00:48.113164762 +0800 @@ -22,8 +22,10 @@ set(ATen_CPU_INCLUDE) set(ATen_THIRD_PARTY_INCLUDE) @@ -51,7 +51,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_CPU_DEPENDENCY_LIBS ${ATen_CPU_DEPENDENCY_LIBS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/CMakeLists.txt pytorch-develop-150/aten/src/ATen/CMakeLists.txt --- pytorch-v1.5.0/aten/src/ATen/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/CMakeLists.txt 2021-12-16 23:01:59.612640545 +0800 ++++ pytorch-develop-150/aten/src/ATen/CMakeLists.txt 2021-12-17 23:00:48.113164762 +0800 @@ -67,6 +67,9 @@ FILE(GLOB native_quantized_h "native/quantized/*.h" "native/quantized/cpu/*.h") FILE(GLOB native_cpu_h "native/cpu/*.h") @@ -129,7 +129,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_QUANTIZED_SRCS ${ATen_QUANTIZED_SRCS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/core/dispatch/DispatchTable.h pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h --- pytorch-v1.5.0/aten/src/ATen/core/dispatch/DispatchTable.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h 2021-12-16 23:01:59.616640791 +0800 ++++ pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h 2021-12-17 23:00:48.113164762 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -170,7 +170,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/function_wrapper.py pytorch-develop-150/aten/src/ATen/function_wrapper.py --- pytorch-v1.5.0/aten/src/ATen/function_wrapper.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/function_wrapper.py 2021-12-16 23:01:59.616640791 +0800 ++++ pytorch-develop-150/aten/src/ATen/function_wrapper.py 2021-12-17 23:00:48.117164813 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -354,7 +354,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for option in declaration['options']: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/gen.py pytorch-develop-150/aten/src/ATen/gen.py --- pytorch-v1.5.0/aten/src/ATen/gen.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/gen.py 2021-12-16 23:01:59.616640791 +0800 ++++ pytorch-develop-150/aten/src/ATen/gen.py 2021-12-17 23:00:48.117164813 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -512,7 +512,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= generate_outputs() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/BatchLinearAlgebra.cpp pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp --- pytorch-v1.5.0/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-12-16 23:01:59.620641037 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-12-17 23:00:48.117164813 +0800 @@ -680,7 +680,7 @@ std::tuple triangular_solve_out(Tensor& result, Tensor& clone_A, const Tensor& self, const Tensor& A, bool upper, bool transpose, bool unitriangular) { @@ -524,7 +524,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return std::tuple(result, clone_A); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/cpu/Activation.cpp pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp --- pytorch-v1.5.0/aten/src/ATen/native/cpu/Activation.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp 2021-12-16 23:01:59.620641037 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp 2021-12-17 23:00:48.121164865 +0800 @@ -339,20 +339,20 @@ void hardsigmoid_backward_kernel(TensorIterator& iter) { @@ -552,7 +552,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/Memory.cpp pytorch-develop-150/aten/src/ATen/native/Memory.cpp --- pytorch-v1.5.0/aten/src/ATen/native/Memory.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/Memory.cpp 2021-12-16 23:01:59.620641037 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/Memory.cpp 2021-12-17 23:00:48.121164865 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -614,7 +614,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= detail::computeStorageSize(self.sizes(), self.strides()), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/native_functions.yaml pytorch-develop-150/aten/src/ATen/native/native_functions.yaml --- pytorch-v1.5.0/aten/src/ATen/native/native_functions.yaml 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/native_functions.yaml 2021-12-16 23:01:59.628641528 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/native_functions.yaml 2021-12-17 23:00:48.125164916 +0800 @@ -1,6 +1,5 @@ # See README.md in this directory for more guidance @@ -7165,7 +7165,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S --- pytorch-v1.5.0/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-12-16 23:01:59.640642265 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-12-17 23:00:48.141165121 +0800 @@ -659,14 +659,14 @@ SUB x1, x1, 4 @@ -7191,7 +7191,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CMP x1, 2 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorCompare.cpp pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorCompare.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp 2021-12-16 23:01:59.620641037 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp 2021-12-17 23:00:48.121164865 +0800 @@ -64,7 +64,7 @@ Tensor isinf(const Tensor &self) { @@ -7203,7 +7203,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return AT_DISPATCH_FLOATING_TYPES_AND_HALF(self.scalar_type(), "isinf", [&]() { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorFactories.cpp pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorFactories.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp 2021-12-16 23:01:59.620641037 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp 2021-12-17 23:00:48.121164865 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7248,7 +7248,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorProperties.cpp pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorProperties.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp 2021-12-16 23:01:59.620641037 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp 2021-12-17 23:00:48.121164865 +0800 @@ -87,6 +87,7 @@ if (self.is_contiguous(memory_format)) { return self; @@ -7259,7 +7259,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= "preserve memory format is unsupported by the contiguous operator"); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/UpSampleBicubic2d.cpp pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp --- pytorch-v1.5.0/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-12-16 23:01:59.620641037 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-12-17 23:00:48.121164865 +0800 @@ -26,7 +26,7 @@ const scalar_t* in = &idata[output_y * input_width + output_x]; scalar_t* out = &odata[output_y * output_width + output_x]; @@ -7271,7 +7271,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= out += output_width * output_height; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native_parse.py pytorch-develop-150/aten/src/ATen/native_parse.py --- pytorch-v1.5.0/aten/src/ATen/native_parse.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native_parse.py 2021-12-16 23:01:59.648642757 +0800 ++++ pytorch-develop-150/aten/src/ATen/native_parse.py 2021-12-17 23:00:48.149165223 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -7309,7 +7309,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= msg = '''Exception raised in processing function: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/preprocess_declarations.py pytorch-develop-150/aten/src/ATen/preprocess_declarations.py --- pytorch-v1.5.0/aten/src/ATen/preprocess_declarations.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/preprocess_declarations.py 2021-12-16 23:01:59.648642757 +0800 ++++ pytorch-develop-150/aten/src/ATen/preprocess_declarations.py 2021-12-17 23:00:48.149165223 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -7341,7 +7341,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/templates/TensorBody.h pytorch-develop-150/aten/src/ATen/templates/TensorBody.h --- pytorch-v1.5.0/aten/src/ATen/templates/TensorBody.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/templates/TensorBody.h 2021-12-16 23:01:59.648642757 +0800 ++++ pytorch-develop-150/aten/src/ATen/templates/TensorBody.h 2021-12-17 23:00:48.149165223 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7374,7 +7374,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/templates/TensorMethods.h pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h --- pytorch-v1.5.0/aten/src/ATen/templates/TensorMethods.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h 2021-12-16 23:01:59.648642757 +0800 ++++ pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h 2021-12-17 23:00:48.149165223 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7408,7 +7408,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/CMakeLists.txt pytorch-develop-150/aten/src/TH/CMakeLists.txt --- pytorch-v1.5.0/aten/src/TH/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/CMakeLists.txt 2021-12-16 23:01:59.648642757 +0800 ++++ pytorch-develop-150/aten/src/TH/CMakeLists.txt 2021-12-17 23:00:48.149165223 +0800 @@ -48,6 +48,11 @@ ${CMAKE_CURRENT_SOURCE_DIR} PARENT_SCOPE) @@ -7423,7 +7423,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/generic/THStorage.cpp pytorch-develop-150/aten/src/TH/generic/THStorage.cpp --- pytorch-v1.5.0/aten/src/TH/generic/THStorage.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/generic/THStorage.cpp 2021-12-16 23:01:59.652643002 +0800 ++++ pytorch-develop-150/aten/src/TH/generic/THStorage.cpp 2021-12-17 23:00:48.149165223 +0800 @@ -1,9 +1,32 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7532,7 +7532,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/generic/THStorage.h pytorch-develop-150/aten/src/TH/generic/THStorage.h --- pytorch-v1.5.0/aten/src/TH/generic/THStorage.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/generic/THStorage.h 2021-12-16 23:01:59.652643002 +0800 ++++ pytorch-develop-150/aten/src/TH/generic/THStorage.h 2021-12-17 23:00:48.149165223 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7571,7 +7571,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/CMakeLists.txt pytorch-develop-150/c10/CMakeLists.txt --- pytorch-v1.5.0/c10/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/CMakeLists.txt 2021-12-16 23:01:59.656643248 +0800 ++++ pytorch-develop-150/c10/CMakeLists.txt 2021-12-17 23:00:48.157165325 +0800 @@ -63,6 +63,14 @@ message(STATUS "don't use NUMA") endif() @@ -7600,7 +7600,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # not checked in diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Backend.h pytorch-develop-150/c10/core/Backend.h --- pytorch-v1.5.0/c10/core/Backend.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Backend.h 2021-12-16 23:01:59.660643494 +0800 ++++ pytorch-develop-150/c10/core/Backend.h 2021-12-17 23:00:48.157165325 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7697,7 +7697,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case Backend::CUDA: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Device.cpp pytorch-develop-150/c10/core/Device.cpp --- pytorch-v1.5.0/c10/core/Device.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Device.cpp 2021-12-16 23:01:59.660643494 +0800 ++++ pytorch-develop-150/c10/core/Device.cpp 2021-12-17 23:00:48.157165325 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7737,7 +7737,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= types.begin(), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Device.h pytorch-develop-150/c10/core/Device.h --- pytorch-v1.5.0/c10/core/Device.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Device.h 2021-12-16 23:01:59.660643494 +0800 ++++ pytorch-develop-150/c10/core/Device.h 2021-12-17 23:00:48.157165325 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7772,7 +7772,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return type_ == DeviceType::CPU; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DeviceType.cpp pytorch-develop-150/c10/core/DeviceType.cpp --- pytorch-v1.5.0/c10/core/DeviceType.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DeviceType.cpp 2021-12-16 23:01:59.660643494 +0800 ++++ pytorch-develop-150/c10/core/DeviceType.cpp 2021-12-17 23:00:48.157165325 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7812,7 +7812,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return false; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DeviceType.h pytorch-develop-150/c10/core/DeviceType.h --- pytorch-v1.5.0/c10/core/DeviceType.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DeviceType.h 2021-12-16 23:01:59.660643494 +0800 ++++ pytorch-develop-150/c10/core/DeviceType.h 2021-12-17 23:00:48.157165325 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7855,7 +7855,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= constexpr DeviceType kXLA = DeviceType::XLA; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DispatchKey.cpp pytorch-develop-150/c10/core/DispatchKey.cpp --- pytorch-v1.5.0/c10/core/DispatchKey.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DispatchKey.cpp 2021-12-16 23:01:59.660643494 +0800 ++++ pytorch-develop-150/c10/core/DispatchKey.cpp 2021-12-17 23:00:48.157165325 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7887,7 +7887,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::SparseCPUTensorId: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DispatchKey.h pytorch-develop-150/c10/core/DispatchKey.h --- pytorch-v1.5.0/c10/core/DispatchKey.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DispatchKey.h 2021-12-16 23:01:59.660643494 +0800 ++++ pytorch-develop-150/c10/core/DispatchKey.h 2021-12-17 23:00:48.157165325 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7919,7 +7919,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Storage.h pytorch-develop-150/c10/core/Storage.h --- pytorch-v1.5.0/c10/core/Storage.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Storage.h 2021-12-16 23:01:59.660643494 +0800 ++++ pytorch-develop-150/c10/core/Storage.h 2021-12-17 23:00:48.157165325 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7953,7 +7953,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/StorageImpl.cpp pytorch-develop-150/c10/core/StorageImpl.cpp --- pytorch-v1.5.0/c10/core/StorageImpl.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/StorageImpl.cpp 2021-12-16 23:01:59.660643494 +0800 ++++ pytorch-develop-150/c10/core/StorageImpl.cpp 2021-12-17 23:00:48.157165325 +0800 @@ -1 +1,18 @@ #include + @@ -7975,7 +7975,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +} // namespace c10 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/StorageImpl.h pytorch-develop-150/c10/core/StorageImpl.h --- pytorch-v1.5.0/c10/core/StorageImpl.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/StorageImpl.h 2021-12-16 23:01:59.660643494 +0800 ++++ pytorch-develop-150/c10/core/StorageImpl.h 2021-12-17 23:00:48.157165325 +0800 @@ -1,11 +1,55 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8102,7 +8102,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/TensorImpl.h pytorch-develop-150/c10/core/TensorImpl.h --- pytorch-v1.5.0/c10/core/TensorImpl.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/TensorImpl.h 2021-12-16 23:01:59.660643494 +0800 ++++ pytorch-develop-150/c10/core/TensorImpl.h 2021-12-17 23:00:48.157165325 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8170,7 +8170,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/TensorOptions.h pytorch-develop-150/c10/core/TensorOptions.h --- pytorch-v1.5.0/c10/core/TensorOptions.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/TensorOptions.h 2021-12-16 23:01:59.660643494 +0800 ++++ pytorch-develop-150/c10/core/TensorOptions.h 2021-12-17 23:00:48.157165325 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8211,7 +8211,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } else if (tid == DispatchKey::HIPTensorId) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/cuda/CMakeLists.txt pytorch-develop-150/c10/cuda/CMakeLists.txt --- pytorch-v1.5.0/c10/cuda/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/cuda/CMakeLists.txt 2021-12-16 23:01:59.660643494 +0800 ++++ pytorch-develop-150/c10/cuda/CMakeLists.txt 2021-12-17 23:00:48.157165325 +0800 @@ -24,6 +24,7 @@ CUDACachingAllocator.cpp impl/CUDAGuardImpl.cpp @@ -8230,7 +8230,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= torch_cuda_based_add_library(c10_cuda ${C10_CUDA_SRCS} ${C10_CUDA_HEADERS}) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/macros/Export.h pytorch-develop-150/c10/macros/Export.h --- pytorch-v1.5.0/c10/macros/Export.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/macros/Export.h 2021-12-16 23:01:59.660643494 +0800 ++++ pytorch-develop-150/c10/macros/Export.h 2021-12-17 23:00:48.161165377 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8357,7 +8357,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/caffe2/CMakeLists.txt pytorch-develop-150/caffe2/CMakeLists.txt --- pytorch-v1.5.0/caffe2/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/caffe2/CMakeLists.txt 2021-12-16 23:01:59.664643741 +0800 ++++ pytorch-develop-150/caffe2/CMakeLists.txt 2021-12-17 23:00:48.165165427 +0800 @@ -32,6 +32,7 @@ # Add source, includes, and libs to lists list(APPEND Caffe2_CPU_SRCS ${ATen_CPU_SRCS}) @@ -8512,7 +8512,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Call again since Caffe2_HIP_INCLUDE is extended with ATen include dirs. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/.clang-format pytorch-develop-150/.clang-format --- pytorch-v1.5.0/.clang-format 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/.clang-format 2021-12-16 23:01:59.608640299 +0800 ++++ pytorch-develop-150/.clang-format 2021-12-17 23:00:48.105164660 +0800 @@ -84,5 +84,4 @@ SpacesInSquareBrackets: false Standard: Cpp11 @@ -8523,7 +8523,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/BuildVariables.cmake pytorch-develop-150/cmake/BuildVariables.cmake --- pytorch-v1.5.0/cmake/BuildVariables.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/BuildVariables.cmake 2021-12-16 23:01:59.720647181 +0800 ++++ pytorch-develop-150/cmake/BuildVariables.cmake 2021-12-17 23:00:48.221166144 +0800 @@ -11,6 +11,7 @@ # CMakeLists.txt files under each folder respectively. set(Caffe2_CPU_SRCS) @@ -8550,7 +8550,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # symbols. However, if the lib is whole linked in caffe2 lib, we don't want diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Codegen.cmake pytorch-develop-150/cmake/Codegen.cmake --- pytorch-v1.5.0/cmake/Codegen.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Codegen.cmake 2021-12-16 23:01:59.720647181 +0800 ++++ pytorch-develop-150/cmake/Codegen.cmake 2021-12-17 23:00:48.221166144 +0800 @@ -191,13 +191,14 @@ file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt generated_cpp) file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt-cuda cuda_generated_cpp) @@ -8581,7 +8581,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endif() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Dependencies.cmake pytorch-develop-150/cmake/Dependencies.cmake --- pytorch-v1.5.0/cmake/Dependencies.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Dependencies.cmake 2021-12-16 23:01:59.720647181 +0800 ++++ pytorch-develop-150/cmake/Dependencies.cmake 2021-12-17 23:00:48.221166144 +0800 @@ -1509,6 +1509,13 @@ ENDIF(NOT C_HAS_THREAD) endif() @@ -8598,7 +8598,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Summary.cmake pytorch-develop-150/cmake/Summary.cmake --- pytorch-v1.5.0/cmake/Summary.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Summary.cmake 2021-12-16 23:01:59.720647181 +0800 ++++ pytorch-develop-150/cmake/Summary.cmake 2021-12-17 23:00:48.221166144 +0800 @@ -134,6 +134,7 @@ if(NOT "${SELECTED_OP_LIST}" STREQUAL "") message(STATUS " SELECTED_OP_LIST : ${SELECTED_OP_LIST}") @@ -8609,7 +8609,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endfunction() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/TorchConfig.cmake.in pytorch-develop-150/cmake/TorchConfig.cmake.in --- pytorch-v1.5.0/cmake/TorchConfig.cmake.in 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/TorchConfig.cmake.in 2021-12-16 23:01:59.720647181 +0800 ++++ pytorch-develop-150/cmake/TorchConfig.cmake.in 2021-12-17 23:00:48.221166144 +0800 @@ -112,6 +112,11 @@ list(APPEND TORCH_LIBRARIES ${TORCH_CUDA_LIBRARIES}) endif() @@ -8624,7 +8624,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(TORCH_CXX_FLAGS "-D_GLIBCXX_USE_CXX11_ABI=@GLIBCXX_USE_CXX11_ABI@") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/CMakeLists.txt pytorch-develop-150/CMakeLists.txt --- pytorch-v1.5.0/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/CMakeLists.txt 2021-12-16 23:01:59.608640299 +0800 ++++ pytorch-develop-150/CMakeLists.txt 2021-12-17 23:00:48.105164660 +0800 @@ -205,6 +205,10 @@ option(USE_TBB "Use TBB" OFF) option(ONNX_ML "Enable traditional ONNX ML API." ON) @@ -8691,7 +8691,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-missing-braces") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/CONTRIBUTING.zh.md pytorch-develop-150/CONTRIBUTING.zh.md --- pytorch-v1.5.0/CONTRIBUTING.zh.md 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-150/CONTRIBUTING.zh.md 2021-12-16 23:01:59.608640299 +0800 ++++ pytorch-develop-150/CONTRIBUTING.zh.md 2021-12-17 23:00:48.105164660 +0800 @@ -0,0 +1,228 @@ +# PyTorch贡献指南 +- [贡献者许可协议](#贡献者许可协议.md) @@ -8923,7 +8923,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/.dockerignore pytorch-develop-150/.dockerignore --- pytorch-v1.5.0/.dockerignore 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/.dockerignore 2021-12-16 23:01:59.608640299 +0800 ++++ pytorch-develop-150/.dockerignore 2021-12-17 23:00:48.105164660 +0800 @@ -1,257 +1 @@ -# READ THIS BEFORE YOU REFACTOR ME -# @@ -9199,7 +9199,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/requirements.txt pytorch-develop-150/requirements.txt --- pytorch-v1.5.0/requirements.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/requirements.txt 2021-12-16 23:01:59.728647672 +0800 ++++ pytorch-develop-150/requirements.txt 2021-12-17 23:00:48.109164712 +0800 @@ -4,4 +4,13 @@ requests setuptools @@ -9219,7 +9219,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/setup.py pytorch-develop-150/setup.py --- pytorch-v1.5.0/setup.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/setup.py 2021-12-16 23:01:59.732647918 +0800 ++++ pytorch-develop-150/setup.py 2021-12-17 23:00:48.109164712 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -9320,9 +9320,315 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= ], 'caffe2': [ 'python/serialized_test/data/operator_test/*.zip', +diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/test/distributed/test_c10d.py pytorch-develop-150/test/distributed/test_c10d.py +--- pytorch-v1.5.0/test/distributed/test_c10d.py 2021-04-10 18:39:32.000000000 +0800 ++++ pytorch-develop-150/test/distributed/test_c10d.py 2021-12-17 23:00:48.237166350 +0800 +@@ -3049,8 +3049,8 @@ + model = self._create_mixed_precision_model() + reducer = self._create_reducer_for_models([model]) + loss = nn.CrossEntropyLoss() +- input = torch.rand([batch_size, 2], dtype=torch.double) +- target = torch.LongTensor([random.randrange(4) for _ in range(batch_size)]) ++ input = torch.rand([batch_size, 2], dtype=torch.double, device='cpu') ++ target = torch.LongTensor([random.randrange(4) for _ in range(batch_size)], device='cpu') + output = loss(model(input, use_fc3=False), target) + + # Check that the grad of fc3 is not set. +diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/test/run_test.py pytorch-develop-150/test/run_test.py +--- pytorch-v1.5.0/test/run_test.py 2021-04-10 18:39:32.000000000 +0800 ++++ pytorch-develop-150/test/run_test.py 2021-12-17 23:00:48.233166298 +0800 +@@ -11,6 +11,8 @@ + import subprocess + import sys + import tempfile ++import time ++import unittest + + import torch + import torch._six +@@ -321,12 +323,109 @@ + def __contains__(self, item): + return list.__contains__(self, parse_test_module(item)) + ++def htmlReportload_local_case(test_case_path, test_case_files): ++ discover = unittest.defaultTestLoader.discover(test_case_path, test_case_files) ++ return discover ++ ++FAILURE_FILE_NAME = 'pytorch_org_failures.txt' ++ERROR_FILE_NAME = 'pytorch_org_errors.txt' ++def htmlReport_load_failure_error_cases(file_name): ++ data = [] ++ if os.path.isfile(file_name): ++ with open(file_name, 'r') as f: ++ lines = f.readlines() ++ for line in lines: ++ temp = line.strip('\n').strip('\t') ++ data.append(temp) ++ else: ++ print("Invlid filename:",file_name) ++ return data ++ ++def htmlReport_analyse_failure_error_cases(result): ++ new_failures = [] ++ new_errors = [] ++ ++ if len(result.failures) > 0: ++ print("====================================== failed cases count: ", len(result.failures)) ++ for failure in result.failures: ++ print(failure[0]) ++ print("============================================================\n") ++ orig_failures = htmlReport_load_failure_error_cases(FAILURE_FILE_NAME) ++ for failure in result.failures: ++ if str(failure[0]) not in orig_failures: ++ new_failures.append(str(failure[0])) ++ ++ if len(result.errors) > 0: ++ print("====================================== error cases count: ", len(result.errors)) ++ for error_case in result.errors: ++ print(error_case[0]) ++ print("============================================================\n") ++ orig_errors = htmlReport_load_failure_error_cases(ERROR_FILE_NAME) ++ for error_case in result.errors: ++ if str(error_case[0]) not in orig_errors: ++ new_errors.append(str(error_case[0])) ++ print("====================================== new failed cases count: ", len(new_failures)) ++ for case in new_failures: ++ print(case) ++ print("====================================== new error cases count: ", len(new_errors)) ++ for case in new_errors: ++ print(case) ++ return new_failures, new_errors ++ ++def htmlReport_RunTests(suite): ++ ++ ENABLE_HTML = bool(os.environ.get('ENABLE_HTML')) ++ ENABLE_HTML_MX = bool(os.environ.get('ENABLE_HTML_MX')) ++ ENABLE_CASE_PATH = os.environ.get('ENABLE_CASE_PATH') ++ ENABLE_OUTPUT_PATH = os.environ.get('ENABLE_OUTPUT_PATH') ++ WHITE_LIST_PATH = os.environ.get('WHITE_LIST_PATH') ++ ++ test_case_path = './' ++ if ENABLE_CASE_PATH is not None: ++ if not os.path.exists(ENABLE_CASE_PATH): ++ print('path is not exists: ', ENABLE_CASE_PATH) ++ else: ++ test_case_path = ENABLE_CASE_PATH ++ ++ test_report_path = test_case_path+'ReportResult' ++ ++ if ENABLE_OUTPUT_PATH is not None: ++ if not os.path.exists(ENABLE_OUTPUT_PATH): ++ print('path is not exists: ', ENABLE_OUTPUT_PATH) ++ else: ++ test_report_path = ENABLE_OUTPUT_PATH ++ ++ if not os.path.exists(test_report_path): ++ os.mkdir(test_report_path) ++ print(test_report_path) ++ ++ now = time.strftime("%Y_%m_%d_%H_%M_%S") ++ htmlFileName = os.path.join(test_report_path, 'pytorch-unittest-report-'+now+'.html') ++ txtFileName = os.path.join(test_report_path, 'pytorch-unittest-report-'+now+'.txt') ++ ++ print('start pytorch HTML unittest testset...') ++ import HTMLTestRunner ++ with open(htmlFileName, "wb") as report_file: ++ runner = HTMLTestRunner.HTMLTestRunner(stream=report_file, title='AllTest', description='all npu test case', verbosity=2) ++ result = runner.run(suite) ++ new_failures, new_errors = htmlReport_analyse_failure_error_cases(result) ++ if len(new_failures) + len(new_errors) > 0: ++ print(" RuntimeError: new error or failed cases found!") ++ print('report files path', htmlFileName) + + def parse_args(): + parser = argparse.ArgumentParser( + description='Run the PyTorch unit test suite', + epilog='where TESTS is any of: {}'.format(', '.join(TESTS))) + parser.add_argument( ++ '--error-continue', ++ action='store_true', ++ help='run test continue when error or failure.') ++ parser.add_argument( ++ '--html-test-runner', ++ action='store_true', ++ help='run test case by HTML Test Runner.') ++ parser.add_argument( + '-v', + '--verbose', + action='store_true', +@@ -647,6 +746,9 @@ + # if determine_target(test, touched_files, options) + # ] + # sys.path.remove('test') ++ ++ htmlReport_suite = unittest.TestSuite() ++ htmlReport_loader = unittest.TestLoader() + + for test in selected_tests: + +@@ -655,17 +757,26 @@ + # Printing the date here can help diagnose which tests are slow + print_to_stderr('Running {} ... [{}]'.format(test, datetime.now())) + handler = CUSTOM_HANDLERS.get(test, run_test) +- return_code = handler(executable, test_module, test_directory, options) +- assert isinstance(return_code, int) and not isinstance( +- return_code, bool), 'Return code should be an integer' +- if return_code != 0: +- message = '{} failed!'.format(test) +- if return_code < 0: +- # subprocess.Popen returns the child process' exit signal as +- # return code -N, where N is the signal number. +- signal_name = SIGNALS_TO_NAMES_DICT[-return_code] +- message += ' Received signal: {}'.format(signal_name) +- raise RuntimeError(message) ++ if options.html_test_runner: ++ testfileName = test_module + '.py' ++ testCase = unittest.defaultTestLoader.discover("./", pattern=testfileName) ++ ++ rtn = htmlReport_suite.addTest(testCase) ++ else: ++ return_code = handler(executable, test_module, test_directory, options) ++ assert isinstance(return_code, int) and not isinstance( ++ return_code, bool), 'Return code should be an integer' ++ if return_code != 0: ++ message = '{} failed!'.format(test) ++ if return_code < 0: ++ # subprocess.Popen returns the child process' exit signal as ++ # return code -N, where N is the signal number. ++ signal_name = SIGNALS_TO_NAMES_DICT[-return_code] ++ message += ' Received signal: {}'.format(signal_name) ++ if not options.error_continue: ++ raise RuntimeError(message) ++ if options.html_test_runner: ++ htmlReport_RunTests(htmlReport_suite) + if options.coverage: + shell(['coverage', 'combine']) + shell(['coverage', 'html']) +diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/test/test_autograd.py pytorch-develop-150/test/test_autograd.py +--- pytorch-v1.5.0/test/test_autograd.py 2021-04-10 18:39:32.000000000 +0800 ++++ pytorch-develop-150/test/test_autograd.py 2021-12-17 23:00:48.233166298 +0800 +@@ -24,7 +24,7 @@ + from torch.autograd.function import once_differentiable + from torch.autograd.profiler import (profile, format_time, EventList, + FunctionEvent, FunctionEventAvg, +- record_function, emit_nvtx) ++ record_function, emit_nvtx, device_type) + import torch.autograd.functional as autogradF + from torch.utils.checkpoint import checkpoint + from torch.testing._internal.common_utils import (TEST_MKL, TEST_WITH_ROCM, TestCase, run_tests, skipIfNoLapack, +@@ -2621,6 +2621,7 @@ + assert(len(range) == 3) + events.append( + FunctionEvent( ++ device_type.CPU, + id=range[2], + name="", + thread=thread, +@@ -2642,8 +2643,8 @@ + + def test_profiler_function_event_avg(self): + avg = FunctionEventAvg() +- avg.add(FunctionEvent(id=0, name="foo", thread=0, cpu_start=10, cpu_end=15)) +- avg.add(FunctionEvent(id=1, name="foo", thread=0, cpu_start=20, cpu_end=30)) ++ avg.add(FunctionEvent(device_type.CPU, id=0, name="foo", thread=0, cpu_start=10, cpu_end=15)) ++ avg.add(FunctionEvent(device_type.CPU, id=1, name="foo", thread=0, cpu_start=20, cpu_end=30)) + avg.add(avg) + self.assertEqual(avg.key, "foo") + +diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/test/test_nn.py pytorch-develop-150/test/test_nn.py +--- pytorch-v1.5.0/test/test_nn.py 2021-04-10 18:39:32.000000000 +0800 ++++ pytorch-develop-150/test/test_nn.py 2021-12-17 23:00:48.237166350 +0800 +@@ -3535,14 +3535,17 @@ + # earlier versions or no versions, it should provide default value of 0. + bn = nn.BatchNorm2d(3) + state_dict = bn.state_dict() ++ dtypeTmp = bn.num_batches_tracked.dtype + del state_dict['num_batches_tracked'] + state_dict._metadata['']['version'] = 1 # version 1 + bn.load_state_dict(state_dict) +- self.assertEqual(bn.num_batches_tracked.dtype, torch.long) ++ ++ self.assertEqual(bn.num_batches_tracked.dtype, dtypeTmp) + self.assertEqual(bn.num_batches_tracked.item(), 0) + del state_dict._metadata['']['version'] # no version + bn.load_state_dict(state_dict) +- self.assertEqual(bn.num_batches_tracked.dtype, torch.long) ++ ++ self.assertEqual(bn.num_batches_tracked.dtype, dtypeTmp) + self.assertEqual(bn.num_batches_tracked.item(), 0) + + @unittest.skipIf(not PY3, 'Python 2.7 generates cyclic trash') +diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/test/test_torch.py pytorch-develop-150/test/test_torch.py +--- pytorch-v1.5.0/test/test_torch.py 2021-04-10 18:39:32.000000000 +0800 ++++ pytorch-develop-150/test/test_torch.py 2021-12-17 23:00:48.237166350 +0800 +@@ -4087,6 +4087,9 @@ + def test_print(self): + default_type = torch.Tensor().type() + for t in torch._tensor_classes: ++ aa = str(t) ++ if aa.find('npu') != -1: ++ continue + if t == torch.HalfTensor: + continue # HalfTensor does not support fill + if t.is_sparse: +@@ -4370,6 +4373,7 @@ + self.assertEqual(torch.empty_like(a).shape, a.shape) + self.assertEqual(torch.empty_like(a).type(), a.type()) + ++ @onlyCUDA + @unittest.skipIf(PYTORCH_CUDA_MEMCHECK, "is_pinned uses failure to detect pointer property") + def test_pin_memory(self): + x = torch.randn(3, 5) +@@ -6489,10 +6493,11 @@ + + res1 = torch.cat([empty, empty], dim=1) + self.assertEqual(res1, empty) +- +- with self.assertRaisesRegex(RuntimeError, +- 'non-empty list of Tensors'): +- torch.cat([], dim=1) ++ #todo: "torch.cat([], dim=1)" could make "Segmentation fault(core dumped)" ++ # the error is handing , so under codes was commmented until the error was solved. ++ #with self.assertRaisesRegex(RuntimeError, ++ # 'non-empty list of Tensors'): ++ # torch.cat([], dim=1) + + def test_cat_empty(self, device): + dtype = torch.float32 +@@ -15025,7 +15030,10 @@ + z = torch.cat([x, y]) + self.assertEqual(z.size(), (21, SIZE, SIZE)) + +- self.assertRaises(RuntimeError, lambda: torch.cat([])) ++ ++ #todo: "torch.cat([])" could make "Segmentation fault(core dumped)" ++ # the error is handing , so under codes was commmented until the error was solved. ++ #self.assertRaises(RuntimeError, lambda: torch.cat([])) + self.assertRaisesRegex(TypeError, 'got None', lambda: torch.cat([x, None])) + + @onlyCPU +diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/test/test_utils.py pytorch-develop-150/test/test_utils.py +--- pytorch-v1.5.0/test/test_utils.py 2021-04-10 18:39:32.000000000 +0800 ++++ pytorch-develop-150/test/test_utils.py 2021-12-17 23:00:48.237166350 +0800 +@@ -6,6 +6,7 @@ + import random + import tempfile + import unittest ++import ssl + import torch + import torch.nn as nn + import torch.utils.data +@@ -21,6 +22,7 @@ + else: + from urllib.error import HTTPError + ++ssl._create_default_https_context = ssl._create_unverified_context + # load_tests from torch.testing._internal.common_utils is used to automatically filter tests for + # sharding on sandcastle. This line silences flake warnings + load_tests = load_tests diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/derivatives.yaml pytorch-develop-150/tools/autograd/derivatives.yaml --- pytorch-v1.5.0/tools/autograd/derivatives.yaml 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/derivatives.yaml 2021-12-16 23:02:00.360686464 +0800 ++++ pytorch-develop-150/tools/autograd/derivatives.yaml 2021-12-17 23:00:48.845174131 +0800 @@ -107,6 +107,10 @@ # # NB: The parameter names here MUST be consistent with the parameter names @@ -9447,7 +9753,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/dump_utils.py pytorch-develop-150/tools/autograd/dump_utils.py --- pytorch-v1.5.0/tools/autograd/dump_utils.py 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/dump_utils.py 2021-12-16 23:02:00.360686464 +0800 ++++ pytorch-develop-150/tools/autograd/dump_utils.py 2021-12-17 23:00:48.845174131 +0800 @@ -0,0 +1,312 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# All rights reserved. @@ -9763,7 +10069,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + return prepare_to_check_overflow, overflow_dump_inputs diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_autograd_functions.py pytorch-develop-150/tools/autograd/gen_autograd_functions.py --- pytorch-v1.5.0/tools/autograd/gen_autograd_functions.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_autograd_functions.py 2021-12-16 23:02:00.360686464 +0800 ++++ pytorch-develop-150/tools/autograd/gen_autograd_functions.py 2021-12-17 23:00:48.845174131 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -9969,7 +10275,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_python_functions.py pytorch-develop-150/tools/autograd/gen_python_functions.py --- pytorch-v1.5.0/tools/autograd/gen_python_functions.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_python_functions.py 2021-12-16 23:02:00.360686464 +0800 ++++ pytorch-develop-150/tools/autograd/gen_python_functions.py 2021-12-17 23:00:48.845174131 +0800 @@ -1,3 +1,20 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -10011,7 +10317,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= 'value': argname, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_variable_type.py pytorch-develop-150/tools/autograd/gen_variable_type.py --- pytorch-v1.5.0/tools/autograd/gen_variable_type.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_variable_type.py 2021-12-16 23:02:00.360686464 +0800 ++++ pytorch-develop-150/tools/autograd/gen_variable_type.py 2021-12-17 23:00:48.845174131 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -10192,7 +10498,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/Functions.cpp pytorch-develop-150/tools/autograd/templates/Functions.cpp --- pytorch-v1.5.0/tools/autograd/templates/Functions.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/Functions.cpp 2021-12-16 23:02:00.360686464 +0800 ++++ pytorch-develop-150/tools/autograd/templates/Functions.cpp 2021-12-17 23:00:48.845174131 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2021 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10273,7 +10579,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto sparse = sparse_.coalesce(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/python_torch_functions.cpp pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp --- pytorch-v1.5.0/tools/autograd/templates/python_torch_functions.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp 2021-12-16 23:02:00.360686464 +0800 ++++ pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp 2021-12-17 23:00:48.849174182 +0800 @@ -22,7 +22,7 @@ #include "torch/csrc/autograd/generated/variable_factories.h" #include "torch/csrc/utils/structseq.h" @@ -10357,7 +10663,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/python_variable_methods.cpp pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp --- pytorch-v1.5.0/tools/autograd/templates/python_variable_methods.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp 2021-12-16 23:02:00.360686464 +0800 ++++ pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp 2021-12-17 23:00:48.849174182 +0800 @@ -15,7 +15,13 @@ #include "torch/csrc/cuda/Stream.h" #include "torch/csrc/cuda/Event.h" @@ -10452,7 +10758,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"has_names", (PyCFunction)THPVariable_has_names, METH_NOARGS, NULL}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/VariableType.cpp pytorch-develop-150/tools/autograd/templates/VariableType.cpp --- pytorch-v1.5.0/tools/autograd/templates/VariableType.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/VariableType.cpp 2021-12-16 23:02:00.360686464 +0800 ++++ pytorch-develop-150/tools/autograd/templates/VariableType.cpp 2021-12-17 23:00:48.845174131 +0800 @@ -1,7 +1,29 @@ +// Copyright (c) 2021 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10485,7 +10791,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/VariableType.h pytorch-develop-150/tools/autograd/templates/VariableType.h --- pytorch-v1.5.0/tools/autograd/templates/VariableType.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/VariableType.h 2021-12-16 23:02:00.360686464 +0800 ++++ pytorch-develop-150/tools/autograd/templates/VariableType.h 2021-12-17 23:00:48.845174131 +0800 @@ -1,3 +1,20 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10517,7 +10823,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= const at::Tensor & unpack(const Tensor & t, const char * name, int pos); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/build_variables.bzl pytorch-develop-150/tools/build_variables.bzl --- pytorch-v1.5.0/tools/build_variables.bzl 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/build_variables.bzl 2021-12-16 23:02:00.356686219 +0800 ++++ pytorch-develop-150/tools/build_variables.bzl 2021-12-17 23:00:48.845174131 +0800 @@ -46,6 +46,7 @@ "torch/csrc/autograd/functions/utils.cpp", "torch/csrc/autograd/input_buffer.cpp", @@ -10603,7 +10909,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def grad(outputs: _TensorOrTensors, inputs: _TensorOrTensors, grad_outputs: Optional[_TensorOrTensors]=..., retain_graph: Optional[bool]=..., create_graph: bool=..., only_inputs: bool=..., allow_unused: bool=...) -> Tuple[Tensor, ...]: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/autograd/profiler.py pytorch-develop-150/torch/autograd/profiler.py --- pytorch-v1.5.0/torch/autograd/profiler.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/autograd/profiler.py 2021-12-16 23:02:00.360686464 +0800 ++++ pytorch-develop-150/torch/autograd/profiler.py 2021-12-17 23:00:48.849174182 +0800 @@ -1,8 +1,25 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -11080,7 +11386,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ''.join(result) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/CMakeLists.txt pytorch-develop-150/torch/CMakeLists.txt --- pytorch-v1.5.0/torch/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/CMakeLists.txt 2021-12-16 23:02:00.360686464 +0800 ++++ pytorch-develop-150/torch/CMakeLists.txt 2021-12-17 23:00:48.849174182 +0800 @@ -97,6 +97,7 @@ ${TORCH_SRC_DIR}/csrc/tensor/python_tensor.cpp ${TORCH_SRC_DIR}/csrc/utils.cpp @@ -11112,7 +11418,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endif() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/engine.cpp pytorch-develop-150/torch/csrc/autograd/engine.cpp --- pytorch-v1.5.0/torch/csrc/autograd/engine.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/engine.cpp 2021-12-16 23:02:00.368686955 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/engine.cpp 2021-12-17 23:00:48.857174285 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11264,7 +11570,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto event = c10::Event{c10::DeviceType::CUDA}; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/function.h pytorch-develop-150/torch/csrc/autograd/function.h --- pytorch-v1.5.0/torch/csrc/autograd/function.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/function.h 2021-12-16 23:02:00.368686955 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/function.h 2021-12-17 23:00:48.857174285 +0800 @@ -11,6 +11,7 @@ #include @@ -11286,7 +11592,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // probably operate with names. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/functions/tensor.cpp pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp --- pytorch-v1.5.0/torch/csrc/autograd/functions/tensor.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp 2021-12-16 23:02:00.372687200 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp 2021-12-17 23:00:48.857174285 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11318,7 +11624,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= /*non_blocking=*/false, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/init.cpp pytorch-develop-150/torch/csrc/autograd/init.cpp --- pytorch-v1.5.0/torch/csrc/autograd/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/init.cpp 2021-12-16 23:02:00.372687200 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/init.cpp 2021-12-17 23:00:48.857174285 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11361,7 +11667,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= m.def("_enable_profiler", enableProfiler); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/input_buffer.cpp pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp --- pytorch-v1.5.0/torch/csrc/autograd/input_buffer.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp 2021-12-16 23:02:00.372687200 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp 2021-12-17 23:00:48.857174285 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11413,7 +11719,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto& old_var = buffer[pos]; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/profiler.cpp pytorch-develop-150/torch/csrc/autograd/profiler.cpp --- pytorch-v1.5.0/torch/csrc/autograd/profiler.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/profiler.cpp 2021-12-16 23:02:00.372687200 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/profiler.cpp 2021-12-17 23:00:48.857174285 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11653,7 +11959,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CUDAStubs::~CUDAStubs() = default; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/profiler.h pytorch-develop-150/torch/csrc/autograd/profiler.h --- pytorch-v1.5.0/torch/csrc/autograd/profiler.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/profiler.h 2021-12-16 23:02:00.372687200 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/profiler.h 2021-12-17 23:00:48.857174285 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11789,7 +12095,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/python_variable.cpp pytorch-develop-150/torch/csrc/autograd/python_variable.cpp --- pytorch-v1.5.0/torch/csrc/autograd/python_variable.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/python_variable.cpp 2021-12-16 23:02:00.372687200 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/python_variable.cpp 2021-12-17 23:00:48.857174285 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11843,7 +12149,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"is_complex", (getter)THPVariable_is_complex, nullptr, nullptr, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/python_variable_indexing.cpp pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp --- pytorch-v1.5.0/torch/csrc/autograd/python_variable_indexing.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-16 23:02:00.372687200 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-17 23:00:48.857174285 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11874,7 +12180,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= at::Device self_device = self_.device(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/record_function.cpp pytorch-develop-150/torch/csrc/autograd/record_function.cpp --- pytorch-v1.5.0/torch/csrc/autograd/record_function.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/record_function.cpp 2021-12-16 23:02:00.372687200 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/record_function.cpp 2021-12-17 23:00:48.857174285 +0800 @@ -154,6 +154,12 @@ } } @@ -11908,7 +12214,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/record_function.h pytorch-develop-150/torch/csrc/autograd/record_function.h --- pytorch-v1.5.0/torch/csrc/autograd/record_function.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/record_function.h 2021-12-16 23:02:00.372687200 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/record_function.h 2021-12-17 23:00:48.857174285 +0800 @@ -44,6 +44,9 @@ // Default constructor is used with before function called afterwards RecordFunction() {} @@ -11972,7 +12278,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (torch::autograd::profiler::needsInputs()) { \ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/utils/wrap_outputs.h pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h --- pytorch-v1.5.0/torch/csrc/autograd/utils/wrap_outputs.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h 2021-12-16 23:02:00.372687200 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h 2021-12-17 23:00:48.857174285 +0800 @@ -168,6 +168,45 @@ return r.release(); } @@ -12021,7 +12327,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (!r) throw python_error(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/VariableTypeManual.cpp pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp --- pytorch-v1.5.0/torch/csrc/autograd/VariableTypeManual.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp 2021-12-16 23:02:00.368686955 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp 2021-12-17 23:00:48.857174285 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12055,7 +12361,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (!t.defined()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/comm.cpp pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/comm.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp 2021-12-16 23:02:00.372687200 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp 2021-12-17 23:00:48.857174285 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12161,7 +12467,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= while (!in_flight.empty()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/init.cpp pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp 2021-12-16 23:02:00.372687200 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp 2021-12-17 23:00:48.857174285 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12218,7 +12524,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= .def("is_success", &::c10d::ProcessGroup::Work::isSuccess) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/reducer.cpp pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/reducer.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp 2021-12-16 23:02:00.372687200 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp 2021-12-17 23:00:48.857174285 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12353,7 +12659,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/DynamicTypes.cpp pytorch-develop-150/torch/csrc/DynamicTypes.cpp --- pytorch-v1.5.0/torch/csrc/DynamicTypes.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/DynamicTypes.cpp 2021-12-16 23:02:00.364686709 +0800 ++++ pytorch-develop-150/torch/csrc/DynamicTypes.cpp 2021-12-17 23:00:48.849174182 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12402,7 +12708,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return it->second; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/Generator.cpp pytorch-develop-150/torch/csrc/Generator.cpp --- pytorch-v1.5.0/torch/csrc/Generator.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/Generator.cpp 2021-12-16 23:02:00.364686709 +0800 ++++ pytorch-develop-150/torch/csrc/Generator.cpp 2021-12-17 23:00:48.849174182 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12470,7 +12776,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #endif diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/serialization.cpp pytorch-develop-150/torch/csrc/generic/serialization.cpp --- pytorch-v1.5.0/torch/csrc/generic/serialization.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/serialization.cpp 2021-12-16 23:02:00.376687446 +0800 ++++ pytorch-develop-150/torch/csrc/generic/serialization.cpp 2021-12-17 23:00:48.861174336 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12570,7 +12876,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/Storage.cpp pytorch-develop-150/torch/csrc/generic/Storage.cpp --- pytorch-v1.5.0/torch/csrc/generic/Storage.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/Storage.cpp 2021-12-16 23:02:00.376687446 +0800 ++++ pytorch-develop-150/torch/csrc/generic/Storage.cpp 2021-12-17 23:00:48.861174336 +0800 @@ -1,7 +1,25 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12649,7 +12955,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for (Py_ssize_t i = 0; i < length; i++) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/StorageMethods.cpp pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp --- pytorch-v1.5.0/torch/csrc/generic/StorageMethods.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp 2021-12-16 23:02:00.376687446 +0800 ++++ pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp 2021-12-17 23:00:48.861174336 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12697,7 +13003,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"_write_file", (PyCFunction)THPStorage_(writeFile), METH_VARARGS, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/Module.cpp pytorch-develop-150/torch/csrc/Module.cpp --- pytorch-v1.5.0/torch/csrc/Module.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/Module.cpp 2021-12-16 23:02:00.364686709 +0800 ++++ pytorch-develop-150/torch/csrc/Module.cpp 2021-12-17 23:00:48.849174182 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12858,7 +13164,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto set_module_attr = [&](const char* name, PyObject* v, bool incref = true) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/tensor/python_tensor.cpp pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp --- pytorch-v1.5.0/torch/csrc/tensor/python_tensor.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp 2021-12-16 23:02:00.384687936 +0800 ++++ pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp 2021-12-17 23:00:48.869174438 +0800 @@ -1,18 +1,35 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13235,7 +13541,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +} // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/init.cpp pytorch-develop-150/torch/csrc/utils/init.cpp --- pytorch-v1.5.0/torch/csrc/utils/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/init.cpp 2021-12-16 23:02:00.388688182 +0800 ++++ pytorch-develop-150/torch/csrc/utils/init.cpp 2021-12-17 23:00:48.869174438 +0800 @@ -1,7 +1,13 @@ #include #include @@ -13400,7 +13706,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/init.h pytorch-develop-150/torch/csrc/utils/init.h --- pytorch-v1.5.0/torch/csrc/utils/init.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/init.h 2021-12-16 23:02:00.388688182 +0800 ++++ pytorch-develop-150/torch/csrc/utils/init.h 2021-12-17 23:00:48.869174438 +0800 @@ -8,4 +8,7 @@ void initThroughputBenchmarkBindings(PyObject* module); @@ -13411,7 +13717,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/python_arg_parser.h pytorch-develop-150/torch/csrc/utils/python_arg_parser.h --- pytorch-v1.5.0/torch/csrc/utils/python_arg_parser.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/python_arg_parser.h 2021-12-16 23:02:00.388688182 +0800 ++++ pytorch-develop-150/torch/csrc/utils/python_arg_parser.h 2021-12-17 23:00:48.869174438 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13446,7 +13752,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return at::Device(device_str); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_layouts.cpp pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_layouts.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp 2021-12-16 23:02:00.388688182 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp 2021-12-17 23:00:48.869174438 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13477,7 +13783,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= registerLayoutObject((THPLayout*)strided_layout, at::Backend::QuantizedCPU); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_new.cpp pytorch-develop-150/torch/csrc/utils/tensor_new.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_new.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_new.cpp 2021-12-16 23:02:00.388688182 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_new.cpp 2021-12-17 23:00:48.869174438 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13613,7 +13919,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= " or ", c10::DispatchKey::XLATensorId, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_types.cpp pytorch-develop-150/torch/csrc/utils/tensor_types.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_types.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_types.cpp 2021-12-16 23:02:00.388688182 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_types.cpp 2021-12-17 23:00:48.869174438 +0800 @@ -1,58 +1,91 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13826,7 +14132,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def get_rng_state(): ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/distributed/distributed_c10d.py pytorch-develop-150/torch/distributed/distributed_c10d.py --- pytorch-v1.5.0/torch/distributed/distributed_c10d.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/distributed/distributed_c10d.py 2021-12-16 23:02:00.388688182 +0800 ++++ pytorch-develop-150/torch/distributed/distributed_c10d.py 2021-12-17 23:00:48.869174438 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -13907,7 +14213,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/__init__.py pytorch-develop-150/torch/__init__.py --- pytorch-v1.5.0/torch/__init__.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/__init__.py 2021-12-16 23:02:00.360686464 +0800 ++++ pytorch-develop-150/torch/__init__.py 2021-12-17 23:00:48.849174182 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -13950,7 +14256,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/lib/c10d/CMakeLists.txt pytorch-develop-150/torch/lib/c10d/CMakeLists.txt --- pytorch-v1.5.0/torch/lib/c10d/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/lib/c10d/CMakeLists.txt 2021-12-16 23:02:00.388688182 +0800 ++++ pytorch-develop-150/torch/lib/c10d/CMakeLists.txt 2021-12-17 23:00:48.873174490 +0800 @@ -28,6 +28,10 @@ option(USE_C10D_NCCL "USE C10D NCCL" ON) endif() @@ -14003,7 +14309,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= copy_header(ProcessGroupMPI.hpp) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/lib/libshm/CMakeLists.txt pytorch-develop-150/torch/lib/libshm/CMakeLists.txt --- pytorch-v1.5.0/torch/lib/libshm/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/lib/libshm/CMakeLists.txt 2021-12-16 23:02:00.392688427 +0800 ++++ pytorch-develop-150/torch/lib/libshm/CMakeLists.txt 2021-12-17 23:00:48.873174490 +0800 @@ -37,8 +37,11 @@ SET_TARGET_PROPERTIES(shm PROPERTIES PREFIX "lib" @@ -14060,7 +14366,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -_maybe_indices_t = _scalar_or_tuple_2_t[Tensor] diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/functional.py pytorch-develop-150/torch/nn/functional.py --- pytorch-v1.5.0/torch/nn/functional.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/functional.py 2021-12-16 23:02:00.392688427 +0800 ++++ pytorch-develop-150/torch/nn/functional.py 2021-12-17 23:00:48.873174490 +0800 @@ -1611,7 +1611,7 @@ else: output = input.matmul(weight.t()) @@ -14083,7 +14389,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -from . import parallel as parallel diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/batchnorm.py pytorch-develop-150/torch/nn/modules/batchnorm.py --- pytorch-v1.5.0/torch/nn/modules/batchnorm.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/batchnorm.py 2021-12-16 23:02:00.392688427 +0800 ++++ pytorch-develop-150/torch/nn/modules/batchnorm.py 2021-12-17 23:00:48.873174490 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14115,7 +14421,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= self.register_parameter('running_var', None) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/module.py pytorch-develop-150/torch/nn/modules/module.py --- pytorch-v1.5.0/torch/nn/modules/module.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/module.py 2021-12-16 23:02:00.392688427 +0800 ++++ pytorch-develop-150/torch/nn/modules/module.py 2021-12-17 23:00:48.873174490 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14275,7 +14581,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return t.to(device, dtype if t.is_floating_point() else None, non_blocking, memory_format=convert_to_format) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/normalization.py pytorch-develop-150/torch/nn/modules/normalization.py --- pytorch-v1.5.0/torch/nn/modules/normalization.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/normalization.py 2021-12-16 23:02:00.392688427 +0800 ++++ pytorch-develop-150/torch/nn/modules/normalization.py 2021-12-17 23:00:48.873174490 +0800 @@ -128,13 +128,14 @@ """ __constants__ = ['normalized_shape', 'eps', 'elementwise_affine'] @@ -14344,7 +14650,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - module_kwargs: Optional[Any] = ...) -> Tensor: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/parallel/distributed.py pytorch-develop-150/torch/nn/parallel/distributed.py --- pytorch-v1.5.0/torch/nn/parallel/distributed.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/parallel/distributed.py 2021-12-16 23:02:00.392688427 +0800 ++++ pytorch-develop-150/torch/nn/parallel/distributed.py 2021-12-17 23:00:48.877174541 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14699,7 +15005,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def remove_weight_norm(module: T_module, name: str = ...) -> T_module: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/onnx/symbolic_opset9.py pytorch-develop-150/torch/onnx/symbolic_opset9.py --- pytorch-v1.5.0/torch/onnx/symbolic_opset9.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/onnx/symbolic_opset9.py 2021-12-16 23:02:00.396688672 +0800 ++++ pytorch-develop-150/torch/onnx/symbolic_opset9.py 2021-12-17 23:00:48.877174541 +0800 @@ -1621,14 +1621,23 @@ slices = [sym_help._slice_helper(g, w, axes=[0], starts=[x * n], ends=[y * n]) for x, y in intervals] return g.op('Concat', *slices, axis_i=0) @@ -14777,7 +15083,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, params: _params_t, lr: float=..., lr_decay: float=..., weight_decay: float=..., initial_accumulator_value: float=..., eps: float=...) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/optim/adamax.py pytorch-develop-150/torch/optim/adamax.py --- pytorch-v1.5.0/torch/optim/adamax.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/optim/adamax.py 2021-12-16 23:02:00.396688672 +0800 ++++ pytorch-develop-150/torch/optim/adamax.py 2021-12-17 23:00:48.877174541 +0800 @@ -80,8 +80,8 @@ exp_inf.mul_(beta2).unsqueeze(0), grad.abs().add_(eps).unsqueeze_(0) @@ -14954,7 +15260,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, params: _params_t, lr: float=..., betas: Tuple[float, float]=..., eps: float=...) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/serialization.py pytorch-develop-150/torch/serialization.py --- pytorch-v1.5.0/torch/serialization.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/serialization.py 2021-12-16 23:02:00.360686464 +0800 ++++ pytorch-develop-150/torch/serialization.py 2021-12-17 23:00:48.849174182 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15038,7 +15344,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def location_tag(storage): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/storage.py pytorch-develop-150/torch/storage.py --- pytorch-v1.5.0/torch/storage.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/storage.py 2021-12-16 23:02:00.360686464 +0800 ++++ pytorch-develop-150/torch/storage.py 2021-12-17 23:00:48.849174182 +0800 @@ -7,6 +7,7 @@ class _StorageBase(object): @@ -15058,7 +15364,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= else: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/tensor.py pytorch-develop-150/torch/tensor.py --- pytorch-v1.5.0/torch/tensor.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/tensor.py 2021-12-16 23:02:00.360686464 +0800 ++++ pytorch-develop-150/torch/tensor.py 2021-12-17 23:00:48.849174182 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15120,7 +15426,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def __reversed__(self): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/_tensor_str.py pytorch-develop-150/torch/_tensor_str.py --- pytorch-v1.5.0/torch/_tensor_str.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/_tensor_str.py 2021-12-16 23:02:00.360686464 +0800 ++++ pytorch-develop-150/torch/_tensor_str.py 2021-12-17 23:00:48.849174182 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15174,7 +15480,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= has_default_dtype = self.dtype in (torch.get_default_dtype(), torch.int64, torch.bool) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/data/dataloader.py pytorch-develop-150/torch/utils/data/dataloader.py --- pytorch-v1.5.0/torch/utils/data/dataloader.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/data/dataloader.py 2021-12-16 23:02:00.400688917 +0800 ++++ pytorch-develop-150/torch/utils/data/dataloader.py 2021-12-17 23:00:48.881174592 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15383,7 +15689,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, sampler: Sampler[int], batch_size: int, drop_last: bool) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/data/_utils/pin_memory.py pytorch-develop-150/torch/utils/data/_utils/pin_memory.py --- pytorch-v1.5.0/torch/utils/data/_utils/pin_memory.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/data/_utils/pin_memory.py 2021-12-16 23:02:00.400688917 +0800 ++++ pytorch-develop-150/torch/utils/data/_utils/pin_memory.py 2021-12-17 23:00:48.877174541 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15444,7 +15750,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/__init__.py pytorch-develop-150/torch/utils/__init__.py --- pytorch-v1.5.0/torch/utils/__init__.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/__init__.py 2021-12-16 23:02:00.396688672 +0800 ++++ pytorch-develop-150/torch/utils/__init__.py 2021-12-17 23:00:48.877174541 +0800 @@ -1,6 +1,9 @@ from __future__ import absolute_import, division, print_function, unicode_literals @@ -15457,7 +15763,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def set_module(obj, mod): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/_utils.py pytorch-develop-150/torch/_utils.py --- pytorch-v1.5.0/torch/_utils.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/_utils.py 2021-12-16 23:02:00.360686464 +0800 ++++ pytorch-develop-150/torch/_utils.py 2021-12-17 23:00:48.849174182 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. diff --git a/patch/pytorch1.8.1_npu.patch b/patch/pytorch1.8.1_npu.patch index acd30dcadbd..03e2c1151de 100644 --- a/patch/pytorch1.8.1_npu.patch +++ b/patch/pytorch1.8.1_npu.patch @@ -1,6 +1,6 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/CMakeLists.txt pytorch-develop-181/aten/CMakeLists.txt --- pytorch-v1.8.1/aten/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/CMakeLists.txt 2021-12-16 23:02:04.448936374 +0800 ++++ pytorch-develop-181/aten/CMakeLists.txt 2021-12-17 23:00:52.929226218 +0800 @@ -22,9 +22,11 @@ set(ATen_CPU_INCLUDE) set(ATen_THIRD_PARTY_INCLUDE) @@ -52,7 +52,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_CPU_DEPENDENCY_LIBS ${ATen_CPU_DEPENDENCY_LIBS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/autocast_mode.h pytorch-develop-181/aten/src/ATen/autocast_mode.h --- pytorch-v1.8.1/aten/src/ATen/autocast_mode.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/autocast_mode.h 2021-12-16 23:02:04.452936617 +0800 ++++ pytorch-develop-181/aten/src/ATen/autocast_mode.h 2021-12-17 23:00:52.933226268 +0800 @@ -5,7 +5,7 @@ namespace { @@ -64,7 +64,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/CMakeLists.txt pytorch-develop-181/aten/src/ATen/CMakeLists.txt --- pytorch-v1.8.1/aten/src/ATen/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/CMakeLists.txt 2021-12-16 23:02:04.448936374 +0800 ++++ pytorch-develop-181/aten/src/ATen/CMakeLists.txt 2021-12-17 23:00:52.929226218 +0800 @@ -85,6 +85,10 @@ file(GLOB native_quantized_h "native/quantized/*.h" "native/quantized/cpu/*.h") file(GLOB native_cpu_h "native/cpu/*.h") @@ -115,7 +115,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_HIP_SRCS ${ATen_HIP_SRCS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/Dispatcher.h pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/Dispatcher.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h 2021-12-16 23:02:04.456936862 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h 2021-12-17 23:00:52.937226320 +0800 @@ -417,6 +417,11 @@ } } @@ -130,7 +130,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.cpp pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-12-16 23:02:04.456936862 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-12-17 23:00:52.937226320 +0800 @@ -6,6 +6,7 @@ namespace c10 { @@ -141,7 +141,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= std::unordered_set not_observed_ops = { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.h pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-12-16 23:02:04.456936862 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-12-17 23:00:52.937226320 +0800 @@ -1,12 +1,16 @@ #pragma once @@ -161,7 +161,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/OperatorEntry.h pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-12-16 23:02:04.456936862 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-12-17 23:00:52.937226320 +0800 @@ -10,6 +10,7 @@ #include #include @@ -172,7 +172,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #include diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/VariableFallbackKernel.cpp pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp --- pytorch-v1.8.1/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-12-16 23:02:04.456936862 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-12-17 23:00:52.933226268 +0800 @@ -48,4 +48,8 @@ m.fallback(torch::CppFunction::makeFallthrough()); } @@ -184,7 +184,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/Convolution.cpp pytorch-develop-181/aten/src/ATen/native/Convolution.cpp --- pytorch-v1.8.1/aten/src/ATen/native/Convolution.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/Convolution.cpp 2021-12-16 23:02:04.468937593 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/Convolution.cpp 2021-12-17 23:00:52.949226472 +0800 @@ -603,7 +603,9 @@ const Tensor& input, const Tensor& weight, const Tensor& bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, @@ -198,7 +198,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= at::Tensor _convolution( diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/Memory.cpp pytorch-develop-181/aten/src/ATen/native/Memory.cpp --- pytorch-v1.8.1/aten/src/ATen/native/Memory.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/Memory.cpp 2021-12-16 23:02:04.472937836 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/Memory.cpp 2021-12-17 23:00:52.949226472 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -249,7 +249,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= detail::computeStorageNbytes( diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/native_functions.yaml pytorch-develop-181/aten/src/ATen/native/native_functions.yaml --- pytorch-v1.8.1/aten/src/ATen/native/native_functions.yaml 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/native_functions.yaml 2021-12-16 23:02:04.504939786 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/native_functions.yaml 2021-12-17 23:00:52.985226931 +0800 @@ -2073,6 +2073,8 @@ dispatch: CPU, CUDA: isnan @@ -480,7 +480,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +- func: npu_apply_adam.out(Scalar beta1_power, Scalar beta2_power, Scalar lr, Scalar beta1, Scalar beta2, Scalar epsilon, Tensor grad, bool? use_locking, bool? use_nesterov, *, Tensor(a!) var, Tensor(b!) m, Tensor(c!) v) -> (Tensor(a!), Tensor(b!), Tensor(c!)) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/NPUVariableMethodStubs.cpp pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp --- pytorch-v1.8.1/aten/src/ATen/native/NPUVariableMethodStubs.cpp 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp 2021-12-16 23:02:04.472937836 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp 2021-12-17 23:00:52.953226524 +0800 @@ -0,0 +1,464 @@ +#include "ATen/native/npu/common/FormatCastHelper.h" +#include "ATen/native/npu/frame/FormatHelper.h" @@ -1864,7 +1864,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -} diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/TensorFactories.cpp pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp --- pytorch-v1.8.1/aten/src/ATen/native/TensorFactories.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp 2021-12-16 23:02:04.484938567 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp 2021-12-17 23:00:52.965226676 +0800 @@ -1,3 +1,20 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -1897,7 +1897,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= namespace { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h --- pytorch-v1.8.1/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-12-16 23:02:04.540941978 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-12-17 23:00:53.021227388 +0800 @@ -1,19229 +1,19229 @@ -// -// Copyright (c) 2017-2020 Advanced Micro Devices, Inc. All rights reserved. @@ -40359,7 +40359,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +#endif // #ifdef VMA_IMPLEMENTATION diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/record_function.cpp pytorch-develop-181/aten/src/ATen/record_function.cpp --- pytorch-v1.8.1/aten/src/ATen/record_function.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/record_function.cpp 2021-12-16 23:02:04.544942222 +0800 ++++ pytorch-develop-181/aten/src/ATen/record_function.cpp 2021-12-17 23:00:53.021227388 +0800 @@ -400,6 +400,9 @@ rf_tls_.tls_record_function_enabled_ = enable; } @@ -40372,7 +40372,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (rf_tls_ptr->tls_record_function_enabled_) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/record_function.h pytorch-develop-181/aten/src/ATen/record_function.h --- pytorch-v1.8.1/aten/src/ATen/record_function.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/record_function.h 2021-12-16 23:02:04.544942222 +0800 ++++ pytorch-develop-181/aten/src/ATen/record_function.h 2021-12-17 23:00:53.021227388 +0800 @@ -2,6 +2,7 @@ #include @@ -40408,7 +40408,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // scope - record scope that this function tracks diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/templates/TensorBody.h pytorch-develop-181/aten/src/ATen/templates/TensorBody.h --- pytorch-v1.8.1/aten/src/ATen/templates/TensorBody.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/templates/TensorBody.h 2021-12-16 23:02:04.544942222 +0800 ++++ pytorch-develop-181/aten/src/ATen/templates/TensorBody.h 2021-12-17 23:00:53.025227439 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40441,7 +40441,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= bool is_xpu() const; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/templates/TensorMethods.cpp pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp --- pytorch-v1.8.1/aten/src/ATen/templates/TensorMethods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp 2021-12-16 23:02:04.544942222 +0800 ++++ pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp 2021-12-17 23:00:53.025227439 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40486,7 +40486,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/Utils.cpp pytorch-develop-181/aten/src/ATen/Utils.cpp --- pytorch-v1.8.1/aten/src/ATen/Utils.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/Utils.cpp 2021-12-16 23:02:04.452936617 +0800 ++++ pytorch-develop-181/aten/src/ATen/Utils.cpp 2021-12-17 23:00:52.933226268 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40530,7 +40530,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/TH/generic/THStorage.cpp pytorch-develop-181/aten/src/TH/generic/THStorage.cpp --- pytorch-v1.8.1/aten/src/TH/generic/THStorage.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/TH/generic/THStorage.cpp 2021-12-16 23:02:04.548942466 +0800 ++++ pytorch-develop-181/aten/src/TH/generic/THStorage.cpp 2021-12-17 23:00:53.029227489 +0800 @@ -1,9 +1,32 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40643,7 +40643,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/TH/generic/THStorage.h pytorch-develop-181/aten/src/TH/generic/THStorage.h --- pytorch-v1.8.1/aten/src/TH/generic/THStorage.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/TH/generic/THStorage.h 2021-12-16 23:02:04.548942466 +0800 ++++ pytorch-develop-181/aten/src/TH/generic/THStorage.h 2021-12-17 23:00:53.029227489 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40682,7 +40682,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/CMakeLists.txt pytorch-develop-181/c10/CMakeLists.txt --- pytorch-v1.8.1/c10/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/CMakeLists.txt 2021-12-16 23:02:04.560943197 +0800 ++++ pytorch-develop-181/c10/CMakeLists.txt 2021-12-17 23:00:53.041227643 +0800 @@ -79,6 +79,14 @@ message(STATUS "don't use NUMA") endif() @@ -40711,7 +40711,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # not checked in diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Backend.h pytorch-develop-181/c10/core/Backend.h --- pytorch-v1.8.1/c10/core/Backend.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Backend.h 2021-12-16 23:02:04.560943197 +0800 ++++ pytorch-develop-181/c10/core/Backend.h 2021-12-17 23:00:53.041227643 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40805,7 +40805,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Device.cpp pytorch-develop-181/c10/core/Device.cpp --- pytorch-v1.8.1/c10/core/Device.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Device.cpp 2021-12-16 23:02:04.560943197 +0800 ++++ pytorch-develop-181/c10/core/Device.cpp 2021-12-17 23:00:53.041227643 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40836,7 +40836,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= types.begin(), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Device.h pytorch-develop-181/c10/core/Device.h --- pytorch-v1.8.1/c10/core/Device.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Device.h 2021-12-16 23:02:04.560943197 +0800 ++++ pytorch-develop-181/c10/core/Device.h 2021-12-17 23:00:53.041227643 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40871,7 +40871,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= bool is_cpu() const noexcept { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DeviceType.cpp pytorch-develop-181/c10/core/DeviceType.cpp --- pytorch-v1.8.1/c10/core/DeviceType.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DeviceType.cpp 2021-12-16 23:02:04.560943197 +0800 ++++ pytorch-develop-181/c10/core/DeviceType.cpp 2021-12-17 23:00:53.041227643 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40911,7 +40911,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return false; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DeviceType.h pytorch-develop-181/c10/core/DeviceType.h --- pytorch-v1.8.1/c10/core/DeviceType.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DeviceType.h 2021-12-16 23:02:04.560943197 +0800 ++++ pytorch-develop-181/c10/core/DeviceType.h 2021-12-17 23:00:53.041227643 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40953,7 +40953,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= constexpr DeviceType kMSNPU = DeviceType::MSNPU; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKey.cpp pytorch-develop-181/c10/core/DispatchKey.cpp --- pytorch-v1.8.1/c10/core/DispatchKey.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKey.cpp 2021-12-16 23:02:04.560943197 +0800 ++++ pytorch-develop-181/c10/core/DispatchKey.cpp 2021-12-17 23:00:53.041227643 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41002,7 +41002,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::PrivateUse1: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKey.h pytorch-develop-181/c10/core/DispatchKey.h --- pytorch-v1.8.1/c10/core/DispatchKey.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKey.h 2021-12-16 23:02:04.560943197 +0800 ++++ pytorch-develop-181/c10/core/DispatchKey.h 2021-12-17 23:00:53.041227643 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41049,7 +41049,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= PrivateUse3_PreAutograd = AutogradPrivateUse3, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKeySet.cpp pytorch-develop-181/c10/core/DispatchKeySet.cpp --- pytorch-v1.8.1/c10/core/DispatchKeySet.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKeySet.cpp 2021-12-16 23:02:04.560943197 +0800 ++++ pytorch-develop-181/c10/core/DispatchKeySet.cpp 2021-12-17 23:00:53.041227643 +0800 @@ -11,6 +11,7 @@ DispatchKey::XLA, DispatchKey::NestedTensor, @@ -41069,7 +41069,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::AutogradXPU: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKeySet.h pytorch-develop-181/c10/core/DispatchKeySet.h --- pytorch-v1.8.1/c10/core/DispatchKeySet.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKeySet.h 2021-12-16 23:02:04.560943197 +0800 ++++ pytorch-develop-181/c10/core/DispatchKeySet.h 2021-12-17 23:00:53.041227643 +0800 @@ -193,6 +193,7 @@ DispatchKey::AutogradCPU, DispatchKey::AutogradCUDA, @@ -41080,7 +41080,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= DispatchKey::AutogradPrivateUse1, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Storage.h pytorch-develop-181/c10/core/Storage.h --- pytorch-v1.8.1/c10/core/Storage.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Storage.h 2021-12-16 23:02:04.560943197 +0800 ++++ pytorch-develop-181/c10/core/Storage.h 2021-12-17 23:00:53.041227643 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41114,7 +41114,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/StorageImpl.h pytorch-develop-181/c10/core/StorageImpl.h --- pytorch-v1.8.1/c10/core/StorageImpl.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/StorageImpl.h 2021-12-16 23:02:04.560943197 +0800 ++++ pytorch-develop-181/c10/core/StorageImpl.h 2021-12-17 23:00:53.041227643 +0800 @@ -1,12 +1,42 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41174,7 +41174,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/TensorImpl.h pytorch-develop-181/c10/core/TensorImpl.h --- pytorch-v1.8.1/c10/core/TensorImpl.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/TensorImpl.h 2021-12-16 23:02:04.564943440 +0800 ++++ pytorch-develop-181/c10/core/TensorImpl.h 2021-12-17 23:00:53.041227643 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41217,7 +41217,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ts.has(DispatchKey::SparseCPU) || diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/TensorOptions.h pytorch-develop-181/c10/core/TensorOptions.h --- pytorch-v1.8.1/c10/core/TensorOptions.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/TensorOptions.h 2021-12-16 23:02:04.564943440 +0800 ++++ pytorch-develop-181/c10/core/TensorOptions.h 2021-12-17 23:00:53.041227643 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41258,7 +41258,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } else if (tid == DispatchKey::QuantizedXPU) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/macros/Export.h pytorch-develop-181/c10/macros/Export.h --- pytorch-v1.8.1/c10/macros/Export.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/macros/Export.h 2021-12-16 23:02:04.564943440 +0800 ++++ pytorch-develop-181/c10/macros/Export.h 2021-12-17 23:00:53.045227693 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41327,7 +41327,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #define C10_API_ENUM C10_API diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/caffe2/CMakeLists.txt pytorch-develop-181/caffe2/CMakeLists.txt --- pytorch-v1.8.1/caffe2/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/caffe2/CMakeLists.txt 2021-12-16 23:02:04.572943928 +0800 ++++ pytorch-develop-181/caffe2/CMakeLists.txt 2021-12-17 23:00:53.049227744 +0800 @@ -76,6 +76,7 @@ # Add source, includes, and libs to lists list(APPEND Caffe2_CPU_SRCS ${ATen_CPU_SRCS}) @@ -41452,7 +41452,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Call again since Caffe2_HIP_INCLUDE is extended with ATen include dirs. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/BuildVariables.cmake pytorch-develop-181/cmake/BuildVariables.cmake --- pytorch-v1.8.1/cmake/BuildVariables.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/BuildVariables.cmake 2021-12-16 23:02:04.644948313 +0800 ++++ pytorch-develop-181/cmake/BuildVariables.cmake 2021-12-17 23:00:53.125228709 +0800 @@ -11,6 +11,7 @@ # CMakeLists.txt files under each folder respectively. set(Caffe2_CPU_SRCS) @@ -41476,7 +41476,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # symbols. However, if the lib is whole linked in caffe2 lib, we don't want diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Codegen.cmake pytorch-develop-181/cmake/Codegen.cmake --- pytorch-v1.8.1/cmake/Codegen.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Codegen.cmake 2021-12-16 23:02:04.644948313 +0800 ++++ pytorch-develop-181/cmake/Codegen.cmake 2021-12-17 23:00:53.125228709 +0800 @@ -208,13 +208,14 @@ file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt generated_cpp) file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt-cuda cuda_generated_cpp) @@ -41509,7 +41509,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= function(append_filelist name outputvar) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Dependencies.cmake pytorch-develop-181/cmake/Dependencies.cmake --- pytorch-v1.8.1/cmake/Dependencies.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Dependencies.cmake 2021-12-16 23:02:04.644948313 +0800 ++++ pytorch-develop-181/cmake/Dependencies.cmake 2021-12-17 23:00:53.125228709 +0800 @@ -1771,6 +1771,13 @@ endif(NOT C_HAS_THREAD) endif() @@ -41526,7 +41526,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Summary.cmake pytorch-develop-181/cmake/Summary.cmake --- pytorch-v1.8.1/cmake/Summary.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Summary.cmake 2021-12-16 23:02:04.648948557 +0800 ++++ pytorch-develop-181/cmake/Summary.cmake 2021-12-17 23:00:53.125228709 +0800 @@ -127,6 +127,7 @@ message(STATUS " USE_MKLDNN_CBLAS : ${USE_MKLDNN_CBLAS}") endif() @@ -41545,7 +41545,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= message(STATUS " Private Dependencies : ${Caffe2_DEPENDENCY_LIBS}") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/TorchConfig.cmake.in pytorch-develop-181/cmake/TorchConfig.cmake.in --- pytorch-v1.8.1/cmake/TorchConfig.cmake.in 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/TorchConfig.cmake.in 2021-12-16 23:02:04.648948557 +0800 ++++ pytorch-develop-181/cmake/TorchConfig.cmake.in 2021-12-17 23:00:53.125228709 +0800 @@ -158,6 +158,11 @@ list(APPEND TORCH_LIBRARIES ${TORCH_CUDA_LIBRARIES}) endif() @@ -41560,7 +41560,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(TORCH_CXX_FLAGS "-D_GLIBCXX_USE_CXX11_ABI=@GLIBCXX_USE_CXX11_ABI@") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/CMakeLists.txt pytorch-develop-181/CMakeLists.txt --- pytorch-v1.8.1/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/CMakeLists.txt 2021-12-16 23:02:04.440935887 +0800 ++++ pytorch-develop-181/CMakeLists.txt 2021-12-17 23:00:52.917226065 +0800 @@ -261,6 +261,10 @@ "USE_DISTRIBUTED" OFF) option(USE_TBB "Use TBB" OFF) @@ -41604,7 +41604,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if(APPLE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/setup.py pytorch-develop-181/setup.py --- pytorch-v1.8.1/setup.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/setup.py 2021-12-16 23:02:04.660949287 +0800 ++++ pytorch-develop-181/setup.py 2021-12-17 23:00:53.137228861 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -41673,7 +41673,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= 'utils/benchmark/utils/valgrind_wrapper/*.h', diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/derivatives.yaml pytorch-develop-181/tools/autograd/derivatives.yaml --- pytorch-v1.8.1/tools/autograd/derivatives.yaml 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/derivatives.yaml 2021-12-16 23:02:05.112976809 +0800 ++++ pytorch-develop-181/tools/autograd/derivatives.yaml 2021-12-17 23:00:53.589234604 +0800 @@ -1976,3 +1976,7 @@ - name: nonzero(Tensor self) -> Tensor @@ -41684,7 +41684,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + mat2: npu_bmm_v2_mat2_backward(grad, self, mat2, mat2.sizes()) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/gen_python_functions.py pytorch-develop-181/tools/autograd/gen_python_functions.py --- pytorch-v1.8.1/tools/autograd/gen_python_functions.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/gen_python_functions.py 2021-12-16 23:02:05.112976809 +0800 ++++ pytorch-develop-181/tools/autograd/gen_python_functions.py 2021-12-17 23:00:53.589234604 +0800 @@ -1,3 +1,20 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -41708,7 +41708,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # The bindings are generated as methods on python_variable or functions on the diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/Functions.cpp pytorch-develop-181/tools/autograd/templates/Functions.cpp --- pytorch-v1.8.1/tools/autograd/templates/Functions.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/Functions.cpp 2021-12-16 23:02:05.112976809 +0800 ++++ pytorch-develop-181/tools/autograd/templates/Functions.cpp 2021-12-17 23:00:53.589234604 +0800 @@ -14,6 +14,35 @@ namespace torch { namespace autograd { namespace generated { @@ -41747,7 +41747,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }}} // namespace torch::autograd::generated diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/python_torch_functions.cpp pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp --- pytorch-v1.8.1/tools/autograd/templates/python_torch_functions.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp 2021-12-16 23:02:05.116977051 +0800 ++++ pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp 2021-12-17 23:00:53.589234604 +0800 @@ -30,7 +30,7 @@ #include "torch/csrc/autograd/generated/variable_factories.h" #include "torch/csrc/utils/structseq.h" @@ -41783,7 +41783,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return torch::range(start, end, step, options); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/python_variable_methods.cpp pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp --- pytorch-v1.8.1/tools/autograd/templates/python_variable_methods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp 2021-12-16 23:02:05.116977051 +0800 ++++ pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp 2021-12-17 23:00:53.589234604 +0800 @@ -20,7 +20,13 @@ #ifdef USE_CUDA #include "torch/csrc/cuda/Event.h" @@ -41851,7 +41851,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"has_names", THPVariable_has_names, METH_NOARGS, NULL}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/build_variables.bzl pytorch-develop-181/tools/build_variables.bzl --- pytorch-v1.8.1/tools/build_variables.bzl 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/build_variables.bzl 2021-12-16 23:02:05.116977051 +0800 ++++ pytorch-develop-181/tools/build_variables.bzl 2021-12-17 23:00:53.589234604 +0800 @@ -362,6 +362,7 @@ libtorch_cuda_core_sources = [ "torch/csrc/CudaIPCTypes.cpp", @@ -41870,7 +41870,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= "torch/csrc/utils/python_arg_parser.cpp", diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/codegen/gen.py pytorch-develop-181/tools/codegen/gen.py --- pytorch-v1.8.1/tools/codegen/gen.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/codegen/gen.py 2021-12-16 23:02:05.116977051 +0800 ++++ pytorch-develop-181/tools/codegen/gen.py 2021-12-17 23:00:53.593234655 +0800 @@ -815,7 +815,7 @@ core_fm = make_file_manager(core_install_dir) cpu_fm = make_file_manager(options.install_dir) @@ -41918,7 +41918,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= main() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/codegen/model.py pytorch-develop-181/tools/codegen/model.py --- pytorch-v1.8.1/tools/codegen/model.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/codegen/model.py 2021-12-16 23:02:05.120977296 +0800 ++++ pytorch-develop-181/tools/codegen/model.py 2021-12-17 23:00:53.593234655 +0800 @@ -79,6 +79,7 @@ SparseHIP = auto() SparseXPU = auto() @@ -42060,7 +42060,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/autograd/profiler.py pytorch-develop-181/torch/autograd/profiler.py --- pytorch-v1.8.1/torch/autograd/profiler.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/autograd/profiler.py 2021-12-16 23:02:05.120977296 +0800 ++++ pytorch-develop-181/torch/autograd/profiler.py 2021-12-17 23:00:53.597234705 +0800 @@ -37,14 +37,17 @@ class EventList(list): """A list of Events (for pretty printing)""" @@ -42739,7 +42739,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ''.join(result) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_C/_autograd.pyi pytorch-develop-181/torch/_C/_autograd.pyi --- pytorch-v1.8.1/torch/_C/_autograd.pyi 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_C/_autograd.pyi 2021-12-16 23:02:05.120977296 +0800 ++++ pytorch-develop-181/torch/_C/_autograd.pyi 2021-12-17 23:00:53.593234655 +0800 @@ -9,14 +9,17 @@ CUDA = ... NVTX = ... @@ -42760,7 +42760,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= class ProfilerConfig: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/CMakeLists.txt pytorch-develop-181/torch/CMakeLists.txt --- pytorch-v1.8.1/torch/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/CMakeLists.txt 2021-12-16 23:02:05.120977296 +0800 ++++ pytorch-develop-181/torch/CMakeLists.txt 2021-12-17 23:00:53.593234655 +0800 @@ -131,6 +131,20 @@ endif() @@ -42784,7 +42784,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= list(APPEND TORCH_PYTHON_SRCS ${GENERATED_THNN_CXX_CUDA}) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/engine.cpp pytorch-develop-181/torch/csrc/autograd/engine.cpp --- pytorch-v1.8.1/torch/csrc/autograd/engine.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/engine.cpp 2021-12-16 23:02:05.128977781 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/engine.cpp 2021-12-17 23:00:53.601234756 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -42881,7 +42881,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto outputs = call_function(graph_task, func, inputs); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/functions/tensor.cpp pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp --- pytorch-v1.8.1/torch/csrc/autograd/functions/tensor.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp 2021-12-16 23:02:05.128977781 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp 2021-12-17 23:00:53.601234756 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -42913,7 +42913,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= /*non_blocking=*/false, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/init.cpp pytorch-develop-181/torch/csrc/autograd/init.cpp --- pytorch-v1.8.1/torch/csrc/autograd/init.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/init.cpp 2021-12-16 23:02:05.128977781 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/init.cpp 2021-12-17 23:00:53.601234756 +0800 @@ -52,6 +52,7 @@ .value("Disabled", ProfilerState::Disabled) .value("CPU", ProfilerState::CPU) @@ -42956,7 +42956,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= .value("OPENCL", c10::DeviceType::OPENCL) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/input_buffer.cpp pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp --- pytorch-v1.8.1/torch/csrc/autograd/input_buffer.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp 2021-12-16 23:02:05.128977781 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp 2021-12-17 23:00:53.601234756 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43008,7 +43008,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto& old_var = buffer[pos]; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.cpp pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp --- pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp 2021-12-16 23:02:05.128977781 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp 2021-12-17 23:00:53.601234756 +0800 @@ -147,7 +147,7 @@ constexpr const CUDAStubs* default_stubs_addr = &default_stubs; // Constant initialization, so it is guaranteed to be initialized before @@ -43251,7 +43251,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CUDAStubs::~CUDAStubs() = default; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.h pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h --- pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h 2021-12-16 23:02:05.128977781 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h 2021-12-17 23:00:53.601234756 +0800 @@ -19,6 +19,8 @@ #include // for gettimeofday() #endif @@ -43440,7 +43440,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= std::vector&& remoteProfiledEvents); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/python_variable.cpp pytorch-develop-181/torch/csrc/autograd/python_variable.cpp --- pytorch-v1.8.1/torch/csrc/autograd/python_variable.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/python_variable.cpp 2021-12-16 23:02:05.128977781 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/python_variable.cpp 2021-12-17 23:00:53.605234807 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43494,7 +43494,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"is_mkldnn", (getter)THPVariable_is_mkldnn, nullptr, nullptr, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/python_variable_indexing.cpp pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp --- pytorch-v1.8.1/torch/csrc/autograd/python_variable_indexing.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-16 23:02:05.128977781 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-17 23:00:53.605234807 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43527,7 +43527,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/distributed/c10d/init.cpp pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp --- pytorch-v1.8.1/torch/csrc/distributed/c10d/init.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp 2021-12-16 23:02:05.132978025 +0800 ++++ pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp 2021-12-17 23:00:53.605234807 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43694,7 +43694,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= torch::class_<::c10d::DistributedC10d>("dist_c10d", "frontend") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/DynamicTypes.cpp pytorch-develop-181/torch/csrc/DynamicTypes.cpp --- pytorch-v1.8.1/torch/csrc/DynamicTypes.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/DynamicTypes.cpp 2021-12-16 23:02:05.124977539 +0800 ++++ pytorch-develop-181/torch/csrc/DynamicTypes.cpp 2021-12-17 23:00:53.597234705 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43743,7 +43743,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return it->second; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/Generator.cpp pytorch-develop-181/torch/csrc/Generator.cpp --- pytorch-v1.8.1/torch/csrc/Generator.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/Generator.cpp 2021-12-16 23:02:05.124977539 +0800 ++++ pytorch-develop-181/torch/csrc/Generator.cpp 2021-12-17 23:00:53.597234705 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43790,7 +43790,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= " is not supported for torch.Generator() api."); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/serialization.cpp pytorch-develop-181/torch/csrc/generic/serialization.cpp --- pytorch-v1.8.1/torch/csrc/generic/serialization.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/serialization.cpp 2021-12-16 23:02:05.136978268 +0800 ++++ pytorch-develop-181/torch/csrc/generic/serialization.cpp 2021-12-17 23:00:53.609234858 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43888,7 +43888,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/Storage.cpp pytorch-develop-181/torch/csrc/generic/Storage.cpp --- pytorch-v1.8.1/torch/csrc/generic/Storage.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/Storage.cpp 2021-12-16 23:02:05.136978268 +0800 ++++ pytorch-develop-181/torch/csrc/generic/Storage.cpp 2021-12-17 23:00:53.609234858 +0800 @@ -1,7 +1,25 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43968,7 +43968,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for (Py_ssize_t i = 0; i < length; i++) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/StorageMethods.cpp pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp --- pytorch-v1.8.1/torch/csrc/generic/StorageMethods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp 2021-12-16 23:02:05.136978268 +0800 ++++ pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp 2021-12-17 23:00:53.609234858 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44048,7 +44048,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/Module.cpp pytorch-develop-181/torch/csrc/Module.cpp --- pytorch-v1.8.1/torch/csrc/Module.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/Module.cpp 2021-12-16 23:02:05.124977539 +0800 ++++ pytorch-develop-181/torch/csrc/Module.cpp 2021-12-17 23:00:53.597234705 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44174,7 +44174,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (incref) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/tensor/python_tensor.cpp pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp --- pytorch-v1.8.1/torch/csrc/tensor/python_tensor.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp 2021-12-16 23:02:05.160979727 +0800 ++++ pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp 2021-12-17 23:00:53.633235163 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44212,7 +44212,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/python_arg_parser.h pytorch-develop-181/torch/csrc/utils/python_arg_parser.h --- pytorch-v1.8.1/torch/csrc/utils/python_arg_parser.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/python_arg_parser.h 2021-12-16 23:02:05.160979727 +0800 ++++ pytorch-develop-181/torch/csrc/utils/python_arg_parser.h 2021-12-17 23:00:53.637235213 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44247,7 +44247,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return at::Device(device_str); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_layouts.cpp pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_layouts.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp 2021-12-16 23:02:05.160979727 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp 2021-12-17 23:00:53.637235213 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44278,7 +44278,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= PyObject *sparse_coo_layout = THPLayout_New(at::Layout::Sparse, "torch.sparse_coo"); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_new.cpp pytorch-develop-181/torch/csrc/utils/tensor_new.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_new.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_new.cpp 2021-12-16 23:02:05.160979727 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_new.cpp 2021-12-17 23:00:53.637235213 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44410,7 +44410,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= dispatch_key); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_types.cpp pytorch-develop-181/torch/csrc/utils/tensor_types.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_types.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_types.cpp 2021-12-16 23:02:05.160979727 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_types.cpp 2021-12-17 23:00:53.637235213 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44460,7 +44460,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/distributed/distributed_c10d.py pytorch-develop-181/torch/distributed/distributed_c10d.py --- pytorch-v1.8.1/torch/distributed/distributed_c10d.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/distributed/distributed_c10d.py 2021-12-16 23:02:05.164979971 +0800 ++++ pytorch-develop-181/torch/distributed/distributed_c10d.py 2021-12-17 23:00:53.637235213 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -44551,7 +44551,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= prefix_store, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/__init__.py pytorch-develop-181/torch/__init__.py --- pytorch-v1.8.1/torch/__init__.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/__init__.py 2021-12-16 23:02:05.120977296 +0800 ++++ pytorch-develop-181/torch/__init__.py 2021-12-17 23:00:53.593234655 +0800 @@ -675,3 +675,11 @@ # class usage. We add these lines here to preserve backward compatibility. quantized_lstm = torch.ops.aten.quantized_lstm @@ -44567,7 +44567,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/CMakeLists.txt pytorch-develop-181/torch/lib/c10d/CMakeLists.txt --- pytorch-v1.8.1/torch/lib/c10d/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/CMakeLists.txt 2021-12-16 23:02:05.168980214 +0800 ++++ pytorch-develop-181/torch/lib/c10d/CMakeLists.txt 2021-12-17 23:00:53.645235316 +0800 @@ -27,6 +27,10 @@ option(USE_C10D_NCCL "USE C10D NCCL" ON) endif() @@ -44620,7 +44620,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= copy_header(ProcessGroupMPI.hpp) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/comm.cpp pytorch-develop-181/torch/lib/c10d/comm.cpp --- pytorch-v1.8.1/torch/lib/c10d/comm.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/comm.cpp 2021-12-16 23:02:05.172980456 +0800 ++++ pytorch-develop-181/torch/lib/c10d/comm.cpp 2021-12-17 23:00:53.645235316 +0800 @@ -12,6 +12,26 @@ class BroadcastWork { @@ -44709,7 +44709,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= while (!in_flight.empty()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/reducer.cpp pytorch-develop-181/torch/lib/c10d/reducer.cpp --- pytorch-v1.8.1/torch/lib/c10d/reducer.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/reducer.cpp 2021-12-16 23:02:05.172980456 +0800 ++++ pytorch-develop-181/torch/lib/c10d/reducer.cpp 2021-12-17 23:00:53.645235316 +0800 @@ -18,6 +18,18 @@ namespace c10d { namespace { @@ -44901,7 +44901,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // A bucket with one or more dense tensors needs to be unflattened. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/libshm/CMakeLists.txt pytorch-develop-181/torch/lib/libshm/CMakeLists.txt --- pytorch-v1.8.1/torch/lib/libshm/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/libshm/CMakeLists.txt 2021-12-16 23:02:05.172980456 +0800 ++++ pytorch-develop-181/torch/lib/libshm/CMakeLists.txt 2021-12-17 23:00:53.645235316 +0800 @@ -41,8 +41,11 @@ set_target_properties(shm PROPERTIES PREFIX "lib" @@ -44917,7 +44917,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # https://github.com/libgit2/libgit2/issues/2128#issuecomment-35649830 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/batchnorm.py pytorch-develop-181/torch/nn/modules/batchnorm.py --- pytorch-v1.8.1/torch/nn/modules/batchnorm.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/batchnorm.py 2021-12-16 23:02:05.176980701 +0800 ++++ pytorch-develop-181/torch/nn/modules/batchnorm.py 2021-12-17 23:00:53.649235366 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -44939,7 +44939,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= from ._functions import SyncBatchNorm as sync_batch_norm diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/module.py pytorch-develop-181/torch/nn/modules/module.py --- pytorch-v1.8.1/torch/nn/modules/module.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/module.py 2021-12-16 23:02:05.176980701 +0800 ++++ pytorch-develop-181/torch/nn/modules/module.py 2021-12-17 23:00:53.649235366 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45065,7 +45065,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if convert_to_format is not None and t.dim() == 4: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/normalization.py pytorch-develop-181/torch/nn/modules/normalization.py --- pytorch-v1.8.1/torch/nn/modules/normalization.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/normalization.py 2021-12-16 23:02:05.176980701 +0800 ++++ pytorch-develop-181/torch/nn/modules/normalization.py 2021-12-17 23:00:53.649235366 +0800 @@ -167,8 +167,11 @@ init.zeros_(self.bias) @@ -45082,7 +45082,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return '{normalized_shape}, eps={eps}, ' \ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/parallel/distributed.py pytorch-develop-181/torch/nn/parallel/distributed.py --- pytorch-v1.8.1/torch/nn/parallel/distributed.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/parallel/distributed.py 2021-12-16 23:02:05.176980701 +0800 ++++ pytorch-develop-181/torch/nn/parallel/distributed.py 2021-12-17 23:00:53.649235366 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45114,7 +45114,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= output = self.module(*inputs[0], **kwargs[0]) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/serialization.py pytorch-develop-181/torch/serialization.py --- pytorch-v1.8.1/torch/serialization.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/serialization.py 2021-12-16 23:02:05.184981187 +0800 ++++ pytorch-develop-181/torch/serialization.py 2021-12-17 23:00:53.657235468 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45203,7 +45203,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= See also: `saving-loading-tensors` diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/storage.py pytorch-develop-181/torch/storage.py --- pytorch-v1.8.1/torch/storage.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/storage.py 2021-12-16 23:02:05.184981187 +0800 ++++ pytorch-develop-181/torch/storage.py 2021-12-17 23:00:53.657235468 +0800 @@ -8,6 +8,7 @@ class _StorageBase(object): _cdata: Any @@ -45223,7 +45223,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= else: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/tensor.py pytorch-develop-181/torch/tensor.py --- pytorch-v1.8.1/torch/tensor.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/tensor.py 2021-12-16 23:02:05.184981187 +0800 ++++ pytorch-develop-181/torch/tensor.py 2021-12-17 23:00:53.657235468 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45285,7 +45285,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def __reversed__(self): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_tensor_str.py pytorch-develop-181/torch/_tensor_str.py --- pytorch-v1.8.1/torch/_tensor_str.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_tensor_str.py 2021-12-16 23:02:05.120977296 +0800 ++++ pytorch-develop-181/torch/_tensor_str.py 2021-12-17 23:00:53.597234705 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45329,7 +45329,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # TODO: add an API to map real -> complex dtypes diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/utils/data/dataloader.py pytorch-develop-181/torch/utils/data/dataloader.py --- pytorch-v1.8.1/torch/utils/data/dataloader.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/utils/data/dataloader.py 2021-12-16 23:02:05.188981430 +0800 ++++ pytorch-develop-181/torch/utils/data/dataloader.py 2021-12-17 23:00:53.665235569 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45390,7 +45390,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= pin_memory_thread.start() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/utils/data/_utils/pin_memory.py pytorch-develop-181/torch/utils/data/_utils/pin_memory.py --- pytorch-v1.8.1/torch/utils/data/_utils/pin_memory.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/utils/data/_utils/pin_memory.py 2021-12-16 23:02:01.372748494 +0800 ++++ pytorch-develop-181/torch/utils/data/_utils/pin_memory.py 2021-12-17 23:00:49.853187017 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45436,7 +45436,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # logic of this function. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_utils.py pytorch-develop-181/torch/_utils.py --- pytorch-v1.8.1/torch/_utils.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_utils.py 2021-12-16 23:02:05.120977296 +0800 ++++ pytorch-develop-181/torch/_utils.py 2021-12-17 23:00:53.597234705 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. -- Gitee From 0254e8151e321eed33572fba3ffd4310625cdc25 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Sat, 18 Dec 2021 23:01:13 +0800 Subject: [PATCH 08/55] =?UTF-8?q?=E5=90=8C=E6=AD=A51.5.0=209b0a379565e3d54?= =?UTF-8?q?a388600c587bc706356778d22=20=20=20=20=20!2895=20=E3=80=90FixUp?= =?UTF-8?q?=E3=80=91`Div`=E7=AE=97=E5=AD=90=E6=9B=BF=E6=8D=A2=E4=B8=BA`Rea?= =?UTF-8?q?lDiv`=EF=BC=8C=E6=BB=A1=E8=B6=B3int32=E4=B8=8B=E7=9A=84?= =?UTF-8?q?=E8=B4=9F=E6=95=B0=E7=9A=84=E5=B0=8F=E6=95=B0=E4=BD=8D=E5=A4=84?= =?UTF-8?q?=E7=90=86=20=20=20=20=20Merge=20pull=20request=20!2895=20from?= =?UTF-8?q?=20wuxiankun/RealDiv=20=E5=90=8C=E6=AD=A51.8.1=20f9f494b14ca91b?= =?UTF-8?q?bf236f99f78aef3927c7c3cdab=20=20=20=20=20!2881=201.8.1=E7=AE=97?= =?UTF-8?q?=E5=AD=90=E9=80=82=E9=85=8D=EF=BC=9Aindex=5Fselect=20=20=20=20?= =?UTF-8?q?=20Merge=20pull=20request=20!2881=20from=20=E7=8E=8B=E6=99=93/1?= =?UTF-8?q?.8=5Findex=5Fselect=5F1?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- patch/pytorch1.5.0_npu.patch | 230 +++++++++--------- patch/pytorch1.8.1_npu.patch | 184 +++++++------- .../aten/src/ATen/native/npu/DivKernelNpu.cpp | 10 +- .../test_npu/test_network_ops/test_div.py | 4 - .../ATen/native/npu/IndexSelectKernelNpu.cpp | 152 ++++++++++++ .../test_network_ops/test_index_select.py | 130 ++++++++++ 6 files changed, 494 insertions(+), 216 deletions(-) create mode 100644 pytorch1.8.1/src/aten/src/ATen/native/npu/IndexSelectKernelNpu.cpp create mode 100644 pytorch1.8.1/test/test_npu/test_network_ops/test_index_select.py diff --git a/patch/pytorch1.5.0_npu.patch b/patch/pytorch1.5.0_npu.patch index a660a60b5c8..085ab421df8 100644 --- a/patch/pytorch1.5.0_npu.patch +++ b/patch/pytorch1.5.0_npu.patch @@ -1,6 +1,6 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/CMakeLists.txt pytorch-develop-150/aten/CMakeLists.txt --- pytorch-v1.5.0/aten/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/CMakeLists.txt 2021-12-17 23:00:48.113164762 +0800 ++++ pytorch-develop-150/aten/CMakeLists.txt 2021-12-18 23:01:06.049043092 +0800 @@ -22,8 +22,10 @@ set(ATen_CPU_INCLUDE) set(ATen_THIRD_PARTY_INCLUDE) @@ -51,7 +51,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_CPU_DEPENDENCY_LIBS ${ATen_CPU_DEPENDENCY_LIBS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/CMakeLists.txt pytorch-develop-150/aten/src/ATen/CMakeLists.txt --- pytorch-v1.5.0/aten/src/ATen/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/CMakeLists.txt 2021-12-17 23:00:48.113164762 +0800 ++++ pytorch-develop-150/aten/src/ATen/CMakeLists.txt 2021-12-18 23:01:06.049043092 +0800 @@ -67,6 +67,9 @@ FILE(GLOB native_quantized_h "native/quantized/*.h" "native/quantized/cpu/*.h") FILE(GLOB native_cpu_h "native/cpu/*.h") @@ -129,7 +129,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_QUANTIZED_SRCS ${ATen_QUANTIZED_SRCS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/core/dispatch/DispatchTable.h pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h --- pytorch-v1.5.0/aten/src/ATen/core/dispatch/DispatchTable.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h 2021-12-17 23:00:48.113164762 +0800 ++++ pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h 2021-12-18 23:01:06.053043123 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -170,7 +170,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/function_wrapper.py pytorch-develop-150/aten/src/ATen/function_wrapper.py --- pytorch-v1.5.0/aten/src/ATen/function_wrapper.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/function_wrapper.py 2021-12-17 23:00:48.117164813 +0800 ++++ pytorch-develop-150/aten/src/ATen/function_wrapper.py 2021-12-18 23:01:06.053043123 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -354,7 +354,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for option in declaration['options']: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/gen.py pytorch-develop-150/aten/src/ATen/gen.py --- pytorch-v1.5.0/aten/src/ATen/gen.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/gen.py 2021-12-17 23:00:48.117164813 +0800 ++++ pytorch-develop-150/aten/src/ATen/gen.py 2021-12-18 23:01:06.053043123 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -512,7 +512,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= generate_outputs() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/BatchLinearAlgebra.cpp pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp --- pytorch-v1.5.0/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-12-17 23:00:48.117164813 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-12-18 23:01:06.053043123 +0800 @@ -680,7 +680,7 @@ std::tuple triangular_solve_out(Tensor& result, Tensor& clone_A, const Tensor& self, const Tensor& A, bool upper, bool transpose, bool unitriangular) { @@ -524,7 +524,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return std::tuple(result, clone_A); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/cpu/Activation.cpp pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp --- pytorch-v1.5.0/aten/src/ATen/native/cpu/Activation.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp 2021-12-17 23:00:48.121164865 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp 2021-12-18 23:01:06.057043153 +0800 @@ -339,20 +339,20 @@ void hardsigmoid_backward_kernel(TensorIterator& iter) { @@ -552,7 +552,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/Memory.cpp pytorch-develop-150/aten/src/ATen/native/Memory.cpp --- pytorch-v1.5.0/aten/src/ATen/native/Memory.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/Memory.cpp 2021-12-17 23:00:48.121164865 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/Memory.cpp 2021-12-18 23:01:06.057043153 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -614,7 +614,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= detail::computeStorageSize(self.sizes(), self.strides()), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/native_functions.yaml pytorch-develop-150/aten/src/ATen/native/native_functions.yaml --- pytorch-v1.5.0/aten/src/ATen/native/native_functions.yaml 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/native_functions.yaml 2021-12-17 23:00:48.125164916 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/native_functions.yaml 2021-12-18 23:01:06.065043215 +0800 @@ -1,6 +1,5 @@ # See README.md in this directory for more guidance @@ -7165,7 +7165,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S --- pytorch-v1.5.0/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-12-17 23:00:48.141165121 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-12-18 23:01:06.077043308 +0800 @@ -659,14 +659,14 @@ SUB x1, x1, 4 @@ -7191,7 +7191,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CMP x1, 2 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorCompare.cpp pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorCompare.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp 2021-12-17 23:00:48.121164865 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp 2021-12-18 23:01:06.057043153 +0800 @@ -64,7 +64,7 @@ Tensor isinf(const Tensor &self) { @@ -7203,7 +7203,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return AT_DISPATCH_FLOATING_TYPES_AND_HALF(self.scalar_type(), "isinf", [&]() { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorFactories.cpp pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorFactories.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp 2021-12-17 23:00:48.121164865 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp 2021-12-18 23:01:06.057043153 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7248,7 +7248,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorProperties.cpp pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorProperties.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp 2021-12-17 23:00:48.121164865 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp 2021-12-18 23:01:06.057043153 +0800 @@ -87,6 +87,7 @@ if (self.is_contiguous(memory_format)) { return self; @@ -7259,7 +7259,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= "preserve memory format is unsupported by the contiguous operator"); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/UpSampleBicubic2d.cpp pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp --- pytorch-v1.5.0/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-12-17 23:00:48.121164865 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-12-18 23:01:06.057043153 +0800 @@ -26,7 +26,7 @@ const scalar_t* in = &idata[output_y * input_width + output_x]; scalar_t* out = &odata[output_y * output_width + output_x]; @@ -7271,7 +7271,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= out += output_width * output_height; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native_parse.py pytorch-develop-150/aten/src/ATen/native_parse.py --- pytorch-v1.5.0/aten/src/ATen/native_parse.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native_parse.py 2021-12-17 23:00:48.149165223 +0800 ++++ pytorch-develop-150/aten/src/ATen/native_parse.py 2021-12-18 23:01:06.085043369 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -7309,7 +7309,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= msg = '''Exception raised in processing function: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/preprocess_declarations.py pytorch-develop-150/aten/src/ATen/preprocess_declarations.py --- pytorch-v1.5.0/aten/src/ATen/preprocess_declarations.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/preprocess_declarations.py 2021-12-17 23:00:48.149165223 +0800 ++++ pytorch-develop-150/aten/src/ATen/preprocess_declarations.py 2021-12-18 23:01:06.085043369 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -7341,7 +7341,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/templates/TensorBody.h pytorch-develop-150/aten/src/ATen/templates/TensorBody.h --- pytorch-v1.5.0/aten/src/ATen/templates/TensorBody.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/templates/TensorBody.h 2021-12-17 23:00:48.149165223 +0800 ++++ pytorch-develop-150/aten/src/ATen/templates/TensorBody.h 2021-12-18 23:01:06.085043369 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7374,7 +7374,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/templates/TensorMethods.h pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h --- pytorch-v1.5.0/aten/src/ATen/templates/TensorMethods.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h 2021-12-17 23:00:48.149165223 +0800 ++++ pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h 2021-12-18 23:01:06.085043369 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7408,7 +7408,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/CMakeLists.txt pytorch-develop-150/aten/src/TH/CMakeLists.txt --- pytorch-v1.5.0/aten/src/TH/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/CMakeLists.txt 2021-12-17 23:00:48.149165223 +0800 ++++ pytorch-develop-150/aten/src/TH/CMakeLists.txt 2021-12-18 23:01:06.085043369 +0800 @@ -48,6 +48,11 @@ ${CMAKE_CURRENT_SOURCE_DIR} PARENT_SCOPE) @@ -7423,7 +7423,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/generic/THStorage.cpp pytorch-develop-150/aten/src/TH/generic/THStorage.cpp --- pytorch-v1.5.0/aten/src/TH/generic/THStorage.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/generic/THStorage.cpp 2021-12-17 23:00:48.149165223 +0800 ++++ pytorch-develop-150/aten/src/TH/generic/THStorage.cpp 2021-12-18 23:01:06.089043399 +0800 @@ -1,9 +1,32 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7532,7 +7532,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/generic/THStorage.h pytorch-develop-150/aten/src/TH/generic/THStorage.h --- pytorch-v1.5.0/aten/src/TH/generic/THStorage.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/generic/THStorage.h 2021-12-17 23:00:48.149165223 +0800 ++++ pytorch-develop-150/aten/src/TH/generic/THStorage.h 2021-12-18 23:01:06.089043399 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7571,7 +7571,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/CMakeLists.txt pytorch-develop-150/c10/CMakeLists.txt --- pytorch-v1.5.0/c10/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/CMakeLists.txt 2021-12-17 23:00:48.157165325 +0800 ++++ pytorch-develop-150/c10/CMakeLists.txt 2021-12-18 23:01:06.093043431 +0800 @@ -63,6 +63,14 @@ message(STATUS "don't use NUMA") endif() @@ -7600,7 +7600,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # not checked in diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Backend.h pytorch-develop-150/c10/core/Backend.h --- pytorch-v1.5.0/c10/core/Backend.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Backend.h 2021-12-17 23:00:48.157165325 +0800 ++++ pytorch-develop-150/c10/core/Backend.h 2021-12-18 23:01:06.093043431 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7697,7 +7697,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case Backend::CUDA: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Device.cpp pytorch-develop-150/c10/core/Device.cpp --- pytorch-v1.5.0/c10/core/Device.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Device.cpp 2021-12-17 23:00:48.157165325 +0800 ++++ pytorch-develop-150/c10/core/Device.cpp 2021-12-18 23:01:06.093043431 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7737,7 +7737,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= types.begin(), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Device.h pytorch-develop-150/c10/core/Device.h --- pytorch-v1.5.0/c10/core/Device.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Device.h 2021-12-17 23:00:48.157165325 +0800 ++++ pytorch-develop-150/c10/core/Device.h 2021-12-18 23:01:06.093043431 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7772,7 +7772,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return type_ == DeviceType::CPU; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DeviceType.cpp pytorch-develop-150/c10/core/DeviceType.cpp --- pytorch-v1.5.0/c10/core/DeviceType.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DeviceType.cpp 2021-12-17 23:00:48.157165325 +0800 ++++ pytorch-develop-150/c10/core/DeviceType.cpp 2021-12-18 23:01:06.093043431 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7812,7 +7812,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return false; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DeviceType.h pytorch-develop-150/c10/core/DeviceType.h --- pytorch-v1.5.0/c10/core/DeviceType.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DeviceType.h 2021-12-17 23:00:48.157165325 +0800 ++++ pytorch-develop-150/c10/core/DeviceType.h 2021-12-18 23:01:06.093043431 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7855,7 +7855,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= constexpr DeviceType kXLA = DeviceType::XLA; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DispatchKey.cpp pytorch-develop-150/c10/core/DispatchKey.cpp --- pytorch-v1.5.0/c10/core/DispatchKey.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DispatchKey.cpp 2021-12-17 23:00:48.157165325 +0800 ++++ pytorch-develop-150/c10/core/DispatchKey.cpp 2021-12-18 23:01:06.097043462 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7887,7 +7887,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::SparseCPUTensorId: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DispatchKey.h pytorch-develop-150/c10/core/DispatchKey.h --- pytorch-v1.5.0/c10/core/DispatchKey.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DispatchKey.h 2021-12-17 23:00:48.157165325 +0800 ++++ pytorch-develop-150/c10/core/DispatchKey.h 2021-12-18 23:01:06.097043462 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7919,7 +7919,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Storage.h pytorch-develop-150/c10/core/Storage.h --- pytorch-v1.5.0/c10/core/Storage.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Storage.h 2021-12-17 23:00:48.157165325 +0800 ++++ pytorch-develop-150/c10/core/Storage.h 2021-12-18 23:01:06.097043462 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7953,7 +7953,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/StorageImpl.cpp pytorch-develop-150/c10/core/StorageImpl.cpp --- pytorch-v1.5.0/c10/core/StorageImpl.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/StorageImpl.cpp 2021-12-17 23:00:48.157165325 +0800 ++++ pytorch-develop-150/c10/core/StorageImpl.cpp 2021-12-18 23:01:06.097043462 +0800 @@ -1 +1,18 @@ #include + @@ -7975,7 +7975,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +} // namespace c10 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/StorageImpl.h pytorch-develop-150/c10/core/StorageImpl.h --- pytorch-v1.5.0/c10/core/StorageImpl.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/StorageImpl.h 2021-12-17 23:00:48.157165325 +0800 ++++ pytorch-develop-150/c10/core/StorageImpl.h 2021-12-18 23:01:06.097043462 +0800 @@ -1,11 +1,55 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8102,7 +8102,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/TensorImpl.h pytorch-develop-150/c10/core/TensorImpl.h --- pytorch-v1.5.0/c10/core/TensorImpl.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/TensorImpl.h 2021-12-17 23:00:48.157165325 +0800 ++++ pytorch-develop-150/c10/core/TensorImpl.h 2021-12-18 23:01:06.097043462 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8170,7 +8170,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/TensorOptions.h pytorch-develop-150/c10/core/TensorOptions.h --- pytorch-v1.5.0/c10/core/TensorOptions.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/TensorOptions.h 2021-12-17 23:00:48.157165325 +0800 ++++ pytorch-develop-150/c10/core/TensorOptions.h 2021-12-18 23:01:06.097043462 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8211,7 +8211,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } else if (tid == DispatchKey::HIPTensorId) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/cuda/CMakeLists.txt pytorch-develop-150/c10/cuda/CMakeLists.txt --- pytorch-v1.5.0/c10/cuda/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/cuda/CMakeLists.txt 2021-12-17 23:00:48.157165325 +0800 ++++ pytorch-develop-150/c10/cuda/CMakeLists.txt 2021-12-18 23:01:06.097043462 +0800 @@ -24,6 +24,7 @@ CUDACachingAllocator.cpp impl/CUDAGuardImpl.cpp @@ -8230,7 +8230,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= torch_cuda_based_add_library(c10_cuda ${C10_CUDA_SRCS} ${C10_CUDA_HEADERS}) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/macros/Export.h pytorch-develop-150/c10/macros/Export.h --- pytorch-v1.5.0/c10/macros/Export.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/macros/Export.h 2021-12-17 23:00:48.161165377 +0800 ++++ pytorch-develop-150/c10/macros/Export.h 2021-12-18 23:01:06.097043462 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8357,7 +8357,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/caffe2/CMakeLists.txt pytorch-develop-150/caffe2/CMakeLists.txt --- pytorch-v1.5.0/caffe2/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/caffe2/CMakeLists.txt 2021-12-17 23:00:48.165165427 +0800 ++++ pytorch-develop-150/caffe2/CMakeLists.txt 2021-12-18 23:01:06.101043493 +0800 @@ -32,6 +32,7 @@ # Add source, includes, and libs to lists list(APPEND Caffe2_CPU_SRCS ${ATen_CPU_SRCS}) @@ -8512,7 +8512,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Call again since Caffe2_HIP_INCLUDE is extended with ATen include dirs. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/.clang-format pytorch-develop-150/.clang-format --- pytorch-v1.5.0/.clang-format 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/.clang-format 2021-12-17 23:00:48.105164660 +0800 ++++ pytorch-develop-150/.clang-format 2021-12-18 23:01:06.045043060 +0800 @@ -84,5 +84,4 @@ SpacesInSquareBrackets: false Standard: Cpp11 @@ -8523,7 +8523,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/BuildVariables.cmake pytorch-develop-150/cmake/BuildVariables.cmake --- pytorch-v1.5.0/cmake/BuildVariables.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/BuildVariables.cmake 2021-12-17 23:00:48.221166144 +0800 ++++ pytorch-develop-150/cmake/BuildVariables.cmake 2021-12-18 23:01:06.157043924 +0800 @@ -11,6 +11,7 @@ # CMakeLists.txt files under each folder respectively. set(Caffe2_CPU_SRCS) @@ -8550,7 +8550,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # symbols. However, if the lib is whole linked in caffe2 lib, we don't want diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Codegen.cmake pytorch-develop-150/cmake/Codegen.cmake --- pytorch-v1.5.0/cmake/Codegen.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Codegen.cmake 2021-12-17 23:00:48.221166144 +0800 ++++ pytorch-develop-150/cmake/Codegen.cmake 2021-12-18 23:01:06.157043924 +0800 @@ -191,13 +191,14 @@ file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt generated_cpp) file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt-cuda cuda_generated_cpp) @@ -8581,7 +8581,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endif() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Dependencies.cmake pytorch-develop-150/cmake/Dependencies.cmake --- pytorch-v1.5.0/cmake/Dependencies.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Dependencies.cmake 2021-12-17 23:00:48.221166144 +0800 ++++ pytorch-develop-150/cmake/Dependencies.cmake 2021-12-18 23:01:06.157043924 +0800 @@ -1509,6 +1509,13 @@ ENDIF(NOT C_HAS_THREAD) endif() @@ -8598,7 +8598,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Summary.cmake pytorch-develop-150/cmake/Summary.cmake --- pytorch-v1.5.0/cmake/Summary.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Summary.cmake 2021-12-17 23:00:48.221166144 +0800 ++++ pytorch-develop-150/cmake/Summary.cmake 2021-12-18 23:01:06.157043924 +0800 @@ -134,6 +134,7 @@ if(NOT "${SELECTED_OP_LIST}" STREQUAL "") message(STATUS " SELECTED_OP_LIST : ${SELECTED_OP_LIST}") @@ -8609,7 +8609,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endfunction() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/TorchConfig.cmake.in pytorch-develop-150/cmake/TorchConfig.cmake.in --- pytorch-v1.5.0/cmake/TorchConfig.cmake.in 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/TorchConfig.cmake.in 2021-12-17 23:00:48.221166144 +0800 ++++ pytorch-develop-150/cmake/TorchConfig.cmake.in 2021-12-18 23:01:06.157043924 +0800 @@ -112,6 +112,11 @@ list(APPEND TORCH_LIBRARIES ${TORCH_CUDA_LIBRARIES}) endif() @@ -8624,7 +8624,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(TORCH_CXX_FLAGS "-D_GLIBCXX_USE_CXX11_ABI=@GLIBCXX_USE_CXX11_ABI@") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/CMakeLists.txt pytorch-develop-150/CMakeLists.txt --- pytorch-v1.5.0/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/CMakeLists.txt 2021-12-17 23:00:48.105164660 +0800 ++++ pytorch-develop-150/CMakeLists.txt 2021-12-18 23:01:06.045043060 +0800 @@ -205,6 +205,10 @@ option(USE_TBB "Use TBB" OFF) option(ONNX_ML "Enable traditional ONNX ML API." ON) @@ -8691,7 +8691,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-missing-braces") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/CONTRIBUTING.zh.md pytorch-develop-150/CONTRIBUTING.zh.md --- pytorch-v1.5.0/CONTRIBUTING.zh.md 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-150/CONTRIBUTING.zh.md 2021-12-17 23:00:48.105164660 +0800 ++++ pytorch-develop-150/CONTRIBUTING.zh.md 2021-12-18 23:01:06.045043060 +0800 @@ -0,0 +1,228 @@ +# PyTorch贡献指南 +- [贡献者许可协议](#贡献者许可协议.md) @@ -8923,7 +8923,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/.dockerignore pytorch-develop-150/.dockerignore --- pytorch-v1.5.0/.dockerignore 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/.dockerignore 2021-12-17 23:00:48.105164660 +0800 ++++ pytorch-develop-150/.dockerignore 2021-12-18 23:01:06.045043060 +0800 @@ -1,257 +1 @@ -# READ THIS BEFORE YOU REFACTOR ME -# @@ -9199,7 +9199,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/requirements.txt pytorch-develop-150/requirements.txt --- pytorch-v1.5.0/requirements.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/requirements.txt 2021-12-17 23:00:48.109164712 +0800 ++++ pytorch-develop-150/requirements.txt 2021-12-18 23:01:06.045043060 +0800 @@ -4,4 +4,13 @@ requests setuptools @@ -9219,7 +9219,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/setup.py pytorch-develop-150/setup.py --- pytorch-v1.5.0/setup.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/setup.py 2021-12-17 23:00:48.109164712 +0800 ++++ pytorch-develop-150/setup.py 2021-12-18 23:01:06.045043060 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -9322,7 +9322,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= 'python/serialized_test/data/operator_test/*.zip', diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/test/distributed/test_c10d.py pytorch-develop-150/test/distributed/test_c10d.py --- pytorch-v1.5.0/test/distributed/test_c10d.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/test/distributed/test_c10d.py 2021-12-17 23:00:48.237166350 +0800 ++++ pytorch-develop-150/test/distributed/test_c10d.py 2021-12-18 23:01:06.173044047 +0800 @@ -3049,8 +3049,8 @@ model = self._create_mixed_precision_model() reducer = self._create_reducer_for_models([model]) @@ -9336,7 +9336,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Check that the grad of fc3 is not set. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/test/run_test.py pytorch-develop-150/test/run_test.py --- pytorch-v1.5.0/test/run_test.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/test/run_test.py 2021-12-17 23:00:48.233166298 +0800 ++++ pytorch-develop-150/test/run_test.py 2021-12-18 23:01:06.169044017 +0800 @@ -11,6 +11,8 @@ import subprocess import sys @@ -9506,7 +9506,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= shell(['coverage', 'html']) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/test/test_autograd.py pytorch-develop-150/test/test_autograd.py --- pytorch-v1.5.0/test/test_autograd.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/test/test_autograd.py 2021-12-17 23:00:48.233166298 +0800 ++++ pytorch-develop-150/test/test_autograd.py 2021-12-18 23:01:06.169044017 +0800 @@ -24,7 +24,7 @@ from torch.autograd.function import once_differentiable from torch.autograd.profiler import (profile, format_time, EventList, @@ -9537,7 +9537,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/test/test_nn.py pytorch-develop-150/test/test_nn.py --- pytorch-v1.5.0/test/test_nn.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/test/test_nn.py 2021-12-17 23:00:48.237166350 +0800 ++++ pytorch-develop-150/test/test_nn.py 2021-12-18 23:01:06.173044047 +0800 @@ -3535,14 +3535,17 @@ # earlier versions or no versions, it should provide default value of 0. bn = nn.BatchNorm2d(3) @@ -9560,7 +9560,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= @unittest.skipIf(not PY3, 'Python 2.7 generates cyclic trash') diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/test/test_torch.py pytorch-develop-150/test/test_torch.py --- pytorch-v1.5.0/test/test_torch.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/test/test_torch.py 2021-12-17 23:00:48.237166350 +0800 ++++ pytorch-develop-150/test/test_torch.py 2021-12-18 23:01:06.173044047 +0800 @@ -4087,6 +4087,9 @@ def test_print(self): default_type = torch.Tensor().type() @@ -9609,7 +9609,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= @onlyCPU diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/test/test_utils.py pytorch-develop-150/test/test_utils.py --- pytorch-v1.5.0/test/test_utils.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/test/test_utils.py 2021-12-17 23:00:48.237166350 +0800 ++++ pytorch-develop-150/test/test_utils.py 2021-12-18 23:01:06.173044047 +0800 @@ -6,6 +6,7 @@ import random import tempfile @@ -9628,7 +9628,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= load_tests = load_tests diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/derivatives.yaml pytorch-develop-150/tools/autograd/derivatives.yaml --- pytorch-v1.5.0/tools/autograd/derivatives.yaml 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/derivatives.yaml 2021-12-17 23:00:48.845174131 +0800 ++++ pytorch-develop-150/tools/autograd/derivatives.yaml 2021-12-18 23:01:06.761048578 +0800 @@ -107,6 +107,10 @@ # # NB: The parameter names here MUST be consistent with the parameter names @@ -9753,7 +9753,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/dump_utils.py pytorch-develop-150/tools/autograd/dump_utils.py --- pytorch-v1.5.0/tools/autograd/dump_utils.py 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/dump_utils.py 2021-12-17 23:00:48.845174131 +0800 ++++ pytorch-develop-150/tools/autograd/dump_utils.py 2021-12-18 23:01:06.761048578 +0800 @@ -0,0 +1,312 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# All rights reserved. @@ -10069,7 +10069,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + return prepare_to_check_overflow, overflow_dump_inputs diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_autograd_functions.py pytorch-develop-150/tools/autograd/gen_autograd_functions.py --- pytorch-v1.5.0/tools/autograd/gen_autograd_functions.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_autograd_functions.py 2021-12-17 23:00:48.845174131 +0800 ++++ pytorch-develop-150/tools/autograd/gen_autograd_functions.py 2021-12-18 23:01:06.761048578 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -10275,7 +10275,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_python_functions.py pytorch-develop-150/tools/autograd/gen_python_functions.py --- pytorch-v1.5.0/tools/autograd/gen_python_functions.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_python_functions.py 2021-12-17 23:00:48.845174131 +0800 ++++ pytorch-develop-150/tools/autograd/gen_python_functions.py 2021-12-18 23:01:06.761048578 +0800 @@ -1,3 +1,20 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -10317,7 +10317,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= 'value': argname, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_variable_type.py pytorch-develop-150/tools/autograd/gen_variable_type.py --- pytorch-v1.5.0/tools/autograd/gen_variable_type.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_variable_type.py 2021-12-17 23:00:48.845174131 +0800 ++++ pytorch-develop-150/tools/autograd/gen_variable_type.py 2021-12-18 23:01:06.761048578 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -10498,7 +10498,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/Functions.cpp pytorch-develop-150/tools/autograd/templates/Functions.cpp --- pytorch-v1.5.0/tools/autograd/templates/Functions.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/Functions.cpp 2021-12-17 23:00:48.845174131 +0800 ++++ pytorch-develop-150/tools/autograd/templates/Functions.cpp 2021-12-18 23:01:06.761048578 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2021 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10579,7 +10579,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto sparse = sparse_.coalesce(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/python_torch_functions.cpp pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp --- pytorch-v1.5.0/tools/autograd/templates/python_torch_functions.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp 2021-12-17 23:00:48.849174182 +0800 ++++ pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp 2021-12-18 23:01:06.761048578 +0800 @@ -22,7 +22,7 @@ #include "torch/csrc/autograd/generated/variable_factories.h" #include "torch/csrc/utils/structseq.h" @@ -10663,7 +10663,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/python_variable_methods.cpp pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp --- pytorch-v1.5.0/tools/autograd/templates/python_variable_methods.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp 2021-12-17 23:00:48.849174182 +0800 ++++ pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp 2021-12-18 23:01:06.761048578 +0800 @@ -15,7 +15,13 @@ #include "torch/csrc/cuda/Stream.h" #include "torch/csrc/cuda/Event.h" @@ -10758,7 +10758,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"has_names", (PyCFunction)THPVariable_has_names, METH_NOARGS, NULL}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/VariableType.cpp pytorch-develop-150/tools/autograd/templates/VariableType.cpp --- pytorch-v1.5.0/tools/autograd/templates/VariableType.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/VariableType.cpp 2021-12-17 23:00:48.845174131 +0800 ++++ pytorch-develop-150/tools/autograd/templates/VariableType.cpp 2021-12-18 23:01:06.761048578 +0800 @@ -1,7 +1,29 @@ +// Copyright (c) 2021 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10791,7 +10791,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/VariableType.h pytorch-develop-150/tools/autograd/templates/VariableType.h --- pytorch-v1.5.0/tools/autograd/templates/VariableType.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/VariableType.h 2021-12-17 23:00:48.845174131 +0800 ++++ pytorch-develop-150/tools/autograd/templates/VariableType.h 2021-12-18 23:01:06.761048578 +0800 @@ -1,3 +1,20 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10823,7 +10823,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= const at::Tensor & unpack(const Tensor & t, const char * name, int pos); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/build_variables.bzl pytorch-develop-150/tools/build_variables.bzl --- pytorch-v1.5.0/tools/build_variables.bzl 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/build_variables.bzl 2021-12-17 23:00:48.845174131 +0800 ++++ pytorch-develop-150/tools/build_variables.bzl 2021-12-18 23:01:06.757048547 +0800 @@ -46,6 +46,7 @@ "torch/csrc/autograd/functions/utils.cpp", "torch/csrc/autograd/input_buffer.cpp", @@ -10909,7 +10909,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def grad(outputs: _TensorOrTensors, inputs: _TensorOrTensors, grad_outputs: Optional[_TensorOrTensors]=..., retain_graph: Optional[bool]=..., create_graph: bool=..., only_inputs: bool=..., allow_unused: bool=...) -> Tuple[Tensor, ...]: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/autograd/profiler.py pytorch-develop-150/torch/autograd/profiler.py --- pytorch-v1.5.0/torch/autograd/profiler.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/autograd/profiler.py 2021-12-17 23:00:48.849174182 +0800 ++++ pytorch-develop-150/torch/autograd/profiler.py 2021-12-18 23:01:06.761048578 +0800 @@ -1,8 +1,25 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -11386,7 +11386,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ''.join(result) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/CMakeLists.txt pytorch-develop-150/torch/CMakeLists.txt --- pytorch-v1.5.0/torch/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/CMakeLists.txt 2021-12-17 23:00:48.849174182 +0800 ++++ pytorch-develop-150/torch/CMakeLists.txt 2021-12-18 23:01:06.761048578 +0800 @@ -97,6 +97,7 @@ ${TORCH_SRC_DIR}/csrc/tensor/python_tensor.cpp ${TORCH_SRC_DIR}/csrc/utils.cpp @@ -11418,7 +11418,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endif() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/engine.cpp pytorch-develop-150/torch/csrc/autograd/engine.cpp --- pytorch-v1.5.0/torch/csrc/autograd/engine.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/engine.cpp 2021-12-17 23:00:48.857174285 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/engine.cpp 2021-12-18 23:01:06.769048640 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11570,7 +11570,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto event = c10::Event{c10::DeviceType::CUDA}; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/function.h pytorch-develop-150/torch/csrc/autograd/function.h --- pytorch-v1.5.0/torch/csrc/autograd/function.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/function.h 2021-12-17 23:00:48.857174285 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/function.h 2021-12-18 23:01:06.769048640 +0800 @@ -11,6 +11,7 @@ #include @@ -11592,7 +11592,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // probably operate with names. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/functions/tensor.cpp pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp --- pytorch-v1.5.0/torch/csrc/autograd/functions/tensor.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp 2021-12-17 23:00:48.857174285 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp 2021-12-18 23:01:06.769048640 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11624,7 +11624,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= /*non_blocking=*/false, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/init.cpp pytorch-develop-150/torch/csrc/autograd/init.cpp --- pytorch-v1.5.0/torch/csrc/autograd/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/init.cpp 2021-12-17 23:00:48.857174285 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/init.cpp 2021-12-18 23:01:06.769048640 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11667,7 +11667,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= m.def("_enable_profiler", enableProfiler); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/input_buffer.cpp pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp --- pytorch-v1.5.0/torch/csrc/autograd/input_buffer.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp 2021-12-17 23:00:48.857174285 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp 2021-12-18 23:01:06.769048640 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11719,7 +11719,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto& old_var = buffer[pos]; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/profiler.cpp pytorch-develop-150/torch/csrc/autograd/profiler.cpp --- pytorch-v1.5.0/torch/csrc/autograd/profiler.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/profiler.cpp 2021-12-17 23:00:48.857174285 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/profiler.cpp 2021-12-18 23:01:06.769048640 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11959,7 +11959,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CUDAStubs::~CUDAStubs() = default; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/profiler.h pytorch-develop-150/torch/csrc/autograd/profiler.h --- pytorch-v1.5.0/torch/csrc/autograd/profiler.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/profiler.h 2021-12-17 23:00:48.857174285 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/profiler.h 2021-12-18 23:01:06.769048640 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12095,7 +12095,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/python_variable.cpp pytorch-develop-150/torch/csrc/autograd/python_variable.cpp --- pytorch-v1.5.0/torch/csrc/autograd/python_variable.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/python_variable.cpp 2021-12-17 23:00:48.857174285 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/python_variable.cpp 2021-12-18 23:01:06.769048640 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12149,7 +12149,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"is_complex", (getter)THPVariable_is_complex, nullptr, nullptr, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/python_variable_indexing.cpp pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp --- pytorch-v1.5.0/torch/csrc/autograd/python_variable_indexing.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-17 23:00:48.857174285 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-18 23:01:06.769048640 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12180,7 +12180,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= at::Device self_device = self_.device(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/record_function.cpp pytorch-develop-150/torch/csrc/autograd/record_function.cpp --- pytorch-v1.5.0/torch/csrc/autograd/record_function.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/record_function.cpp 2021-12-17 23:00:48.857174285 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/record_function.cpp 2021-12-18 23:01:06.769048640 +0800 @@ -154,6 +154,12 @@ } } @@ -12214,7 +12214,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/record_function.h pytorch-develop-150/torch/csrc/autograd/record_function.h --- pytorch-v1.5.0/torch/csrc/autograd/record_function.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/record_function.h 2021-12-17 23:00:48.857174285 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/record_function.h 2021-12-18 23:01:06.769048640 +0800 @@ -44,6 +44,9 @@ // Default constructor is used with before function called afterwards RecordFunction() {} @@ -12278,7 +12278,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (torch::autograd::profiler::needsInputs()) { \ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/utils/wrap_outputs.h pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h --- pytorch-v1.5.0/torch/csrc/autograd/utils/wrap_outputs.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h 2021-12-17 23:00:48.857174285 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h 2021-12-18 23:01:06.769048640 +0800 @@ -168,6 +168,45 @@ return r.release(); } @@ -12327,7 +12327,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (!r) throw python_error(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/VariableTypeManual.cpp pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp --- pytorch-v1.5.0/torch/csrc/autograd/VariableTypeManual.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp 2021-12-17 23:00:48.857174285 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp 2021-12-18 23:01:06.769048640 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12361,7 +12361,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (!t.defined()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/comm.cpp pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/comm.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp 2021-12-17 23:00:48.857174285 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp 2021-12-18 23:01:06.773048671 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12467,7 +12467,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= while (!in_flight.empty()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/init.cpp pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp 2021-12-17 23:00:48.857174285 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp 2021-12-18 23:01:06.773048671 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12524,7 +12524,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= .def("is_success", &::c10d::ProcessGroup::Work::isSuccess) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/reducer.cpp pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/reducer.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp 2021-12-17 23:00:48.857174285 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp 2021-12-18 23:01:06.773048671 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12659,7 +12659,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/DynamicTypes.cpp pytorch-develop-150/torch/csrc/DynamicTypes.cpp --- pytorch-v1.5.0/torch/csrc/DynamicTypes.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/DynamicTypes.cpp 2021-12-17 23:00:48.849174182 +0800 ++++ pytorch-develop-150/torch/csrc/DynamicTypes.cpp 2021-12-18 23:01:06.765048609 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12708,7 +12708,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return it->second; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/Generator.cpp pytorch-develop-150/torch/csrc/Generator.cpp --- pytorch-v1.5.0/torch/csrc/Generator.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/Generator.cpp 2021-12-17 23:00:48.849174182 +0800 ++++ pytorch-develop-150/torch/csrc/Generator.cpp 2021-12-18 23:01:06.765048609 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12776,7 +12776,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #endif diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/serialization.cpp pytorch-develop-150/torch/csrc/generic/serialization.cpp --- pytorch-v1.5.0/torch/csrc/generic/serialization.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/serialization.cpp 2021-12-17 23:00:48.861174336 +0800 ++++ pytorch-develop-150/torch/csrc/generic/serialization.cpp 2021-12-18 23:01:06.773048671 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12876,7 +12876,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/Storage.cpp pytorch-develop-150/torch/csrc/generic/Storage.cpp --- pytorch-v1.5.0/torch/csrc/generic/Storage.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/Storage.cpp 2021-12-17 23:00:48.861174336 +0800 ++++ pytorch-develop-150/torch/csrc/generic/Storage.cpp 2021-12-18 23:01:06.773048671 +0800 @@ -1,7 +1,25 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12955,7 +12955,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for (Py_ssize_t i = 0; i < length; i++) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/StorageMethods.cpp pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp --- pytorch-v1.5.0/torch/csrc/generic/StorageMethods.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp 2021-12-17 23:00:48.861174336 +0800 ++++ pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp 2021-12-18 23:01:06.773048671 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13003,7 +13003,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"_write_file", (PyCFunction)THPStorage_(writeFile), METH_VARARGS, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/Module.cpp pytorch-develop-150/torch/csrc/Module.cpp --- pytorch-v1.5.0/torch/csrc/Module.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/Module.cpp 2021-12-17 23:00:48.849174182 +0800 ++++ pytorch-develop-150/torch/csrc/Module.cpp 2021-12-18 23:01:06.765048609 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13164,7 +13164,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto set_module_attr = [&](const char* name, PyObject* v, bool incref = true) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/tensor/python_tensor.cpp pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp --- pytorch-v1.5.0/torch/csrc/tensor/python_tensor.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp 2021-12-17 23:00:48.869174438 +0800 ++++ pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp 2021-12-18 23:01:06.781048733 +0800 @@ -1,18 +1,35 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13541,7 +13541,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +} // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/init.cpp pytorch-develop-150/torch/csrc/utils/init.cpp --- pytorch-v1.5.0/torch/csrc/utils/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/init.cpp 2021-12-17 23:00:48.869174438 +0800 ++++ pytorch-develop-150/torch/csrc/utils/init.cpp 2021-12-18 23:01:06.781048733 +0800 @@ -1,7 +1,13 @@ #include #include @@ -13706,7 +13706,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/init.h pytorch-develop-150/torch/csrc/utils/init.h --- pytorch-v1.5.0/torch/csrc/utils/init.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/init.h 2021-12-17 23:00:48.869174438 +0800 ++++ pytorch-develop-150/torch/csrc/utils/init.h 2021-12-18 23:01:06.781048733 +0800 @@ -8,4 +8,7 @@ void initThroughputBenchmarkBindings(PyObject* module); @@ -13717,7 +13717,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/python_arg_parser.h pytorch-develop-150/torch/csrc/utils/python_arg_parser.h --- pytorch-v1.5.0/torch/csrc/utils/python_arg_parser.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/python_arg_parser.h 2021-12-17 23:00:48.869174438 +0800 ++++ pytorch-develop-150/torch/csrc/utils/python_arg_parser.h 2021-12-18 23:01:06.781048733 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13752,7 +13752,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return at::Device(device_str); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_layouts.cpp pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_layouts.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp 2021-12-17 23:00:48.869174438 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp 2021-12-18 23:01:06.781048733 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13783,7 +13783,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= registerLayoutObject((THPLayout*)strided_layout, at::Backend::QuantizedCPU); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_new.cpp pytorch-develop-150/torch/csrc/utils/tensor_new.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_new.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_new.cpp 2021-12-17 23:00:48.869174438 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_new.cpp 2021-12-18 23:01:06.781048733 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13919,7 +13919,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= " or ", c10::DispatchKey::XLATensorId, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_types.cpp pytorch-develop-150/torch/csrc/utils/tensor_types.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_types.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_types.cpp 2021-12-17 23:00:48.869174438 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_types.cpp 2021-12-18 23:01:06.781048733 +0800 @@ -1,58 +1,91 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -14132,7 +14132,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def get_rng_state(): ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/distributed/distributed_c10d.py pytorch-develop-150/torch/distributed/distributed_c10d.py --- pytorch-v1.5.0/torch/distributed/distributed_c10d.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/distributed/distributed_c10d.py 2021-12-17 23:00:48.869174438 +0800 ++++ pytorch-develop-150/torch/distributed/distributed_c10d.py 2021-12-18 23:01:06.785048763 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14213,7 +14213,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/__init__.py pytorch-develop-150/torch/__init__.py --- pytorch-v1.5.0/torch/__init__.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/__init__.py 2021-12-17 23:00:48.849174182 +0800 ++++ pytorch-develop-150/torch/__init__.py 2021-12-18 23:01:06.761048578 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14256,7 +14256,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/lib/c10d/CMakeLists.txt pytorch-develop-150/torch/lib/c10d/CMakeLists.txt --- pytorch-v1.5.0/torch/lib/c10d/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/lib/c10d/CMakeLists.txt 2021-12-17 23:00:48.873174490 +0800 ++++ pytorch-develop-150/torch/lib/c10d/CMakeLists.txt 2021-12-18 23:01:06.785048763 +0800 @@ -28,6 +28,10 @@ option(USE_C10D_NCCL "USE C10D NCCL" ON) endif() @@ -14309,7 +14309,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= copy_header(ProcessGroupMPI.hpp) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/lib/libshm/CMakeLists.txt pytorch-develop-150/torch/lib/libshm/CMakeLists.txt --- pytorch-v1.5.0/torch/lib/libshm/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/lib/libshm/CMakeLists.txt 2021-12-17 23:00:48.873174490 +0800 ++++ pytorch-develop-150/torch/lib/libshm/CMakeLists.txt 2021-12-18 23:01:06.785048763 +0800 @@ -37,8 +37,11 @@ SET_TARGET_PROPERTIES(shm PROPERTIES PREFIX "lib" @@ -14366,7 +14366,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -_maybe_indices_t = _scalar_or_tuple_2_t[Tensor] diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/functional.py pytorch-develop-150/torch/nn/functional.py --- pytorch-v1.5.0/torch/nn/functional.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/functional.py 2021-12-17 23:00:48.873174490 +0800 ++++ pytorch-develop-150/torch/nn/functional.py 2021-12-18 23:01:06.785048763 +0800 @@ -1611,7 +1611,7 @@ else: output = input.matmul(weight.t()) @@ -14389,7 +14389,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -from . import parallel as parallel diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/batchnorm.py pytorch-develop-150/torch/nn/modules/batchnorm.py --- pytorch-v1.5.0/torch/nn/modules/batchnorm.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/batchnorm.py 2021-12-17 23:00:48.873174490 +0800 ++++ pytorch-develop-150/torch/nn/modules/batchnorm.py 2021-12-18 23:01:06.789048794 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14421,7 +14421,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= self.register_parameter('running_var', None) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/module.py pytorch-develop-150/torch/nn/modules/module.py --- pytorch-v1.5.0/torch/nn/modules/module.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/module.py 2021-12-17 23:00:48.873174490 +0800 ++++ pytorch-develop-150/torch/nn/modules/module.py 2021-12-18 23:01:06.789048794 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14581,7 +14581,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return t.to(device, dtype if t.is_floating_point() else None, non_blocking, memory_format=convert_to_format) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/normalization.py pytorch-develop-150/torch/nn/modules/normalization.py --- pytorch-v1.5.0/torch/nn/modules/normalization.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/normalization.py 2021-12-17 23:00:48.873174490 +0800 ++++ pytorch-develop-150/torch/nn/modules/normalization.py 2021-12-18 23:01:06.789048794 +0800 @@ -128,13 +128,14 @@ """ __constants__ = ['normalized_shape', 'eps', 'elementwise_affine'] @@ -14650,7 +14650,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - module_kwargs: Optional[Any] = ...) -> Tensor: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/parallel/distributed.py pytorch-develop-150/torch/nn/parallel/distributed.py --- pytorch-v1.5.0/torch/nn/parallel/distributed.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/parallel/distributed.py 2021-12-17 23:00:48.877174541 +0800 ++++ pytorch-develop-150/torch/nn/parallel/distributed.py 2021-12-18 23:01:06.789048794 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15005,7 +15005,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def remove_weight_norm(module: T_module, name: str = ...) -> T_module: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/onnx/symbolic_opset9.py pytorch-develop-150/torch/onnx/symbolic_opset9.py --- pytorch-v1.5.0/torch/onnx/symbolic_opset9.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/onnx/symbolic_opset9.py 2021-12-17 23:00:48.877174541 +0800 ++++ pytorch-develop-150/torch/onnx/symbolic_opset9.py 2021-12-18 23:01:06.789048794 +0800 @@ -1621,14 +1621,23 @@ slices = [sym_help._slice_helper(g, w, axes=[0], starts=[x * n], ends=[y * n]) for x, y in intervals] return g.op('Concat', *slices, axis_i=0) @@ -15083,7 +15083,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, params: _params_t, lr: float=..., lr_decay: float=..., weight_decay: float=..., initial_accumulator_value: float=..., eps: float=...) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/optim/adamax.py pytorch-develop-150/torch/optim/adamax.py --- pytorch-v1.5.0/torch/optim/adamax.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/optim/adamax.py 2021-12-17 23:00:48.877174541 +0800 ++++ pytorch-develop-150/torch/optim/adamax.py 2021-12-18 23:01:06.789048794 +0800 @@ -80,8 +80,8 @@ exp_inf.mul_(beta2).unsqueeze(0), grad.abs().add_(eps).unsqueeze_(0) @@ -15260,7 +15260,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, params: _params_t, lr: float=..., betas: Tuple[float, float]=..., eps: float=...) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/serialization.py pytorch-develop-150/torch/serialization.py --- pytorch-v1.5.0/torch/serialization.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/serialization.py 2021-12-17 23:00:48.849174182 +0800 ++++ pytorch-develop-150/torch/serialization.py 2021-12-18 23:01:06.761048578 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15344,7 +15344,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def location_tag(storage): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/storage.py pytorch-develop-150/torch/storage.py --- pytorch-v1.5.0/torch/storage.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/storage.py 2021-12-17 23:00:48.849174182 +0800 ++++ pytorch-develop-150/torch/storage.py 2021-12-18 23:01:06.761048578 +0800 @@ -7,6 +7,7 @@ class _StorageBase(object): @@ -15364,7 +15364,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= else: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/tensor.py pytorch-develop-150/torch/tensor.py --- pytorch-v1.5.0/torch/tensor.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/tensor.py 2021-12-17 23:00:48.849174182 +0800 ++++ pytorch-develop-150/torch/tensor.py 2021-12-18 23:01:06.761048578 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15426,7 +15426,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def __reversed__(self): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/_tensor_str.py pytorch-develop-150/torch/_tensor_str.py --- pytorch-v1.5.0/torch/_tensor_str.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/_tensor_str.py 2021-12-17 23:00:48.849174182 +0800 ++++ pytorch-develop-150/torch/_tensor_str.py 2021-12-18 23:01:06.761048578 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15480,7 +15480,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= has_default_dtype = self.dtype in (torch.get_default_dtype(), torch.int64, torch.bool) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/data/dataloader.py pytorch-develop-150/torch/utils/data/dataloader.py --- pytorch-v1.5.0/torch/utils/data/dataloader.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/data/dataloader.py 2021-12-17 23:00:48.881174592 +0800 ++++ pytorch-develop-150/torch/utils/data/dataloader.py 2021-12-18 23:01:06.793048825 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15689,7 +15689,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, sampler: Sampler[int], batch_size: int, drop_last: bool) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/data/_utils/pin_memory.py pytorch-develop-150/torch/utils/data/_utils/pin_memory.py --- pytorch-v1.5.0/torch/utils/data/_utils/pin_memory.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/data/_utils/pin_memory.py 2021-12-17 23:00:48.877174541 +0800 ++++ pytorch-develop-150/torch/utils/data/_utils/pin_memory.py 2021-12-18 23:01:06.793048825 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15750,7 +15750,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/__init__.py pytorch-develop-150/torch/utils/__init__.py --- pytorch-v1.5.0/torch/utils/__init__.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/__init__.py 2021-12-17 23:00:48.877174541 +0800 ++++ pytorch-develop-150/torch/utils/__init__.py 2021-12-18 23:01:06.793048825 +0800 @@ -1,6 +1,9 @@ from __future__ import absolute_import, division, print_function, unicode_literals @@ -15763,7 +15763,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def set_module(obj, mod): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/_utils.py pytorch-develop-150/torch/_utils.py --- pytorch-v1.5.0/torch/_utils.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/_utils.py 2021-12-17 23:00:48.849174182 +0800 ++++ pytorch-develop-150/torch/_utils.py 2021-12-18 23:01:06.761048578 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. diff --git a/patch/pytorch1.8.1_npu.patch b/patch/pytorch1.8.1_npu.patch index 03e2c1151de..a674923f004 100644 --- a/patch/pytorch1.8.1_npu.patch +++ b/patch/pytorch1.8.1_npu.patch @@ -1,6 +1,6 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/CMakeLists.txt pytorch-develop-181/aten/CMakeLists.txt --- pytorch-v1.8.1/aten/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/CMakeLists.txt 2021-12-17 23:00:52.929226218 +0800 ++++ pytorch-develop-181/aten/CMakeLists.txt 2021-12-18 23:01:10.905080428 +0800 @@ -22,9 +22,11 @@ set(ATen_CPU_INCLUDE) set(ATen_THIRD_PARTY_INCLUDE) @@ -52,7 +52,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_CPU_DEPENDENCY_LIBS ${ATen_CPU_DEPENDENCY_LIBS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/autocast_mode.h pytorch-develop-181/aten/src/ATen/autocast_mode.h --- pytorch-v1.8.1/aten/src/ATen/autocast_mode.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/autocast_mode.h 2021-12-17 23:00:52.933226268 +0800 ++++ pytorch-develop-181/aten/src/ATen/autocast_mode.h 2021-12-18 23:01:10.909080458 +0800 @@ -5,7 +5,7 @@ namespace { @@ -64,7 +64,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/CMakeLists.txt pytorch-develop-181/aten/src/ATen/CMakeLists.txt --- pytorch-v1.8.1/aten/src/ATen/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/CMakeLists.txt 2021-12-17 23:00:52.929226218 +0800 ++++ pytorch-develop-181/aten/src/ATen/CMakeLists.txt 2021-12-18 23:01:10.905080428 +0800 @@ -85,6 +85,10 @@ file(GLOB native_quantized_h "native/quantized/*.h" "native/quantized/cpu/*.h") file(GLOB native_cpu_h "native/cpu/*.h") @@ -115,7 +115,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_HIP_SRCS ${ATen_HIP_SRCS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/Dispatcher.h pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/Dispatcher.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h 2021-12-17 23:00:52.937226320 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h 2021-12-18 23:01:10.913080489 +0800 @@ -417,6 +417,11 @@ } } @@ -130,7 +130,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.cpp pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-12-17 23:00:52.937226320 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-12-18 23:01:10.913080489 +0800 @@ -6,6 +6,7 @@ namespace c10 { @@ -141,7 +141,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= std::unordered_set not_observed_ops = { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.h pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-12-17 23:00:52.937226320 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-12-18 23:01:10.913080489 +0800 @@ -1,12 +1,16 @@ #pragma once @@ -161,7 +161,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/OperatorEntry.h pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-12-17 23:00:52.937226320 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-12-18 23:01:10.913080489 +0800 @@ -10,6 +10,7 @@ #include #include @@ -172,7 +172,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #include diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/VariableFallbackKernel.cpp pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp --- pytorch-v1.8.1/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-12-17 23:00:52.933226268 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-12-18 23:01:10.909080458 +0800 @@ -48,4 +48,8 @@ m.fallback(torch::CppFunction::makeFallthrough()); } @@ -184,7 +184,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/Convolution.cpp pytorch-develop-181/aten/src/ATen/native/Convolution.cpp --- pytorch-v1.8.1/aten/src/ATen/native/Convolution.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/Convolution.cpp 2021-12-17 23:00:52.949226472 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/Convolution.cpp 2021-12-18 23:01:10.921080550 +0800 @@ -603,7 +603,9 @@ const Tensor& input, const Tensor& weight, const Tensor& bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, @@ -198,7 +198,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= at::Tensor _convolution( diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/Memory.cpp pytorch-develop-181/aten/src/ATen/native/Memory.cpp --- pytorch-v1.8.1/aten/src/ATen/native/Memory.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/Memory.cpp 2021-12-17 23:00:52.949226472 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/Memory.cpp 2021-12-18 23:01:10.925080581 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -249,7 +249,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= detail::computeStorageNbytes( diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/native_functions.yaml pytorch-develop-181/aten/src/ATen/native/native_functions.yaml --- pytorch-v1.8.1/aten/src/ATen/native/native_functions.yaml 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/native_functions.yaml 2021-12-17 23:00:52.985226931 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/native_functions.yaml 2021-12-18 23:01:10.961080857 +0800 @@ -2073,6 +2073,8 @@ dispatch: CPU, CUDA: isnan @@ -480,7 +480,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +- func: npu_apply_adam.out(Scalar beta1_power, Scalar beta2_power, Scalar lr, Scalar beta1, Scalar beta2, Scalar epsilon, Tensor grad, bool? use_locking, bool? use_nesterov, *, Tensor(a!) var, Tensor(b!) m, Tensor(c!) v) -> (Tensor(a!), Tensor(b!), Tensor(c!)) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/NPUVariableMethodStubs.cpp pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp --- pytorch-v1.8.1/aten/src/ATen/native/NPUVariableMethodStubs.cpp 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp 2021-12-17 23:00:52.953226524 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp 2021-12-18 23:01:10.925080581 +0800 @@ -0,0 +1,464 @@ +#include "ATen/native/npu/common/FormatCastHelper.h" +#include "ATen/native/npu/frame/FormatHelper.h" @@ -1864,7 +1864,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -} diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/TensorFactories.cpp pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp --- pytorch-v1.8.1/aten/src/ATen/native/TensorFactories.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp 2021-12-17 23:00:52.965226676 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp 2021-12-18 23:01:10.941080704 +0800 @@ -1,3 +1,20 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -1897,7 +1897,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= namespace { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h --- pytorch-v1.8.1/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-12-17 23:00:53.021227388 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-12-18 23:01:10.997081133 +0800 @@ -1,19229 +1,19229 @@ -// -// Copyright (c) 2017-2020 Advanced Micro Devices, Inc. All rights reserved. @@ -40359,7 +40359,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +#endif // #ifdef VMA_IMPLEMENTATION diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/record_function.cpp pytorch-develop-181/aten/src/ATen/record_function.cpp --- pytorch-v1.8.1/aten/src/ATen/record_function.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/record_function.cpp 2021-12-17 23:00:53.021227388 +0800 ++++ pytorch-develop-181/aten/src/ATen/record_function.cpp 2021-12-18 23:01:11.001081164 +0800 @@ -400,6 +400,9 @@ rf_tls_.tls_record_function_enabled_ = enable; } @@ -40372,7 +40372,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (rf_tls_ptr->tls_record_function_enabled_) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/record_function.h pytorch-develop-181/aten/src/ATen/record_function.h --- pytorch-v1.8.1/aten/src/ATen/record_function.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/record_function.h 2021-12-17 23:00:53.021227388 +0800 ++++ pytorch-develop-181/aten/src/ATen/record_function.h 2021-12-18 23:01:11.001081164 +0800 @@ -2,6 +2,7 @@ #include @@ -40408,7 +40408,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // scope - record scope that this function tracks diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/templates/TensorBody.h pytorch-develop-181/aten/src/ATen/templates/TensorBody.h --- pytorch-v1.8.1/aten/src/ATen/templates/TensorBody.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/templates/TensorBody.h 2021-12-17 23:00:53.025227439 +0800 ++++ pytorch-develop-181/aten/src/ATen/templates/TensorBody.h 2021-12-18 23:01:11.001081164 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40441,7 +40441,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= bool is_xpu() const; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/templates/TensorMethods.cpp pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp --- pytorch-v1.8.1/aten/src/ATen/templates/TensorMethods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp 2021-12-17 23:00:53.025227439 +0800 ++++ pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp 2021-12-18 23:01:11.001081164 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40486,7 +40486,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/Utils.cpp pytorch-develop-181/aten/src/ATen/Utils.cpp --- pytorch-v1.8.1/aten/src/ATen/Utils.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/Utils.cpp 2021-12-17 23:00:52.933226268 +0800 ++++ pytorch-develop-181/aten/src/ATen/Utils.cpp 2021-12-18 23:01:10.909080458 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40530,7 +40530,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/TH/generic/THStorage.cpp pytorch-develop-181/aten/src/TH/generic/THStorage.cpp --- pytorch-v1.8.1/aten/src/TH/generic/THStorage.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/TH/generic/THStorage.cpp 2021-12-17 23:00:53.029227489 +0800 ++++ pytorch-develop-181/aten/src/TH/generic/THStorage.cpp 2021-12-18 23:01:11.005081195 +0800 @@ -1,9 +1,32 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40643,7 +40643,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/TH/generic/THStorage.h pytorch-develop-181/aten/src/TH/generic/THStorage.h --- pytorch-v1.8.1/aten/src/TH/generic/THStorage.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/TH/generic/THStorage.h 2021-12-17 23:00:53.029227489 +0800 ++++ pytorch-develop-181/aten/src/TH/generic/THStorage.h 2021-12-18 23:01:11.005081195 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40682,7 +40682,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/CMakeLists.txt pytorch-develop-181/c10/CMakeLists.txt --- pytorch-v1.8.1/c10/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/CMakeLists.txt 2021-12-17 23:00:53.041227643 +0800 ++++ pytorch-develop-181/c10/CMakeLists.txt 2021-12-18 23:01:11.017081287 +0800 @@ -79,6 +79,14 @@ message(STATUS "don't use NUMA") endif() @@ -40711,7 +40711,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # not checked in diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Backend.h pytorch-develop-181/c10/core/Backend.h --- pytorch-v1.8.1/c10/core/Backend.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Backend.h 2021-12-17 23:00:53.041227643 +0800 ++++ pytorch-develop-181/c10/core/Backend.h 2021-12-18 23:01:11.017081287 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40805,7 +40805,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Device.cpp pytorch-develop-181/c10/core/Device.cpp --- pytorch-v1.8.1/c10/core/Device.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Device.cpp 2021-12-17 23:00:53.041227643 +0800 ++++ pytorch-develop-181/c10/core/Device.cpp 2021-12-18 23:01:11.017081287 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40836,7 +40836,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= types.begin(), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Device.h pytorch-develop-181/c10/core/Device.h --- pytorch-v1.8.1/c10/core/Device.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Device.h 2021-12-17 23:00:53.041227643 +0800 ++++ pytorch-develop-181/c10/core/Device.h 2021-12-18 23:01:11.017081287 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40871,7 +40871,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= bool is_cpu() const noexcept { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DeviceType.cpp pytorch-develop-181/c10/core/DeviceType.cpp --- pytorch-v1.8.1/c10/core/DeviceType.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DeviceType.cpp 2021-12-17 23:00:53.041227643 +0800 ++++ pytorch-develop-181/c10/core/DeviceType.cpp 2021-12-18 23:01:11.017081287 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40911,7 +40911,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return false; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DeviceType.h pytorch-develop-181/c10/core/DeviceType.h --- pytorch-v1.8.1/c10/core/DeviceType.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DeviceType.h 2021-12-17 23:00:53.041227643 +0800 ++++ pytorch-develop-181/c10/core/DeviceType.h 2021-12-18 23:01:11.017081287 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40953,7 +40953,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= constexpr DeviceType kMSNPU = DeviceType::MSNPU; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKey.cpp pytorch-develop-181/c10/core/DispatchKey.cpp --- pytorch-v1.8.1/c10/core/DispatchKey.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKey.cpp 2021-12-17 23:00:53.041227643 +0800 ++++ pytorch-develop-181/c10/core/DispatchKey.cpp 2021-12-18 23:01:11.017081287 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41002,7 +41002,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::PrivateUse1: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKey.h pytorch-develop-181/c10/core/DispatchKey.h --- pytorch-v1.8.1/c10/core/DispatchKey.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKey.h 2021-12-17 23:00:53.041227643 +0800 ++++ pytorch-develop-181/c10/core/DispatchKey.h 2021-12-18 23:01:11.017081287 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41049,7 +41049,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= PrivateUse3_PreAutograd = AutogradPrivateUse3, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKeySet.cpp pytorch-develop-181/c10/core/DispatchKeySet.cpp --- pytorch-v1.8.1/c10/core/DispatchKeySet.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKeySet.cpp 2021-12-17 23:00:53.041227643 +0800 ++++ pytorch-develop-181/c10/core/DispatchKeySet.cpp 2021-12-18 23:01:11.017081287 +0800 @@ -11,6 +11,7 @@ DispatchKey::XLA, DispatchKey::NestedTensor, @@ -41069,7 +41069,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::AutogradXPU: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKeySet.h pytorch-develop-181/c10/core/DispatchKeySet.h --- pytorch-v1.8.1/c10/core/DispatchKeySet.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKeySet.h 2021-12-17 23:00:53.041227643 +0800 ++++ pytorch-develop-181/c10/core/DispatchKeySet.h 2021-12-18 23:01:11.017081287 +0800 @@ -193,6 +193,7 @@ DispatchKey::AutogradCPU, DispatchKey::AutogradCUDA, @@ -41080,7 +41080,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= DispatchKey::AutogradPrivateUse1, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Storage.h pytorch-develop-181/c10/core/Storage.h --- pytorch-v1.8.1/c10/core/Storage.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Storage.h 2021-12-17 23:00:53.041227643 +0800 ++++ pytorch-develop-181/c10/core/Storage.h 2021-12-18 23:01:11.021081318 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41114,7 +41114,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/StorageImpl.h pytorch-develop-181/c10/core/StorageImpl.h --- pytorch-v1.8.1/c10/core/StorageImpl.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/StorageImpl.h 2021-12-17 23:00:53.041227643 +0800 ++++ pytorch-develop-181/c10/core/StorageImpl.h 2021-12-18 23:01:11.021081318 +0800 @@ -1,12 +1,42 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41174,7 +41174,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/TensorImpl.h pytorch-develop-181/c10/core/TensorImpl.h --- pytorch-v1.8.1/c10/core/TensorImpl.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/TensorImpl.h 2021-12-17 23:00:53.041227643 +0800 ++++ pytorch-develop-181/c10/core/TensorImpl.h 2021-12-18 23:01:11.021081318 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41217,7 +41217,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ts.has(DispatchKey::SparseCPU) || diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/TensorOptions.h pytorch-develop-181/c10/core/TensorOptions.h --- pytorch-v1.8.1/c10/core/TensorOptions.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/TensorOptions.h 2021-12-17 23:00:53.041227643 +0800 ++++ pytorch-develop-181/c10/core/TensorOptions.h 2021-12-18 23:01:11.021081318 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41258,7 +41258,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } else if (tid == DispatchKey::QuantizedXPU) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/macros/Export.h pytorch-develop-181/c10/macros/Export.h --- pytorch-v1.8.1/c10/macros/Export.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/macros/Export.h 2021-12-17 23:00:53.045227693 +0800 ++++ pytorch-develop-181/c10/macros/Export.h 2021-12-18 23:01:11.021081318 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41327,7 +41327,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #define C10_API_ENUM C10_API diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/caffe2/CMakeLists.txt pytorch-develop-181/caffe2/CMakeLists.txt --- pytorch-v1.8.1/caffe2/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/caffe2/CMakeLists.txt 2021-12-17 23:00:53.049227744 +0800 ++++ pytorch-develop-181/caffe2/CMakeLists.txt 2021-12-18 23:01:11.029081378 +0800 @@ -76,6 +76,7 @@ # Add source, includes, and libs to lists list(APPEND Caffe2_CPU_SRCS ${ATen_CPU_SRCS}) @@ -41452,7 +41452,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Call again since Caffe2_HIP_INCLUDE is extended with ATen include dirs. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/BuildVariables.cmake pytorch-develop-181/cmake/BuildVariables.cmake --- pytorch-v1.8.1/cmake/BuildVariables.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/BuildVariables.cmake 2021-12-17 23:00:53.125228709 +0800 ++++ pytorch-develop-181/cmake/BuildVariables.cmake 2021-12-18 23:01:11.105081961 +0800 @@ -11,6 +11,7 @@ # CMakeLists.txt files under each folder respectively. set(Caffe2_CPU_SRCS) @@ -41476,7 +41476,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # symbols. However, if the lib is whole linked in caffe2 lib, we don't want diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Codegen.cmake pytorch-develop-181/cmake/Codegen.cmake --- pytorch-v1.8.1/cmake/Codegen.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Codegen.cmake 2021-12-17 23:00:53.125228709 +0800 ++++ pytorch-develop-181/cmake/Codegen.cmake 2021-12-18 23:01:11.105081961 +0800 @@ -208,13 +208,14 @@ file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt generated_cpp) file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt-cuda cuda_generated_cpp) @@ -41509,7 +41509,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= function(append_filelist name outputvar) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Dependencies.cmake pytorch-develop-181/cmake/Dependencies.cmake --- pytorch-v1.8.1/cmake/Dependencies.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Dependencies.cmake 2021-12-17 23:00:53.125228709 +0800 ++++ pytorch-develop-181/cmake/Dependencies.cmake 2021-12-18 23:01:11.105081961 +0800 @@ -1771,6 +1771,13 @@ endif(NOT C_HAS_THREAD) endif() @@ -41526,7 +41526,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Summary.cmake pytorch-develop-181/cmake/Summary.cmake --- pytorch-v1.8.1/cmake/Summary.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Summary.cmake 2021-12-17 23:00:53.125228709 +0800 ++++ pytorch-develop-181/cmake/Summary.cmake 2021-12-18 23:01:11.105081961 +0800 @@ -127,6 +127,7 @@ message(STATUS " USE_MKLDNN_CBLAS : ${USE_MKLDNN_CBLAS}") endif() @@ -41545,7 +41545,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= message(STATUS " Private Dependencies : ${Caffe2_DEPENDENCY_LIBS}") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/TorchConfig.cmake.in pytorch-develop-181/cmake/TorchConfig.cmake.in --- pytorch-v1.8.1/cmake/TorchConfig.cmake.in 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/TorchConfig.cmake.in 2021-12-17 23:00:53.125228709 +0800 ++++ pytorch-develop-181/cmake/TorchConfig.cmake.in 2021-12-18 23:01:11.105081961 +0800 @@ -158,6 +158,11 @@ list(APPEND TORCH_LIBRARIES ${TORCH_CUDA_LIBRARIES}) endif() @@ -41560,7 +41560,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(TORCH_CXX_FLAGS "-D_GLIBCXX_USE_CXX11_ABI=@GLIBCXX_USE_CXX11_ABI@") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/CMakeLists.txt pytorch-develop-181/CMakeLists.txt --- pytorch-v1.8.1/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/CMakeLists.txt 2021-12-17 23:00:52.917226065 +0800 ++++ pytorch-develop-181/CMakeLists.txt 2021-12-18 23:01:10.893080335 +0800 @@ -261,6 +261,10 @@ "USE_DISTRIBUTED" OFF) option(USE_TBB "Use TBB" OFF) @@ -41604,7 +41604,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if(APPLE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/setup.py pytorch-develop-181/setup.py --- pytorch-v1.8.1/setup.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/setup.py 2021-12-17 23:00:53.137228861 +0800 ++++ pytorch-develop-181/setup.py 2021-12-18 23:01:11.121082083 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -41673,7 +41673,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= 'utils/benchmark/utils/valgrind_wrapper/*.h', diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/derivatives.yaml pytorch-develop-181/tools/autograd/derivatives.yaml --- pytorch-v1.8.1/tools/autograd/derivatives.yaml 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/derivatives.yaml 2021-12-17 23:00:53.589234604 +0800 ++++ pytorch-develop-181/tools/autograd/derivatives.yaml 2021-12-18 23:01:11.577085578 +0800 @@ -1976,3 +1976,7 @@ - name: nonzero(Tensor self) -> Tensor @@ -41684,7 +41684,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + mat2: npu_bmm_v2_mat2_backward(grad, self, mat2, mat2.sizes()) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/gen_python_functions.py pytorch-develop-181/tools/autograd/gen_python_functions.py --- pytorch-v1.8.1/tools/autograd/gen_python_functions.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/gen_python_functions.py 2021-12-17 23:00:53.589234604 +0800 ++++ pytorch-develop-181/tools/autograd/gen_python_functions.py 2021-12-18 23:01:11.577085578 +0800 @@ -1,3 +1,20 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -41708,7 +41708,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # The bindings are generated as methods on python_variable or functions on the diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/Functions.cpp pytorch-develop-181/tools/autograd/templates/Functions.cpp --- pytorch-v1.8.1/tools/autograd/templates/Functions.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/Functions.cpp 2021-12-17 23:00:53.589234604 +0800 ++++ pytorch-develop-181/tools/autograd/templates/Functions.cpp 2021-12-18 23:01:11.577085578 +0800 @@ -14,6 +14,35 @@ namespace torch { namespace autograd { namespace generated { @@ -41747,7 +41747,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }}} // namespace torch::autograd::generated diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/python_torch_functions.cpp pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp --- pytorch-v1.8.1/tools/autograd/templates/python_torch_functions.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp 2021-12-17 23:00:53.589234604 +0800 ++++ pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp 2021-12-18 23:01:11.577085578 +0800 @@ -30,7 +30,7 @@ #include "torch/csrc/autograd/generated/variable_factories.h" #include "torch/csrc/utils/structseq.h" @@ -41783,7 +41783,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return torch::range(start, end, step, options); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/python_variable_methods.cpp pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp --- pytorch-v1.8.1/tools/autograd/templates/python_variable_methods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp 2021-12-17 23:00:53.589234604 +0800 ++++ pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp 2021-12-18 23:01:11.577085578 +0800 @@ -20,7 +20,13 @@ #ifdef USE_CUDA #include "torch/csrc/cuda/Event.h" @@ -41851,7 +41851,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"has_names", THPVariable_has_names, METH_NOARGS, NULL}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/build_variables.bzl pytorch-develop-181/tools/build_variables.bzl --- pytorch-v1.8.1/tools/build_variables.bzl 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/build_variables.bzl 2021-12-17 23:00:53.589234604 +0800 ++++ pytorch-develop-181/tools/build_variables.bzl 2021-12-18 23:01:11.577085578 +0800 @@ -362,6 +362,7 @@ libtorch_cuda_core_sources = [ "torch/csrc/CudaIPCTypes.cpp", @@ -41870,7 +41870,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= "torch/csrc/utils/python_arg_parser.cpp", diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/codegen/gen.py pytorch-develop-181/tools/codegen/gen.py --- pytorch-v1.8.1/tools/codegen/gen.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/codegen/gen.py 2021-12-17 23:00:53.593234655 +0800 ++++ pytorch-develop-181/tools/codegen/gen.py 2021-12-18 23:01:11.581085608 +0800 @@ -815,7 +815,7 @@ core_fm = make_file_manager(core_install_dir) cpu_fm = make_file_manager(options.install_dir) @@ -41918,7 +41918,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= main() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/codegen/model.py pytorch-develop-181/tools/codegen/model.py --- pytorch-v1.8.1/tools/codegen/model.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/codegen/model.py 2021-12-17 23:00:53.593234655 +0800 ++++ pytorch-develop-181/tools/codegen/model.py 2021-12-18 23:01:11.581085608 +0800 @@ -79,6 +79,7 @@ SparseHIP = auto() SparseXPU = auto() @@ -42060,7 +42060,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/autograd/profiler.py pytorch-develop-181/torch/autograd/profiler.py --- pytorch-v1.8.1/torch/autograd/profiler.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/autograd/profiler.py 2021-12-17 23:00:53.597234705 +0800 ++++ pytorch-develop-181/torch/autograd/profiler.py 2021-12-18 23:01:11.585085639 +0800 @@ -37,14 +37,17 @@ class EventList(list): """A list of Events (for pretty printing)""" @@ -42739,7 +42739,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ''.join(result) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_C/_autograd.pyi pytorch-develop-181/torch/_C/_autograd.pyi --- pytorch-v1.8.1/torch/_C/_autograd.pyi 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_C/_autograd.pyi 2021-12-17 23:00:53.593234655 +0800 ++++ pytorch-develop-181/torch/_C/_autograd.pyi 2021-12-18 23:01:11.581085608 +0800 @@ -9,14 +9,17 @@ CUDA = ... NVTX = ... @@ -42760,7 +42760,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= class ProfilerConfig: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/CMakeLists.txt pytorch-develop-181/torch/CMakeLists.txt --- pytorch-v1.8.1/torch/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/CMakeLists.txt 2021-12-17 23:00:53.593234655 +0800 ++++ pytorch-develop-181/torch/CMakeLists.txt 2021-12-18 23:01:11.581085608 +0800 @@ -131,6 +131,20 @@ endif() @@ -42784,7 +42784,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= list(APPEND TORCH_PYTHON_SRCS ${GENERATED_THNN_CXX_CUDA}) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/engine.cpp pytorch-develop-181/torch/csrc/autograd/engine.cpp --- pytorch-v1.8.1/torch/csrc/autograd/engine.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/engine.cpp 2021-12-17 23:00:53.601234756 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/engine.cpp 2021-12-18 23:01:11.589085670 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -42881,7 +42881,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto outputs = call_function(graph_task, func, inputs); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/functions/tensor.cpp pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp --- pytorch-v1.8.1/torch/csrc/autograd/functions/tensor.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp 2021-12-17 23:00:53.601234756 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp 2021-12-18 23:01:11.589085670 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -42913,7 +42913,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= /*non_blocking=*/false, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/init.cpp pytorch-develop-181/torch/csrc/autograd/init.cpp --- pytorch-v1.8.1/torch/csrc/autograd/init.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/init.cpp 2021-12-17 23:00:53.601234756 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/init.cpp 2021-12-18 23:01:11.589085670 +0800 @@ -52,6 +52,7 @@ .value("Disabled", ProfilerState::Disabled) .value("CPU", ProfilerState::CPU) @@ -42956,7 +42956,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= .value("OPENCL", c10::DeviceType::OPENCL) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/input_buffer.cpp pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp --- pytorch-v1.8.1/torch/csrc/autograd/input_buffer.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp 2021-12-17 23:00:53.601234756 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp 2021-12-18 23:01:11.589085670 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43008,7 +43008,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto& old_var = buffer[pos]; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.cpp pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp --- pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp 2021-12-17 23:00:53.601234756 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp 2021-12-18 23:01:11.589085670 +0800 @@ -147,7 +147,7 @@ constexpr const CUDAStubs* default_stubs_addr = &default_stubs; // Constant initialization, so it is guaranteed to be initialized before @@ -43251,7 +43251,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CUDAStubs::~CUDAStubs() = default; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.h pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h --- pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h 2021-12-17 23:00:53.601234756 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h 2021-12-18 23:01:11.589085670 +0800 @@ -19,6 +19,8 @@ #include // for gettimeofday() #endif @@ -43440,7 +43440,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= std::vector&& remoteProfiledEvents); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/python_variable.cpp pytorch-develop-181/torch/csrc/autograd/python_variable.cpp --- pytorch-v1.8.1/torch/csrc/autograd/python_variable.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/python_variable.cpp 2021-12-17 23:00:53.605234807 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/python_variable.cpp 2021-12-18 23:01:11.593085701 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43494,7 +43494,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"is_mkldnn", (getter)THPVariable_is_mkldnn, nullptr, nullptr, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/python_variable_indexing.cpp pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp --- pytorch-v1.8.1/torch/csrc/autograd/python_variable_indexing.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-17 23:00:53.605234807 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-18 23:01:11.593085701 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43527,7 +43527,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/distributed/c10d/init.cpp pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp --- pytorch-v1.8.1/torch/csrc/distributed/c10d/init.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp 2021-12-17 23:00:53.605234807 +0800 ++++ pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp 2021-12-18 23:01:11.593085701 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43694,7 +43694,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= torch::class_<::c10d::DistributedC10d>("dist_c10d", "frontend") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/DynamicTypes.cpp pytorch-develop-181/torch/csrc/DynamicTypes.cpp --- pytorch-v1.8.1/torch/csrc/DynamicTypes.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/DynamicTypes.cpp 2021-12-17 23:00:53.597234705 +0800 ++++ pytorch-develop-181/torch/csrc/DynamicTypes.cpp 2021-12-18 23:01:11.585085639 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43743,7 +43743,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return it->second; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/Generator.cpp pytorch-develop-181/torch/csrc/Generator.cpp --- pytorch-v1.8.1/torch/csrc/Generator.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/Generator.cpp 2021-12-17 23:00:53.597234705 +0800 ++++ pytorch-develop-181/torch/csrc/Generator.cpp 2021-12-18 23:01:11.585085639 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43790,7 +43790,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= " is not supported for torch.Generator() api."); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/serialization.cpp pytorch-develop-181/torch/csrc/generic/serialization.cpp --- pytorch-v1.8.1/torch/csrc/generic/serialization.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/serialization.cpp 2021-12-17 23:00:53.609234858 +0800 ++++ pytorch-develop-181/torch/csrc/generic/serialization.cpp 2021-12-18 23:01:11.597085731 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43888,7 +43888,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/Storage.cpp pytorch-develop-181/torch/csrc/generic/Storage.cpp --- pytorch-v1.8.1/torch/csrc/generic/Storage.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/Storage.cpp 2021-12-17 23:00:53.609234858 +0800 ++++ pytorch-develop-181/torch/csrc/generic/Storage.cpp 2021-12-18 23:01:11.597085731 +0800 @@ -1,7 +1,25 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43968,7 +43968,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for (Py_ssize_t i = 0; i < length; i++) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/StorageMethods.cpp pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp --- pytorch-v1.8.1/torch/csrc/generic/StorageMethods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp 2021-12-17 23:00:53.609234858 +0800 ++++ pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp 2021-12-18 23:01:11.597085731 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44048,7 +44048,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/Module.cpp pytorch-develop-181/torch/csrc/Module.cpp --- pytorch-v1.8.1/torch/csrc/Module.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/Module.cpp 2021-12-17 23:00:53.597234705 +0800 ++++ pytorch-develop-181/torch/csrc/Module.cpp 2021-12-18 23:01:11.585085639 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44174,7 +44174,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (incref) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/tensor/python_tensor.cpp pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp --- pytorch-v1.8.1/torch/csrc/tensor/python_tensor.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp 2021-12-17 23:00:53.633235163 +0800 ++++ pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp 2021-12-18 23:01:11.625085946 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44212,7 +44212,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/python_arg_parser.h pytorch-develop-181/torch/csrc/utils/python_arg_parser.h --- pytorch-v1.8.1/torch/csrc/utils/python_arg_parser.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/python_arg_parser.h 2021-12-17 23:00:53.637235213 +0800 ++++ pytorch-develop-181/torch/csrc/utils/python_arg_parser.h 2021-12-18 23:01:11.625085946 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44247,7 +44247,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return at::Device(device_str); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_layouts.cpp pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_layouts.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp 2021-12-17 23:00:53.637235213 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp 2021-12-18 23:01:11.625085946 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44278,7 +44278,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= PyObject *sparse_coo_layout = THPLayout_New(at::Layout::Sparse, "torch.sparse_coo"); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_new.cpp pytorch-develop-181/torch/csrc/utils/tensor_new.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_new.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_new.cpp 2021-12-17 23:00:53.637235213 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_new.cpp 2021-12-18 23:01:11.625085946 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44410,7 +44410,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= dispatch_key); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_types.cpp pytorch-develop-181/torch/csrc/utils/tensor_types.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_types.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_types.cpp 2021-12-17 23:00:53.637235213 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_types.cpp 2021-12-18 23:01:11.625085946 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44460,7 +44460,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/distributed/distributed_c10d.py pytorch-develop-181/torch/distributed/distributed_c10d.py --- pytorch-v1.8.1/torch/distributed/distributed_c10d.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/distributed/distributed_c10d.py 2021-12-17 23:00:53.637235213 +0800 ++++ pytorch-develop-181/torch/distributed/distributed_c10d.py 2021-12-18 23:01:11.625085946 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -44551,7 +44551,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= prefix_store, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/__init__.py pytorch-develop-181/torch/__init__.py --- pytorch-v1.8.1/torch/__init__.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/__init__.py 2021-12-17 23:00:53.593234655 +0800 ++++ pytorch-develop-181/torch/__init__.py 2021-12-18 23:01:11.581085608 +0800 @@ -675,3 +675,11 @@ # class usage. We add these lines here to preserve backward compatibility. quantized_lstm = torch.ops.aten.quantized_lstm @@ -44567,7 +44567,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/CMakeLists.txt pytorch-develop-181/torch/lib/c10d/CMakeLists.txt --- pytorch-v1.8.1/torch/lib/c10d/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/CMakeLists.txt 2021-12-17 23:00:53.645235316 +0800 ++++ pytorch-develop-181/torch/lib/c10d/CMakeLists.txt 2021-12-18 23:01:11.633086007 +0800 @@ -27,6 +27,10 @@ option(USE_C10D_NCCL "USE C10D NCCL" ON) endif() @@ -44620,7 +44620,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= copy_header(ProcessGroupMPI.hpp) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/comm.cpp pytorch-develop-181/torch/lib/c10d/comm.cpp --- pytorch-v1.8.1/torch/lib/c10d/comm.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/comm.cpp 2021-12-17 23:00:53.645235316 +0800 ++++ pytorch-develop-181/torch/lib/c10d/comm.cpp 2021-12-18 23:01:11.633086007 +0800 @@ -12,6 +12,26 @@ class BroadcastWork { @@ -44709,7 +44709,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= while (!in_flight.empty()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/reducer.cpp pytorch-develop-181/torch/lib/c10d/reducer.cpp --- pytorch-v1.8.1/torch/lib/c10d/reducer.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/reducer.cpp 2021-12-17 23:00:53.645235316 +0800 ++++ pytorch-develop-181/torch/lib/c10d/reducer.cpp 2021-12-18 23:01:11.633086007 +0800 @@ -18,6 +18,18 @@ namespace c10d { namespace { @@ -44901,7 +44901,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // A bucket with one or more dense tensors needs to be unflattened. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/libshm/CMakeLists.txt pytorch-develop-181/torch/lib/libshm/CMakeLists.txt --- pytorch-v1.8.1/torch/lib/libshm/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/libshm/CMakeLists.txt 2021-12-17 23:00:53.645235316 +0800 ++++ pytorch-develop-181/torch/lib/libshm/CMakeLists.txt 2021-12-18 23:01:11.637086037 +0800 @@ -41,8 +41,11 @@ set_target_properties(shm PROPERTIES PREFIX "lib" @@ -44917,7 +44917,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # https://github.com/libgit2/libgit2/issues/2128#issuecomment-35649830 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/batchnorm.py pytorch-develop-181/torch/nn/modules/batchnorm.py --- pytorch-v1.8.1/torch/nn/modules/batchnorm.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/batchnorm.py 2021-12-17 23:00:53.649235366 +0800 ++++ pytorch-develop-181/torch/nn/modules/batchnorm.py 2021-12-18 23:01:11.637086037 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -44939,7 +44939,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= from ._functions import SyncBatchNorm as sync_batch_norm diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/module.py pytorch-develop-181/torch/nn/modules/module.py --- pytorch-v1.8.1/torch/nn/modules/module.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/module.py 2021-12-17 23:00:53.649235366 +0800 ++++ pytorch-develop-181/torch/nn/modules/module.py 2021-12-18 23:01:11.637086037 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45065,7 +45065,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if convert_to_format is not None and t.dim() == 4: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/normalization.py pytorch-develop-181/torch/nn/modules/normalization.py --- pytorch-v1.8.1/torch/nn/modules/normalization.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/normalization.py 2021-12-17 23:00:53.649235366 +0800 ++++ pytorch-develop-181/torch/nn/modules/normalization.py 2021-12-18 23:01:11.637086037 +0800 @@ -167,8 +167,11 @@ init.zeros_(self.bias) @@ -45082,7 +45082,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return '{normalized_shape}, eps={eps}, ' \ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/parallel/distributed.py pytorch-develop-181/torch/nn/parallel/distributed.py --- pytorch-v1.8.1/torch/nn/parallel/distributed.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/parallel/distributed.py 2021-12-17 23:00:53.649235366 +0800 ++++ pytorch-develop-181/torch/nn/parallel/distributed.py 2021-12-18 23:01:11.641086068 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45114,7 +45114,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= output = self.module(*inputs[0], **kwargs[0]) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/serialization.py pytorch-develop-181/torch/serialization.py --- pytorch-v1.8.1/torch/serialization.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/serialization.py 2021-12-17 23:00:53.657235468 +0800 ++++ pytorch-develop-181/torch/serialization.py 2021-12-18 23:01:11.649086129 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45203,7 +45203,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= See also: `saving-loading-tensors` diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/storage.py pytorch-develop-181/torch/storage.py --- pytorch-v1.8.1/torch/storage.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/storage.py 2021-12-17 23:00:53.657235468 +0800 ++++ pytorch-develop-181/torch/storage.py 2021-12-18 23:01:11.649086129 +0800 @@ -8,6 +8,7 @@ class _StorageBase(object): _cdata: Any @@ -45223,7 +45223,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= else: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/tensor.py pytorch-develop-181/torch/tensor.py --- pytorch-v1.8.1/torch/tensor.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/tensor.py 2021-12-17 23:00:53.657235468 +0800 ++++ pytorch-develop-181/torch/tensor.py 2021-12-18 23:01:11.649086129 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45285,7 +45285,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def __reversed__(self): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_tensor_str.py pytorch-develop-181/torch/_tensor_str.py --- pytorch-v1.8.1/torch/_tensor_str.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_tensor_str.py 2021-12-17 23:00:53.597234705 +0800 ++++ pytorch-develop-181/torch/_tensor_str.py 2021-12-18 23:01:11.585085639 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45329,7 +45329,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # TODO: add an API to map real -> complex dtypes diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/utils/data/dataloader.py pytorch-develop-181/torch/utils/data/dataloader.py --- pytorch-v1.8.1/torch/utils/data/dataloader.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/utils/data/dataloader.py 2021-12-17 23:00:53.665235569 +0800 ++++ pytorch-develop-181/torch/utils/data/dataloader.py 2021-12-18 23:01:11.653086160 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45390,7 +45390,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= pin_memory_thread.start() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/utils/data/_utils/pin_memory.py pytorch-develop-181/torch/utils/data/_utils/pin_memory.py --- pytorch-v1.8.1/torch/utils/data/_utils/pin_memory.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/utils/data/_utils/pin_memory.py 2021-12-17 23:00:49.853187017 +0800 ++++ pytorch-develop-181/torch/utils/data/_utils/pin_memory.py 2021-12-18 23:01:07.749056184 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45436,7 +45436,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # logic of this function. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_utils.py pytorch-develop-181/torch/_utils.py --- pytorch-v1.8.1/torch/_utils.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_utils.py 2021-12-17 23:00:53.597234705 +0800 ++++ pytorch-develop-181/torch/_utils.py 2021-12-18 23:01:11.585085639 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. diff --git a/pytorch1.5.0/src/aten/src/ATen/native/npu/DivKernelNpu.cpp b/pytorch1.5.0/src/aten/src/ATen/native/npu/DivKernelNpu.cpp index e4596ab1ec1..af794312a52 100644 --- a/pytorch1.5.0/src/aten/src/ATen/native/npu/DivKernelNpu.cpp +++ b/pytorch1.5.0/src/aten/src/ATen/native/npu/DivKernelNpu.cpp @@ -24,7 +24,7 @@ using namespace at::native::npu; Tensor& div_out_npu(Tensor& result, const Tensor& self, const Scalar other) { auto unified_result = OpPreparation::binary_op_check(result, self, other, true); OpCommand cmd; - cmd.Name("Div") + cmd.Name("RealDiv") .Expect(unified_result) .Input(self) .Input(other, self.scalar_type()) @@ -42,7 +42,7 @@ Tensor& div_out_npu_nocheck(Tensor& result, const Tensor& self, const Tensor& ot } else { auto unified_result = OpPreparation::binary_op_check(result, self, other, true); OpCommand cmd; - cmd.Name("Div") + cmd.Name("RealDiv") .Expect(unified_result) .Input(self) .Input(other) @@ -58,10 +58,10 @@ Tensor& div_out_npu(Tensor& result, const Tensor& self, const Tensor& other) { Tensor outputTensor = CalcuOpUtil::is_scalar_wrapped_to_tensor(self) ? other : self; auto outputSize = broadcast_ops_npu_output_size(self, other); OpPreparation::CheckOut( - {self}, - result, + {self}, + result, CalcuOpUtil::get_tensor_npu_format(outputTensor), - self.scalar_type(), + self.scalar_type(), outputSize); div_out_npu_nocheck(result, self, other); diff --git a/pytorch1.5.0/test/test_npu/test_network_ops/test_div.py b/pytorch1.5.0/test/test_npu/test_network_ops/test_div.py index 8ada03b8f1f..0f5f43dc54b 100755 --- a/pytorch1.5.0/test/test_npu/test_network_ops/test_div.py +++ b/pytorch1.5.0/test/test_npu/test_network_ops/test_div.py @@ -50,10 +50,6 @@ class TestDiv(TestCase): cpu_input2, npu_input2 = create_dtype_tensor((2,3,4,5), dtype, no_zero=True) cpu_output, npu_output = self.get_outputs([cpu_input1, cpu_input2], [npu_input1, npu_input2], dtype) - # div 在int结果为负数时采用截断而不是向下取整的方式取整，所以选用numpy比较 - if dtype == torch.int: - cpu_output = np.floor_divide(cpu_input1.numpy(), cpu_input2.numpy()) - self.assertRtolEqual(cpu_output, npu_output) @unittest.skipIf(UT_FAST_MODE, "Run UT in fast mode") diff --git a/pytorch1.8.1/src/aten/src/ATen/native/npu/IndexSelectKernelNpu.cpp b/pytorch1.8.1/src/aten/src/ATen/native/npu/IndexSelectKernelNpu.cpp new file mode 100644 index 00000000000..93a8eaf634d --- /dev/null +++ b/pytorch1.8.1/src/aten/src/ATen/native/npu/IndexSelectKernelNpu.cpp @@ -0,0 +1,152 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. +// All rights reserved. +// +// Licensed under the BSD 3-Clause License (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// https://opensource.org/licenses/BSD-3-Clause +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "ATen/native/npu/utils/OpAdapter.h" +#include "ATen/native/npu/utils/CalcuOpUtil.h" + +namespace at { +namespace native { +using namespace at::native::npu; + +Tensor& index_select_out_npu_nocheck( + const Tensor& self, + int64_t dim, + const Tensor& index, + Tensor& result) { + if (self.scalar_type() == at::kLong) { + TORCH_WARN_ONCE("The oprator of index_select is executed, Currently High Accuracy but Low Performance OP with 64-bit has been used," + "Please Do Some Cast at Python Functions with 32-bit for Better Performance!"); + } + SmallVector dimVec = {dim}; + OpCommand cmd; + cmd.Name("GatherV2") + .Input(self) + .Input(index) + .Input(dimVec, at::kInt) + .Output(result) + .Run(); + + return result; +} + +Tensor& index_select_out_npu( + const Tensor& self, + int64_t dim, + const Tensor& index, + Tensor& result) { + Tensor indexTmp(index); + if (indexTmp.ndimension() == 0) { + indexTmp = index.unsqueeze(0); + } + // calculate the output size + auto outputSize = index_select_npu_output_size(self, dim, indexTmp); + + int64_t npu_format = CalcuOpUtil::get_tensor_npu_format(self); + // scalar scene no support nz + if (outputSize.empty()) { + npu_format = ACL_FORMAT_ND; + } + + Tensor input = self; + if (self.dtype() == kBool) { + // bool to int dtype + input = input.npu_dtype_cast(at::kInt); + } + + OpPreparation::CheckOut( + {input}, + result, + npu_format, + input.scalar_type(), + outputSize); + + OpPipeWithDefinedOut pipe; + result = pipe.CheckMemory({input, indexTmp}, {result}) + .Func([&input, &dim, &indexTmp](Tensor& result) + {index_select_out_npu_nocheck(input, dim, indexTmp, result);}) + .Call(result); + + if (self.dtype() == kBool) { + result = result.to(kBool); + } + + return result; +} + +Tensor index_select_npu( + const Tensor& self, + int64_t dim, + const Tensor& index) { + Tensor indexTmp(index); + if (indexTmp.ndimension() == 0) { + indexTmp = index.unsqueeze(0); + } + // calculate the output size + auto outputSize = index_select_npu_output_size(self, dim, indexTmp); + + int64_t npu_format = CalcuOpUtil::get_tensor_npu_format(self); + // scalar scene no support nz + if (outputSize.empty()) { + npu_format = ACL_FORMAT_ND; + } + + Tensor input = self; + if (self.dtype() == kBool) { + // bool to int dtype + input = input.npu_dtype_cast(at::kInt); + } + + Tensor result = OpPreparation::ApplyTensorWithFormat(input, outputSize, npu_format); + + index_select_out_npu_nocheck(input, dim, indexTmp, result); + + if (self.dtype() == kBool) { + // int to bool dtype 这里不转变回bool也能通过测试的比较 + result = result.to(kBool); + } + + return result; +} + +Tensor& index_select_dimname_out_npu( + const Tensor& self, + Dimname dim, + const Tensor& index, + Tensor& result) { + Tensor indexTmp(index); + if (indexTmp.ndimension() == 0) { + indexTmp = index.unsqueeze(0); + } + return index_select_out_npu( + self, dimname_to_position(self, dim), indexTmp, result); +} + +Tensor index_select_dimname_npu( + const Tensor& self, + Dimname dim, + const Tensor& index) { + return index_select_npu(self, dimname_to_position(self, dim), index); +} + +TORCH_LIBRARY_IMPL(aten, NPU, m) { + m.impl("index_select.out", TORCH_FN(index_select_out_npu)); + m.impl("index_select", TORCH_FN(index_select_npu)); + m.impl("index_select.dimname_out", TORCH_FN(index_select_dimname_out_npu)); + m.impl("index_select.dimname", TORCH_FN(index_select_dimname_npu)); +} + +} // namespace native +} // namespace at diff --git a/pytorch1.8.1/test/test_npu/test_network_ops/test_index_select.py b/pytorch1.8.1/test/test_npu/test_network_ops/test_index_select.py new file mode 100644 index 00000000000..00573580f8a --- /dev/null +++ b/pytorch1.8.1/test/test_npu/test_network_ops/test_index_select.py @@ -0,0 +1,130 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. +# All rights reserved. +# +# Licensed under the BSD 3-Clause License (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://opensource.org/licenses/BSD-3-Clause +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import sys +import torch +import numpy as np +from common_utils import TestCase, run_tests +from common_device_type import dtypes, instantiate_device_type_tests +from util_test import create_common_tensor + +class TestIndexSelect(TestCase): + + def cpu_op_exec(self, input1, axis, indices): + '''the shape of input:float16, float32,int8,uint8,int32,uint32,int16,uint16,int64,uint64,''' + output = torch.index_select(input1, dim=axis, index=indices) + output = output.numpy() + return output + + def npu_op_exec(self, input1, axis, indices): + output = torch.index_select(input1, dim=axis, index=indices) + output = output.to('cpu') + output = output.numpy() + return output + + def cpu_op_out_exec(self, input1, axis, indices, output): + '''the shape of input:float16, float32,int8,uint8,int32,uint32,int16,uint16,int64,uint64,''' + torch.index_select(input1, dim=axis, index=indices,out=output) + output = output.numpy() + return output + + def npu_op_out_exec(self, input1, axis, indices, output): + torch.index_select(input1, dim=axis, index=indices, out=output) + output = output.to('cpu') + output = output.numpy() + return output + + def test_index_select(self, device): + shape_format = [ + [[np.float32, 0, (3, )], torch.tensor(0, dtype=torch.int64), 0], + [[np.float32, 0, (3, )], torch.tensor([0, 1], dtype=torch.int64), 0], + [[np.float32, 0, (2, 4)], torch.tensor([0, 1, 2], dtype=torch.int64), 1], + [[np.float32, 0, (3, 4, 6)], torch.tensor([1, 2, 4], dtype=torch.int64), 2], + [[np.float32, 3, (4, 5, 6, 7)], torch.tensor([3, 5, 6], dtype=torch.int64), 3], + [[np.float32, -1, (3, 4, 8, 9, 12)], torch.tensor([2, 3, 5, 6], dtype=torch.int64), 4], + + [[np.int8, 0, (3,)], torch.tensor([0, 1], dtype=torch.int64), 0], + [[np.int8, 0, (2, 4)], torch.tensor([0, 1, 2], dtype=torch.int64), 1], + [[np.int8, 0, (3, 4, 6)], torch.tensor([1, 2, 4], dtype=torch.int64), 2], + [[np.int8, 0, (4, 5, 6, 7)], torch.tensor([3, 5, 6], dtype=torch.int64), 3], + [[np.int8, -1, (3, 4, 8, 9, 12)], torch.tensor([2, 3, 5, 6], dtype=torch.int64), 4], + + [[np.uint8, 0, (3,)], torch.tensor([0, 1], dtype=torch.int64), 0], + [[np.uint8, 0, (2, 4)], torch.tensor([0, 1, 2], dtype=torch.int64), 1], + [[np.uint8, 0, (3, 4, 6)], torch.tensor([1, 2, 4], dtype=torch.int64), 2], + [[np.uint8, 0, (4, 5, 6, 7)], torch.tensor([3, 5, 6], dtype=torch.int64), 3], + [[np.uint8, -1, (3, 4, 8, 9, 12)], torch.tensor([2, 3, 5, 6], dtype=torch.int64), 4], + + [[np.int32, 0, (3,)], torch.tensor([0, 1], dtype=torch.int64), 0], + [[np.int32, 0, (2, 4)], torch.tensor([0, 1, 2], dtype=torch.int64), 1], + [[np.int32, 0, (3, 4, 6)], torch.tensor([1, 2, 4], dtype=torch.int64), 2], + [[np.int32, 0, (4, 5, 6, 7)], torch.tensor([3, 5, 6], dtype=torch.int64), 3], + [[np.int32, -1, (3, 4, 8, 9, 12)], torch.tensor([2, 3, 5, 6], dtype=torch.int64), 4], + + [[np.uint8, 0, (3,)], torch.tensor([0, 1], dtype=torch.int64), 0], + [[np.uint8, 0, (2, 4)], torch.tensor([0, 1, 2], dtype=torch.int64), 1], + [[np.uint8, 0, (3, 4, 6)], torch.tensor([1, 2, 4], dtype=torch.int64), 2], + [[np.uint8, 0, (4, 5, 6, 7)], torch.tensor([3, 5, 6], dtype=torch.int64), 3], + [[np.uint8, -1, (3, 4, 8, 9, 12)], torch.tensor([2, 3, 5, 6], dtype=torch.int64), 4], + + [[np.uint8, 0, (3,)], torch.tensor([0, 1], dtype=torch.int64), 0], + [[np.uint8, 0, (2, 4)], torch.tensor([0, 1, 2], dtype=torch.int64), 1], + [[np.uint8, 0, (3, 4, 6)], torch.tensor([1, 2, 4], dtype=torch.int64), 2], + [[np.uint8, 0, (4, 5, 6, 7)], torch.tensor([3, 5, 6], dtype=torch.int64), 3], + [[np.uint8, -1, (3, 4, 8, 9, 12)], torch.tensor([2, 3, 5, 6], dtype=torch.int64), 4], + + [[np.int16, 0, (3,)], torch.tensor([0, 1], dtype=torch.int64), 0], + [[np.int16, 0, (2, 4)], torch.tensor([0, 1, 2], dtype=torch.int64), 1], + [[np.int16, 0, (3, 4, 6)], torch.tensor([1, 2, 4], dtype=torch.int64), 2], + [[np.int16, 0, (4, 5, 6, 7)], torch.tensor([3, 5, 6], dtype=torch.int64), 3], + [[np.int16, -1, (3, 4, 8, 9, 12)], torch.tensor([2, 3, 5, 6], dtype=torch.int64), 4], + + ] + for item in shape_format: + input1, npu_input = create_common_tensor(item[0], 1, 100) + _, npu_out = create_common_tensor(item[0], 1, 100) + cpu_output = self.cpu_op_exec(input1, item[2], item[1]) + npu_output = self.npu_op_exec(npu_input, item[2], item[1].to('npu')) + npu_output_out = self.npu_op_out_exec(npu_input, item[2], item[1].to('npu'), npu_out) + self.assertRtolEqual(cpu_output, npu_output) + self.assertRtolEqual(cpu_output, npu_output_out) + + + def test_index_select_fp16(self, device): + shape_format = [ + [[np.float16, 0, (3,)], torch.tensor([0, 1], dtype=torch.int64), 0], + [[np.float16, 0, (2, 4)], torch.tensor([0, 1, 2], dtype=torch.int64), 1], + [[np.float16, 0, (3, 4, 6)], torch.tensor([1, 2, 4], dtype=torch.int64), 2], + [[np.float16, 3, (4, 5, 6, 7)], torch.tensor([3, 5, 6], dtype=torch.int64), 3], + [[np.float16, -1, (3, 4, 8, 9, 12)], torch.tensor([2, 3, 5, 6], dtype=torch.int64), 4], + [[np.float16, 0, (3, )], torch.tensor(0, dtype=torch.int64), 0], + ] + for item in shape_format: + input1, npu_input = create_common_tensor(item[0], 1, 100) + + input1 = input1.to(torch.float32) + + cpu_output = self.cpu_op_exec(input1, item[2], item[1]) + npu_output = self.npu_op_exec(npu_input, item[2], item[1].to('npu')) + + cpu_output = cpu_output.astype(np.float16) + + self.assertRtolEqual(cpu_output, npu_output) + +instantiate_device_type_tests(TestIndexSelect, globals(), except_for="cpu") +if __name__ == "__main__": + run_tests() -- Gitee From 5bb56f1c8e87f2d0959ea72f1c588642e6292f8a Mon Sep 17 00:00:00 2001 From: pengzirong Date: Fri, 19 Nov 2021 16:25:27 +0800 Subject: [PATCH 09/55] update --- ...55\347\273\203\346\214\207\345\215\227.md" | 66 ++++++++++++++----- 1 file changed, 48 insertions(+), 18 deletions(-) diff --git "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" index 73bc7283119..9c08219d40f 100644 --- "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" +++ "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" @@ -1575,34 +1575,69 @@ def main(): - 解决方案：建议联系华为方支撑人员，提供稳定复现的单P和多P脚本。 - -

精度调优方法

模型出现精度问题一般有：因算子溢出导致的训练loss不收敛或者精度不达标问题，整个网络训练引起的性能不达标问题。用户可通过单算子溢出检测和整网调测适度解决模型精度不达标问题。 - **[单算子溢出检测](#单算子溢出检测md)** - - **[整网调测](#整网调测md)** +##### 环境准备 -
单算子溢出检测
+- 安装hdf5工具以支持算子dump功能，安装详情请参见[编译安装hdf5](#编译安装hdf5md)。 -用户通过算子溢出检测功能检测算子是否有溢出，然后采集溢出算子的数据，从而帮助开发人员快速定位并解决算子精度问题。 + 若使用模型算子精度对比功能，需要同时在NPU和GPU环境安装hdf5。否则，仅在NPU环境安装hdf5即可。 -约束说明： +- 安装支持dump功能的Ascend PyTorch框架，编译前请修改build.sh脚本，其余操作请参见《PyTorch安装指南》。 -- 需要安装hdf5工具以支持算子dump功能，安装详情请参见[编译安装hdf5](#编译安装hdf5md)。 -- 本功能只提供IR级别的算子溢出检测，且只支持AICORE，不支持Atomic。 -- 须在PyTorch源代码“build.sh“文件中添加“USE\_DUMP=1”字段。 + - 在NPU环境PyTorch安装 + + 编译前修改build.sh脚本，在脚本中增加`USE_DUMP=1`字段。 + ```bash + DEBUG=0 USE_DISTRIBUTED=1 USE_HCCL=1 USE_MKLDNN=0 USE_CUDA=0 USE_NPU=1 BUILD_TEST=0 USE_NNPACK=0 USE_DUMP=1 python"${PY_VERSION}" setup.py build bdist_wheel ``` - 修改前: DEBUG=0 USE_DISTRIBUTED=1 USE_HCCL=1 USE_MKLDNN=0 USE_CUDA=0 USE_NPU=1 BUILD_TEST=0 USE_NNPACK=0 python3 setup.py build bdist_wheel - 修改后: DEBUG=0 USE_DISTRIBUTED=1 USE_HCCL=1 USE_MKLDNN=0 USE_CUDA=0 USE_NPU=1 BUILD_TEST=0 USE_NNPACK=0 USE_DUMP=1 python3 setup.py build + + - （可选）在GPU环境PyTorch安装，若对模型算子精度对比，请执行此操作，否则请忽略。 + + 编译前修改build.sh，在脚本中增加`USE_DUMP=1`、`USE_NCCL=0`字段，将 `USE_HCCL`、`USE_NPU`字段的值修改为0，将`USE_CUDA`字段的值修改为1。 + + ```bash + DEBUG=0 USE_DISTRIBUTED=1 USE_HCCL=0 USE_NCCL=0 USE_MKLDNN=0 USE_CUDA=1 USE_NPU=0 BUILD_TEST=0 USE_NNPACK=0 USE_DUMP=1 python"${PY_VERSION}" setup.py build bdist_wheel ``` - 并参见《PyTorch安装指南》的“手动编译安装”章节重新编译并安装PyTorch。 +##### 模型算子精度对比 + +用户使用精度对比工具，在相同输入的情况下，获取模型在GPU和NPU进行训练时模型内算子输出的精度差异，从而帮助开发者实现算子精度问题定位。 + +约束说明： + +- 建议使用小batchsize，一般设置为8及以下。 + + 由于每个算子输入、输出数据会存储在硬盘中，会占用较大空间，故建议使用小batchsize节省硬盘空间。 + +- 建议仅dump一个step的数据进行精度对比。 + +对比模式： + + + + + + + + + + + +
单算子溢出检测
+ +用户通过算子溢出检测功能检测算子是否有溢出，然后采集溢出算子的数据，从而帮助开发人员快速定位并解决算子精度问题。 + +约束说明： + +- 本功能只提供IR级别的算子溢出检测，且只支持AICORE，不支持Atomic。 - 使用单算子溢出检测功能时，请不要同时开启apex的动态loss scale模式和使用tensor融合功能。 采集溢出算子数据： @@ -1626,15 +1661,10 @@ with torch.utils.dumper(check_overflow=check_overflow, dump_path=dump_path, load 2. 请将算子溢出的打印截图及映射后的TBE算子输入输出文件通过Issue附件形式反馈给华为开发人员。 -**IR与TBE算子映射** +##### IR与TBE算子映射 前提条件： -- 开启PyTorch框架dump功能。 - - 在PyTorch源代码 “build.sh“ 文件中添加“USE\_DUMP=1”字段，编译安装PyTorch框架。 - -- 需要安装hdf5工具以支持算子dump功能，安装详情请参见[编译安装hdf5](#编译安装hdf5md)。 - 设置环境变量`export ACL_DUMP_DATA=0`。 - 在脚本中避免使用`torch.npu.init.dump()`和`torch.npu.set.dump()`接口。 -- Gitee From 8df22cc73209838ab1ccb76f29c4288b49d02ae1 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Tue, 23 Nov 2021 19:46:23 +0800 Subject: [PATCH 10/55] GPU dump NPU load --- ...55\347\273\203\346\214\207\345\215\227.md" | 63 ++++++++++++++++++- 1 file changed, 62 insertions(+), 1 deletion(-) diff --git "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" index 9c08219d40f..44375686946 100644 --- "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" +++ "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" @@ -1618,17 +1618,74 @@ def main(): - 建议仅dump一个step的数据进行精度对比。 -对比模式： +- 目前支持精度为fp32、O1或O2训练过程的算子精度对比。 +对比模式： +- GPU的输入和输出为已知数据，将GPU的输入数据加载到NPU上执行得到输出数据，NPU与GPU输出数据对比。 +- NPU的输入和输出为已知数据，将NPU的输入数据加载到GPU上执行得到输出数据，NPU与GPU输出数据对比。 +操作步骤： + +1. 在GPU或NPU环境，使用dumper工具获取GPU或NPU的模型输入和算子输出数据。 + + 修改训练代码，增加数据dump功能。在模型训练代码的正向、反向计算位置使用`with`语句增加`torch.utils.dumper()`方法dump数据。例如，在GPU环境下修改示例： + + ```python + for i, data in enumerate(dataloader): + with torch.utils.dumper(use_dump=True, dump_path="./model_gpu.h5") as dump: + # 模型训练代码 + xxx # forward code + xxx # backward code + exit() + xxx # optimizer code + ``` + dump_path参数为dump数据保存文件路径及名称。建议仅dump一个step的数据用于精度对比，同时参数更新代码放在with语句外。 +2. 将在GPU(NPU)环境dump的数据model_gpu.h5拷贝到NPU(GPU)环境。 +3. 在NPU或NPU环境，使用dumper工具加载已经dump出的数据，并获取算子输出数据。 + 修改训练代码，增加数据load、dump功能。在模型训练代码的正向、反向计算位置使用`with`语句增加`torch.utils.dumper()`方法load、dump数据。例如，在NPU环境下修改示例： + ```python + for i, data in enumerate(dataloader): + with torch.utils.dumper(use_dump=True, load_file_path="./model_gpu.h5", dump_path="./model_npu.h5") as dump: + # 模型训练代码 + xxx # forward code + xxx # backward code + exit() + xxx # optimizer code + ``` + + load_file_path参数为从GPU或NPU获取的dump数据路径，dump_path参数为dump数据保存文件路径及名称。建议仅dump一个step的数据用于精度对比，同时参数更新代码放在with语句外。 +4. 使用msaccucmp.py对算子输出数据对比。 + 1. ascend-toolkit提供了msaccucmp.py工具脚本用具精度对比。 + + - 该脚本路径为："/user/local/Ascend/ascend-toolkit/latest/tools/operator_cmp/compare/msaccucmp.py"， + + 路径仅供参考，请以ascend-toolkit实际安装路径为准。 + + - 也可以使用如下命令查找msaccucmp.py路径。 + + ```linux + find / -name msaccucmp.py + ``` + + 2. 执行msaccucmp.py脚本，进行精度对比。 + + ``` + python3 /user/local/Ascend/ascend-toolkit/latest/tools/operator_cmp/compare/msaccucmp.py compare -m ./model_npu.h5 -g ./model_gpu.h5 + ``` + + 参数说明： + + `-g`参数传入使用GPU获得的dump数据文件路径。 + + `-m`参数传入使用NPU获得的dump数据文件路径。
单算子溢出检测
@@ -1737,6 +1794,10 @@ with torch.utils.dumper(check_overflow=check_overflow, dump_path=dump_path, load 运行成功后，在acl.json配置文件中的`dump_path`路径下查看输出结果文件。 +##### NPU与GPU算子映射 + +请参见《开发辅助工具指南》中 ”精度对比工具使用指南（训练）“中 “数据准备章节” 中的 “[准备以PyTorch为原始训练网络的精度比对数据文件](https://support.huawei.com/enterprise/zh/doc/EDOC1100219269/2324edc8#ZH-CN_TOPIC_0000001162580808)”。 +
整网调测
用户也可通过分析整个网络的方式来进行网络模型的精度调测。 -- Gitee From 2d01893c2946a3076a515f78949b9534b214b0db Mon Sep 17 00:00:00 2001 From: pengzirong Date: Sat, 4 Dec 2021 15:51:23 +0800 Subject: [PATCH 11/55] support python3.9 --- ...ch\345\256\211\350\243\205\346\214\207\345\215\227.md" | 8 +++++--- ...5&\350\256\255\347\273\203\346\214\207\345\215\227.md" | 4 ++++ 2 files changed, 9 insertions(+), 3 deletions(-) diff --git "a/docs/zh/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227.md" index d4dd6209eb1..47ded36e5d8 100644 --- "a/docs/zh/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227.md" +++ "b/docs/zh/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227.md" @@ -36,7 +36,7 @@ - 需完成CANN开发或运行环境的安装，具体操作请参考《CANN 软件安装指南》。 - 需安装3.12.0以上版本的CMake，安装方法请参考[CMake安装方法](#CMake安装方法md)。 - 需确保已安装7.3.0以上版本的gcc，7.3.0版本gcc具体安装及使用方式请参考[安装7.3.0版本gcc](#安装7-3-0版本gccmd)。 -- 需安装python版本为3.7.5或3.8。 +- 需安装python版本为3.7.5、3.8、3.9。 - 需确保环境中已安装patch、git工具，以Ubuntu和CentOS系统为例，命令如下： - Ubuntu系统 @@ -145,10 +145,12 @@ bash build.sh --python=3.7 或 bash build.sh --python=3.8 + 或 + bash build.sh --python=3.9 ``` - + 请指定环境中python版本进行编译。生成的二进制包在当前的dist目录下，即“pytorch/pytorch/dist”文件夹目录下。 - + 5. 安装PyTorch。 进入“pytorch/pytorch/dist“文件夹目录，执行如下命令安装。 diff --git "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" index 44375686946..deb69bcb26c 100644 --- "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" +++ "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" @@ -1579,7 +1579,11 @@ def main(): 模型出现精度问题一般有：因算子溢出导致的训练loss不收敛或者精度不达标问题，整个网络训练引起的性能不达标问题。用户可通过单算子溢出检测和整网调测适度解决模型精度不达标问题。 +- **[环境准备](#环境准备md)** +- **[模型算子精度对比](模型算子精度对比)** - **[单算子溢出检测](#单算子溢出检测md)** +- **[IR与TBE算子映射](IR与TBE算子映射)** +- **[NPU与GPU算子映射](NPU与GPU算子映射)** - **[整网调测](#整网调测md)** ##### 环境准备 -- Gitee From 626af539c77fae9a2c34934c6f907515bae05b7f Mon Sep 17 00:00:00 2001 From: pengzirong Date: Mon, 6 Dec 2021 11:25:29 +0800 Subject: [PATCH 12/55] add pytorch 1.8.1 API --- ...\214\201\346\270\205\345\215\225_1.8.1.md" | 1139 +++++++++++++++++ 1 file changed, 1139 insertions(+) create mode 100644 "docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.8.1.md" diff --git "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.8.1.md" "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.8.1.md" new file mode 100644 index 00000000000..35fd77cdc68 --- /dev/null +++ "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.8.1.md" @@ -0,0 +1,1139 @@ +# Torch + +## Tensors + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [is_tensor](https://pytorch.org/docs/1.8.1/generated/torch.is_tensor.html) | | +| 2 | [is_storage](https://pytorch.org/docs/1.8.1/generated/torch.is_storage.html) | | +| 3 | [is_complex](https://pytorch.org/docs/1.8.1/generated/torch.is_complex.html) | | +| 4 | [is_floating_point](https://pytorch.org/docs/1.8.1/generated/torch.is_floating_point.html) | | +| 5 | [is_nonzero](https://pytorch.org/docs/1.8.1/generated/torch.is_nonzero.html) | | +| 6 | [set_default_dtype](https://pytorch.org/docs/1.8.1/generated/torch.set_default_dtype.html) | | +| 7 | [get_default_dtype](https://pytorch.org/docs/1.8.1/generated/torch.get_default_dtype.html) | | +| 8 | [set_default_tensor_type](https://pytorch.org/docs/1.8.1/generated/torch.set_default_tensor_type.html) | | +| 9 | [numel](https://pytorch.org/docs/1.8.1/generated/torch.numel.html) | | +| 10 | [set_printoptions](https://pytorch.org/docs/1.8.1/generated/torch.set_printoptions.html) | | +| 11 | [set_flush_denormal](https://pytorch.org/docs/1.8.1/generated/torch.set_flush_denormal.html) | | + +### Creation Ops + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [tensor](https://pytorch.org/docs/1.8.1/generated/torch.tensor.html) | | +| 2 | [sparse_coo_tensor](https://pytorch.org/docs/1.8.1/generated/torch.sparse_coo_tensor.html) | | +| 3 | [as_tensor](https://pytorch.org/docs/1.8.1/generated/torch.as_tensor.html) | | +| 4 | [as_strided](https://pytorch.org/docs/1.8.1/generated/torch.as_strided.html) | | +| 5 | [from_numpy](https://pytorch.org/docs/1.8.1/generated/torch.from_numpy.html) | | +| 6 | [zeros](https://pytorch.org/docs/1.8.1/generated/torch.zeros.html) | | +| 7 | [zeros_like](https://pytorch.org/docs/1.8.1/generated/torch.zeros_like.html) | | +| 8 | [ones](https://pytorch.org/docs/1.8.1/generated/torch.ones.html) | | +| 9 | [ones_like](https://pytorch.org/docs/1.8.1/generated/torch.ones_like.html) | | +| 10 | [arange](https://pytorch.org/docs/1.8.1/generated/torch.arange.html) | | +| 11 | [range](https://pytorch.org/docs/1.8.1/generated/torch.range.html) | | +| 12 | [linspace](https://pytorch.org/docs/1.8.1/generated/torch.linspace.html) | | +| 13 | [logspace](https://pytorch.org/docs/1.8.1/generated/torch.logspace.html) | | +| 14 | [eye](https://pytorch.org/docs/1.8.1/generated/torch.eye.html) | | +| 15 | [empty](https://pytorch.org/docs/1.8.1/generated/torch.empty.html) | | +| 16 | [empty_like](https://pytorch.org/docs/1.8.1/generated/torch.empty_like.html) | | +| 17 | [empty_strided](https://pytorch.org/docs/1.8.1/generated/torch.empty_strided.html) | | +| 18 | [full](https://pytorch.org/docs/1.8.1/generated/torch.full.html) | | +| 19 | [full_like](https://pytorch.org/docs/1.8.1/generated/torch.full_like.html) | | +| 20 | [quantize_per_tensor](https://pytorch.org/docs/1.8.1/generated/torch.quantize_per_tensor.html) | | +| 21 | [quantize_per_channel](https://pytorch.org/docs/1.8.1/generated/torch.quantize_per_channel.html) | | +| 22 | [dequantize](https://pytorch.org/docs/1.8.1/generated/torch.dequantize.html) | | +| 23 | [complex](https://pytorch.org/docs/1.8.1/generated/torch.complex.html) | | +| 24 | [polar](https://pytorch.org/docs/1.8.1/generated/torch.polar.html) | | +| 25 | [heaviside](https://pytorch.org/docs/1.8.1/generated/torch.heaviside.html) | | + +### Indexing, Slicing, Joining, Mutating Ops + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [cat](https://pytorch.org/docs/1.8.1/generated/torch.cat.html) | | +| 2 | [chunk](https://pytorch.org/docs/1.8.1/generated/torch.chunk.html) | | +| 3 | [column_stack](https://pytorch.org/docs/1.8.1/generated/torch.column_stack.html) | | +| 4 | [dstack](https://pytorch.org/docs/1.8.1/generated/torch.dstack.html) | | +| 5 | [gather](https://pytorch.org/docs/1.8.1/generated/torch.gather.html) | | +| 6 | [hstack](https://pytorch.org/docs/1.8.1/generated/torch.hstack.html) | | +| 7 | [index_select](https://pytorch.org/docs/1.8.1/generated/torch.index_select.html) | | +| 8 | [masked_select](https://pytorch.org/docs/1.8.1/generated/torch.masked_select.html) | | +| 9 | [movedim](https://pytorch.org/docs/1.8.1/generated/torch.movedim.html) | | +| 10 | [moveaxis](https://pytorch.org/docs/1.8.1/generated/torch.moveaxis.html) | | +| 11 | [narrow](https://pytorch.org/docs/1.8.1/generated/torch.narrow.html) | | +| 12 | [nonzero](https://pytorch.org/docs/1.8.1/generated/torch.nonzero.html) | | +| 13 | [reshape](https://pytorch.org/docs/1.8.1/generated/torch.reshape.html) | | +| 14 | [row_stack](https://pytorch.org/docs/1.8.1/generated/torch.row_stack.html) | | +| 15 | [scatter](https://pytorch.org/docs/1.8.1/generated/torch.scatter.html) | | +| 16 | [scatter_add](https://pytorch.org/docs/1.8.1/generated/torch.scatter_add.html) | | +| 17 | [split](https://pytorch.org/docs/1.8.1/generated/torch.split.html) | | +| 18 | [squeeze](https://pytorch.org/docs/1.8.1/generated/torch.squeeze.html) | | +| 19 | [stack](https://pytorch.org/docs/1.8.1/generated/torch.stack.html) | | +| 20 | [swapaxes](https://pytorch.org/docs/1.8.1/generated/torch.swapaxes.html) | | +| 21 | [swapdims](https://pytorch.org/docs/1.8.1/generated/torch.swapdims.html) | | +| 22 | [t](https://pytorch.org/docs/1.8.1/generated/torch.t.html) | | +| 23 | [take](https://pytorch.org/docs/1.8.1/generated/torch.take.html) | | +| 24 | [tensor_split](https://pytorch.org/docs/1.8.1/generated/torch.tensor_split.html) | | +| 25 | [tile](https://pytorch.org/docs/1.8.1/generated/torch.tile.html) | | +| 26 | [transpose](https://pytorch.org/docs/1.8.1/generated/torch.transpose.html) | | +| 27 | [unbind](https://pytorch.org/docs/1.8.1/generated/torch.unbind.html) | | +| 28 | [unsqueeze](https://pytorch.org/docs/1.8.1/generated/torch.unsqueeze.html) | | +| 29 | [vstack](https://pytorch.org/docs/1.8.1/generated/torch.vstack.html) | | +| 30 | [where](https://pytorch.org/docs/1.8.1/generated/torch.where.html) | | + +## Generators + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [Generator](https://pytorch.org/docs/1.8.1/generated/torch.Generator.html) | | + +## Random sampling + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [seed](https://pytorch.org/docs/1.8.1/generated/torch.seed.html) | | +| 2 | [manual_seed](https://pytorch.org/docs/1.8.1/generated/torch.manual_seed.html) | | +| 3 | [initial_seed](https://pytorch.org/docs/1.8.1/generated/torch.initial_seed.html) | | +| 4 | [get_rng_state](https://pytorch.org/docs/1.8.1/generated/torch.get_rng_state.html) | | +| 5 | [set_rng_state](https://pytorch.org/docs/1.8.1/generated/torch.set_rng_state.html) | | +| 6 | [bernoulli](https://pytorch.org/docs/1.8.1/generated/torch.bernoulli.html) | | +| 7 | [multinomial](https://pytorch.org/docs/1.8.1/generated/torch.multinomial.html) | | +| 8 | [normal](https://pytorch.org/docs/1.8.1/generated/torch.normal.html) | | +| 9 | [poisson](https://pytorch.org/docs/1.8.1/generated/torch.poisson.html) | | +| 10 | [rand](https://pytorch.org/docs/1.8.1/generated/torch.rand.html) | | +| 11 | [rand_like](https://pytorch.org/docs/1.8.1/generated/torch.rand_like.html) | | +| 12 | [randint](https://pytorch.org/docs/1.8.1/generated/torch.randint.html) | | +| 13 | [randint_like](https://pytorch.org/docs/1.8.1/generated/torch.randint_like.html) | | +| 14 | [randn](https://pytorch.org/docs/1.8.1/generated/torch.randn.html) | | +| 15 | [randn_like](https://pytorch.org/docs/1.8.1/generated/torch.randn_like.html) | | +| 16 | [randperm](https://pytorch.org/docs/1.8.1/generated/torch.randperm.html) | | + +### In-place random sampling + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [torch.Tensor.bernoulli_()](https://pytorch.org/docs/1.8.1/tensors.html) | | +| 2 | [torch.Tensor.cauchy_()](https://pytorch.org/docs/1.8.1/tensors.html) | | +| 3 | [torch.Tensor.exponential_()](https://pytorch.org/docs/1.8.1/tensors.html) | | +| 4 | [torch.Tensor.geometric_()](https://pytorch.org/docs/1.8.1/tensors.html) | | +| 5 | [torch.Tensor.log_normal_()](https://pytorch.org/docs/1.8.1/tensors.html) | | +| 6 | [torch.Tensor.normal_()](https://pytorch.org/docs/1.8.1/tensors.html) | | +| 7 | [torch.Tensor.random_()](https://pytorch.org/docs/1.8.1/tensors.html) | | +| 8 | [torch.Tensor.uniform_()](https://pytorch.org/docs/1.8.1/tensors.html) | | + +### Quasi-random sampling + +| | | | +| ---- | ------------------------------------------------------------ | ---- | +| 1 | [quasirandom.SobolEngine](https://pytorch.org/docs/1.8.1/generated/torch.quasirandom.SobolEngine.html) | | + +## Serialization + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [save](https://pytorch.org/docs/1.8.1/generated/torch.save.html) | | +| 2 | [load](https://pytorch.org/docs/1.8.1/generated/torch.load.html) | | + +## Parallelism + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [get_num_threads](https://pytorch.org/docs/1.8.1/generated/torch.get_num_threads.html) | | +| 2 | [set_num_threads](https://pytorch.org/docs/1.8.1/generated/torch.set_num_threads.html) | | +| 3 | [get_num_interop_threads](https://pytorch.org/docs/1.8.1/generated/torch.get_num_interop_threads.html) | | +| 4 | [set_num_interop_threads](https://pytorch.org/docs/1.8.1/generated/torch.set_num_interop_threads.html) | | + +## Locally disabling gradient computation + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [no_grad](https://pytorch.org/docs/1.8.1/generated/torch.no_grad.html#torch.no_grad) | | +| 2 | [enable_grad](https://pytorch.org/docs/1.8.1/generated/torch.enable_grad.html#torch.enable_grad) | | +| 3 | set_grad_enabled | | + +## Math operations + +### Pointwise Ops + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [abs](https://pytorch.org/docs/1.8.1/generated/torch.abs.html#torch.abs) | | +| 2 | [absolute](https://pytorch.org/docs/1.8.1/generated/torch.absolute.html#torch.absolute) | | +| 3 | [acos](https://pytorch.org/docs/1.8.1/generated/torch.acos.html#torch.acos) | | +| 4 | [arccos](https://pytorch.org/docs/1.8.1/generated/torch.arccos.html#torch.arccos) | | +| 5 | [acosh](https://pytorch.org/docs/1.8.1/generated/torch.acosh.html#torch.acosh) | | +| 6 | [arccosh](https://pytorch.org/docs/1.8.1/generated/torch.arccosh.html#torch.arccosh) | | +| 7 | [add](https://pytorch.org/docs/1.8.1/generated/torch.add.html#torch.add) | | +| 8 | [addcdiv](https://pytorch.org/docs/1.8.1/generated/torch.addcdiv.html#torch.addcdiv) | | +| 9 | [addcmul](https://pytorch.org/docs/1.8.1/generated/torch.addcmul.html#torch.addcmul) | | +| 10 | [angle](https://pytorch.org/docs/1.8.1/generated/torch.angle.html#torch.angle) | | +| 11 | [asin](https://pytorch.org/docs/1.8.1/generated/torch.asin.html#torch.asin) | | +| 12 | [arcsin](https://pytorch.org/docs/1.8.1/generated/torch.arcsin.html#torch.arcsin) | | +| 13 | [asinh](https://pytorch.org/docs/1.8.1/generated/torch.asinh.html#torch.asinh) | | +| 14 | [arcsinh](https://pytorch.org/docs/1.8.1/generated/torch.arcsinh.html#torch.arcsinh) | | +| 15 | [atan](https://pytorch.org/docs/1.8.1/generated/torch.atan.html#torch.atan) | | +| 16 | [arctan](https://pytorch.org/docs/1.8.1/generated/torch.arctan.html#torch.arctan) | | +| 17 | [atanh](https://pytorch.org/docs/1.8.1/generated/torch.atanh.html#torch.atanh) | | +| 18 | [arctanh](https://pytorch.org/docs/1.8.1/generated/torch.arctanh.html#torch.arctanh) | | +| 19 | [atan2](https://pytorch.org/docs/1.8.1/generated/torch.atan2.html#torch.atan2) | | +| 20 | [bitwise_not](https://pytorch.org/docs/1.8.1/generated/torch.bitwise_not.html#torch.bitwise_not) | | +| 21 | [bitwise_and](https://pytorch.org/docs/1.8.1/generated/torch.bitwise_and.html#torch.bitwise_and) | | +| 22 | [bitwise_or](https://pytorch.org/docs/1.8.1/generated/torch.bitwise_or.html#torch.bitwise_or) | | +| 23 | [bitwise_xor](https://pytorch.org/docs/1.8.1/generated/torch.bitwise_xor.html#torch.bitwise_xor) | | +| 24 | [ceil](https://pytorch.org/docs/1.8.1/generated/torch.ceil.html#torch.ceil) | | +| 25 | [clamp](https://pytorch.org/docs/1.8.1/generated/torch.clamp.html#torch.clamp) | | +| 26 | [clip](https://pytorch.org/docs/1.8.1/generated/torch.clip.html#torch.clip) | | +| 27 | [conj](https://pytorch.org/docs/1.8.1/generated/torch.conj.html#torch.conj) | | +| 28 | [copysign](https://pytorch.org/docs/1.8.1/generated/torch.copysign.html#torch.copysign) | | +| 29 | [cos](https://pytorch.org/docs/1.8.1/generated/torch.cos.html#torch.cos) | | +| 30 | [cosh](https://pytorch.org/docs/1.8.1/generated/torch.cosh.html#torch.cosh) | | +| 31 | [deg2rad](https://pytorch.org/docs/1.8.1/generated/torch.deg2rad.html#torch.deg2rad) | | +| 32 | [div](https://pytorch.org/docs/1.8.1/generated/torch.div.html#torch.div) | | +| 33 | [divide](https://pytorch.org/docs/1.8.1/generated/torch.divide.html#torch.divide) | | +| 34 | [digamma](https://pytorch.org/docs/1.8.1/generated/torch.digamma.html#torch.digamma) | | +| 35 | [erf](https://pytorch.org/docs/1.8.1/generated/torch.erf.html#torch.erf) | | +| 36 | [erfc](https://pytorch.org/docs/1.8.1/generated/torch.erfc.html#torch.erfc) | | +| 37 | [erfinv](https://pytorch.org/docs/1.8.1/generated/torch.erfinv.html#torch.erfinv) | | +| 38 | [exp](https://pytorch.org/docs/1.8.1/generated/torch.exp.html#torch.exp) | | +| 39 | [exp2](https://pytorch.org/docs/1.8.1/generated/torch.exp2.html#torch.exp2) | | +| 40 | [expm1](https://pytorch.org/docs/1.8.1/generated/torch.expm1.html#torch.expm1) | | +| 41 | [fake_quantize_per_channel_affine](https://pytorch.org/docs/1.8.1/generated/torch.fake_quantize_per_channel_affine.html#torch.fake_quantize_per_channel_affine) | | +| 42 | [fake_quantize_per_tensor_affine](https://pytorch.org/docs/1.8.1/generated/torch.fake_quantize_per_tensor_affine.html#torch.fake_quantize_per_tensor_affine) | | +| 43 | [fix](https://pytorch.org/docs/1.8.1/generated/torch.fix.html#torch.fix) | | +| 44 | [float_power](https://pytorch.org/docs/1.8.1/generated/torch.float_power.html#torch.float_power) | | +| 45 | [floor](https://pytorch.org/docs/1.8.1/generated/torch.floor.html#torch.floor) | | +| 46 | [floor_divide](https://pytorch.org/docs/1.8.1/generated/torch.floor_divide.html#torch.floor_divide) | | +| 47 | [fmod](https://pytorch.org/docs/1.8.1/generated/torch.fmod.html#torch.fmod) | | +| 48 | [frac](https://pytorch.org/docs/1.8.1/generated/torch.frac.html#torch.frac) | | +| 49 | [imag](https://pytorch.org/docs/1.8.1/generated/torch.imag.html#torch.imag) | | +| 50 | [ldexp](https://pytorch.org/docs/1.8.1/generated/torch.ldexp.html#torch.ldexp) | | +| 51 | [lerp](https://pytorch.org/docs/1.8.1/generated/torch.lerp.html#torch.lerp) | | +| 52 | [lgamma](https://pytorch.org/docs/1.8.1/generated/torch.lgamma.html#torch.lgamma) | | +| 53 | [log](https://pytorch.org/docs/1.8.1/generated/torch.log.html#torch.log) | | +| 54 | [log10](https://pytorch.org/docs/1.8.1/generated/torch.log10.html#torch.log10) | | +| 55 | [log1p](https://pytorch.org/docs/1.8.1/generated/torch.log1p.html#torch.log1p) | | +| 56 | [log2](https://pytorch.org/docs/1.8.1/generated/torch.log2.html#torch.log2) | | +| 57 | [logaddexp](https://pytorch.org/docs/1.8.1/generated/torch.logaddexp.html#torch.logaddexp) | | +| 58 | [logaddexp2](https://pytorch.org/docs/1.8.1/generated/torch.logaddexp2.html#torch.logaddexp2) | | +| 59 | [logical_and](https://pytorch.org/docs/1.8.1/generated/torch.logical_and.html#torch.logical_and) | | +| 60 | [logical_not](https://pytorch.org/docs/1.8.1/generated/torch.logical_not.html#torch.logical_not) | | +| 61 | [logical_or](https://pytorch.org/docs/1.8.1/generated/torch.logical_or.html#torch.logical_or) | | +| 62 | [logical_xor](https://pytorch.org/docs/1.8.1/generated/torch.logical_xor.html#torch.logical_xor) | | +| 63 | [logit](https://pytorch.org/docs/1.8.1/generated/torch.logit.html#torch.logit) | | +| 64 | [hypot](https://pytorch.org/docs/1.8.1/generated/torch.hypot.html#torch.hypot) | | +| 65 | [i0](https://pytorch.org/docs/1.8.1/generated/torch.i0.html#torch.i0) | | +| 66 | [igamma](https://pytorch.org/docs/1.8.1/generated/torch.igamma.html#torch.igamma) | | +| 67 | [igammac](https://pytorch.org/docs/1.8.1/generated/torch.igammac.html#torch.igammac) | | +| 68 | [mul](https://pytorch.org/docs/1.8.1/generated/torch.mul.html#torch.mul) | | +| 69 | [multiply](https://pytorch.org/docs/1.8.1/generated/torch.multiply.html#torch.multiply) | | +| 70 | [mvlgamma](https://pytorch.org/docs/1.8.1/generated/torch.mvlgamma.html#torch.mvlgamma) | | +| 71 | [nan_to_num](https://pytorch.org/docs/1.8.1/generated/torch.nan_to_num.html#torch.nan_to_num) | | +| 72 | [neg](https://pytorch.org/docs/1.8.1/generated/torch.neg.html#torch.neg) | | +| 73 | [negative](https://pytorch.org/docs/1.8.1/generated/torch.negative.html#torch.negative) | | +| 74 | [nextafter](https://pytorch.org/docs/1.8.1/generated/torch.nextafter.html#torch.nextafter) | | +| 75 | [polygamma](https://pytorch.org/docs/1.8.1/generated/torch.polygamma.html#torch.polygamma) | | +| 76 | [pow](https://pytorch.org/docs/1.8.1/generated/torch.pow.html#torch.pow) | | +| 77 | [rad2deg](https://pytorch.org/docs/1.8.1/generated/torch.rad2deg.html#torch.rad2deg) | | +| 78 | [real](https://pytorch.org/docs/1.8.1/generated/torch.real.html#torch.real) | | +| 79 | [reciprocal](https://pytorch.org/docs/1.8.1/generated/torch.reciprocal.html#torch.reciprocal) | | +| 80 | [remainder](https://pytorch.org/docs/1.8.1/generated/torch.remainder.html#torch.remainder) | | +| 81 | [round](https://pytorch.org/docs/1.8.1/generated/torch.round.html#torch.round) | | +| 82 | [rsqrt](https://pytorch.org/docs/1.8.1/generated/torch.rsqrt.html#torch.rsqrt) | | +| 83 | [sigmoid](https://pytorch.org/docs/1.8.1/generated/torch.sigmoid.html#torch.sigmoid) | | +| 84 | [sign](https://pytorch.org/docs/1.8.1/generated/torch.sign.html#torch.sign) | | +| 85 | [sgn](https://pytorch.org/docs/1.8.1/generated/torch.sgn.html#torch.sgn) | | +| 86 | [signbit](https://pytorch.org/docs/1.8.1/generated/torch.signbit.html#torch.signbit) | | +| 87 | [sin](https://pytorch.org/docs/1.8.1/generated/torch.sin.html#torch.sin) | | +| 88 | [sinc](https://pytorch.org/docs/1.8.1/generated/torch.sinc.html#torch.sinc) | | +| 89 | [sinh](https://pytorch.org/docs/1.8.1/generated/torch.sinh.html#torch.sinh) | | +| 90 | [sqrt](https://pytorch.org/docs/1.8.1/generated/torch.sqrt.html#torch.sqrt) | | +| 91 | [square](https://pytorch.org/docs/1.8.1/generated/torch.square.html#torch.square) | | +| 92 | [sub](https://pytorch.org/docs/1.8.1/generated/torch.sub.html#torch.sub) | | +| 93 | [subtract](https://pytorch.org/docs/1.8.1/generated/torch.subtract.html#torch.subtract) | | +| 94 | [tan](https://pytorch.org/docs/1.8.1/generated/torch.tan.html#torch.tan) | | +| 95 | [tanh](https://pytorch.org/docs/1.8.1/generated/torch.tanh.html#torch.tanh) | | +| 96 | [true_divide](https://pytorch.org/docs/1.8.1/generated/torch.true_divide.html#torch.true_divide) | | +| 97 | [trunc](https://pytorch.org/docs/1.8.1/generated/torch.trunc.html#torch.trunc) | | +| 98 | [xlogy](https://pytorch.org/docs/1.8.1/generated/torch.xlogy.html#torch.xlogy) | | + +### Reduction Ops + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [argmax](https://pytorch.org/docs/1.8.1/generated/torch.argmax.html#torch.argmax) | | +| 2 | [argmin](https://pytorch.org/docs/1.8.1/generated/torch.argmin.html#torch.argmin) | | +| 3 | [amax](https://pytorch.org/docs/1.8.1/generated/torch.amax.html#torch.amax) | | +| 4 | [amin](https://pytorch.org/docs/1.8.1/generated/torch.amin.html#torch.amin) | | +| 5 | [all](https://pytorch.org/docs/1.8.1/generated/torch.all.html#torch.all) | | +| 6 | [any](https://pytorch.org/docs/1.8.1/generated/torch.any.html#torch.any) | | +| 7 | [max](https://pytorch.org/docs/1.8.1/generated/torch.max.html#torch.max) | | +| 8 | [min](https://pytorch.org/docs/1.8.1/generated/torch.min.html#torch.min) | | +| 9 | [dist](https://pytorch.org/docs/1.8.1/generated/torch.dist.html#torch.dist) | | +| 10 | [logsumexp](https://pytorch.org/docs/1.8.1/generated/torch.logsumexp.html#torch.logsumexp) | | +| 11 | [mean](https://pytorch.org/docs/1.8.1/generated/torch.mean.html#torch.mean) | | +| 12 | [median](https://pytorch.org/docs/1.8.1/generated/torch.median.html#torch.median) | | +| 13 | [nanmedian](https://pytorch.org/docs/1.8.1/generated/torch.nanmedian.html#torch.nanmedian) | | +| 14 | [mode](https://pytorch.org/docs/1.8.1/generated/torch.mode.html#torch.mode) | | +| 15 | [norm](https://pytorch.org/docs/1.8.1/generated/torch.norm.html#torch.norm) | | +| 16 | [nansum](https://pytorch.org/docs/1.8.1/generated/torch.nansum.html#torch.nansum) | | +| 17 | [prod](https://pytorch.org/docs/1.8.1/generated/torch.prod.html#torch.prod) | | +| 18 | [quantile](https://pytorch.org/docs/1.8.1/generated/torch.quantile.html#torch.quantile) | | +| 19 | [nanquantile](https://pytorch.org/docs/1.8.1/generated/torch.nanquantile.html#torch.nanquantile) | | +| 20 | [std](https://pytorch.org/docs/1.8.1/generated/torch.std.html#torch.std) | | +| 21 | [std_mean](https://pytorch.org/docs/1.8.1/generated/torch.std_mean.html#torch.std_mean) | | +| 22 | [sum](https://pytorch.org/docs/1.8.1/generated/torch.sum.html#torch.sum) | | +| 23 | [unique](https://pytorch.org/docs/1.8.1/generated/torch.unique.html#torch.unique) | | +| 24 | [unique_consecutive](https://pytorch.org/docs/1.8.1/generated/torch.unique_consecutive.html#torch.unique_consecutive) | | +| 25 | [var](https://pytorch.org/docs/1.8.1/generated/torch.var.html#torch.var) | | +| 26 | [var_mean](https://pytorch.org/docs/1.8.1/generated/torch.var_mean.html#torch.var_mean) | | +| 27 | [count_nonzero](https://pytorch.org/docs/1.8.1/generated/torch.count_nonzero.html#torch.count_nonzero) | | + +### Comparison Ops + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [allclose](https://pytorch.org/docs/1.8.1/generated/torch.allclose.html#torch.allclose) | | +| 2 | [argsort](https://pytorch.org/docs/1.8.1/generated/torch.argsort.html#torch.argsort) | | +| 3 | [eq](https://pytorch.org/docs/1.8.1/generated/torch.eq.html#torch.eq) | | +| 4 | [equal](https://pytorch.org/docs/1.8.1/generated/torch.equal.html#torch.equal) | | +| 5 | [ge](https://pytorch.org/docs/1.8.1/generated/torch.ge.html#torch.ge) | | +| 6 | [greater_equal](https://pytorch.org/docs/1.8.1/generated/torch.greater_equal.html#torch.greater_equal) | | +| 7 | [gt](https://pytorch.org/docs/1.8.1/generated/torch.gt.html#torch.gt) | | +| 8 | [greater](https://pytorch.org/docs/1.8.1/generated/torch.greater.html#torch.greater) | | +| 9 | [isclose](https://pytorch.org/docs/1.8.1/generated/torch.isclose.html#torch.isclose) | | +| 10 | [isfinite](https://pytorch.org/docs/1.8.1/generated/torch.isfinite.html#torch.isfinite) | | +| 11 | [isinf](https://pytorch.org/docs/1.8.1/generated/torch.isinf.html#torch.isinf) | | +| 12 | [isposinf](https://pytorch.org/docs/1.8.1/generated/torch.isposinf.html#torch.isposinf) | | +| 13 | [isneginf](https://pytorch.org/docs/1.8.1/generated/torch.isneginf.html#torch.isneginf) | | +| 14 | [isnan](https://pytorch.org/docs/1.8.1/generated/torch.isnan.html#torch.isnan) | | +| 15 | [isreal](https://pytorch.org/docs/1.8.1/generated/torch.isreal.html#torch.isreal) | | +| 16 | [kthvalue](https://pytorch.org/docs/1.8.1/generated/torch.kthvalue.html#torch.kthvalue) | | +| 17 | [le](https://pytorch.org/docs/1.8.1/generated/torch.le.html#torch.le) | | +| 18 | [less_equal](https://pytorch.org/docs/1.8.1/generated/torch.less_equal.html#torch.less_equal) | | +| 19 | [lt](https://pytorch.org/docs/1.8.1/generated/torch.lt.html#torch.lt) | | +| 20 | [less](https://pytorch.org/docs/1.8.1/generated/torch.less.html#torch.less) | | +| 21 | [maximum](https://pytorch.org/docs/1.8.1/generated/torch.maximum.html#torch.maximum) | | +| 22 | [minimum](https://pytorch.org/docs/1.8.1/generated/torch.minimum.html#torch.minimum) | | +| 23 | [fmax](https://pytorch.org/docs/1.8.1/generated/torch.fmax.html#torch.fmax) | | +| 24 | [fmin](https://pytorch.org/docs/1.8.1/generated/torch.fmin.html#torch.fmin) | | +| 25 | [ne](https://pytorch.org/docs/1.8.1/generated/torch.ne.html#torch.ne) | | +| 26 | [not_equal](https://pytorch.org/docs/1.8.1/generated/torch.not_equal.html#torch.not_equal) | | +| 27 | [sort](https://pytorch.org/docs/1.8.1/generated/torch.sort.html#torch.sort) | | +| 28 | [topk](https://pytorch.org/docs/1.8.1/generated/torch.topk.html#torch.topk) | | +| 29 | [msort](https://pytorch.org/docs/1.8.1/generated/torch.msort.html#torch.msort) | | + +### Spectral Ops + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [stft](https://pytorch.org/docs/1.8.1/generated/torch.stft.html#torch.stft) | | +| 2 | [istft](https://pytorch.org/docs/1.8.1/generated/torch.istft.html#torch.istft) | | +| 3 | [bartlett_window](https://pytorch.org/docs/1.8.1/generated/torch.bartlett_window.html#torch.bartlett_window) | | +| 4 | [blackman_window](https://pytorch.org/docs/1.8.1/generated/torch.blackman_window.html#torch.blackman_window) | | +| 5 | [hamming_window](https://pytorch.org/docs/1.8.1/generated/torch.hamming_window.html#torch.hamming_window) | | +| 6 | [hann_window](https://pytorch.org/docs/1.8.1/generated/torch.hann_window.html#torch.hann_window) | | +| 7 | [kaiser_window](https://pytorch.org/docs/1.8.1/generated/torch.kaiser_window.html#torch.kaiser_window) | | + +### Other Operations + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [atleast_1d](https://pytorch.org/docs/1.8.1/generated/torch.atleast_1d.html#torch.atleast_1d) | | +| 2 | [atleast_2d](https://pytorch.org/docs/1.8.1/generated/torch.atleast_2d.html#torch.atleast_2d) | | +| 3 | [atleast_3d](https://pytorch.org/docs/1.8.1/generated/torch.atleast_3d.html#torch.atleast_3d) | | +| 4 | [bincount](https://pytorch.org/docs/1.8.1/generated/torch.bincount.html#torch.bincount) | | +| 5 | [block_diag](https://pytorch.org/docs/1.8.1/generated/torch.block_diag.html#torch.block_diag) | | +| 6 | [broadcast_tensors](https://pytorch.org/docs/1.8.1/generated/torch.broadcast_tensors.html#torch.broadcast_tensors) | | +| 7 | [broadcast_to](https://pytorch.org/docs/1.8.1/generated/torch.broadcast_to.html#torch.broadcast_to) | | +| 8 | [broadcast_shapes](https://pytorch.org/docs/1.8.1/generated/torch.broadcast_shapes.html#torch.broadcast_shapes) | | +| 9 | [bucketize](https://pytorch.org/docs/1.8.1/generated/torch.bucketize.html#torch.bucketize) | | +| 10 | [cartesian_prod](https://pytorch.org/docs/1.8.1/generated/torch.cartesian_prod.html#torch.cartesian_prod) | | +| 11 | [cdist](https://pytorch.org/docs/1.8.1/generated/torch.cdist.html#torch.cdist) | | +| 12 | [clone](https://pytorch.org/docs/1.8.1/generated/torch.clone.html#torch.clone) | | +| 13 | [combinations](https://pytorch.org/docs/1.8.1/generated/torch.combinations.html#torch.combinations) | | +| 14 | [cross](https://pytorch.org/docs/1.8.1/generated/torch.cross.html#torch.cross) | | +| 15 | [cummax](https://pytorch.org/docs/1.8.1/generated/torch.cummax.html#torch.cummax) | | +| 16 | [cummin](https://pytorch.org/docs/1.8.1/generated/torch.cummin.html#torch.cummin) | | +| 17 | [cumprod](https://pytorch.org/docs/1.8.1/generated/torch.cumprod.html#torch.cumprod) | | +| 18 | [cumsum](https://pytorch.org/docs/1.8.1/generated/torch.cumsum.html#torch.cumsum) | | +| 19 | [diag](https://pytorch.org/docs/1.8.1/generated/torch.diag.html#torch.diag) | | +| 20 | [diag_embed](https://pytorch.org/docs/1.8.1/generated/torch.diag_embed.html#torch.diag_embed) | | +| 21 | [diagflat](https://pytorch.org/docs/1.8.1/generated/torch.diagflat.html#torch.diagflat) | | +| 22 | [diagonal](https://pytorch.org/docs/1.8.1/generated/torch.diagonal.html#torch.diagonal) | | +| 23 | [diff](https://pytorch.org/docs/1.8.1/generated/torch.diff.html#torch.diff) | | +| 24 | [einsum](https://pytorch.org/docs/1.8.1/generated/torch.einsum.html#torch.einsum) | | +| 25 | [flatten](https://pytorch.org/docs/1.8.1/generated/torch.flatten.html#torch.flatten) | | +| 26 | [flip](https://pytorch.org/docs/1.8.1/generated/torch.flip.html#torch.flip) | | +| 27 | [fliplr](https://pytorch.org/docs/1.8.1/generated/torch.fliplr.html#torch.fliplr) | | +| 28 | [flipud](https://pytorch.org/docs/1.8.1/generated/torch.flipud.html#torch.flipud) | | +| 29 | [kron](https://pytorch.org/docs/1.8.1/generated/torch.kron.html#torch.kron) | | +| 30 | [rot90](https://pytorch.org/docs/1.8.1/generated/torch.rot90.html#torch.rot90) | | +| 31 | [gcd](https://pytorch.org/docs/1.8.1/generated/torch.gcd.html#torch.gcd) | | +| 32 | [histc](https://pytorch.org/docs/1.8.1/generated/torch.histc.html#torch.histc) | | +| 33 | [meshgrid](https://pytorch.org/docs/1.8.1/generated/torch.meshgrid.html#torch.meshgrid) | | +| 34 | [lcm](https://pytorch.org/docs/1.8.1/generated/torch.lcm.html#torch.lcm) | | +| 35 | [logcumsumexp](https://pytorch.org/docs/1.8.1/generated/torch.logcumsumexp.html#torch.logcumsumexp) | | +| 36 | [ravel](https://pytorch.org/docs/1.8.1/generated/torch.ravel.html#torch.ravel) | | +| 37 | [renorm](https://pytorch.org/docs/1.8.1/generated/torch.renorm.html#torch.renorm) | | +| 38 | [repeat_interleave](https://pytorch.org/docs/1.8.1/generated/torch.repeat_interleave.html#torch.repeat_interleave) | | +| 39 | [roll](https://pytorch.org/docs/1.8.1/generated/torch.roll.html#torch.roll) | | +| 40 | [searchsorted](https://pytorch.org/docs/1.8.1/generated/torch.searchsorted.html#torch.searchsorted) | | +| 41 | [tensordot](https://pytorch.org/docs/1.8.1/generated/torch.tensordot.html#torch.tensordot) | | +| 42 | [trace](https://pytorch.org/docs/1.8.1/generated/torch.trace.html#torch.trace) | | +| 43 | [tril](https://pytorch.org/docs/1.8.1/generated/torch.tril.html#torch.tril) | | +| 44 | [tril_indices](https://pytorch.org/docs/1.8.1/generated/torch.tril_indices.html#torch.tril_indices) | | +| 45 | [triu](https://pytorch.org/docs/1.8.1/generated/torch.triu.html#torch.triu) | | +| 46 | [triu_indices](https://pytorch.org/docs/1.8.1/generated/torch.triu_indices.html#torch.triu_indices) | | +| 47 | [vander](https://pytorch.org/docs/1.8.1/generated/torch.vander.html#torch.vander) | | +| 48 | [view_as_real](https://pytorch.org/docs/1.8.1/generated/torch.view_as_real.html#torch.view_as_real) | | +| 49 | [view_as_complex](https://pytorch.org/docs/1.8.1/generated/torch.view_as_complex.html#torch.view_as_complex) | | + +### BLAS and LAPACK Operations + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [addbmm](https://pytorch.org/docs/1.8.1/generated/torch.addbmm.html#torch.addbmm) | | +| 2 | [addmm](https://pytorch.org/docs/1.8.1/generated/torch.addmm.html#torch.addmm) | | +| 3 | [addmv](https://pytorch.org/docs/1.8.1/generated/torch.addmv.html#torch.addmv) | | +| 4 | [addr](https://pytorch.org/docs/1.8.1/generated/torch.addr.html#torch.addr) | | +| 5 | [baddbmm](https://pytorch.org/docs/1.8.1/generated/torch.baddbmm.html#torch.baddbmm) | | +| 6 | [bmm](https://pytorch.org/docs/1.8.1/generated/torch.bmm.html#torch.bmm) | | +| 7 | [chain_matmul](https://pytorch.org/docs/1.8.1/generated/torch.chain_matmul.html#torch.chain_matmul) | | +| 8 | [cholesky](https://pytorch.org/docs/1.8.1/generated/torch.cholesky.html#torch.cholesky) | | +| 9 | [cholesky_inverse](https://pytorch.org/docs/1.8.1/generated/torch.cholesky_inverse.html#torch.cholesky_inverse) | | +| 10 | [cholesky_solve](https://pytorch.org/docs/1.8.1/generated/torch.cholesky_solve.html#torch.cholesky_solve) | | +| 11 | [dot](https://pytorch.org/docs/1.8.1/generated/torch.dot.html#torch.dot) | | +| 12 | [eig](https://pytorch.org/docs/1.8.1/generated/torch.eig.html#torch.eig) | | +| 13 | [geqrf](https://pytorch.org/docs/1.8.1/generated/torch.geqrf.html#torch.geqrf) | | +| 14 | [ger](https://pytorch.org/docs/1.8.1/generated/torch.ger.html#torch.ger) | | +| 15 | [inner](https://pytorch.org/docs/1.8.1/generated/torch.inner.html#torch.inner) | | +| 16 | [inverse](https://pytorch.org/docs/1.8.1/generated/torch.inverse.html#torch.inverse) | | +| 17 | [det](https://pytorch.org/docs/1.8.1/generated/torch.det.html#torch.det) | | +| 18 | [logdet](https://pytorch.org/docs/1.8.1/generated/torch.logdet.html#torch.logdet) | | +| 19 | [slogdet](https://pytorch.org/docs/1.8.1/generated/torch.slogdet.html#torch.slogdet) | | +| 20 | [lstsq](https://pytorch.org/docs/1.8.1/generated/torch.lstsq.html#torch.lstsq) | | +| 21 | [lu](https://pytorch.org/docs/1.8.1/generated/torch.lu.html#torch.lu) | | +| 22 | [lu_solve](https://pytorch.org/docs/1.8.1/generated/torch.lu_solve.html#torch.lu_solve) | | +| 23 | [lu_unpack](https://pytorch.org/docs/1.8.1/generated/torch.lu_unpack.html#torch.lu_unpack) | | +| 24 | [matmul](https://pytorch.org/docs/1.8.1/generated/torch.matmul.html#torch.matmul) | | +| 25 | [matrix_power](https://pytorch.org/docs/1.8.1/generated/torch.matrix_power.html#torch.matrix_power) | | +| 26 | [matrix_rank](https://pytorch.org/docs/1.8.1/generated/torch.matrix_rank.html#torch.matrix_rank) | | +| 27 | [matrix_exp](https://pytorch.org/docs/1.8.1/generated/torch.matrix_exp.html#torch.matrix_exp) | | +| 28 | [mm](https://pytorch.org/docs/1.8.1/generated/torch.mm.html#torch.mm) | | +| 29 | [mv](https://pytorch.org/docs/1.8.1/generated/torch.mv.html#torch.mv) | | +| 30 | [orgqr](https://pytorch.org/docs/1.8.1/generated/torch.orgqr.html#torch.orgqr) | | +| 31 | [ormqr](https://pytorch.org/docs/1.8.1/generated/torch.ormqr.html#torch.ormqr) | | +| 32 | [outer](https://pytorch.org/docs/1.8.1/generated/torch.outer.html#torch.outer) | | +| 33 | [pinverse](https://pytorch.org/docs/1.8.1/generated/torch.pinverse.html#torch.pinverse) | | +| 34 | [qr](https://pytorch.org/docs/1.8.1/generated/torch.qr.html#torch.qr) | | +| 35 | [solve](https://pytorch.org/docs/1.8.1/generated/torch.solve.html#torch.solve) | | +| 36 | [svd](https://pytorch.org/docs/1.8.1/generated/torch.svd.html#torch.svd) | | +| 37 | [svd_lowrank](https://pytorch.org/docs/1.8.1/generated/torch.svd_lowrank.html#torch.svd_lowrank) | | +| 38 | [pca_lowrank](https://pytorch.org/docs/1.8.1/generated/torch.pca_lowrank.html#torch.pca_lowrank) | | +| 39 | [symeig](https://pytorch.org/docs/1.8.1/generated/torch.symeig.html#torch.symeig) | | +| 40 | [lobpcg](https://pytorch.org/docs/1.8.1/generated/torch.lobpcg.html#torch.lobpcg) | | +| 41 | [trapz](https://pytorch.org/docs/1.8.1/generated/torch.trapz.html#torch.trapz) | | +| 42 | [triangular_solve](https://pytorch.org/docs/1.8.1/generated/torch.triangular_solve.html#torch.triangular_solve) | | +| 43 | [vdot](https://pytorch.org/docs/1.8.1/generated/torch.vdot.html#torch.vdot) | | + +## Utilities + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [compiled_with_cxx11_abi](https://pytorch.org/docs/1.8.1/generated/torch.compiled_with_cxx11_abi.html#torch.compiled_with_cxx11_abi) | | +| 2 | [result_type](https://pytorch.org/docs/1.8.1/generated/torch.result_type.html#torch.result_type) | | +| 3 | [can_cast](https://pytorch.org/docs/1.8.1/generated/torch.can_cast.html#torch.can_cast) | | +| 4 | [promote_types](https://pytorch.org/docs/1.8.1/generated/torch.promote_types.html#torch.promote_types) | | +| 5 | [use_deterministic_algorithms](https://pytorch.org/docs/1.8.1/generated/torch.use_deterministic_algorithms.html#torch.use_deterministic_algorithms) | | +| 6 | [are_deterministic_algorithms_enabled](https://pytorch.org/docs/1.8.1/generated/torch.are_deterministic_algorithms_enabled.html#torch.are_deterministic_algorithms_enabled) | | +| 7 | [_assert](https://pytorch.org/docs/1.8.1/generated/torch._assert.html#torch._assert) | | + +## Other + +## torch.Tensor + +# Layers (torch.nn) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [Parameter](https://pytorch.org/docs/1.8.1/generated/torch.nn.parameter.Parameter.html#torch.nn.parameter.Parameter) | | +| 2 | [UninitializedParameter](https://pytorch.org/docs/1.8.1/generated/torch.nn.parameter.UninitializedParameter.html#torch.nn.parameter.UninitializedParameter) | | + +## [Containers](https://pytorch.org/docs/1.8.1/nn.html#id1) + + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [Module](https://pytorch.org/docs/1.8.1/generated/torch.nn.Module.html#torch.nn.Module) | | +| 2 | [Sequential](https://pytorch.org/docs/1.8.1/generated/torch.nn.Sequential.html#torch.nn.Sequential) | | +| 3 | [ModuleList](https://pytorch.org/docs/1.8.1/generated/torch.nn.ModuleList.html#torch.nn.ModuleList) | | +| 4 | [ModuleDict](https://pytorch.org/docs/1.8.1/generated/torch.nn.ModuleDict.html#torch.nn.ModuleDict) | | +| 5 | [ParameterList](https://pytorch.org/docs/1.8.1/generated/torch.nn.ParameterList.html#torch.nn.ParameterList) | | +| 6 | [ParameterDict](https://pytorch.org/docs/1.8.1/generated/torch.nn.ParameterDict.html#torch.nn.ParameterDict) | | + +### Global Hooks For Module + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [register_module_forward_pre_hook](https://pytorch.org/docs/1.8.1/generated/torch.nn.modules.module.register_module_forward_pre_hook.html#torch.nn.modules.module.register_module_forward_pre_hook) | | +| 2 | [register_module_forward_hook](https://pytorch.org/docs/1.8.1/generated/torch.nn.modules.module.register_module_forward_hook.html#torch.nn.modules.module.register_module_forward_hook) | | +| 3 | [register_module_backward_hook](https://pytorch.org/docs/1.8.1/generated/torch.nn.modules.module.register_module_backward_hook.html#torch.nn.modules.module.register_module_backward_hook) | | + +## [Convolution Layers](https://pytorch.org/docs/1.8.1/nn.html#id1) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [nn.Conv1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.Conv1d.html#torch.nn.Conv1d) | | +| 2 | [nn.Conv2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.Conv2d.html#torch.nn.Conv2d) | | +| 3 | [nn.Conv3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.Conv3d.html#torch.nn.Conv3d) | | +| 4 | [nn.ConvTranspose1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ConvTranspose1d.html#torch.nn.ConvTranspose1d) | | +| 5 | [nn.ConvTranspose2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ConvTranspose2d.html#torch.nn.ConvTranspose2d) | | +| 6 | [nn.ConvTranspose3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ConvTranspose3d.html#torch.nn.ConvTranspose3d) | | +| 7 | [nn.LazyConv1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.LazyConv1d.html#torch.nn.LazyConv1d) | | +| 8 | [nn.LazyConv2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.LazyConv2d.html#torch.nn.LazyConv2d) | | +| 9 | [nn.LazyConv3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.LazyConv3d.html#torch.nn.LazyConv3d) | | +| 10 | [nn.LazyConvTranspose1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.LazyConvTranspose1d.html#torch.nn.LazyConvTranspose1d) | | +| 11 | [nn.LazyConvTranspose2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.LazyConvTranspose2d.html#torch.nn.LazyConvTranspose2d) | | +| 12 | [nn.LazyConvTranspose3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.LazyConvTranspose3d.html#torch.nn.LazyConvTranspose3d) | | +| 13 | [nn.Unfold](https://pytorch.org/docs/1.8.1/generated/torch.nn.Unfold.html#torch.nn.Unfold) | | +| 14 | [nn.Fold](https://pytorch.org/docs/1.8.1/generated/torch.nn.Fold.html#torch.nn.Fold) | | + +## [Pooling layers](https://pytorch.org/docs/1.8.1/nn.html#id1) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [nn.MaxPool1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.MaxPool1d.html#torch.nn.MaxPool1d) | | +| 2 | [nn.MaxPool2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.MaxPool2d.html#torch.nn.MaxPool2d) | | +| 3 | [nn.MaxPool3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.MaxPool3d.html#torch.nn.MaxPool3d) | | +| 4 | [nn.MaxUnpool1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.MaxUnpool1d.html#torch.nn.MaxUnpool1d) | | +| 5 | [nn.MaxUnpool2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.MaxUnpool2d.html#torch.nn.MaxUnpool2d) | | +| 6 | [nn.MaxUnpool3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.MaxUnpool3d.html#torch.nn.MaxUnpool3d) | | +| 7 | [nn.AvgPool1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AvgPool1d.html#torch.nn.AvgPool1d) | | +| 8 | [nn.AvgPool2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AvgPool2d.html#torch.nn.AvgPool2d) | | +| 9 | [nn.AvgPool3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AvgPool3d.html#torch.nn.AvgPool3d) | | +| 10 | [nn.FractionalMaxPool2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.FractionalMaxPool2d.html#torch.nn.FractionalMaxPool2d) | | +| 11 | [nn.LPPool1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.LPPool1d.html#torch.nn.LPPool1d) | | +| 12 | [nn.LPPool2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.LPPool2d.html#torch.nn.LPPool2d) | | +| 13 | [nn.AdaptiveMaxPool1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AdaptiveMaxPool1d.html#torch.nn.AdaptiveMaxPool1d) | | +| 14 | [nn.AdaptiveMaxPool2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AdaptiveMaxPool2d.html#torch.nn.AdaptiveMaxPool2d) | | +| 15 | [nn.AdaptiveMaxPool3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AdaptiveMaxPool3d.html#torch.nn.AdaptiveMaxPool3d) | | +| 16 | [nn.AdaptiveAvgPool1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AdaptiveAvgPool1d.html#torch.nn.AdaptiveAvgPool1d) | | +| 17 | [nn.AdaptiveAvgPool2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AdaptiveAvgPool2d.html#torch.nn.AdaptiveAvgPool2d) | | +| 18 | [nn.AdaptiveAvgPool3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AdaptiveAvgPool3d.html#torch.nn.AdaptiveAvgPool3d) | | + + + +## [Padding Layers](https://pytorch.org/docs/1.8.1/nn.html#id1) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [nn.ReflectionPad1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ReflectionPad1d.html#torch.nn.ReflectionPad1d) | | +| 2 | [nn.ReflectionPad2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ReflectionPad2d.html#torch.nn.ReflectionPad2d) | | +| 3 | [nn.ReplicationPad1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ReplicationPad1d.html#torch.nn.ReplicationPad1d) | | +| 4 | [nn.ReplicationPad2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ReplicationPad2d.html#torch.nn.ReplicationPad2d) | | +| 5 | [nn.ReplicationPad3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ReplicationPad3d.html#torch.nn.ReplicationPad3d) | | +| 6 | [nn.ZeroPad2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ZeroPad2d.html#torch.nn.ZeroPad2d) | | +| 7 | [nn.ConstantPad1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ConstantPad1d.html#torch.nn.ConstantPad1d) | | +| 8 | [nn.ConstantPad2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ConstantPad2d.html#torch.nn.ConstantPad2d) | | +| 9 | [nn.ConstantPad3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ConstantPad3d.html#torch.nn.ConstantPad3d) | | + + + +## [Non-linear Activations (weighted sum, nonlinearity)](https://pytorch.org/docs/1.8.1/nn.html#id1) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [nn.ELU](https://pytorch.org/docs/1.8.1/generated/torch.nn.ELU.html#torch.nn.ELU) | | +| 2 | [nn.Hardshrink](https://pytorch.org/docs/1.8.1/generated/torch.nn.Hardshrink.html#torch.nn.Hardshrink) | | +| 3 | [nn.Hardsigmoid](https://pytorch.org/docs/1.8.1/generated/torch.nn.Hardsigmoid.html#torch.nn.Hardsigmoid) | | +| 4 | [nn.Hardtanh](https://pytorch.org/docs/1.8.1/generated/torch.nn.Hardtanh.html#torch.nn.Hardtanh) | | +| 5 | [nn.Hardswish](https://pytorch.org/docs/1.8.1/generated/torch.nn.Hardswish.html#torch.nn.Hardswish) | | +| 6 | [nn.LeakyReLU](https://pytorch.org/docs/1.8.1/generated/torch.nn.LeakyReLU.html#torch.nn.LeakyReLU) | | +| 7 | [nn.LogSigmoid](https://pytorch.org/docs/1.8.1/generated/torch.nn.LogSigmoid.html#torch.nn.LogSigmoid) | | +| 8 | [nn.MultiheadAttention](https://pytorch.org/docs/1.8.1/generated/torch.nn.MultiheadAttention.html#torch.nn.MultiheadAttention) | | +| 9 | [nn.PReLU](https://pytorch.org/docs/1.8.1/generated/torch.nn.PReLU.html#torch.nn.PReLU) | | +| 10 | [nn.ReLU](https://pytorch.org/docs/1.8.1/generated/torch.nn.ReLU.html#torch.nn.ReLU) | | +| 11 | [nn.ReLU6](https://pytorch.org/docs/1.8.1/generated/torch.nn.ReLU6.html#torch.nn.ReLU6) | | +| 12 | [nn.RReLU](https://pytorch.org/docs/1.8.1/generated/torch.nn.RReLU.html#torch.nn.RReLU) | | +| 13 | [nn.SELU](https://pytorch.org/docs/1.8.1/generated/torch.nn.SELU.html#torch.nn.SELU) | | +| 14 | [nn.CELU](https://pytorch.org/docs/1.8.1/generated/torch.nn.CELU.html#torch.nn.CELU) | | +| 15 | [nn.GELU](https://pytorch.org/docs/1.8.1/generated/torch.nn.GELU.html#torch.nn.GELU) | | +| 16 | [nn.Sigmoid](https://pytorch.org/docs/1.8.1/generated/torch.nn.Sigmoid.html#torch.nn.Sigmoid) | | +| 17 | [nn.SiLU](https://pytorch.org/docs/1.8.1/generated/torch.nn.SiLU.html#torch.nn.SiLU) | | +| 18 | [nn.Softplus](https://pytorch.org/docs/1.8.1/generated/torch.nn.Softplus.html#torch.nn.Softplus) | | +| 19 | [nn.Softshrink](https://pytorch.org/docs/1.8.1/generated/torch.nn.Softshrink.html#torch.nn.Softshrink) | | +| 20 | [nn.Softsign](https://pytorch.org/docs/1.8.1/generated/torch.nn.Softsign.html#torch.nn.Softsign) | | +| 21 | [nn.Tanh](https://pytorch.org/docs/1.8.1/generated/torch.nn.Tanh.html#torch.nn.Tanh) | | +| 22 | [nn.Tanhshrink](https://pytorch.org/docs/1.8.1/generated/torch.nn.Tanhshrink.html#torch.nn.Tanhshrink) | | +| 23 | [nn.Threshold](https://pytorch.org/docs/1.8.1/generated/torch.nn.Threshold.html#torch.nn.Threshold) | | + +## [Non-linear Activations (other)](https://pytorch.org/docs/1.8.1/nn.html#id1) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [nn.Softmin](https://pytorch.org/docs/1.8.1/generated/torch.nn.Softmin.html#torch.nn.Softmin) | | +| 2 | [nn.Softmax](https://pytorch.org/docs/1.8.1/generated/torch.nn.Softmax.html#torch.nn.Softmax) | | +| 3 | [nn.Softmax2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.Softmax2d.html#torch.nn.Softmax2d) | | +| 4 | [nn.LogSoftmax](https://pytorch.org/docs/1.8.1/generated/torch.nn.LogSoftmax.html#torch.nn.LogSoftmax) | | +| 5 | [nn.AdaptiveLogSoftmaxWithLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.AdaptiveLogSoftmaxWithLoss.html#torch.nn.AdaptiveLogSoftmaxWithLoss) | | + +## [Normalization Layers](https://pytorch.org/docs/1.8.1/nn.html#id1) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [nn.BatchNorm1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.BatchNorm1d.html#torch.nn.BatchNorm1d) | | +| 2 | [nn.BatchNorm2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.BatchNorm2d.html#torch.nn.BatchNorm2d) | | +| 3 | [nn.BatchNorm3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.BatchNorm3d.html#torch.nn.BatchNorm3d) | | +| 4 | [nn.GroupNorm](https://pytorch.org/docs/1.8.1/generated/torch.nn.GroupNorm.html#torch.nn.GroupNorm) | | +| 5 | [nn.SyncBatchNorm](https://pytorch.org/docs/1.8.1/generated/torch.nn.SyncBatchNorm.html#torch.nn.SyncBatchNorm) | | +| 6 | [nn.InstanceNorm1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.InstanceNorm1d.html#torch.nn.InstanceNorm1d) | | +| 7 | [nn.InstanceNorm2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.InstanceNorm2d.html#torch.nn.InstanceNorm2d) | | +| 8 | [nn.InstanceNorm3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.InstanceNorm3d.html#torch.nn.InstanceNorm3d) | | +| 9 | [nn.LayerNorm](https://pytorch.org/docs/1.8.1/generated/torch.nn.LayerNorm.html#torch.nn.LayerNorm) | | +| 10 | [nn.LocalResponseNorm](https://pytorch.org/docs/1.8.1/generated/torch.nn.LocalResponseNorm.html#torch.nn.LocalResponseNorm) | | + + + +## [Recurrent Layers](https://pytorch.org/docs/1.8.1/nn.html#id1) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [nn.RNNBase](https://pytorch.org/docs/1.8.1/generated/torch.nn.RNNBase.html#torch.nn.RNNBase) | | +| 2 | [nn.RNN](https://pytorch.org/docs/1.8.1/generated/torch.nn.RNN.html#torch.nn.RNN) | | +| 3 | [nn.LSTM](https://pytorch.org/docs/1.8.1/generated/torch.nn.LSTM.html#torch.nn.LSTM) | | +| 4 | [nn.GRU](https://pytorch.org/docs/1.8.1/generated/torch.nn.GRU.html#torch.nn.GRU) | | +| 5 | [nn.RNNCell](https://pytorch.org/docs/1.8.1/generated/torch.nn.RNNCell.html#torch.nn.RNNCell) | | +| 6 | [nn.LSTMCell](https://pytorch.org/docs/1.8.1/generated/torch.nn.LSTMCell.html#torch.nn.LSTMCell) | | +| 7 | [nn.GRUCell](https://pytorch.org/docs/1.8.1/generated/torch.nn.GRUCell.html#torch.nn.GRUCell) | | + + + +## [Transformer Layers](https://pytorch.org/docs/1.8.1/nn.html#id1) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [nn.Transformer](https://pytorch.org/docs/1.8.1/generated/torch.nn.Transformer.html#torch.nn.Transformer) | | +| 2 | [nn.TransformerEncoder](https://pytorch.org/docs/1.8.1/generated/torch.nn.TransformerEncoder.html#torch.nn.TransformerEncoder) | | +| 3 | [nn.TransformerDecoder](https://pytorch.org/docs/1.8.1/generated/torch.nn.TransformerDecoder.html#torch.nn.TransformerDecoder) | | +| 4 | [nn.TransformerEncoderLayer](https://pytorch.org/docs/1.8.1/generated/torch.nn.TransformerEncoderLayer.html#torch.nn.TransformerEncoderLayer) | | +| 5 | [nn.TransformerDecoderLayer](https://pytorch.org/docs/1.8.1/generated/torch.nn.TransformerDecoderLayer.html#torch.nn.TransformerDecoderLayer) | | + + + +## [Linear Layers](https://pytorch.org/docs/1.8.1/nn.html#id1) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [nn.Identity](https://pytorch.org/docs/1.8.1/generated/torch.nn.Identity.html#torch.nn.Identity) | | +| 2 | [nn.Linear](https://pytorch.org/docs/1.8.1/generated/torch.nn.Linear.html#torch.nn.Linear) | | +| 3 | [nn.Bilinear](https://pytorch.org/docs/1.8.1/generated/torch.nn.Bilinear.html#torch.nn.Bilinear) | | +| 4 | [nn.LazyLinear](https://pytorch.org/docs/1.8.1/generated/torch.nn.LazyLinear.html#torch.nn.LazyLinear) | | + + + +## [Dropout Layers](https://pytorch.org/docs/1.8.1/nn.html#id1) + + + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [nn.Dropout](https://pytorch.org/docs/1.8.1/generated/torch.nn.Dropout.html#torch.nn.Dropout) | | +| 2 | [nn.Dropout2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.Dropout2d.html#torch.nn.Dropout2d) | | +| 3 | [nn.Dropout3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.Dropout3d.html#torch.nn.Dropout3d) | | +| 4 | [nn.AlphaDropout](https://pytorch.org/docs/1.8.1/generated/torch.nn.AlphaDropout.html#torch.nn.AlphaDropout) | | + +## [Sparse Layers](https://pytorch.org/docs/1.8.1/nn.html#id1) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [nn.Embedding](https://pytorch.org/docs/1.8.1/generated/torch.nn.Embedding.html#torch.nn.Embedding) | | +| 2 | [nn.EmbeddingBag](https://pytorch.org/docs/1.8.1/generated/torch.nn.EmbeddingBag.html#torch.nn.EmbeddingBag) | | + + + +## [Distance Functions](https://pytorch.org/docs/1.8.1/nn.html#id1) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [nn.CosineSimilarity](https://pytorch.org/docs/1.8.1/generated/torch.nn.CosineSimilarity.html#torch.nn.CosineSimilarity) | | +| 2 | [nn.PairwiseDistance](https://pytorch.org/docs/1.8.1/generated/torch.nn.PairwiseDistance.html#torch.nn.PairwiseDistance) | | + + + +## [Loss Functions](https://pytorch.org/docs/1.8.1/nn.html#id1) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [nn.L1Loss](https://pytorch.org/docs/1.8.1/generated/torch.nn.L1Loss.html#torch.nn.L1Loss) | | +| 2 | [nn.MSELoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.MSELoss.html#torch.nn.MSELoss) | | +| 3 | [nn.CrossEntropyLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss) | | +| 4 | [nn.CTCLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.CTCLoss.html#torch.nn.CTCLoss) | | +| 5 | [nn.NLLLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss) | | +| 6 | [nn.PoissonNLLLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.PoissonNLLLoss.html#torch.nn.PoissonNLLLoss) | | +| 7 | [nn.GaussianNLLLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.GaussianNLLLoss.html#torch.nn.GaussianNLLLoss) | | +| 8 | [nn.KLDivLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.KLDivLoss.html#torch.nn.KLDivLoss) | | +| 9 | [nn.BCELoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.BCELoss.html#torch.nn.BCELoss) | | +| 10 | [nn.BCEWithLogitsLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.BCEWithLogitsLoss.html#torch.nn.BCEWithLogitsLoss) | | +| 11 | [nn.MarginRankingLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.MarginRankingLoss.html#torch.nn.MarginRankingLoss) | | +| 12 | [nn.HingeEmbeddingLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.HingeEmbeddingLoss.html#torch.nn.HingeEmbeddingLoss) | | +| 13 | [nn.MultiLabelMarginLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.MultiLabelMarginLoss.html#torch.nn.MultiLabelMarginLoss) | | +| 14 | [nn.SmoothL1Loss](https://pytorch.org/docs/1.8.1/generated/torch.nn.SmoothL1Loss.html#torch.nn.SmoothL1Loss) | | +| 15 | [nn.SoftMarginLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.SoftMarginLoss.html#torch.nn.SoftMarginLoss) | | +| 16 | [nn.MultiLabelSoftMarginLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.MultiLabelSoftMarginLoss.html#torch.nn.MultiLabelSoftMarginLoss) | | +| 17 | [nn.CosineEmbeddingLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.CosineEmbeddingLoss.html#torch.nn.CosineEmbeddingLoss) | | +| 18 | [nn.MultiMarginLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.MultiMarginLoss.html#torch.nn.MultiMarginLoss) | | +| 19 | [nn.TripletMarginLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.TripletMarginLoss.html#torch.nn.TripletMarginLoss) | | +| 20 | [nn.TripletMarginWithDistanceLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.TripletMarginWithDistanceLoss.html#torch.nn.TripletMarginWithDistanceLoss) | | + +## [Vision Layers](https://pytorch.org/docs/1.8.1/nn.html#id1) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [nn.PixelShuffle](https://pytorch.org/docs/1.8.1/generated/torch.nn.PixelShuffle.html#torch.nn.PixelShuffle) | | +| 2 | [nn.PixelUnshuffle](https://pytorch.org/docs/1.8.1/generated/torch.nn.PixelUnshuffle.html#torch.nn.PixelUnshuffle) | | +| 3 | [nn.Upsample](https://pytorch.org/docs/1.8.1/generated/torch.nn.Upsample.html#torch.nn.Upsample) | | +| 4 | [nn.UpsamplingNearest2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.UpsamplingNearest2d.html#torch.nn.UpsamplingNearest2d) | | +| 5 | [nn.UpsamplingBilinear2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.UpsamplingBilinear2d.html#torch.nn.UpsamplingBilinear2d) | | + + + +## [Shuffle Layers](https://pytorch.org/docs/1.8.1/nn.html#id1) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [nn.ChannelShuffle](https://pytorch.org/docs/1.8.1/generated/torch.nn.ChannelShuffle.html#torch.nn.ChannelShuffle) | | + + + +## [DataParallel Layers (multi-GPU, distributed)](https://pytorch.org/docs/1.8.1/nn.html#id1) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [nn.DataParallel](https://pytorch.org/docs/1.8.1/generated/torch.nn.DataParallel.html#torch.nn.DataParallel) | | +| 2 | [nn.parallel.DistributedDataParallel](https://pytorch.org/docs/1.8.1/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel) | | + +## [Utilities](https://pytorch.org/docs/1.8.1/nn.html#id1) + + + +From the `torch.nn.utils` module + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [clip_grad_norm_](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.clip_grad_norm_.html#torch.nn.utils.clip_grad_norm_) | | +| 2 | [clip_grad_value_](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.clip_grad_value_.html#torch.nn.utils.clip_grad_value_) | | +| 3 | [parameters_to_vector](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.parameters_to_vector.html#torch.nn.utils.parameters_to_vector) | | +| 4 | [vector_to_parameters](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.vector_to_parameters.html#torch.nn.utils.vector_to_parameters) | | +| 5 | [prune.BasePruningMethod](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.BasePruningMethod.html#torch.nn.utils.prune.BasePruningMethod) | | +| 6 | [prune.PruningContainer](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.PruningContainer.html#torch.nn.utils.prune.PruningContainer) | | +| 7 | [prune.Identity](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.Identity.html#torch.nn.utils.prune.Identity) | | +| 8 | [prune.RandomUnstructured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.RandomUnstructured.html#torch.nn.utils.prune.RandomUnstructured) | | +| 9 | [prune.L1Unstructured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.L1Unstructured.html#torch.nn.utils.prune.L1Unstructured) | | +| 10 | [prune.RandomStructured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.RandomStructured.html#torch.nn.utils.prune.RandomStructured) | | +| 11 | [prune.LnStructured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.LnStructured.html#torch.nn.utils.prune.LnStructured) | | +| 12 | [prune.CustomFromMask](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.CustomFromMask.html#torch.nn.utils.prune.CustomFromMask) | | +| 13 | [prune.identity](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.identity.html#torch.nn.utils.prune.identity) | | +| 14 | [prune.random_unstructured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.random_unstructured.html#torch.nn.utils.prune.random_unstructured) | | +| 15 | [prune.l1_unstructured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.l1_unstructured.html#torch.nn.utils.prune.l1_unstructured) | | +| 16 | [prune.random_structured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.random_structured.html#torch.nn.utils.prune.random_structured) | | +| 17 | [prune.ln_structured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.ln_structured.html#torch.nn.utils.prune.ln_structured) | | +| 18 | [prune.global_unstructured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.global_unstructured.html#torch.nn.utils.prune.global_unstructured) | | +| 19 | [prune.custom_from_mask](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.custom_from_mask.html#torch.nn.utils.prune.custom_from_mask) | | +| 20 | [prune.remove](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.remove.html#torch.nn.utils.prune.remove) | | +| 21 | [prune.is_pruned](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.is_pruned.html#torch.nn.utils.prune.is_pruned) | | +| 22 | [weight_norm](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.weight_norm.html#torch.nn.utils.weight_norm) | | +| 23 | [remove_weight_norm](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.remove_weight_norm.html#torch.nn.utils.remove_weight_norm) | | +| 24 | [spectral_norm](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.spectral_norm.html#torch.nn.utils.spectral_norm) | | +| 25 | [remove_spectral_norm](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.remove_spectral_norm.html#torch.nn.utils.remove_spectral_norm) | | + + + +### Utility functions in other modules + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [nn.utils.rnn.PackedSequence](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.rnn.PackedSequence.html#torch.nn.utils.rnn.PackedSequence) | | +| 2 | [nn.utils.rnn.pack_padded_sequence](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.rnn.pack_padded_sequence.html#torch.nn.utils.rnn.pack_padded_sequence) | | +| 3 | [nn.utils.rnn.pad_packed_sequence](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.rnn.pad_packed_sequence.html#torch.nn.utils.rnn.pad_packed_sequence) | | +| 4 | [nn.utils.rnn.pad_sequence](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.rnn.pad_sequence.html#torch.nn.utils.rnn.pad_sequence) | | +| 5 | [nn.utils.rnn.pack_sequence](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.rnn.pack_sequence.html#torch.nn.utils.rnn.pack_sequence) | | +| 6 | [nn.Flatten](https://pytorch.org/docs/1.8.1/generated/torch.nn.Flatten.html#torch.nn.Flatten) | | +| 7 | [nn.Unflatten](https://pytorch.org/docs/1.8.1/generated/torch.nn.Unflatten.html#torch.nn.Unflatten) | | + +### Lazy Modules Initialization + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [nn.modules.lazy.LazyModuleMixin](https://pytorch.org/docs/1.8.1/generated/torch.nn.modules.lazy.LazyModuleMixin.html#torch.nn.modules.lazy.LazyModuleMixin) | | + + + + + + + + + + + +## Functions(torch.nn.functional) + +### [Convolution functions](https://pytorch.org/docs/1.8.1/nn.functional.html#convolution-functions) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [conv1d](https://pytorch.org/docs/1.8.1/nn.functional.html#conv1d) | | +| 2 | [conv2d](https://pytorch.org/docs/1.8.1/nn.functional.html#conv2d) | | +| 3 | [conv3d](https://pytorch.org/docs/1.8.1/nn.functional.html#conv3d) | | +| 4 | [conv_transpose1d](https://pytorch.org/docs/1.8.1/nn.functional.html#conv-transpose1d) | | +| 5 | [conv_transpose2d](https://pytorch.org/docs/1.8.1/nn.functional.html#conv-transpose2d) | | +| 6 | [conv_transpose3d](https://pytorch.org/docs/1.8.1/nn.functional.html#conv-transpose3d) | | +| 7 | [unfold](https://pytorch.org/docs/1.8.1/nn.functional.html#unfold) | | +| 8 | [fold](https://pytorch.org/docs/1.8.1/nn.functional.html#fold) | | + +### [Pooling functions](https://pytorch.org/docs/1.8.1/nn.functional.html#pooling-functions) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [avg_pool1d](https://pytorch.org/docs/1.8.1/nn.functional.html#avg-pool1d) | | +| 2 | [avg_pool2d](https://pytorch.org/docs/1.8.1/nn.functional.html#avg-pool2d) | | +| 3 | [avg_pool3d](https://pytorch.org/docs/1.8.1/nn.functional.html#avg-pool3d) | | +| 4 | [max_pool1d](https://pytorch.org/docs/1.8.1/nn.functional.html#max-pool1d) | | +| 5 | [max_pool2d](https://pytorch.org/docs/1.8.1/nn.functional.html#max-pool2d) | | +| 6 | [max_pool3d](https://pytorch.org/docs/1.8.1/nn.functional.html#max-pool3d) | | +| 7 | [max_unpool1d](https://pytorch.org/docs/1.8.1/nn.functional.html#max-unpool1d) | | +| 8 | [max_unpool2d](https://pytorch.org/docs/1.8.1/nn.functional.html#max-unpool2d) | | +| 9 | [max_unpool3d](https://pytorch.org/docs/1.8.1/nn.functional.html#max-unpool3d) | | +| 10 | [lp_pool1d](https://pytorch.org/docs/1.8.1/nn.functional.html#lp-pool1d) | | +| 11 | [lp_pool2d](https://pytorch.org/docs/1.8.1/nn.functional.html#lp-pool2d) | | +| 12 | [adaptive_max_pool1d](https://pytorch.org/docs/1.8.1/nn.functional.html#adaptive-max-pool1d) | | +| 13 | [adaptive_max_pool2d](https://pytorch.org/docs/1.8.1/nn.functional.html#adaptive-max-pool2d) | | +| 14 | [adaptive_max_pool3d](https://pytorch.org/docs/1.8.1/nn.functional.html#adaptive-max-pool3d) | | +| 15 | [adaptive_avg_pool1d](https://pytorch.org/docs/1.8.1/nn.functional.html#adaptive-avg-pool1d) | | +| 16 | [adaptive_avg_pool2d](https://pytorch.org/docs/1.8.1/nn.functional.html#adaptive-avg-pool2d) | | +| 17 | [adaptive_avg_pool3d](https://pytorch.org/docs/1.8.1/nn.functional.html#adaptive-avg-pool3d) | | + +### [Non-linear activation functions](https://pytorch.org/docs/1.8.1/nn.functional.html#non-linear-activation-functions) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [threshold](https://pytorch.org/docs/1.8.1/nn.functional.html#threshold) | | +| 2 | [relu](https://pytorch.org/docs/1.8.1/nn.functional.html#relu) | | +| 3 | [hardtanh](https://pytorch.org/docs/1.8.1/nn.functional.html#hardtanh) | | +| 4 | [hardswish](https://pytorch.org/docs/1.8.1/nn.functional.html#hardswish) | | +| 5 | [relu6](https://pytorch.org/docs/1.8.1/nn.functional.html#relu6) | | +| 6 | [elu](https://pytorch.org/docs/1.8.1/nn.functional.html#elu) | | +| 7 | [selu](https://pytorch.org/docs/1.8.1/nn.functional.html#selu) | | +| 8 | [celu](https://pytorch.org/docs/1.8.1/nn.functional.html#celu) | | +| 9 | [leaky_relu](https://pytorch.org/docs/1.8.1/nn.functional.html#leaky-relu) | | +| 10 | [prelu](https://pytorch.org/docs/1.8.1/nn.functional.html#prelu) | | +| 11 | [rrelu](https://pytorch.org/docs/1.8.1/nn.functional.html#rrelu) | | +| 12 | [glu](https://pytorch.org/docs/1.8.1/nn.functional.html#glu) | | +| 13 | [gelu](https://pytorch.org/docs/1.8.1/nn.functional.html#gelu) | | +| 14 | [logsigmoid](https://pytorch.org/docs/1.8.1/nn.functional.html#logsigmoid) | | +| 15 | [hardshrink](https://pytorch.org/docs/1.8.1/nn.functional.html#hardshrink) | | +| 16 | [tanhshrink](https://pytorch.org/docs/1.8.1/nn.functional.html#tanhshrink) | | +| 17 | [softsign](https://pytorch.org/docs/1.8.1/nn.functional.html#softsign) | | +| 18 | [softplus](https://pytorch.org/docs/1.8.1/nn.functional.html#softplus) | | +| 19 | [softmin](https://pytorch.org/docs/1.8.1/nn.functional.html#softmin) | | +| 20 | [softmax](https://pytorch.org/docs/1.8.1/nn.functional.html#softmax) | | +| 21 | [softshrink](https://pytorch.org/docs/1.8.1/nn.functional.html#softshrink) | | +| 22 | [gumbel_softmax](https://pytorch.org/docs/1.8.1/nn.functional.html#gumbel-softmax) | | +| 23 | [log_softmax](https://pytorch.org/docs/1.8.1/nn.functional.html#log-softmax) | | +| 24 | [tanh](https://pytorch.org/docs/1.8.1/nn.functional.html#tanh) | | +| 25 | [sigmoid](https://pytorch.org/docs/1.8.1/nn.functional.html#sigmoid) | | +| 26 | [hardsigmoid](https://pytorch.org/docs/1.8.1/nn.functional.html#hardsigmoid) | | +| 27 | [silu](https://pytorch.org/docs/1.8.1/nn.functional.html#silu) | | + +### [Normalization functions](https://pytorch.org/docs/1.8.1/nn.functional.html#normalization-functions) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [batch_norm](https://pytorch.org/docs/1.8.1/nn.functional.html#batch-norm) | | +| 2 | [instance_norm](https://pytorch.org/docs/1.8.1/nn.functional.html#instance-norm) | | +| 3 | [layer_norm](https://pytorch.org/docs/1.8.1/nn.functional.html#layer-norm) | | +| 4 | [local_response_norm](https://pytorch.org/docs/1.8.1/nn.functional.html#local-response-norm) | | +| 5 | [normalize](https://pytorch.org/docs/1.8.1/nn.functional.html#normalize) | | + +### [Linear functions](https://pytorch.org/docs/1.8.1/nn.functional.html#linear-functions)[Linear functions](https://pytorch.org/docs/1.8.1/nn.functional.html#linear-functions) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [linear](https://pytorch.org/docs/1.8.1/nn.functional.html#linear) | | +| 2 | [bilinear](https://pytorch.org/docs/1.8.1/nn.functional.html#bilinear) | | + +### [Dropout functions](https://pytorch.org/docs/1.8.1/nn.functional.html#dropout-functions) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [dropout](https://pytorch.org/docs/1.8.1/nn.functional.html#dropout) | | +| 2 | [alpha_dropout](https://pytorch.org/docs/1.8.1/nn.functional.html#alpha-dropout) | | +| 3 | [feature_alpha_dropout](https://pytorch.org/docs/1.8.1/nn.functional.html#feature-alpha-dropout) | | +| 4 | [dropout2d](https://pytorch.org/docs/1.8.1/nn.functional.html#dropout2d) | | +| 5 | [dropout3d](https://pytorch.org/docs/1.8.1/nn.functional.html#dropout3d) | | + +### [Sparse functions](https://pytorch.org/docs/1.8.1/nn.functional.html#sparse-functions) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [embedding](https://pytorch.org/docs/1.8.1/nn.functional.html#embedding) | | +| 2 | [embedding_bag](https://pytorch.org/docs/1.8.1/nn.functional.html#embedding-bag) | | +| 3 | [one_hot](https://pytorch.org/docs/1.8.1/nn.functional.html#one-hot) | | + +### [Distance functions](https://pytorch.org/docs/1.8.1/nn.functional.html#distance-functions) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [pairwise_distance](https://pytorch.org/docs/1.8.1/nn.functional.html#pairwise-distance) | | +| 2 | [cosine_similarity](https://pytorch.org/docs/1.8.1/nn.functional.html#cosine-similarity) | | +| 3 | [pdist](https://pytorch.org/docs/1.8.1/nn.functional.html#pdist) | | + +### [Loss functions](https://pytorch.org/docs/1.8.1/nn.functional.html#loss-functions) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [binary_cross_entropy](https://pytorch.org/docs/1.8.1/nn.functional.html#binary-cross-entropy) | | +| 2 | [binary_cross_entropy_with_logits](https://pytorch.org/docs/1.8.1/nn.functional.html#binary-cross-entropy-with-logits) | | +| 3 | [poisson_nll_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#poisson-nll-loss) | | +| 4 | [cosine_embedding_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#cosine-embedding-loss) | | +| 5 | [cross_entropy](https://pytorch.org/docs/1.8.1/nn.functional.html#cross-entropy) | | +| 6 | [ctc_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#ctc-loss) | | +| 7 | [hinge_embedding_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#hinge-embedding-loss) | | +| 8 | [kl_div](https://pytorch.org/docs/1.8.1/nn.functional.html#kl-div) | | +| 9 | [l1_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#l1-loss) | | +| 10 | [mse_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#mse-loss) | | +| 11 | [margin_ranking_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#margin-ranking-loss) | | +| 12 | [multilabel_margin_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#multilabel-margin-loss) | | +| 13 | [multilabel_soft_margin_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#multilabel-soft-margin-loss) | | +| 14 | [multi_margin_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#multi-margin-loss) | | +| 15 | [nll_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#nll-loss) | | +| 16 | [smooth_l1_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#smooth-l1-loss) | | +| 17 | [soft_margin_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#soft-margin-loss) | | +| 18 | [triplet_margin_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#triplet-margin-loss) | | +| 19 | [triplet_margin_with_distance_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#triplet-margin-with-distance-loss) | | + +### [Vision functions](https://pytorch.org/docs/1.8.1/nn.functional.html#vision-functions) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [pixel_shuffle](https://pytorch.org/docs/1.8.1/nn.functional.html#pixel-shuffle) | | +| 2 | [pixel_unshuffle](https://pytorch.org/docs/1.8.1/nn.functional.html#pixel-unshuffle) | | +| 3 | [pad](https://pytorch.org/docs/1.8.1/nn.functional.html#pad) | | +| 4 | [interpolate](https://pytorch.org/docs/1.8.1/nn.functional.html#interpolate) | | +| 5 | [upsample](https://pytorch.org/docs/1.8.1/nn.functional.html#upsample) | | +| 6 | [upsample_nearest](https://pytorch.org/docs/1.8.1/nn.functional.html#upsample-nearest) | | +| 7 | [upsample_bilinear](https://pytorch.org/docs/1.8.1/nn.functional.html#upsample-bilinear) | | +| 8 | [grid_sample](https://pytorch.org/docs/1.8.1/nn.functional.html#grid-sample) | | +| 9 | [affine_grid](https://pytorch.org/docs/1.8.1/nn.functional.html#affine-grid) | | + +### [DataParallel functions (multi-GPU, distributed)](https://pytorch.org/docs/1.8.1/nn.functional.html#dataparallel-functions-multi-gpu-distributed) + +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [data_parallel](https://pytorch.org/docs/1.8.1/nn.functional.html#data-parallel) | | + + + + + +## torch.distributed + +| 序号 | API名称 | 是否支持（PyTorch1.5.0） | +| ---- | ------------------------------------- | ------------------------ | +| | torch.distributed.is_available | | +| 1 | torch.distributed.init_process_group | 是 | +| 2 | torch.distributed.Backend | 是 | +| 3 | torch.distributed.get_backend | 是 | +| 4 | torch.distributed.get_rank | 是 | +| 5 | torch.distributed.get_world_size | 是 | +| 6 | torch.distributed.is_initialized | 是 | +| 7 | torch.distributed.is_mpi_available | 是 | +| 8 | torch.distributed.is_nccl_available | 是 | +| 9 | torch.distributed.new_group | 是 | +| 10 | torch.distributed.send | 否 | +| 11 | torch.distributed.recv | 否 | +| 12 | torch.distributed.isend | 否 | +| 13 | torch.distributed.irecv | 否 | +| 14 | is_completed | 是 | +| 15 | wait | 是 | +| 16 | torch.distributed.broadcast | 是 | +| 17 | torch.distributed.all_reduce | 是 | +| 18 | torch.distributed.reduce | 否 | +| 19 | torch.distributed.all_gather | 是 | +| 20 | torch.distributed.gather | 是 | +| 21 | torch.distributed.scatter | 是 | +| 22 | torch.distributed.barrier | 是 | +| 23 | torch.distributed.ReduceOp | 是 | +| 24 | torch.distributed.reduce_op | 是 | +| 25 | torch.distributed.broadcast_multigpu | 否 | +| 26 | torch.distributed.all_reduce_multigpu | 否 | +| 27 | torch.distributed.reduce_multigpu | 否 | +| 28 | torch.distributed.all_gather_multigpu | 否 | +| 29 | torch.distributed.launch | 是 | +| 30 | torch.multiprocessing.spawn | 是 | + + + +## torch.npu + +| 序号 | API名称 | npu对应API名称 | 是否支持（PyTorch1.5.0） | +| ---- | ------------------------------------- | ------------------------------------ | ------------------------ | +| 1 | torch.cuda.current_blas_handle | torch.npu.current_blas_handle | 否 | +| 2 | torch.cuda.current_device | torch.npu.current_device | 是 | +| 3 | torch.cuda.current_stream | torch.npu.current_stream | 是 | +| 4 | torch.cuda.default_stream | torch.npu.default_stream | 是 | +| 5 | torch.cuda.device | torch.npu.device | 否 | +| 6 | torch.cuda.device_count | torch.npu.device_count | 是 | +| 7 | torch.cuda.device_of | torch.npu.device_of | 否 | +| 8 | torch.cuda.get_device_capability | torch.npu.get_device_capability | 否 | +| 9 | torch.cuda.get_device_name | torch.npu.get_device_name | 否 | +| 10 | torch.cuda.init | torch.npu.init | 是 | +| 11 | torch.cuda.ipc_collect | torch.npu.ipc_collect | 否 | +| 12 | torch.cuda.is_available | torch.npu.is_available | 是 | +| 13 | torch.cuda.is_initialized | torch.npu.is_initialized | 是 | +| 14 | torch.cuda.set_device | torch.npu.set_device | 部分支持 | +| 15 | torch.cuda.stream | torch.npu.stream | 是 | +| 16 | torch.cuda.synchronize | torch.npu.synchronize | 是 | +| 17 | torch.cuda.get_rng_state | torch.npu.get_rng_state | 否 | +| 18 | torch.cuda.get_rng_state_all | torch.npu.get_rng_state_all | 否 | +| 19 | torch.cuda.set_rng_state | torch.npu.set_rng_state | 否 | +| 20 | torch.cuda.set_rng_state_all | torch.npu.set_rng_state_all | 否 | +| 21 | torch.cuda.manual_seed | torch.npu.manual_seed | 否 | +| 22 | torch.cuda.manual_seed_all | torch.npu.manual_seed_all | 否 | +| 23 | torch.cuda.seed | torch.npu.seed | 否 | +| 24 | torch.cuda.seed_all | torch.npu.seed_all | 否 | +| 25 | torch.cuda.initial_seed | torch.npu.initial_seed | 否 | +| 26 | torch.cuda.comm.broadcast | torch.npu.comm.broadcast | 否 | +| 27 | torch.cuda.comm.broadcast_coalesced | torch.npu.comm.broadcast_coalesced | 否 | +| 28 | torch.cuda.comm.reduce_add | torch.npu.comm.reduce_add | 否 | +| 29 | torch.cuda.comm.scatter | torch.npu.comm.scatter | 否 | +| 30 | torch.cuda.comm.gather | torch.npu.comm.gather | 否 | +| 31 | torch.cuda.Stream | torch.npu.Stream | 是 | +| 32 | torch.cuda.Stream.query | torch.npu.Stream.query | 否 | +| 33 | torch.cuda.Stream.record_event | torch.npu.Stream.record_event | 是 | +| 34 | torch.cuda.Stream.synchronize | torch.npu.Stream.synchronize | 是 | +| 35 | torch.cuda.Stream.wait_event | torch.npu.Stream.wait_event | 是 | +| 36 | torch.cuda.Stream.wait_stream | torch.npu.Stream.wait_stream | 是 | +| 37 | torch.cuda.Event | torch.npu.Event | 是 | +| 38 | torch.cuda.Event.elapsed_time | torch.npu.Event.elapsed_time | 是 | +| 39 | torch.cuda.Event.from_ipc_handle | torch.npu.Event.from_ipc_handle | 否 | +| 40 | torch.cuda.Event.ipc_handle | torch.npu.Event.ipc_handle | 否 | +| 41 | torch.cuda.Event.query | torch.npu.Event.query | 是 | +| 42 | torch.cuda.Event.record | torch.npu.Event.record | 是 | +| 43 | torch.cuda.Event.synchronize | torch.npu.Event.synchronize | 是 | +| 44 | torch.cuda.Event.wait | torch.npu.Event.wait | 是 | +| 45 | torch.cuda.empty_cache | torch.npu.empty_cache | 是 | +| 46 | torch.cuda.memory_stats | torch.npu.memory_stats | 是 | +| 47 | torch.cuda.memory_summary | torch.npu.memory_summary | 是 | +| 48 | torch.cuda.memory_snapshot | torch.npu.memory_snapshot | 是 | +| 49 | torch.cuda.memory_allocated | torch.npu.memory_allocated | 是 | +| 50 | torch.cuda.max_memory_allocated | torch.npu.max_memory_allocated | 是 | +| 51 | torch.cuda.reset_max_memory_allocated | torch.npu.reset_max_memory_allocated | 是 | +| 52 | torch.cuda.memory_reserved | torch.npu.memory_reserved | 是 | +| 53 | torch.cuda.max_memory_reserved | torch.npu.max_memory_reserved | 是 | +| 54 | torch.cuda.memory_cached | torch.npu.memory_cached | 是 | +| 55 | torch.cuda.max_memory_cached | torch.npu.max_memory_cached | 是 | +| 56 | torch.cuda.reset_max_memory_cached | torch.npu.reset_max_memory_cached | 是 | +| 57 | torch.cuda.nvtx.mark | torch.npu.nvtx.mark | 否 | +| 58 | torch.cuda.nvtx.range_push | torch.npu.nvtx.range_push | 否 | +| 59 | torch.cuda.nvtx.range_pop | torch.npu.nvtx.range_pop | 否 | +| 60 | torch.cuda._sleep | torch.npu._sleep | 否 | +| 61 | torch.cuda.Stream.priority_range | torch.npu.Stream.priority_range | 否 | +| 62 | torch.cuda.get_device_properties | torch.npu.get_device_properties | 否 | +| 63 | torch.cuda.amp.GradScaler | torch.npu.amp.GradScaler | 否 | + +## NPU自定义算子清单 + +| 序号 | PyTorch 算子（由昇腾开发） | 昇腾适配算子 | +| ---- | ---------------------------------------------- | ---------------------------------------------- | +| 1 | npu_convolution_transpose | npu_convolution_transpose | +| 2 | npu_conv_transpose2d | conv_transpose2d_npu | +| 3 | npu_convolution_transpose_backward | npu_convolution_transpose_backward | +| 4 | npu_conv_transpose2d_backward | conv_transpose2d_backward_npu | +| 5 | npu_conv_transpose3d_backward | conv_transpose3d_backward_npu | +| 6 | npu_convolution | npu_convolution | +| 7 | npu_convolution_backward | npu_convolution_backward | +| 8 | npu_convolution_double_backward | npu_convolution_double_backward | +| 9 | npu_conv2d | conv2d_npu | +| 10 | npu_conv2d.out | conv2d_out_npu | +| 11 | npu_conv2d_backward | conv2d_backward_npu | +| 12 | npu_conv3d | conv3d_npu | +| 13 | npu_conv3d.out | conv3d_out_npu | +| 14 | npu_conv3d_backward | conv3d_backward_npu | +| 15 | one_ | one_npu_ | +| 16 | npu_sort_v2.out | sort_without_indices_out_npu | +| 17 | npu_sort_v2 | sort_without_indices_npu | +| 18 | npu_format_cast | format_cast_npu | +| 19 | npu_format_cast_.acl_format | format_cast_npu_ | +| 20 | npu_format_cast_.src | format_cast_npu_ | +| 21 | npu_transpose_to_contiguous | transpose_to_contiguous_npu | +| 22 | npu_transpose | transpose_npu | +| 23 | npu_transpose.out | transpose_out_npu | +| 24 | npu_broadcast | broadcast_npu | +| 25 | npu_broadcast.out | broadcast_out_npu | +| 26 | npu_dtype_cast | dtype_cast_npu | +| 27 | npu_dtype_cast_.Tensor | dtype_cast_npu_ | +| 28 | npu_roi_alignbk | roi_align_backward_npu | +| 29 | empty_with_format | empty_with_format_npu | +| 30 | empty_with_format.names | empty_with_format_npu | +| 31 | copy_memory_ | copy_memory_npu_ | +| 32 | npu_one_hot | one_hot_npu | +| 33 | npu_stride_add | stride_add_npu | +| 34 | npu_softmax_cross_entropy_with_logits | softmax_cross_entropy_with_logits_npu | +| 35 | npu_softmax_cross_entropy_with_logits_backward | softmax_cross_entropy_with_logits_backward_npu | +| 36 | npu_ps_roi_pooling | ps_roi_pooling_npu | +| 37 | npu_ps_roi_pooling_backward | ps_roi_pooling_backward_npu | +| 38 | npu_roi_align | roi_align_npu | +| 39 | npu_nms_v4 | nms_v4_npu | +| 40 | npu_lstm | lstm_npu | +| 41 | npu_lstm_backward | lstm_backward_npu | +| 42 | npu_iou | iou_npu | +| 43 | npu_ptiou | ptiou_npu | +| 44 | npu_nms_with_mask | nms_with_mask_npu | +| 45 | npu_pad | pad_npu | +| 46 | npu_bounding_box_encode | bounding_box_encode_npu | +| 47 | npu_bounding_box_decode | bounding_box_decode_npu | +| 48 | npu_gru | gru_npu | +| 49 | npu_gru_backward | gru_backward_npu | +| 50 | npu_set_.source_Storage_storage_offset_format | set_npu_ | +| 51 | npu_random_choice_with_mask | random_choice_with_mask_npu | +| 52 | npu_batch_nms | batch_nms_npu | +| 53 | npu_slice | slice_npu | +| 54 | npu_slice.out | slice_out_npu | +| 55 | npu_dropoutV2 | dropout_v2_npu | +| 56 | npu_dropoutV2_backward | dropout_v2_backward_npu | +| 57 | _npu_dropout | _dropout_npu | +| 58 | _npu_dropout_inplace | _dropout_npu_inplace | +| 59 | npu_dropout_backward | dropout_backward_npu | +| 60 | npu_indexing | indexing_npu | +| 61 | npu_indexing.out | indexing_out_npu | +| 62 | npu_ifmr | ifmr_npu | +| 63 | npu_max.dim | max_v1_npu | +| 64 | npu_max.names_dim | max_v1_npu | +| 65 | npu_scatter | scatter_npu | +| 66 | npu_max_backward | max_backward_npu | +| 67 | npu_apply_adam | apply_adam_npu | +| 68 | npu_layer_norm_eval | layer_norm_eval_npu | +| 69 | npu_alloc_float_status | alloc_float_status_npu | +| 70 | npu_get_float_status | get_float_status_npu | +| 71 | npu_clear_float_status | clear_float_status_npu | +| 72 | npu_confusion_transpose | confusion_transpose_npu | +| 73 | npu_confusion_transpose_backward | confusion_transpose_backward_npu | +| 74 | npu_bmmV2 | bmm_v2_npu | +| 75 | fast_gelu | fast_gelu_npu | +| 76 | fast_gelu_backward | fast_gelu_backward_npu | +| 77 | npu_sub_sample | sub_sample_npu | +| 78 | npu_deformable_conv2d | deformable_conv2d_npu | +| 79 | npu_deformable_conv2dbk | deformable_conv2d_backward_npu | +| 80 | npu_mish | mish_npu | +| 81 | npu_anchor_response_flags | anchor_response_flags_npu | +| 82 | npu_yolo_boxes_encode | yolo_boxes_encode_npu | +| 83 | npu_grid_assign_positive | grid_assign_positive_npu | +| 84 | npu_mish_backward | mish_backward_npu | +| 85 | npu_normalize_batch | normalize_batch_npu | +| 86 | npu_masked_fill_range | masked_fill_range_npu | +| 87 | npu_linear | linear_npu | +| 88 | npu_linear_backward | linear_backward_npu | +| 89 | npu_bert_apply_adam | bert_apply_adam_npu | +| 90 | npu_giou | giou_npu | +| 91 | npu_giou_backward | giou_backward_npu | \ No newline at end of file -- Gitee From 9bcfb1e84e9505f238305048a47c074cc7f09f17 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Mon, 6 Dec 2021 22:06:42 +0800 Subject: [PATCH 13/55] updata API --- ...\214\201\346\270\205\345\215\225_1.5.0.md" | 1346 +++++++++++++++++ ...\214\201\346\270\205\345\215\225_1.8.1.md" | 60 +- 2 files changed, 1404 insertions(+), 2 deletions(-) create mode 100644 "docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" diff --git "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" new file mode 100644 index 00000000000..aafb7db50cd --- /dev/null +++ "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" @@ -0,0 +1,1346 @@ +## [Tensors](https://pytorch.org/docs/1.5.0/torch.html) + +| 序号 | API名称 | PyTorch1.5.0支持情况 | +| ---- | ----------------------------- | -------------------------------------- | +| 1 | torch.is_tensor | 是 | +| 2 | torch.is_storage | 是 | +| 3 | torch.is_complex | 是，支持判断，但当前硬件限制不支持复数 | +| 4 | torch.is_floating_point | 是 | +| 5 | torch.set_default_dtype | 是 | +| 6 | torch.get_default_dtype | 是 | +| 7 | torch.set_default_tensor_type | 是 | +| 8 | torch.numel | 是 | +| 9 | torch.set_printoptions | 是 | +| 10 | torch.set_flush_denormal | 是 | +| 11 | torch.tensor | 是 | +| 12 | torch.sparse_coo_tensor | 否 | +| 13 | torch.as_tensor | 是 | +| 14 | torch.as_strided | 是 | +| 15 | torch.from_numpy | 是 | +| 16 | torch.zeros | 是 | +| 17 | torch.zeros_like | 是 | +| 18 | torch.ones | 是 | +| 19 | torch.ones_like | 是 | +| 20 | torch.arange | 是 | +| 21 | torch.range | 是 | +| 22 | torch.linspace | 是 | +| 23 | torch.logspace | 是 | +| 24 | torch.eye | 是 | +| 25 | torch.empty | 是 | +| 26 | torch.empty_like | 是 | +| 27 | torch.empty_strided | 是 | +| 28 | torch.full | 是 | +| 29 | torch.full_like | 是 | +| 30 | torch.quantize_per_tensor | 是 | +| 31 | torch.quantize_per_channel | 是 | +| 32 | torch.cat | 是 | +| 33 | torch.chunk | 是 | +| 34 | torch.gather | 是 | +| 35 | torch.index_select | 是 | +| 36 | torch.masked_select | 是 | +| 37 | torch.narrow | 是 | +| 38 | torch.nonzero | 是 | +| 39 | torch.reshape | 是 | +| 40 | torch.split | 是 | +| 41 | torch.squeeze | 是 | +| 42 | torch.stack | 是 | +| 43 | torch.t | 是 | +| 44 | torch.take | 是 | +| 45 | torch.transpose | 是 | +| 46 | torch.unbind | 是 | +| 47 | torch.unsqueeze | 是 | +| 48 | torch.where | 是 | + +## Generators + +| 序号 | API名称 | 是否支持（PyTorch1.5.0） | +| ---- | ------------------------------- | ------------------------ | +| 1 | torch._C.Generator | 是 | +| 2 | torch._C.Generator.device | 是 | +| 3 | torch._C.Generator.get_state | 否 | +| 4 | torch._C.Generator.initial_seed | 是 | +| 5 | torch._C.Generator.manual_seed | 是 | +| 6 | torch._C.Generator.seed | 是 | +| 7 | torch._C.Generator.set_state | 否 | + +## Random sampling + +| 序号 | API名称 | 是否支持（PyTorch1.5.0） | +| ---- | ------------------------------------------ | ------------------------ | +| 1 | torch.seed | 是 | +| 2 | torch.manual_seed | 是 | +| 3 | torch.initial_seed | 是 | +| 4 | torch.get_rng_state | 是 | +| 5 | torch.set_rng_state | 是 | +| 6 | torch.torch.default_generator | 是 | +| 7 | torch.bernoulli | 是 | +| 8 | torch.multinomial | 是 | +| 9 | torch.normal | 是 | +| 10 | torch.poisson | 否 | +| 11 | torch.rand | 是 | +| 12 | torch.rand_like | 是 | +| 13 | torch.randint | 是 | +| 14 | torch.randint_like | 是 | +| 15 | torch.randn | 是 | +| 16 | torch.randn_like | 是 | +| 17 | torch.randperm | 是 | +| 18 | torch.Tensor.bernoulli_() | 是 | +| 19 | torch.Tensor.bernoulli_() | 是 | +| 20 | torch.Tensor.exponential_() | 否 | +| 21 | torch.Tensor.geometric_() | 否 | +| 22 | torch.Tensor.log_normal_() | 否 | +| 23 | torch.Tensor.normal_() | 是 | +| 24 | torch.Tensor.random_() | 是 | +| 25 | torch.Tensor.uniform_() | 是 | +| 26 | torch.quasirandom.SobolEngine | 是 | +| 27 | torch.quasirandom.SobolEngine.draw | 是 | +| 28 | torch.quasirandom.SobolEngine.fast_forward | 是 | +| 29 | torch.quasirandom.SobolEngine.reset | 是 | + +## Serialization + +| 序号 | API名称 | 是否支持（PyTorch1.5.0） | +| ---- | ---------- | ------------------------ | +| 1 | torch.save | 是 | +| 2 | torch.load | 是 | + +## Math operations + +| 序号 | API名称 | 是否支持（PyTorch1.5.0） | +| ---- | ------------------------ | ------------------------ | +| 1 | torch.abs | 是 | +| 2 | torch.acos | 是 | +| 3 | torch.add | 是 | +| 4 | torch.addcdiv | 是 | +| 5 | torch.addcmul | 是 | +| 6 | torch.angle | 否 | +| 7 | torch.asin | 是 | +| 8 | torch.atan | 是 | +| 9 | torch.atan2 | 是 | +| 10 | torch.bitwise_not | 是 | +| 11 | torch.bitwise_and | 是 | +| 12 | torch.bitwise_or | 是 | +| 13 | torch.bitwise_xor | 是 | +| 14 | torch.ceil | 是 | +| 15 | torch.clamp | 是 | +| 16 | torch.conj | 否 | +| 17 | torch.cos | 是 | +| 18 | torch.cosh | 是 | +| 19 | torch.div | 是 | +| 20 | torch.digamma | 否 | +| 21 | torch.erf | 是 | +| 22 | torch.erfc | 是 | +| 23 | torch.erfinv | 是 | +| 24 | torch.exp | 是 | +| 25 | torch.expm1 | 是 | +| 26 | torch.floor | 是 | +| 27 | torch.floor_divide | 是 | +| 28 | torch.fmod | 是 | +| 29 | torch.frac | 是 | +| 30 | torch.imag | 否 | +| 31 | torch.lerp | 是 | +| 32 | torch.lgamma | 否 | +| 33 | torch.log | 是 | +| 34 | torch.log10 | 是 | +| 35 | torch.log1p | 是 | +| 36 | torch.log2 | 是 | +| 37 | torch.logical_and | 是 | +| 38 | torch.logical_not | 是 | +| 39 | torch.logical_or | 是 | +| 40 | torch.logical_xor | 是 | +| 41 | torch.mul | 是 | +| 42 | torch.mvlgamma | 否 | +| 43 | torch.neg | 是 | +| 44 | torch.polygamma | 否 | +| 45 | torch.pow | 是 | +| 46 | torch.real | 是 | +| 47 | torch.reciprocal | 是 | +| 48 | torch.remainder | 是 | +| 49 | torch.round | 是 | +| 50 | torch.rsqrt | 是 | +| 51 | torch.sigmoid | 是 | +| 52 | torch.sign | 是 | +| 53 | torch.sin | 是 | +| 54 | torch.sinh | 是 | +| 55 | torch.sqrt | 是 | +| 56 | torch.square | 是 | +| 57 | torch.tan | 是 | +| 58 | torch.tanh | 是 | +| 59 | torch.true_divide | 是 | +| 60 | torch.trunc | 是 | +| 61 | torch.argmax | 是 | +| 62 | torch.argmin | 是 | +| 63 | torch.dist | 是 | +| 64 | torch.logsumexp | 是 | +| 65 | torch.mean | 是 | +| 66 | torch.median | 是 | +| 67 | torch.mode | 否 | +| 68 | torch.norm | 是 | +| 69 | torch.prod | 是 | +| 70 | torch.std | 是 | +| 71 | torch.std_mean | 是 | +| 72 | torch.sum | 是 | +| 73 | torch.unique | 是 | +| 74 | torch.unique_consecutive | 否 | +| 75 | torch.var | 否 | +| 76 | torch.var_mean | 否 | +| 77 | torch.allclose | 是 | +| 78 | torch.argsort | 是 | +| 79 | torch.eq | 是 | +| 80 | torch.equal | 是 | +| 81 | torch.ge | 是 | +| 82 | torch.gt | 是 | +| 83 | torch.isfinite | 是 | +| 84 | torch.isinf | 是 | +| 85 | torch.isnan | 是 | +| 86 | torch.kthvalue | 是 | +| 87 | torch.le | 是 | +| 88 | torch.lt | 是 | +| 89 | torch.max | 是 | +| 90 | torch.min | 是 | +| 91 | torch.ne | 是 | +| 92 | torch.sort | 是 | +| 93 | torch.topk | 是 | +| 94 | torch.fft | 否 | +| 95 | torch.ifft | 否 | +| 96 | torch.rfft | 否 | +| 97 | torch.irfft | 否 | +| 98 | torch.stft | 否 | +| 99 | torch.bartlett_window | 是 | +| 100 | torch.blackman_window | 是 | +| 101 | torch.hamming_window | 是 | +| 102 | torch.hann_window | 是 | +| 103 | torch.bincount | 是 | +| 104 | torch.broadcast_tensors | 是 | +| 105 | torch.cartesian_prod | 是 | +| 106 | torch.cdist | 是 | +| 107 | torch.combinations | 否 | +| 108 | torch.cross | 是 | +| 109 | torch.cummax | 是 | +| 110 | torch.cummin | 是 | +| 111 | torch.cumprod | 是 | +| 112 | torch.cumsum | 是 | +| 113 | torch.diag | 是 | +| 114 | torch.diag_embed | 是 | +| 115 | torch.diagflat | 是 | +| 116 | torch.diagonal | 是 | +| 117 | torch.einsum | 是 | +| 118 | torch.flatten | 是 | +| 119 | torch.flip | 是 | +| 120 | torch.rot90 | 是 | +| 121 | torch.histc | 否 | +| 122 | torch.meshgrid | 是 | +| 123 | torch.renorm | 是 | +| 124 | torch.repeat_interleave | 是 | +| 125 | torch.roll | 是 | +| 126 | torch.tensordot | 是 | +| 127 | torch.trace | 否 | +| 128 | torch.tril | 是 | +| 129 | torch.tril_indices | 是 | +| 130 | torch.triu | 是 | +| 131 | torch.triu_indices | 是 | +| 132 | torch.addbmm | 是 | +| 133 | torch.addmm | 是 | +| 134 | torch.addmv | 是 | +| 135 | torch.addr | 是 | +| 136 | torch.baddbmm | 是 | +| 137 | torch.bmm | 是 | +| 138 | torch.chain_matmul | 是 | +| 139 | torch.cholesky | 否 | +| 140 | torch.cholesky_inverse | 否 | +| 141 | torch.cholesky_solve | 否 | +| 142 | torch.dot | 是 | +| 143 | torch.eig | 否 | +| 144 | torch.geqrf | 否 | +| 145 | torch.ger | 是 | +| 146 | torch.inverse | 是 | +| 147 | torch.det | 否 | +| 148 | torch.logdet | 否 | +| 149 | torch.slogdet | 是 | +| 150 | torch.lstsq | 否 | +| 151 | torch.lu | 否 | +| 152 | torch.lu_solve | 否 | +| 153 | torch.lu_unpack | 否 | +| 154 | torch.matmul | 是 | +| 155 | torch.matrix_power | 是 | +| 156 | torch.matrix_rank | 是 | +| 157 | torch.mm | 是 | +| 158 | torch.mv | 是 | +| 159 | torch.orgqr | 否 | +| 160 | torch.ormqr | 否 | +| 161 | torch.pinverse | 是 | +| 162 | torch.qr | 是 | +| 163 | torch.solve | 否 | +| 164 | torch.svd | 是 | +| 165 | torch.svd_lowrank | 是 | +| 166 | torch.pca_lowrank | 是 | +| 167 | torch.symeig | 是 | +| 168 | torch.lobpcg | 否 | +| 169 | torch.trapz | 是 | +| 170 | torch.triangular_solve | 是 | + +## Utilities + +| 序号 | API名称 | 是否支持（PyTorch1.5.0） | +| ---- | ----------------------------- | ------------------------ | +| 1 | torch.compiled_with_cxx11_abi | 是 | +| 2 | torch.result_type | 是 | +| 3 | torch.can_cast | 是 | +| 4 | torch.promote_types | 是 | + +## Other + +| 序号 | API名称 | 是否支持（PyTorch1.5.0） | +| ---- | ----------------------------- | ------------------------ | +| 1 | torch.no_grad | 是 | +| 2 | torch.enable_grad | 是 | +| 3 | torch.set_grad_enabled | 是 | +| 4 | torch.get_num_threads | 是 | +| 5 | torch.set_num_threads | 是 | +| 6 | torch.get_num_interop_threads | 是 | +| 7 | torch.set_num_interop_threads | 是 | + +## torch.Tensor + +| 序号 | API名称 | 是否支持（PyTorch1.5.0） | +| ---- | -------------------------------------- | ------------------------ | +| 1 | torch.Tensor | 是 | +| 2 | torch.Tensor.new_tensor | 是 | +| 3 | torch.Tensor.new_full | 是 | +| 4 | torch.Tensor.new_empty | 是 | +| 5 | torch.Tensor.new_ones | 是 | +| 6 | torch.Tensor.new_zeros | 是 | +| 7 | torch.Tensor.is_cuda | 是 | +| 8 | torch.Tensor.is_quantized | 是 | +| 9 | torch.Tensor.device | 是 | +| 10 | torch.Tensor.ndim | 是 | +| 11 | torch.Tensor.T | 是 | +| 12 | torch.Tensor.abs | 是 | +| 13 | torch.Tensor.abs_ | 是 | +| 14 | torch.Tensor.acos | 是 | +| 15 | torch.Tensor.acos_ | 是 | +| 16 | torch.Tensor.add | 是 | +| 17 | torch.Tensor.add_ | 是 | +| 18 | torch.Tensor.addbmm | 是 | +| 19 | torch.Tensor.addbmm_ | 是 | +| 20 | torch.Tensor.addcdiv | 是 | +| 21 | torch.Tensor.addcdiv_ | 是 | +| 22 | torch.Tensor.addcmul | 是 | +| 23 | torch.Tensor.addcmul_ | 是 | +| 24 | torch.Tensor.addmm | 是 | +| 25 | torch.Tensor.addmm_ | 是 | +| 26 | torch.Tensor.addmv | 是 | +| 27 | torch.Tensor.addmv_ | 是 | +| 28 | torch.Tensor.addr | 是 | +| 29 | torch.Tensor.addr_ | 是 | +| 30 | torch.Tensor.allclose | 是 | +| 31 | torch.Tensor.angle | 否 | +| 32 | torch.Tensor.apply_ | 否 | +| 33 | torch.Tensor.argmax | 是 | +| 34 | torch.Tensor.argmin | 是 | +| 35 | torch.Tensor.argsort | 是 | +| 36 | torch.Tensor.asin | 是 | +| 37 | torch.Tensor.asin_ | 是 | +| 38 | torch.Tensor.as_strided | 是 | +| 39 | torch.Tensor.atan | 是 | +| 40 | torch.Tensor.atan2 | 是 | +| 41 | torch.Tensor.atan2_ | 是 | +| 42 | torch.Tensor.atan_ | 是 | +| 43 | torch.Tensor.baddbmm | 是 | +| 44 | torch.Tensor.baddbmm_ | 是 | +| 45 | torch.Tensor.bernoulli | 是 | +| 46 | torch.Tensor.bernoulli_ | 是 | +| 47 | torch.Tensor.bfloat16 | 否 | +| 48 | torch.Tensor.bincount | 是 | +| 49 | torch.Tensor.bitwise_not | 是 | +| 50 | torch.Tensor.bitwise_not_ | 是 | +| 51 | torch.Tensor.bitwise_and | 是 | +| 52 | torch.Tensor.bitwise_and_ | 是 | +| 53 | torch.Tensor.bitwise_or | 是 | +| 54 | torch.Tensor.bitwise_or_ | 是 | +| 55 | torch.Tensor.bitwise_xor | 是 | +| 56 | torch.Tensor.bitwise_xor_ | 是 | +| 57 | torch.Tensor.bmm | 是 | +| 58 | torch.Tensor.bool | 是 | +| 59 | torch.Tensor.byte | 是 | +| 60 | torch.Tensor.cauchy_ | 否 | +| 61 | torch.Tensor.ceil | 是 | +| 62 | torch.Tensor.ceil_ | 是 | +| 63 | torch.Tensor.char | 是 | +| 64 | torch.Tensor.cholesky | 否 | +| 65 | torch.Tensor.cholesky_inverse | 否 | +| 66 | torch.Tensor.cholesky_solve | 否 | +| 67 | torch.Tensor.chunk | 是 | +| 68 | torch.Tensor.clamp | 是 | +| 69 | torch.Tensor.clamp_ | 是 | +| 70 | torch.Tensor.clone | 是 | +| 71 | torch.Tensor.contiguous | 是 | +| 72 | torch.Tensor.copy_ | 是 | +| 73 | torch.Tensor.conj | 否 | +| 74 | torch.Tensor.cos | 是 | +| 75 | torch.Tensor.cos_ | 是 | +| 76 | torch.Tensor.cosh | 是 | +| 77 | torch.Tensor.cosh_ | 是 | +| 78 | torch.Tensor.cpu | 是 | +| 79 | torch.Tensor.cross | 是 | +| 80 | torch.Tensor.cuda | 否 | +| 81 | torch.Tensor.cummax | 是 | +| 82 | torch.Tensor.cummin | 是 | +| 83 | torch.Tensor.cumprod | 是 | +| 84 | torch.Tensor.cumsum | 是 | +| 85 | torch.Tensor.data_ptr | 是 | +| 86 | torch.Tensor.dequantize | 否 | +| 87 | torch.Tensor.det | 否 | +| 88 | torch.Tensor.dense_dim | 否 | +| 89 | torch.Tensor.diag | 是 | +| 90 | torch.Tensor.diag_embed | 是 | +| 91 | torch.Tensor.diagflat | 是 | +| 92 | torch.Tensor.diagonal | 是 | +| 93 | torch.Tensor.fill_diagonal_ | 是 | +| 94 | torch.Tensor.digamma | 否 | +| 95 | torch.Tensor.digamma_ | 否 | +| 96 | torch.Tensor.dim | 是 | +| 97 | torch.Tensor.dist | 是 | +| 98 | torch.Tensor.div | 是 | +| 99 | torch.Tensor.div_ | 是 | +| 100 | torch.Tensor.dot | 是 | +| 101 | torch.Tensor.double | 否 | +| 102 | torch.Tensor.eig | 否 | +| 103 | torch.Tensor.element_size | 是 | +| 104 | torch.Tensor.eq | 是 | +| 105 | torch.Tensor.eq_ | 是 | +| 106 | torch.Tensor.equal | 是 | +| 107 | torch.Tensor.erf | 是 | +| 108 | torch.Tensor.erf_ | 是 | +| 109 | torch.Tensor.erfc | 是 | +| 110 | torch.Tensor.erfc_ | 是 | +| 111 | torch.Tensor.erfinv | 是 | +| 112 | torch.Tensor.erfinv_ | 是 | +| 113 | torch.Tensor.exp | 是 | +| 114 | torch.Tensor.exp_ | 是 | +| 115 | torch.Tensor.expm1 | 是 | +| 116 | torch.Tensor.expm1_ | 是 | +| 117 | torch.Tensor.expand | 是 | +| 118 | torch.Tensor.expand_as | 是 | +| 119 | torch.Tensor.exponential_ | 否 | +| 120 | torch.Tensor.fft | 否 | +| 121 | torch.Tensor.fill_ | 是 | +| 122 | torch.Tensor.flatten | 是 | +| 123 | torch.Tensor.flip | 是 | +| 124 | torch.Tensor.float | 是 | +| 125 | torch.Tensor.floor | 是 | +| 126 | torch.Tensor.floor_ | 是 | +| 127 | torch.Tensor.floor_divide | 是 | +| 128 | torch.Tensor.floor_divide_ | 是 | +| 129 | torch.Tensor.fmod | 是 | +| 130 | torch.Tensor.fmod_ | 是 | +| 131 | torch.Tensor.frac | 是 | +| 132 | torch.Tensor.frac_ | 是 | +| 133 | torch.Tensor.gather | 是 | +| 134 | torch.Tensor.ge | 是 | +| 135 | torch.Tensor.ge_ | 是 | +| 136 | torch.Tensor.geometric_ | 否 | +| 137 | torch.Tensor.geqrf | 否 | +| 138 | torch.Tensor.ger | 是 | +| 139 | torch.Tensor.get_device | 是 | +| 140 | torch.Tensor.gt | 是 | +| 141 | torch.Tensor.gt_ | 是 | +| 142 | torch.Tensor.half | 是 | +| 143 | torch.Tensor.hardshrink | 是 | +| 144 | torch.Tensor.histc | 否 | +| 145 | torch.Tensor.ifft | 否 | +| 146 | torch.Tensor.index_add_ | 是 | +| 147 | torch.Tensor.index_add | 是 | +| 148 | torch.Tensor.index_copy_ | 是 | +| 149 | torch.Tensor.index_copy | 是 | +| 150 | torch.Tensor.index_fill_ | 是 | +| 151 | torch.Tensor.index_fill | 是 | +| 152 | torch.Tensor.index_put_ | 是 | +| 153 | torch.Tensor.index_put | 是 | +| 154 | torch.Tensor.index_select | 是 | +| 155 | torch.Tensor.indices | 否 | +| 156 | torch.Tensor.int | 是 | +| 157 | torch.Tensor.int_repr | 否 | +| 158 | torch.Tensor.inverse | 是 | +| 159 | torch.Tensor.irfft | 否 | +| 160 | torch.Tensor.is_contiguous | 是 | +| 161 | torch.Tensor.is_complex | 是 | +| 162 | torch.Tensor.is_floating_point | 是 | +| 163 | torch.Tensor.is_pinned | 是 | +| 164 | torch.Tensor.is_set_to | 否 | +| 165 | torch.Tensor.is_shared | 是 | +| 166 | torch.Tensor.is_signed | 是 | +| 167 | torch.Tensor.is_sparse | 是 | +| 168 | torch.Tensor.item | 是 | +| 169 | torch.Tensor.kthvalue | 是 | +| 170 | torch.Tensor.le | 是 | +| 171 | torch.Tensor.le_ | 是 | +| 172 | torch.Tensor.lerp | 是 | +| 173 | torch.Tensor.lerp_ | 是 | +| 174 | torch.Tensor.lgamma | 否 | +| 175 | torch.Tensor.lgamma_ | 否 | +| 176 | torch.Tensor.log | 是 | +| 177 | torch.Tensor.log_ | 是 | +| 178 | torch.Tensor.logdet | 否 | +| 179 | torch.Tensor.log10 | 是 | +| 180 | torch.Tensor.log10_ | 是 | +| 181 | torch.Tensor.log1p | 是 | +| 182 | torch.Tensor.log1p_ | 是 | +| 183 | torch.Tensor.log2 | 是 | +| 184 | torch.Tensor.log2_ | 是 | +| 185 | torch.Tensor.log_normal_ | 是 | +| 186 | torch.Tensor.logsumexp | 是 | +| 187 | torch.Tensor.logical_and | 是 | +| 188 | torch.Tensor.logical_and_ | 是 | +| 189 | torch.Tensor.logical_not | 是 | +| 190 | torch.Tensor.logical_not_ | 是 | +| 191 | torch.Tensor.logical_or | 是 | +| 192 | torch.Tensor.logical_or_ | 是 | +| 193 | torch.Tensor.logical_xor | 否 | +| 194 | torch.Tensor.logical_xor_ | 否 | +| 195 | torch.Tensor.long | 是 | +| 196 | torch.Tensor.lstsq | 否 | +| 197 | torch.Tensor.lt | 是 | +| 198 | torch.Tensor.lt_ | 是 | +| 199 | torch.Tensor.lu | 是 | +| 200 | torch.Tensor.lu_solve | 是 | +| 201 | torch.Tensor.map_ | 否 | +| 202 | torch.Tensor.masked_scatter_ | 是 | +| 203 | torch.Tensor.masked_scatter | 是 | +| 204 | torch.Tensor.masked_fill_ | 是 | +| 205 | torch.Tensor.masked_fill | 是 | +| 206 | torch.Tensor.masked_select | 是 | +| 207 | torch.Tensor.matmul | 是 | +| 208 | torch.Tensor.matrix_power | 是 | +| 209 | torch.Tensor.max | 是 | +| 210 | torch.Tensor.mean | 是 | +| 211 | torch.Tensor.median | 是 | +| 212 | torch.Tensor.min | 是 | +| 213 | torch.Tensor.mm | 是 | +| 214 | torch.Tensor.mode | 否 | +| 215 | torch.Tensor.mul | 是 | +| 216 | torch.Tensor.mul_ | 是 | +| 217 | torch.Tensor.multinomial | 是 | +| 218 | torch.Tensor.mv | 是 | +| 219 | torch.Tensor.mvlgamma | 否 | +| 220 | torch.Tensor.mvlgamma_ | 否 | +| 221 | torch.Tensor.narrow | 是 | +| 222 | torch.Tensor.narrow_copy | 是 | +| 223 | torch.Tensor.ndimension | 是 | +| 224 | torch.Tensor.ne | 是 | +| 225 | torch.Tensor.ne_ | 是 | +| 226 | torch.Tensor.neg | 是 | +| 227 | torch.Tensor.neg_ | 是 | +| 228 | torch.Tensor.nelement | 是 | +| 229 | torch.Tensor.nonzero | 是 | +| 230 | torch.Tensor.norm | 是 | +| 231 | torch.Tensor.normal_ | 是 | +| 232 | torch.Tensor.numel | 是 | +| 233 | torch.Tensor.numpy | 否 | +| 234 | torch.Tensor.orgqr | 否 | +| 235 | torch.Tensor.ormqr | 否 | +| 236 | torch.Tensor.permute | 是 | +| 237 | torch.Tensor.pin_memory | 否 | +| 238 | torch.Tensor.pinverse | 是 | +| 239 | torch.Tensor.polygamma | 否 | +| 240 | torch.Tensor.polygamma_ | 否 | +| 241 | torch.Tensor.pow | 是 | +| 242 | torch.Tensor.pow_ | 是 | +| 243 | torch.Tensor.prod | 是 | +| 244 | torch.Tensor.put_ | 是 | +| 245 | torch.Tensor.qr | 是 | +| 246 | torch.Tensor.qscheme | 否 | +| 247 | torch.Tensor.q_scale | 否 | +| 248 | torch.Tensor.q_zero_point | 否 | +| 249 | torch.Tensor.q_per_channel_scales | 否 | +| 250 | torch.Tensor.q_per_channel_zero_points | 否 | +| 251 | torch.Tensor.q_per_channel_axis | 否 | +| 252 | torch.Tensor.random_ | 是 | +| 253 | torch.Tensor.reciprocal | 是 | +| 254 | torch.Tensor.reciprocal_ | 是 | +| 255 | torch.Tensor.record_stream | 是 | +| 256 | torch.Tensor.remainder | 是 | +| 257 | torch.Tensor.remainder_ | 是 | +| 258 | torch.Tensor.renorm | 是 | +| 259 | torch.Tensor.renorm_ | 是 | +| 260 | torch.Tensor.repeat | 是 | +| 261 | torch.Tensor.repeat_interleave | 是 | +| 262 | torch.Tensor.requires_grad_ | 是 | +| 263 | torch.Tensor.reshape | 是 | +| 264 | torch.Tensor.reshape_as | 是 | +| 265 | torch.Tensor.resize_ | 是 | +| 266 | torch.Tensor.resize_as_ | 是 | +| 267 | torch.Tensor.rfft | 否 | +| 268 | torch.Tensor.roll | 是 | +| 269 | torch.Tensor.rot90 | 是 | +| 270 | torch.Tensor.round | 是 | +| 271 | torch.Tensor.round_ | 是 | +| 272 | torch.Tensor.rsqrt | 是 | +| 273 | torch.Tensor.rsqrt_ | 是 | +| 274 | torch.Tensor.scatter | 是 | +| 275 | torch.Tensor.scatter_ | 是 | +| 276 | torch.Tensor.scatter_add_ | 是 | +| 277 | torch.Tensor.scatter_add | 是 | +| 278 | torch.Tensor.select | 是 | +| 279 | torch.Tensor.set_ | 是 | +| 280 | torch.Tensor.share_memory_ | 否 | +| 281 | torch.Tensor.short | 是 | +| 282 | torch.Tensor.sigmoid | 是 | +| 283 | torch.Tensor.sigmoid_ | 是 | +| 284 | torch.Tensor.sign | 是 | +| 285 | torch.Tensor.sign_ | 是 | +| 286 | torch.Tensor.sin | 是 | +| 287 | torch.Tensor.sin_ | 是 | +| 288 | torch.Tensor.sinh | 是 | +| 289 | torch.Tensor.sinh_ | 是 | +| 290 | torch.Tensor.size | 是 | +| 291 | torch.Tensor.slogdet | 是 | +| 292 | torch.Tensor.solve | 否 | +| 293 | torch.Tensor.sort | 是 | +| 294 | torch.Tensor.split | 是 | +| 295 | torch.Tensor.sparse_mask | 否 | +| 296 | torch.Tensor.sparse_dim | 否 | +| 297 | torch.Tensor.sqrt | 是 | +| 298 | torch.Tensor.sqrt_ | 是 | +| 299 | torch.Tensor.square | 是 | +| 300 | torch.Tensor.square_ | 是 | +| 301 | torch.Tensor.squeeze | 是 | +| 302 | torch.Tensor.squeeze_ | 是 | +| 303 | torch.Tensor.std | 是 | +| 304 | torch.Tensor.stft | 否 | +| 305 | torch.Tensor.storage | 是 | +| 306 | torch.Tensor.storage_offset | 是 | +| 307 | torch.Tensor.storage_type | 是 | +| 308 | torch.Tensor.stride | 是 | +| 309 | torch.Tensor.sub | 是 | +| 310 | torch.Tensor.sub_ | 是 | +| 311 | torch.Tensor.sum | 是 | +| 312 | torch.Tensor.sum_to_size | 是 | +| 313 | torch.Tensor.svd | 是 | +| 314 | torch.Tensor.symeig | 是 | +| 315 | torch.Tensor.t | 是 | +| 316 | torch.Tensor.t_ | 是 | +| 317 | torch.Tensor.to | 是 | +| 318 | torch.Tensor.to_mkldnn | 否 | +| 319 | torch.Tensor.take | 是 | +| 320 | torch.Tensor.tan | 是 | +| 321 | torch.Tensor.tan_ | 是 | +| 322 | torch.Tensor.tanh | 是 | +| 323 | torch.Tensor.tanh_ | 是 | +| 324 | torch.Tensor.tolist | 是 | +| 325 | torch.Tensor.topk | 是 | +| 326 | torch.Tensor.to_sparse | 否 | +| 327 | torch.Tensor.trace | 否 | +| 328 | torch.Tensor.transpose | 是 | +| 329 | torch.Tensor.transpose_ | 是 | +| 330 | torch.Tensor.triangular_solve | 是 | +| 331 | torch.Tensor.tril | 是 | +| 332 | torch.Tensor.tril_ | 是 | +| 333 | torch.Tensor.triu | 是 | +| 334 | torch.Tensor.triu_ | 是 | +| 335 | torch.Tensor.true_divide | 是 | +| 336 | torch.Tensor.true_divide_ | 是 | +| 337 | torch.Tensor.trunc | 是 | +| 338 | torch.Tensor.trunc_ | 是 | +| 339 | torch.Tensor.type | 是 | +| 340 | torch.Tensor.type_as | 是 | +| 341 | torch.Tensor.unbind | 是 | +| 342 | torch.Tensor.unfold | 是 | +| 343 | torch.Tensor.uniform_ | 是 | +| 344 | torch.Tensor.unique | 是 | +| 345 | torch.Tensor.unique_consecutive | 否 | +| 346 | torch.Tensor.unsqueeze | 是 | +| 347 | torch.Tensor.unsqueeze_ | 是 | +| 348 | torch.Tensor.values | 否 | +| 349 | torch.Tensor.var | 否 | +| 350 | torch.Tensor.view | 是 | +| 351 | torch.Tensor.view_as | 是 | +| 352 | torch.Tensor.where | 是 | +| 353 | torch.Tensor.zero_ | 是 | +| 354 | torch.BoolTensor | 是 | +| 355 | torch.BoolTensor.all | 是 | +| 356 | torch.BoolTensor.any | 是 | + +## Layers (torch.nn) + +| 序号 | API名称 | 是否支持（PyTorch1.5.0） | +| ---- | -------------------------------------------------------- | ---------------------------- | +| 1 | torch.nn.Parameter | 是 | +| 2 | torch.nn.Module | 是 | +| 3 | torch.nn.Module.add_module | 是 | +| 4 | torch.nn.Module.apply | 是 | +| 5 | torch.nn.Module.bfloat16 | 否 | +| 6 | torch.nn.Module.buffers | 是 | +| 7 | torch.nn.Module.children | 是 | +| 8 | torch.nn.Module.cpu | 是 | +| 9 | torch.nn.Module.cuda | 否 | +| 10 | torch.nn.Module.double | 否 | +| 11 | torch.nn.Module.dump_patches | 是 | +| 12 | torch.nn.Module.eval | 是 | +| 13 | torch.nn.Module.extra_repr | 是 | +| 14 | torch.nn.Module.float | 是 | +| 15 | torch.nn.Module.forward | 是 | +| 16 | torch.nn.Module.half | 是 | +| 17 | torch.nn.Module.load_state_dict | 是 | +| 18 | torch.nn.Module.modules | 是 | +| 19 | torch.nn.Module.named_buffers | 是 | +| 20 | torch.nn.Module.named_children | 是 | +| 21 | torch.nn.Module.named_modules | 是 | +| 22 | torch.nn.Module.named_parameters | 是 | +| 23 | torch.nn.Module.parameters | 是 | +| 24 | torch.nn.Module.register_backward_hook | 是 | +| 25 | torch.nn.Module.register_buffer | 是 | +| 26 | torch.nn.Module.register_forward_hook | 是 | +| 27 | torch.nn.Module.register_forward_pre_hook | 是 | +| 28 | torch.nn.Module.register_parameter | 是 | +| 29 | torch.nn.Module.requires_grad_ | 是 | +| 30 | torch.nn.Module.state_dict | 是 | +| 31 | torch.nn.Module.to | 是 | +| 32 | torch.nn.Module.train | 是 | +| 33 | torch.nn.Module.type | 是 | +| 34 | torch.nn.Module.zero_grad | 是 | +| 35 | torch.nn.Sequential | 是 | +| 36 | torch.nn.ModuleList | 是 | +| 37 | torch.nn.ModuleList.append | 是 | +| 38 | torch.nn.ModuleList.extend | 是 | +| 39 | torch.nn.ModuleList.insert | 是 | +| 40 | torch.nn.ModuleDict | 是 | +| 41 | torch.nn.ModuleDict.clear | 是 | +| 42 | torch.nn.ModuleDict.items | 是 | +| 43 | torch.nn.ModuleDict.keys | 是 | +| 44 | torch.nn.ModuleDict.pop | 是 | +| 45 | torch.nn.ModuleDict.update | 是 | +| 46 | torch.nn.ModuleDict.values | 是 | +| 47 | torch.nn.ParameterList | 是 | +| 48 | torch.nn.ParameterList.append | 是 | +| 49 | torch.nn.ParameterList.extend | 是 | +| 50 | torch.nn.ParameterDict | 是 | +| 51 | torch.nn.ParameterDict.clear | 是 | +| 52 | torch.nn.ParameterDict.items | 是 | +| 53 | torch.nn.ParameterDict.keys | 是 | +| 54 | torch.nn.ParameterDict.pop | 是 | +| 55 | torch.nn.ParameterDict.update | 是 | +| 56 | torch.nn.ParameterDict.values | 是 | +| 57 | torch.nn.Conv1d | 是 | +| 58 | torch.nn.Conv2d | 是 | +| 59 | torch.nn.Conv3d | 是 | +| 60 | torch.nn.ConvTranspose1d | 是 | +| 61 | torch.nn.ConvTranspose2d | 是 | +| 62 | torch.nn.ConvTranspose3d | 是 | +| 63 | torch.nn.Unfold | 是 | +| 64 | torch.nn.Fold | 是 | +| 65 | torch.nn.MaxPool1d | 是 | +| 66 | torch.nn.MaxPool2d | 是 | +| 67 | torch.nn.MaxPool3d | 是 | +| 68 | torch.nn.MaxUnpool1d | 是 | +| 69 | torch.nn.MaxUnpool2d | 是 | +| 70 | torch.nn.MaxUnpool3d | 是 | +| 71 | torch.nn.AvgPool1d | 是 | +| 72 | torch.nn.AvgPool2d | 是 | +| 73 | torch.nn.AvgPool3d | 是 | +| 74 | torch.nn.FractionalMaxPool2d | 否 | +| 75 | torch.nn.LPPool1d | 是 | +| 76 | torch.nn.LPPool2d | 是 | +| 77 | torch.nn.AdaptiveMaxPool1d | 是 | +| 78 | torch.nn.AdaptiveMaxPool2d | 是 | +| 79 | torch.nn.AdaptiveMaxPool3d | 否 | +| 80 | torch.nn.AdaptiveAvgPool1d | 是 | +| 81 | torch.nn.AdaptiveAvgPool2d | 是 | +| 82 | torch.nn.AdaptiveAvgPool3d | 是，仅支持D=1，H=1，W=1场景 | +| 83 | torch.nn.ReflectionPad1d | 否 | +| 84 | torch.nn.ReflectionPad2d | 是 | +| 85 | torch.nn.ReplicationPad1d | 否 | +| 86 | torch.nn.ReplicationPad2d | 是 | +| 87 | torch.nn.ReplicationPad3d | 否 | +| 88 | torch.nn.ZeroPad2d | 是 | +| 89 | torch.nn.ConstantPad1d | 是 | +| 90 | torch.nn.ConstantPad2d | 是 | +| 91 | torch.nn.ConstantPad3d | 是 | +| 92 | torch.nn.ELU | 是 | +| 93 | torch.nn.Hardshrink | 是 | +| 94 | torch.nn.Hardtanh | 是 | +| 95 | torch.nn.LeakyReLU | 是 | +| 96 | torch.nn.LogSigmoid | 是 | +| 97 | torch.nn.MultiheadAttention | 是 | +| 98 | torch.nn.PReLU | 是 | +| 99 | torch.nn.ReLU | 是 | +| 100 | torch.nn.ReLU6 | 是 | +| 101 | torch.nn.RReLU | 是 | +| 102 | torch.nn.SELU | 是 | +| 103 | torch.nn.CELU | 是 | +| 104 | torch.nn.GELU | 是 | +| 105 | torch.nn.Sigmoid | 是 | +| 106 | torch.nn.Softplus | 是 | +| 107 | torch.nn.Softshrink | 是，SoftShrink场景暂不支持 | +| 108 | torch.nn.Softsign | 是 | +| 109 | torch.nn.Tanh | 是 | +| 110 | torch.nn.Tanhshrink | 是 | +| 111 | torch.nn.Threshold | 是 | +| 112 | torch.nn.Softmin | 是 | +| 113 | torch.nn.Softmax | 是 | +| 114 | torch.nn.Softmax2d | 是 | +| 115 | torch.nn.LogSoftmax | 是 | +| 116 | torch.nn.AdaptiveLogSoftmaxWithLoss | 否 | +| 117 | torch.nn.AdaptiveLogSoftmaxWithLoss.log_prob | 否 | +| 118 | torch.nn.AdaptiveLogSoftmaxWithLoss.predict | 否 | +| 119 | torch.nn.BatchNorm1d | 是 | +| 120 | torch.nn.BatchNorm2d | 是 | +| 121 | torch.nn.BatchNorm3d | 是 | +| 122 | torch.nn.GroupNorm | 是 | +| 123 | torch.nn.SyncBatchNorm | 否 | +| 124 | torch.nn.SyncBatchNorm.convert_sync_batchnorm | 否 | +| 125 | torch.nn.InstanceNorm1d | 是 | +| 126 | torch.nn.InstanceNorm2d | 是 | +| 127 | torch.nn.InstanceNorm3d | 是 | +| 128 | torch.nn.LayerNorm | 是 | +| 129 | torch.nn.LocalResponseNorm | 是 | +| 130 | torch.nn.RNNBase | 是 | +| 131 | torch.nn.RNNBase.flatten_parameters | 是 | +| 132 | torch.nn.RNN | 是 | +| 133 | torch.nn.LSTM | 是 | +| 134 | torch.nn.GRU | 是，DynamicGRUV2场景暂不支持 | +| 135 | torch.nn.RNNCell | 是 | +| 136 | torch.nn.LSTMCell | 是 | +| 137 | torch.nn.GRUCell | 是 | +| 138 | torch.nn.Transformer | 是 | +| 139 | torch.nn.Transformer.forward | 是 | +| 140 | torch.nn.Transformer.generate_square_subsequent_mask | 是 | +| 141 | torch.nn.TransformerEncoder | 是 | +| 142 | torch.nn.TransformerEncoder.forward | 是 | +| 143 | torch.nn.TransformerDecoder | 是 | +| 144 | torch.nn.TransformerDecoder.forward | 是 | +| 145 | torch.nn.TransformerEncoderLayer | 是 | +| 146 | torch.nn.TransformerEncoderLayer.forward | 是 | +| 147 | torch.nn.TransformerDecoderLayer | 是 | +| 148 | torch.nn.TransformerDecoderLayer.forward | 是 | +| 149 | torch.nn.Identity | 是 | +| 150 | torch.nn.Linear | 是 | +| 151 | torch.nn.Bilinear | 是 | +| 152 | torch.nn.Dropout | 是 | +| 153 | torch.nn.Dropout2d | 是 | +| 154 | torch.nn.Dropout3d | 是 | +| 155 | torch.nn.AlphaDropout | 是 | +| 156 | torch.nn.Embedding | 是 | +| 157 | torch.nn.Embedding.from_pretrained | 是 | +| 158 | torch.nn.EmbeddingBag | 是 | +| 159 | torch.nn.EmbeddingBag.from_pretrained | 是 | +| 160 | torch.nn.CosineSimilarity | 是 | +| 161 | torch.nn.PairwiseDistance | 是 | +| 162 | torch.nn.L1Loss | 是 | +| 163 | torch.nn.MSELoss | 是 | +| 164 | torch.nn.CrossEntropyLoss | 是 | +| 165 | torch.nn.CTCLoss | 是 | +| 166 | torch.nn.NLLLoss | 是 | +| 167 | torch.nn.PoissonNLLLoss | 是 | +| 168 | torch.nn.KLDivLoss | 是 | +| 169 | torch.nn.BCELoss | 是 | +| 170 | torch.nn.BCEWithLogitsLoss | 是 | +| 171 | torch.nn.MarginRankingLoss | 是 | +| 172 | torch.nn.HingeEmbeddingLoss | 是 | +| 173 | torch.nn.MultiLabelMarginLoss | 是 | +| 174 | torch.nn.SmoothL1Loss | 是 | +| 175 | torch.nn.SoftMarginLoss | 是 | +| 176 | torch.nn.MultiLabelSoftMarginLoss | 是 | +| 177 | torch.nn.CosineEmbeddingLoss | 是 | +| 178 | torch.nn.MultiMarginLoss | 否 | +| 179 | torch.nn.TripletMarginLoss | 是 | +| 180 | torch.nn.PixelShuffle | 是 | +| 181 | torch.nn.Upsample | 是 | +| 182 | torch.nn.UpsamplingNearest2d | 是 | +| 183 | torch.nn.UpsamplingBilinear2d | 是 | +| 184 | torch.nn.DataParallel | 否 | +| 185 | torch.nn.parallel.DistributedDataParallel | 是 | +| 186 | torch.nn.parallel.DistributedDataParallel.no_sync | 是 | +| 187 | torch.nn.utils.clip_grad_norm_ | 是 | +| 188 | torch.nn.utils.clip_grad_value_ | 是 | +| 189 | torch.nn.utils.parameters_to_vector | 是 | +| 190 | torch.nn.utils.vector_to_parameters | 是 | +| 197 | torch.nn.utils.prune.PruningContainer | 是 | +| 198 | torch.nn.utils.prune.PruningContainer.add_pruning_method | 是 | +| 199 | torch.nn.utils.prune.PruningContainer.apply | 是 | +| 200 | torch.nn.utils.prune.PruningContainer.apply_mask | 是 | +| 201 | torch.nn.utils.prune.PruningContainer.compute_mask | 是 | +| 202 | torch.nn.utils.prune.PruningContainer.prune | 是 | +| 203 | torch.nn.utils.prune.PruningContainer.remove | 是 | +| 204 | torch.nn.utils.prune.Identity | 是 | +| 205 | torch.nn.utils.prune.Identity.apply | 是 | +| 206 | torch.nn.utils.prune.Identity.apply_mask | 是 | +| 207 | torch.nn.utils.prune.Identity.prune | 是 | +| 208 | torch.nn.utils.prune.Identity.remove | 是 | +| 209 | torch.nn.utils.prune.RandomUnstructured | 是 | +| 210 | torch.nn.utils.prune.RandomUnstructured.apply | 是 | +| 211 | torch.nn.utils.prune.RandomUnstructured.apply_mask | 是 | +| 212 | torch.nn.utils.prune.RandomUnstructured.prune | 是 | +| 213 | torch.nn.utils.prune.RandomUnstructured.remove | 是 | +| 214 | torch.nn.utils.prune.L1Unstructured | 是 | +| 215 | torch.nn.utils.prune.L1Unstructured.apply | 是 | +| 216 | torch.nn.utils.prune.L1Unstructured.apply_mask | 是 | +| 217 | torch.nn.utils.prune.L1Unstructured.prune | 是 | +| 218 | torch.nn.utils.prune.L1Unstructured.remove | 是 | +| 219 | torch.nn.utils.prune.RandomStructured | 是 | +| 220 | torch.nn.utils.prune.RandomStructured.apply | 是 | +| 221 | torch.nn.utils.prune.RandomStructured.apply_mask | 是 | +| 222 | torch.nn.utils.prune.RandomStructured.compute_mask | 是 | +| 223 | torch.nn.utils.prune.RandomStructured.prune | 是 | +| 224 | torch.nn.utils.prune.RandomStructured.remove | 是 | +| 225 | torch.nn.utils.prune.LnStructured | 是 | +| 226 | torch.nn.utils.prune.LnStructured.apply | 是 | +| 227 | torch.nn.utils.prune.LnStructured.apply_mask | 是 | +| 228 | torch.nn.utils.prune.LnStructured.compute_mask | 是 | +| 229 | torch.nn.utils.prune.LnStructured.prune | 是 | +| 230 | torch.nn.utils.prune.LnStructured.remove | 是 | +| 231 | torch.nn.utils.prune.CustomFromMask | 是 | +| 232 | torch.nn.utils.prune.CustomFromMask.apply | 是 | +| 233 | torch.nn.utils.prune.CustomFromMask.apply_mask | 是 | +| 234 | torch.nn.utils.prune.CustomFromMask.prune | 是 | +| 235 | torch.nn.utils.prune.CustomFromMask.remove | 是 | +| 236 | torch.nn.utils.prune.identity | 是 | +| 237 | torch.nn.utils.prune.random_unstructured | 是 | +| 238 | torch.nn.utils.prune.l1_unstructured | 是 | +| 239 | torch.nn.utils.prune.random_structured | 是 | +| 240 | torch.nn.utils.prune.ln_structured | 是 | +| 241 | torch.nn.utils.prune.global_unstructured | 是 | +| 242 | torch.nn.utils.prune.custom_from_mask | 是 | +| 243 | torch.nn.utils.prune.remove | 是 | +| 244 | torch.nn.utils.prune.is_pruned | 是 | +| 245 | torch.nn.utils.weight_norm | 是 | +| 246 | torch.nn.utils.remove_weight_norm | 是 | +| 247 | torch.nn.utils.spectral_norm | 是 | +| 248 | torch.nn.utils.remove_spectral_norm | 是 | +| 249 | torch.nn.utils.rnn.PackedSequence | 是 | +| 250 | torch.nn.utils.rnn.pack_padded_sequence | 是 | +| 251 | torch.nn.utils.rnn.pad_packed_sequence | 否 | +| 252 | torch.nn.utils.rnn.pad_sequence | 是 | +| 253 | torch.nn.utils.rnn.pack_sequence | 否 | +| 254 | torch.nn.Flatten | 是 | +| 255 | torch.quantization.quantize | 否 | +| 256 | torch.quantization.quantize_dynamic | 否 | +| 257 | torch.quantization.quantize_qat | 否 | +| 258 | torch.quantization.prepare | 是 | +| 259 | torch.quantization.prepare_qat | 否 | +| 260 | torch.quantization.convert | 否 | +| 261 | torch.quantization.QConfig | 是 | +| 262 | torch.quantization.QConfigDynamic | 是 | +| 263 | torch.quantization.fuse_modules | 是 | +| 264 | torch.quantization.QuantStub | 是 | +| 265 | torch.quantization.DeQuantStub | 是 | +| 266 | torch.quantization.QuantWrapper | 是 | +| 267 | torch.quantization.add_quant_dequant | 是 | +| 268 | torch.quantization.add_observer_ | 是 | +| 269 | torch.quantization.swap_module | 是 | +| 270 | torch.quantization.propagate_qconfig_ | 是 | +| 271 | torch.quantization.default_eval_fn | 是 | +| 272 | torch.quantization.MinMaxObserver | 是 | +| 273 | torch.quantization.MovingAverageMinMaxObserver | 是 | +| 274 | torch.quantization.PerChannelMinMaxObserver | 是 | +| 275 | torch.quantization.MovingAveragePerChannelMinMaxObserver | 是 | +| 276 | torch.quantization.HistogramObserver | 否 | +| 277 | torch.quantization.FakeQuantize | 否 | +| 278 | torch.quantization.NoopObserver | 是 | +| 279 | torch.quantization.get_observer_dict | 是 | +| 280 | torch.quantization.RecordingObserver | 是 | +| 281 | torch.nn.intrinsic.ConvBn2d | 是 | +| 282 | torch.nn.intrinsic.ConvBnReLU2d | 是 | +| 283 | torch.nn.intrinsic.ConvReLU2d | 是 | +| 284 | torch.nn.intrinsic.ConvReLU3d | 是 | +| 285 | torch.nn.intrinsic.LinearReLU | 是 | +| 286 | torch.nn.intrinsic.qat.ConvBn2d | 否 | +| 287 | torch.nn.intrinsic.qat.ConvBnReLU2d | 否 | +| 288 | torch.nn.intrinsic.qat.ConvReLU2d | 否 | +| 289 | torch.nn.intrinsic.qat.LinearReLU | 否 | +| 290 | torch.nn.intrinsic.quantized.ConvReLU2d | 否 | +| 291 | torch.nn.intrinsic.quantized.ConvReLU3d | 否 | +| 292 | torch.nn.intrinsic.quantized.LinearReLU | 否 | +| 293 | torch.nn.qat.Conv2d | 否 | +| 294 | torch.nn.qat.Conv2d.from_float | 否 | +| 295 | torch.nn.qat.Linear | 否 | +| 296 | torch.nn.qat.Linear.from_float | 否 | +| 297 | torch.nn.quantized.functional.relu | 否 | +| 298 | torch.nn.quantized.functional.linear | 否 | +| 299 | torch.nn.quantized.functional.conv2d | 否 | +| 300 | torch.nn.quantized.functional.conv3d | 否 | +| 301 | torch.nn.quantized.functional.max_pool2d | 否 | +| 302 | torch.nn.quantized.functional.adaptive_avg_pool2d | 否 | +| 303 | torch.nn.quantized.functional.avg_pool2d | 否 | +| 304 | torch.nn.quantized.functional.interpolate | 否 | +| 305 | torch.nn.quantized.functional.upsample | 否 | +| 306 | torch.nn.quantized.functional.upsample_bilinear | 否 | +| 307 | torch.nn.quantized.functional.upsample_nearest | 否 | +| 308 | torch.nn.quantized.ReLU | 否 | +| 309 | torch.nn.quantized.ReLU6 | 否 | +| 310 | torch.nn.quantized.Conv2d | 否 | +| 311 | torch.nn.quantized.Conv2d.from_float | 否 | +| 312 | torch.nn.quantized.Conv3d | 否 | +| 313 | torch.nn.quantized.Conv3d.from_float | 否 | +| 314 | torch.nn.quantized.FloatFunctional | 是 | +| 315 | torch.nn.quantized.QFunctional | 否 | +| 316 | torch.nn.quantized.Quantize | 是 | +| 317 | torch.nn.quantized.DeQuantize | 否 | +| 318 | torch.nn.quantized.Linear | 否 | +| 319 | torch.nn.quantized.Linear.from_float | 否 | +| 320 | torch.nn.quantized.dynamic.Linear | 否 | +| 321 | torch.nn.quantized.dynamic.Linear.from_float | 否 | +| 322 | torch.nn.quantized.dynamic.LSTM | 否 | + +## Functions(torch.nn.functional) + +| 序号 | API名称 | 是否支持（PyTorch1.5.0） | +| ---- | ---------------------------------------------------- | --------------------------- | +| 1 | torch.nn.functional.conv1d | 是 | +| 2 | torch.nn.functional.conv2d | 是 | +| 3 | torch.nn.functional.conv3d | 是 | +| 4 | torch.nn.functional.conv_transpose1d | 是 | +| 5 | torch.nn.functional.conv_transpose2d | 是 | +| 6 | torch.nn.functional.conv_transpose3d | 是 | +| 7 | torch.nn.functional.unfold | 是 | +| 8 | torch.nn.functional.fold | 是 | +| 9 | torch.nn.functional.avg_pool1d | 是 | +| 10 | torch.nn.functional.avg_pool2d | 是 | +| 11 | torch.nn.functional.avg_pool3d | 是 | +| 12 | torch.nn.functional.max_pool1d | 是 | +| 13 | torch.nn.functional.max_pool2d | 是 | +| 14 | torch.nn.functional.max_pool3d | 是 | +| 15 | torch.nn.functional.max_unpool1d | 是 | +| 16 | torch.nn.functional.max_unpool2d | 是 | +| 17 | torch.nn.functional.max_unpool3d | 是 | +| 18 | torch.nn.functional.lp_pool1d | 是 | +| 19 | torch.nn.functional.lp_pool2d | 是 | +| 20 | torch.nn.functional.adaptive_max_pool1d | 是 | +| 21 | torch.nn.functional.adaptive_max_pool2d | 是 | +| 22 | torch.nn.functional.adaptive_max_pool3d | 否 | +| 23 | torch.nn.functional.adaptive_avg_pool1d | 是 | +| 24 | torch.nn.functional.adaptive_avg_pool2d | 是 | +| 25 | torch.nn.functional.adaptive_avg_pool3d | 是，仅支持D=1，H=1，W=1场景 | +| 26 | torch.nn.functional.threshold | 是 | +| 27 | torch.nn.functional.threshold_ | 是 | +| 28 | torch.nn.functional.relu | 是 | +| 29 | torch.nn.functional.relu_ | 是 | +| 30 | torch.nn.functional.hardtanh | 是 | +| 31 | torch.nn.functional.hardtanh_ | 是 | +| 32 | torch.nn.functional.relu6 | 是 | +| 33 | torch.nn.functional.elu | 是 | +| 34 | torch.nn.functional.elu_ | 是 | +| 35 | torch.nn.functional.selu | 是 | +| 36 | torch.nn.functional.celu | 是 | +| 37 | torch.nn.functional.leaky_relu | 是 | +| 38 | torch.nn.functional.leaky_relu_ | 是 | +| 39 | torch.nn.functional.prelu | 是 | +| 40 | torch.nn.functional.rrelu | 是 | +| 41 | torch.nn.functional.rrelu_ | 是 | +| 42 | torch.nn.functional.glu | 是 | +| 43 | torch.nn.functional.gelu | 是 | +| 44 | torch.nn.functional.logsigmoid | 是 | +| 45 | torch.nn.functional.hardshrink | 是 | +| 46 | torch.nn.functional.tanhshrink | 是 | +| 47 | torch.nn.functional.softsign | 是 | +| 48 | torch.nn.functional.softplus | 是 | +| 49 | torch.nn.functional.softmin | 是 | +| 50 | torch.nn.functional.softmax | 是 | +| 51 | torch.nn.functional.softshrink | 是 | +| 52 | torch.nn.functional.gumbel_softmax | 否 | +| 53 | torch.nn.functional.log_softmax | 是 | +| 54 | torch.nn.functional.tanh | 是 | +| 55 | torch.nn.functional.sigmoid | 是 | +| 56 | torch.nn.functional.batch_norm | 是 | +| 57 | torch.nn.functional.instance_norm | 是 | +| 58 | torch.nn.functional.layer_norm | 是 | +| 59 | torch.nn.functional.local_response_norm | 是 | +| 60 | torch.nn.functional.normalize | 是 | +| 61 | torch.nn.functional.linear | 是 | +| 62 | torch.nn.functional.bilinear | 是 | +| 63 | torch.nn.functional.dropout | 是 | +| 64 | torch.nn.functional.alpha_dropout | 是 | +| 65 | torch.nn.functional.dropout2d | 是 | +| 66 | torch.nn.functional.dropout3d | 是 | +| 67 | torch.nn.functional.embedding | 是 | +| 68 | torch.nn.functional.embedding_bag | 是 | +| 69 | torch.nn.functional.one_hot | 是 | +| 70 | torch.nn.functional.pairwise_distance | 是 | +| 71 | torch.nn.functional.cosine_similarity | 是 | +| 72 | torch.nn.functional.pdist | 是 | +| 73 | torch.nn.functional.binary_cross_entropy | 是 | +| 74 | torch.nn.functional.binary_cross_entropy_with_logits | 是 | +| 75 | torch.nn.functional.poisson_nll_loss | 是 | +| 76 | torch.nn.functional.cosine_embedding_loss | 是 | +| 77 | torch.nn.functional.cross_entropy | 是 | +| 78 | torch.nn.functional.ctc_loss | 是（仅支持2维输入） | +| 79 | torch.nn.functional.hinge_embedding_loss | 是 | +| 80 | torch.nn.functional.kl_div | 是 | +| 81 | torch.nn.functional.l1_loss | 是 | +| 82 | torch.nn.functional.mse_loss | 是 | +| 83 | torch.nn.functional.margin_ranking_loss | 是 | +| 84 | torch.nn.functional.multilabel_margin_loss | 是 | +| 85 | torch.nn.functional.multilabel_soft_margin_loss | 是 | +| 86 | torch.nn.functional.multi_margin_loss | 否 | +| 87 | torch.nn.functional.nll_loss | 是 | +| 88 | torch.nn.functional.smooth_l1_loss | 是 | +| 89 | torch.nn.functional.soft_margin_loss | 是 | +| 90 | torch.nn.functional.triplet_margin_loss | 是 | +| 91 | torch.nn.functional.pixel_shuffle | 是 | +| 92 | torch.nn.functional.pad | 是 | +| 93 | torch.nn.functional.interpolate | 是 | +| 94 | torch.nn.functional.upsample | 是 | +| 95 | torch.nn.functional.upsample_nearest | 是 | +| 96 | torch.nn.functional.upsample_bilinear | 是 | +| 97 | torch.nn.functional.grid_sample | 是 | +| 98 | torch.nn.functional.affine_grid | 是 | +| 99 | torch.nn.parallel.data_parallel | 否 | + +## torch.distributed + +| 序号 | API名称 | 是否支持（PyTorch1.5.0） | +| ---- | ------------------------------------- | ------------------------ | +| 1 | torch.distributed.init_process_group | 是 | +| 2 | torch.distributed.Backend | 是 | +| 3 | torch.distributed.get_backend | 是 | +| 4 | torch.distributed.get_rank | 是 | +| 5 | torch.distributed.get_world_size | 是 | +| 6 | torch.distributed.is_initialized | 是 | +| 7 | torch.distributed.is_mpi_available | 是 | +| 8 | torch.distributed.is_nccl_available | 是 | +| 9 | torch.distributed.new_group | 是 | +| 10 | torch.distributed.send | 否 | +| 11 | torch.distributed.recv | 否 | +| 12 | torch.distributed.isend | 否 | +| 13 | torch.distributed.irecv | 否 | +| 14 | is_completed | 是 | +| 15 | wait | 是 | +| 16 | torch.distributed.broadcast | 是 | +| 17 | torch.distributed.all_reduce | 是 | +| 18 | torch.distributed.reduce | 否 | +| 19 | torch.distributed.all_gather | 是 | +| 20 | torch.distributed.gather | 否 | +| 21 | torch.distributed.scatter | 否 | +| 22 | torch.distributed.barrier | 是 | +| 23 | torch.distributed.ReduceOp | 是 | +| 24 | torch.distributed.reduce_op | 是 | +| 25 | torch.distributed.broadcast_multigpu | 否 | +| 26 | torch.distributed.all_reduce_multigpu | 否 | +| 27 | torch.distributed.reduce_multigpu | 否 | +| 28 | torch.distributed.all_gather_multigpu | 否 | +| 29 | torch.distributed.launch | 是 | +| 30 | torch.multiprocessing.spawn | 是 | + +## torch.npu + +| 序号 | API名称 | npu对应API名称 | 是否支持（PyTorch1.5.0） | +| ---- | ------------------------------------- | ------------------------------------ | ------------------------ | +| 1 | torch.cuda.current_blas_handle | torch.npu.current_blas_handle | 否 | +| 2 | torch.cuda.current_device | torch.npu.current_device | 是 | +| 3 | torch.cuda.current_stream | torch.npu.current_stream | 是 | +| 4 | torch.cuda.default_stream | torch.npu.default_stream | 是 | +| 5 | torch.cuda.device | torch.npu.device | 是 | +| 6 | torch.cuda.device_count | torch.npu.device_count | 是 | +| 7 | torch.cuda.device_of | torch.npu.device_of | 是 | +| 8 | torch.cuda.get_device_capability | torch.npu.get_device_capability | 否 | +| 9 | torch.cuda.get_device_name | torch.npu.get_device_name | 否 | +| 10 | torch.cuda.init | torch.npu.init | 是 | +| 11 | torch.cuda.ipc_collect | torch.npu.ipc_collect | 否 | +| 12 | torch.cuda.is_available | torch.npu.is_available | 是 | +| 13 | torch.cuda.is_initialized | torch.npu.is_initialized | 是 | +| 14 | torch.cuda.set_device | torch.npu.set_device | 部分支持 | +| 15 | torch.cuda.stream | torch.npu.stream | 是 | +| 16 | torch.cuda.synchronize | torch.npu.synchronize | 是 | +| 17 | torch.cuda.get_rng_state | torch.npu.get_rng_state | 否 | +| 18 | torch.cuda.get_rng_state_all | torch.npu.get_rng_state_all | 否 | +| 19 | torch.cuda.set_rng_state | torch.npu.set_rng_state | 否 | +| 20 | torch.cuda.set_rng_state_all | torch.npu.set_rng_state_all | 否 | +| 21 | torch.cuda.manual_seed | torch.npu.manual_seed | 否 | +| 22 | torch.cuda.manual_seed_all | torch.npu.manual_seed_all | 否 | +| 23 | torch.cuda.seed | torch.npu.seed | 否 | +| 24 | torch.cuda.seed_all | torch.npu.seed_all | 否 | +| 25 | torch.cuda.initial_seed | torch.npu.initial_seed | 否 | +| 26 | torch.cuda.comm.broadcast | torch.npu.comm.broadcast | 否 | +| 27 | torch.cuda.comm.broadcast_coalesced | torch.npu.comm.broadcast_coalesced | 否 | +| 28 | torch.cuda.comm.reduce_add | torch.npu.comm.reduce_add | 否 | +| 29 | torch.cuda.comm.scatter | torch.npu.comm.scatter | 否 | +| 30 | torch.cuda.comm.gather | torch.npu.comm.gather | 否 | +| 31 | torch.cuda.Stream | torch.npu.Stream | 是 | +| 32 | torch.cuda.Stream.query | torch.npu.Stream.query | 是 | +| 33 | torch.cuda.Stream.record_event | torch.npu.Stream.record_event | 是 | +| 34 | torch.cuda.Stream.synchronize | torch.npu.Stream.synchronize | 是 | +| 35 | torch.cuda.Stream.wait_event | torch.npu.Stream.wait_event | 是 | +| 36 | torch.cuda.Stream.wait_stream | torch.npu.Stream.wait_stream | 是 | +| 37 | torch.cuda.Event | torch.npu.Event | 是 | +| 38 | torch.cuda.Event.elapsed_time | torch.npu.Event.elapsed_time | 是 | +| 39 | torch.cuda.Event.from_ipc_handle | torch.npu.Event.from_ipc_handle | 否 | +| 40 | torch.cuda.Event.ipc_handle | torch.npu.Event.ipc_handle | 否 | +| 41 | torch.cuda.Event.query | torch.npu.Event.query | 是 | +| 42 | torch.cuda.Event.record | torch.npu.Event.record | 是 | +| 43 | torch.cuda.Event.synchronize | torch.npu.Event.synchronize | 是 | +| 44 | torch.cuda.Event.wait | torch.npu.Event.wait | 是 | +| 45 | torch.cuda.empty_cache | torch.npu.empty_cache | 是 | +| 46 | torch.cuda.memory_stats | torch.npu.memory_stats | 是 | +| 47 | torch.cuda.memory_summary | torch.npu.memory_summary | 是 | +| 48 | torch.cuda.memory_snapshot | torch.npu.memory_snapshot | 是 | +| 49 | torch.cuda.memory_allocated | torch.npu.memory_allocated | 是 | +| 50 | torch.cuda.max_memory_allocated | torch.npu.max_memory_allocated | 是 | +| 51 | torch.cuda.reset_max_memory_allocated | torch.npu.reset_max_memory_allocated | 是 | +| 52 | torch.cuda.memory_reserved | torch.npu.memory_reserved | 是 | +| 53 | torch.cuda.max_memory_reserved | torch.npu.max_memory_reserved | 是 | +| 54 | torch.cuda.memory_cached | torch.npu.memory_cached | 是 | +| 55 | torch.cuda.max_memory_cached | torch.npu.max_memory_cached | 是 | +| 56 | torch.cuda.reset_max_memory_cached | torch.npu.reset_max_memory_cached | 是 | +| 57 | torch.cuda.nvtx.mark | torch.npu.nvtx.mark | 否 | +| 58 | torch.cuda.nvtx.range_push | torch.npu.nvtx.range_push | 否 | +| 59 | torch.cuda.nvtx.range_pop | torch.npu.nvtx.range_pop | 否 | +| 60 | torch.cuda._sleep | torch.npu._sleep | 否 | +| 61 | torch.cuda.Stream.priority_range | torch.npu.Stream.priority_range | 否 | +| 62 | torch.cuda.get_device_properties | torch.npu.get_device_properties | 否 | +| 63 | torch.cuda.amp.GradScaler | torch.npu.amp.GradScaler | 否 | + +torch.npu.set_device()接口只支持在程序开始的位置通过set_device进行指定，不支持多次指定和with torch.npu.device(id)方式的device切换 + +## NPU自定义算子清单 + +| 序号 | PyTorch 算子（由昇腾开发） | 昇腾适配算子 | +| ---- | ---------------------------------------------- | ---------------------------------------------- | +| 1 | npu_convolution_transpose | npu_convolution_transpose | +| 2 | npu_conv_transpose2d | conv_transpose2d_npu | +| 3 | npu_convolution_transpose_backward | npu_convolution_transpose_backward | +| 4 | npu_conv_transpose2d_backward | conv_transpose2d_backward_npu | +| 5 | npu_conv_transpose3d_backward | conv_transpose3d_backward_npu | +| 6 | npu_convolution | npu_convolution | +| 7 | npu_convolution_backward | npu_convolution_backward | +| 8 | npu_convolution_double_backward | npu_convolution_double_backward | +| 9 | npu_conv2d | conv2d_npu | +| 10 | npu_conv2d.out | conv2d_out_npu | +| 11 | npu_conv2d_backward | conv2d_backward_npu | +| 12 | npu_conv3d | conv3d_npu | +| 13 | npu_conv3d.out | conv3d_out_npu | +| 14 | npu_conv3d_backward | conv3d_backward_npu | +| 15 | one_ | one_npu_ | +| 16 | npu_sort_v2.out | sort_without_indices_out_npu | +| 17 | npu_sort_v2 | sort_without_indices_npu | +| 18 | npu_format_cast | format_cast_npu | +| 19 | npu_format_cast_.acl_format | format_cast_npu_ | +| 20 | npu_format_cast_.src | format_cast_npu_ | +| 21 | npu_transpose_to_contiguous | transpose_to_contiguous_npu | +| 22 | npu_transpose | transpose_npu | +| 23 | npu_transpose.out | transpose_out_npu | +| 24 | npu_broadcast | broadcast_npu | +| 25 | npu_broadcast.out | broadcast_out_npu | +| 26 | npu_dtype_cast | dtype_cast_npu | +| 27 | npu_dtype_cast_.Tensor | dtype_cast_npu_ | +| 28 | npu_roi_alignbk | roi_align_backward_npu | +| 29 | empty_with_format | empty_with_format_npu | +| 30 | empty_with_format.names | empty_with_format_npu | +| 31 | copy_memory_ | copy_memory_npu_ | +| 32 | npu_one_hot | one_hot_npu | +| 33 | npu_stride_add | stride_add_npu | +| 34 | npu_softmax_cross_entropy_with_logits | softmax_cross_entropy_with_logits_npu | +| 35 | npu_softmax_cross_entropy_with_logits_backward | softmax_cross_entropy_with_logits_backward_npu | +| 36 | npu_ps_roi_pooling | ps_roi_pooling_npu | +| 37 | npu_ps_roi_pooling_backward | ps_roi_pooling_backward_npu | +| 38 | npu_roi_align | roi_align_npu | +| 39 | npu_nms_v4 | nms_v4_npu | +| 40 | npu_lstm | lstm_npu | +| 41 | npu_lstm_backward | lstm_backward_npu | +| 42 | npu_iou | iou_npu | +| 43 | npu_ptiou | ptiou_npu | +| 44 | npu_nms_with_mask | nms_with_mask_npu | +| 45 | npu_pad | pad_npu | +| 46 | npu_bounding_box_encode | bounding_box_encode_npu | +| 47 | npu_bounding_box_decode | bounding_box_decode_npu | +| 48 | npu_gru | gru_npu | +| 49 | npu_gru_backward | gru_backward_npu | +| 50 | npu_set_.source_Storage_storage_offset_format | set_npu_ | +| 51 | npu_random_choice_with_mask | random_choice_with_mask_npu | +| 52 | npu_batch_nms | batch_nms_npu | +| 53 | npu_slice | slice_npu | +| 54 | npu_slice.out | slice_out_npu | +| 55 | npu_dropoutV2 | dropout_v2_npu | +| 56 | npu_dropoutV2_backward | dropout_v2_backward_npu | +| 57 | _npu_dropout | _dropout_npu | +| 58 | _npu_dropout_inplace | _dropout_npu_inplace | +| 59 | npu_dropout_backward | dropout_backward_npu | +| 60 | npu_indexing | indexing_npu | +| 61 | npu_indexing.out | indexing_out_npu | +| 62 | npu_ifmr | ifmr_npu | +| 63 | npu_max.dim | max_v1_npu | +| 64 | npu_max.names_dim | max_v1_npu | +| 65 | npu_scatter | scatter_npu | +| 66 | npu_max_backward | max_backward_npu | +| 67 | npu_apply_adam | apply_adam_npu | +| 68 | npu_layer_norm_eval | layer_norm_eval_npu | +| 69 | npu_alloc_float_status | alloc_float_status_npu | +| 70 | npu_get_float_status | get_float_status_npu | +| 71 | npu_clear_float_status | clear_float_status_npu | +| 72 | npu_confusion_transpose | confusion_transpose_npu | +| 73 | npu_confusion_transpose_backward | confusion_transpose_backward_npu | +| 74 | npu_bmmV2 | bmm_v2_npu | +| 75 | fast_gelu | fast_gelu_npu | +| 76 | fast_gelu_backward | fast_gelu_backward_npu | +| 77 | npu_sub_sample | sub_sample_npu | +| 78 | npu_deformable_conv2d | deformable_conv2d_npu | +| 79 | npu_deformable_conv2dbk | deformable_conv2d_backward_npu | +| 80 | npu_mish | mish_npu | +| 81 | npu_anchor_response_flags | anchor_response_flags_npu | +| 82 | npu_yolo_boxes_encode | yolo_boxes_encode_npu | +| 83 | npu_grid_assign_positive | grid_assign_positive_npu | +| 84 | npu_mish_backward | mish_backward_npu | +| 85 | npu_normalize_batch | normalize_batch_npu | +| 86 | npu_masked_fill_range | masked_fill_range_npu | +| 87 | npu_linear | linear_npu | +| 88 | npu_linear_backward | linear_backward_npu | +| 89 | npu_bert_apply_adam | bert_apply_adam_npu | +| 90 | npu_giou | giou_npu | +| 91 | npu_giou_backward | giou_backward_npu | + +算子接口说明： + +> ``` +> npu_apply_adam(beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, use_locking, use_nesterov, out = (var, m, v)) +> ``` + +count adam result. + +- Parameters： + - **beta1_power** (Number) - + - **beta2_power** (Number) - + - **lr** (Number) - learning rate. + - **beta1** (Number) - exponential decay rate for the 1st moment estimates. + - **beta2** (Number) - exponential decay rate for the 2nd moment estimates. + - **epsilon** (Number) - term added to the denominator to improve numerical stability. + - **grad** (Tensor) - the gradient. + - **use_locking** (bool) - If `True` use locks for update operations. + - **use_nesterov** (bool) -If `True`, uses the nesterov update. + - **var** (Tensor) - + - **m** (Tensor) - + - **v** (Tensor) - + +- constraints： + + None + +- Examples： + + None + +> npu_bert_apply_adam(lr, beta1, beta2, epsilon, grad, max_grad_norm, global_grad_norm, weight_decay, out = (var, m, v)) + +count adam result in bert. + +- Parameters： + - **lr** (Number) - learning rate. + - **beta1** (Number) - exponential decay rate for the 1st moment estimates. + - **beta2** (Number) - exponential decay rate for the 2nd moment estimates. + - **epsilon** (Number) - term added to the denominator to improve numerical stability. + - **grad** (Tensor) - the gradient. + - **max_grad_norm** (Number) - + - **global_grad_norm** (Number) - + - **weight_decay** (Number) - weight decay + - **var** (Tensor) - + - **m** (Tensor) - + - **v** (Tensor) - + +- constraints： + + None + +- Examples： + + None + + + diff --git "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.8.1.md" "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.8.1.md" index 35fd77cdc68..dc01f67e671 100644 --- "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.8.1.md" +++ "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.8.1.md" @@ -1042,7 +1042,7 @@ From the `torch.nn.utils` module | 62 | torch.cuda.get_device_properties | torch.npu.get_device_properties | 否 | | 63 | torch.cuda.amp.GradScaler | torch.npu.amp.GradScaler | 否 | -## NPU自定义算子清单 +## NPU自定义算子 | 序号 | PyTorch 算子（由昇腾开发） | 昇腾适配算子 | | ---- | ---------------------------------------------- | ---------------------------------------------- | @@ -1136,4 +1136,60 @@ From the `torch.nn.utils` module | 88 | npu_linear_backward | linear_backward_npu | | 89 | npu_bert_apply_adam | bert_apply_adam_npu | | 90 | npu_giou | giou_npu | -| 91 | npu_giou_backward | giou_backward_npu | \ No newline at end of file +| 91 | npu_giou_backward | giou_backward_npu | + +算子接口算子接口说明： + +> ``` +> npu_apply_adam(beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, use_locking, use_nesterov, out = (var, m, v)) +> ``` + +count adam result. + +- Parameters： + - **beta1_power** (Number) - + - **beta2_power** (Number) - + - **lr** (Number) - learning rate. + - **beta1** (Number) - exponential decay rate for the 1st moment estimates. + - **beta2** (Number) - exponential decay rate for the 2nd moment estimates. + - **epsilon** (Number) - term added to the denominator to improve numerical stability. + - **grad** (Tensor) - the gradient. + - **use_locking** (bool) - If `True` use locks for update operations. + - **use_nesterov** (bool) -If `True`, uses the nesterov update. + - **var** (Tensor) - + - **m** (Tensor) - + - **v** (Tensor) - + +- constraints： + + None + +- Examples： + + None + +> npu_bert_apply_adam(lr, beta1, beta2, epsilon, grad, max_grad_norm, global_grad_norm, weight_decay, out = (var, m, v)) + +count adam result in bert. + +- Parameters： + - **lr** (Number) - learning rate. + - **beta1** (Number) - exponential decay rate for the 1st moment estimates. + - **beta2** (Number) - exponential decay rate for the 2nd moment estimates. + - **epsilon** (Number) - term added to the denominator to improve numerical stability. + - **grad** (Tensor) - the gradient. + - **max_grad_norm** (Number) - + - **global_grad_norm** (Number) - + - **weight_decay** (Number) - weight decay + - **var** (Tensor) - + - **m** (Tensor) - + - **v** (Tensor) - + +- constraints： + + None + +- Examples： + + None + -- Gitee From bbb497e49574371f5f40be9fe0d262ad904d8742 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Tue, 7 Dec 2021 15:10:42 +0800 Subject: [PATCH 14/55] add pytorch train --- ...02\351\205\215\346\214\207\345\215\227.md" | 195 ++++++++++++++++++ 1 file changed, 195 insertions(+) create mode 100644 "docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" diff --git "a/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" new file mode 100644 index 00000000000..0b3fba33562 --- /dev/null +++ "b/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" @@ -0,0 +1,195 @@ +# 概述 + +该文档帮助用户快速实现Pytorch单机多卡模型在集群上使用DDP模式训练。 + +# 适配流程 + +1. 准备环境。 + + 准备模型多机多卡训练的软件、硬件、网络环境，包括开发和运行环境搭建、集群组网链接、芯片IP设置、防火墙设置等。 + +2. 准备单机多卡模型。 + + 该文档帮助用户实现单机多卡训练模型到多机多卡训练的适配，故用户需提前准备单机多卡模型，可以从开源社区获取，也可以自行实现。 + +3. 修改模型脚本。 + + 修改单机多卡模型启动脚本和环境变量，使模型支持在多机多卡训练。 + +4. 启动训练。 + + 实现在多机多卡环境启动模型训练并查看训练日志。 + + + +# 快速上手 + +## 概述 + +使用resnet50模型实现两台计算机8卡训练。两台计算机分别命名为AI Server0、AI Server1，每台计算机上的8个Ascend 910处理器命名为device 0~7。 + +## 环境准备 + +首先您需要具有至少两台装有Ascend 910处理器的计算机，并保证每台计算机都正确的安装NPU固件和驱动。 + +1. 准备开发和运行环境。 + + 在每台计算机上完成下面开发和运行环境准备。 + + - 完成CANN开发和运行环境的安装，请参见《CANN 软件安装指南》。 + + - 安装适配NPU的PyTorch，安装方法请参见《PyTorch安装指南》。 + +2. 准备组网。 + + 通过交换机或光口直连的方式完成计算设备组网搭建，搭建方法请参见《[数据中心训练场景组网](https://support.huawei.com/enterprise/zh/doc/EDOC1100221993/229cc0e4)》。 + + 该示例中采用两台计算机8卡进行训练，故可以采用光口直连的方式准备组网。 + +3. 配置device IP + + 在AI Server0上配置device IP。 + + ```shell + hccn_tool -i 0 -ip -s address 192.168.100.101 netmask 255.255.255.0 + hccn_tool -i 1 -ip -s address 192.168.101.101 netmask 255.255.255.0 + hccn_tool -i 2 -ip -s address 192.168.102.101 netmask 255.255.255.0 + hccn_tool -i 3 -ip -s address 192.168.103.101 netmask 255.255.255.0 + hccn_tool -i 4 -ip -s address 192.168.100.100 netmask 255.255.255.0 + hccn_tool -i 5 -ip -s address 192.168.101.100 netmask 255.255.255.0 + hccn_tool -i 6 -ip -s address 192.168.102.100 netmask 255.255.255.0 + hccn_tool -i 7 -ip -s address 192.168.103.100 netmask 255.255.255.0 + ``` + + 在AI Server1上配置device IP。 + + ```shell + hccn_tool -i 0 -ip -s address 192.168.100.111 netmask 255.255.255.0 + hccn_tool -i 1 -ip -s address 192.168.101.111 netmask 255.255.255.0 + hccn_tool -i 2 -ip -s address 192.168.102.111 netmask 255.255.255.0 + hccn_tool -i 3 -ip -s address 192.168.103.111 netmask 255.255.255.0 + hccn_tool -i 4 -ip -s address 192.168.100.110 netmask 255.255.255.0 + hccn_tool -i 5 -ip -s address 192.168.101.110 netmask 255.255.255.0 + hccn_tool -i 6 -ip -s address 192.168.102.110 netmask 255.255.255.0 + hccn_tool -i 7 -ip -s address 192.168.103.110 netmask 255.255.255.0 + ``` + +4. 配置防火墙 + + - Ubuntu系统防火墙关闭命令 + + ```shell + ufw disable + + - Redhat或CentOS 7系统防火墙关闭命令 + + ```shell + systemctl stop firewalld + +## 准备单机多卡模型 + +1. 获取resnet50模型。 + + ```shell + git clone https://gitee.com/ascend/modelzoo.git + cd modelzoo + git reset --hard ca699fcc4022fbea5a0bcf892223abbfaed07a85 + cd built-in/PyTorch/Official/cv/image_classification/ResNet50_for_PyTorch + ``` + +2. 在单机多卡上运行训练，确保模型正确。 + + 运行操作参见[README.md](https://gitee.com/ascend/modelzoo/blob/master/built-in/PyTorch/Official/cv/image_classification/ResNet50_for_PyTorch/README.md)。 + + + +## 修改模型脚本 + +1. 声明的**MASTER_ADDR**变量的值从脚本外部接收** + +os.environ['MASTER_ADDR'] = addr + +![img](file:///C:\Users\ZoeJ\AppData\Local\Temp\ksohtml\wps39A.tmp.jpg) + +其中addr变量是master节点的host ip，该点体现在启动命令中， 举例模型脚本中已经有该声明，不用再添加； + +2. **声明HCCL环境变量** + +在启动2*8集群训练之前，不同于单机多卡的是， 要声明下HCCL_IF_IP变量 + +和HCCL_WHITELIST_DISABLE这2个环境变量。 + +（1）HCCL_WHITELIST_DISABLE，HCCL通道白名单，一般设置为1，表示关闭白名单。在2台服务器上都声明export HCCL_WHITELIST_DISABLE=1 + +（2）HCCL_IF_IP, HCCL初始化通信网卡IP，设置为当前服务器的host网卡。2*8集群中，在AI Server0上声明export HCCL_IF_IP=AI Server0 host ip, 在AI Server1上声明export HCCL_IF_IP=AI Server1 host ip。 + + + +本举例模型脚本中已存在相关改动点， 执行单机多卡训练和多机多卡训练的区别体现在启动命令上， 多机多卡脚本使用上面的mian.py即可。 + + + +## 启动训练 + +（1）将模型脚本上传至AI Server0和AIServer1； + +（2）对照requirements.txt，检查AI Server0和AIServer1是否缺少相关三方库， 该模型用到了DLLogger模块， 需源码安装； + +比如AI Server0服务器的host ip为：192.168.xx.22， AI Server1服务器的host ip为：192.168.xx.23。AI Server0为master节点，我们现在拉起2*8的集群。在拉起之前请先将脚本防止服务器相应位置， 确保python相关库已安装。 + +**在AI** **server0服务器上启动命令：** + +source env_npu.sh + +export HCCL_WHITELIST_DISABLE=1 + +export HCCL_IF_IP=192.168.xx.22 + +python3.7 -m torch.distributed.launch --nnodes=2 --node_rank=0 --nproc_per_node 8 --master_addr 192.168.xx.22 --master_port 29501 main.py --addr 192.168.xx.22 + +**在AI** **server****1****服务器上启动命令：** + +source env_npu.sh + +export HCCL_WHITELIST_DISABLE=1 + +export HCCL_IF_IP=192.168.xx.23 + +python3.7 -m torch.distributed.launch --nnodes=2 --node_rank=1 --nproc_per_node 8 --master_addr 192.168.xx.22--master_port 29501 main.py --addr 192.168.xx.22 + +以上2个命令的差别是HCCL_IF_IP/node_rank/addr的值不同， 用户可将命令写入shell脚本， 对不同的参数以shell脚本外传值方式启动。 + +**host日志查看** + +cd ~/ascend/log下， 查看host日志 + +![img](file:///C:\Users\ZoeJ\AppData\Local\Temp\ksohtml\wps499D.tmp.jpg) + + + +# 适配原理及步骤 + + + + + + + + + + + + + + + + + + + + + + + + + -- Gitee From 62204e1733ce0ff11f4713e0dda46671cfad2d2f Mon Sep 17 00:00:00 2001 From: pengzirong Date: Tue, 7 Dec 2021 15:54:47 +0800 Subject: [PATCH 15/55] add code --- ...02\351\205\215\346\214\207\345\215\227.md" | 145 ++++++++++++++++-- 1 file changed, 136 insertions(+), 9 deletions(-) diff --git "a/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" index 0b3fba33562..c231dab886a 100644 --- "a/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" +++ "b/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" @@ -26,7 +26,7 @@ ## 概述 -使用resnet50模型实现两台计算机8卡训练。两台计算机分别命名为AI Server0、AI Server1，每台计算机上的8个Ascend 910处理器命名为device 0~7。 +使用自定义模型实现两台计算机8卡训练。两台计算机分别命名为AI Server0、AI Server1，每台计算机上的8个Ascend 910处理器命名为device 0~7。 ## 环境准备 @@ -88,14 +88,141 @@ ## 准备单机多卡模型 -1. 获取resnet50模型。 - - ```shell - git clone https://gitee.com/ascend/modelzoo.git - cd modelzoo - git reset --hard ca699fcc4022fbea5a0bcf892223abbfaed07a85 - cd built-in/PyTorch/Official/cv/image_classification/ResNet50_for_PyTorch - ``` +1. 获取自定义模型。 + + - 从开源社区下载样例。 + + ``` + git clone https://gitee.com/ascend/modelzoo.git + cd modelzoo + git reset --hard ca699fcc4022fbea5a0bcf892223abbfaed07a85 + cd built-in/PyTorch/Official/cv/image_classification/ResNet50_for_PyTorch + ``` + + - 手动搭建简单样例。 + + ```python + import argparse + import os + import torch + import torchvision + import torch.nn as nn + import torch.nn.functional as F + # 新增： + import torch.distributed as dist + from torch.nn.parallel import DistributedDataParallel as DDP + + + ### 1. 基础模块 ### + # 假设我们的模型是这个，与DDP无关 + class ToyModel(nn.Module): + def __init__(self): + super(ToyModel, self).__init__() + self.conv1 = nn.Conv2d(3, 6, 5) + self.pool = nn.MaxPool2d(2, 2) + self.conv2 = nn.Conv2d(6, 16, 5) + self.fc1 = nn.Linear(16 * 5 * 5, 120) + self.fc2 = nn.Linear(120, 84) + self.fc3 = nn.Linear(84, 10) + + def forward(self, x): + x = self.pool(F.relu(self.conv1(x))) + x = self.pool(F.relu(self.conv2(x))) + x = x.view(-1, 16 * 5 * 5) + x = F.relu(self.fc1(x)) + x = F.relu(self.fc2(x)) + x = self.fc3(x) + return x + + + # 假设我们的数据是这个 + def get_dataset(): + transform = torchvision.transforms.Compose([ + torchvision.transforms.ToTensor(), + torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) + ]) + my_trainset = torchvision.datasets.CIFAR10(root='./data', train=True, + download=True, transform=transform) + # DDP：使用DistributedSampler，DDP帮我们把细节都封装起来了。 + # 用，就完事儿！sampler的原理，第二篇中有介绍。 + train_sampler = torch.utils.data.distributed.DistributedSampler(my_trainset) + # DDP：需要注意的是，这里的batch_size指的是每个进程下的batch_size。 + # 也就是说，总batch_size是这里的batch_size再乘以并行数(world_size)。 + trainloader = torch.utils.data.DataLoader(my_trainset, + batch_size=16, num_workers=2, sampler=train_sampler) + return trainloader + + + ### 2. 初始化我们的模型、数据、各种配置 #### + # DDP：从外部得到local_rank参数 + parser = argparse.ArgumentParser() + parser.add_argument("--local_rank", default=-1, type=int) + parser.add_argument("--addr", default='127.0.0.1', type=str, help='master addr') + + FLAGS = parser.parse_args() + local_rank = FLAGS.local_rank + addr = FLAGS.addr + + os.environ['MASTER_ADDR'] = addr + os.environ['MASTER_PORT'] = '29501' + + # DDP：DDP backend初始化 + loc = 'npu:{}'.format(local_rank) + torch.npu.set_device(loc) + dist.init_process_group(backend='hccl') # hccl是NPU设备上的后端 + + + # 准备数据，要在DDP初始化之后进行 + trainloader = get_dataset() + + # 构造模型 + model = ToyModel().to(loc) + # DDP: Load模型要在构造DDP模型之前，且只需要在master上加载就行了。 + ckpt_path = None + if dist.get_rank() == 0 and ckpt_path is not None: + model.load_state_dict(torch.load(ckpt_path)) + # DDP: 构造DDP model + model = DDP(model, device_ids=[local_rank], output_device=local_rank) + + # DDP: 要在构造DDP model之后，才能用model初始化optimizer。 + optimizer = torch.optim.SGD(model.parameters(), lr=0.001) + + # 假设我们的loss是这个 + loss_func = nn.CrossEntropyLoss().to(loc) + + ### 3. 网络训练 ### + model.train() + iterator = range(100) + for epoch in iterator: + # DDP：设置sampler的epoch， + # DistributedSampler需要这个来指定shuffle方式， + # 通过维持各个进程之间的相同随机数种子使不同进程能获得同样的shuffle效果。 + trainloader.sampler.set_epoch(epoch) + # 后面这部分，则与原来完全一致了。 + for data, label in trainloader: + data, label = data.to(local_rank), label.to(local_rank) + optimizer.zero_grad() + prediction = model(data) + loss = loss_func(prediction, label) + loss.backward() + print("loss = %0.3f \n" % loss) + optimizer.step() + # DDP: + # 1. save模型的时候，和DP模式一样，有一个需要注意的点：保存的是model.module而不是model。 + # 因为model其实是DDP model，参数是被`model=DDP(model)`包起来的。 + # 2. 只需要在进程0上保存一次就行了，避免多次保存重复的东西。 + if dist.get_rank() == 0: + torch.save(model.module.state_dict(), "%d.ckpt" % epoch) + + + ################ + ## Bash运行 + # DDP: 使用torch.distributed.launch启动DDP模式 + # python -m torch.distributed.launch --nproc_per_node 8 main.py + + ``` + + 2. 在单机多卡上运行训练，确保模型正确。 -- Gitee From 039920b22c24c0f223e76a784332171f2b1a8a37 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Tue, 7 Dec 2021 18:59:02 +0800 Subject: [PATCH 16/55] updata --- ...02\351\205\215\346\214\207\345\215\227.md" | 312 ++++++++++-------- 1 file changed, 173 insertions(+), 139 deletions(-) diff --git "a/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" index c231dab886a..af6fd42a255 100644 --- "a/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" +++ "b/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" @@ -36,7 +36,7 @@ 在每台计算机上完成下面开发和运行环境准备。 - - 完成CANN开发和运行环境的安装，请参见《CANN 软件安装指南》。 + - 完成CANN开发和运行环境的安装，请参见《CANN 软件安装指南》，支持5.0.3以后版本。 - 安装适配NPU的PyTorch，安装方法请参见《PyTorch安装指南》。 @@ -90,143 +90,181 @@ 1. 获取自定义模型。 - - 从开源社区下载样例。 - - ``` - git clone https://gitee.com/ascend/modelzoo.git - cd modelzoo - git reset --hard ca699fcc4022fbea5a0bcf892223abbfaed07a85 - cd built-in/PyTorch/Official/cv/image_classification/ResNet50_for_PyTorch - ``` - - - 手动搭建简单样例。 - - ```python - import argparse - import os - import torch - import torchvision - import torch.nn as nn - import torch.nn.functional as F - # 新增： - import torch.distributed as dist - from torch.nn.parallel import DistributedDataParallel as DDP - - - ### 1. 基础模块 ### - # 假设我们的模型是这个，与DDP无关 - class ToyModel(nn.Module): - def __init__(self): - super(ToyModel, self).__init__() - self.conv1 = nn.Conv2d(3, 6, 5) - self.pool = nn.MaxPool2d(2, 2) - self.conv2 = nn.Conv2d(6, 16, 5) - self.fc1 = nn.Linear(16 * 5 * 5, 120) - self.fc2 = nn.Linear(120, 84) - self.fc3 = nn.Linear(84, 10) - - def forward(self, x): - x = self.pool(F.relu(self.conv1(x))) - x = self.pool(F.relu(self.conv2(x))) - x = x.view(-1, 16 * 5 * 5) - x = F.relu(self.fc1(x)) - x = F.relu(self.fc2(x)) - x = self.fc3(x) - return x - - - # 假设我们的数据是这个 - def get_dataset(): - transform = torchvision.transforms.Compose([ - torchvision.transforms.ToTensor(), - torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) - ]) - my_trainset = torchvision.datasets.CIFAR10(root='./data', train=True, - download=True, transform=transform) - # DDP：使用DistributedSampler，DDP帮我们把细节都封装起来了。 - # 用，就完事儿！sampler的原理，第二篇中有介绍。 - train_sampler = torch.utils.data.distributed.DistributedSampler(my_trainset) - # DDP：需要注意的是，这里的batch_size指的是每个进程下的batch_size。 - # 也就是说，总batch_size是这里的batch_size再乘以并行数(world_size)。 - trainloader = torch.utils.data.DataLoader(my_trainset, - batch_size=16, num_workers=2, sampler=train_sampler) - return trainloader - - - ### 2. 初始化我们的模型、数据、各种配置 #### - # DDP：从外部得到local_rank参数 - parser = argparse.ArgumentParser() - parser.add_argument("--local_rank", default=-1, type=int) - parser.add_argument("--addr", default='127.0.0.1', type=str, help='master addr') - - FLAGS = parser.parse_args() - local_rank = FLAGS.local_rank - addr = FLAGS.addr - - os.environ['MASTER_ADDR'] = addr - os.environ['MASTER_PORT'] = '29501' - - # DDP：DDP backend初始化 - loc = 'npu:{}'.format(local_rank) - torch.npu.set_device(loc) - dist.init_process_group(backend='hccl') # hccl是NPU设备上的后端 - - - # 准备数据，要在DDP初始化之后进行 - trainloader = get_dataset() - - # 构造模型 - model = ToyModel().to(loc) - # DDP: Load模型要在构造DDP模型之前，且只需要在master上加载就行了。 - ckpt_path = None - if dist.get_rank() == 0 and ckpt_path is not None: - model.load_state_dict(torch.load(ckpt_path)) - # DDP: 构造DDP model - model = DDP(model, device_ids=[local_rank], output_device=local_rank) - - # DDP: 要在构造DDP model之后，才能用model初始化optimizer。 - optimizer = torch.optim.SGD(model.parameters(), lr=0.001) - - # 假设我们的loss是这个 - loss_func = nn.CrossEntropyLoss().to(loc) - - ### 3. 网络训练 ### - model.train() - iterator = range(100) - for epoch in iterator: - # DDP：设置sampler的epoch， - # DistributedSampler需要这个来指定shuffle方式， - # 通过维持各个进程之间的相同随机数种子使不同进程能获得同样的shuffle效果。 - trainloader.sampler.set_epoch(epoch) - # 后面这部分，则与原来完全一致了。 - for data, label in trainloader: - data, label = data.to(local_rank), label.to(local_rank) - optimizer.zero_grad() - prediction = model(data) - loss = loss_func(prediction, label) - loss.backward() - print("loss = %0.3f \n" % loss) - optimizer.step() - # DDP: - # 1. save模型的时候，和DP模式一样，有一个需要注意的点：保存的是model.module而不是model。 - # 因为model其实是DDP model，参数是被`model=DDP(model)`包起来的。 - # 2. 只需要在进程0上保存一次就行了，避免多次保存重复的东西。 - if dist.get_rank() == 0: - torch.save(model.module.state_dict(), "%d.ckpt" % epoch) - - - ################ - ## Bash运行 - # DDP: 使用torch.distributed.launch启动DDP模式 - # python -m torch.distributed.launch --nproc_per_node 8 main.py - - ``` - - + 实现一个简单的样例，创建main.py，也可以通过开源社区下载。 + + ```python + import argparse + import os + import torch + import torchvision + import torch.nn as nn + import torch.nn.functional as F + import torch.distributed as dist + from torch.nn.parallel import DistributedDataParallel as DDP + + ### 1.基础模块 ### + # 搭建模型 + class ToyModel(nn.Module): + def __init__(self): + super(ToyModel, self).__init__() + self.conv1 = nn.Conv2d(3, 6, 5) + self.pool = nn.MaxPool2d(2, 2) + self.conv2 = nn.Conv2d(6, 16, 5) + self.fc1 = nn.Linear(16 * 5 * 5, 120) + self.fc2 = nn.Linear(120, 84) + self.fc3 = nn.Linear(84, 10) + + def forward(self, x): + x = self.pool(F.relu(self.conv1(x))) + x = self.pool(F.relu(self.conv2(x))) + x = x.view(-1, 16 * 5 * 5) + x = F.relu(self.fc1(x)) + x = F.relu(self.fc2(x)) + x = self.fc3(x) + return x + + # 获取数据集方法 + def get_dataset(): + transform = torchvision.transforms.Compose([ + torchvision.transforms.ToTensor(), + torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) + ]) + my_trainset = torchvision.datasets.CIFAR10(root='./data', train=True, + download=True, transform=transform) + + train_sampler = torch.utils.data.distributed.DistributedSampler(my_trainset) + trainloader = torch.utils.data.DataLoader(my_trainset, + batch_size=16, num_workers=2, sampler=train_sampler) + return trainloader + + + ### 2. 初始化参数、数据、模型、损失函数、优化器 #### + # 获取local_rank和addr参数 + parser = argparse.ArgumentParser() + parser.add_argument("--local_rank", default=-1, type=int) + parser.add_argument("--addr", default='127.0.0.1', type=str, help='master addr') + + FLAGS = parser.parse_args() + local_rank = FLAGS.local_rank + addr = FLAGS.addr + + # 设置系统的Master地址和端口 + os.environ['MASTER_ADDR'] = addr + os.environ['MASTER_PORT'] = '29501' + + # DDP：DDP backend初始化 + loc = 'npu:{}'.format(local_rank) + torch.npu.set_device(loc) + dist.init_process_group(backend='hccl') # hccl是NPU设备上的后端 + + + # 准备数据，要在DDP初始化之后进行 + trainloader = get_dataset() + + # 实例化模型 + model = ToyModel().to(loc) + + # 加载模型权重，在构造DDP模型之前，且只需要在master上加载就行了。 + ckpt_path = None + if dist.get_rank() == 0 and ckpt_path is not None: + model.load_state_dict(torch.load(ckpt_path)) + + # 构造DDP model + model = DDP(model, device_ids=[local_rank], output_device=local_rank) + + # 初始化优化器，在构造DDP model之后，用model初始化optimizer。 + optimizer = torch.optim.SGD(model.parameters(), lr=0.001) + + # 初始化损失函数 + loss_func = nn.CrossEntropyLoss().to(loc) + + ### 3. 网络训练 ### + model.train() + iterator = range(100) + for epoch in iterator: + trainloader.sampler.set_epoch(epoch) + for data, label in trainloader: + data, label = data.to(local_rank), label.to(local_rank) + optimizer.zero_grad() + prediction = model(data) + loss = loss_func(prediction, label) + loss.backward() + print("loss = %0.3f \n" % loss) + optimizer.step() + + # 1. save模型的时候，和DP模式一样，有一个需要注意的点：保存的是model.module而不是model。 + # 因为model其实是DDP model，参数是被`model=DDP(model)`包起来的。 + # 2. 只需要在进程0上保存一次就行了，避免多次保存重复的东西。 + if dist.get_rank() == 0: + torch.save(model.module.state_dict(), "%d.ckpt" % epoch) + ``` + + 2. 在单机多卡上运行训练，确保模型正确。 - 运行操作参见[README.md](https://gitee.com/ascend/modelzoo/blob/master/built-in/PyTorch/Official/cv/image_classification/ResNet50_for_PyTorch/README.md)。 + 1. 用户自行安装模型脚本需要的Python第三方库。 + + 2. 使用torch.distributed.launch执行main.py如下命令在单机多卡上训练模型。 + + ```shell + python -m torch.distributed.launch --nproc_per_node 8 main.py + ``` + + `--nproc_per_node` 为训练卡的数量。 + + 运行成功后，模型在该设备的8张NPU上进行训练。 + + + +## 修改模型脚本 + +该快速上手提供的示例，已经对多机多卡训练进行了适配，不需要对脚本进行修改。其他模型的多机多卡适配修改，请参考详细介绍。 + +## 启动多机多卡训练 + + + +1. 将maion.py模型脚本上传至AI Server0和AI Server1任意路径下。 +2. 比如AI Server0服务器的host ip为：192.168.xx.22， AI Server1服务器的host ip为：192.168.xx.23。AI Server0为master节点，我们现在拉起2*8的集群。 + + + +**在AI** **server0服务器上启动命令：** + +source env_npu.sh + +export HCCL_WHITELIST_DISABLE=1 + +export HCCL_IF_IP=192.168.xx.22 + +python3.7 -m torch.distributed.launch --nnodes=2 --node_rank=0 --nproc_per_node 8 --master_addr 192.168.xx.22 --master_port 29501 main.py --addr 192.168.xx.22 + +**在AI** **server****1****服务器上启动命令：** + +source env_npu.sh + +export HCCL_WHITELIST_DISABLE=1 + +export HCCL_IF_IP=192.168.xx.23 + +python3.7 -m torch.distributed.launch --nnodes=2 --node_rank=1 --nproc_per_node 8 --master_addr 192.168.xx.22--master_port 29501 main.py --addr 192.168.xx.22 + +以上2个命令的差别是HCCL_IF_IP/node_rank/addr的值不同， 用户可将命令写入shell脚本， 对不同的参数以shell脚本外传值方式启动。 + +**host日志查看** + +cd ~/ascend/log下， 查看host日志 + +![img](file:///C:\Users\ZoeJ\AppData\Local\Temp\ksohtml\wps499D.tmp.jpg) + + + +# 适配原理及步骤 + +## 原理 + +## 常用参数 @@ -254,8 +292,6 @@ os.environ['MASTER_ADDR'] = addr 本举例模型脚本中已存在相关改动点， 执行单机多卡训练和多机多卡训练的区别体现在启动命令上， 多机多卡脚本使用上面的mian.py即可。 - - ## 启动训练 （1）将模型脚本上传至AI Server0和AIServer1； @@ -294,8 +330,6 @@ cd ~/ascend/log下， 查看host日志 -# 适配原理及步骤 - -- Gitee From 7f53d5aabe77885268394f8eac4166325dc64443 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Wed, 8 Dec 2021 10:11:23 +0800 Subject: [PATCH 17/55] updata --- ...02\351\205\215\346\214\207\345\215\227.md" | 51 ++++++++++--------- 1 file changed, 28 insertions(+), 23 deletions(-) diff --git "a/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" index af6fd42a255..492f20b19d2 100644 --- "a/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" +++ "b/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" @@ -225,38 +225,43 @@ -1. 将maion.py模型脚本上传至AI Server0和AI Server1任意路径下。 -2. 比如AI Server0服务器的host ip为：192.168.xx.22， AI Server1服务器的host ip为：192.168.xx.23。AI Server0为master节点，我们现在拉起2*8的集群。 +1. 将main.py模型脚本上传至AI Server0和AI Server1任意路径下。 +2. 使用AI Server0为master节点，我们现在拉起2*8的集群。比如AI Server0服务器的host ip为：192.168.xx.22， AI Server1服务器的host ip为：192.168.xx.23。 + 在AI server0服务器上启动命令： -**在AI** **server0服务器上启动命令：** - -source env_npu.sh - -export HCCL_WHITELIST_DISABLE=1 - -export HCCL_IF_IP=192.168.xx.22 - -python3.7 -m torch.distributed.launch --nnodes=2 --node_rank=0 --nproc_per_node 8 --master_addr 192.168.xx.22 --master_port 29501 main.py --addr 192.168.xx.22 - -**在AI** **server****1****服务器上启动命令：** - -source env_npu.sh + ```shell + # 设置环境变量 + source env_npu.sh + # 关闭HCCL通道白名单 + export HCCL_WHITELIST_DISABLE=1 + # HCCL初始化通信网卡IP，设置为当前服务器的host网卡 + export HCCL_IF_IP=192.168.xx.22 + # + python3.7 -m torch.distributed.launch --nnodes=2 --node_rank=0 --nproc_per_node 8 --master_addr 192.168.xx.22 --master_port 29501 main.py --addr 192.168.xx.22 + ``` -export HCCL_WHITELIST_DISABLE=1 + 在AI server1服务器上启动命令： -export HCCL_IF_IP=192.168.xx.23 - -python3.7 -m torch.distributed.launch --nnodes=2 --node_rank=1 --nproc_per_node 8 --master_addr 192.168.xx.22--master_port 29501 main.py --addr 192.168.xx.22 + ```shell + # 设置环境变量 + source env_npu.sh + # 关闭HCCL通道白名单 + export HCCL_WHITELIST_DISABLE=1 + # HCCL初始化通信网卡IP，设置为当前服务器的host网卡 + export HCCL_IF_IP=192.168.xx.23 + + python3.7 -m torch.distributed.launch --nnodes=2 --node_rank=1 --nproc_per_node 8 --master_addr 192.168.xx.22--master_port 29501 main.py --addr 192.168.xx.22 + ``` -以上2个命令的差别是HCCL_IF_IP/node_rank/addr的值不同， 用户可将命令写入shell脚本， 对不同的参数以shell脚本外传值方式启动。 + 以上2个命令的差别是HCCL_IF_IP/node_rank/addr的值不同， 用户可将命令写入shell脚本， 对不同的参数以shell脚本外传值方式启动。 -**host日志查看** +3. host日志查看 -cd ~/ascend/log下， 查看host日志 + cd ~/ascend/log下， 查看host日志 -![img](file:///C:\Users\ZoeJ\AppData\Local\Temp\ksohtml\wps499D.tmp.jpg) + ![img](file:///C:\Users\ZoeJ\AppData\Local\Temp\ksohtml\wps499D.tmp.jpg) -- Gitee From b68ca6e46b86e81d9561b550ea8adf039c0c69ec Mon Sep 17 00:00:00 2001 From: pengzirong Date: Wed, 8 Dec 2021 21:30:55 +0800 Subject: [PATCH 18/55] updata --- ...02\351\205\215\346\214\207\345\215\227.md" | 524 +++++++++++++++--- 1 file changed, 462 insertions(+), 62 deletions(-) diff --git "a/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" index 492f20b19d2..a4aee0155b6 100644 --- "a/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" +++ "b/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" @@ -1,20 +1,22 @@ # 概述 -该文档帮助用户快速实现Pytorch单机多卡模型在集群上使用DDP模式训练。 +用户可以从Ascend ModelZoo获得PyTorch训练模型，但不支持多机多卡训练，需要根据模型的实际代码进行修改。本文帮助用户快速实现Pytorch模型在多机多卡上使用DDP（Distributed Data Parallel）模式训练。 -# 适配流程 +# 训练流程 + +PyTorch模型多机多卡训练流程一般包括准备环境、准备模型、修改模型、启动训练四个部分。 1. 准备环境。 - 准备模型多机多卡训练的软件、硬件、网络环境，包括开发和运行环境搭建、集群组网链接、芯片IP设置、防火墙设置等。 + 准备多机多卡训练的软件、硬件、网络环境，包括开发和运行环境搭建、集群组网链接、芯片IP设置、防火墙设置等。 -2. 准备单机多卡模型。 +2. 准备模型。 - 该文档帮助用户实现单机多卡训练模型到多机多卡训练的适配，故用户需提前准备单机多卡模型，可以从开源社区获取，也可以自行实现。 + 准备PyTorch模型、数据加载器、优化器等训练需要的模块，从[开源社区](https://gitee.com/ascend/modelzoo/tree/master/built-in/PyTorch)获取，也可以自行实现。 -3. 修改模型脚本。 +3. 修改模型。 - 修改单机多卡模型启动脚本和环境变量，使模型支持在多机多卡训练。 + 在基础模型上进行修改，添加DDP需要的代码和环境变量，使模型支持在多机多卡训练。 4. 启动训练。 @@ -26,15 +28,15 @@ ## 概述 -使用自定义模型实现两台计算机8卡训练。两台计算机分别命名为AI Server0、AI Server1，每台计算机上的8个Ascend 910处理器命名为device 0~7。 +通过示例帮助用户快速了解PyTorch模型是如何在多机多卡上训练的。该示例使用自定义模型实现两台计算机8卡训练。两台计算机分别命名为AI Server0、AI Server1，每台计算机上的8个Ascend 910处理器命名为device 0~7。 -## 环境准备 +## 准备环境 首先您需要具有至少两台装有Ascend 910处理器的计算机，并保证每台计算机都正确的安装NPU固件和驱动。 1. 准备开发和运行环境。 - 在每台计算机上完成下面开发和运行环境准备。 + 在每台计算机上分别完成开发和运行环境准备。 - 完成CANN开发和运行环境的安装，请参见《CANN 软件安装指南》，支持5.0.3以后版本。 @@ -86,11 +88,13 @@ ```shell systemctl stop firewalld -## 准备单机多卡模型 +## 准备模型 + +该示例创建一个简单的模型，供用户快速了解多机多卡训练。用户也可以从[开源社区](https://gitee.com/ascend/modelzoo/tree/master/built-in/PyTorch)获取基于Ascned NPU的PyTorch训练模型。 -1. 获取自定义模型。 +1. DDP模型。 - 实现一个简单的样例，创建main.py，也可以通过开源社区下载。 + 实现一个简单的样例main.py用于多机多卡训练。 ```python import argparse @@ -152,7 +156,7 @@ os.environ['MASTER_ADDR'] = addr os.environ['MASTER_PORT'] = '29501' - # DDP：DDP backend初始化 + # DDP backend初始化 loc = 'npu:{}'.format(local_rank) torch.npu.set_device(loc) dist.init_process_group(backend='hccl') # hccl是NPU设备上的后端 @@ -205,7 +209,13 @@ 1. 用户自行安装模型脚本需要的Python第三方库。 - 2. 使用torch.distributed.launch执行main.py如下命令在单机多卡上训练模型。 + 2. 配置NPU环境变量，env_npu.sh脚本请参见附录。 + + ```shell + source env_npu.sh + ``` + + 3. 使用torch.distributed.launch执行main.py如下命令在单机多卡上训练模型。 ```shell python -m torch.distributed.launch --nproc_per_node 8 main.py @@ -217,26 +227,34 @@ -## 修改模型脚本 +## 修改模型 -该快速上手提供的示例，已经对多机多卡训练进行了适配，不需要对脚本进行修改。其他模型的多机多卡适配修改，请参考详细介绍。 +该快速上手提供的示例，已经对多机多卡训练进行了适配，不需要对脚本进行修改。其他模型的多机多卡适配修改，请参考”多机多卡训练“章节。 -## 启动多机多卡训练 +## 启动训练 +1. 将main.py模型脚本上传至AI Server0和AI Server1任意路径下。 +2. 查询服务器的host IP： -1. 将main.py模型脚本上传至AI Server0和AI Server1任意路径下。 + ```shell + hostname -I + ``` + + 打印出所有IP，第一个为IP当前服务器的host IP。 -2. 使用AI Server0为master节点，我们现在拉起2*8的集群。比如AI Server0服务器的host ip为：192.168.xx.22， AI Server1服务器的host ip为：192.168.xx.23。 + 比如：AI Server0服务器的host IP为：192.168.xx.22， AI Server1服务器的host IP为：192.168.xx.23。 + +3. 使用AI Server0为master节点，我们现在拉起2 x 8的集群。 在AI server0服务器上启动命令： ```shell - # 设置环境变量 + # 设置环境变量,env_npu.sh脚本内容从附录获取 source env_npu.sh # 关闭HCCL通道白名单 export HCCL_WHITELIST_DISABLE=1 - # HCCL初始化通信网卡IP，设置为当前服务器的host网卡 + # HCCL初始化通信网卡IP，设置为当前服务器的host IP export HCCL_IF_IP=192.168.xx.22 # python3.7 -m torch.distributed.launch --nnodes=2 --node_rank=0 --nproc_per_node 8 --master_addr 192.168.xx.22 --master_port 29501 main.py --addr 192.168.xx.22 @@ -245,105 +263,487 @@ 在AI server1服务器上启动命令： ```shell - # 设置环境变量 + # 设置环境变量,env_npu.sh脚本内容从附录获取 source env_npu.sh # 关闭HCCL通道白名单 export HCCL_WHITELIST_DISABLE=1 - # HCCL初始化通信网卡IP，设置为当前服务器的host网卡 + # HCCL初始化通信网卡IP，设置为当前服务器的host IP export HCCL_IF_IP=192.168.xx.23 - python3.7 -m torch.distributed.launch --nnodes=2 --node_rank=1 --nproc_per_node 8 --master_addr 192.168.xx.22--master_port 29501 main.py --addr 192.168.xx.22 + python3.7 -m torch.distributed.launch --nnodes=2 --node_rank=1 --nproc_per_node 8 --master_addr 192.168.xx.22 --master_port 29501 main.py --addr 192.168.xx.22 ``` - 以上2个命令的差别是HCCL_IF_IP/node_rank/addr的值不同， 用户可将命令写入shell脚本， 对不同的参数以shell脚本外传值方式启动。 + 参数说明。 + + --nnode：指定用来分布式训练脚本的节点数。 + + --node_rank：多节点分布式训练时，指定当前节点的 rank。 + + --nproc_per_node：指定当前节点上，使用GPU训练的进程数。 + + --master_addr：master节点（rank为0）的地址，应该为ip地址或者node 0 的 hostname。 + + --master_port：指定分布式训练中，master 节点使用的端口号。 + + --addr：main.py 脚本的入参，输入master节点的host IP。 3. host日志查看 - cd ~/ascend/log下， 查看host日志 + host日志保存在`~/ascend/log`路径下，用户可以到该路径下查看每个host的device日志 - ![img](file:///C:\Users\ZoeJ\AppData\Local\Temp\ksohtml\wps499D.tmp.jpg) +通过该简单的示例您已经完成了多机多卡模型的训练。 +# 多机多卡训练 -# 适配原理及步骤 -## 原理 +## 常用概念及参数介绍 -## 常用参数 +pytorch分布式训练基本概念 +| 基本概念 | 说明 | +| ---------- | ------------------------------------------------------------ | +| AI Server | 带有Ascend 910处理器的计算机，多台计算机用AI Server0+序号表示，如AI Server0、AI Server1。 | +| device | AI Server上Ascend 910卡，多卡用device 0、device 1……device 7表示。 | +| host | AI Server主机。 | +| master | 在多台AI Server选取一台作为master，作为数据通信的主机。 | +| group | 即进程组。默认情况下，只有一个组，采用用默认的就行。 | +| world size | 表示全局的进程并行数，可通过torch.distributed.get_world_size()获取， 在不同进程里，该值是一样的。 | +| rank | 表示当前进的序号， 用于进程间通讯。比如是2x8的集群，world size 就是16，rank在每个进程里依次是0,1,2,…,15。 | +| local_rank | 每台机子上的进程的序号。机器一上有0,1,2,3,4,5,6,7，机器二上也有0,1,2,3,4,5,6,7。一般情况下，你需要用这个local_rank来设置当前模型是跑在当前机器的哪块GPU/NPU上面的。 | +使用torch.distributed.launch启动多卡训练时的参数 -## 修改模型脚本 +| 参数名称 | 说明 | +| ------------------ | ------------------------------------------------------------ | +| **nnodes** | 指定用来分布式训练脚本的节点数 | +| **node_rank** | 多节点分布式训练时，指定当前节点的 rank | +| **nproc_per_node** | 指定当前节点上，使用GPU训练的进程数。建议将该参数设置为当前节点的GPU数量，这样每个进程都能单独控制一个GPU，效率最高。 | +| **master_addr** | master节点（rank为0）的地址，应该为ip地址或者node 0 的 hostname。对于单节点多进程训练的情况，该参数可以设置为 127.0.0.1。 | +| **master_port**: | 指定分布式训练中，master 节点使用的端口号，必须与其他应用的端口号不冲突。 | -1. 声明的**MASTER_ADDR**变量的值从脚本外部接收** +## 多机多卡训练流程 -os.environ['MASTER_ADDR'] = addr +### 准备环境 -![img](file:///C:\Users\ZoeJ\AppData\Local\Temp\ksohtml\wps39A.tmp.jpg) +首先您需要具有至少两台AI Server（装有Ascend 910处理器的计算机），并保证每台计算机已安装正确版本的NPU固件和驱动。 -其中addr变量是master节点的host ip，该点体现在启动命令中， 举例模型脚本中已经有该声明，不用再添加； +1. 准备开发和运行环境。 -2. **声明HCCL环境变量** + 在每台计算机上分别完成开发和运行环境准备。 -在启动2*8集群训练之前，不同于单机多卡的是， 要声明下HCCL_IF_IP变量 + - 完成CANN开发和运行环境的安装，请参见《CANN 软件安装指南》，支持5.0.3以后版本。 -和HCCL_WHITELIST_DISABLE这2个环境变量。 + - 安装适配NPU的PyTorch，安装方法请参见《PyTorch安装指南》。 -（1）HCCL_WHITELIST_DISABLE，HCCL通道白名单，一般设置为1，表示关闭白名单。在2台服务器上都声明export HCCL_WHITELIST_DISABLE=1 +2. 准备组网。 -（2）HCCL_IF_IP, HCCL初始化通信网卡IP，设置为当前服务器的host网卡。2*8集群中，在AI Server0上声明export HCCL_IF_IP=AI Server0 host ip, 在AI Server1上声明export HCCL_IF_IP=AI Server1 host ip。 + 集群训练由多台装有Ascend 910处理器的计算机完成（最多128台），需要配合交换机组成数据面全连接主备网络，支持8 x n卡训练场景，2台机器可以采用光口直连的方式。搭建方法请参见《[数据中心训练场景组网](https://support.huawei.com/enterprise/zh/doc/EDOC1100221993/229cc0e4)》。 - +3. 配置device IP -本举例模型脚本中已存在相关改动点， 执行单机多卡训练和多机多卡训练的区别体现在启动命令上， 多机多卡脚本使用上面的mian.py即可。 + 使用hccn_tool工具配置device IP，hccn_tool工具CANN软件以提供。 -## 启动训练 + ```shell + hccn_tool -i 0 -ip -s address 192.168.100.111 netmask 255.255.255.0 + ``` -（1）将模型脚本上传至AI Server0和AIServer1； + 配置device IP需遵守以下规则： -（2）对照requirements.txt，检查AI Server0和AIServer1是否缺少相关三方库， 该模型用到了DLLogger模块， 需源码安装； + 1. AI Server中的第0/4，1/5，2/6，3/7号device需处于同一网段，第0/1/2/3号device在不同网段，第4/5/6/7号device在不同网段。 + 2. 对于集群场景，各AI Server对应的位置的device需处于同一网段，AI Server0和AI Server1的0号网卡需处于同一网段、1号网卡需要在同一网段 + 3. 每个IP都不能冲突，相同网段下的IP需在最后8位做区分 -比如AI Server0服务器的host ip为：192.168.xx.22， AI Server1服务器的host ip为：192.168.xx.23。AI Server0为master节点，我们现在拉起2*8的集群。在拉起之前请先将脚本防止服务器相应位置， 确保python相关库已安装。 + 使用hccn_tool工具验证device IP是否配置正确。 -**在AI** **server0服务器上启动命令：** + - 查询每个device的ip。 -source env_npu.sh + ```shell + hccn_tool -i 0 -ip –g + ``` -export HCCL_WHITELIST_DISABLE=1 + 打印查询结果: + + > ipaddr:192.168.100.101 + > + > netmask:255.255.255.0 -export HCCL_IF_IP=192.168.xx.22 + - 使用hccn_tool 确保2机器的卡间连通性，从device0 - devcie7 测试8次，确保所有两机间所有卡都连通。 -python3.7 -m torch.distributed.launch --nnodes=2 --node_rank=0 --nproc_per_node 8 --master_addr 192.168.xx.22 --master_port 29501 main.py --addr 192.168.xx.22 + ```shell + hccn_tool -i 0 -netdetect -s address xx.xx.xx.xx + + hccn_tool -i 0 -net_health –g + ``` -**在AI** **server****1****服务器上启动命令：** + -i：device序号 -source env_npu.sh + -s address：xx.xx.xx.xx是另外一台机器的device i的IP -export HCCL_WHITELIST_DISABLE=1 + 如果返回`success`则表示已经连通。 + +4. 配置防火墙 + + 在进行HCCL通信的时候可能出现防火墙拦截通信端口导致通信超时，因此在运行pytorch的集群训练的时候需要注意将服务器上的防火墙关闭。 + + - Ubuntu系统防火墙关闭命令 + + ```shell + ufw disable + ``` + + - Redhat或CentOS 7系统防火墙关闭命令 + + ```shell + systemctl stop firewalld + ``` + +### 准备模型 + +准备模型阶段主要有两种方式。 + +- 从[开源社区](https://gitee.com/ascend/modelzoo/tree/master/built-in/PyTorch)下载PyTorch训练模型 +- 手动搭建PyTorch训练模型 + +1. 准备PyTorch训练模型、数据加载器 + + 准备PyTorch模型。 + + ```python + class ToyModel(nn.Module): + def __init__(self): + ... + def forward(self,x): + ... + ``` + + 准备数据获取方法。 + + ```python + def get_dataset(): + transform = torchvision.transforms.Compose([ + torchvision.transforms.ToTensor(), + torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) + ]) + my_trainset = torchvision.datasets.CIFAR10(root='./data', train=True, + download=True, transform=transform) + + trainloader = torch.utils.data.DataLoader(my_trainset,batch_size=16,) + return trainloader + ``` + +2. 实例化模型 + + ```python + # 实例化模型 + model = ToyModel().to(loc) + + # 加载模型权重，在构造DDP模型之前，且只需要在master上加载就行了。 + ckpt_path = None + if dist.get_rank() == 0 and ckpt_path is not None: + model.load_state_dict(torch.load(ckpt_path)) + + # 构造DDP model + model = DDP(model, device_ids=[local_rank], output_device=local_rank) + ``` + +3. 准备损失函数和优化器。 + + ```python + # 初始化优化器，在构造DDP model之后，用model初始化optimizer。 + optimizer = torch.optim.SGD(model.parameters(), lr=0.001) + + # 初始化损失函数 + loss_func = nn.CrossEntropyLoss().to(loc) + ``` + +4. 训练模型 + + ```python + ### 3. 网络训练 ### + model.train() + iterator = range(100) + for epoch in iterator: + trainloader.sampler.set_epoch(epoch) + for data, label in trainloader: + data, label = data.to(local_rank), label.to(local_rank) + optimizer.zero_grad() + prediction = model(data) + loss = loss_func(prediction, label) + loss.backward() + print("loss = %0.3f \n" % loss) + optimizer.step() + + # 1. save模型的时候，和DP模式一样，有一个需要注意的点：保存的是model.module而不是model。 + # 因为model其实是DDP model，参数是被`model=DDP(model)`包起来的。 + # 2. 只需要在进程0上保存一次就行了，避免多次保存重复的东西。 + if dist.get_rank() == 0: + torch.save(model.module.state_dict(), "%d.ckpt" % epoch) + ``` -export HCCL_IF_IP=192.168.xx.23 + + +### 修改模型 + +1. 设置系统的Master地址和端口 + + ```python + os.environ['MASTER_ADDR'] = addr + os.environ['MASTER_PORT'] = '29501 + ``` + + 在NPU进行分布式训练使用HCCL进行通信，在PyTorch中使用的是自动拓扑探测的HCCL通信机制，即不需要使用RANK_TABLE_FLIE，但是其依赖于host侧的网卡进行通信，因此需要在代码中设置环境变量来设置通信网卡。其代码形式如下:： + + os.environ['MASTER_ADDR'] = '192.168.xx.xx' + + os.environ['MASTER_PORT'] = '29561' + + 上述值在多机情况下需要设置为： + + MASTER_ADDR：设置为集群中master的IP（任意挑选一台作为master即可） + + MASTER_PORT：设置为master的一个空闲端口 + + 上述两个参数在模型代码中一般会设置为传参的形式，但也有可能某些代码中写为"127.0.0.1"，需要进行修改。上述变量需在调用torch.distributed.init_process_group()之前声明。 + +2. distributed初始化 + + 修改torch.distributed.init_process_group() + + PyTorch中使用torch.distributed.init_process_group()来初始化线程组。 + + 其代码形式如下： + + import torch.distributed as dist + + dist.init_process_group(backend='hccl', world_size=world_size, rank=rank) + + 上述值的意义为： + + backend：进行分布式训练的使用的通信协议，**在NPU上只能使用"hccl"** + + world_size：进行训练时使用的device的总数 + + rank: 当前初始化的device的rank_id，也就是全局的逻辑ID + + - 使用torch.distributed.launch启动多卡训练。 + + ```python + dist.init_process_group(backend='hccl') # hccl是NPU设备上的后端 + + ``` + + - 使用mp.spawn启动多卡训练。 + + ```python + def main_worker(pid_idx, device_nums_per_node, args): + + args.distributed_rank = args.rank * device_nums_per_node + pid_idx + + dist.init_process_group(backend=args.dist_backend, world_size=args.distributed_world_size, rank=args.distributed_rank) + ``` + +3. 模型DDP初始化 + + ```python + # 实例化模型 + model = ToyModel().to(loc) + + # 加载模型权重，在构造DDP模型之前，且只需要在master上加载就行了。 + ckpt_path = None + if dist.get_rank() == 0 and ckpt_path is not None: + model.load_state_dict(torch.load(ckpt_path)) + + # 构造DDP model + model = DDP(model, device_ids=[local_rank], output_device=local_rank) + ``` + +4. 数据DDP初始化 + + ```python + def get_dataset(): + transform = torchvision.transforms.Compose([ + torchvision.transforms.ToTensor(), + torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) + ]) + my_trainset = torchvision.datasets.CIFAR10(root='./data', train=True, + download=True, transform=transform) + trainloader = torch.utils.data.DataLoader(my_trainset,batch_size=16,) + return trainloader + + trainloader = get_dataset() + ``` -python3.7 -m torch.distributed.launch --nnodes=2 --node_rank=1 --nproc_per_node 8 --master_addr 192.168.xx.22--master_port 29501 main.py --addr 192.168.xx.22 + -以上2个命令的差别是HCCL_IF_IP/node_rank/addr的值不同， 用户可将命令写入shell脚本， 对不同的参数以shell脚本外传值方式启动。 + -**host日志查看** +5. 损失方法、优化器。 -cd ~/ascend/log下， 查看host日志 + ```python + # 初始化优化器，在构造DDP model之后，用model初始化optimizer。 + optimizer = torch.optim.SGD(model.parameters(), lr=0.001) + + # 初始化损失函数 + loss_func = nn.CrossEntropyLoss().to(loc) + ``` -![img](file:///C:\Users\ZoeJ\AppData\Local\Temp\ksohtml\wps499D.tmp.jpg) +6. DDP模型训练 + ```python + ### 3. 网络训练 ### + model.train() + iterator = range(100) + for epoch in iterator: + trainloader.sampler.set_epoch(epoch) + for data, label in trainloader: + data, label = data.to(local_rank), label.to(local_rank) + optimizer.zero_grad() + prediction = model(data) + loss = loss_func(prediction, label) + loss.backward() + print("loss = %0.3f \n" % loss) + optimizer.step() + + # 1. save模型的时候，和DP模式一样，有一个需要注意的点：保存的是model.module而不是model。 + # 因为model其实是DDP model，参数是被`model=DDP(model)`包起来的。 + # 2. 只需要在进程0上保存一次就行了，避免多次保存重复的东西。 + if dist.get_rank() == 0: + torch.save(model.module.state_dict(), "%d.ckpt" % epoch) + ``` + +### 启动训练 + +启动训练提供两种方式，手动启动和shell脚本启动 + +- 手动启动 + + 添加环境变量 + + 相较于8p的启动脚本，多机的脚本需要增加环境变量： + + HCCL_WHITELIST_DISABLE，HCCL通道白名单，一般性设置为1表示关闭白名单。 + + HCCL_IF_IP, HCCL初始化通信网卡IP，设置为当前服务器的host网卡。AI Server0 脚本中该参数的值为AI Server0的host ip, AI Server1脚本中该参数的值为AI Server1的host ip。 + + 修改点这部分其中最重要的参数是需要设置addr，也就是上述提到的"MASTER_ADDR"需要将集群中每一台机器上启动脚本的该参数设置为master节点的ip。 + + 本部分的说明中使用的是torch.distributed.launch来启动多卡训练 + + （1）将模型脚本上传至AI Server0和AIServer1； + + （2）对照requirements.txt，检查AI Server0和AIServer1是否缺少相关三方库， 该模型用到了DLLogger模块， 需源码安装； + + 比如AI Server0服务器的host ip为：192.168.xx.22， AI Server1服务器的host ip为：192.168.xx.23。AI Server0为master节点，我们现在拉起2*8的集群。在拉起之前请先将脚本防止服务器相应位置， 确保python相关库已安装。 + **在AI** **server0服务器上启动命令：** + source env_npu.sh + export HCCL_WHITELIST_DISABLE=1 + export HCCL_IF_IP=192.168.xx.22 + python3.7 -m torch.distributed.launch --nnodes=2 --node_rank=0 --nproc_per_node 8 --master_addr 192.168.xx.22 --master_port 29501 main.py --addr 192.168.xx.22 + **在AI** **server****1****服务器上启动命令：** + source env_npu.sh + export HCCL_WHITELIST_DISABLE=1 + export HCCL_IF_IP=192.168.xx.23 + + python3.7 -m torch.distributed.launch --nnodes=2 --node_rank=1 --nproc_per_node 8 --master_addr 192.168.xx.22--master_port 29501 main.py --addr 192.168.xx.22 + + 以上2个命令的差别是HCCL_IF_IP/node_rank/addr的值不同， 用户可将命令写入shell脚本， 对不同的参数以shell脚本外传值方式启动。 + +- 使用sh脚本启动 + + 用户可将命令写入shell脚本， 对不同的参数以shell脚本外传值方式启动。 + +训练成功后可以查看日志信息 + +**host日志查看** + + + +# 附录 + +NPU环境变量配置脚本env_npu.sh，可使用该脚本进行运行和开发环境变量的配置。 + +```shell +#!/bin/bash +export install_path=/usr/local/Ascend + +if [ -d ${install_path}/toolkit ]; then + export LD_LIBRARY_PATH=/usr/include/hdf5/lib/:/usr/local/:/usr/local/lib/:/usr/lib/:${install_path}/fwkacllib/lib64/:${install_path}/driver/lib64/common/:${install_path}/driver/lib64/driver/:${install_path}/add-ons:${path_lib}:${LD_LIBRARY_PATH} + export PATH=${install_path}/fwkacllib/ccec_compiler/bin:${install_path}/fwkacllib/bin:$PATH + export PYTHONPATH=${install_path}/fwkacllib/python/site-packages:${install_path}/tfplugin/python/site-packages:${install_path}/toolkit/python/site-packages:$PYTHONPATH + export PYTHONPATH=/usr/local/python3.7.5/lib/python3.7/site-packages:$PYTHONPATH + export ASCEND_OPP_PATH=${install_path}/opp +else + if [ -d ${install_path}/nnae/latest ];then +exportLD_LIBRARY_PATH=/usr/local/:/usr/local/python3.7.5/lib/:/usr/local/openblas/lib:/usr/local/lib/:/usr/lib64/:/usr/lib/:${install_path}/nnae/latest/fwkacllib/lib64/:${install_path}/driver/lib64/common/:${install_path}/driver/lib64/driver/:${install_path}/add-ons/:/usr/lib/aarch64_64-linux-gnu:$LD_LIBRARY_PATH + export PATH=$PATH:${install_path}/nnae/latest/fwkacllib/ccec_compiler/bin/:${install_path}/nnae/latest/toolkit/tools/ide_daemon/bin/ + export ASCEND_OPP_PATH=${install_path}/nnae/latest/opp/ + export OPTION_EXEC_EXTERN_PLUGIN_PATH=${install_path}/nnae/latest/fwkacllib/lib64/plugin/opskernel/libfe.so:${install_path}/nnae/latest/fwkacllib/lib64/plugin/opskernel/libaicpu_engine.so:${install_path}/nnae/latest/fwkacllib/lib64/plugin/opskernel/libge_local_engine.so + export PYTHONPATH=${install_path}/nnae/latest/fwkacllib/python/site-packages/:${install_path}/nnae/latest/fwkacllib/python/site-packages/auto_tune.egg/auto_tune:${install_path}/nnae/latest/fwkacllib/python/site-packages/schedule_search.egg:$PYTHONPATH + export ASCEND_AICPU_PATH=${install_path}/nnae/latest + else + export LD_LIBRARY_PATH=/usr/local/:/usr/local/lib/:/usr/lib64/:/usr/lib/:/usr/local/python3.7.5/lib/:/usr/local/openblas/lib:${install_path}/ascend-toolkit/latest/fwkacllib/lib64/:${install_path}/driver/lib64/common/:${install_path}/driver/lib64/driver/:${install_path}/add-ons/:/usr/lib/aarch64-linux-gnu:$LD_LIBRARY_PATH + export PATH=$PATH:${install_path}/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin/:${install_path}/ascend-toolkit/latest/toolkit/tools/ide_daemon/bin/ + export ASCEND_OPP_PATH=${install_path}/ascend-toolkit/latest/opp/ + export OPTION_EXEC_EXTERN_PLUGIN_PATH=${install_path}/ascend-toolkit/latest/fwkacllib/lib64/plugin/opskernel/libfe.so:${install_path}/ascend-toolkit/latest/fwkacllib/lib64/plugin/opskernel/libaicpu_engine.so:${install_path}/ascend-toolkit/latest/fwkacllib/lib64/plugin/opskernel/libge_local_engine.so + export PYTHONPATH=${install_path}/ascend-toolkit/latest/fwkacllib/python/site-packages/:${install_path}/ascend-toolkit/latest/fwkacllib/python/site-packages/auto_tune.egg/auto_tune:${install_path}/ascend-toolkit/latest/fwkacllib/python/site-packages/schedule_search.egg:$PYTHONPATH + export ASCEND_AICPU_PATH=${install_path}/ascend-toolkit/latest + fi +fi + +#将Host日志输出到串口,0-关闭/1-开启 +export ASCEND_SLOG_PRINT_TO_STDOUT=0 +#设置默认日志级别,0-debug/1-info/2-warning/3-error +export ASCEND_GLOBAL_LOG_LEVEL=3 +#设置Event日志开启标志,0-关闭/1-开启 +export ASCEND_GLOBAL_EVENT_ENABLE=0 +#设置是否开启taskque,0-关闭/1-开启 +export TASK_QUEUE_ENABLE=1 +#HCCL白名单开关,1-关闭/0-开启 +export HCCL_WHITELIST_DISABLE=1 - +#设置device侧日志登记为error +${install_path}/driver/tools/msnpureport -g error -d 0 +${install_path}/driver/tools/msnpureport -g error -d 1 +${install_path}/driver/tools/msnpureport -g error -d 2 +${install_path}/driver/tools/msnpureport -g error -d 3 +${install_path}/driver/tools/msnpureport -g error -d 4 +${install_path}/driver/tools/msnpureport -g error -d 5 +${install_path}/driver/tools/msnpureport -g error -d 6 +${install_path}/driver/tools/msnpureport -g error -d 7 +#关闭Device侧Event日志 +${install_path}/driver/tools/msnpureport -e disable + +path_lib=$(python3.7 -c """ +import sys +import re +result='' +for index in range(len(sys.path)): + match_sit = re.search('-packages', sys.path[index]) + if match_sit is not None: + match_lib = re.search('lib', sys.path[index]) + + if match_lib is not None: + end=match_lib.span()[1] + result += sys.path[index][0:end] + ':' + + result+=sys.path[index] + '/torch/lib:' +print(result)""" +) + +echo ${path_lib} + +export LD_LIBRARY_PATH=/usr/local/python3.7.5/lib/:${path_lib}:$LD_LIBRARY_PATH +``` -- Gitee From 9aa734bedc680b26a84c0867eb9ee5061b8fe7c7 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Wed, 8 Dec 2021 22:07:31 +0800 Subject: [PATCH 19/55] updata --- ...02\351\205\215\346\214\207\345\215\227.md" | 115 +++++++----------- 1 file changed, 45 insertions(+), 70 deletions(-) diff --git "a/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" index a4aee0155b6..76e42504a73 100644 --- "a/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" +++ "b/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" @@ -203,8 +203,6 @@ PyTorch模型多机多卡训练流程一般包括准备环境、准备模型、 torch.save(model.module.state_dict(), "%d.ckpt" % epoch) ``` - - 2. 在单机多卡上运行训练，确保模型正确。 1. 用户自行安装模型脚本需要的Python第三方库。 @@ -403,6 +401,9 @@ pytorch分布式训练基本概念 准备模型阶段主要有两种方式。 - 从[开源社区](https://gitee.com/ascend/modelzoo/tree/master/built-in/PyTorch)下载PyTorch训练模型 + + 从开源社区获取的模型已经支持单机多卡训练，请用户参照“修改模型”小节需要修改的项目，根据具体模型完成相应修改。 + - 手动搭建PyTorch训练模型 1. 准备PyTorch训练模型、数据加载器 @@ -430,6 +431,8 @@ pytorch分布式训练基本概念 trainloader = torch.utils.data.DataLoader(my_trainset,batch_size=16,) return trainloader + + trainloader=get_dataset() ``` 2. 实例化模型 @@ -438,13 +441,9 @@ pytorch分布式训练基本概念 # 实例化模型 model = ToyModel().to(loc) - # 加载模型权重，在构造DDP模型之前，且只需要在master上加载就行了。 - ckpt_path = None - if dist.get_rank() == 0 and ckpt_path is not None: + # 加载模型权重 + if ckpt_path is not None: model.load_state_dict(torch.load(ckpt_path)) - - # 构造DDP model - model = DDP(model, device_ids=[local_rank], output_device=local_rank) ``` 3. 准备损失函数和优化器。 @@ -464,7 +463,6 @@ pytorch分布式训练基本概念 model.train() iterator = range(100) for epoch in iterator: - trainloader.sampler.set_epoch(epoch) for data, label in trainloader: data, label = data.to(local_rank), label.to(local_rank) optimizer.zero_grad() @@ -474,83 +472,73 @@ pytorch分布式训练基本概念 print("loss = %0.3f \n" % loss) optimizer.step() - # 1. save模型的时候，和DP模式一样，有一个需要注意的点：保存的是model.module而不是model。 - # 因为model其实是DDP model，参数是被`model=DDP(model)`包起来的。 - # 2. 只需要在进程0上保存一次就行了，避免多次保存重复的东西。 - if dist.get_rank() == 0: - torch.save(model.module.state_dict(), "%d.ckpt" % epoch) + torch.save(model.state_dict(), "%d.ckpt" % epoch) ``` ### 修改模型 -1. 设置系统的Master地址和端口 +模型修改主要涉及以下6项，包括master ip地址和端口的设置，distributed初始化，模型DDP初始化，数据DDP初始化，优化器初始化，DDP模型训练方法修改。请用户结合初始模型代码，灵活修改。 + +1. 设置master ip地址和端口，在NPU进行分布式训练使用HCCL进行通信，在PyTorch中使用的是自动拓扑探测的HCCL通信机制，即不需要使用RANK_TABLE_FLIE，但是其依赖于host侧的网卡进行通信，因此需要在代码中设置环境变量来设置通信网卡。 ```python - os.environ['MASTER_ADDR'] = addr - os.environ['MASTER_PORT'] = '29501 + os.environ['MASTER_ADDR'] = xxx.xxx.xxx.xxx + os.environ['MASTER_PORT'] = 'xxx' ``` - 在NPU进行分布式训练使用HCCL进行通信，在PyTorch中使用的是自动拓扑探测的HCCL通信机制，即不需要使用RANK_TABLE_FLIE，但是其依赖于host侧的网卡进行通信，因此需要在代码中设置环境变量来设置通信网卡。其代码形式如下:： - - os.environ['MASTER_ADDR'] = '192.168.xx.xx' - - os.environ['MASTER_PORT'] = '29561' - - 上述值在多机情况下需要设置为： - MASTER_ADDR：设置为集群中master的IP（任意挑选一台作为master即可） MASTER_PORT：设置为master的一个空闲端口 - 上述两个参数在模型代码中一般会设置为传参的形式，但也有可能某些代码中写为"127.0.0.1"，需要进行修改。上述变量需在调用torch.distributed.init_process_group()之前声明。 - -2. distributed初始化 - - 修改torch.distributed.init_process_group() + master ip地址和端口在模型代码中一般会设置为传参的形式，也有可能某些开源代码中设置为"127.0.0.1"，需要进行修改。 - PyTorch中使用torch.distributed.init_process_group()来初始化线程组。 + 上述变量需在调用torch.distributed.init_process_group()之前声明。 - 其代码形式如下： - - import torch.distributed as dist +2. distributed初始化 - dist.init_process_group(backend='hccl', world_size=world_size, rank=rank) + PyTorch中使用`dist.init_process_group(backend='hccl', world_size=world_size, rank=rank)`来初始化线程组其中参数含义如下。 - 上述值的意义为： + `backend`：进行分布式训练的使用的通信协议，在NPU上只能使用"hccl" - backend：进行分布式训练的使用的通信协议，**在NPU上只能使用"hccl"** + `world_size`：进行训练时使用的device的总数 - world_size：进行训练时使用的device的总数 + `rank`： 当前初始化的device的rank_id，也就是全局的逻辑ID - rank: 当前初始化的device的rank_id，也就是全局的逻辑ID + 有两种方法启动多卡训练，分别初始化的方法如下。 - 使用torch.distributed.launch启动多卡训练。 ```python - dist.init_process_group(backend='hccl') # hccl是NPU设备上的后端 + import torch.distributed as dist + dist.init_process_group(backend='hccl') # hccl是NPU设备上的后端 ``` - 使用mp.spawn启动多卡训练。 ```python - def main_worker(pid_idx, device_nums_per_node, args): + import torch.distributed as dist - args.distributed_rank = args.rank * device_nums_per_node + pid_idx - - dist.init_process_group(backend=args.dist_backend, world_size=args.distributed_world_size, rank=args.distributed_rank) + def main_worker(pid_idx, device_nums_per_node, args): + args.distributed_rank = args.rank * device_nums_per_node + pid_idx + dist.init_process_group(backend=args.dist_backend, world_size=args.distributed_world_size, rank=args.distributed_rank) ``` + 其中： + + `pid_idx`：device序号。 + + `device_nums_per_node`：每个AI Server的device数量。 + 3. 模型DDP初始化 ```python # 实例化模型 model = ToyModel().to(loc) - # 加载模型权重，在构造DDP模型之前，且只需要在master上加载就行了。 - ckpt_path = None + # 加载模型权重，在构造DDP模型之前，且只需要在master上加载。 if dist.get_rank() == 0 and ckpt_path is not None: model.load_state_dict(torch.load(ckpt_path)) @@ -565,19 +553,18 @@ pytorch分布式训练基本概念 transform = torchvision.transforms.Compose([ torchvision.transforms.ToTensor(), torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) - ]) + ]) my_trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform) - trainloader = torch.utils.data.DataLoader(my_trainset,batch_size=16,) + + train_sampler = torch.utils.data.distributed.DistributedSampler(my_trainset) + trainloader = torch.utils.data.DataLoader(my_trainset, + batch_size=16, num_workers=2, sampler=train_sampler) return trainloader trainloader = get_dataset() ``` - - - - 5. 损失方法、优化器。 ```python @@ -618,15 +605,13 @@ pytorch分布式训练基本概念 - 手动启动 - 添加环境变量 - - 相较于8p的启动脚本，多机的脚本需要增加环境变量： + 1. 添加环境变量，多机训练需要增加`HCCL_WHITELIST_DISABLE`和`HCCL_IF_IP`环境变量。 - HCCL_WHITELIST_DISABLE，HCCL通道白名单，一般性设置为1表示关闭白名单。 + - HCCL_WHITELIST_DISABLE：HCCL通道白名单，一般性设置为1表示关闭白名单。 - HCCL_IF_IP, HCCL初始化通信网卡IP，设置为当前服务器的host网卡。AI Server0 脚本中该参数的值为AI Server0的host ip, AI Server1脚本中该参数的值为AI Server1的host ip。 + - HCCL_IF_IP：HCCL初始化通信网卡IP，设置为当前服务器的host网卡IP。 - 修改点这部分其中最重要的参数是需要设置addr，也就是上述提到的"MASTER_ADDR"需要将集群中每一台机器上启动脚本的该参数设置为master节点的ip。 + 本部分的说明中使用的是torch.distributed.launch来启动多卡训练 @@ -636,26 +621,16 @@ pytorch分布式训练基本概念 比如AI Server0服务器的host ip为：192.168.xx.22， AI Server1服务器的host ip为：192.168.xx.23。AI Server0为master节点，我们现在拉起2*8的集群。在拉起之前请先将脚本防止服务器相应位置， 确保python相关库已安装。 - **在AI** **server0服务器上启动命令：** + **在AI** **serveri服务器上启动命令：** source env_npu.sh export HCCL_WHITELIST_DISABLE=1 - export HCCL_IF_IP=192.168.xx.22 + export HCCL_IF_IP=192.168.xx.xx python3.7 -m torch.distributed.launch --nnodes=2 --node_rank=0 --nproc_per_node 8 --master_addr 192.168.xx.22 --master_port 29501 main.py --addr 192.168.xx.22 - **在AI** **server****1****服务器上启动命令：** - - source env_npu.sh - - export HCCL_WHITELIST_DISABLE=1 - - export HCCL_IF_IP=192.168.xx.23 - - python3.7 -m torch.distributed.launch --nnodes=2 --node_rank=1 --nproc_per_node 8 --master_addr 192.168.xx.22--master_port 29501 main.py --addr 192.168.xx.22 - 以上2个命令的差别是HCCL_IF_IP/node_rank/addr的值不同， 用户可将命令写入shell脚本， 对不同的参数以shell脚本外传值方式启动。 - 使用sh脚本启动 -- Gitee From 2f95fbc330ce2cf25ce5097efbe0a912b4d61ed0 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Thu, 9 Dec 2021 11:12:45 +0800 Subject: [PATCH 20/55] updata --- ...02\351\205\215\346\214\207\345\215\227.md" | 228 ++++++++++++++++-- 1 file changed, 207 insertions(+), 21 deletions(-) diff --git "a/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" index 76e42504a73..5dd8b21b756 100644 --- "a/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" +++ "b/docs/zh/PyTorch\346\250\241\345\236\213\345\244\232\346\234\272\345\244\232\345\215\241\350\256\255\347\273\203\351\200\202\351\205\215\346\214\207\345\215\227.md" @@ -287,7 +287,7 @@ PyTorch模型多机多卡训练流程一般包括准备环境、准备模型、 3. host日志查看 - host日志保存在`~/ascend/log`路径下，用户可以到该路径下查看每个host的device日志 + host日志保存在`~/ascend/log`路径下，用户可以到该路径下查看每个host的device日志。 通过该简单的示例您已经完成了多机多卡模型的训练。 @@ -578,11 +578,12 @@ pytorch分布式训练基本概念 6. DDP模型训练 ```python - ### 3. 网络训练 ### model.train() iterator = range(100) for epoch in iterator: + # 设置epoch trainloader.sampler.set_epoch(epoch) + for data, label in trainloader: data, label = data.to(local_rank), label.to(local_rank) optimizer.zero_grad() @@ -603,45 +604,230 @@ pytorch分布式训练基本概念 启动训练提供两种方式，手动启动和shell脚本启动 -- 手动启动 +- 手动启动（使用torch.distributed.launch方式启动） + + 1. 配置NPU 环境变量，请参考附录提供的env_npu.sh脚本 + + 2. 添加环境变量，多机训练需要增加`HCCL_WHITELIST_DISABLE`和`HCCL_IF_IP`环境变量。 + + - HCCL_WHITELIST_DISABLE：HCCL通道白名单，一般性设置为1表示关闭白名单。 + - HCCL_IF_IP：HCCL初始化通信网卡IP，设置为当前服务器的host网卡IP。 + + 3. 将修改的模型脚本上传至每个AI Server + + 4. 在每个AI Server上安装必要Python库 + + 5. 任选一个AI Server作为masker节点，并查询每个AI Server的IP。 + + 6. 在每个AI server服务器上启动命令 + + ``` + python3 -m torch.distributed.launch --nnodes=${nnodes} --node_rank=i --nproc_per_node 8 --master_addr 192.168.xx.22 --master_port 29501 main.py --addr 192.168.xx.22 + ``` + + 其中： + + --nnodes：用来分布式训练脚本的AI server数。 + + --node_rank：AI server的序号，每个AI server有对应的序号。 + + --nproc_per_node：每个AI server的device数量。 + + --master_addr：作为maskter节点的AI server IP地址。 + + --master_port： 作为maskter节点的AI server 端口号。 + + main.py：请修改为启动脚本名称。 + + --addr ：传入启动脚本的参数，master IP地址。 + +- 使用OpenMPI启动 + + 1. 安装 OpenMPI开源库 + + 多机多卡环境下分布式训练部署依赖于OpenMPI开源库，参与模型训练的每台服务器都需要安装OpenMPI开源库。目前OpenMPI开源库的版本要求为4.0.1、4.0.2或4.0.3。 + 执行`mpirun --version`命令检查是否已安装OpenMPI。如果返回`mpirun (Open MPI) 4.0.2 Report bugs to http://www.open-mpi.org/community/help/` ，说明已经安装。如果已安装，且版本为4.0.1、4.0.2或4.0.3其中一个，则无需再安装。 + + 否则请按照如下步骤安装OpenMPI： + + 1. 访问如下链接下载OpenMPI软件包。例如openmpi-4.0.2.tar.bz2。 + https://www.open-mpi.org/software/ompi/v4.0/ + + 2. 以root用户登录安装环境。 + + 3. 将下载的OpenMPI软件包上传到安装环境的某一目录下。 + + 4. 进入软件包所在目录，执行如下命令解压软件包。 + + ```shell + tar -jxvf openmpi-4.0.2.tar.bz2 + ``` + + 5. 进入解压后的目录，执行如下命令配置、编译和安装。 + + ```shell + ./configure --prefix=/usr/local/mpirun4.0.2 + make + make install + ``` + + 其中“--prefix”参数用于指定OpenMPI安装路径，用户根据实际情况进行修改。 + + 6. 执行vi ~/.bashrc命令，打开.bashrc文件，在文件的最后添加如下环境变量。 + + ```shell + export OPENMPI=/usr/local/mpirun4.0.2 + export LD_LIBRARY_PATH=$OPENMPI/lib + export PATH=$OPENMPI/bin:$PATH + ``` + + 其中“/usr/local/mpirun4.0.2”为OpenMPI安装路径，用户需要根据实际进行修改。 + 执行:wq!命令保存文件并退出。 + + 7. 执行如下命令使环境变量生效。 + + ``` + source ~/.bashrc + ``` + + 8. 安装完成之后，执行如下命令查看安装版本，如果返回正确的版本信息，则说明安装成功。 + + ``` + mpirun --version + ``` + + 2. 配置AI Server SSH免密登录 + + 如果使用OpenMPI在多机多卡环境下分布式训练部署，需要在每两台服务器之间配置SSH免密登录，确保服务器之间可以互通。具体操作步骤如下： + + 1. 以root用户登录每台服务器。 + + 2. 配置集群中主机间的可信度。 + + 打开并编辑**/etc/ssh/ssh_config**文件，在文件最后增加如下字段： + + ``` + StrictHostKeyChecking no + UserKnownHostsFile /dev/null + ``` + + 3. 在每台服务器上分别打开**/etc/hosts**文件，在该文件中添加本服务器对应的IP地址和主机名，且需要添加到该文件的首行。如果文件中已添加，则跳过此步骤。添加内容示例如下： + + ``` + 10.90.140.199 ubuntu + ``` + + 其中10.90.140.199为该服务器的IP地址，ubuntu为主机名。 + + 4. 在第一台服务器执行如下命令生成公钥（例如第一台服务器IP为10.90.140.199）。 + + ``` + cd ~/.ssh/ # 若没有该目录，请先执行一次ssh localhost + ssh-keygen -t rsa # 生成秘钥，会出现提示，连续点击3次"Enter"即可 + mv id_rsa.pub authorized_keys # 将生成的秘钥id_rsa.pub重命名为公钥authorized_keys + ``` + + 5. 在其他每台服务器上都生成秘钥，并复制到第一台服务器的authorized_keys文件中。 + + 1. 在其他每台服务器上执行如下命令生成秘钥。 + + cd ~/.ssh/ + ssh-keygen -t rsa + + 2. 将其他每台服务器上生成的秘钥文件id_rsa.pub下载到本地，并复制该文件中的秘钥。 + + 3. 在第一台服务器内执行如下命令打开公钥文件authorized_keys，将[5.b](#li361025101116 "link to:#li361025101116 + ctrl +click to jump")复制的其他每台服务器的秘钥粘贴到第一台服务器的公钥后面。 + + ``` + vi ~/.ssh/authorized_keys + ``` + + 执行**:wq!**保存该文件。 + + 6. 在其他每台服务器上执行如下命令将第一台服务器制作好的公钥复制到其他每台服务器内。 + + cd ~/.ssh/ + scp root@10.90.140.199:~/.ssh/authorized_keys ./ + + 7. 在每台服务器执行如下命令测试免密登录。 + + ``` + ssh 用户名@IP地址 + ``` + + 例如：在第一台服务器10.90.140.199免密登录服务器10.90.140.231，执行**ssh root@10.90.140.231**命令。 - 1. 添加环境变量，多机训练需要增加`HCCL_WHITELIST_DISABLE`和`HCCL_IF_IP`环境变量。 + 若显示类似如下信息，说明已免密登录服务器10.90.140.231。 - - HCCL_WHITELIST_DISABLE：HCCL通道白名单，一般性设置为1表示关闭白名单。 + ``` + Linux ubuntu 4.19.28 #1 SMP Tue Jun 23 19:05:23 EDT 2020 x86_64 + + The programs included with the ubuntu GNU/Linux system are free software; + the exact distribution terms for each program are described in the + individual files in /usr/share/doc/*/copyright. + + ubuntu GNU/Linux comes with ABSOLUTELY NO WARRANTY, to the extent + permitted by applicable law. + Last login: Tue Sep 15 14:37:21 2020 from 10.254.88.75 + ``` - - HCCL_IF_IP：HCCL初始化通信网卡IP，设置为当前服务器的host网卡IP。 + 执行**exit**命令可以退出登录，若显示类似如下信息，说明已退出登录。 - + ``` + logout + Connection to 10.90.140.231 closed. + ``` - 本部分的说明中使用的是torch.distributed.launch来启动多卡训练 + 3. 使用OpenMPI拉起模型训练。 - （1）将模型脚本上传至AI Server0和AIServer1； + 1. 分别为每一个AI server编写启动脚本，如train.sh，并将启动脚本分别移到相应AI server的同一路径下。 - （2）对照requirements.txt，检查AI Server0和AIServer1是否缺少相关三方库， 该模型用到了DLLogger模块， 需源码安装； + ``` + # 配置NPU环境变量，env_npu.sh脚本参见附录 + source env_npu.sh + #关闭HCCL通道白名单 + export HCCL_WHITELIST_DISABLE=1 + # HCCL初始化通信网卡IP，设置为当前服务器的host IP + export HCCL_IF_IP=xxx.xxx.xx.xxx + python3 -m torch.distributed.launch --nnodes=${nnodes} --node_rank=i --nproc_per_node 8 --master_addr xxx.xxx.xx.xxx --master_port 29501 main.py --addr xxx.xxx.xx.xxx + ``` - 比如AI Server0服务器的host ip为：192.168.xx.22， AI Server1服务器的host ip为：192.168.xx.23。AI Server0为master节点，我们现在拉起2*8的集群。在拉起之前请先将脚本防止服务器相应位置， 确保python相关库已安装。 + 脚本参数请参见手动启动。 - **在AI** **serveri服务器上启动命令：** + 2. 编写启动脚本 - source env_npu.sh + ``` + # 配置mpirun环境变量 + export PATH=$PATH:/usr/local/mpirun4.0.2/bin + # 执行mpirun工具 + mpirun -H xxx.xxx.xxx.xxx:1,xxx.xxx.xxx.xxx:1 \ + --bind-to none -map-by slot \ + --mca btl_tcp_if_exclude lo,docker0,endvnic\ + --allow-run-as-root \ + --prefix /usr/local/mpirun4.0.2/ \ + ./train.sh + ``` - export HCCL_WHITELIST_DISABLE=1 + 其中 - export HCCL_IF_IP=192.168.xx.xx + -H：每个AI server的IP:启动进程数。 - python3.7 -m torch.distributed.launch --nnodes=2 --node_rank=0 --nproc_per_node 8 --master_addr 192.168.xx.22 --master_port 29501 main.py --addr 192.168.xx.22 + --bind-to：绑定进程的策略。 - 以上2个命令的差别是HCCL_IF_IP/node_rank/addr的值不同， 用户可将命令写入shell脚本， 对不同的参数以shell脚本外传值方式启动。 + --mca：特定上下文的MCA参数，arg0为参数名，arg1为参数值。 -- 使用sh脚本启动 + --allow-run-as-root：允许使用root用户运行。 - 用户可将命令写入shell脚本， 对不同的参数以shell脚本外传值方式启动。 + --prefix：mpirun4.0.2路径。 -训练成功后可以查看日志信息 + ./train.sh：各个AI server的启动脚本路径。 -**host日志查看** + 4. +训练成功后可以查看日志信息。 +host日志保存在`~/ascend/log`路径下，用户可以到该路径下查看每个host的device日志。 # 附录 -- Gitee From 72f9481869c76067ea14a933d8f4063fb331c5c8 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Thu, 9 Dec 2021 14:32:24 +0800 Subject: [PATCH 21/55] pytorch --- ...\214\201\346\270\205\345\215\225_1.8.1.md" | 1507 +++++++++-------- 1 file changed, 761 insertions(+), 746 deletions(-) diff --git "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.8.1.md" "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.8.1.md" index dc01f67e671..c5d00a6cb65 100644 --- "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.8.1.md" +++ "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.8.1.md" @@ -4,152 +4,152 @@ | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [is_tensor](https://pytorch.org/docs/1.8.1/generated/torch.is_tensor.html) | | -| 2 | [is_storage](https://pytorch.org/docs/1.8.1/generated/torch.is_storage.html) | | -| 3 | [is_complex](https://pytorch.org/docs/1.8.1/generated/torch.is_complex.html) | | -| 4 | [is_floating_point](https://pytorch.org/docs/1.8.1/generated/torch.is_floating_point.html) | | -| 5 | [is_nonzero](https://pytorch.org/docs/1.8.1/generated/torch.is_nonzero.html) | | -| 6 | [set_default_dtype](https://pytorch.org/docs/1.8.1/generated/torch.set_default_dtype.html) | | -| 7 | [get_default_dtype](https://pytorch.org/docs/1.8.1/generated/torch.get_default_dtype.html) | | -| 8 | [set_default_tensor_type](https://pytorch.org/docs/1.8.1/generated/torch.set_default_tensor_type.html) | | -| 9 | [numel](https://pytorch.org/docs/1.8.1/generated/torch.numel.html) | | -| 10 | [set_printoptions](https://pytorch.org/docs/1.8.1/generated/torch.set_printoptions.html) | | -| 11 | [set_flush_denormal](https://pytorch.org/docs/1.8.1/generated/torch.set_flush_denormal.html) | | +| 1 | [is_tensor](https://pytorch.org/docs/1.8.1/generated/torch.is_tensor.html) | 否 | +| 2 | [is_storage](https://pytorch.org/docs/1.8.1/generated/torch.is_storage.html) | 否 | +| 3 | [is_complex](https://pytorch.org/docs/1.8.1/generated/torch.is_complex.html) | 否 | +| 4 | [is_floating_point](https://pytorch.org/docs/1.8.1/generated/torch.is_floating_point.html) | 否 | +| 5 | [is_nonzero](https://pytorch.org/docs/1.8.1/generated/torch.is_nonzero.html) | 否 | +| 6 | [set_default_dtype](https://pytorch.org/docs/1.8.1/generated/torch.set_default_dtype.html) | 否 | +| 7 | [get_default_dtype](https://pytorch.org/docs/1.8.1/generated/torch.get_default_dtype.html) | 否 | +| 8 | [set_default_tensor_type](https://pytorch.org/docs/1.8.1/generated/torch.set_default_tensor_type.html) | 否 | +| 9 | [numel](https://pytorch.org/docs/1.8.1/generated/torch.numel.html) | 否 | +| 10 | [set_printoptions](https://pytorch.org/docs/1.8.1/generated/torch.set_printoptions.html) | 否 | +| 11 | [set_flush_denormal](https://pytorch.org/docs/1.8.1/generated/torch.set_flush_denormal.html) | 否 | ### Creation Ops | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [tensor](https://pytorch.org/docs/1.8.1/generated/torch.tensor.html) | | -| 2 | [sparse_coo_tensor](https://pytorch.org/docs/1.8.1/generated/torch.sparse_coo_tensor.html) | | -| 3 | [as_tensor](https://pytorch.org/docs/1.8.1/generated/torch.as_tensor.html) | | -| 4 | [as_strided](https://pytorch.org/docs/1.8.1/generated/torch.as_strided.html) | | -| 5 | [from_numpy](https://pytorch.org/docs/1.8.1/generated/torch.from_numpy.html) | | -| 6 | [zeros](https://pytorch.org/docs/1.8.1/generated/torch.zeros.html) | | -| 7 | [zeros_like](https://pytorch.org/docs/1.8.1/generated/torch.zeros_like.html) | | -| 8 | [ones](https://pytorch.org/docs/1.8.1/generated/torch.ones.html) | | -| 9 | [ones_like](https://pytorch.org/docs/1.8.1/generated/torch.ones_like.html) | | -| 10 | [arange](https://pytorch.org/docs/1.8.1/generated/torch.arange.html) | | -| 11 | [range](https://pytorch.org/docs/1.8.1/generated/torch.range.html) | | -| 12 | [linspace](https://pytorch.org/docs/1.8.1/generated/torch.linspace.html) | | -| 13 | [logspace](https://pytorch.org/docs/1.8.1/generated/torch.logspace.html) | | -| 14 | [eye](https://pytorch.org/docs/1.8.1/generated/torch.eye.html) | | -| 15 | [empty](https://pytorch.org/docs/1.8.1/generated/torch.empty.html) | | -| 16 | [empty_like](https://pytorch.org/docs/1.8.1/generated/torch.empty_like.html) | | -| 17 | [empty_strided](https://pytorch.org/docs/1.8.1/generated/torch.empty_strided.html) | | -| 18 | [full](https://pytorch.org/docs/1.8.1/generated/torch.full.html) | | -| 19 | [full_like](https://pytorch.org/docs/1.8.1/generated/torch.full_like.html) | | -| 20 | [quantize_per_tensor](https://pytorch.org/docs/1.8.1/generated/torch.quantize_per_tensor.html) | | -| 21 | [quantize_per_channel](https://pytorch.org/docs/1.8.1/generated/torch.quantize_per_channel.html) | | -| 22 | [dequantize](https://pytorch.org/docs/1.8.1/generated/torch.dequantize.html) | | -| 23 | [complex](https://pytorch.org/docs/1.8.1/generated/torch.complex.html) | | -| 24 | [polar](https://pytorch.org/docs/1.8.1/generated/torch.polar.html) | | -| 25 | [heaviside](https://pytorch.org/docs/1.8.1/generated/torch.heaviside.html) | | +| 1 | [tensor](https://pytorch.org/docs/1.8.1/generated/torch.tensor.html) | 否 | +| 2 | [sparse_coo_tensor](https://pytorch.org/docs/1.8.1/generated/torch.sparse_coo_tensor.html) | 否 | +| 3 | [as_tensor](https://pytorch.org/docs/1.8.1/generated/torch.as_tensor.html) | 否 | +| 4 | [as_strided](https://pytorch.org/docs/1.8.1/generated/torch.as_strided.html) | 否 | +| 5 | [from_numpy](https://pytorch.org/docs/1.8.1/generated/torch.from_numpy.html) | 否 | +| 6 | [zeros](https://pytorch.org/docs/1.8.1/generated/torch.zeros.html) | 否 | +| 7 | [zeros_like](https://pytorch.org/docs/1.8.1/generated/torch.zeros_like.html) | 否 | +| 8 | [ones](https://pytorch.org/docs/1.8.1/generated/torch.ones.html) | 否 | +| 9 | [ones_like](https://pytorch.org/docs/1.8.1/generated/torch.ones_like.html) | 否 | +| 10 | [arange](https://pytorch.org/docs/1.8.1/generated/torch.arange.html) | 否 | +| 11 | [range](https://pytorch.org/docs/1.8.1/generated/torch.range.html) | 否 | +| 12 | [linspace](https://pytorch.org/docs/1.8.1/generated/torch.linspace.html) | 否 | +| 13 | [logspace](https://pytorch.org/docs/1.8.1/generated/torch.logspace.html) | 否 | +| 14 | [eye](https://pytorch.org/docs/1.8.1/generated/torch.eye.html) | 否 | +| 15 | [empty](https://pytorch.org/docs/1.8.1/generated/torch.empty.html) | 否 | +| 16 | [empty_like](https://pytorch.org/docs/1.8.1/generated/torch.empty_like.html) | 否 | +| 17 | [empty_strided](https://pytorch.org/docs/1.8.1/generated/torch.empty_strided.html) | 否 | +| 18 | [full](https://pytorch.org/docs/1.8.1/generated/torch.full.html) | 否 | +| 19 | [full_like](https://pytorch.org/docs/1.8.1/generated/torch.full_like.html) | 否 | +| 20 | [quantize_per_tensor](https://pytorch.org/docs/1.8.1/generated/torch.quantize_per_tensor.html) | 否 | +| 21 | [quantize_per_channel](https://pytorch.org/docs/1.8.1/generated/torch.quantize_per_channel.html) | 否 | +| 22 | [dequantize](https://pytorch.org/docs/1.8.1/generated/torch.dequantize.html) | 否 | +| 23 | [complex](https://pytorch.org/docs/1.8.1/generated/torch.complex.html) | 否 | +| 24 | [polar](https://pytorch.org/docs/1.8.1/generated/torch.polar.html) | 否 | +| 25 | [heaviside](https://pytorch.org/docs/1.8.1/generated/torch.heaviside.html) | 否 | ### Indexing, Slicing, Joining, Mutating Ops | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [cat](https://pytorch.org/docs/1.8.1/generated/torch.cat.html) | | -| 2 | [chunk](https://pytorch.org/docs/1.8.1/generated/torch.chunk.html) | | -| 3 | [column_stack](https://pytorch.org/docs/1.8.1/generated/torch.column_stack.html) | | -| 4 | [dstack](https://pytorch.org/docs/1.8.1/generated/torch.dstack.html) | | -| 5 | [gather](https://pytorch.org/docs/1.8.1/generated/torch.gather.html) | | -| 6 | [hstack](https://pytorch.org/docs/1.8.1/generated/torch.hstack.html) | | -| 7 | [index_select](https://pytorch.org/docs/1.8.1/generated/torch.index_select.html) | | -| 8 | [masked_select](https://pytorch.org/docs/1.8.1/generated/torch.masked_select.html) | | -| 9 | [movedim](https://pytorch.org/docs/1.8.1/generated/torch.movedim.html) | | -| 10 | [moveaxis](https://pytorch.org/docs/1.8.1/generated/torch.moveaxis.html) | | -| 11 | [narrow](https://pytorch.org/docs/1.8.1/generated/torch.narrow.html) | | -| 12 | [nonzero](https://pytorch.org/docs/1.8.1/generated/torch.nonzero.html) | | -| 13 | [reshape](https://pytorch.org/docs/1.8.1/generated/torch.reshape.html) | | -| 14 | [row_stack](https://pytorch.org/docs/1.8.1/generated/torch.row_stack.html) | | -| 15 | [scatter](https://pytorch.org/docs/1.8.1/generated/torch.scatter.html) | | -| 16 | [scatter_add](https://pytorch.org/docs/1.8.1/generated/torch.scatter_add.html) | | -| 17 | [split](https://pytorch.org/docs/1.8.1/generated/torch.split.html) | | -| 18 | [squeeze](https://pytorch.org/docs/1.8.1/generated/torch.squeeze.html) | | -| 19 | [stack](https://pytorch.org/docs/1.8.1/generated/torch.stack.html) | | -| 20 | [swapaxes](https://pytorch.org/docs/1.8.1/generated/torch.swapaxes.html) | | -| 21 | [swapdims](https://pytorch.org/docs/1.8.1/generated/torch.swapdims.html) | | -| 22 | [t](https://pytorch.org/docs/1.8.1/generated/torch.t.html) | | -| 23 | [take](https://pytorch.org/docs/1.8.1/generated/torch.take.html) | | -| 24 | [tensor_split](https://pytorch.org/docs/1.8.1/generated/torch.tensor_split.html) | | -| 25 | [tile](https://pytorch.org/docs/1.8.1/generated/torch.tile.html) | | -| 26 | [transpose](https://pytorch.org/docs/1.8.1/generated/torch.transpose.html) | | -| 27 | [unbind](https://pytorch.org/docs/1.8.1/generated/torch.unbind.html) | | -| 28 | [unsqueeze](https://pytorch.org/docs/1.8.1/generated/torch.unsqueeze.html) | | -| 29 | [vstack](https://pytorch.org/docs/1.8.1/generated/torch.vstack.html) | | -| 30 | [where](https://pytorch.org/docs/1.8.1/generated/torch.where.html) | | +| 1 | [cat](https://pytorch.org/docs/1.8.1/generated/torch.cat.html) | 否 | +| 2 | [chunk](https://pytorch.org/docs/1.8.1/generated/torch.chunk.html) | 否 | +| 3 | [column_stack](https://pytorch.org/docs/1.8.1/generated/torch.column_stack.html) | 否 | +| 4 | [dstack](https://pytorch.org/docs/1.8.1/generated/torch.dstack.html) | 否 | +| 5 | [gather](https://pytorch.org/docs/1.8.1/generated/torch.gather.html) | 否 | +| 6 | [hstack](https://pytorch.org/docs/1.8.1/generated/torch.hstack.html) | 否 | +| 7 | [index_select](https://pytorch.org/docs/1.8.1/generated/torch.index_select.html) | 否 | +| 8 | [masked_select](https://pytorch.org/docs/1.8.1/generated/torch.masked_select.html) | 否 | +| 9 | [movedim](https://pytorch.org/docs/1.8.1/generated/torch.movedim.html) | 否 | +| 10 | [moveaxis](https://pytorch.org/docs/1.8.1/generated/torch.moveaxis.html) | 否 | +| 11 | [narrow](https://pytorch.org/docs/1.8.1/generated/torch.narrow.html) | 否 | +| 12 | [nonzero](https://pytorch.org/docs/1.8.1/generated/torch.nonzero.html) | 否 | +| 13 | [reshape](https://pytorch.org/docs/1.8.1/generated/torch.reshape.html) | 否 | +| 14 | [row_stack](https://pytorch.org/docs/1.8.1/generated/torch.row_stack.html) | 否 | +| 15 | [scatter](https://pytorch.org/docs/1.8.1/generated/torch.scatter.html) | 否 | +| 16 | [scatter_add](https://pytorch.org/docs/1.8.1/generated/torch.scatter_add.html) | 否 | +| 17 | [split](https://pytorch.org/docs/1.8.1/generated/torch.split.html) | 否 | +| 18 | [squeeze](https://pytorch.org/docs/1.8.1/generated/torch.squeeze.html) | 否 | +| 19 | [stack](https://pytorch.org/docs/1.8.1/generated/torch.stack.html) | 否 | +| 20 | [swapaxes](https://pytorch.org/docs/1.8.1/generated/torch.swapaxes.html) | 否 | +| 21 | [swapdims](https://pytorch.org/docs/1.8.1/generated/torch.swapdims.html) | 否 | +| 22 | [t](https://pytorch.org/docs/1.8.1/generated/torch.t.html) | 否 | +| 23 | [take](https://pytorch.org/docs/1.8.1/generated/torch.take.html) | 否 | +| 24 | [tensor_split](https://pytorch.org/docs/1.8.1/generated/torch.tensor_split.html) | 否 | +| 25 | [tile](https://pytorch.org/docs/1.8.1/generated/torch.tile.html) | 否 | +| 26 | [transpose](https://pytorch.org/docs/1.8.1/generated/torch.transpose.html) | 否 | +| 27 | [unbind](https://pytorch.org/docs/1.8.1/generated/torch.unbind.html) | 否 | +| 28 | [unsqueeze](https://pytorch.org/docs/1.8.1/generated/torch.unsqueeze.html) | 否 | +| 29 | [vstack](https://pytorch.org/docs/1.8.1/generated/torch.vstack.html) | 否 | +| 30 | [where](https://pytorch.org/docs/1.8.1/generated/torch.where.html) | 否 | ## Generators | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [Generator](https://pytorch.org/docs/1.8.1/generated/torch.Generator.html) | | +| 1 | [Generator](https://pytorch.org/docs/1.8.1/generated/torch.Generator.html) | 否 | ## Random sampling | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [seed](https://pytorch.org/docs/1.8.1/generated/torch.seed.html) | | -| 2 | [manual_seed](https://pytorch.org/docs/1.8.1/generated/torch.manual_seed.html) | | -| 3 | [initial_seed](https://pytorch.org/docs/1.8.1/generated/torch.initial_seed.html) | | -| 4 | [get_rng_state](https://pytorch.org/docs/1.8.1/generated/torch.get_rng_state.html) | | -| 5 | [set_rng_state](https://pytorch.org/docs/1.8.1/generated/torch.set_rng_state.html) | | -| 6 | [bernoulli](https://pytorch.org/docs/1.8.1/generated/torch.bernoulli.html) | | -| 7 | [multinomial](https://pytorch.org/docs/1.8.1/generated/torch.multinomial.html) | | -| 8 | [normal](https://pytorch.org/docs/1.8.1/generated/torch.normal.html) | | -| 9 | [poisson](https://pytorch.org/docs/1.8.1/generated/torch.poisson.html) | | -| 10 | [rand](https://pytorch.org/docs/1.8.1/generated/torch.rand.html) | | -| 11 | [rand_like](https://pytorch.org/docs/1.8.1/generated/torch.rand_like.html) | | -| 12 | [randint](https://pytorch.org/docs/1.8.1/generated/torch.randint.html) | | -| 13 | [randint_like](https://pytorch.org/docs/1.8.1/generated/torch.randint_like.html) | | -| 14 | [randn](https://pytorch.org/docs/1.8.1/generated/torch.randn.html) | | -| 15 | [randn_like](https://pytorch.org/docs/1.8.1/generated/torch.randn_like.html) | | -| 16 | [randperm](https://pytorch.org/docs/1.8.1/generated/torch.randperm.html) | | +| 1 | [seed](https://pytorch.org/docs/1.8.1/generated/torch.seed.html) | 否 | +| 2 | [manual_seed](https://pytorch.org/docs/1.8.1/generated/torch.manual_seed.html) | 否 | +| 3 | [initial_seed](https://pytorch.org/docs/1.8.1/generated/torch.initial_seed.html) | 否 | +| 4 | [get_rng_state](https://pytorch.org/docs/1.8.1/generated/torch.get_rng_state.html) | 否 | +| 5 | [set_rng_state](https://pytorch.org/docs/1.8.1/generated/torch.set_rng_state.html) | 否 | +| 6 | [bernoulli](https://pytorch.org/docs/1.8.1/generated/torch.bernoulli.html) | 否 | +| 7 | [multinomial](https://pytorch.org/docs/1.8.1/generated/torch.multinomial.html) | 否 | +| 8 | [normal](https://pytorch.org/docs/1.8.1/generated/torch.normal.html) | 否 | +| 9 | [poisson](https://pytorch.org/docs/1.8.1/generated/torch.poisson.html) | 否 | +| 10 | [rand](https://pytorch.org/docs/1.8.1/generated/torch.rand.html) | 否 | +| 11 | [rand_like](https://pytorch.org/docs/1.8.1/generated/torch.rand_like.html) | 否 | +| 12 | [randint](https://pytorch.org/docs/1.8.1/generated/torch.randint.html) | 否 | +| 13 | [randint_like](https://pytorch.org/docs/1.8.1/generated/torch.randint_like.html) | 否 | +| 14 | [randn](https://pytorch.org/docs/1.8.1/generated/torch.randn.html) | 否 | +| 15 | [randn_like](https://pytorch.org/docs/1.8.1/generated/torch.randn_like.html) | 否 | +| 16 | [randperm](https://pytorch.org/docs/1.8.1/generated/torch.randperm.html) | 否 | ### In-place random sampling | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [torch.Tensor.bernoulli_()](https://pytorch.org/docs/1.8.1/tensors.html) | | -| 2 | [torch.Tensor.cauchy_()](https://pytorch.org/docs/1.8.1/tensors.html) | | -| 3 | [torch.Tensor.exponential_()](https://pytorch.org/docs/1.8.1/tensors.html) | | -| 4 | [torch.Tensor.geometric_()](https://pytorch.org/docs/1.8.1/tensors.html) | | -| 5 | [torch.Tensor.log_normal_()](https://pytorch.org/docs/1.8.1/tensors.html) | | -| 6 | [torch.Tensor.normal_()](https://pytorch.org/docs/1.8.1/tensors.html) | | -| 7 | [torch.Tensor.random_()](https://pytorch.org/docs/1.8.1/tensors.html) | | -| 8 | [torch.Tensor.uniform_()](https://pytorch.org/docs/1.8.1/tensors.html) | | +| 1 | [torch.Tensor.bernoulli_()](https://pytorch.org/docs/1.8.1/tensors.html) | 否 | +| 2 | [torch.Tensor.cauchy_()](https://pytorch.org/docs/1.8.1/tensors.html) | 否 | +| 3 | [torch.Tensor.exponential_()](https://pytorch.org/docs/1.8.1/tensors.html) | 否 | +| 4 | [torch.Tensor.geometric_()](https://pytorch.org/docs/1.8.1/tensors.html) | 否 | +| 5 | [torch.Tensor.log_normal_()](https://pytorch.org/docs/1.8.1/tensors.html) | 否 | +| 6 | [torch.Tensor.normal_()](https://pytorch.org/docs/1.8.1/tensors.html) | 否 | +| 7 | [torch.Tensor.random_()](https://pytorch.org/docs/1.8.1/tensors.html) | 否 | +| 8 | [torch.Tensor.uniform_()](https://pytorch.org/docs/1.8.1/tensors.html) | 否 | ### Quasi-random sampling -| | | | -| ---- | ------------------------------------------------------------ | ---- | -| 1 | [quasirandom.SobolEngine](https://pytorch.org/docs/1.8.1/generated/torch.quasirandom.SobolEngine.html) | | +| 序号 | API名称 | 支持情况 | +| ---- | ------------------------------------------------------------ | -------- | +| 1 | [quasirandom.SobolEngine](https://pytorch.org/docs/1.8.1/generated/torch.quasirandom.SobolEngine.html) | 否 | ## Serialization | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [save](https://pytorch.org/docs/1.8.1/generated/torch.save.html) | | -| 2 | [load](https://pytorch.org/docs/1.8.1/generated/torch.load.html) | | +| 1 | [save](https://pytorch.org/docs/1.8.1/generated/torch.save.html) | 否 | +| 2 | [load](https://pytorch.org/docs/1.8.1/generated/torch.load.html) | 否 | ## Parallelism | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [get_num_threads](https://pytorch.org/docs/1.8.1/generated/torch.get_num_threads.html) | | -| 2 | [set_num_threads](https://pytorch.org/docs/1.8.1/generated/torch.set_num_threads.html) | | -| 3 | [get_num_interop_threads](https://pytorch.org/docs/1.8.1/generated/torch.get_num_interop_threads.html) | | -| 4 | [set_num_interop_threads](https://pytorch.org/docs/1.8.1/generated/torch.set_num_interop_threads.html) | | +| 1 | [get_num_threads](https://pytorch.org/docs/1.8.1/generated/torch.get_num_threads.html) | 否 | +| 2 | [set_num_threads](https://pytorch.org/docs/1.8.1/generated/torch.set_num_threads.html) | 否 | +| 3 | [get_num_interop_threads](https://pytorch.org/docs/1.8.1/generated/torch.get_num_interop_threads.html) | 否 | +| 4 | [set_num_interop_threads](https://pytorch.org/docs/1.8.1/generated/torch.set_num_interop_threads.html) | 否 | ## Locally disabling gradient computation | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [no_grad](https://pytorch.org/docs/1.8.1/generated/torch.no_grad.html#torch.no_grad) | | -| 2 | [enable_grad](https://pytorch.org/docs/1.8.1/generated/torch.enable_grad.html#torch.enable_grad) | | -| 3 | set_grad_enabled | | +| 1 | [no_grad](https://pytorch.org/docs/1.8.1/generated/torch.no_grad.html#torch.no_grad) | 否 | +| 2 | [enable_grad](https://pytorch.org/docs/1.8.1/generated/torch.enable_grad.html#torch.enable_grad) | 否 | +| 3 | set_grad_enabled | 否 | ## Math operations @@ -157,296 +157,296 @@ | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [abs](https://pytorch.org/docs/1.8.1/generated/torch.abs.html#torch.abs) | | -| 2 | [absolute](https://pytorch.org/docs/1.8.1/generated/torch.absolute.html#torch.absolute) | | -| 3 | [acos](https://pytorch.org/docs/1.8.1/generated/torch.acos.html#torch.acos) | | -| 4 | [arccos](https://pytorch.org/docs/1.8.1/generated/torch.arccos.html#torch.arccos) | | -| 5 | [acosh](https://pytorch.org/docs/1.8.1/generated/torch.acosh.html#torch.acosh) | | -| 6 | [arccosh](https://pytorch.org/docs/1.8.1/generated/torch.arccosh.html#torch.arccosh) | | -| 7 | [add](https://pytorch.org/docs/1.8.1/generated/torch.add.html#torch.add) | | -| 8 | [addcdiv](https://pytorch.org/docs/1.8.1/generated/torch.addcdiv.html#torch.addcdiv) | | -| 9 | [addcmul](https://pytorch.org/docs/1.8.1/generated/torch.addcmul.html#torch.addcmul) | | -| 10 | [angle](https://pytorch.org/docs/1.8.1/generated/torch.angle.html#torch.angle) | | -| 11 | [asin](https://pytorch.org/docs/1.8.1/generated/torch.asin.html#torch.asin) | | -| 12 | [arcsin](https://pytorch.org/docs/1.8.1/generated/torch.arcsin.html#torch.arcsin) | | -| 13 | [asinh](https://pytorch.org/docs/1.8.1/generated/torch.asinh.html#torch.asinh) | | -| 14 | [arcsinh](https://pytorch.org/docs/1.8.1/generated/torch.arcsinh.html#torch.arcsinh) | | -| 15 | [atan](https://pytorch.org/docs/1.8.1/generated/torch.atan.html#torch.atan) | | -| 16 | [arctan](https://pytorch.org/docs/1.8.1/generated/torch.arctan.html#torch.arctan) | | -| 17 | [atanh](https://pytorch.org/docs/1.8.1/generated/torch.atanh.html#torch.atanh) | | -| 18 | [arctanh](https://pytorch.org/docs/1.8.1/generated/torch.arctanh.html#torch.arctanh) | | -| 19 | [atan2](https://pytorch.org/docs/1.8.1/generated/torch.atan2.html#torch.atan2) | | -| 20 | [bitwise_not](https://pytorch.org/docs/1.8.1/generated/torch.bitwise_not.html#torch.bitwise_not) | | -| 21 | [bitwise_and](https://pytorch.org/docs/1.8.1/generated/torch.bitwise_and.html#torch.bitwise_and) | | -| 22 | [bitwise_or](https://pytorch.org/docs/1.8.1/generated/torch.bitwise_or.html#torch.bitwise_or) | | -| 23 | [bitwise_xor](https://pytorch.org/docs/1.8.1/generated/torch.bitwise_xor.html#torch.bitwise_xor) | | -| 24 | [ceil](https://pytorch.org/docs/1.8.1/generated/torch.ceil.html#torch.ceil) | | -| 25 | [clamp](https://pytorch.org/docs/1.8.1/generated/torch.clamp.html#torch.clamp) | | -| 26 | [clip](https://pytorch.org/docs/1.8.1/generated/torch.clip.html#torch.clip) | | -| 27 | [conj](https://pytorch.org/docs/1.8.1/generated/torch.conj.html#torch.conj) | | -| 28 | [copysign](https://pytorch.org/docs/1.8.1/generated/torch.copysign.html#torch.copysign) | | -| 29 | [cos](https://pytorch.org/docs/1.8.1/generated/torch.cos.html#torch.cos) | | -| 30 | [cosh](https://pytorch.org/docs/1.8.1/generated/torch.cosh.html#torch.cosh) | | -| 31 | [deg2rad](https://pytorch.org/docs/1.8.1/generated/torch.deg2rad.html#torch.deg2rad) | | -| 32 | [div](https://pytorch.org/docs/1.8.1/generated/torch.div.html#torch.div) | | -| 33 | [divide](https://pytorch.org/docs/1.8.1/generated/torch.divide.html#torch.divide) | | -| 34 | [digamma](https://pytorch.org/docs/1.8.1/generated/torch.digamma.html#torch.digamma) | | -| 35 | [erf](https://pytorch.org/docs/1.8.1/generated/torch.erf.html#torch.erf) | | -| 36 | [erfc](https://pytorch.org/docs/1.8.1/generated/torch.erfc.html#torch.erfc) | | -| 37 | [erfinv](https://pytorch.org/docs/1.8.1/generated/torch.erfinv.html#torch.erfinv) | | -| 38 | [exp](https://pytorch.org/docs/1.8.1/generated/torch.exp.html#torch.exp) | | -| 39 | [exp2](https://pytorch.org/docs/1.8.1/generated/torch.exp2.html#torch.exp2) | | -| 40 | [expm1](https://pytorch.org/docs/1.8.1/generated/torch.expm1.html#torch.expm1) | | -| 41 | [fake_quantize_per_channel_affine](https://pytorch.org/docs/1.8.1/generated/torch.fake_quantize_per_channel_affine.html#torch.fake_quantize_per_channel_affine) | | -| 42 | [fake_quantize_per_tensor_affine](https://pytorch.org/docs/1.8.1/generated/torch.fake_quantize_per_tensor_affine.html#torch.fake_quantize_per_tensor_affine) | | -| 43 | [fix](https://pytorch.org/docs/1.8.1/generated/torch.fix.html#torch.fix) | | -| 44 | [float_power](https://pytorch.org/docs/1.8.1/generated/torch.float_power.html#torch.float_power) | | -| 45 | [floor](https://pytorch.org/docs/1.8.1/generated/torch.floor.html#torch.floor) | | -| 46 | [floor_divide](https://pytorch.org/docs/1.8.1/generated/torch.floor_divide.html#torch.floor_divide) | | -| 47 | [fmod](https://pytorch.org/docs/1.8.1/generated/torch.fmod.html#torch.fmod) | | -| 48 | [frac](https://pytorch.org/docs/1.8.1/generated/torch.frac.html#torch.frac) | | -| 49 | [imag](https://pytorch.org/docs/1.8.1/generated/torch.imag.html#torch.imag) | | -| 50 | [ldexp](https://pytorch.org/docs/1.8.1/generated/torch.ldexp.html#torch.ldexp) | | -| 51 | [lerp](https://pytorch.org/docs/1.8.1/generated/torch.lerp.html#torch.lerp) | | -| 52 | [lgamma](https://pytorch.org/docs/1.8.1/generated/torch.lgamma.html#torch.lgamma) | | -| 53 | [log](https://pytorch.org/docs/1.8.1/generated/torch.log.html#torch.log) | | -| 54 | [log10](https://pytorch.org/docs/1.8.1/generated/torch.log10.html#torch.log10) | | -| 55 | [log1p](https://pytorch.org/docs/1.8.1/generated/torch.log1p.html#torch.log1p) | | -| 56 | [log2](https://pytorch.org/docs/1.8.1/generated/torch.log2.html#torch.log2) | | -| 57 | [logaddexp](https://pytorch.org/docs/1.8.1/generated/torch.logaddexp.html#torch.logaddexp) | | -| 58 | [logaddexp2](https://pytorch.org/docs/1.8.1/generated/torch.logaddexp2.html#torch.logaddexp2) | | -| 59 | [logical_and](https://pytorch.org/docs/1.8.1/generated/torch.logical_and.html#torch.logical_and) | | -| 60 | [logical_not](https://pytorch.org/docs/1.8.1/generated/torch.logical_not.html#torch.logical_not) | | -| 61 | [logical_or](https://pytorch.org/docs/1.8.1/generated/torch.logical_or.html#torch.logical_or) | | -| 62 | [logical_xor](https://pytorch.org/docs/1.8.1/generated/torch.logical_xor.html#torch.logical_xor) | | -| 63 | [logit](https://pytorch.org/docs/1.8.1/generated/torch.logit.html#torch.logit) | | -| 64 | [hypot](https://pytorch.org/docs/1.8.1/generated/torch.hypot.html#torch.hypot) | | -| 65 | [i0](https://pytorch.org/docs/1.8.1/generated/torch.i0.html#torch.i0) | | -| 66 | [igamma](https://pytorch.org/docs/1.8.1/generated/torch.igamma.html#torch.igamma) | | -| 67 | [igammac](https://pytorch.org/docs/1.8.1/generated/torch.igammac.html#torch.igammac) | | -| 68 | [mul](https://pytorch.org/docs/1.8.1/generated/torch.mul.html#torch.mul) | | -| 69 | [multiply](https://pytorch.org/docs/1.8.1/generated/torch.multiply.html#torch.multiply) | | -| 70 | [mvlgamma](https://pytorch.org/docs/1.8.1/generated/torch.mvlgamma.html#torch.mvlgamma) | | -| 71 | [nan_to_num](https://pytorch.org/docs/1.8.1/generated/torch.nan_to_num.html#torch.nan_to_num) | | -| 72 | [neg](https://pytorch.org/docs/1.8.1/generated/torch.neg.html#torch.neg) | | -| 73 | [negative](https://pytorch.org/docs/1.8.1/generated/torch.negative.html#torch.negative) | | -| 74 | [nextafter](https://pytorch.org/docs/1.8.1/generated/torch.nextafter.html#torch.nextafter) | | -| 75 | [polygamma](https://pytorch.org/docs/1.8.1/generated/torch.polygamma.html#torch.polygamma) | | -| 76 | [pow](https://pytorch.org/docs/1.8.1/generated/torch.pow.html#torch.pow) | | -| 77 | [rad2deg](https://pytorch.org/docs/1.8.1/generated/torch.rad2deg.html#torch.rad2deg) | | -| 78 | [real](https://pytorch.org/docs/1.8.1/generated/torch.real.html#torch.real) | | -| 79 | [reciprocal](https://pytorch.org/docs/1.8.1/generated/torch.reciprocal.html#torch.reciprocal) | | -| 80 | [remainder](https://pytorch.org/docs/1.8.1/generated/torch.remainder.html#torch.remainder) | | -| 81 | [round](https://pytorch.org/docs/1.8.1/generated/torch.round.html#torch.round) | | -| 82 | [rsqrt](https://pytorch.org/docs/1.8.1/generated/torch.rsqrt.html#torch.rsqrt) | | -| 83 | [sigmoid](https://pytorch.org/docs/1.8.1/generated/torch.sigmoid.html#torch.sigmoid) | | -| 84 | [sign](https://pytorch.org/docs/1.8.1/generated/torch.sign.html#torch.sign) | | -| 85 | [sgn](https://pytorch.org/docs/1.8.1/generated/torch.sgn.html#torch.sgn) | | -| 86 | [signbit](https://pytorch.org/docs/1.8.1/generated/torch.signbit.html#torch.signbit) | | -| 87 | [sin](https://pytorch.org/docs/1.8.1/generated/torch.sin.html#torch.sin) | | -| 88 | [sinc](https://pytorch.org/docs/1.8.1/generated/torch.sinc.html#torch.sinc) | | -| 89 | [sinh](https://pytorch.org/docs/1.8.1/generated/torch.sinh.html#torch.sinh) | | -| 90 | [sqrt](https://pytorch.org/docs/1.8.1/generated/torch.sqrt.html#torch.sqrt) | | -| 91 | [square](https://pytorch.org/docs/1.8.1/generated/torch.square.html#torch.square) | | -| 92 | [sub](https://pytorch.org/docs/1.8.1/generated/torch.sub.html#torch.sub) | | -| 93 | [subtract](https://pytorch.org/docs/1.8.1/generated/torch.subtract.html#torch.subtract) | | -| 94 | [tan](https://pytorch.org/docs/1.8.1/generated/torch.tan.html#torch.tan) | | -| 95 | [tanh](https://pytorch.org/docs/1.8.1/generated/torch.tanh.html#torch.tanh) | | -| 96 | [true_divide](https://pytorch.org/docs/1.8.1/generated/torch.true_divide.html#torch.true_divide) | | -| 97 | [trunc](https://pytorch.org/docs/1.8.1/generated/torch.trunc.html#torch.trunc) | | -| 98 | [xlogy](https://pytorch.org/docs/1.8.1/generated/torch.xlogy.html#torch.xlogy) | | +| 1 | [abs](https://pytorch.org/docs/1.8.1/generated/torch.abs.html#torch.abs) | 否 | +| 2 | [absolute](https://pytorch.org/docs/1.8.1/generated/torch.absolute.html#torch.absolute) | 否 | +| 3 | [acos](https://pytorch.org/docs/1.8.1/generated/torch.acos.html#torch.acos) | 否 | +| 4 | [arccos](https://pytorch.org/docs/1.8.1/generated/torch.arccos.html#torch.arccos) | 否 | +| 5 | [acosh](https://pytorch.org/docs/1.8.1/generated/torch.acosh.html#torch.acosh) | 否 | +| 6 | [arccosh](https://pytorch.org/docs/1.8.1/generated/torch.arccosh.html#torch.arccosh) | 否 | +| 7 | [add](https://pytorch.org/docs/1.8.1/generated/torch.add.html#torch.add) | 否 | +| 8 | [addcdiv](https://pytorch.org/docs/1.8.1/generated/torch.addcdiv.html#torch.addcdiv) | 否 | +| 9 | [addcmul](https://pytorch.org/docs/1.8.1/generated/torch.addcmul.html#torch.addcmul) | 否 | +| 10 | [angle](https://pytorch.org/docs/1.8.1/generated/torch.angle.html#torch.angle) | 否 | +| 11 | [asin](https://pytorch.org/docs/1.8.1/generated/torch.asin.html#torch.asin) | 否 | +| 12 | [arcsin](https://pytorch.org/docs/1.8.1/generated/torch.arcsin.html#torch.arcsin) | 否 | +| 13 | [asinh](https://pytorch.org/docs/1.8.1/generated/torch.asinh.html#torch.asinh) | 否 | +| 14 | [arcsinh](https://pytorch.org/docs/1.8.1/generated/torch.arcsinh.html#torch.arcsinh) | 否 | +| 15 | [atan](https://pytorch.org/docs/1.8.1/generated/torch.atan.html#torch.atan) | 否 | +| 16 | [arctan](https://pytorch.org/docs/1.8.1/generated/torch.arctan.html#torch.arctan) | 否 | +| 17 | [atanh](https://pytorch.org/docs/1.8.1/generated/torch.atanh.html#torch.atanh) | 否 | +| 18 | [arctanh](https://pytorch.org/docs/1.8.1/generated/torch.arctanh.html#torch.arctanh) | 否 | +| 19 | [atan2](https://pytorch.org/docs/1.8.1/generated/torch.atan2.html#torch.atan2) | 否 | +| 20 | [bitwise_not](https://pytorch.org/docs/1.8.1/generated/torch.bitwise_not.html#torch.bitwise_not) | 否 | +| 21 | [bitwise_and](https://pytorch.org/docs/1.8.1/generated/torch.bitwise_and.html#torch.bitwise_and) | 否 | +| 22 | [bitwise_or](https://pytorch.org/docs/1.8.1/generated/torch.bitwise_or.html#torch.bitwise_or) | 否 | +| 23 | [bitwise_xor](https://pytorch.org/docs/1.8.1/generated/torch.bitwise_xor.html#torch.bitwise_xor) | 否 | +| 24 | [ceil](https://pytorch.org/docs/1.8.1/generated/torch.ceil.html#torch.ceil) | 否 | +| 25 | [clamp](https://pytorch.org/docs/1.8.1/generated/torch.clamp.html#torch.clamp) | 否 | +| 26 | [clip](https://pytorch.org/docs/1.8.1/generated/torch.clip.html#torch.clip) | 否 | +| 27 | [conj](https://pytorch.org/docs/1.8.1/generated/torch.conj.html#torch.conj) | 否 | +| 28 | [copysign](https://pytorch.org/docs/1.8.1/generated/torch.copysign.html#torch.copysign) | 否 | +| 29 | [cos](https://pytorch.org/docs/1.8.1/generated/torch.cos.html#torch.cos) | 否 | +| 30 | [cosh](https://pytorch.org/docs/1.8.1/generated/torch.cosh.html#torch.cosh) | 否 | +| 31 | [deg2rad](https://pytorch.org/docs/1.8.1/generated/torch.deg2rad.html#torch.deg2rad) | 否 | +| 32 | [div](https://pytorch.org/docs/1.8.1/generated/torch.div.html#torch.div) | 否 | +| 33 | [divide](https://pytorch.org/docs/1.8.1/generated/torch.divide.html#torch.divide) | 否 | +| 34 | [digamma](https://pytorch.org/docs/1.8.1/generated/torch.digamma.html#torch.digamma) | 否 | +| 35 | [erf](https://pytorch.org/docs/1.8.1/generated/torch.erf.html#torch.erf) | 否 | +| 36 | [erfc](https://pytorch.org/docs/1.8.1/generated/torch.erfc.html#torch.erfc) | 否 | +| 37 | [erfinv](https://pytorch.org/docs/1.8.1/generated/torch.erfinv.html#torch.erfinv) | 否 | +| 38 | [exp](https://pytorch.org/docs/1.8.1/generated/torch.exp.html#torch.exp) | 否 | +| 39 | [exp2](https://pytorch.org/docs/1.8.1/generated/torch.exp2.html#torch.exp2) | 否 | +| 40 | [expm1](https://pytorch.org/docs/1.8.1/generated/torch.expm1.html#torch.expm1) | 否 | +| 41 | [fake_quantize_per_channel_affine](https://pytorch.org/docs/1.8.1/generated/torch.fake_quantize_per_channel_affine.html#torch.fake_quantize_per_channel_affine) | 否 | +| 42 | [fake_quantize_per_tensor_affine](https://pytorch.org/docs/1.8.1/generated/torch.fake_quantize_per_tensor_affine.html#torch.fake_quantize_per_tensor_affine) | 否 | +| 43 | [fix](https://pytorch.org/docs/1.8.1/generated/torch.fix.html#torch.fix) | 否 | +| 44 | [float_power](https://pytorch.org/docs/1.8.1/generated/torch.float_power.html#torch.float_power) | 否 | +| 45 | [floor](https://pytorch.org/docs/1.8.1/generated/torch.floor.html#torch.floor) | 否 | +| 46 | [floor_divide](https://pytorch.org/docs/1.8.1/generated/torch.floor_divide.html#torch.floor_divide) | 否 | +| 47 | [fmod](https://pytorch.org/docs/1.8.1/generated/torch.fmod.html#torch.fmod) | 否 | +| 48 | [frac](https://pytorch.org/docs/1.8.1/generated/torch.frac.html#torch.frac) | 否 | +| 49 | [imag](https://pytorch.org/docs/1.8.1/generated/torch.imag.html#torch.imag) | 否 | +| 50 | [ldexp](https://pytorch.org/docs/1.8.1/generated/torch.ldexp.html#torch.ldexp) | 否 | +| 51 | [lerp](https://pytorch.org/docs/1.8.1/generated/torch.lerp.html#torch.lerp) | 否 | +| 52 | [lgamma](https://pytorch.org/docs/1.8.1/generated/torch.lgamma.html#torch.lgamma) | 否 | +| 53 | [log](https://pytorch.org/docs/1.8.1/generated/torch.log.html#torch.log) | 否 | +| 54 | [log10](https://pytorch.org/docs/1.8.1/generated/torch.log10.html#torch.log10) | 否 | +| 55 | [log1p](https://pytorch.org/docs/1.8.1/generated/torch.log1p.html#torch.log1p) | 否 | +| 56 | [log2](https://pytorch.org/docs/1.8.1/generated/torch.log2.html#torch.log2) | 否 | +| 57 | [logaddexp](https://pytorch.org/docs/1.8.1/generated/torch.logaddexp.html#torch.logaddexp) | 否 | +| 58 | [logaddexp2](https://pytorch.org/docs/1.8.1/generated/torch.logaddexp2.html#torch.logaddexp2) | 否 | +| 59 | [logical_and](https://pytorch.org/docs/1.8.1/generated/torch.logical_and.html#torch.logical_and) | 否 | +| 60 | [logical_not](https://pytorch.org/docs/1.8.1/generated/torch.logical_not.html#torch.logical_not) | 否 | +| 61 | [logical_or](https://pytorch.org/docs/1.8.1/generated/torch.logical_or.html#torch.logical_or) | 否 | +| 62 | [logical_xor](https://pytorch.org/docs/1.8.1/generated/torch.logical_xor.html#torch.logical_xor) | 否 | +| 63 | [logit](https://pytorch.org/docs/1.8.1/generated/torch.logit.html#torch.logit) | 否 | +| 64 | [hypot](https://pytorch.org/docs/1.8.1/generated/torch.hypot.html#torch.hypot) | 否 | +| 65 | [i0](https://pytorch.org/docs/1.8.1/generated/torch.i0.html#torch.i0) | 否 | +| 66 | [igamma](https://pytorch.org/docs/1.8.1/generated/torch.igamma.html#torch.igamma) | 否 | +| 67 | [igammac](https://pytorch.org/docs/1.8.1/generated/torch.igammac.html#torch.igammac) | 否 | +| 68 | [mul](https://pytorch.org/docs/1.8.1/generated/torch.mul.html#torch.mul) | 否 | +| 69 | [multiply](https://pytorch.org/docs/1.8.1/generated/torch.multiply.html#torch.multiply) | 否 | +| 70 | [mvlgamma](https://pytorch.org/docs/1.8.1/generated/torch.mvlgamma.html#torch.mvlgamma) | 否 | +| 71 | [nan_to_num](https://pytorch.org/docs/1.8.1/generated/torch.nan_to_num.html#torch.nan_to_num) | 否 | +| 72 | [neg](https://pytorch.org/docs/1.8.1/generated/torch.neg.html#torch.neg) | 否 | +| 73 | [negative](https://pytorch.org/docs/1.8.1/generated/torch.negative.html#torch.negative) | 否 | +| 74 | [nextafter](https://pytorch.org/docs/1.8.1/generated/torch.nextafter.html#torch.nextafter) | 否 | +| 75 | [polygamma](https://pytorch.org/docs/1.8.1/generated/torch.polygamma.html#torch.polygamma) | 否 | +| 76 | [pow](https://pytorch.org/docs/1.8.1/generated/torch.pow.html#torch.pow) | 否 | +| 77 | [rad2deg](https://pytorch.org/docs/1.8.1/generated/torch.rad2deg.html#torch.rad2deg) | 否 | +| 78 | [real](https://pytorch.org/docs/1.8.1/generated/torch.real.html#torch.real) | 否 | +| 79 | [reciprocal](https://pytorch.org/docs/1.8.1/generated/torch.reciprocal.html#torch.reciprocal) | 否 | +| 80 | [remainder](https://pytorch.org/docs/1.8.1/generated/torch.remainder.html#torch.remainder) | 否 | +| 81 | [round](https://pytorch.org/docs/1.8.1/generated/torch.round.html#torch.round) | 否 | +| 82 | [rsqrt](https://pytorch.org/docs/1.8.1/generated/torch.rsqrt.html#torch.rsqrt) | 否 | +| 83 | [sigmoid](https://pytorch.org/docs/1.8.1/generated/torch.sigmoid.html#torch.sigmoid) | 否 | +| 84 | [sign](https://pytorch.org/docs/1.8.1/generated/torch.sign.html#torch.sign) | 否 | +| 85 | [sgn](https://pytorch.org/docs/1.8.1/generated/torch.sgn.html#torch.sgn) | 否 | +| 86 | [signbit](https://pytorch.org/docs/1.8.1/generated/torch.signbit.html#torch.signbit) | 否 | +| 87 | [sin](https://pytorch.org/docs/1.8.1/generated/torch.sin.html#torch.sin) | 否 | +| 88 | [sinc](https://pytorch.org/docs/1.8.1/generated/torch.sinc.html#torch.sinc) | 否 | +| 89 | [sinh](https://pytorch.org/docs/1.8.1/generated/torch.sinh.html#torch.sinh) | 否 | +| 90 | [sqrt](https://pytorch.org/docs/1.8.1/generated/torch.sqrt.html#torch.sqrt) | 否 | +| 91 | [square](https://pytorch.org/docs/1.8.1/generated/torch.square.html#torch.square) | 否 | +| 92 | [sub](https://pytorch.org/docs/1.8.1/generated/torch.sub.html#torch.sub) | 否 | +| 93 | [subtract](https://pytorch.org/docs/1.8.1/generated/torch.subtract.html#torch.subtract) | 否 | +| 94 | [tan](https://pytorch.org/docs/1.8.1/generated/torch.tan.html#torch.tan) | 否 | +| 95 | [tanh](https://pytorch.org/docs/1.8.1/generated/torch.tanh.html#torch.tanh) | 否 | +| 96 | [true_divide](https://pytorch.org/docs/1.8.1/generated/torch.true_divide.html#torch.true_divide) | 否 | +| 97 | [trunc](https://pytorch.org/docs/1.8.1/generated/torch.trunc.html#torch.trunc) | 否 | +| 98 | [xlogy](https://pytorch.org/docs/1.8.1/generated/torch.xlogy.html#torch.xlogy) | 否 | ### Reduction Ops | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [argmax](https://pytorch.org/docs/1.8.1/generated/torch.argmax.html#torch.argmax) | | -| 2 | [argmin](https://pytorch.org/docs/1.8.1/generated/torch.argmin.html#torch.argmin) | | -| 3 | [amax](https://pytorch.org/docs/1.8.1/generated/torch.amax.html#torch.amax) | | -| 4 | [amin](https://pytorch.org/docs/1.8.1/generated/torch.amin.html#torch.amin) | | -| 5 | [all](https://pytorch.org/docs/1.8.1/generated/torch.all.html#torch.all) | | -| 6 | [any](https://pytorch.org/docs/1.8.1/generated/torch.any.html#torch.any) | | -| 7 | [max](https://pytorch.org/docs/1.8.1/generated/torch.max.html#torch.max) | | -| 8 | [min](https://pytorch.org/docs/1.8.1/generated/torch.min.html#torch.min) | | -| 9 | [dist](https://pytorch.org/docs/1.8.1/generated/torch.dist.html#torch.dist) | | -| 10 | [logsumexp](https://pytorch.org/docs/1.8.1/generated/torch.logsumexp.html#torch.logsumexp) | | -| 11 | [mean](https://pytorch.org/docs/1.8.1/generated/torch.mean.html#torch.mean) | | -| 12 | [median](https://pytorch.org/docs/1.8.1/generated/torch.median.html#torch.median) | | -| 13 | [nanmedian](https://pytorch.org/docs/1.8.1/generated/torch.nanmedian.html#torch.nanmedian) | | -| 14 | [mode](https://pytorch.org/docs/1.8.1/generated/torch.mode.html#torch.mode) | | -| 15 | [norm](https://pytorch.org/docs/1.8.1/generated/torch.norm.html#torch.norm) | | -| 16 | [nansum](https://pytorch.org/docs/1.8.1/generated/torch.nansum.html#torch.nansum) | | -| 17 | [prod](https://pytorch.org/docs/1.8.1/generated/torch.prod.html#torch.prod) | | -| 18 | [quantile](https://pytorch.org/docs/1.8.1/generated/torch.quantile.html#torch.quantile) | | -| 19 | [nanquantile](https://pytorch.org/docs/1.8.1/generated/torch.nanquantile.html#torch.nanquantile) | | -| 20 | [std](https://pytorch.org/docs/1.8.1/generated/torch.std.html#torch.std) | | -| 21 | [std_mean](https://pytorch.org/docs/1.8.1/generated/torch.std_mean.html#torch.std_mean) | | -| 22 | [sum](https://pytorch.org/docs/1.8.1/generated/torch.sum.html#torch.sum) | | -| 23 | [unique](https://pytorch.org/docs/1.8.1/generated/torch.unique.html#torch.unique) | | -| 24 | [unique_consecutive](https://pytorch.org/docs/1.8.1/generated/torch.unique_consecutive.html#torch.unique_consecutive) | | -| 25 | [var](https://pytorch.org/docs/1.8.1/generated/torch.var.html#torch.var) | | -| 26 | [var_mean](https://pytorch.org/docs/1.8.1/generated/torch.var_mean.html#torch.var_mean) | | -| 27 | [count_nonzero](https://pytorch.org/docs/1.8.1/generated/torch.count_nonzero.html#torch.count_nonzero) | | +| 1 | [argmax](https://pytorch.org/docs/1.8.1/generated/torch.argmax.html#torch.argmax) | 否 | +| 2 | [argmin](https://pytorch.org/docs/1.8.1/generated/torch.argmin.html#torch.argmin) | 否 | +| 3 | [amax](https://pytorch.org/docs/1.8.1/generated/torch.amax.html#torch.amax) | 否 | +| 4 | [amin](https://pytorch.org/docs/1.8.1/generated/torch.amin.html#torch.amin) | 否 | +| 5 | [all](https://pytorch.org/docs/1.8.1/generated/torch.all.html#torch.all) | 否 | +| 6 | [any](https://pytorch.org/docs/1.8.1/generated/torch.any.html#torch.any) | 否 | +| 7 | [max](https://pytorch.org/docs/1.8.1/generated/torch.max.html#torch.max) | 否 | +| 8 | [min](https://pytorch.org/docs/1.8.1/generated/torch.min.html#torch.min) | 否 | +| 9 | [dist](https://pytorch.org/docs/1.8.1/generated/torch.dist.html#torch.dist) | 否 | +| 10 | [logsumexp](https://pytorch.org/docs/1.8.1/generated/torch.logsumexp.html#torch.logsumexp) | 否 | +| 11 | [mean](https://pytorch.org/docs/1.8.1/generated/torch.mean.html#torch.mean) | 否 | +| 12 | [median](https://pytorch.org/docs/1.8.1/generated/torch.median.html#torch.median) | 否 | +| 13 | [nanmedian](https://pytorch.org/docs/1.8.1/generated/torch.nanmedian.html#torch.nanmedian) | 否 | +| 14 | [mode](https://pytorch.org/docs/1.8.1/generated/torch.mode.html#torch.mode) | 否 | +| 15 | [norm](https://pytorch.org/docs/1.8.1/generated/torch.norm.html#torch.norm) | 否 | +| 16 | [nansum](https://pytorch.org/docs/1.8.1/generated/torch.nansum.html#torch.nansum) | 否 | +| 17 | [prod](https://pytorch.org/docs/1.8.1/generated/torch.prod.html#torch.prod) | 否 | +| 18 | [quantile](https://pytorch.org/docs/1.8.1/generated/torch.quantile.html#torch.quantile) | 否 | +| 19 | [nanquantile](https://pytorch.org/docs/1.8.1/generated/torch.nanquantile.html#torch.nanquantile) | 否 | +| 20 | [std](https://pytorch.org/docs/1.8.1/generated/torch.std.html#torch.std) | 否 | +| 21 | [std_mean](https://pytorch.org/docs/1.8.1/generated/torch.std_mean.html#torch.std_mean) | 否 | +| 22 | [sum](https://pytorch.org/docs/1.8.1/generated/torch.sum.html#torch.sum) | 否 | +| 23 | [unique](https://pytorch.org/docs/1.8.1/generated/torch.unique.html#torch.unique) | 否 | +| 24 | [unique_consecutive](https://pytorch.org/docs/1.8.1/generated/torch.unique_consecutive.html#torch.unique_consecutive) | 否 | +| 25 | [var](https://pytorch.org/docs/1.8.1/generated/torch.var.html#torch.var) | 否 | +| 26 | [var_mean](https://pytorch.org/docs/1.8.1/generated/torch.var_mean.html#torch.var_mean) | 否 | +| 27 | [count_nonzero](https://pytorch.org/docs/1.8.1/generated/torch.count_nonzero.html#torch.count_nonzero) | 否 | ### Comparison Ops | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [allclose](https://pytorch.org/docs/1.8.1/generated/torch.allclose.html#torch.allclose) | | -| 2 | [argsort](https://pytorch.org/docs/1.8.1/generated/torch.argsort.html#torch.argsort) | | -| 3 | [eq](https://pytorch.org/docs/1.8.1/generated/torch.eq.html#torch.eq) | | -| 4 | [equal](https://pytorch.org/docs/1.8.1/generated/torch.equal.html#torch.equal) | | -| 5 | [ge](https://pytorch.org/docs/1.8.1/generated/torch.ge.html#torch.ge) | | -| 6 | [greater_equal](https://pytorch.org/docs/1.8.1/generated/torch.greater_equal.html#torch.greater_equal) | | -| 7 | [gt](https://pytorch.org/docs/1.8.1/generated/torch.gt.html#torch.gt) | | -| 8 | [greater](https://pytorch.org/docs/1.8.1/generated/torch.greater.html#torch.greater) | | -| 9 | [isclose](https://pytorch.org/docs/1.8.1/generated/torch.isclose.html#torch.isclose) | | -| 10 | [isfinite](https://pytorch.org/docs/1.8.1/generated/torch.isfinite.html#torch.isfinite) | | -| 11 | [isinf](https://pytorch.org/docs/1.8.1/generated/torch.isinf.html#torch.isinf) | | -| 12 | [isposinf](https://pytorch.org/docs/1.8.1/generated/torch.isposinf.html#torch.isposinf) | | -| 13 | [isneginf](https://pytorch.org/docs/1.8.1/generated/torch.isneginf.html#torch.isneginf) | | -| 14 | [isnan](https://pytorch.org/docs/1.8.1/generated/torch.isnan.html#torch.isnan) | | -| 15 | [isreal](https://pytorch.org/docs/1.8.1/generated/torch.isreal.html#torch.isreal) | | -| 16 | [kthvalue](https://pytorch.org/docs/1.8.1/generated/torch.kthvalue.html#torch.kthvalue) | | -| 17 | [le](https://pytorch.org/docs/1.8.1/generated/torch.le.html#torch.le) | | -| 18 | [less_equal](https://pytorch.org/docs/1.8.1/generated/torch.less_equal.html#torch.less_equal) | | -| 19 | [lt](https://pytorch.org/docs/1.8.1/generated/torch.lt.html#torch.lt) | | -| 20 | [less](https://pytorch.org/docs/1.8.1/generated/torch.less.html#torch.less) | | -| 21 | [maximum](https://pytorch.org/docs/1.8.1/generated/torch.maximum.html#torch.maximum) | | -| 22 | [minimum](https://pytorch.org/docs/1.8.1/generated/torch.minimum.html#torch.minimum) | | -| 23 | [fmax](https://pytorch.org/docs/1.8.1/generated/torch.fmax.html#torch.fmax) | | -| 24 | [fmin](https://pytorch.org/docs/1.8.1/generated/torch.fmin.html#torch.fmin) | | -| 25 | [ne](https://pytorch.org/docs/1.8.1/generated/torch.ne.html#torch.ne) | | -| 26 | [not_equal](https://pytorch.org/docs/1.8.1/generated/torch.not_equal.html#torch.not_equal) | | -| 27 | [sort](https://pytorch.org/docs/1.8.1/generated/torch.sort.html#torch.sort) | | -| 28 | [topk](https://pytorch.org/docs/1.8.1/generated/torch.topk.html#torch.topk) | | -| 29 | [msort](https://pytorch.org/docs/1.8.1/generated/torch.msort.html#torch.msort) | | +| 1 | [allclose](https://pytorch.org/docs/1.8.1/generated/torch.allclose.html#torch.allclose) | 否 | +| 2 | [argsort](https://pytorch.org/docs/1.8.1/generated/torch.argsort.html#torch.argsort) | 否 | +| 3 | [eq](https://pytorch.org/docs/1.8.1/generated/torch.eq.html#torch.eq) | 否 | +| 4 | [equal](https://pytorch.org/docs/1.8.1/generated/torch.equal.html#torch.equal) | 否 | +| 5 | [ge](https://pytorch.org/docs/1.8.1/generated/torch.ge.html#torch.ge) | 否 | +| 6 | [greater_equal](https://pytorch.org/docs/1.8.1/generated/torch.greater_equal.html#torch.greater_equal) | 否 | +| 7 | [gt](https://pytorch.org/docs/1.8.1/generated/torch.gt.html#torch.gt) | 否 | +| 8 | [greater](https://pytorch.org/docs/1.8.1/generated/torch.greater.html#torch.greater) | 否 | +| 9 | [isclose](https://pytorch.org/docs/1.8.1/generated/torch.isclose.html#torch.isclose) | 否 | +| 10 | [isfinite](https://pytorch.org/docs/1.8.1/generated/torch.isfinite.html#torch.isfinite) | 否 | +| 11 | [isinf](https://pytorch.org/docs/1.8.1/generated/torch.isinf.html#torch.isinf) | 否 | +| 12 | [isposinf](https://pytorch.org/docs/1.8.1/generated/torch.isposinf.html#torch.isposinf) | 否 | +| 13 | [isneginf](https://pytorch.org/docs/1.8.1/generated/torch.isneginf.html#torch.isneginf) | 否 | +| 14 | [isnan](https://pytorch.org/docs/1.8.1/generated/torch.isnan.html#torch.isnan) | 否 | +| 15 | [isreal](https://pytorch.org/docs/1.8.1/generated/torch.isreal.html#torch.isreal) | 否 | +| 16 | [kthvalue](https://pytorch.org/docs/1.8.1/generated/torch.kthvalue.html#torch.kthvalue) | 否 | +| 17 | [le](https://pytorch.org/docs/1.8.1/generated/torch.le.html#torch.le) | 否 | +| 18 | [less_equal](https://pytorch.org/docs/1.8.1/generated/torch.less_equal.html#torch.less_equal) | 否 | +| 19 | [lt](https://pytorch.org/docs/1.8.1/generated/torch.lt.html#torch.lt) | 否 | +| 20 | [less](https://pytorch.org/docs/1.8.1/generated/torch.less.html#torch.less) | 否 | +| 21 | [maximum](https://pytorch.org/docs/1.8.1/generated/torch.maximum.html#torch.maximum) | 否 | +| 22 | [minimum](https://pytorch.org/docs/1.8.1/generated/torch.minimum.html#torch.minimum) | 否 | +| 23 | [fmax](https://pytorch.org/docs/1.8.1/generated/torch.fmax.html#torch.fmax) | 否 | +| 24 | [fmin](https://pytorch.org/docs/1.8.1/generated/torch.fmin.html#torch.fmin) | 否 | +| 25 | [ne](https://pytorch.org/docs/1.8.1/generated/torch.ne.html#torch.ne) | 否 | +| 26 | [not_equal](https://pytorch.org/docs/1.8.1/generated/torch.not_equal.html#torch.not_equal) | 否 | +| 27 | [sort](https://pytorch.org/docs/1.8.1/generated/torch.sort.html#torch.sort) | 否 | +| 28 | [topk](https://pytorch.org/docs/1.8.1/generated/torch.topk.html#torch.topk) | 否 | +| 29 | [msort](https://pytorch.org/docs/1.8.1/generated/torch.msort.html#torch.msort) | 否 | ### Spectral Ops | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [stft](https://pytorch.org/docs/1.8.1/generated/torch.stft.html#torch.stft) | | -| 2 | [istft](https://pytorch.org/docs/1.8.1/generated/torch.istft.html#torch.istft) | | -| 3 | [bartlett_window](https://pytorch.org/docs/1.8.1/generated/torch.bartlett_window.html#torch.bartlett_window) | | -| 4 | [blackman_window](https://pytorch.org/docs/1.8.1/generated/torch.blackman_window.html#torch.blackman_window) | | -| 5 | [hamming_window](https://pytorch.org/docs/1.8.1/generated/torch.hamming_window.html#torch.hamming_window) | | -| 6 | [hann_window](https://pytorch.org/docs/1.8.1/generated/torch.hann_window.html#torch.hann_window) | | -| 7 | [kaiser_window](https://pytorch.org/docs/1.8.1/generated/torch.kaiser_window.html#torch.kaiser_window) | | +| 1 | [stft](https://pytorch.org/docs/1.8.1/generated/torch.stft.html#torch.stft) | 否 | +| 2 | [istft](https://pytorch.org/docs/1.8.1/generated/torch.istft.html#torch.istft) | 否 | +| 3 | [bartlett_window](https://pytorch.org/docs/1.8.1/generated/torch.bartlett_window.html#torch.bartlett_window) | 否 | +| 4 | [blackman_window](https://pytorch.org/docs/1.8.1/generated/torch.blackman_window.html#torch.blackman_window) | 否 | +| 5 | [hamming_window](https://pytorch.org/docs/1.8.1/generated/torch.hamming_window.html#torch.hamming_window) | 否 | +| 6 | [hann_window](https://pytorch.org/docs/1.8.1/generated/torch.hann_window.html#torch.hann_window) | 否 | +| 7 | [kaiser_window](https://pytorch.org/docs/1.8.1/generated/torch.kaiser_window.html#torch.kaiser_window) | 否 | ### Other Operations | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [atleast_1d](https://pytorch.org/docs/1.8.1/generated/torch.atleast_1d.html#torch.atleast_1d) | | -| 2 | [atleast_2d](https://pytorch.org/docs/1.8.1/generated/torch.atleast_2d.html#torch.atleast_2d) | | -| 3 | [atleast_3d](https://pytorch.org/docs/1.8.1/generated/torch.atleast_3d.html#torch.atleast_3d) | | -| 4 | [bincount](https://pytorch.org/docs/1.8.1/generated/torch.bincount.html#torch.bincount) | | -| 5 | [block_diag](https://pytorch.org/docs/1.8.1/generated/torch.block_diag.html#torch.block_diag) | | -| 6 | [broadcast_tensors](https://pytorch.org/docs/1.8.1/generated/torch.broadcast_tensors.html#torch.broadcast_tensors) | | -| 7 | [broadcast_to](https://pytorch.org/docs/1.8.1/generated/torch.broadcast_to.html#torch.broadcast_to) | | -| 8 | [broadcast_shapes](https://pytorch.org/docs/1.8.1/generated/torch.broadcast_shapes.html#torch.broadcast_shapes) | | -| 9 | [bucketize](https://pytorch.org/docs/1.8.1/generated/torch.bucketize.html#torch.bucketize) | | -| 10 | [cartesian_prod](https://pytorch.org/docs/1.8.1/generated/torch.cartesian_prod.html#torch.cartesian_prod) | | -| 11 | [cdist](https://pytorch.org/docs/1.8.1/generated/torch.cdist.html#torch.cdist) | | -| 12 | [clone](https://pytorch.org/docs/1.8.1/generated/torch.clone.html#torch.clone) | | -| 13 | [combinations](https://pytorch.org/docs/1.8.1/generated/torch.combinations.html#torch.combinations) | | -| 14 | [cross](https://pytorch.org/docs/1.8.1/generated/torch.cross.html#torch.cross) | | -| 15 | [cummax](https://pytorch.org/docs/1.8.1/generated/torch.cummax.html#torch.cummax) | | -| 16 | [cummin](https://pytorch.org/docs/1.8.1/generated/torch.cummin.html#torch.cummin) | | -| 17 | [cumprod](https://pytorch.org/docs/1.8.1/generated/torch.cumprod.html#torch.cumprod) | | -| 18 | [cumsum](https://pytorch.org/docs/1.8.1/generated/torch.cumsum.html#torch.cumsum) | | -| 19 | [diag](https://pytorch.org/docs/1.8.1/generated/torch.diag.html#torch.diag) | | -| 20 | [diag_embed](https://pytorch.org/docs/1.8.1/generated/torch.diag_embed.html#torch.diag_embed) | | -| 21 | [diagflat](https://pytorch.org/docs/1.8.1/generated/torch.diagflat.html#torch.diagflat) | | -| 22 | [diagonal](https://pytorch.org/docs/1.8.1/generated/torch.diagonal.html#torch.diagonal) | | -| 23 | [diff](https://pytorch.org/docs/1.8.1/generated/torch.diff.html#torch.diff) | | -| 24 | [einsum](https://pytorch.org/docs/1.8.1/generated/torch.einsum.html#torch.einsum) | | -| 25 | [flatten](https://pytorch.org/docs/1.8.1/generated/torch.flatten.html#torch.flatten) | | -| 26 | [flip](https://pytorch.org/docs/1.8.1/generated/torch.flip.html#torch.flip) | | -| 27 | [fliplr](https://pytorch.org/docs/1.8.1/generated/torch.fliplr.html#torch.fliplr) | | -| 28 | [flipud](https://pytorch.org/docs/1.8.1/generated/torch.flipud.html#torch.flipud) | | -| 29 | [kron](https://pytorch.org/docs/1.8.1/generated/torch.kron.html#torch.kron) | | -| 30 | [rot90](https://pytorch.org/docs/1.8.1/generated/torch.rot90.html#torch.rot90) | | -| 31 | [gcd](https://pytorch.org/docs/1.8.1/generated/torch.gcd.html#torch.gcd) | | -| 32 | [histc](https://pytorch.org/docs/1.8.1/generated/torch.histc.html#torch.histc) | | -| 33 | [meshgrid](https://pytorch.org/docs/1.8.1/generated/torch.meshgrid.html#torch.meshgrid) | | -| 34 | [lcm](https://pytorch.org/docs/1.8.1/generated/torch.lcm.html#torch.lcm) | | -| 35 | [logcumsumexp](https://pytorch.org/docs/1.8.1/generated/torch.logcumsumexp.html#torch.logcumsumexp) | | -| 36 | [ravel](https://pytorch.org/docs/1.8.1/generated/torch.ravel.html#torch.ravel) | | -| 37 | [renorm](https://pytorch.org/docs/1.8.1/generated/torch.renorm.html#torch.renorm) | | -| 38 | [repeat_interleave](https://pytorch.org/docs/1.8.1/generated/torch.repeat_interleave.html#torch.repeat_interleave) | | -| 39 | [roll](https://pytorch.org/docs/1.8.1/generated/torch.roll.html#torch.roll) | | -| 40 | [searchsorted](https://pytorch.org/docs/1.8.1/generated/torch.searchsorted.html#torch.searchsorted) | | -| 41 | [tensordot](https://pytorch.org/docs/1.8.1/generated/torch.tensordot.html#torch.tensordot) | | -| 42 | [trace](https://pytorch.org/docs/1.8.1/generated/torch.trace.html#torch.trace) | | -| 43 | [tril](https://pytorch.org/docs/1.8.1/generated/torch.tril.html#torch.tril) | | -| 44 | [tril_indices](https://pytorch.org/docs/1.8.1/generated/torch.tril_indices.html#torch.tril_indices) | | -| 45 | [triu](https://pytorch.org/docs/1.8.1/generated/torch.triu.html#torch.triu) | | -| 46 | [triu_indices](https://pytorch.org/docs/1.8.1/generated/torch.triu_indices.html#torch.triu_indices) | | -| 47 | [vander](https://pytorch.org/docs/1.8.1/generated/torch.vander.html#torch.vander) | | -| 48 | [view_as_real](https://pytorch.org/docs/1.8.1/generated/torch.view_as_real.html#torch.view_as_real) | | -| 49 | [view_as_complex](https://pytorch.org/docs/1.8.1/generated/torch.view_as_complex.html#torch.view_as_complex) | | +| 1 | [atleast_1d](https://pytorch.org/docs/1.8.1/generated/torch.atleast_1d.html#torch.atleast_1d) | 否 | +| 2 | [atleast_2d](https://pytorch.org/docs/1.8.1/generated/torch.atleast_2d.html#torch.atleast_2d) | 否 | +| 3 | [atleast_3d](https://pytorch.org/docs/1.8.1/generated/torch.atleast_3d.html#torch.atleast_3d) | 否 | +| 4 | [bincount](https://pytorch.org/docs/1.8.1/generated/torch.bincount.html#torch.bincount) | 否 | +| 5 | [block_diag](https://pytorch.org/docs/1.8.1/generated/torch.block_diag.html#torch.block_diag) | 否 | +| 6 | [broadcast_tensors](https://pytorch.org/docs/1.8.1/generated/torch.broadcast_tensors.html#torch.broadcast_tensors) | 否 | +| 7 | [broadcast_to](https://pytorch.org/docs/1.8.1/generated/torch.broadcast_to.html#torch.broadcast_to) | 否 | +| 8 | [broadcast_shapes](https://pytorch.org/docs/1.8.1/generated/torch.broadcast_shapes.html#torch.broadcast_shapes) | 否 | +| 9 | [bucketize](https://pytorch.org/docs/1.8.1/generated/torch.bucketize.html#torch.bucketize) | 否 | +| 10 | [cartesian_prod](https://pytorch.org/docs/1.8.1/generated/torch.cartesian_prod.html#torch.cartesian_prod) | 否 | +| 11 | [cdist](https://pytorch.org/docs/1.8.1/generated/torch.cdist.html#torch.cdist) | 否 | +| 12 | [clone](https://pytorch.org/docs/1.8.1/generated/torch.clone.html#torch.clone) | 否 | +| 13 | [combinations](https://pytorch.org/docs/1.8.1/generated/torch.combinations.html#torch.combinations) | 否 | +| 14 | [cross](https://pytorch.org/docs/1.8.1/generated/torch.cross.html#torch.cross) | 否 | +| 15 | [cummax](https://pytorch.org/docs/1.8.1/generated/torch.cummax.html#torch.cummax) | 否 | +| 16 | [cummin](https://pytorch.org/docs/1.8.1/generated/torch.cummin.html#torch.cummin) | 否 | +| 17 | [cumprod](https://pytorch.org/docs/1.8.1/generated/torch.cumprod.html#torch.cumprod) | 否 | +| 18 | [cumsum](https://pytorch.org/docs/1.8.1/generated/torch.cumsum.html#torch.cumsum) | 否 | +| 19 | [diag](https://pytorch.org/docs/1.8.1/generated/torch.diag.html#torch.diag) | 否 | +| 20 | [diag_embed](https://pytorch.org/docs/1.8.1/generated/torch.diag_embed.html#torch.diag_embed) | 否 | +| 21 | [diagflat](https://pytorch.org/docs/1.8.1/generated/torch.diagflat.html#torch.diagflat) | 否 | +| 22 | [diagonal](https://pytorch.org/docs/1.8.1/generated/torch.diagonal.html#torch.diagonal) | 否 | +| 23 | [diff](https://pytorch.org/docs/1.8.1/generated/torch.diff.html#torch.diff) | 否 | +| 24 | [einsum](https://pytorch.org/docs/1.8.1/generated/torch.einsum.html#torch.einsum) | 否 | +| 25 | [flatten](https://pytorch.org/docs/1.8.1/generated/torch.flatten.html#torch.flatten) | 否 | +| 26 | [flip](https://pytorch.org/docs/1.8.1/generated/torch.flip.html#torch.flip) | 否 | +| 27 | [fliplr](https://pytorch.org/docs/1.8.1/generated/torch.fliplr.html#torch.fliplr) | 否 | +| 28 | [flipud](https://pytorch.org/docs/1.8.1/generated/torch.flipud.html#torch.flipud) | 否 | +| 29 | [kron](https://pytorch.org/docs/1.8.1/generated/torch.kron.html#torch.kron) | 否 | +| 30 | [rot90](https://pytorch.org/docs/1.8.1/generated/torch.rot90.html#torch.rot90) | 否 | +| 31 | [gcd](https://pytorch.org/docs/1.8.1/generated/torch.gcd.html#torch.gcd) | 否 | +| 32 | [histc](https://pytorch.org/docs/1.8.1/generated/torch.histc.html#torch.histc) | 否 | +| 33 | [meshgrid](https://pytorch.org/docs/1.8.1/generated/torch.meshgrid.html#torch.meshgrid) | 否 | +| 34 | [lcm](https://pytorch.org/docs/1.8.1/generated/torch.lcm.html#torch.lcm) | 否 | +| 35 | [logcumsumexp](https://pytorch.org/docs/1.8.1/generated/torch.logcumsumexp.html#torch.logcumsumexp) | 否 | +| 36 | [ravel](https://pytorch.org/docs/1.8.1/generated/torch.ravel.html#torch.ravel) | 否 | +| 37 | [renorm](https://pytorch.org/docs/1.8.1/generated/torch.renorm.html#torch.renorm) | 否 | +| 38 | [repeat_interleave](https://pytorch.org/docs/1.8.1/generated/torch.repeat_interleave.html#torch.repeat_interleave) | 否 | +| 39 | [roll](https://pytorch.org/docs/1.8.1/generated/torch.roll.html#torch.roll) | 否 | +| 40 | [searchsorted](https://pytorch.org/docs/1.8.1/generated/torch.searchsorted.html#torch.searchsorted) | 否 | +| 41 | [tensordot](https://pytorch.org/docs/1.8.1/generated/torch.tensordot.html#torch.tensordot) | 否 | +| 42 | [trace](https://pytorch.org/docs/1.8.1/generated/torch.trace.html#torch.trace) | 否 | +| 43 | [tril](https://pytorch.org/docs/1.8.1/generated/torch.tril.html#torch.tril) | 否 | +| 44 | [tril_indices](https://pytorch.org/docs/1.8.1/generated/torch.tril_indices.html#torch.tril_indices) | 否 | +| 45 | [triu](https://pytorch.org/docs/1.8.1/generated/torch.triu.html#torch.triu) | 否 | +| 46 | [triu_indices](https://pytorch.org/docs/1.8.1/generated/torch.triu_indices.html#torch.triu_indices) | 否 | +| 47 | [vander](https://pytorch.org/docs/1.8.1/generated/torch.vander.html#torch.vander) | 否 | +| 48 | [view_as_real](https://pytorch.org/docs/1.8.1/generated/torch.view_as_real.html#torch.view_as_real) | 否 | +| 49 | [view_as_complex](https://pytorch.org/docs/1.8.1/generated/torch.view_as_complex.html#torch.view_as_complex) | 否 | ### BLAS and LAPACK Operations | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [addbmm](https://pytorch.org/docs/1.8.1/generated/torch.addbmm.html#torch.addbmm) | | -| 2 | [addmm](https://pytorch.org/docs/1.8.1/generated/torch.addmm.html#torch.addmm) | | -| 3 | [addmv](https://pytorch.org/docs/1.8.1/generated/torch.addmv.html#torch.addmv) | | -| 4 | [addr](https://pytorch.org/docs/1.8.1/generated/torch.addr.html#torch.addr) | | -| 5 | [baddbmm](https://pytorch.org/docs/1.8.1/generated/torch.baddbmm.html#torch.baddbmm) | | -| 6 | [bmm](https://pytorch.org/docs/1.8.1/generated/torch.bmm.html#torch.bmm) | | -| 7 | [chain_matmul](https://pytorch.org/docs/1.8.1/generated/torch.chain_matmul.html#torch.chain_matmul) | | -| 8 | [cholesky](https://pytorch.org/docs/1.8.1/generated/torch.cholesky.html#torch.cholesky) | | -| 9 | [cholesky_inverse](https://pytorch.org/docs/1.8.1/generated/torch.cholesky_inverse.html#torch.cholesky_inverse) | | -| 10 | [cholesky_solve](https://pytorch.org/docs/1.8.1/generated/torch.cholesky_solve.html#torch.cholesky_solve) | | -| 11 | [dot](https://pytorch.org/docs/1.8.1/generated/torch.dot.html#torch.dot) | | -| 12 | [eig](https://pytorch.org/docs/1.8.1/generated/torch.eig.html#torch.eig) | | -| 13 | [geqrf](https://pytorch.org/docs/1.8.1/generated/torch.geqrf.html#torch.geqrf) | | -| 14 | [ger](https://pytorch.org/docs/1.8.1/generated/torch.ger.html#torch.ger) | | -| 15 | [inner](https://pytorch.org/docs/1.8.1/generated/torch.inner.html#torch.inner) | | -| 16 | [inverse](https://pytorch.org/docs/1.8.1/generated/torch.inverse.html#torch.inverse) | | -| 17 | [det](https://pytorch.org/docs/1.8.1/generated/torch.det.html#torch.det) | | -| 18 | [logdet](https://pytorch.org/docs/1.8.1/generated/torch.logdet.html#torch.logdet) | | -| 19 | [slogdet](https://pytorch.org/docs/1.8.1/generated/torch.slogdet.html#torch.slogdet) | | -| 20 | [lstsq](https://pytorch.org/docs/1.8.1/generated/torch.lstsq.html#torch.lstsq) | | -| 21 | [lu](https://pytorch.org/docs/1.8.1/generated/torch.lu.html#torch.lu) | | -| 22 | [lu_solve](https://pytorch.org/docs/1.8.1/generated/torch.lu_solve.html#torch.lu_solve) | | -| 23 | [lu_unpack](https://pytorch.org/docs/1.8.1/generated/torch.lu_unpack.html#torch.lu_unpack) | | -| 24 | [matmul](https://pytorch.org/docs/1.8.1/generated/torch.matmul.html#torch.matmul) | | -| 25 | [matrix_power](https://pytorch.org/docs/1.8.1/generated/torch.matrix_power.html#torch.matrix_power) | | -| 26 | [matrix_rank](https://pytorch.org/docs/1.8.1/generated/torch.matrix_rank.html#torch.matrix_rank) | | -| 27 | [matrix_exp](https://pytorch.org/docs/1.8.1/generated/torch.matrix_exp.html#torch.matrix_exp) | | -| 28 | [mm](https://pytorch.org/docs/1.8.1/generated/torch.mm.html#torch.mm) | | -| 29 | [mv](https://pytorch.org/docs/1.8.1/generated/torch.mv.html#torch.mv) | | -| 30 | [orgqr](https://pytorch.org/docs/1.8.1/generated/torch.orgqr.html#torch.orgqr) | | -| 31 | [ormqr](https://pytorch.org/docs/1.8.1/generated/torch.ormqr.html#torch.ormqr) | | -| 32 | [outer](https://pytorch.org/docs/1.8.1/generated/torch.outer.html#torch.outer) | | -| 33 | [pinverse](https://pytorch.org/docs/1.8.1/generated/torch.pinverse.html#torch.pinverse) | | -| 34 | [qr](https://pytorch.org/docs/1.8.1/generated/torch.qr.html#torch.qr) | | -| 35 | [solve](https://pytorch.org/docs/1.8.1/generated/torch.solve.html#torch.solve) | | -| 36 | [svd](https://pytorch.org/docs/1.8.1/generated/torch.svd.html#torch.svd) | | -| 37 | [svd_lowrank](https://pytorch.org/docs/1.8.1/generated/torch.svd_lowrank.html#torch.svd_lowrank) | | -| 38 | [pca_lowrank](https://pytorch.org/docs/1.8.1/generated/torch.pca_lowrank.html#torch.pca_lowrank) | | -| 39 | [symeig](https://pytorch.org/docs/1.8.1/generated/torch.symeig.html#torch.symeig) | | -| 40 | [lobpcg](https://pytorch.org/docs/1.8.1/generated/torch.lobpcg.html#torch.lobpcg) | | -| 41 | [trapz](https://pytorch.org/docs/1.8.1/generated/torch.trapz.html#torch.trapz) | | -| 42 | [triangular_solve](https://pytorch.org/docs/1.8.1/generated/torch.triangular_solve.html#torch.triangular_solve) | | -| 43 | [vdot](https://pytorch.org/docs/1.8.1/generated/torch.vdot.html#torch.vdot) | | +| 1 | [addbmm](https://pytorch.org/docs/1.8.1/generated/torch.addbmm.html#torch.addbmm) | 否 | +| 2 | [addmm](https://pytorch.org/docs/1.8.1/generated/torch.addmm.html#torch.addmm) | 否 | +| 3 | [addmv](https://pytorch.org/docs/1.8.1/generated/torch.addmv.html#torch.addmv) | 否 | +| 4 | [addr](https://pytorch.org/docs/1.8.1/generated/torch.addr.html#torch.addr) | 否 | +| 5 | [baddbmm](https://pytorch.org/docs/1.8.1/generated/torch.baddbmm.html#torch.baddbmm) | 否 | +| 6 | [bmm](https://pytorch.org/docs/1.8.1/generated/torch.bmm.html#torch.bmm) | 否 | +| 7 | [chain_matmul](https://pytorch.org/docs/1.8.1/generated/torch.chain_matmul.html#torch.chain_matmul) | 否 | +| 8 | [cholesky](https://pytorch.org/docs/1.8.1/generated/torch.cholesky.html#torch.cholesky) | 否 | +| 9 | [cholesky_inverse](https://pytorch.org/docs/1.8.1/generated/torch.cholesky_inverse.html#torch.cholesky_inverse) | 否 | +| 10 | [cholesky_solve](https://pytorch.org/docs/1.8.1/generated/torch.cholesky_solve.html#torch.cholesky_solve) | 否 | +| 11 | [dot](https://pytorch.org/docs/1.8.1/generated/torch.dot.html#torch.dot) | 否 | +| 12 | [eig](https://pytorch.org/docs/1.8.1/generated/torch.eig.html#torch.eig) | 否 | +| 13 | [geqrf](https://pytorch.org/docs/1.8.1/generated/torch.geqrf.html#torch.geqrf) | 否 | +| 14 | [ger](https://pytorch.org/docs/1.8.1/generated/torch.ger.html#torch.ger) | 否 | +| 15 | [inner](https://pytorch.org/docs/1.8.1/generated/torch.inner.html#torch.inner) | 否 | +| 16 | [inverse](https://pytorch.org/docs/1.8.1/generated/torch.inverse.html#torch.inverse) | 否 | +| 17 | [det](https://pytorch.org/docs/1.8.1/generated/torch.det.html#torch.det) | 否 | +| 18 | [logdet](https://pytorch.org/docs/1.8.1/generated/torch.logdet.html#torch.logdet) | 否 | +| 19 | [slogdet](https://pytorch.org/docs/1.8.1/generated/torch.slogdet.html#torch.slogdet) | 否 | +| 20 | [lstsq](https://pytorch.org/docs/1.8.1/generated/torch.lstsq.html#torch.lstsq) | 否 | +| 21 | [lu](https://pytorch.org/docs/1.8.1/generated/torch.lu.html#torch.lu) | 否 | +| 22 | [lu_solve](https://pytorch.org/docs/1.8.1/generated/torch.lu_solve.html#torch.lu_solve) | 否 | +| 23 | [lu_unpack](https://pytorch.org/docs/1.8.1/generated/torch.lu_unpack.html#torch.lu_unpack) | 否 | +| 24 | [matmul](https://pytorch.org/docs/1.8.1/generated/torch.matmul.html#torch.matmul) | 否 | +| 25 | [matrix_power](https://pytorch.org/docs/1.8.1/generated/torch.matrix_power.html#torch.matrix_power) | 否 | +| 26 | [matrix_rank](https://pytorch.org/docs/1.8.1/generated/torch.matrix_rank.html#torch.matrix_rank) | 否 | +| 27 | [matrix_exp](https://pytorch.org/docs/1.8.1/generated/torch.matrix_exp.html#torch.matrix_exp) | 否 | +| 28 | [mm](https://pytorch.org/docs/1.8.1/generated/torch.mm.html#torch.mm) | 否 | +| 29 | [mv](https://pytorch.org/docs/1.8.1/generated/torch.mv.html#torch.mv) | 否 | +| 30 | [orgqr](https://pytorch.org/docs/1.8.1/generated/torch.orgqr.html#torch.orgqr) | 否 | +| 31 | [ormqr](https://pytorch.org/docs/1.8.1/generated/torch.ormqr.html#torch.ormqr) | 否 | +| 32 | [outer](https://pytorch.org/docs/1.8.1/generated/torch.outer.html#torch.outer) | 否 | +| 33 | [pinverse](https://pytorch.org/docs/1.8.1/generated/torch.pinverse.html#torch.pinverse) | 否 | +| 34 | [qr](https://pytorch.org/docs/1.8.1/generated/torch.qr.html#torch.qr) | 否 | +| 35 | [solve](https://pytorch.org/docs/1.8.1/generated/torch.solve.html#torch.solve) | 否 | +| 36 | [svd](https://pytorch.org/docs/1.8.1/generated/torch.svd.html#torch.svd) | 否 | +| 37 | [svd_lowrank](https://pytorch.org/docs/1.8.1/generated/torch.svd_lowrank.html#torch.svd_lowrank) | 否 | +| 38 | [pca_lowrank](https://pytorch.org/docs/1.8.1/generated/torch.pca_lowrank.html#torch.pca_lowrank) | 否 | +| 39 | [symeig](https://pytorch.org/docs/1.8.1/generated/torch.symeig.html#torch.symeig) | 否 | +| 40 | [lobpcg](https://pytorch.org/docs/1.8.1/generated/torch.lobpcg.html#torch.lobpcg) | 否 | +| 41 | [trapz](https://pytorch.org/docs/1.8.1/generated/torch.trapz.html#torch.trapz) | 否 | +| 42 | [triangular_solve](https://pytorch.org/docs/1.8.1/generated/torch.triangular_solve.html#torch.triangular_solve) | 否 | +| 43 | [vdot](https://pytorch.org/docs/1.8.1/generated/torch.vdot.html#torch.vdot) | 否 | ## Utilities | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [compiled_with_cxx11_abi](https://pytorch.org/docs/1.8.1/generated/torch.compiled_with_cxx11_abi.html#torch.compiled_with_cxx11_abi) | | -| 2 | [result_type](https://pytorch.org/docs/1.8.1/generated/torch.result_type.html#torch.result_type) | | -| 3 | [can_cast](https://pytorch.org/docs/1.8.1/generated/torch.can_cast.html#torch.can_cast) | | -| 4 | [promote_types](https://pytorch.org/docs/1.8.1/generated/torch.promote_types.html#torch.promote_types) | | -| 5 | [use_deterministic_algorithms](https://pytorch.org/docs/1.8.1/generated/torch.use_deterministic_algorithms.html#torch.use_deterministic_algorithms) | | -| 6 | [are_deterministic_algorithms_enabled](https://pytorch.org/docs/1.8.1/generated/torch.are_deterministic_algorithms_enabled.html#torch.are_deterministic_algorithms_enabled) | | -| 7 | [_assert](https://pytorch.org/docs/1.8.1/generated/torch._assert.html#torch._assert) | | +| 1 | [compiled_with_cxx11_abi](https://pytorch.org/docs/1.8.1/generated/torch.compiled_with_cxx11_abi.html#torch.compiled_with_cxx11_abi) | 否 | +| 2 | [result_type](https://pytorch.org/docs/1.8.1/generated/torch.result_type.html#torch.result_type) | 否 | +| 3 | [can_cast](https://pytorch.org/docs/1.8.1/generated/torch.can_cast.html#torch.can_cast) | 否 | +| 4 | [promote_types](https://pytorch.org/docs/1.8.1/generated/torch.promote_types.html#torch.promote_types) | 否 | +| 5 | [use_deterministic_algorithms](https://pytorch.org/docs/1.8.1/generated/torch.use_deterministic_algorithms.html#torch.use_deterministic_algorithms) | 否 | +| 6 | [are_deterministic_algorithms_enabled](https://pytorch.org/docs/1.8.1/generated/torch.are_deterministic_algorithms_enabled.html#torch.are_deterministic_algorithms_enabled) | 否 | +| 7 | [_assert](https://pytorch.org/docs/1.8.1/generated/torch._assert.html#torch._assert) | 否 | ## Other @@ -456,86 +456,84 @@ | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [Parameter](https://pytorch.org/docs/1.8.1/generated/torch.nn.parameter.Parameter.html#torch.nn.parameter.Parameter) | | -| 2 | [UninitializedParameter](https://pytorch.org/docs/1.8.1/generated/torch.nn.parameter.UninitializedParameter.html#torch.nn.parameter.UninitializedParameter) | | +| 1 | [Parameter](https://pytorch.org/docs/1.8.1/generated/torch.nn.parameter.Parameter.html#torch.nn.parameter.Parameter) | 否 | +| 2 | [UninitializedParameter](https://pytorch.org/docs/1.8.1/generated/torch.nn.parameter.UninitializedParameter.html#torch.nn.parameter.UninitializedParameter) | 否 | ## [Containers](https://pytorch.org/docs/1.8.1/nn.html#id1) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [Module](https://pytorch.org/docs/1.8.1/generated/torch.nn.Module.html#torch.nn.Module) | | -| 2 | [Sequential](https://pytorch.org/docs/1.8.1/generated/torch.nn.Sequential.html#torch.nn.Sequential) | | -| 3 | [ModuleList](https://pytorch.org/docs/1.8.1/generated/torch.nn.ModuleList.html#torch.nn.ModuleList) | | -| 4 | [ModuleDict](https://pytorch.org/docs/1.8.1/generated/torch.nn.ModuleDict.html#torch.nn.ModuleDict) | | -| 5 | [ParameterList](https://pytorch.org/docs/1.8.1/generated/torch.nn.ParameterList.html#torch.nn.ParameterList) | | -| 6 | [ParameterDict](https://pytorch.org/docs/1.8.1/generated/torch.nn.ParameterDict.html#torch.nn.ParameterDict) | | +| 1 | [Module](https://pytorch.org/docs/1.8.1/generated/torch.nn.Module.html#torch.nn.Module) | 否 | +| 2 | [Sequential](https://pytorch.org/docs/1.8.1/generated/torch.nn.Sequential.html#torch.nn.Sequential) | 否 | +| 3 | [ModuleList](https://pytorch.org/docs/1.8.1/generated/torch.nn.ModuleList.html#torch.nn.ModuleList) | 否 | +| 4 | [ModuleDict](https://pytorch.org/docs/1.8.1/generated/torch.nn.ModuleDict.html#torch.nn.ModuleDict) | 否 | +| 5 | [ParameterList](https://pytorch.org/docs/1.8.1/generated/torch.nn.ParameterList.html#torch.nn.ParameterList) | 否 | +| 6 | [ParameterDict](https://pytorch.org/docs/1.8.1/generated/torch.nn.ParameterDict.html#torch.nn.ParameterDict) | 否 | ### Global Hooks For Module | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [register_module_forward_pre_hook](https://pytorch.org/docs/1.8.1/generated/torch.nn.modules.module.register_module_forward_pre_hook.html#torch.nn.modules.module.register_module_forward_pre_hook) | | -| 2 | [register_module_forward_hook](https://pytorch.org/docs/1.8.1/generated/torch.nn.modules.module.register_module_forward_hook.html#torch.nn.modules.module.register_module_forward_hook) | | -| 3 | [register_module_backward_hook](https://pytorch.org/docs/1.8.1/generated/torch.nn.modules.module.register_module_backward_hook.html#torch.nn.modules.module.register_module_backward_hook) | | +| 1 | [register_module_forward_pre_hook](https://pytorch.org/docs/1.8.1/generated/torch.nn.modules.module.register_module_forward_pre_hook.html#torch.nn.modules.module.register_module_forward_pre_hook) | 否 | +| 2 | [register_module_forward_hook](https://pytorch.org/docs/1.8.1/generated/torch.nn.modules.module.register_module_forward_hook.html#torch.nn.modules.module.register_module_forward_hook) | 否 | +| 3 | [register_module_backward_hook](https://pytorch.org/docs/1.8.1/generated/torch.nn.modules.module.register_module_backward_hook.html#torch.nn.modules.module.register_module_backward_hook) | 否 | ## [Convolution Layers](https://pytorch.org/docs/1.8.1/nn.html#id1) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [nn.Conv1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.Conv1d.html#torch.nn.Conv1d) | | -| 2 | [nn.Conv2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.Conv2d.html#torch.nn.Conv2d) | | -| 3 | [nn.Conv3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.Conv3d.html#torch.nn.Conv3d) | | -| 4 | [nn.ConvTranspose1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ConvTranspose1d.html#torch.nn.ConvTranspose1d) | | -| 5 | [nn.ConvTranspose2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ConvTranspose2d.html#torch.nn.ConvTranspose2d) | | -| 6 | [nn.ConvTranspose3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ConvTranspose3d.html#torch.nn.ConvTranspose3d) | | -| 7 | [nn.LazyConv1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.LazyConv1d.html#torch.nn.LazyConv1d) | | -| 8 | [nn.LazyConv2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.LazyConv2d.html#torch.nn.LazyConv2d) | | -| 9 | [nn.LazyConv3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.LazyConv3d.html#torch.nn.LazyConv3d) | | -| 10 | [nn.LazyConvTranspose1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.LazyConvTranspose1d.html#torch.nn.LazyConvTranspose1d) | | -| 11 | [nn.LazyConvTranspose2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.LazyConvTranspose2d.html#torch.nn.LazyConvTranspose2d) | | -| 12 | [nn.LazyConvTranspose3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.LazyConvTranspose3d.html#torch.nn.LazyConvTranspose3d) | | -| 13 | [nn.Unfold](https://pytorch.org/docs/1.8.1/generated/torch.nn.Unfold.html#torch.nn.Unfold) | | -| 14 | [nn.Fold](https://pytorch.org/docs/1.8.1/generated/torch.nn.Fold.html#torch.nn.Fold) | | +| 1 | [nn.Conv1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.Conv1d.html#torch.nn.Conv1d) | 否 | +| 2 | [nn.Conv2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.Conv2d.html#torch.nn.Conv2d) | 否 | +| 3 | [nn.Conv3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.Conv3d.html#torch.nn.Conv3d) | 否 | +| 4 | [nn.ConvTranspose1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ConvTranspose1d.html#torch.nn.ConvTranspose1d) | 否 | +| 5 | [nn.ConvTranspose2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ConvTranspose2d.html#torch.nn.ConvTranspose2d) | 否 | +| 6 | [nn.ConvTranspose3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ConvTranspose3d.html#torch.nn.ConvTranspose3d) | 否 | +| 7 | [nn.LazyConv1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.LazyConv1d.html#torch.nn.LazyConv1d) | 否 | +| 8 | [nn.LazyConv2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.LazyConv2d.html#torch.nn.LazyConv2d) | 否 | +| 9 | [nn.LazyConv3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.LazyConv3d.html#torch.nn.LazyConv3d) | 否 | +| 10 | [nn.LazyConvTranspose1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.LazyConvTranspose1d.html#torch.nn.LazyConvTranspose1d) | 否 | +| 11 | [nn.LazyConvTranspose2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.LazyConvTranspose2d.html#torch.nn.LazyConvTranspose2d) | 否 | +| 12 | [nn.LazyConvTranspose3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.LazyConvTranspose3d.html#torch.nn.LazyConvTranspose3d) | 否 | +| 13 | [nn.Unfold](https://pytorch.org/docs/1.8.1/generated/torch.nn.Unfold.html#torch.nn.Unfold) | 否 | +| 14 | [nn.Fold](https://pytorch.org/docs/1.8.1/generated/torch.nn.Fold.html#torch.nn.Fold) | 否 | ## [Pooling layers](https://pytorch.org/docs/1.8.1/nn.html#id1) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [nn.MaxPool1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.MaxPool1d.html#torch.nn.MaxPool1d) | | -| 2 | [nn.MaxPool2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.MaxPool2d.html#torch.nn.MaxPool2d) | | -| 3 | [nn.MaxPool3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.MaxPool3d.html#torch.nn.MaxPool3d) | | -| 4 | [nn.MaxUnpool1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.MaxUnpool1d.html#torch.nn.MaxUnpool1d) | | -| 5 | [nn.MaxUnpool2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.MaxUnpool2d.html#torch.nn.MaxUnpool2d) | | -| 6 | [nn.MaxUnpool3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.MaxUnpool3d.html#torch.nn.MaxUnpool3d) | | -| 7 | [nn.AvgPool1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AvgPool1d.html#torch.nn.AvgPool1d) | | -| 8 | [nn.AvgPool2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AvgPool2d.html#torch.nn.AvgPool2d) | | -| 9 | [nn.AvgPool3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AvgPool3d.html#torch.nn.AvgPool3d) | | -| 10 | [nn.FractionalMaxPool2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.FractionalMaxPool2d.html#torch.nn.FractionalMaxPool2d) | | -| 11 | [nn.LPPool1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.LPPool1d.html#torch.nn.LPPool1d) | | -| 12 | [nn.LPPool2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.LPPool2d.html#torch.nn.LPPool2d) | | -| 13 | [nn.AdaptiveMaxPool1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AdaptiveMaxPool1d.html#torch.nn.AdaptiveMaxPool1d) | | -| 14 | [nn.AdaptiveMaxPool2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AdaptiveMaxPool2d.html#torch.nn.AdaptiveMaxPool2d) | | -| 15 | [nn.AdaptiveMaxPool3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AdaptiveMaxPool3d.html#torch.nn.AdaptiveMaxPool3d) | | -| 16 | [nn.AdaptiveAvgPool1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AdaptiveAvgPool1d.html#torch.nn.AdaptiveAvgPool1d) | | -| 17 | [nn.AdaptiveAvgPool2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AdaptiveAvgPool2d.html#torch.nn.AdaptiveAvgPool2d) | | -| 18 | [nn.AdaptiveAvgPool3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AdaptiveAvgPool3d.html#torch.nn.AdaptiveAvgPool3d) | | - - +| 1 | [nn.MaxPool1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.MaxPool1d.html#torch.nn.MaxPool1d) | 否 | +| 2 | [nn.MaxPool2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.MaxPool2d.html#torch.nn.MaxPool2d) | 否 | +| 3 | [nn.MaxPool3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.MaxPool3d.html#torch.nn.MaxPool3d) | 否 | +| 4 | [nn.MaxUnpool1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.MaxUnpool1d.html#torch.nn.MaxUnpool1d) | 否 | +| 5 | [nn.MaxUnpool2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.MaxUnpool2d.html#torch.nn.MaxUnpool2d) | 否 | +| 6 | [nn.MaxUnpool3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.MaxUnpool3d.html#torch.nn.MaxUnpool3d) | 否 | +| 7 | [nn.AvgPool1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AvgPool1d.html#torch.nn.AvgPool1d) | 否 | +| 8 | [nn.AvgPool2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AvgPool2d.html#torch.nn.AvgPool2d) | 否 | +| 9 | [nn.AvgPool3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AvgPool3d.html#torch.nn.AvgPool3d) | 否 | +| 10 | [nn.FractionalMaxPool2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.FractionalMaxPool2d.html#torch.nn.FractionalMaxPool2d) | 否 | +| 11 | [nn.LPPool1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.LPPool1d.html#torch.nn.LPPool1d) | 否 | +| 12 | [nn.LPPool2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.LPPool2d.html#torch.nn.LPPool2d) | 否 | +| 13 | [nn.AdaptiveMaxPool1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AdaptiveMaxPool1d.html#torch.nn.AdaptiveMaxPool1d) | 否 | +| 14 | [nn.AdaptiveMaxPool2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AdaptiveMaxPool2d.html#torch.nn.AdaptiveMaxPool2d) | 否 | +| 15 | [nn.AdaptiveMaxPool3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AdaptiveMaxPool3d.html#torch.nn.AdaptiveMaxPool3d) | 否 | +| 16 | [nn.AdaptiveAvgPool1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AdaptiveAvgPool1d.html#torch.nn.AdaptiveAvgPool1d) | 否 | +| 17 | [nn.AdaptiveAvgPool2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AdaptiveAvgPool2d.html#torch.nn.AdaptiveAvgPool2d) | 否 | +| 18 | [nn.AdaptiveAvgPool3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.AdaptiveAvgPool3d.html#torch.nn.AdaptiveAvgPool3d) | 否 | ## [Padding Layers](https://pytorch.org/docs/1.8.1/nn.html#id1) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [nn.ReflectionPad1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ReflectionPad1d.html#torch.nn.ReflectionPad1d) | | -| 2 | [nn.ReflectionPad2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ReflectionPad2d.html#torch.nn.ReflectionPad2d) | | -| 3 | [nn.ReplicationPad1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ReplicationPad1d.html#torch.nn.ReplicationPad1d) | | -| 4 | [nn.ReplicationPad2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ReplicationPad2d.html#torch.nn.ReplicationPad2d) | | -| 5 | [nn.ReplicationPad3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ReplicationPad3d.html#torch.nn.ReplicationPad3d) | | -| 6 | [nn.ZeroPad2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ZeroPad2d.html#torch.nn.ZeroPad2d) | | -| 7 | [nn.ConstantPad1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ConstantPad1d.html#torch.nn.ConstantPad1d) | | -| 8 | [nn.ConstantPad2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ConstantPad2d.html#torch.nn.ConstantPad2d) | | -| 9 | [nn.ConstantPad3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ConstantPad3d.html#torch.nn.ConstantPad3d) | | +| 1 | [nn.ReflectionPad1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ReflectionPad1d.html#torch.nn.ReflectionPad1d) | 否 | +| 2 | [nn.ReflectionPad2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ReflectionPad2d.html#torch.nn.ReflectionPad2d) | 否 | +| 3 | [nn.ReplicationPad1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ReplicationPad1d.html#torch.nn.ReplicationPad1d) | 否 | +| 4 | [nn.ReplicationPad2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ReplicationPad2d.html#torch.nn.ReplicationPad2d) | 否 | +| 5 | [nn.ReplicationPad3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ReplicationPad3d.html#torch.nn.ReplicationPad3d) | 否 | +| 6 | [nn.ZeroPad2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ZeroPad2d.html#torch.nn.ZeroPad2d) | 否 | +| 7 | [nn.ConstantPad1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ConstantPad1d.html#torch.nn.ConstantPad1d) | 否 | +| 8 | [nn.ConstantPad2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ConstantPad2d.html#torch.nn.ConstantPad2d) | 否 | +| 9 | [nn.ConstantPad3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.ConstantPad3d.html#torch.nn.ConstantPad3d) | 否 | @@ -543,54 +541,54 @@ | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [nn.ELU](https://pytorch.org/docs/1.8.1/generated/torch.nn.ELU.html#torch.nn.ELU) | | -| 2 | [nn.Hardshrink](https://pytorch.org/docs/1.8.1/generated/torch.nn.Hardshrink.html#torch.nn.Hardshrink) | | -| 3 | [nn.Hardsigmoid](https://pytorch.org/docs/1.8.1/generated/torch.nn.Hardsigmoid.html#torch.nn.Hardsigmoid) | | -| 4 | [nn.Hardtanh](https://pytorch.org/docs/1.8.1/generated/torch.nn.Hardtanh.html#torch.nn.Hardtanh) | | -| 5 | [nn.Hardswish](https://pytorch.org/docs/1.8.1/generated/torch.nn.Hardswish.html#torch.nn.Hardswish) | | -| 6 | [nn.LeakyReLU](https://pytorch.org/docs/1.8.1/generated/torch.nn.LeakyReLU.html#torch.nn.LeakyReLU) | | -| 7 | [nn.LogSigmoid](https://pytorch.org/docs/1.8.1/generated/torch.nn.LogSigmoid.html#torch.nn.LogSigmoid) | | -| 8 | [nn.MultiheadAttention](https://pytorch.org/docs/1.8.1/generated/torch.nn.MultiheadAttention.html#torch.nn.MultiheadAttention) | | -| 9 | [nn.PReLU](https://pytorch.org/docs/1.8.1/generated/torch.nn.PReLU.html#torch.nn.PReLU) | | -| 10 | [nn.ReLU](https://pytorch.org/docs/1.8.1/generated/torch.nn.ReLU.html#torch.nn.ReLU) | | -| 11 | [nn.ReLU6](https://pytorch.org/docs/1.8.1/generated/torch.nn.ReLU6.html#torch.nn.ReLU6) | | -| 12 | [nn.RReLU](https://pytorch.org/docs/1.8.1/generated/torch.nn.RReLU.html#torch.nn.RReLU) | | -| 13 | [nn.SELU](https://pytorch.org/docs/1.8.1/generated/torch.nn.SELU.html#torch.nn.SELU) | | -| 14 | [nn.CELU](https://pytorch.org/docs/1.8.1/generated/torch.nn.CELU.html#torch.nn.CELU) | | -| 15 | [nn.GELU](https://pytorch.org/docs/1.8.1/generated/torch.nn.GELU.html#torch.nn.GELU) | | -| 16 | [nn.Sigmoid](https://pytorch.org/docs/1.8.1/generated/torch.nn.Sigmoid.html#torch.nn.Sigmoid) | | -| 17 | [nn.SiLU](https://pytorch.org/docs/1.8.1/generated/torch.nn.SiLU.html#torch.nn.SiLU) | | -| 18 | [nn.Softplus](https://pytorch.org/docs/1.8.1/generated/torch.nn.Softplus.html#torch.nn.Softplus) | | -| 19 | [nn.Softshrink](https://pytorch.org/docs/1.8.1/generated/torch.nn.Softshrink.html#torch.nn.Softshrink) | | -| 20 | [nn.Softsign](https://pytorch.org/docs/1.8.1/generated/torch.nn.Softsign.html#torch.nn.Softsign) | | -| 21 | [nn.Tanh](https://pytorch.org/docs/1.8.1/generated/torch.nn.Tanh.html#torch.nn.Tanh) | | -| 22 | [nn.Tanhshrink](https://pytorch.org/docs/1.8.1/generated/torch.nn.Tanhshrink.html#torch.nn.Tanhshrink) | | -| 23 | [nn.Threshold](https://pytorch.org/docs/1.8.1/generated/torch.nn.Threshold.html#torch.nn.Threshold) | | +| 1 | [nn.ELU](https://pytorch.org/docs/1.8.1/generated/torch.nn.ELU.html#torch.nn.ELU) | 否 | +| 2 | [nn.Hardshrink](https://pytorch.org/docs/1.8.1/generated/torch.nn.Hardshrink.html#torch.nn.Hardshrink) | 否 | +| 3 | [nn.Hardsigmoid](https://pytorch.org/docs/1.8.1/generated/torch.nn.Hardsigmoid.html#torch.nn.Hardsigmoid) | 否 | +| 4 | [nn.Hardtanh](https://pytorch.org/docs/1.8.1/generated/torch.nn.Hardtanh.html#torch.nn.Hardtanh) | 否 | +| 5 | [nn.Hardswish](https://pytorch.org/docs/1.8.1/generated/torch.nn.Hardswish.html#torch.nn.Hardswish) | 否 | +| 6 | [nn.LeakyReLU](https://pytorch.org/docs/1.8.1/generated/torch.nn.LeakyReLU.html#torch.nn.LeakyReLU) | 否 | +| 7 | [nn.LogSigmoid](https://pytorch.org/docs/1.8.1/generated/torch.nn.LogSigmoid.html#torch.nn.LogSigmoid) | 否 | +| 8 | [nn.MultiheadAttention](https://pytorch.org/docs/1.8.1/generated/torch.nn.MultiheadAttention.html#torch.nn.MultiheadAttention) | 否 | +| 9 | [nn.PReLU](https://pytorch.org/docs/1.8.1/generated/torch.nn.PReLU.html#torch.nn.PReLU) | 否 | +| 10 | [nn.ReLU](https://pytorch.org/docs/1.8.1/generated/torch.nn.ReLU.html#torch.nn.ReLU) | 否 | +| 11 | [nn.ReLU6](https://pytorch.org/docs/1.8.1/generated/torch.nn.ReLU6.html#torch.nn.ReLU6) | 否 | +| 12 | [nn.RReLU](https://pytorch.org/docs/1.8.1/generated/torch.nn.RReLU.html#torch.nn.RReLU) | 否 | +| 13 | [nn.SELU](https://pytorch.org/docs/1.8.1/generated/torch.nn.SELU.html#torch.nn.SELU) | 否 | +| 14 | [nn.CELU](https://pytorch.org/docs/1.8.1/generated/torch.nn.CELU.html#torch.nn.CELU) | 否 | +| 15 | [nn.GELU](https://pytorch.org/docs/1.8.1/generated/torch.nn.GELU.html#torch.nn.GELU) | 否 | +| 16 | [nn.Sigmoid](https://pytorch.org/docs/1.8.1/generated/torch.nn.Sigmoid.html#torch.nn.Sigmoid) | 否 | +| 17 | [nn.SiLU](https://pytorch.org/docs/1.8.1/generated/torch.nn.SiLU.html#torch.nn.SiLU) | 否 | +| 18 | [nn.Softplus](https://pytorch.org/docs/1.8.1/generated/torch.nn.Softplus.html#torch.nn.Softplus) | 否 | +| 19 | [nn.Softshrink](https://pytorch.org/docs/1.8.1/generated/torch.nn.Softshrink.html#torch.nn.Softshrink) | 否 | +| 20 | [nn.Softsign](https://pytorch.org/docs/1.8.1/generated/torch.nn.Softsign.html#torch.nn.Softsign) | 否 | +| 21 | [nn.Tanh](https://pytorch.org/docs/1.8.1/generated/torch.nn.Tanh.html#torch.nn.Tanh) | 否 | +| 22 | [nn.Tanhshrink](https://pytorch.org/docs/1.8.1/generated/torch.nn.Tanhshrink.html#torch.nn.Tanhshrink) | 否 | +| 23 | [nn.Threshold](https://pytorch.org/docs/1.8.1/generated/torch.nn.Threshold.html#torch.nn.Threshold) | 否 | ## [Non-linear Activations (other)](https://pytorch.org/docs/1.8.1/nn.html#id1) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [nn.Softmin](https://pytorch.org/docs/1.8.1/generated/torch.nn.Softmin.html#torch.nn.Softmin) | | -| 2 | [nn.Softmax](https://pytorch.org/docs/1.8.1/generated/torch.nn.Softmax.html#torch.nn.Softmax) | | -| 3 | [nn.Softmax2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.Softmax2d.html#torch.nn.Softmax2d) | | -| 4 | [nn.LogSoftmax](https://pytorch.org/docs/1.8.1/generated/torch.nn.LogSoftmax.html#torch.nn.LogSoftmax) | | -| 5 | [nn.AdaptiveLogSoftmaxWithLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.AdaptiveLogSoftmaxWithLoss.html#torch.nn.AdaptiveLogSoftmaxWithLoss) | | +| 1 | [nn.Softmin](https://pytorch.org/docs/1.8.1/generated/torch.nn.Softmin.html#torch.nn.Softmin) | 否 | +| 2 | [nn.Softmax](https://pytorch.org/docs/1.8.1/generated/torch.nn.Softmax.html#torch.nn.Softmax) | 否 | +| 3 | [nn.Softmax2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.Softmax2d.html#torch.nn.Softmax2d) | 否 | +| 4 | [nn.LogSoftmax](https://pytorch.org/docs/1.8.1/generated/torch.nn.LogSoftmax.html#torch.nn.LogSoftmax) | 否 | +| 5 | [nn.AdaptiveLogSoftmaxWithLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.AdaptiveLogSoftmaxWithLoss.html#torch.nn.AdaptiveLogSoftmaxWithLoss) | 否 | ## [Normalization Layers](https://pytorch.org/docs/1.8.1/nn.html#id1) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [nn.BatchNorm1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.BatchNorm1d.html#torch.nn.BatchNorm1d) | | -| 2 | [nn.BatchNorm2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.BatchNorm2d.html#torch.nn.BatchNorm2d) | | -| 3 | [nn.BatchNorm3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.BatchNorm3d.html#torch.nn.BatchNorm3d) | | -| 4 | [nn.GroupNorm](https://pytorch.org/docs/1.8.1/generated/torch.nn.GroupNorm.html#torch.nn.GroupNorm) | | -| 5 | [nn.SyncBatchNorm](https://pytorch.org/docs/1.8.1/generated/torch.nn.SyncBatchNorm.html#torch.nn.SyncBatchNorm) | | -| 6 | [nn.InstanceNorm1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.InstanceNorm1d.html#torch.nn.InstanceNorm1d) | | -| 7 | [nn.InstanceNorm2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.InstanceNorm2d.html#torch.nn.InstanceNorm2d) | | -| 8 | [nn.InstanceNorm3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.InstanceNorm3d.html#torch.nn.InstanceNorm3d) | | -| 9 | [nn.LayerNorm](https://pytorch.org/docs/1.8.1/generated/torch.nn.LayerNorm.html#torch.nn.LayerNorm) | | -| 10 | [nn.LocalResponseNorm](https://pytorch.org/docs/1.8.1/generated/torch.nn.LocalResponseNorm.html#torch.nn.LocalResponseNorm) | | +| 1 | [nn.BatchNorm1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.BatchNorm1d.html#torch.nn.BatchNorm1d) | 否 | +| 2 | [nn.BatchNorm2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.BatchNorm2d.html#torch.nn.BatchNorm2d) | 否 | +| 3 | [nn.BatchNorm3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.BatchNorm3d.html#torch.nn.BatchNorm3d) | 否 | +| 4 | [nn.GroupNorm](https://pytorch.org/docs/1.8.1/generated/torch.nn.GroupNorm.html#torch.nn.GroupNorm) | 否 | +| 5 | [nn.SyncBatchNorm](https://pytorch.org/docs/1.8.1/generated/torch.nn.SyncBatchNorm.html#torch.nn.SyncBatchNorm) | 否 | +| 6 | [nn.InstanceNorm1d](https://pytorch.org/docs/1.8.1/generated/torch.nn.InstanceNorm1d.html#torch.nn.InstanceNorm1d) | 否 | +| 7 | [nn.InstanceNorm2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.InstanceNorm2d.html#torch.nn.InstanceNorm2d) | 否 | +| 8 | [nn.InstanceNorm3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.InstanceNorm3d.html#torch.nn.InstanceNorm3d) | 否 | +| 9 | [nn.LayerNorm](https://pytorch.org/docs/1.8.1/generated/torch.nn.LayerNorm.html#torch.nn.LayerNorm) | 否 | +| 10 | [nn.LocalResponseNorm](https://pytorch.org/docs/1.8.1/generated/torch.nn.LocalResponseNorm.html#torch.nn.LocalResponseNorm) | 否 | @@ -598,13 +596,13 @@ | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [nn.RNNBase](https://pytorch.org/docs/1.8.1/generated/torch.nn.RNNBase.html#torch.nn.RNNBase) | | -| 2 | [nn.RNN](https://pytorch.org/docs/1.8.1/generated/torch.nn.RNN.html#torch.nn.RNN) | | -| 3 | [nn.LSTM](https://pytorch.org/docs/1.8.1/generated/torch.nn.LSTM.html#torch.nn.LSTM) | | -| 4 | [nn.GRU](https://pytorch.org/docs/1.8.1/generated/torch.nn.GRU.html#torch.nn.GRU) | | -| 5 | [nn.RNNCell](https://pytorch.org/docs/1.8.1/generated/torch.nn.RNNCell.html#torch.nn.RNNCell) | | -| 6 | [nn.LSTMCell](https://pytorch.org/docs/1.8.1/generated/torch.nn.LSTMCell.html#torch.nn.LSTMCell) | | -| 7 | [nn.GRUCell](https://pytorch.org/docs/1.8.1/generated/torch.nn.GRUCell.html#torch.nn.GRUCell) | | +| 1 | [nn.RNNBase](https://pytorch.org/docs/1.8.1/generated/torch.nn.RNNBase.html#torch.nn.RNNBase) | 否 | +| 2 | [nn.RNN](https://pytorch.org/docs/1.8.1/generated/torch.nn.RNN.html#torch.nn.RNN) | 否 | +| 3 | [nn.LSTM](https://pytorch.org/docs/1.8.1/generated/torch.nn.LSTM.html#torch.nn.LSTM) | 否 | +| 4 | [nn.GRU](https://pytorch.org/docs/1.8.1/generated/torch.nn.GRU.html#torch.nn.GRU) | 否 | +| 5 | [nn.RNNCell](https://pytorch.org/docs/1.8.1/generated/torch.nn.RNNCell.html#torch.nn.RNNCell) | 否 | +| 6 | [nn.LSTMCell](https://pytorch.org/docs/1.8.1/generated/torch.nn.LSTMCell.html#torch.nn.LSTMCell) | 否 | +| 7 | [nn.GRUCell](https://pytorch.org/docs/1.8.1/generated/torch.nn.GRUCell.html#torch.nn.GRUCell) | 否 | @@ -612,11 +610,11 @@ | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [nn.Transformer](https://pytorch.org/docs/1.8.1/generated/torch.nn.Transformer.html#torch.nn.Transformer) | | -| 2 | [nn.TransformerEncoder](https://pytorch.org/docs/1.8.1/generated/torch.nn.TransformerEncoder.html#torch.nn.TransformerEncoder) | | -| 3 | [nn.TransformerDecoder](https://pytorch.org/docs/1.8.1/generated/torch.nn.TransformerDecoder.html#torch.nn.TransformerDecoder) | | -| 4 | [nn.TransformerEncoderLayer](https://pytorch.org/docs/1.8.1/generated/torch.nn.TransformerEncoderLayer.html#torch.nn.TransformerEncoderLayer) | | -| 5 | [nn.TransformerDecoderLayer](https://pytorch.org/docs/1.8.1/generated/torch.nn.TransformerDecoderLayer.html#torch.nn.TransformerDecoderLayer) | | +| 1 | [nn.Transformer](https://pytorch.org/docs/1.8.1/generated/torch.nn.Transformer.html#torch.nn.Transformer) | 否 | +| 2 | [nn.TransformerEncoder](https://pytorch.org/docs/1.8.1/generated/torch.nn.TransformerEncoder.html#torch.nn.TransformerEncoder) | 否 | +| 3 | [nn.TransformerDecoder](https://pytorch.org/docs/1.8.1/generated/torch.nn.TransformerDecoder.html#torch.nn.TransformerDecoder) | 否 | +| 4 | [nn.TransformerEncoderLayer](https://pytorch.org/docs/1.8.1/generated/torch.nn.TransformerEncoderLayer.html#torch.nn.TransformerEncoderLayer) | 否 | +| 5 | [nn.TransformerDecoderLayer](https://pytorch.org/docs/1.8.1/generated/torch.nn.TransformerDecoderLayer.html#torch.nn.TransformerDecoderLayer) | 否 | @@ -624,10 +622,10 @@ | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [nn.Identity](https://pytorch.org/docs/1.8.1/generated/torch.nn.Identity.html#torch.nn.Identity) | | -| 2 | [nn.Linear](https://pytorch.org/docs/1.8.1/generated/torch.nn.Linear.html#torch.nn.Linear) | | -| 3 | [nn.Bilinear](https://pytorch.org/docs/1.8.1/generated/torch.nn.Bilinear.html#torch.nn.Bilinear) | | -| 4 | [nn.LazyLinear](https://pytorch.org/docs/1.8.1/generated/torch.nn.LazyLinear.html#torch.nn.LazyLinear) | | +| 1 | [nn.Identity](https://pytorch.org/docs/1.8.1/generated/torch.nn.Identity.html#torch.nn.Identity) | 否 | +| 2 | [nn.Linear](https://pytorch.org/docs/1.8.1/generated/torch.nn.Linear.html#torch.nn.Linear) | 否 | +| 3 | [nn.Bilinear](https://pytorch.org/docs/1.8.1/generated/torch.nn.Bilinear.html#torch.nn.Bilinear) | 否 | +| 4 | [nn.LazyLinear](https://pytorch.org/docs/1.8.1/generated/torch.nn.LazyLinear.html#torch.nn.LazyLinear) | 否 | @@ -637,17 +635,17 @@ | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [nn.Dropout](https://pytorch.org/docs/1.8.1/generated/torch.nn.Dropout.html#torch.nn.Dropout) | | -| 2 | [nn.Dropout2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.Dropout2d.html#torch.nn.Dropout2d) | | -| 3 | [nn.Dropout3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.Dropout3d.html#torch.nn.Dropout3d) | | -| 4 | [nn.AlphaDropout](https://pytorch.org/docs/1.8.1/generated/torch.nn.AlphaDropout.html#torch.nn.AlphaDropout) | | +| 1 | [nn.Dropout](https://pytorch.org/docs/1.8.1/generated/torch.nn.Dropout.html#torch.nn.Dropout) | 否 | +| 2 | [nn.Dropout2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.Dropout2d.html#torch.nn.Dropout2d) | 否 | +| 3 | [nn.Dropout3d](https://pytorch.org/docs/1.8.1/generated/torch.nn.Dropout3d.html#torch.nn.Dropout3d) | 否 | +| 4 | [nn.AlphaDropout](https://pytorch.org/docs/1.8.1/generated/torch.nn.AlphaDropout.html#torch.nn.AlphaDropout) | 否 | ## [Sparse Layers](https://pytorch.org/docs/1.8.1/nn.html#id1) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [nn.Embedding](https://pytorch.org/docs/1.8.1/generated/torch.nn.Embedding.html#torch.nn.Embedding) | | -| 2 | [nn.EmbeddingBag](https://pytorch.org/docs/1.8.1/generated/torch.nn.EmbeddingBag.html#torch.nn.EmbeddingBag) | | +| 1 | [nn.Embedding](https://pytorch.org/docs/1.8.1/generated/torch.nn.Embedding.html#torch.nn.Embedding) | 否 | +| 2 | [nn.EmbeddingBag](https://pytorch.org/docs/1.8.1/generated/torch.nn.EmbeddingBag.html#torch.nn.EmbeddingBag) | 否 | @@ -655,8 +653,8 @@ | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [nn.CosineSimilarity](https://pytorch.org/docs/1.8.1/generated/torch.nn.CosineSimilarity.html#torch.nn.CosineSimilarity) | | -| 2 | [nn.PairwiseDistance](https://pytorch.org/docs/1.8.1/generated/torch.nn.PairwiseDistance.html#torch.nn.PairwiseDistance) | | +| 1 | [nn.CosineSimilarity](https://pytorch.org/docs/1.8.1/generated/torch.nn.CosineSimilarity.html#torch.nn.CosineSimilarity) | 否 | +| 2 | [nn.PairwiseDistance](https://pytorch.org/docs/1.8.1/generated/torch.nn.PairwiseDistance.html#torch.nn.PairwiseDistance) | 否 | @@ -664,36 +662,36 @@ | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [nn.L1Loss](https://pytorch.org/docs/1.8.1/generated/torch.nn.L1Loss.html#torch.nn.L1Loss) | | -| 2 | [nn.MSELoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.MSELoss.html#torch.nn.MSELoss) | | -| 3 | [nn.CrossEntropyLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss) | | -| 4 | [nn.CTCLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.CTCLoss.html#torch.nn.CTCLoss) | | -| 5 | [nn.NLLLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss) | | -| 6 | [nn.PoissonNLLLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.PoissonNLLLoss.html#torch.nn.PoissonNLLLoss) | | -| 7 | [nn.GaussianNLLLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.GaussianNLLLoss.html#torch.nn.GaussianNLLLoss) | | -| 8 | [nn.KLDivLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.KLDivLoss.html#torch.nn.KLDivLoss) | | -| 9 | [nn.BCELoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.BCELoss.html#torch.nn.BCELoss) | | -| 10 | [nn.BCEWithLogitsLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.BCEWithLogitsLoss.html#torch.nn.BCEWithLogitsLoss) | | -| 11 | [nn.MarginRankingLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.MarginRankingLoss.html#torch.nn.MarginRankingLoss) | | -| 12 | [nn.HingeEmbeddingLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.HingeEmbeddingLoss.html#torch.nn.HingeEmbeddingLoss) | | -| 13 | [nn.MultiLabelMarginLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.MultiLabelMarginLoss.html#torch.nn.MultiLabelMarginLoss) | | -| 14 | [nn.SmoothL1Loss](https://pytorch.org/docs/1.8.1/generated/torch.nn.SmoothL1Loss.html#torch.nn.SmoothL1Loss) | | -| 15 | [nn.SoftMarginLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.SoftMarginLoss.html#torch.nn.SoftMarginLoss) | | -| 16 | [nn.MultiLabelSoftMarginLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.MultiLabelSoftMarginLoss.html#torch.nn.MultiLabelSoftMarginLoss) | | -| 17 | [nn.CosineEmbeddingLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.CosineEmbeddingLoss.html#torch.nn.CosineEmbeddingLoss) | | -| 18 | [nn.MultiMarginLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.MultiMarginLoss.html#torch.nn.MultiMarginLoss) | | -| 19 | [nn.TripletMarginLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.TripletMarginLoss.html#torch.nn.TripletMarginLoss) | | -| 20 | [nn.TripletMarginWithDistanceLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.TripletMarginWithDistanceLoss.html#torch.nn.TripletMarginWithDistanceLoss) | | +| 1 | [nn.L1Loss](https://pytorch.org/docs/1.8.1/generated/torch.nn.L1Loss.html#torch.nn.L1Loss) | 否 | +| 2 | [nn.MSELoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.MSELoss.html#torch.nn.MSELoss) | 否 | +| 3 | [nn.CrossEntropyLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss) | 否 | +| 4 | [nn.CTCLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.CTCLoss.html#torch.nn.CTCLoss) | 否 | +| 5 | [nn.NLLLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss) | 否 | +| 6 | [nn.PoissonNLLLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.PoissonNLLLoss.html#torch.nn.PoissonNLLLoss) | 否 | +| 7 | [nn.GaussianNLLLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.GaussianNLLLoss.html#torch.nn.GaussianNLLLoss) | 否 | +| 8 | [nn.KLDivLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.KLDivLoss.html#torch.nn.KLDivLoss) | 否 | +| 9 | [nn.BCELoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.BCELoss.html#torch.nn.BCELoss) | 否 | +| 10 | [nn.BCEWithLogitsLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.BCEWithLogitsLoss.html#torch.nn.BCEWithLogitsLoss) | 否 | +| 11 | [nn.MarginRankingLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.MarginRankingLoss.html#torch.nn.MarginRankingLoss) | 否 | +| 12 | [nn.HingeEmbeddingLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.HingeEmbeddingLoss.html#torch.nn.HingeEmbeddingLoss) | 否 | +| 13 | [nn.MultiLabelMarginLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.MultiLabelMarginLoss.html#torch.nn.MultiLabelMarginLoss) | 否 | +| 14 | [nn.SmoothL1Loss](https://pytorch.org/docs/1.8.1/generated/torch.nn.SmoothL1Loss.html#torch.nn.SmoothL1Loss) | 否 | +| 15 | [nn.SoftMarginLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.SoftMarginLoss.html#torch.nn.SoftMarginLoss) | 否 | +| 16 | [nn.MultiLabelSoftMarginLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.MultiLabelSoftMarginLoss.html#torch.nn.MultiLabelSoftMarginLoss) | 否 | +| 17 | [nn.CosineEmbeddingLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.CosineEmbeddingLoss.html#torch.nn.CosineEmbeddingLoss) | 否 | +| 18 | [nn.MultiMarginLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.MultiMarginLoss.html#torch.nn.MultiMarginLoss) | 否 | +| 19 | [nn.TripletMarginLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.TripletMarginLoss.html#torch.nn.TripletMarginLoss) | 否 | +| 20 | [nn.TripletMarginWithDistanceLoss](https://pytorch.org/docs/1.8.1/generated/torch.nn.TripletMarginWithDistanceLoss.html#torch.nn.TripletMarginWithDistanceLoss) | 否 | ## [Vision Layers](https://pytorch.org/docs/1.8.1/nn.html#id1) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [nn.PixelShuffle](https://pytorch.org/docs/1.8.1/generated/torch.nn.PixelShuffle.html#torch.nn.PixelShuffle) | | -| 2 | [nn.PixelUnshuffle](https://pytorch.org/docs/1.8.1/generated/torch.nn.PixelUnshuffle.html#torch.nn.PixelUnshuffle) | | -| 3 | [nn.Upsample](https://pytorch.org/docs/1.8.1/generated/torch.nn.Upsample.html#torch.nn.Upsample) | | -| 4 | [nn.UpsamplingNearest2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.UpsamplingNearest2d.html#torch.nn.UpsamplingNearest2d) | | -| 5 | [nn.UpsamplingBilinear2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.UpsamplingBilinear2d.html#torch.nn.UpsamplingBilinear2d) | | +| 1 | [nn.PixelShuffle](https://pytorch.org/docs/1.8.1/generated/torch.nn.PixelShuffle.html#torch.nn.PixelShuffle) | 否 | +| 2 | [nn.PixelUnshuffle](https://pytorch.org/docs/1.8.1/generated/torch.nn.PixelUnshuffle.html#torch.nn.PixelUnshuffle) | 否 | +| 3 | [nn.Upsample](https://pytorch.org/docs/1.8.1/generated/torch.nn.Upsample.html#torch.nn.Upsample) | 否 | +| 4 | [nn.UpsamplingNearest2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.UpsamplingNearest2d.html#torch.nn.UpsamplingNearest2d) | 否 | +| 5 | [nn.UpsamplingBilinear2d](https://pytorch.org/docs/1.8.1/generated/torch.nn.UpsamplingBilinear2d.html#torch.nn.UpsamplingBilinear2d) | 否 | @@ -701,7 +699,7 @@ | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [nn.ChannelShuffle](https://pytorch.org/docs/1.8.1/generated/torch.nn.ChannelShuffle.html#torch.nn.ChannelShuffle) | | +| 1 | [nn.ChannelShuffle](https://pytorch.org/docs/1.8.1/generated/torch.nn.ChannelShuffle.html#torch.nn.ChannelShuffle) | 否 | @@ -709,8 +707,8 @@ | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [nn.DataParallel](https://pytorch.org/docs/1.8.1/generated/torch.nn.DataParallel.html#torch.nn.DataParallel) | | -| 2 | [nn.parallel.DistributedDataParallel](https://pytorch.org/docs/1.8.1/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel) | | +| 1 | [nn.DataParallel](https://pytorch.org/docs/1.8.1/generated/torch.nn.DataParallel.html#torch.nn.DataParallel) | 否 | +| 2 | [nn.parallel.DistributedDataParallel](https://pytorch.org/docs/1.8.1/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel) | 否 | ## [Utilities](https://pytorch.org/docs/1.8.1/nn.html#id1) @@ -720,31 +718,31 @@ From the `torch.nn.utils` module | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [clip_grad_norm_](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.clip_grad_norm_.html#torch.nn.utils.clip_grad_norm_) | | -| 2 | [clip_grad_value_](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.clip_grad_value_.html#torch.nn.utils.clip_grad_value_) | | -| 3 | [parameters_to_vector](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.parameters_to_vector.html#torch.nn.utils.parameters_to_vector) | | -| 4 | [vector_to_parameters](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.vector_to_parameters.html#torch.nn.utils.vector_to_parameters) | | -| 5 | [prune.BasePruningMethod](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.BasePruningMethod.html#torch.nn.utils.prune.BasePruningMethod) | | -| 6 | [prune.PruningContainer](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.PruningContainer.html#torch.nn.utils.prune.PruningContainer) | | -| 7 | [prune.Identity](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.Identity.html#torch.nn.utils.prune.Identity) | | -| 8 | [prune.RandomUnstructured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.RandomUnstructured.html#torch.nn.utils.prune.RandomUnstructured) | | -| 9 | [prune.L1Unstructured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.L1Unstructured.html#torch.nn.utils.prune.L1Unstructured) | | -| 10 | [prune.RandomStructured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.RandomStructured.html#torch.nn.utils.prune.RandomStructured) | | -| 11 | [prune.LnStructured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.LnStructured.html#torch.nn.utils.prune.LnStructured) | | -| 12 | [prune.CustomFromMask](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.CustomFromMask.html#torch.nn.utils.prune.CustomFromMask) | | -| 13 | [prune.identity](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.identity.html#torch.nn.utils.prune.identity) | | -| 14 | [prune.random_unstructured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.random_unstructured.html#torch.nn.utils.prune.random_unstructured) | | -| 15 | [prune.l1_unstructured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.l1_unstructured.html#torch.nn.utils.prune.l1_unstructured) | | -| 16 | [prune.random_structured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.random_structured.html#torch.nn.utils.prune.random_structured) | | -| 17 | [prune.ln_structured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.ln_structured.html#torch.nn.utils.prune.ln_structured) | | -| 18 | [prune.global_unstructured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.global_unstructured.html#torch.nn.utils.prune.global_unstructured) | | -| 19 | [prune.custom_from_mask](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.custom_from_mask.html#torch.nn.utils.prune.custom_from_mask) | | -| 20 | [prune.remove](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.remove.html#torch.nn.utils.prune.remove) | | -| 21 | [prune.is_pruned](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.is_pruned.html#torch.nn.utils.prune.is_pruned) | | -| 22 | [weight_norm](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.weight_norm.html#torch.nn.utils.weight_norm) | | -| 23 | [remove_weight_norm](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.remove_weight_norm.html#torch.nn.utils.remove_weight_norm) | | -| 24 | [spectral_norm](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.spectral_norm.html#torch.nn.utils.spectral_norm) | | -| 25 | [remove_spectral_norm](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.remove_spectral_norm.html#torch.nn.utils.remove_spectral_norm) | | +| 1 | [clip_grad_norm_](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.clip_grad_norm_.html#torch.nn.utils.clip_grad_norm_) | 否 | +| 2 | [clip_grad_value_](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.clip_grad_value_.html#torch.nn.utils.clip_grad_value_) | 否 | +| 3 | [parameters_to_vector](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.parameters_to_vector.html#torch.nn.utils.parameters_to_vector) | 否 | +| 4 | [vector_to_parameters](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.vector_to_parameters.html#torch.nn.utils.vector_to_parameters) | 否 | +| 5 | [prune.BasePruningMethod](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.BasePruningMethod.html#torch.nn.utils.prune.BasePruningMethod) | 否 | +| 6 | [prune.PruningContainer](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.PruningContainer.html#torch.nn.utils.prune.PruningContainer) | 否 | +| 7 | [prune.Identity](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.Identity.html#torch.nn.utils.prune.Identity) | 否 | +| 8 | [prune.RandomUnstructured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.RandomUnstructured.html#torch.nn.utils.prune.RandomUnstructured) | 否 | +| 9 | [prune.L1Unstructured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.L1Unstructured.html#torch.nn.utils.prune.L1Unstructured) | 否 | +| 10 | [prune.RandomStructured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.RandomStructured.html#torch.nn.utils.prune.RandomStructured) | 否 | +| 11 | [prune.LnStructured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.LnStructured.html#torch.nn.utils.prune.LnStructured) | 否 | +| 12 | [prune.CustomFromMask](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.CustomFromMask.html#torch.nn.utils.prune.CustomFromMask) | 否 | +| 13 | [prune.identity](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.identity.html#torch.nn.utils.prune.identity) | 否 | +| 14 | [prune.random_unstructured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.random_unstructured.html#torch.nn.utils.prune.random_unstructured) | 否 | +| 15 | [prune.l1_unstructured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.l1_unstructured.html#torch.nn.utils.prune.l1_unstructured) | 否 | +| 16 | [prune.random_structured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.random_structured.html#torch.nn.utils.prune.random_structured) | 否 | +| 17 | [prune.ln_structured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.ln_structured.html#torch.nn.utils.prune.ln_structured) | 否 | +| 18 | [prune.global_unstructured](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.global_unstructured.html#torch.nn.utils.prune.global_unstructured) | 否 | +| 19 | [prune.custom_from_mask](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.custom_from_mask.html#torch.nn.utils.prune.custom_from_mask) | 否 | +| 20 | [prune.remove](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.remove.html#torch.nn.utils.prune.remove) | 否 | +| 21 | [prune.is_pruned](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.prune.is_pruned.html#torch.nn.utils.prune.is_pruned) | 否 | +| 22 | [weight_norm](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.weight_norm.html#torch.nn.utils.weight_norm) | 否 | +| 23 | [remove_weight_norm](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.remove_weight_norm.html#torch.nn.utils.remove_weight_norm) | 否 | +| 24 | [spectral_norm](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.spectral_norm.html#torch.nn.utils.spectral_norm) | 否 | +| 25 | [remove_spectral_norm](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.remove_spectral_norm.html#torch.nn.utils.remove_spectral_norm) | 否 | @@ -752,19 +750,19 @@ From the `torch.nn.utils` module | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [nn.utils.rnn.PackedSequence](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.rnn.PackedSequence.html#torch.nn.utils.rnn.PackedSequence) | | -| 2 | [nn.utils.rnn.pack_padded_sequence](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.rnn.pack_padded_sequence.html#torch.nn.utils.rnn.pack_padded_sequence) | | -| 3 | [nn.utils.rnn.pad_packed_sequence](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.rnn.pad_packed_sequence.html#torch.nn.utils.rnn.pad_packed_sequence) | | -| 4 | [nn.utils.rnn.pad_sequence](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.rnn.pad_sequence.html#torch.nn.utils.rnn.pad_sequence) | | -| 5 | [nn.utils.rnn.pack_sequence](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.rnn.pack_sequence.html#torch.nn.utils.rnn.pack_sequence) | | -| 6 | [nn.Flatten](https://pytorch.org/docs/1.8.1/generated/torch.nn.Flatten.html#torch.nn.Flatten) | | -| 7 | [nn.Unflatten](https://pytorch.org/docs/1.8.1/generated/torch.nn.Unflatten.html#torch.nn.Unflatten) | | +| 1 | [nn.utils.rnn.PackedSequence](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.rnn.PackedSequence.html#torch.nn.utils.rnn.PackedSequence) | 否 | +| 2 | [nn.utils.rnn.pack_padded_sequence](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.rnn.pack_padded_sequence.html#torch.nn.utils.rnn.pack_padded_sequence) | 否 | +| 3 | [nn.utils.rnn.pad_packed_sequence](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.rnn.pad_packed_sequence.html#torch.nn.utils.rnn.pad_packed_sequence) | 否 | +| 4 | [nn.utils.rnn.pad_sequence](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.rnn.pad_sequence.html#torch.nn.utils.rnn.pad_sequence) | 否 | +| 5 | [nn.utils.rnn.pack_sequence](https://pytorch.org/docs/1.8.1/generated/torch.nn.utils.rnn.pack_sequence.html#torch.nn.utils.rnn.pack_sequence) | 否 | +| 6 | [nn.Flatten](https://pytorch.org/docs/1.8.1/generated/torch.nn.Flatten.html#torch.nn.Flatten) | 否 | +| 7 | [nn.Unflatten](https://pytorch.org/docs/1.8.1/generated/torch.nn.Unflatten.html#torch.nn.Unflatten) | 否 | ### Lazy Modules Initialization | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [nn.modules.lazy.LazyModuleMixin](https://pytorch.org/docs/1.8.1/generated/torch.nn.modules.lazy.LazyModuleMixin.html#torch.nn.modules.lazy.LazyModuleMixin) | | +| 1 | [nn.modules.lazy.LazyModuleMixin](https://pytorch.org/docs/1.8.1/generated/torch.nn.modules.lazy.LazyModuleMixin.html#torch.nn.modules.lazy.LazyModuleMixin) | 否 | @@ -782,265 +780,282 @@ From the `torch.nn.utils` module | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [conv1d](https://pytorch.org/docs/1.8.1/nn.functional.html#conv1d) | | -| 2 | [conv2d](https://pytorch.org/docs/1.8.1/nn.functional.html#conv2d) | | -| 3 | [conv3d](https://pytorch.org/docs/1.8.1/nn.functional.html#conv3d) | | -| 4 | [conv_transpose1d](https://pytorch.org/docs/1.8.1/nn.functional.html#conv-transpose1d) | | -| 5 | [conv_transpose2d](https://pytorch.org/docs/1.8.1/nn.functional.html#conv-transpose2d) | | -| 6 | [conv_transpose3d](https://pytorch.org/docs/1.8.1/nn.functional.html#conv-transpose3d) | | -| 7 | [unfold](https://pytorch.org/docs/1.8.1/nn.functional.html#unfold) | | -| 8 | [fold](https://pytorch.org/docs/1.8.1/nn.functional.html#fold) | | +| 1 | [conv1d](https://pytorch.org/docs/1.8.1/nn.functional.html#conv1d) | 否 | +| 2 | [conv2d](https://pytorch.org/docs/1.8.1/nn.functional.html#conv2d) | 否 | +| 3 | [conv3d](https://pytorch.org/docs/1.8.1/nn.functional.html#conv3d) | 否 | +| 4 | [conv_transpose1d](https://pytorch.org/docs/1.8.1/nn.functional.html#conv-transpose1d) | 否 | +| 5 | [conv_transpose2d](https://pytorch.org/docs/1.8.1/nn.functional.html#conv-transpose2d) | 否 | +| 6 | [conv_transpose3d](https://pytorch.org/docs/1.8.1/nn.functional.html#conv-transpose3d) | 否 | +| 7 | [unfold](https://pytorch.org/docs/1.8.1/nn.functional.html#unfold) | 否 | +| 8 | [fold](https://pytorch.org/docs/1.8.1/nn.functional.html#fold) | 否 | ### [Pooling functions](https://pytorch.org/docs/1.8.1/nn.functional.html#pooling-functions) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [avg_pool1d](https://pytorch.org/docs/1.8.1/nn.functional.html#avg-pool1d) | | -| 2 | [avg_pool2d](https://pytorch.org/docs/1.8.1/nn.functional.html#avg-pool2d) | | -| 3 | [avg_pool3d](https://pytorch.org/docs/1.8.1/nn.functional.html#avg-pool3d) | | -| 4 | [max_pool1d](https://pytorch.org/docs/1.8.1/nn.functional.html#max-pool1d) | | -| 5 | [max_pool2d](https://pytorch.org/docs/1.8.1/nn.functional.html#max-pool2d) | | -| 6 | [max_pool3d](https://pytorch.org/docs/1.8.1/nn.functional.html#max-pool3d) | | -| 7 | [max_unpool1d](https://pytorch.org/docs/1.8.1/nn.functional.html#max-unpool1d) | | -| 8 | [max_unpool2d](https://pytorch.org/docs/1.8.1/nn.functional.html#max-unpool2d) | | -| 9 | [max_unpool3d](https://pytorch.org/docs/1.8.1/nn.functional.html#max-unpool3d) | | -| 10 | [lp_pool1d](https://pytorch.org/docs/1.8.1/nn.functional.html#lp-pool1d) | | -| 11 | [lp_pool2d](https://pytorch.org/docs/1.8.1/nn.functional.html#lp-pool2d) | | -| 12 | [adaptive_max_pool1d](https://pytorch.org/docs/1.8.1/nn.functional.html#adaptive-max-pool1d) | | -| 13 | [adaptive_max_pool2d](https://pytorch.org/docs/1.8.1/nn.functional.html#adaptive-max-pool2d) | | -| 14 | [adaptive_max_pool3d](https://pytorch.org/docs/1.8.1/nn.functional.html#adaptive-max-pool3d) | | -| 15 | [adaptive_avg_pool1d](https://pytorch.org/docs/1.8.1/nn.functional.html#adaptive-avg-pool1d) | | -| 16 | [adaptive_avg_pool2d](https://pytorch.org/docs/1.8.1/nn.functional.html#adaptive-avg-pool2d) | | -| 17 | [adaptive_avg_pool3d](https://pytorch.org/docs/1.8.1/nn.functional.html#adaptive-avg-pool3d) | | +| 1 | [avg_pool1d](https://pytorch.org/docs/1.8.1/nn.functional.html#avg-pool1d) | 否 | +| 2 | [avg_pool2d](https://pytorch.org/docs/1.8.1/nn.functional.html#avg-pool2d) | 否 | +| 3 | [avg_pool3d](https://pytorch.org/docs/1.8.1/nn.functional.html#avg-pool3d) | 否 | +| 4 | [max_pool1d](https://pytorch.org/docs/1.8.1/nn.functional.html#max-pool1d) | 否 | +| 5 | [max_pool2d](https://pytorch.org/docs/1.8.1/nn.functional.html#max-pool2d) | 否 | +| 6 | [max_pool3d](https://pytorch.org/docs/1.8.1/nn.functional.html#max-pool3d) | 否 | +| 7 | [max_unpool1d](https://pytorch.org/docs/1.8.1/nn.functional.html#max-unpool1d) | 否 | +| 8 | [max_unpool2d](https://pytorch.org/docs/1.8.1/nn.functional.html#max-unpool2d) | 否 | +| 9 | [max_unpool3d](https://pytorch.org/docs/1.8.1/nn.functional.html#max-unpool3d) | 否 | +| 10 | [lp_pool1d](https://pytorch.org/docs/1.8.1/nn.functional.html#lp-pool1d) | 否 | +| 11 | [lp_pool2d](https://pytorch.org/docs/1.8.1/nn.functional.html#lp-pool2d) | 否 | +| 12 | [adaptive_max_pool1d](https://pytorch.org/docs/1.8.1/nn.functional.html#adaptive-max-pool1d) | 否 | +| 13 | [adaptive_max_pool2d](https://pytorch.org/docs/1.8.1/nn.functional.html#adaptive-max-pool2d) | 否 | +| 14 | [adaptive_max_pool3d](https://pytorch.org/docs/1.8.1/nn.functional.html#adaptive-max-pool3d) | 否 | +| 15 | [adaptive_avg_pool1d](https://pytorch.org/docs/1.8.1/nn.functional.html#adaptive-avg-pool1d) | 否 | +| 16 | [adaptive_avg_pool2d](https://pytorch.org/docs/1.8.1/nn.functional.html#adaptive-avg-pool2d) | 否 | +| 17 | [adaptive_avg_pool3d](https://pytorch.org/docs/1.8.1/nn.functional.html#adaptive-avg-pool3d) | 否 | ### [Non-linear activation functions](https://pytorch.org/docs/1.8.1/nn.functional.html#non-linear-activation-functions) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [threshold](https://pytorch.org/docs/1.8.1/nn.functional.html#threshold) | | -| 2 | [relu](https://pytorch.org/docs/1.8.1/nn.functional.html#relu) | | -| 3 | [hardtanh](https://pytorch.org/docs/1.8.1/nn.functional.html#hardtanh) | | -| 4 | [hardswish](https://pytorch.org/docs/1.8.1/nn.functional.html#hardswish) | | -| 5 | [relu6](https://pytorch.org/docs/1.8.1/nn.functional.html#relu6) | | -| 6 | [elu](https://pytorch.org/docs/1.8.1/nn.functional.html#elu) | | -| 7 | [selu](https://pytorch.org/docs/1.8.1/nn.functional.html#selu) | | -| 8 | [celu](https://pytorch.org/docs/1.8.1/nn.functional.html#celu) | | -| 9 | [leaky_relu](https://pytorch.org/docs/1.8.1/nn.functional.html#leaky-relu) | | -| 10 | [prelu](https://pytorch.org/docs/1.8.1/nn.functional.html#prelu) | | -| 11 | [rrelu](https://pytorch.org/docs/1.8.1/nn.functional.html#rrelu) | | -| 12 | [glu](https://pytorch.org/docs/1.8.1/nn.functional.html#glu) | | -| 13 | [gelu](https://pytorch.org/docs/1.8.1/nn.functional.html#gelu) | | -| 14 | [logsigmoid](https://pytorch.org/docs/1.8.1/nn.functional.html#logsigmoid) | | -| 15 | [hardshrink](https://pytorch.org/docs/1.8.1/nn.functional.html#hardshrink) | | -| 16 | [tanhshrink](https://pytorch.org/docs/1.8.1/nn.functional.html#tanhshrink) | | -| 17 | [softsign](https://pytorch.org/docs/1.8.1/nn.functional.html#softsign) | | -| 18 | [softplus](https://pytorch.org/docs/1.8.1/nn.functional.html#softplus) | | -| 19 | [softmin](https://pytorch.org/docs/1.8.1/nn.functional.html#softmin) | | -| 20 | [softmax](https://pytorch.org/docs/1.8.1/nn.functional.html#softmax) | | -| 21 | [softshrink](https://pytorch.org/docs/1.8.1/nn.functional.html#softshrink) | | -| 22 | [gumbel_softmax](https://pytorch.org/docs/1.8.1/nn.functional.html#gumbel-softmax) | | -| 23 | [log_softmax](https://pytorch.org/docs/1.8.1/nn.functional.html#log-softmax) | | -| 24 | [tanh](https://pytorch.org/docs/1.8.1/nn.functional.html#tanh) | | -| 25 | [sigmoid](https://pytorch.org/docs/1.8.1/nn.functional.html#sigmoid) | | -| 26 | [hardsigmoid](https://pytorch.org/docs/1.8.1/nn.functional.html#hardsigmoid) | | -| 27 | [silu](https://pytorch.org/docs/1.8.1/nn.functional.html#silu) | | +| 1 | [threshold](https://pytorch.org/docs/1.8.1/nn.functional.html#threshold) | 否 | +| 2 | [relu](https://pytorch.org/docs/1.8.1/nn.functional.html#relu) | 否 | +| 3 | [hardtanh](https://pytorch.org/docs/1.8.1/nn.functional.html#hardtanh) | 否 | +| 4 | [hardswish](https://pytorch.org/docs/1.8.1/nn.functional.html#hardswish) | 否 | +| 5 | [relu6](https://pytorch.org/docs/1.8.1/nn.functional.html#relu6) | 否 | +| 6 | [elu](https://pytorch.org/docs/1.8.1/nn.functional.html#elu) | 否 | +| 7 | [selu](https://pytorch.org/docs/1.8.1/nn.functional.html#selu) | 否 | +| 8 | [celu](https://pytorch.org/docs/1.8.1/nn.functional.html#celu) | 否 | +| 9 | [leaky_relu](https://pytorch.org/docs/1.8.1/nn.functional.html#leaky-relu) | 否 | +| 10 | [prelu](https://pytorch.org/docs/1.8.1/nn.functional.html#prelu) | 否 | +| 11 | [rrelu](https://pytorch.org/docs/1.8.1/nn.functional.html#rrelu) | 否 | +| 12 | [glu](https://pytorch.org/docs/1.8.1/nn.functional.html#glu) | 否 | +| 13 | [gelu](https://pytorch.org/docs/1.8.1/nn.functional.html#gelu) | 否 | +| 14 | [logsigmoid](https://pytorch.org/docs/1.8.1/nn.functional.html#logsigmoid) | 否 | +| 15 | [hardshrink](https://pytorch.org/docs/1.8.1/nn.functional.html#hardshrink) | 否 | +| 16 | [tanhshrink](https://pytorch.org/docs/1.8.1/nn.functional.html#tanhshrink) | 否 | +| 17 | [softsign](https://pytorch.org/docs/1.8.1/nn.functional.html#softsign) | 否 | +| 18 | [softplus](https://pytorch.org/docs/1.8.1/nn.functional.html#softplus) | 否 | +| 19 | [softmin](https://pytorch.org/docs/1.8.1/nn.functional.html#softmin) | 否 | +| 20 | [softmax](https://pytorch.org/docs/1.8.1/nn.functional.html#softmax) | 否 | +| 21 | [softshrink](https://pytorch.org/docs/1.8.1/nn.functional.html#softshrink) | 否 | +| 22 | [gumbel_softmax](https://pytorch.org/docs/1.8.1/nn.functional.html#gumbel-softmax) | 否 | +| 23 | [log_softmax](https://pytorch.org/docs/1.8.1/nn.functional.html#log-softmax) | 否 | +| 24 | [tanh](https://pytorch.org/docs/1.8.1/nn.functional.html#tanh) | 否 | +| 25 | [sigmoid](https://pytorch.org/docs/1.8.1/nn.functional.html#sigmoid) | 否 | +| 26 | [hardsigmoid](https://pytorch.org/docs/1.8.1/nn.functional.html#hardsigmoid) | 否 | +| 27 | [silu](https://pytorch.org/docs/1.8.1/nn.functional.html#silu) | 否 | ### [Normalization functions](https://pytorch.org/docs/1.8.1/nn.functional.html#normalization-functions) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [batch_norm](https://pytorch.org/docs/1.8.1/nn.functional.html#batch-norm) | | -| 2 | [instance_norm](https://pytorch.org/docs/1.8.1/nn.functional.html#instance-norm) | | -| 3 | [layer_norm](https://pytorch.org/docs/1.8.1/nn.functional.html#layer-norm) | | -| 4 | [local_response_norm](https://pytorch.org/docs/1.8.1/nn.functional.html#local-response-norm) | | -| 5 | [normalize](https://pytorch.org/docs/1.8.1/nn.functional.html#normalize) | | +| 1 | [batch_norm](https://pytorch.org/docs/1.8.1/nn.functional.html#batch-norm) | 否 | +| 2 | [instance_norm](https://pytorch.org/docs/1.8.1/nn.functional.html#instance-norm) | 否 | +| 3 | [layer_norm](https://pytorch.org/docs/1.8.1/nn.functional.html#layer-norm) | 否 | +| 4 | [local_response_norm](https://pytorch.org/docs/1.8.1/nn.functional.html#local-response-norm) | 否 | +| 5 | [normalize](https://pytorch.org/docs/1.8.1/nn.functional.html#normalize) | 否 | ### [Linear functions](https://pytorch.org/docs/1.8.1/nn.functional.html#linear-functions)[Linear functions](https://pytorch.org/docs/1.8.1/nn.functional.html#linear-functions) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [linear](https://pytorch.org/docs/1.8.1/nn.functional.html#linear) | | -| 2 | [bilinear](https://pytorch.org/docs/1.8.1/nn.functional.html#bilinear) | | +| 1 | [linear](https://pytorch.org/docs/1.8.1/nn.functional.html#linear) | 否 | +| 2 | [bilinear](https://pytorch.org/docs/1.8.1/nn.functional.html#bilinear) | 否 | ### [Dropout functions](https://pytorch.org/docs/1.8.1/nn.functional.html#dropout-functions) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [dropout](https://pytorch.org/docs/1.8.1/nn.functional.html#dropout) | | -| 2 | [alpha_dropout](https://pytorch.org/docs/1.8.1/nn.functional.html#alpha-dropout) | | -| 3 | [feature_alpha_dropout](https://pytorch.org/docs/1.8.1/nn.functional.html#feature-alpha-dropout) | | -| 4 | [dropout2d](https://pytorch.org/docs/1.8.1/nn.functional.html#dropout2d) | | -| 5 | [dropout3d](https://pytorch.org/docs/1.8.1/nn.functional.html#dropout3d) | | +| 1 | [dropout](https://pytorch.org/docs/1.8.1/nn.functional.html#dropout) | 否 | +| 2 | [alpha_dropout](https://pytorch.org/docs/1.8.1/nn.functional.html#alpha-dropout) | 否 | +| 3 | [feature_alpha_dropout](https://pytorch.org/docs/1.8.1/nn.functional.html#feature-alpha-dropout) | 否 | +| 4 | [dropout2d](https://pytorch.org/docs/1.8.1/nn.functional.html#dropout2d) | 否 | +| 5 | [dropout3d](https://pytorch.org/docs/1.8.1/nn.functional.html#dropout3d) | 否 | ### [Sparse functions](https://pytorch.org/docs/1.8.1/nn.functional.html#sparse-functions) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [embedding](https://pytorch.org/docs/1.8.1/nn.functional.html#embedding) | | -| 2 | [embedding_bag](https://pytorch.org/docs/1.8.1/nn.functional.html#embedding-bag) | | -| 3 | [one_hot](https://pytorch.org/docs/1.8.1/nn.functional.html#one-hot) | | +| 1 | [embedding](https://pytorch.org/docs/1.8.1/nn.functional.html#embedding) | 否 | +| 2 | [embedding_bag](https://pytorch.org/docs/1.8.1/nn.functional.html#embedding-bag) | 否 | +| 3 | [one_hot](https://pytorch.org/docs/1.8.1/nn.functional.html#one-hot) | 否 | ### [Distance functions](https://pytorch.org/docs/1.8.1/nn.functional.html#distance-functions) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [pairwise_distance](https://pytorch.org/docs/1.8.1/nn.functional.html#pairwise-distance) | | -| 2 | [cosine_similarity](https://pytorch.org/docs/1.8.1/nn.functional.html#cosine-similarity) | | -| 3 | [pdist](https://pytorch.org/docs/1.8.1/nn.functional.html#pdist) | | +| 1 | [pairwise_distance](https://pytorch.org/docs/1.8.1/nn.functional.html#pairwise-distance) | 否 | +| 2 | [cosine_similarity](https://pytorch.org/docs/1.8.1/nn.functional.html#cosine-similarity) | 否 | +| 3 | [pdist](https://pytorch.org/docs/1.8.1/nn.functional.html#pdist) | 否 | ### [Loss functions](https://pytorch.org/docs/1.8.1/nn.functional.html#loss-functions) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [binary_cross_entropy](https://pytorch.org/docs/1.8.1/nn.functional.html#binary-cross-entropy) | | -| 2 | [binary_cross_entropy_with_logits](https://pytorch.org/docs/1.8.1/nn.functional.html#binary-cross-entropy-with-logits) | | -| 3 | [poisson_nll_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#poisson-nll-loss) | | -| 4 | [cosine_embedding_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#cosine-embedding-loss) | | -| 5 | [cross_entropy](https://pytorch.org/docs/1.8.1/nn.functional.html#cross-entropy) | | -| 6 | [ctc_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#ctc-loss) | | -| 7 | [hinge_embedding_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#hinge-embedding-loss) | | -| 8 | [kl_div](https://pytorch.org/docs/1.8.1/nn.functional.html#kl-div) | | -| 9 | [l1_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#l1-loss) | | -| 10 | [mse_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#mse-loss) | | -| 11 | [margin_ranking_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#margin-ranking-loss) | | -| 12 | [multilabel_margin_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#multilabel-margin-loss) | | -| 13 | [multilabel_soft_margin_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#multilabel-soft-margin-loss) | | -| 14 | [multi_margin_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#multi-margin-loss) | | -| 15 | [nll_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#nll-loss) | | -| 16 | [smooth_l1_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#smooth-l1-loss) | | -| 17 | [soft_margin_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#soft-margin-loss) | | -| 18 | [triplet_margin_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#triplet-margin-loss) | | -| 19 | [triplet_margin_with_distance_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#triplet-margin-with-distance-loss) | | +| 1 | [binary_cross_entropy](https://pytorch.org/docs/1.8.1/nn.functional.html#binary-cross-entropy) | 否 | +| 2 | [binary_cross_entropy_with_logits](https://pytorch.org/docs/1.8.1/nn.functional.html#binary-cross-entropy-with-logits) | 否 | +| 3 | [poisson_nll_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#poisson-nll-loss) | 否 | +| 4 | [cosine_embedding_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#cosine-embedding-loss) | 否 | +| 5 | [cross_entropy](https://pytorch.org/docs/1.8.1/nn.functional.html#cross-entropy) | 否 | +| 6 | [ctc_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#ctc-loss) | 否 | +| 7 | [hinge_embedding_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#hinge-embedding-loss) | 否 | +| 8 | [kl_div](https://pytorch.org/docs/1.8.1/nn.functional.html#kl-div) | 否 | +| 9 | [l1_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#l1-loss) | 否 | +| 10 | [mse_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#mse-loss) | 否 | +| 11 | [margin_ranking_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#margin-ranking-loss) | 否 | +| 12 | [multilabel_margin_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#multilabel-margin-loss) | 否 | +| 13 | [multilabel_soft_margin_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#multilabel-soft-margin-loss) | 否 | +| 14 | [multi_margin_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#multi-margin-loss) | 否 | +| 15 | [nll_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#nll-loss) | 否 | +| 16 | [smooth_l1_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#smooth-l1-loss) | 否 | +| 17 | [soft_margin_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#soft-margin-loss) | 否 | +| 18 | [triplet_margin_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#triplet-margin-loss) | 否 | +| 19 | [triplet_margin_with_distance_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#triplet-margin-with-distance-loss) | 否 | ### [Vision functions](https://pytorch.org/docs/1.8.1/nn.functional.html#vision-functions) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [pixel_shuffle](https://pytorch.org/docs/1.8.1/nn.functional.html#pixel-shuffle) | | -| 2 | [pixel_unshuffle](https://pytorch.org/docs/1.8.1/nn.functional.html#pixel-unshuffle) | | -| 3 | [pad](https://pytorch.org/docs/1.8.1/nn.functional.html#pad) | | -| 4 | [interpolate](https://pytorch.org/docs/1.8.1/nn.functional.html#interpolate) | | -| 5 | [upsample](https://pytorch.org/docs/1.8.1/nn.functional.html#upsample) | | -| 6 | [upsample_nearest](https://pytorch.org/docs/1.8.1/nn.functional.html#upsample-nearest) | | -| 7 | [upsample_bilinear](https://pytorch.org/docs/1.8.1/nn.functional.html#upsample-bilinear) | | -| 8 | [grid_sample](https://pytorch.org/docs/1.8.1/nn.functional.html#grid-sample) | | -| 9 | [affine_grid](https://pytorch.org/docs/1.8.1/nn.functional.html#affine-grid) | | +| 1 | [pixel_shuffle](https://pytorch.org/docs/1.8.1/nn.functional.html#pixel-shuffle) | 否 | +| 2 | [pixel_unshuffle](https://pytorch.org/docs/1.8.1/nn.functional.html#pixel-unshuffle) | 否 | +| 3 | [pad](https://pytorch.org/docs/1.8.1/nn.functional.html#pad) | 否 | +| 4 | [interpolate](https://pytorch.org/docs/1.8.1/nn.functional.html#interpolate) | 否 | +| 5 | [upsample](https://pytorch.org/docs/1.8.1/nn.functional.html#upsample) | 否 | +| 6 | [upsample_nearest](https://pytorch.org/docs/1.8.1/nn.functional.html#upsample-nearest) | 否 | +| 7 | [upsample_bilinear](https://pytorch.org/docs/1.8.1/nn.functional.html#upsample-bilinear) | 否 | +| 8 | [grid_sample](https://pytorch.org/docs/1.8.1/nn.functional.html#grid-sample) | 否 | +| 9 | [affine_grid](https://pytorch.org/docs/1.8.1/nn.functional.html#affine-grid) | 否 | ### [DataParallel functions (multi-GPU, distributed)](https://pytorch.org/docs/1.8.1/nn.functional.html#dataparallel-functions-multi-gpu-distributed) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | -| 1 | [data_parallel](https://pytorch.org/docs/1.8.1/nn.functional.html#data-parallel) | | - - - - - -## torch.distributed - -| 序号 | API名称 | 是否支持（PyTorch1.5.0） | -| ---- | ------------------------------------- | ------------------------ | -| | torch.distributed.is_available | | -| 1 | torch.distributed.init_process_group | 是 | -| 2 | torch.distributed.Backend | 是 | -| 3 | torch.distributed.get_backend | 是 | -| 4 | torch.distributed.get_rank | 是 | -| 5 | torch.distributed.get_world_size | 是 | -| 6 | torch.distributed.is_initialized | 是 | -| 7 | torch.distributed.is_mpi_available | 是 | -| 8 | torch.distributed.is_nccl_available | 是 | -| 9 | torch.distributed.new_group | 是 | -| 10 | torch.distributed.send | 否 | -| 11 | torch.distributed.recv | 否 | -| 12 | torch.distributed.isend | 否 | -| 13 | torch.distributed.irecv | 否 | -| 14 | is_completed | 是 | -| 15 | wait | 是 | -| 16 | torch.distributed.broadcast | 是 | -| 17 | torch.distributed.all_reduce | 是 | -| 18 | torch.distributed.reduce | 否 | -| 19 | torch.distributed.all_gather | 是 | -| 20 | torch.distributed.gather | 是 | -| 21 | torch.distributed.scatter | 是 | -| 22 | torch.distributed.barrier | 是 | -| 23 | torch.distributed.ReduceOp | 是 | -| 24 | torch.distributed.reduce_op | 是 | -| 25 | torch.distributed.broadcast_multigpu | 否 | -| 26 | torch.distributed.all_reduce_multigpu | 否 | -| 27 | torch.distributed.reduce_multigpu | 否 | -| 28 | torch.distributed.all_gather_multigpu | 否 | -| 29 | torch.distributed.launch | 是 | -| 30 | torch.multiprocessing.spawn | 是 | - - +| 1 | [data_parallel](https://pytorch.org/docs/1.8.1/nn.functional.html#data-parallel) | 否 | + + + + + +## [torch.distributed](https://pytorch.org/docs/1.8.1/distributed.html) + +| 序号 | API名称 | 支持情况 | +| ---- | ----------------------------------------- | -------- | +| 1 | torch.distributed.is_available | 否 | +| 2 | torch.distributed.init_process_group | 否 | +| 3 | torch.distributed.Backend | 否 | +| 4 | torch.distributed.get_backend | 否 | +| 5 | torch.distributed.get_rank | 否 | +| 6 | torch.distributed.get_world_size | 否 | +| 7 | torch.distributed.is_initialized | 否 | +| 8 | torch.distributed.is_mpi_available | 否 | +| 9 | torch.distributed.is_nccl_available | 否 | +| 10 | torch.distributed.Store | 否 | +| 11 | torch.distributed.TCPStore | 否 | +| 12 | torch.distributed.HashStore | 否 | +| 13 | torch.distributed.FileStore | 否 | +| 14 | torch.distributed.PrefixStore | 否 | +| 15 | torch.distributed.Store.set | 否 | +| 16 | torch.distributed.Store.get | 否 | +| 17 | torch.distributed.Store.add | 否 | +| 18 | torch.distributed.Store.wait | 否 | +| 19 | torch.distributed.Store.num_keys | 否 | +| 20 | torch.distributed.Store.delete_key | 否 | +| 21 | torch.distributed.Store.set_timeout | 否 | +| 22 | torch.distributed.new_group | 否 | +| 23 | torch.distributed.send | 否 | +| 24 | torch.distributed.recv | 否 | +| 25 | torch.distributed.isend | 否 | +| 26 | torch.distributed.irecv | 否 | +| 27 | is_completed | 否 | +| 28 | wait | 否 | +| 29 | torch.distributed.broadcast | 否 | +| 30 | torch.distributed.broadcast_object_list | 否 | +| 31 | torch.distributed.all_reduce | 否 | +| 32 | torch.distributed.reduce | 否 | +| 33 | torch.distributed.all_gather | 否 | +| 34 | torch.distributed.all_gather_object | 否 | +| 35 | torch.distributed.gather | 否 | +| 36 | torch.distributed.gather_object | 否 | +| 37 | torch.distributed.scatter | 否 | +| 38 | torch.distributed.scatter_object_list | 否 | +| 39 | torch.distributed.reduce_scatter | 否 | +| 40 | torch.distributed.all_to_all | 否 | +| 41 | torch.distributed.barrier | 否 | +| 42 | torch.distributed.ReduceOp | 否 | +| 43 | torch.distributed.reduce_op | 否 | +| 44 | torch.distributed.broadcast_multigpu | 否 | +| 45 | torch.distributed.all_reduce_multigpu | 否 | +| 46 | torch.distributed.reduce_multigpu | 否 | +| 47 | torch.distributed.all_gather_multigpu | 否 | +| 48 | torch.distributed.reduce_scatter_multigpu | 否 | +| 49 | torch.distributed.launch | 否 | +| 50 | torch.multiprocessing.spawn | 否 | ## torch.npu -| 序号 | API名称 | npu对应API名称 | 是否支持（PyTorch1.5.0） | -| ---- | ------------------------------------- | ------------------------------------ | ------------------------ | -| 1 | torch.cuda.current_blas_handle | torch.npu.current_blas_handle | 否 | -| 2 | torch.cuda.current_device | torch.npu.current_device | 是 | -| 3 | torch.cuda.current_stream | torch.npu.current_stream | 是 | -| 4 | torch.cuda.default_stream | torch.npu.default_stream | 是 | -| 5 | torch.cuda.device | torch.npu.device | 否 | -| 6 | torch.cuda.device_count | torch.npu.device_count | 是 | -| 7 | torch.cuda.device_of | torch.npu.device_of | 否 | -| 8 | torch.cuda.get_device_capability | torch.npu.get_device_capability | 否 | -| 9 | torch.cuda.get_device_name | torch.npu.get_device_name | 否 | -| 10 | torch.cuda.init | torch.npu.init | 是 | -| 11 | torch.cuda.ipc_collect | torch.npu.ipc_collect | 否 | -| 12 | torch.cuda.is_available | torch.npu.is_available | 是 | -| 13 | torch.cuda.is_initialized | torch.npu.is_initialized | 是 | -| 14 | torch.cuda.set_device | torch.npu.set_device | 部分支持 | -| 15 | torch.cuda.stream | torch.npu.stream | 是 | -| 16 | torch.cuda.synchronize | torch.npu.synchronize | 是 | -| 17 | torch.cuda.get_rng_state | torch.npu.get_rng_state | 否 | -| 18 | torch.cuda.get_rng_state_all | torch.npu.get_rng_state_all | 否 | -| 19 | torch.cuda.set_rng_state | torch.npu.set_rng_state | 否 | -| 20 | torch.cuda.set_rng_state_all | torch.npu.set_rng_state_all | 否 | -| 21 | torch.cuda.manual_seed | torch.npu.manual_seed | 否 | -| 22 | torch.cuda.manual_seed_all | torch.npu.manual_seed_all | 否 | -| 23 | torch.cuda.seed | torch.npu.seed | 否 | -| 24 | torch.cuda.seed_all | torch.npu.seed_all | 否 | -| 25 | torch.cuda.initial_seed | torch.npu.initial_seed | 否 | -| 26 | torch.cuda.comm.broadcast | torch.npu.comm.broadcast | 否 | -| 27 | torch.cuda.comm.broadcast_coalesced | torch.npu.comm.broadcast_coalesced | 否 | -| 28 | torch.cuda.comm.reduce_add | torch.npu.comm.reduce_add | 否 | -| 29 | torch.cuda.comm.scatter | torch.npu.comm.scatter | 否 | -| 30 | torch.cuda.comm.gather | torch.npu.comm.gather | 否 | -| 31 | torch.cuda.Stream | torch.npu.Stream | 是 | -| 32 | torch.cuda.Stream.query | torch.npu.Stream.query | 否 | -| 33 | torch.cuda.Stream.record_event | torch.npu.Stream.record_event | 是 | -| 34 | torch.cuda.Stream.synchronize | torch.npu.Stream.synchronize | 是 | -| 35 | torch.cuda.Stream.wait_event | torch.npu.Stream.wait_event | 是 | -| 36 | torch.cuda.Stream.wait_stream | torch.npu.Stream.wait_stream | 是 | -| 37 | torch.cuda.Event | torch.npu.Event | 是 | -| 38 | torch.cuda.Event.elapsed_time | torch.npu.Event.elapsed_time | 是 | -| 39 | torch.cuda.Event.from_ipc_handle | torch.npu.Event.from_ipc_handle | 否 | -| 40 | torch.cuda.Event.ipc_handle | torch.npu.Event.ipc_handle | 否 | -| 41 | torch.cuda.Event.query | torch.npu.Event.query | 是 | -| 42 | torch.cuda.Event.record | torch.npu.Event.record | 是 | -| 43 | torch.cuda.Event.synchronize | torch.npu.Event.synchronize | 是 | -| 44 | torch.cuda.Event.wait | torch.npu.Event.wait | 是 | -| 45 | torch.cuda.empty_cache | torch.npu.empty_cache | 是 | -| 46 | torch.cuda.memory_stats | torch.npu.memory_stats | 是 | -| 47 | torch.cuda.memory_summary | torch.npu.memory_summary | 是 | -| 48 | torch.cuda.memory_snapshot | torch.npu.memory_snapshot | 是 | -| 49 | torch.cuda.memory_allocated | torch.npu.memory_allocated | 是 | -| 50 | torch.cuda.max_memory_allocated | torch.npu.max_memory_allocated | 是 | -| 51 | torch.cuda.reset_max_memory_allocated | torch.npu.reset_max_memory_allocated | 是 | -| 52 | torch.cuda.memory_reserved | torch.npu.memory_reserved | 是 | -| 53 | torch.cuda.max_memory_reserved | torch.npu.max_memory_reserved | 是 | -| 54 | torch.cuda.memory_cached | torch.npu.memory_cached | 是 | -| 55 | torch.cuda.max_memory_cached | torch.npu.max_memory_cached | 是 | -| 56 | torch.cuda.reset_max_memory_cached | torch.npu.reset_max_memory_cached | 是 | -| 57 | torch.cuda.nvtx.mark | torch.npu.nvtx.mark | 否 | -| 58 | torch.cuda.nvtx.range_push | torch.npu.nvtx.range_push | 否 | -| 59 | torch.cuda.nvtx.range_pop | torch.npu.nvtx.range_pop | 否 | -| 60 | torch.cuda._sleep | torch.npu._sleep | 否 | -| 61 | torch.cuda.Stream.priority_range | torch.npu.Stream.priority_range | 否 | -| 62 | torch.cuda.get_device_properties | torch.npu.get_device_properties | 否 | -| 63 | torch.cuda.amp.GradScaler | torch.npu.amp.GradScaler | 否 | +| 序号 | API名称 | npu对应API名称 | 是否支持 | +| ---- | ------------------------------------- | ------------------------------------ | -------- | +| 1 | torch.cuda.current_blas_handle | torch.npu.current_blas_handle | 否 | +| 2 | torch.cuda.current_device | torch.npu.current_device | 是 | +| 3 | torch.cuda.current_stream | torch.npu.current_stream | 是 | +| 4 | torch.cuda.default_stream | torch.npu.default_stream | 是 | +| 5 | torch.cuda.device | torch.npu.device | 否 | +| 6 | torch.cuda.device_count | torch.npu.device_count | 是 | +| 7 | torch.cuda.device_of | torch.npu.device_of | 否 | +| 8 | torch.cuda.get_device_capability | torch.npu.get_device_capability | 否 | +| 9 | torch.cuda.get_device_name | torch.npu.get_device_name | 否 | +| 10 | torch.cuda.init | torch.npu.init | 是 | +| 11 | torch.cuda.ipc_collect | torch.npu.ipc_collect | 否 | +| 12 | torch.cuda.is_available | torch.npu.is_available | 是 | +| 13 | torch.cuda.is_initialized | torch.npu.is_initialized | 是 | +| 14 | torch.cuda.set_device | torch.npu.set_device | 部分支持 | +| 15 | torch.cuda.stream | torch.npu.stream | 是 | +| 16 | torch.cuda.synchronize | torch.npu.synchronize | 是 | +| 17 | torch.cuda.get_rng_state | torch.npu.get_rng_state | 否 | +| 18 | torch.cuda.get_rng_state_all | torch.npu.get_rng_state_all | 否 | +| 19 | torch.cuda.set_rng_state | torch.npu.set_rng_state | 否 | +| 20 | torch.cuda.set_rng_state_all | torch.npu.set_rng_state_all | 否 | +| 21 | torch.cuda.manual_seed | torch.npu.manual_seed | 否 | +| 22 | torch.cuda.manual_seed_all | torch.npu.manual_seed_all | 否 | +| 23 | torch.cuda.seed | torch.npu.seed | 否 | +| 24 | torch.cuda.seed_all | torch.npu.seed_all | 否 | +| 25 | torch.cuda.initial_seed | torch.npu.initial_seed | 否 | +| 26 | torch.cuda.comm.broadcast | torch.npu.comm.broadcast | 否 | +| 27 | torch.cuda.comm.broadcast_coalesced | torch.npu.comm.broadcast_coalesced | 否 | +| 28 | torch.cuda.comm.reduce_add | torch.npu.comm.reduce_add | 否 | +| 29 | torch.cuda.comm.scatter | torch.npu.comm.scatter | 否 | +| 30 | torch.cuda.comm.gather | torch.npu.comm.gather | 否 | +| 31 | torch.cuda.Stream | torch.npu.Stream | 是 | +| 32 | torch.cuda.Stream.query | torch.npu.Stream.query | 否 | +| 33 | torch.cuda.Stream.record_event | torch.npu.Stream.record_event | 是 | +| 34 | torch.cuda.Stream.synchronize | torch.npu.Stream.synchronize | 是 | +| 35 | torch.cuda.Stream.wait_event | torch.npu.Stream.wait_event | 是 | +| 36 | torch.cuda.Stream.wait_stream | torch.npu.Stream.wait_stream | 是 | +| 37 | torch.cuda.Event | torch.npu.Event | 是 | +| 38 | torch.cuda.Event.elapsed_time | torch.npu.Event.elapsed_time | 是 | +| 39 | torch.cuda.Event.from_ipc_handle | torch.npu.Event.from_ipc_handle | 否 | +| 40 | torch.cuda.Event.ipc_handle | torch.npu.Event.ipc_handle | 否 | +| 41 | torch.cuda.Event.query | torch.npu.Event.query | 是 | +| 42 | torch.cuda.Event.record | torch.npu.Event.record | 是 | +| 43 | torch.cuda.Event.synchronize | torch.npu.Event.synchronize | 是 | +| 44 | torch.cuda.Event.wait | torch.npu.Event.wait | 是 | +| 45 | torch.cuda.empty_cache | torch.npu.empty_cache | 是 | +| 46 | torch.cuda.memory_stats | torch.npu.memory_stats | 是 | +| 47 | torch.cuda.memory_summary | torch.npu.memory_summary | 是 | +| 48 | torch.cuda.memory_snapshot | torch.npu.memory_snapshot | 是 | +| 49 | torch.cuda.memory_allocated | torch.npu.memory_allocated | 是 | +| 50 | torch.cuda.max_memory_allocated | torch.npu.max_memory_allocated | 是 | +| 51 | torch.cuda.reset_max_memory_allocated | torch.npu.reset_max_memory_allocated | 是 | +| 52 | torch.cuda.memory_reserved | torch.npu.memory_reserved | 是 | +| 53 | torch.cuda.max_memory_reserved | torch.npu.max_memory_reserved | 是 | +| 54 | torch.cuda.memory_cached | torch.npu.memory_cached | 是 | +| 55 | torch.cuda.max_memory_cached | torch.npu.max_memory_cached | 是 | +| 56 | torch.cuda.reset_max_memory_cached | torch.npu.reset_max_memory_cached | 是 | +| 57 | torch.cuda.nvtx.mark | torch.npu.nvtx.mark | 否 | +| 58 | torch.cuda.nvtx.range_push | torch.npu.nvtx.range_push | 否 | +| 59 | torch.cuda.nvtx.range_pop | torch.npu.nvtx.range_pop | 否 | +| 60 | torch.cuda._sleep | torch.npu._sleep | 否 | +| 61 | torch.cuda.Stream.priority_range | torch.npu.Stream.priority_range | 否 | +| 62 | torch.cuda.get_device_properties | torch.npu.get_device_properties | 否 | +| 63 | torch.cuda.amp.GradScaler | torch.npu.amp.GradScaler | 否 | ## NPU自定义算子 -- Gitee From e825120cc300d2cc482bd88b65dbeedff7c7bb20 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Fri, 10 Dec 2021 16:12:35 +0800 Subject: [PATCH 22/55] updata API --- ...\214\201\346\270\205\345\215\225_1.8.1.md" | 56 ++++++++++++++----- 1 file changed, 43 insertions(+), 13 deletions(-) diff --git "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.8.1.md" "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.8.1.md" index c5d00a6cb65..dc429ac253c 100644 --- "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.8.1.md" +++ "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.8.1.md" @@ -1162,8 +1162,8 @@ From the `torch.nn.utils` module count adam result. - Parameters： - - **beta1_power** (Number) - - - **beta2_power** (Number) - + - **beta1_power** (Number) - power of beta1. + - **beta2_power** (Number) - power of beta2. - **lr** (Number) - learning rate. - **beta1** (Number) - exponential decay rate for the 1st moment estimates. - **beta2** (Number) - exponential decay rate for the 2nd moment estimates. @@ -1171,9 +1171,9 @@ count adam result. - **grad** (Tensor) - the gradient. - **use_locking** (bool) - If `True` use locks for update operations. - **use_nesterov** (bool) -If `True`, uses the nesterov update. - - **var** (Tensor) - - - **m** (Tensor) - - - **v** (Tensor) - + - **var** (Tensor) - variables to be optimized. + - **m** (Tensor) - mean value of variables. + - **v** (Tensor) - variance of variables. - constraints： @@ -1181,9 +1181,9 @@ count adam result. - Examples： - None + None -> npu_bert_apply_adam(lr, beta1, beta2, epsilon, grad, max_grad_norm, global_grad_norm, weight_decay, out = (var, m, v)) +> npu_bert_apply_adam(var, m, v, lr, beta1, beta2, epsilon, grad, max_grad_norm, global_grad_norm, weight_decay, ) count adam result in bert. @@ -1193,12 +1193,12 @@ count adam result in bert. - **beta2** (Number) - exponential decay rate for the 2nd moment estimates. - **epsilon** (Number) - term added to the denominator to improve numerical stability. - **grad** (Tensor) - the gradient. - - **max_grad_norm** (Number) - - - **global_grad_norm** (Number) - + - **max_grad_norm** (Number) - maximum norm for the gradients. + - **global_grad_norm** (Number) - L2_norm for the gradients. - **weight_decay** (Number) - weight decay - - **var** (Tensor) - - - **m** (Tensor) - - - **v** (Tensor) - + - **var** (Tensor) - variables to be optimized. + - **m** (Tensor) -mean value of variables. + - **v** (Tensor) - variance of variables. - constraints： @@ -1206,5 +1206,35 @@ count adam result in bert. - Examples： - None + ```python + >>> var_in = torch.rand(321538).uniform_(-32.,21.).npu() + >>> var_in + tensor([ 0.6119, 5.8193, 3.0683, ..., -28.5832, 12.9402, -24.0488], + device='npu:0') + >>> m_in = torch.zeros(321538).npu() + >>> v_in = torchzeros(321538).npu() + >>> grad = torch.rand(321538).uniform_(-0.05,0.03).npu() + >>> grad + tensor([-0.0315, -0.0113, -0.0132, ..., 0.0106, -0.0226, -0.0252], + device='npu:0') + >>> max_grad_norm = -1. + >>> beta1 = 0.9 + >>> beta2 = 0.99 + >>> weight_decay = 0. + >>> lr = 0.1 + >>> epsilon = 1e-06 + >>> global_grad_norm = 0. + >>> var_out, m_out, v_out = torch.npu_bert_apply_adam(var_in, m_in, v_in, lr, beta1, beta2, epsilon, grad, max_grad_norm, global_grad_norm, weight_decay) + >>> var_out + tensor([ 0.7118, 5.9192, 3.1682, ..., -28.6831, 13.0402, -23.9489], + device='npu:0') + >>> m_out + tensor([-0.0032, -0.0011, -0.0013, ..., 0.0011, -0.0023, -0.0025], + device='npu:0') + >>> v_out + tensor([9.9431e-06, 1.2659e-06, 1.7328e-06, ..., 1.1206e-06, 5.0933e-06, + 6.3495e-06], device='npu:0') + ``` + + -- Gitee From 34ab60ef56d657a586a70318e1ae4d38059a3750 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Fri, 10 Dec 2021 16:57:30 +0800 Subject: [PATCH 23/55] remove PyTorch API --- ...57\346\214\201\346\270\205\345\215\225.md" | 8229 ----------------- .../public_sys-resources/icon-caution.gif" | Bin 580 -> 0 bytes .../public_sys-resources/icon-danger.gif" | Bin 580 -> 0 bytes .../public_sys-resources/icon-note.gif" | Bin 394 -> 0 bytes .../public_sys-resources/icon-notice.gif" | Bin 406 -> 0 bytes .../public_sys-resources/icon-tip.gif" | Bin 253 -> 0 bytes .../public_sys-resources/icon-warning.gif" | Bin 580 -> 0 bytes ...\214\201\346\270\205\345\215\225_1.5.0.md" | 52 +- 8 files changed, 40 insertions(+), 8241 deletions(-) delete mode 100644 "docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225.md" delete mode 100644 "docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225/public_sys-resources/icon-caution.gif" delete mode 100644 "docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225/public_sys-resources/icon-danger.gif" delete mode 100644 "docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225/public_sys-resources/icon-note.gif" delete mode 100644 "docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225/public_sys-resources/icon-notice.gif" delete mode 100644 "docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225/public_sys-resources/icon-tip.gif" delete mode 100644 "docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225/public_sys-resources/icon-warning.gif" diff --git "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225.md" "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225.md" deleted file mode 100644 index b2c94fdbf2b..00000000000 --- "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225.md" +++ /dev/null @@ -1,8229 +0,0 @@ -# PyTorch API支持清单 -- [Tensors](#Tensorsmd) -- [Generators](#Generatorsmd) -- [Random sampling](#Random-samplingmd) -- [Serialization](#Serializationmd) -- [Math operations](#Math-operationsmd) -- [Utilities](#Utilitiesmd) -- [Other](#Othermd) -- [torch.Tensor](#torch-Tensormd) -- [Layers \(torch.nn\)](#Layers-torch-nnmd) -- [Functions\(torch.nn.functional\)](#Functionstorch-nn-functionalmd) -- [torch.distributed](#torch-distributedmd) -- [NPU和CUDA功能对齐](#NPU和CUDA功能对齐md) -

Tensors

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

序号

-

API名称

-

是否支持（PyTorch1.5.0）

-

1

-

torch.is_tensor

-

是

-

2

-

torch.is_storage

-

是

-

3

-

torch.is_complex

-

否

-

4

-

torch.is_floating_point

-

是

-

5

-

torch.set_default_dtype

-

是

-

6

-

torch.get_default_dtype

-

是

-

7

-

torch.set_default_tensor_type

-

是

-

8

-

torch.numel

-

是

-

9

-

torch.set_printoptions

-

是

-

10

-

torch.set_flush_denormal

-

是

-

11

-

torch.tensor

-

是

-

12

-

torch.sparse_coo_tensor

-

否

-

13

-

torch.as_tensor

-

是

-

14

-

torch.as_strided

-

是

-

15

-

torch.from_numpy

-

是

-

16

-

torch.zeros

-

是

-

17

-

torch.zeros_like

-

是

-

18

-

torch.ones

-

是

-

19

-

torch.ones_like

-

是

-

20

-

torch.arange

-

是

-

21

-

torch.range

-

是

-

22

-

torch.linspace

-

是

-

23

-

torch.logspace

-

是

-

24

-

torch.eye

-

是

-

25

-

torch.empty

-

是

-

26

-

torch.empty_like

-

是

-

27

-

torch.empty_strided

-

是

-

28

-

torch.full

-

是

-

29

-

torch.full_like

-

是

-

30

-

torch.quantize_per_tensor

-

是

-

31

-

torch.quantize_per_channel

-

否

-

32

-

torch.cat

-

是

-

33

-

torch.chunk

-

是

-

34

-

torch.gather

-

是

-

35

-

torch.index_select

-

是

-

36

-

torch.masked_select

-

是

-

37

-

torch.narrow

-

是

-

38

-

torch.nonzero

-

是

-

39

-

torch.reshape

-

是

-

40

-

torch.split

-

是

-

41

-

torch.squeeze

-

是

-

42

-

torch.stack

-

是

-

43

-

torch.t

-

是

-

44

-

torch.take

-

是

-

45

-

torch.transpose

-

是

-

46

-

torch.unbind

-

是

-

47

-

torch.unsqueeze

-

是

-

48

-

torch.where

-

是

-
- -

Generators

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

序号

-

API名称

-

是否支持（PyTorch1.5.0）

-

1

-

torch._C.Generator

-

否

-

2

-

torch._C.Generator.device

-

否

-

3

-

torch._C.Generator.get_state

-

否

-

4

-

torch._C.Generator.initial_seed

-

否

-

5

-

torch._C.Generator.manual_seed

-

否

-

6

-

torch._C.Generator.seed

-

否

-

7

-

torch._C.Generator.set_state

-

否

-
- -

Random sampling

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

序号

-

API名称

-

是否支持（PyTorch1.5.0）

-

1

-

torch.seed

-

否

-

2

-

torch.manual_seed

-

否

-

3

-

torch.initial_seed

-

否

-

4

-

torch.get_rng_state

-

否

-

5

-

torch.set_rng_state

-

否

-

6

-

torch.torch.default_generator

-

否

-

7

-

torch.bernoulli

-

是

-

8

-

torch.multinomial

-

是

-

9

-

torch.normal

-

是

-

10

-

torch.poisson

-

否

-

11

-

torch.rand

-

是

-

12

-

torch.rand_like

-

是

-

13

-

torch.randint

-

是

-

14

-

torch.randint_like

-

是

-

15

-

torch.randn

-

是

-

16

-

torch.randn_like

-

是

-

17

-

torch.randperm

-

是

-

18

-

torch.Tensor.bernoulli_()

-

是

-

19

-

torch.Tensor.bernoulli_()

-

是

-

20

-

torch.Tensor.exponential_()

-

否

-

21

-

torch.Tensor.geometric_()

-

否

-

22

-

torch.Tensor.log_normal_()

-

否

-

23

-

torch.Tensor.normal_()

-

是

-

24

-

torch.Tensor.random_()

-

是

-

25

-

torch.Tensor.uniform_()

-

是

-

26

-

torch.quasirandom.SobolEngine

-

否

-

27

-

torch.quasirandom.SobolEngine.draw

-

否

-

28

-

torch.quasirandom.SobolEngine.fast_forward

-

否

-

29

-

torch.quasirandom.SobolEngine.reset

-

否

-
- -

Serialization

- - - - - - - - - - - - - - - - -

序号

-

API名称

-

是否支持（PyTorch1.5.0）

-

1

-

torch.save

-

是

-

2

-

torch.load

-

是

-
- -

Math operations

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

序号

-

API名称

-

是否支持（PyTorch1.5.0）

-

1

-

torch.abs

-

是

-

2

-

torch.acos

-

是

-

3

-

torch.add

-

是

-

4

-

torch.addcdiv

-

是

-

5

-

torch.addcmul

-

是

-

6

-

torch.angle

-

否

-

7

-

torch.asin

-

是

-

8

-

torch.atan

-

是

-

9

-

torch.atan2

-

是

-

10

-

torch.bitwise_not

-

是

-

11

-

torch.bitwise_and

-

是

-

12

-

torch.bitwise_or

-

是

-

13

-

torch.bitwise_xor

-

是

-

14

-

torch.ceil

-

是

-

15

-

torch.clamp

-

是

-

16

-

torch.conj

-

否

-

17

-

torch.cos

-

是

-

18

-

torch.cosh

-

是

-

19

-

torch.div

-

是

-

20

-

torch.digamma

-

否

-

21

-

torch.erf

-

是

-

22

-

torch.erfc

-

否

-

23

-

torch.erfinv

-

是

-

24

-

torch.exp

-

是

-

25

-

torch.expm1

-

是

-

26

-

torch.floor

-

是

-

27

-

torch.floor_divide

-

是

-

28

-

torch.fmod

-

是

-

29

-

torch.frac

-

是

-

30

-

torch.imag

-

否

-

31

-

torch.lerp

-

是

-

32

-

torch.lgamma

-

否

-

33

-

torch.log

-

是

-

34

-

torch.log10

-

是

-

35

-

torch.log1p

-

是

-

36

-

torch.log2

-

是

-

37

-

torch.logical_and

-

是

-

38

-

torch.logical_not

-

是

-

39

-

torch.logical_or

-

是

-

40

-

torch.logical_xor

-

是

-

41

-

torch.mul

-

是

-

42

-

torch.mvlgamma

-

否

-

43

-

torch.neg

-

是

-

44

-

torch.polygamma

-

否

-

45

-

torch.pow

-

是

-

46

-

torch.real

-

是

-

47

-

torch.reciprocal

-

是

-

48

-

torch.remainder

-

是

-

49

-

torch.round

-

是

-

50

-

torch.rsqrt

-

是

-

51

-

torch.sigmoid

-

是

-

52

-

torch.sign

-

是

-

53

-

torch.sin

-

是

-

54

-

torch.sinh

-

是

-

55

-

torch.sqrt

-

是

-

56

-

torch.square

-

是

-

57

-

torch.tan

-

是

-

58

-

torch.tanh

-

是

-

59

-

torch.true_divide

-

是

-

60

-

torch.trunc

-

是

-

61

-

torch.argmax

-

是

-

62

-

torch.argmin

-

是

-

63

-

torch.dist

-

是

-

64

-

torch.logsumexp

-

是

-

65

-

torch.mean

-

是

-

66

-

torch.median

-

是

-

67

-

torch.mode

-

否

-

68

-

torch.norm

-

是

-

69

-

torch.prod

-

是

-

70

-

torch.std

-

是

-

71

-

torch.std_mean

-

是

-

72

-

torch.sum

-

是

-

73

-

torch.unique

-

是

-

74

-

torch.unique_consecutive

-

否

-

75

-

torch.var

-

否

-

76

-

torch.var_mean

-

否

-

77

-

torch.allclose

-

是

-

78

-

torch.argsort

-

是

-

79

-

torch.eq

-

是

-

80

-

torch.equal

-

是

-

81

-

torch.ge

-

是

-

82

-

torch.gt

-

是

-

83

-

torch.isfinite

-

是

-

84

-

torch.isinf

-

是

-

85

-

torch.isnan

-

是

-

86

-

torch.kthvalue

-

是

-

87

-

torch.le

-

是

-

88

-

torch.lt

-

是

-

89

-

torch.max

-

是

-

90

-

torch.min

-

是

-

91

-

torch.ne

-

是

-

92

-

torch.sort

-

是

-

93

-

torch.topk

-

是

-

94

-

torch.fft

-

否

-

95

-

torch.ifft

-

否

-

96

-

torch.rfft

-

否

-

97

-

torch.irfft

-

否

-

98

-

torch.stft

-

否

-

99

-

torch.bartlett_window

-

是

-

100

-

torch.blackman_window

-

是

-

101

-

torch.hamming_window

-

是

-

102

-

torch.hann_window

-

是

-

103

-

torch.bincount

-

否

-

104

-

torch.broadcast_tensors

-

是

-

105

-

torch.cartesian_prod

-

是

-

106

-

torch.cdist

-

是

-

107

-

torch.combinations

-

否

-

108

-

torch.cross

-

是

-

109

-

torch.cummax

-

是

-

110

-

torch.cummin

-

是

-

111

-

torch.cumprod

-

是

-

112

-

torch.cumsum

-

是

-

113

-

torch.diag

-

是

-

114

-

torch.diag_embed

-

是

-

115

-

torch.diagflat

-

是

-

116

-

torch.diagonal

-

是

-

117

-

torch.einsum

-

是

-

118

-

torch.flatten

-

是

-

119

-

torch.flip

-

是

-

120

-

torch.rot90

-

是

-

121

-

torch.histc

-

否

-

122

-

torch.meshgrid

-

是

-

123

-

torch.renorm

-

是

-

124

-

torch.repeat_interleave

-

否

-

125

-

torch.roll

-

否

-

126

-

torch.tensordot

-

是

-

127

-

torch.trace

-

否

-

128

-

torch.tril

-

是

-

129

-

torch.tril_indices

-

否

-

130

-

torch.triu

-

是

-

131

-

torch.triu_indices

-

是

-

132

-

torch.addbmm

-

是

-

133

-

torch.addmm

-

是

-

134

-

torch.addmv

-

是

-

135

-

torch.addr

-

是

-

136

-

torch.baddbmm

-

是

-

137

-

torch.bmm

-

是

-

138

-

torch.chain_matmul

-

是

-

139

-

torch.cholesky

-

否

-

140

-

torch.cholesky_inverse

-

否

-

141

-

torch.cholesky_solve

-

否

-

142

-

torch.dot

-

是

-

143

-

torch.eig

-

否

-

144

-

torch.geqrf

-

否

-

145

-

torch.ger

-

是

-

146

-

torch.inverse

-

是

-

147

-

torch.det

-

否

-

148

-

torch.logdet

-

否

-

149

-

torch.slogdet

-

是

-

150

-

torch.lstsq

-

否

-

151

-

torch.lu

-

否

-

152

-

torch.lu_solve

-

否

-

153

-

torch.lu_unpack

-

否

-

154

-

torch.matmul

-

是

-

155

-

torch.matrix_power

-

是

-

156

-

torch.matrix_rank

-

是

-

157

-

torch.mm

-

是

-

158

-

torch.mv

-

是

-

159

-

torch.orgqr

-

否

-

160

-

torch.ormqr

-

否

-

161

-

torch.pinverse

-

否

-

162

-

torch.qr

-

是

-

163

-

torch.solve

-

否

-

164

-

torch.svd

-

是

-

165

-

torch.svd_lowrank

-

否

-

166

-

torch.pca_lowrank

-

否

-

167

-

torch.symeig

-

是

-

168

-

torch.lobpcg

-

否

-

169

-

torch.trapz

-

是

-

170

-

torch.triangular_solve

-

是

-
- -

Utilities

- - - - - - - - - - - - - - - - - - - - - - - - -

序号

-

API名称

-

是否支持（PyTorch1.5.0）

-

1

-

torch.compiled_with_cxx11_abi

-

是

-

2

-

torch.result_type

-

是

-

3

-

torch.can_cast

-

是

-

4

-

torch.promote_types

-

是

-
- -

Other

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

序号

-

API名称

-

是否支持（PyTorch1.5.0）

-

1

-

torch.no_grad

-

是

-

2

-

torch.enable_grad

-

是

-

3

-

torch.set_grad_enabled

-

是

-

4

-

torch.get_num_threads

-

是

-

5

-

torch.set_num_threads

-

是

-

6

-

torch.get_num_interop_threads

-

是

-

7

-

torch.set_num_interop_threads

-

是

-
- -

torch.Tensor

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

序号

-

API名称

-

是否支持（PyTorch1.5.0）

-

1

-

torch.Tensor

-

是

-

2

-

torch.Tensor.new_tensor

-

是

-

3

-

torch.Tensor.new_full

-

是

-

4

-

torch.Tensor.new_empty

-

是

-

5

-

torch.Tensor.new_ones

-

是

-

6

-

torch.Tensor.new_zeros

-

是

-

7

-

torch.Tensor.is_cuda

-

是

-

8

-

torch.Tensor.is_quantized

-

是

-

9

-

torch.Tensor.device

-

是

-

10

-

torch.Tensor.ndim

-

是

-

11

-

torch.Tensor.T

-

是

-

12

-

torch.Tensor.abs

-

是

-

13

-

torch.Tensor.abs_

-

是

-

14

-

torch.Tensor.acos

-

是

-

15

-

torch.Tensor.acos_

-

是

-

16

-

torch.Tensor.add

-

是

-

17

-

torch.Tensor.add_

-

是

-

18

-

torch.Tensor.addbmm

-

是

-

19

-

torch.Tensor.addbmm_

-

是

-

20

-

torch.Tensor.addcdiv

-

是

-

21

-

torch.Tensor.addcdiv_

-

是

-

22

-

torch.Tensor.addcmul

-

是

-

23

-

torch.Tensor.addcmul_

-

是

-

24

-

torch.Tensor.addmm

-

是

-

25

-

torch.Tensor.addmm_

-

是

-

26

-

torch.Tensor.addmv

-

是

-

27

-

torch.Tensor.addmv_

-

是

-

28

-

torch.Tensor.addr

-

是

-

29

-

torch.Tensor.addr_

-

是

-

30

-

torch.Tensor.allclose

-

是

-

31

-

torch.Tensor.angle

-

否

-

32

-

torch.Tensor.apply_

-

否

-

33

-

torch.Tensor.argmax

-

是

-

34

-

torch.Tensor.argmin

-

是

-

35

-

torch.Tensor.argsort

-

是

-

36

-

torch.Tensor.asin

-

是

-

37

-

torch.Tensor.asin_

-

是

-

38

-

torch.Tensor.as_strided

-

是

-

39

-

torch.Tensor.atan

-

是

-

40

-

torch.Tensor.atan2

-

是

-

41

-

torch.Tensor.atan2_

-

是

-

42

-

torch.Tensor.atan_

-

是

-

43

-

torch.Tensor.baddbmm

-

是

-

44

-

torch.Tensor.baddbmm_

-

是

-

45

-

torch.Tensor.bernoulli

-

是

-

46

-

torch.Tensor.bernoulli_

-

是

-

47

-

torch.Tensor.bfloat16

-

否

-

48

-

torch.Tensor.bincount

-

否

-

49

-

torch.Tensor.bitwise_not

-

是

-

50

-

torch.Tensor.bitwise_not_

-

是

-

51

-

torch.Tensor.bitwise_and

-

是

-

52

-

torch.Tensor.bitwise_and_

-

是

-

53

-

torch.Tensor.bitwise_or

-

是

-

54

-

torch.Tensor.bitwise_or_

-

是

-

55

-

torch.Tensor.bitwise_xor

-

是

-

56

-

torch.Tensor.bitwise_xor_

-

是

-

57

-

torch.Tensor.bmm

-

是

-

58

-

torch.Tensor.bool

-

是

-

59

-

torch.Tensor.byte

-

是

-

60

-

torch.Tensor.cauchy_

-

否

-

61

-

torch.Tensor.ceil

-

是

-

62

-

torch.Tensor.ceil_

-

是

-

63

-

torch.Tensor.char

-

是

-

64

-

torch.Tensor.cholesky

-

否

-

65

-

torch.Tensor.cholesky_inverse

-

否

-

66

-

torch.Tensor.cholesky_solve

-

否

-

67

-

torch.Tensor.chunk

-

是

-

68

-

torch.Tensor.clamp

-

是

-

69

-

torch.Tensor.clamp_

-

是

-

70

-

torch.Tensor.clone

-

是

-

71

-

torch.Tensor.contiguous

-

是

-

72

-

torch.Tensor.copy_

-

是

-

73

-

torch.Tensor.conj

-

否

-

74

-

torch.Tensor.cos

-

是

-

75

-

torch.Tensor.cos_

-

是

-

76

-

torch.Tensor.cosh

-

是

-

77

-

torch.Tensor.cosh_

-

是

-

78

-

torch.Tensor.cpu

-

是

-

79

-

torch.Tensor.cross

-

是

-

80

-

torch.Tensor.cuda

-

否

-

81

-

torch.Tensor.cummax

-

是

-

82

-

torch.Tensor.cummin

-

是

-

83

-

torch.Tensor.cumprod

-

是

-

84

-

torch.Tensor.cumsum

-

是

-

85

-

torch.Tensor.data_ptr

-

是

-

86

-

torch.Tensor.dequantize

-

否

-

87

-

torch.Tensor.det

-

否

-

88

-

torch.Tensor.dense_dim

-

否

-

89

-

torch.Tensor.diag

-

是

-

90

-

torch.Tensor.diag_embed

-

是

-

91

-

torch.Tensor.diagflat

-

是

-

92

-

torch.Tensor.diagonal

-

是

-

93

-

torch.Tensor.fill_diagonal_

-

是

-

94

-

torch.Tensor.digamma

-

否

-

95

-

torch.Tensor.digamma_

-

否

-

96

-

torch.Tensor.dim

-

是

-

97

-

torch.Tensor.dist

-

是

-

98

-

torch.Tensor.div

-

是

-

99

-

torch.Tensor.div_

-

是

-

100

-

torch.Tensor.dot

-

是

-

101

-

torch.Tensor.double

-

否

-

102

-

torch.Tensor.eig

-

否

-

103

-

torch.Tensor.element_size

-

是

-

104

-

torch.Tensor.eq

-

是

-

105

-

torch.Tensor.eq_

-

是

-

106

-

torch.Tensor.equal

-

是

-

107

-

torch.Tensor.erf

-

是

-

108

-

torch.Tensor.erf_

-

是

-

109

-

torch.Tensor.erfc

-

是

-

110

-

torch.Tensor.erfc_

-

是

-

111

-

torch.Tensor.erfinv

-

是

-

112

-

torch.Tensor.erfinv_

-

是

-

113

-

torch.Tensor.exp

-

是

-

114

-

torch.Tensor.exp_

-

是

-

115

-

torch.Tensor.expm1

-

是

-

116

-

torch.Tensor.expm1_

-

是

-

117

-

torch.Tensor.expand

-

是

-

118

-

torch.Tensor.expand_as

-

是

-

119

-

torch.Tensor.exponential_

-

否

-

120

-

torch.Tensor.fft

-

否

-

121

-

torch.Tensor.fill_

-

是

-

122

-

torch.Tensor.flatten

-

是

-

123

-

torch.Tensor.flip

-

是

-

124

-

torch.Tensor.float

-

是

-

125

-

torch.Tensor.floor

-

是

-

126

-

torch.Tensor.floor_

-

是

-

127

-

torch.Tensor.floor_divide

-

是

-

128

-

torch.Tensor.floor_divide_

-

是

-

129

-

torch.Tensor.fmod

-

是

-

130

-

torch.Tensor.fmod_

-

是

-

131

-

torch.Tensor.frac

-

是

-

132

-

torch.Tensor.frac_

-

是

-

133

-

torch.Tensor.gather

-

是

-

134

-

torch.Tensor.ge

-

是

-

135

-

torch.Tensor.ge_

-

是

-

136

-

torch.Tensor.geometric_

-

否

-

137

-

torch.Tensor.geqrf

-

否

-

138

-

torch.Tensor.ger

-

是

-

139

-

torch.Tensor.get_device

-

是

-

140

-

torch.Tensor.gt

-

是

-

141

-

torch.Tensor.gt_

-

是

-

142

-

torch.Tensor.half

-

是

-

143

-

torch.Tensor.hardshrink

-

是

-

144

-

torch.Tensor.histc

-

否

-

145

-

torch.Tensor.ifft

-

否

-

146

-

torch.Tensor.index_add_

-

是

-

147

-

torch.Tensor.index_add

-

是

-

148

-

torch.Tensor.index_copy_

-

是

-

149

-

torch.Tensor.index_copy

-

是

-

150

-

torch.Tensor.index_fill_

-

是

-

151

-

torch.Tensor.index_fill

-

是

-

152

-

torch.Tensor.index_put_

-

是

-

153

-

torch.Tensor.index_put

-

是

-

154

-

torch.Tensor.index_select

-

是

-

155

-

torch.Tensor.indices

-

否

-

156

-

torch.Tensor.int

-

是

-

157

-

torch.Tensor.int_repr

-

否

-

158

-

torch.Tensor.inverse

-

是

-

159

-

torch.Tensor.irfft

-

否

-

160

-

torch.Tensor.is_contiguous

-

是

-

161

-

torch.Tensor.is_complex

-

是

-

162

-

torch.Tensor.is_floating_point

-

是

-

163

-

torch.Tensor.is_pinned

-

是

-

164

-

torch.Tensor.is_set_to

-

否

-

165

-

torch.Tensor.is_shared

-

是

-

166

-

torch.Tensor.is_signed

-

是

-

167

-

torch.Tensor.is_sparse

-

是

-

168

-

torch.Tensor.item

-

是

-

169

-

torch.Tensor.kthvalue

-

是

-

170

-

torch.Tensor.le

-

是

-

171

-

torch.Tensor.le_

-

是

-

172

-

torch.Tensor.lerp

-

是

-

173

-

torch.Tensor.lerp_

-

是

-

174

-

torch.Tensor.lgamma

-

否

-

175

-

torch.Tensor.lgamma_

-

否

-

176

-

torch.Tensor.log

-

是

-

177

-

torch.Tensor.log_

-

是

-

178

-

torch.Tensor.logdet

-

否

-

179

-

torch.Tensor.log10

-

是

-

180

-

torch.Tensor.log10_

-

是

-

181

-

torch.Tensor.log1p

-

是

-

182

-

torch.Tensor.log1p_

-

是

-

183

-

torch.Tensor.log2

-

是

-

184

-

torch.Tensor.log2_

-

是

-

185

-

torch.Tensor.log_normal_

-

是

-

186

-

torch.Tensor.logsumexp

-

是

-

187

-

torch.Tensor.logical_and

-

是

-

188

-

torch.Tensor.logical_and_

-

是

-

189

-

torch.Tensor.logical_not

-

是

-

190

-

torch.Tensor.logical_not_

-

是

-

191

-

torch.Tensor.logical_or

-

是

-

192

-

torch.Tensor.logical_or_

-

是

-

193

-

torch.Tensor.logical_xor

-

否

-

194

-

torch.Tensor.logical_xor_

-

否

-

195

-

torch.Tensor.long

-

是

-

196

-

torch.Tensor.lstsq

-

否

-

197

-

torch.Tensor.lt

-

是

-

198

-

torch.Tensor.lt_

-

是

-

199

-

torch.Tensor.lu

-

是

-

200

-

torch.Tensor.lu_solve

-

是

-

201

-

torch.Tensor.map_

-

否

-

202

-

torch.Tensor.masked_scatter_

-

是

-

203

-

torch.Tensor.masked_scatter

-

是

-

204

-

torch.Tensor.masked_fill_

-

是

-

205

-

torch.Tensor.masked_fill

-

是

-

206

-

torch.Tensor.masked_select

-

是

-

207

-

torch.Tensor.matmul

-

是

-

208

-

torch.Tensor.matrix_power

-

是

-

209

-

torch.Tensor.max

-

是

-

210

-

torch.Tensor.mean

-

是

-

211

-

torch.Tensor.median

-

是

-

212

-

torch.Tensor.min

-

是

-

213

-

torch.Tensor.mm

-

是

-

214

-

torch.Tensor.mode

-

否

-

215

-

torch.Tensor.mul

-

是

-

216

-

torch.Tensor.mul_

-

是

-

217

-

torch.Tensor.multinomial

-

是

-

218

-

torch.Tensor.mv

-

是

-

219

-

torch.Tensor.mvlgamma

-

否

-

220

-

torch.Tensor.mvlgamma_

-

否

-

221

-

torch.Tensor.narrow

-

是

-

222

-

torch.Tensor.narrow_copy

-

是

-

223

-

torch.Tensor.ndimension

-

是

-

224

-

torch.Tensor.ne

-

是

-

225

-

torch.Tensor.ne_

-

是

-

226

-

torch.Tensor.neg

-

是

-

227

-

torch.Tensor.neg_

-

是

-

228

-

torch.Tensor.nelement

-

是

-

229

-

torch.Tensor.nonzero

-

是

-

230

-

torch.Tensor.norm

-

是

-

231

-

torch.Tensor.normal_

-

是

-

232

-

torch.Tensor.numel

-

是

-

233

-

torch.Tensor.numpy

-

否

-

234

-

torch.Tensor.orgqr

-

否

-

235

-

torch.Tensor.ormqr

-

否

-

236

-

torch.Tensor.permute

-

是

-

237

-

torch.Tensor.pin_memory

-

否

-

238

-

torch.Tensor.pinverse

-

否

-

239

-

torch.Tensor.polygamma

-

否

-

240

-

torch.Tensor.polygamma_

-

否

-

241

-

torch.Tensor.pow

-

是

-

242

-

torch.Tensor.pow_

-

是

-

243

-

torch.Tensor.prod

-

是

-

244

-

torch.Tensor.put_

-

是

-

245

-

torch.Tensor.qr

-

是

-

246

-

torch.Tensor.qscheme

-

否

-

247

-

torch.Tensor.q_scale

-

否

-

248

-

torch.Tensor.q_zero_point

-

否

-

249

-

torch.Tensor.q_per_channel_scales

-

否

-

250

-

torch.Tensor.q_per_channel_zero_points

-

否

-

251

-

torch.Tensor.q_per_channel_axis

-

否

-

252

-

torch.Tensor.random_

-

是

-

253

-

torch.Tensor.reciprocal

-

是

-

254

-

torch.Tensor.reciprocal_

-

是

-

255

-

torch.Tensor.record_stream

-

否

-

256

-

torch.Tensor.remainder

-

是

-

257

-

torch.Tensor.remainder_

-

是

-

258

-

torch.Tensor.renorm

-

是

-

259

-

torch.Tensor.renorm_

-

是

-

260

-

torch.Tensor.repeat

-

是

-

261

-

torch.Tensor.repeat_interleave

-

是

-

262

-

torch.Tensor.requires_grad_

-

是

-

263

-

torch.Tensor.reshape

-

是

-

264

-

torch.Tensor.reshape_as

-

是

-

265

-

torch.Tensor.resize_

-

是

-

266

-

torch.Tensor.resize_as_

-

是

-

267

-

torch.Tensor.rfft

-

否

-

268

-

torch.Tensor.roll

-

否

-

269

-

torch.Tensor.rot90

-

是

-

270

-

torch.Tensor.round

-

是

-

271

-

torch.Tensor.round_

-

是

-

272

-

torch.Tensor.rsqrt

-

是

-

273

-

torch.Tensor.rsqrt_

-

是

-

274

-

torch.Tensor.scatter

-

是

-

275

-

torch.Tensor.scatter_

-

是

-

276

-

torch.Tensor.scatter_add_

-

是

-

277

-

torch.Tensor.scatter_add

-

是

-

278

-

torch.Tensor.select

-

是

-

279

-

torch.Tensor.set_

-

是

-

280

-

torch.Tensor.share_memory_

-

否

-

281

-

torch.Tensor.short

-

是

-

282

-

torch.Tensor.sigmoid

-

是

-

283

-

torch.Tensor.sigmoid_

-

是

-

284

-

torch.Tensor.sign

-

是

-

285

-

torch.Tensor.sign_

-

是

-

286

-

torch.Tensor.sin

-

是

-

287

-

torch.Tensor.sin_

-

是

-

288

-

torch.Tensor.sinh

-

是

-

289

-

torch.Tensor.sinh_

-

是

-

290

-

torch.Tensor.size

-

是

-

291

-

torch.Tensor.slogdet

-

是

-

292

-

torch.Tensor.solve

-

否

-

293

-

torch.Tensor.sort

-

是

-

294

-

torch.Tensor.split

-

是

-

295

-

torch.Tensor.sparse_mask

-

否

-

296

-

torch.Tensor.sparse_dim

-

否

-

297

-

torch.Tensor.sqrt

-

是

-

298

-

torch.Tensor.sqrt_

-

是

-

299

-

torch.Tensor.square

-

是

-

300

-

torch.Tensor.square_

-

是

-

301

-

torch.Tensor.squeeze

-

是

-

302

-

torch.Tensor.squeeze_

-

是

-

303

-

torch.Tensor.std

-

是

-

304

-

torch.Tensor.stft

-

否

-

305

-

torch.Tensor.storage

-

是

-

306

-

torch.Tensor.storage_offset

-

是

-

307

-

torch.Tensor.storage_type

-

是

-

308

-

torch.Tensor.stride

-

是

-

309

-

torch.Tensor.sub

-

是

-

310

-

torch.Tensor.sub_

-

是

-

311

-

torch.Tensor.sum

-

是

-

312

-

torch.Tensor.sum_to_size

-

是

-

313

-

torch.Tensor.svd

-

是

-

314

-

torch.Tensor.symeig

-

是

-

315

-

torch.Tensor.t

-

是

-

316

-

torch.Tensor.t_

-

是

-

317

-

torch.Tensor.to

-

是

-

318

-

torch.Tensor.to_mkldnn

-

否

-

319

-

torch.Tensor.take

-

是

-

320

-

torch.Tensor.tan

-

是

-

321

-

torch.Tensor.tan_

-

是

-

322

-

torch.Tensor.tanh

-

是

-

323

-

torch.Tensor.tanh_

-

是

-

324

-

torch.Tensor.tolist

-

是

-

325

-

torch.Tensor.topk

-

是

-

326

-

torch.Tensor.to_sparse

-

否

-

327

-

torch.Tensor.trace

-

否

-

328

-

torch.Tensor.transpose

-

是

-

329

-

torch.Tensor.transpose_

-

是

-

330

-

torch.Tensor.triangular_solve

-

是

-

331

-

torch.Tensor.tril

-

是

-

332

-

torch.Tensor.tril_

-

是

-

333

-

torch.Tensor.triu

-

是

-

334

-

torch.Tensor.triu_

-

是

-

335

-

torch.Tensor.true_divide

-

是

-

336

-

torch.Tensor.true_divide_

-

是

-

337

-

torch.Tensor.trunc

-

是

-

338

-

torch.Tensor.trunc_

-

是

-

339

-

torch.Tensor.type

-

是

-

340

-

torch.Tensor.type_as

-

是

-

341

-

torch.Tensor.unbind

-

是

-

342

-

torch.Tensor.unfold

-

是

-

343

-

torch.Tensor.uniform_

-

是

-

344

-

torch.Tensor.unique

-

是

-

345

-

torch.Tensor.unique_consecutive

-

否

-

346

-

torch.Tensor.unsqueeze

-

是

-

347

-

torch.Tensor.unsqueeze_

-

是

-

348

-

torch.Tensor.values

-

否

-

349

-

torch.Tensor.var

-

否

-

350

-

torch.Tensor.view

-

是

-

351

-

torch.Tensor.view_as

-

是

-

352

-

torch.Tensor.where

-

是

-

353

-

torch.Tensor.zero_

-

是

-

354

-

torch.BoolTensor

-

是

-

355

-

torch.BoolTensor.all

-

是

-

356

-

torch.BoolTensor.any

-

是

-
- -

Layers (torch.nn)

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

序号

-

API名称

-

是否支持（PyTorch1.5.0）

-

1

-

torch.nn.Parameter

-

是

-

2

-

torch.nn.Module

-

是

-

3

-

torch.nn.Module.add_module

-

是

-

4

-

torch.nn.Module.apply

-

是

-

5

-

torch.nn.Module.bfloat16

-

否

-

6

-

torch.nn.Module.buffers

-

是

-

7

-

torch.nn.Module.children

-

是

-

8

-

torch.nn.Module.cpu

-

是

-

9

-

torch.nn.Module.cuda

-

否

-

10

-

torch.nn.Module.double

-

否

-

11

-

torch.nn.Module.dump_patches

-

是

-

12

-

torch.nn.Module.eval

-

是

-

13

-

torch.nn.Module.extra_repr

-

是

-

14

-

torch.nn.Module.float

-

是

-

15

-

torch.nn.Module.forward

-

是

-

16

-

torch.nn.Module.half

-

是

-

17

-

torch.nn.Module.load_state_dict

-

是

-

18

-

torch.nn.Module.modules

-

是

-

19

-

torch.nn.Module.named_buffers

-

是

-

20

-

torch.nn.Module.named_children

-

是

-

21

-

torch.nn.Module.named_modules

-

是

-

22

-

torch.nn.Module.named_parameters

-

是

-

23

-

torch.nn.Module.parameters

-

是

-

24

-

torch.nn.Module.register_backward_hook

-

是

-

25

-

torch.nn.Module.register_buffer

-

是

-

26

-

torch.nn.Module.register_forward_hook

-

是

-

27

-

torch.nn.Module.register_forward_pre_hook

-

是

-

28

-

torch.nn.Module.register_parameter

-

是

-

29

-

torch.nn.Module.requires_grad_

-

是

-

30

-

torch.nn.Module.state_dict

-

是

-

31

-

torch.nn.Module.to

-

是

-

32

-

torch.nn.Module.train

-

是

-

33

-

torch.nn.Module.type

-

是

-

34

-

torch.nn.Module.zero_grad

-

是

-

35

-

torch.nn.Sequential

-

是

-

36

-

torch.nn.ModuleList

-

是

-

37

-

torch.nn.ModuleList.append

-

是

-

38

-

torch.nn.ModuleList.extend

-

是

-

39

-

torch.nn.ModuleList.insert

-

是

-

40

-

torch.nn.ModuleDict

-

是

-

41

-

torch.nn.ModuleDict.clear

-

是

-

42

-

torch.nn.ModuleDict.items

-

是

-

43

-

torch.nn.ModuleDict.keys

-

是

-

44

-

torch.nn.ModuleDict.pop

-

是

-

45

-

torch.nn.ModuleDict.update

-

是

-

46

-

torch.nn.ModuleDict.values

-

是

-

47

-

torch.nn.ParameterList

-

是

-

48

-

torch.nn.ParameterList.append

-

是

-

49

-

torch.nn.ParameterList.extend

-

是

-

50

-

torch.nn.ParameterDict

-

是

-

51

-

torch.nn.ParameterDict.clear

-

是

-

52

-

torch.nn.ParameterDict.items

-

是

-

53

-

torch.nn.ParameterDict.keys

-

是

-

54

-

torch.nn.ParameterDict.pop

-

是

-

55

-

torch.nn.ParameterDict.update

-

是

-

56

-

torch.nn.ParameterDict.values

-

是

-

57

-

torch.nn.Conv1d

-

是

-

58

-

torch.nn.Conv2d

-

是

-

59

-

torch.nn.Conv3d

-

是

-

60

-

torch.nn.ConvTranspose1d

-

否

-

61

-

torch.nn.ConvTranspose2d

-

是

-

62

-

torch.nn.ConvTranspose3d

-

否

-

63

-

torch.nn.Unfold

-

否

-

64

-

torch.nn.Fold

-

是

-

65

-

torch.nn.MaxPool1d

-

是

-

66

-

torch.nn.MaxPool2d

-

是

-

67

-

torch.nn.MaxPool3d

-

是

-

68

-

torch.nn.MaxUnpool1d

-

否

-

69

-

torch.nn.MaxUnpool2d

-

是

-

70

-

torch.nn.MaxUnpool3d

-

是

-

71

-

torch.nn.AvgPool1d

-

是

-

72

-

torch.nn.AvgPool2d

-

是

-

73

-

torch.nn.AvgPool3d

-

是

-

74

-

torch.nn.FractionalMaxPool2d

-

否

-

75

-

torch.nn.LPPool1d

-

是

-

76

-

torch.nn.LPPool2d

-

是

-

77

-

torch.nn.AdaptiveMaxPool1d

-

否

-

78

-

torch.nn.AdaptiveMaxPool2d

-

否

-

79

-

torch.nn.AdaptiveMaxPool3d

-

否

-

80

-

torch.nn.AdaptiveAvgPool1d

-

是

-

81

-

torch.nn.AdaptiveAvgPool2d

-

是

-

82

-

torch.nn.AdaptiveAvgPool3d

-

否

-

83

-

torch.nn.ReflectionPad1d

-

否

-

84

-

torch.nn.ReflectionPad2d

-

是

-

85

-

torch.nn.ReplicationPad1d

-

否

-

86

-

torch.nn.ReplicationPad2d

-

是

-

87

-

torch.nn.ReplicationPad3d

-

否

-

88

-

torch.nn.ZeroPad2d

-

是

-

89

-

torch.nn.ConstantPad1d

-

是

-

90

-

torch.nn.ConstantPad2d

-

是

-

91

-

torch.nn.ConstantPad3d

-

是

-

92

-

torch.nn.ELU

-

是

-

93

-

torch.nn.Hardshrink

-

是

-

94

-

torch.nn.Hardtanh

-

是

-

95

-

torch.nn.LeakyReLU

-

是

-

96

-

torch.nn.LogSigmoid

-

是

-

97

-

torch.nn.MultiheadAttention

-

否

-

98

-

torch.nn.PReLU

-

否

-

99

-

torch.nn.ReLU

-

是

-

100

-

torch.nn.ReLU6

-

是

-

101

-

torch.nn.RReLU

-

否

-

102

-

torch.nn.SELU

-

是

-

103

-

torch.nn.CELU

-

是

-

104

-

torch.nn.GELU

-

是

-

105

-

torch.nn.Sigmoid

-

是

-

106

-

torch.nn.Softplus

-

否

-

107

-

torch.nn.Softshrink

-

是，SoftShrink场景暂不支持

-

108

-

torch.nn.Softsign

-

是

-

109

-

torch.nn.Tanh

-

是

-

110

-

torch.nn.Tanhshrink

-

是

-

111

-

torch.nn.Threshold

-

是

-

112

-

torch.nn.Softmin

-

是

-

113

-

torch.nn.Softmax

-

是

-

114

-

torch.nn.Softmax2d

-

是

-

115

-

torch.nn.LogSoftmax

-

否

-

116

-

torch.nn.AdaptiveLogSoftmaxWithLoss

-

否

-

117

-

torch.nn.AdaptiveLogSoftmaxWithLoss.log_prob

-

否

-

118

-

torch.nn.AdaptiveLogSoftmaxWithLoss.predict

-

否

-

119

-

torch.nn.BatchNorm1d

-

是

-

120

-

torch.nn.BatchNorm2d

-

是

-

121

-

torch.nn.BatchNorm3d

-

否

-

122

-

torch.nn.GroupNorm

-

是

-

123

-

torch.nn.SyncBatchNorm

-

否

-

124

-

torch.nn.SyncBatchNorm.convert_sync_batchnorm

-

否

-

125

-

torch.nn.InstanceNorm1d

-

是

-

126

-

torch.nn.InstanceNorm2d

-

是

-

127

-

torch.nn.InstanceNorm3d

-

是

-

128

-

torch.nn.LayerNorm

-

是

-

129

-

torch.nn.LocalResponseNorm

-

是

-

130

-

torch.nn.RNNBase

-

是

-

131

-

torch.nn.RNNBase.flatten_parameters

-

是

-

132

-

torch.nn.RNN

-

是

-

133

-

torch.nn.LSTM

-

是

-

134

-

torch.nn.GRU

-

是，DynamicGRUV2场景暂不支持

-

135

-

torch.nn.RNNCell

-

是

-

136

-

torch.nn.LSTMCell

-

是

-

137

-

torch.nn.GRUCell

-

是

-

138

-

torch.nn.Transformer

-

否

-

139

-

torch.nn.Transformer.forward

-

否

-

140

-

torch.nn.Transformer.generate_square_subsequent_mask

-

否

-

141

-

torch.nn.TransformerEncoder

-

是

-

142

-

torch.nn.TransformerEncoder.forward

-

是

-

143

-

torch.nn.TransformerDecoder

-

否

-

144

-

torch.nn.TransformerDecoder.forward

-

否

-

145

-

torch.nn.TransformerEncoderLayer

-

是

-

146

-

torch.nn.TransformerEncoderLayer.forward

-

是

-

147

-

torch.nn.TransformerDecoderLayer

-

否

-

148

-

torch.nn.TransformerDecoderLayer.forward

-

否

-

149

-

torch.nn.Identity

-

是

-

150

-

torch.nn.Linear

-

是

-

151

-

torch.nn.Bilinear

-

是

-

152

-

torch.nn.Dropout

-

是

-

153

-

torch.nn.Dropout2d

-

是

-

154

-

torch.nn.Dropout3d

-

是

-

155

-

torch.nn.AlphaDropout

-

是

-

156

-

torch.nn.Embedding

-

是

-

157

-

torch.nn.Embedding.from_pretrained

-

是

-

158

-

torch.nn.EmbeddingBag

-

否

-

159

-

torch.nn.EmbeddingBag.from_pretrained

-

否

-

160

-

torch.nn.CosineSimilarity

-

是

-

161

-

torch.nn.PairwiseDistance

-

是

-

162

-

torch.nn.L1Loss

-

是

-

163

-

torch.nn.MSELoss

-

是

-

164

-

torch.nn.CrossEntropyLoss

-

是

-

165

-

torch.nn.CTCLoss

-

是

-

166

-

torch.nn.NLLLoss

-

是

-

167

-

torch.nn.PoissonNLLLoss

-

是

-

168

-

torch.nn.KLDivLoss

-

是

-

169

-

torch.nn.BCELoss

-

是

-

170

-

torch.nn.BCEWithLogitsLoss

-

是

-

171

-

torch.nn.MarginRankingLoss

-

是

-

172

-

torch.nn.HingeEmbeddingLoss

-

是

-

173

-

torch.nn.MultiLabelMarginLoss

-

否

-

174

-

torch.nn.SmoothL1Loss

-

是

-

175

-

torch.nn.SoftMarginLoss

-

否

-

176

-

torch.nn.MultiLabelSoftMarginLoss

-

是

-

177

-

torch.nn.CosineEmbeddingLoss

-

是

-

178

-

torch.nn.MultiMarginLoss

-

否

-

179

-

torch.nn.TripletMarginLoss

-

是

-

180

-

torch.nn.PixelShuffle

-

是

-

181

-

torch.nn.Upsample

-

是

-

182

-

torch.nn.UpsamplingNearest2d

-

是

-

183

-

torch.nn.UpsamplingBilinear2d

-

是

-

184

-

torch.nn.DataParallel

-

否

-

185

-

torch.nn.parallel.DistributedDataParallel

-

是

-

186

-

torch.nn.parallel.DistributedDataParallel.no_sync

-

是

-

187

-

torch.nn.utils.clip_grad_norm_

-

否

-

188

-

torch.nn.utils.clip_grad_value_

-

否

-

189

-

torch.nn.utils.parameters_to_vector

-

是

-

190

-

torch.nn.utils.vector_to_parameters

-

是

-

197

-

torch.nn.utils.prune.PruningContainer

-

是

-

198

-

torch.nn.utils.prune.PruningContainer.add_pruning_method

-

否

-

199

-

torch.nn.utils.prune.PruningContainer.apply

-

是

-

200

-

torch.nn.utils.prune.PruningContainer.apply_mask

-

否

-

201

-

torch.nn.utils.prune.PruningContainer.compute_mask

-

是

-

202

-

torch.nn.utils.prune.PruningContainer.prune

-

是

-

203

-

torch.nn.utils.prune.PruningContainer.remove

-

否

-

204

-

torch.nn.utils.prune.Identity

-

是

-

205

-

torch.nn.utils.prune.Identity.apply

-

是

-

206

-

torch.nn.utils.prune.Identity.apply_mask

-

否

-

207

-

torch.nn.utils.prune.Identity.prune

-

是

-

208

-

torch.nn.utils.prune.Identity.remove

-

否

-

209

-

torch.nn.utils.prune.RandomUnstructured

-

是

-

210

-

torch.nn.utils.prune.RandomUnstructured.apply

-

否

-

211

-

torch.nn.utils.prune.RandomUnstructured.apply_mask

-

否

-

212

-

torch.nn.utils.prune.RandomUnstructured.prune

-

否

-

213

-

torch.nn.utils.prune.RandomUnstructured.remove

-

否

-

214

-

torch.nn.utils.prune.L1Unstructured

-

是

-

215

-

torch.nn.utils.prune.L1Unstructured.apply

-

否

-

216

-

torch.nn.utils.prune.L1Unstructured.apply_mask

-

否

-

217

-

torch.nn.utils.prune.L1Unstructured.prune

-

否

-

218

-

torch.nn.utils.prune.L1Unstructured.remove

-

否

-

219

-

torch.nn.utils.prune.RandomStructured

-

是

-

220

-

torch.nn.utils.prune.RandomStructured.apply

-

是

-

221

-

torch.nn.utils.prune.RandomStructured.apply_mask

-

否

-

222

-

torch.nn.utils.prune.RandomStructured.compute_mask

-

是

-

223

-

torch.nn.utils.prune.RandomStructured.prune

-

是

-

224

-

torch.nn.utils.prune.RandomStructured.remove

-

否

-

225

-

torch.nn.utils.prune.LnStructured

-

是

-

226

-

torch.nn.utils.prune.LnStructured.apply

-

否

-

227

-

torch.nn.utils.prune.LnStructured.apply_mask

-

否

-

228

-

torch.nn.utils.prune.LnStructured.compute_mask

-

否

-

229

-

torch.nn.utils.prune.LnStructured.prune

-

否

-

230

-

torch.nn.utils.prune.LnStructured.remove

-

否

-

231

-

torch.nn.utils.prune.CustomFromMask

-

是

-

232

-

torch.nn.utils.prune.CustomFromMask.apply

-

是

-

233

-

torch.nn.utils.prune.CustomFromMask.apply_mask

-

否

-

234

-

torch.nn.utils.prune.CustomFromMask.prune

-

是

-

235

-

torch.nn.utils.prune.CustomFromMask.remove

-

否

-

236

-

torch.nn.utils.prune.identity

-

是

-

237

-

torch.nn.utils.prune.random_unstructured

-

否

-

238

-

torch.nn.utils.prune.l1_unstructured

-

否

-

239

-

torch.nn.utils.prune.random_structured

-

是

-

240

-

torch.nn.utils.prune.ln_structured

-

否

-

241

-

torch.nn.utils.prune.global_unstructured

-

否

-

242

-

torch.nn.utils.prune.custom_from_mask

-

是

-

243

-

torch.nn.utils.prune.remove

-

是

-

244

-

torch.nn.utils.prune.is_pruned

-

是

-

245

-

torch.nn.utils.weight_norm

-

是

-

246

-

torch.nn.utils.remove_weight_norm

-

是

-

247

-

torch.nn.utils.spectral_norm

-

是

-

248

-

torch.nn.utils.remove_spectral_norm

-

否

-

249

-

torch.nn.utils.rnn.PackedSequence

-

是

-

250

-

torch.nn.utils.rnn.pack_padded_sequence

-

是

-

251

-

torch.nn.utils.rnn.pad_packed_sequence

-

否

-

252

-

torch.nn.utils.rnn.pad_sequence

-

是

-

253

-

torch.nn.utils.rnn.pack_sequence

-

否

-

254

-

torch.nn.Flatten

-

是

-

255

-

torch.quantization.quantize

-

否

-

256

-

torch.quantization.quantize_dynamic

-

否

-

257

-

torch.quantization.quantize_qat

-

否

-

258

-

torch.quantization.prepare

-

否

-

259

-

torch.quantization.prepare_qat

-

否

-

260

-

torch.quantization.convert

-

否

-

261

-

torch.quantization.QConfig

-

否

-

262

-

torch.quantization.QConfigDynamic

-

否

-

263

-

torch.quantization.fuse_modules

-

否

-

264

-

torch.quantization.QuantStub

-

否

-

265

-

torch.quantization.DeQuantStub

-

否

-

266

-

torch.quantization.QuantWrapper

-

否

-

267

-

torch.quantization.add_quant_dequant

-

否

-

268

-

torch.quantization.add_observer_

-

否

-

269

-

torch.quantization.swap_module

-

否

-

270

-

torch.quantization.propagate_qconfig_

-

否

-

271

-

torch.quantization.default_eval_fn

-

否

-

272

-

torch.quantization.MinMaxObserver

-

否

-

273

-

torch.quantization.MovingAverageMinMaxObserver

-

否

-

274

-

torch.quantization.PerChannelMinMaxObserver

-

否

-

275

-

torch.quantization.MovingAveragePerChannelMinMaxObserver

-

否

-

276

-

torch.quantization.HistogramObserver

-

否

-

277

-

torch.quantization.FakeQuantize

-

否

-

278

-

torch.quantization.NoopObserver

-

否

-

279

-

torch.quantization.get_observer_dict

-

否

-

280

-

torch.quantization.RecordingObserver

-

否

-

281

-

torch.nn.intrinsic.ConvBn2d

-

是

-

282

-

torch.nn.intrinsic.ConvBnReLU2d

-

是

-

283

-

torch.nn.intrinsic.ConvReLU2d

-

是

-

284

-

torch.nn.intrinsic.ConvReLU3d

-

否

-

285

-

torch.nn.intrinsic.LinearReLU

-

是

-

286

-

torch.nn.intrinsic.qat.ConvBn2d

-

否

-

287

-

torch.nn.intrinsic.qat.ConvBnReLU2d

-

否

-

288

-

torch.nn.intrinsic.qat.ConvReLU2d

-

否

-

289

-

torch.nn.intrinsic.qat.LinearReLU

-

否

-

290

-

torch.nn.intrinsic.quantized.ConvReLU2d

-

否

-

291

-

torch.nn.intrinsic.quantized.ConvReLU3d

-

否

-

292

-

torch.nn.intrinsic.quantized.LinearReLU

-

否

-

293

-

torch.nn.qat.Conv2d

-

否

-

294

-

torch.nn.qat.Conv2d.from_float

-

否

-

295

-

torch.nn.qat.Linear

-

否

-

296

-

torch.nn.qat.Linear.from_float

-

否

-

297

-

torch.nn.quantized.functional.relu

-

否

-

298

-

torch.nn.quantized.functional.linear

-

否

-

299

-

torch.nn.quantized.functional.conv2d

-

否

-

300

-

torch.nn.quantized.functional.conv3d

-

否

-

301

-

torch.nn.quantized.functional.max_pool2d

-

否

-

302

-

torch.nn.quantized.functional.adaptive_avg_pool2d

-

否

-

303

-

torch.nn.quantized.functional.avg_pool2d

-

否

-

304

-

torch.nn.quantized.functional.interpolate

-

否

-

305

-

torch.nn.quantized.functional.upsample

-

否

-

306

-

torch.nn.quantized.functional.upsample_bilinear

-

否

-

307

-

torch.nn.quantized.functional.upsample_nearest

-

否

-

308

-

torch.nn.quantized.ReLU

-

否

-

309

-

torch.nn.quantized.ReLU6

-

否

-

310

-

torch.nn.quantized.Conv2d

-

否

-

311

-

torch.nn.quantized.Conv2d.from_float

-

否

-

312

-

torch.nn.quantized.Conv3d

-

否

-

313

-

torch.nn.quantized.Conv3d.from_float

-

否

-

314

-

torch.nn.quantized.FloatFunctional

-

否

-

315

-

torch.nn.quantized.QFunctional

-

否

-

316

-

torch.nn.quantized.Quantize

-

否

-

317

-

torch.nn.quantized.DeQuantize

-

否

-

318

-

torch.nn.quantized.Linear

-

否

-

319

-

torch.nn.quantized.Linear.from_float

-

否

-

320

-

torch.nn.quantized.dynamic.Linear

-

否

-

321

-

torch.nn.quantized.dynamic.Linear.from_float

-

否

-

322

-

torch.nn.quantized.dynamic.LSTM

-

否

-
- -

Functions(torch.nn.functional)

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

序号

-

API名称

-

是否支持（PyTorch1.5.0）

-

1

-

torch.nn.functional.conv1d

-

是

-

2

-

torch.nn.functional.conv2d

-

是

-

3

-

torch.nn.functional.conv3d

-

是

-

4

-

torch.nn.functional.conv_transpose1d

-

是

-

5

-

torch.nn.functional.conv_transpose2d

-

是

-

6

-

torch.nn.functional.conv_transpose3d

-

否

-

7

-

torch.nn.functional.unfold

-

否

-

8

-

torch.nn.functional.fold

-

是

-

9

-

torch.nn.functional.avg_pool1d

-

是

-

10

-

torch.nn.functional.avg_pool2d

-

是

-

11

-

torch.nn.functional.avg_pool3d

-

是

-

12

-

torch.nn.functional.max_pool1d

-

是

-

13

-

torch.nn.functional.max_pool2d

-

是

-

14

-

torch.nn.functional.max_pool3d

-

是

-

15

-

torch.nn.functional.max_unpool1d

-

否

-

16

-

torch.nn.functional.max_unpool2d

-

否

-

17

-

torch.nn.functional.max_unpool3d

-

否

-

18

-

torch.nn.functional.lp_pool1d

-

是

-

19

-

torch.nn.functional.lp_pool2d

-

是

-

20

-

torch.nn.functional.adaptive_max_pool1d

-

是

-

21

-

torch.nn.functional.adaptive_max_pool2d

-

是

-

22

-

torch.nn.functional.adaptive_max_pool3d

-

否

-

23

-

torch.nn.functional.adaptive_avg_pool1d

-

是

-

24

-

torch.nn.functional.adaptive_avg_pool2d

-

是

-

25

-

torch.nn.functional.adaptive_avg_pool3d

-

是

-

26

-

torch.nn.functional.threshold

-

是

-

27

-

torch.nn.functional.threshold_

-

是

-

28

-

torch.nn.functional.relu

-

是

-

29

-

torch.nn.functional.relu_

-

是

-

30

-

torch.nn.functional.hardtanh

-

是

-

31

-

torch.nn.functional.hardtanh_

-

是

-

32

-

torch.nn.functional.relu6

-

是

-

33

-

torch.nn.functional.elu

-

是

-

34

-

torch.nn.functional.elu_

-

是

-

35

-

torch.nn.functional.selu

-

是

-

36

-

torch.nn.functional.celu

-

是

-

37

-

torch.nn.functional.leaky_relu

-

是

-

38

-

torch.nn.functional.leaky_relu_

-

是

-

39

-

torch.nn.functional.prelu

-

是

-

40

-

torch.nn.functional.rrelu

-

否

-

41

-

torch.nn.functional.rrelu_

-

否

-

42

-

torch.nn.functional.glu

-

是

-

43

-

torch.nn.functional.gelu

-

是

-

44

-

torch.nn.functional.logsigmoid

-

是

-

45

-

torch.nn.functional.hardshrink

-

是

-

46

-

torch.nn.functional.tanhshrink

-

是

-

47

-

torch.nn.functional.softsign

-

是

-

48

-

torch.nn.functional.softplus

-

是

-

49

-

torch.nn.functional.softmin

-

是

-

50

-

torch.nn.functional.softmax

-

是

-

51

-

torch.nn.functional.softshrink

-

否

-

52

-

torch.nn.functional.gumbel_softmax

-

否

-

53

-

torch.nn.functional.log_softmax

-

是

-

54

-

torch.nn.functional.tanh

-

是

-

55

-

torch.nn.functional.sigmoid

-

是

-

56

-

torch.nn.functional.batch_norm

-

是

-

57

-

torch.nn.functional.instance_norm

-

是

-

58

-

torch.nn.functional.layer_norm

-

是

-

59

-

torch.nn.functional.local_response_norm

-

是

-

60

-

torch.nn.functional.normalize

-

是

-

61

-

torch.nn.functional.linear

-

是

-

62

-

torch.nn.functional.bilinear

-

是

-

63

-

torch.nn.functional.dropout

-

是

-

64

-

torch.nn.functional.alpha_dropout

-

是

-

65

-

torch.nn.functional.dropout2d

-

否

-

66

-

torch.nn.functional.dropout3d

-

否

-

67

-

torch.nn.functional.embedding

-

是

-

68

-

torch.nn.functional.embedding_bag

-

否

-

69

-

torch.nn.functional.one_hot

-

是

-

70

-

torch.nn.functional.pairwise_distance

-

是

-

71

-

torch.nn.functional.cosine_similarity

-

是

-

72

-

torch.nn.functional.pdist

-

是

-

73

-

torch.nn.functional.binary_cross_entropy

-

是

-

74

-

torch.nn.functional.binary_cross_entropy_with_logits

-

是

-

75

-

torch.nn.functional.poisson_nll_loss

-

是

-

76

-

torch.nn.functional.cosine_embedding_loss

-

是

-

77

-

torch.nn.functional.cross_entropy

-

是

-

78

-

torch.nn.functional.ctc_loss

-

是

-

79

-

torch.nn.functional.hinge_embedding_loss

-

是

-

80

-

torch.nn.functional.kl_div

-

是

-

81

-

torch.nn.functional.l1_loss

-

是

-

82

-

torch.nn.functional.mse_loss

-

是

-

83

-

torch.nn.functional.margin_ranking_loss

-

是

-

84

-

torch.nn.functional.multilabel_margin_loss

-

是

-

85

-

torch.nn.functional.multilabel_soft_margin_loss

-

是

-

86

-

torch.nn.functional.multi_margin_loss

-

否

-

87

-

torch.nn.functional.nll_loss

-

是

-

88

-

torch.nn.functional.smooth_l1_loss

-

是

-

89

-

torch.nn.functional.soft_margin_loss

-

是

-

90

-

torch.nn.functional.triplet_margin_loss

-

是

-

91

-

torch.nn.functional.pixel_shuffle

-

是

-

92

-

torch.nn.functional.pad

-

是

-

93

-

torch.nn.functional.interpolate

-

否

-

94

-

torch.nn.functional.upsample

-

否

-

95

-

torch.nn.functional.upsample_nearest

-

否

-

96

-

torch.nn.functional.upsample_bilinear

-

是

-

97

-

torch.nn.functional.grid_sample

-

是

-

98

-

torch.nn.functional.affine_grid

-

否

-

99

-

torch.nn.parallel.data_parallel

-

否

-
- -

torch.distributed

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

序号

-

API名称

-

是否支持（PyTorch1.5.0）

-

1

-

torch.distributed.init_process_group

-

是

-

2

-

torch.distributed.Backend

-

是

-

3

-

torch.distributed.get_backend

-

是

-

4

-

torch.distributed.get_rank

-

是

-

5

-

torch.distributed.get_world_size

-

是

-

6

-

torch.distributed.is_initialized

-

是

-

7

-

torch.distributed.is_mpi_available

-

是

-

8

-

torch.distributed.is_nccl_available

-

是

-

9

-

torch.distributed.new_group

-

是

-

10

-

torch.distributed.send

-

否

-

11

-

torch.distributed.recv

-

否

-

12

-

torch.distributed.isend

-

否

-

13

-

torch.distributed.irecv

-

否

-

14

-

is_completed

-

是

-

15

-

wait

-

是

-

16

-

torch.distributed.broadcast

-

是

-

17

-

torch.distributed.all_reduce

-

是

-

18

-

torch.distributed.reduce

-

否

-

19

-

torch.distributed.all_gather

-

是

-

20

-

torch.distributed.gather

-

是

-

21

-

torch.distributed.scatter

-

是

-

22

-

torch.distributed.barrier

-

是

-

23

-

torch.distributed.ReduceOp

-

是

-

24

-

torch.distributed.reduce_op

-

是

-

25

-

torch.distributed.broadcast_multigpu

-

否

-

26

-

torch.distributed.all_reduce_multigpu

-

否

-

27

-

torch.distributed.reduce_multigpu

-

否

-

28

-

torch.distributed.all_gather_multigpu

-

否

-

29

-

torch.distributed.launch

-

是

-

30

-

torch.multiprocessing.spawn

-

是

-
- -

NPU和CUDA功能对齐

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

序号

-

API名称

-

npu对应API名称

-

是否支持（PyTorch1.5.0）

-

1

-

torch.cuda.current_blas_handle

-

torch.npu.current_blas_handle

-

否

-

2

-

torch.cuda.current_device

-

torch.npu.current_device

-

是

-

3

-

torch.cuda.current_stream

-

torch.npu.current_stream

-

是

-

4

-

torch.cuda.default_stream

-

torch.npu.default_stream

-

是

-

5

-

torch.cuda.device

-

torch.npu.device

-

否

-

6

-

torch.cuda.device_count

-

torch.npu.device_count

-

是

-

7

-

torch.cuda.device_of

-

torch.npu.device_of

-

否

-

8

-

torch.cuda.get_device_capability

-

torch.npu.get_device_capability

-

否

-

9

-

torch.cuda.get_device_name

-

torch.npu.get_device_name

-

否

-

10

-

torch.cuda.init

-

torch.npu.init

-

是

-

11

-

torch.cuda.ipc_collect

-

torch.npu.ipc_collect

-

否

-

12

-

torch.cuda.is_available

-

torch.npu.is_available

-

是

-

13

-

torch.cuda.is_initialized

-

torch.npu.is_initialized

-

是

-

14

-

torch.cuda.set_device

-

torch.npu.set_device

-

部分支持

-

15

-

torch.cuda.stream

-

torch.npu.stream

-

是

-

16

-

torch.cuda.synchronize

-

torch.npu.synchronize

-

是

-

17

-

torch.cuda.get_rng_state

-

torch.npu.get_rng_state

-

否

-

18

-

torch.cuda.get_rng_state_all

-

torch.npu.get_rng_state_all

-

否

-

19

-

torch.cuda.set_rng_state

-

torch.npu.set_rng_state

-

否

-

20

-

torch.cuda.set_rng_state_all

-

torch.npu.set_rng_state_all

-

否

-

21

-

torch.cuda.manual_seed

-

torch.npu.manual_seed

-

否

-

22

-

torch.cuda.manual_seed_all

-

torch.npu.manual_seed_all

-

否

-

23

-

torch.cuda.seed

-

torch.npu.seed

-

否

-

24

-

torch.cuda.seed_all

-

torch.npu.seed_all

-

否

-

25

-

torch.cuda.initial_seed

-

torch.npu.initial_seed

-

否

-

26

-

torch.cuda.comm.broadcast

-

torch.npu.comm.broadcast

-

否

-

27

-

torch.cuda.comm.broadcast_coalesced

-

torch.npu.comm.broadcast_coalesced

-

否

-

28

-

torch.cuda.comm.reduce_add

-

torch.npu.comm.reduce_add

-

否

-

29

-

torch.cuda.comm.scatter

-

torch.npu.comm.scatter

-

否

-

30

-

torch.cuda.comm.gather

-

torch.npu.comm.gather

-

否

-

31

-

torch.cuda.Stream

-

torch.npu.Stream

-

是

-

32

-

torch.cuda.Stream.query

-

torch.npu.Stream.query

-

否

-

33

-

torch.cuda.Stream.record_event

-

torch.npu.Stream.record_event

-

是

-

34

-

torch.cuda.Stream.synchronize

-

torch.npu.Stream.synchronize

-

是

-

35

-

torch.cuda.Stream.wait_event

-

torch.npu.Stream.wait_event

-

是

-

36

-

torch.cuda.Stream.wait_stream

-

torch.npu.Stream.wait_stream

-

是

-

37

-

torch.cuda.Event

-

torch.npu.Event

-

是

-

38

-

torch.cuda.Event.elapsed_time

-

torch.npu.Event.elapsed_time

-

是

-

39

-

torch.cuda.Event.from_ipc_handle

-

torch.npu.Event.from_ipc_handle

-

否

-

40

-

torch.cuda.Event.ipc_handle

-

torch.npu.Event.ipc_handle

-

否

-

41

-

torch.cuda.Event.query

-

torch.npu.Event.query

-

是

-

42

-

torch.cuda.Event.record

-

torch.npu.Event.record

-

是

-

43

-

torch.cuda.Event.synchronize

-

torch.npu.Event.synchronize

-

是

-

44

-

torch.cuda.Event.wait

-

torch.npu.Event.wait

-

是

-

45

-

torch.cuda.empty_cache

-

torch.npu.empty_cache

-

是

-

46

-

torch.cuda.memory_stats

-

torch.npu.memory_stats

-

是

-

47

-

torch.cuda.memory_summary

-

torch.npu.memory_summary

-

是

-

48

-

torch.cuda.memory_snapshot

-

torch.npu.memory_snapshot

-

是

-

49

-

torch.cuda.memory_allocated

-

torch.npu.memory_allocated

-

是

-

50

-

torch.cuda.max_memory_allocated

-

torch.npu.max_memory_allocated

-

是

-

51

-

torch.cuda.reset_max_memory_allocated

-

torch.npu.reset_max_memory_allocated

-

是

-

52

-

torch.cuda.memory_reserved

-

torch.npu.memory_reserved

-

是

-

53

-

torch.cuda.max_memory_reserved

-

torch.npu.max_memory_reserved

-

是

-

54

-

torch.cuda.memory_cached

-

torch.npu.memory_cached

-

是

-

55

-

torch.cuda.max_memory_cached

-

torch.npu.max_memory_cached

-

是

-

56

-

torch.cuda.reset_max_memory_cached

-

torch.npu.reset_max_memory_cached

-

是

-

57

-

torch.cuda.nvtx.mark

-

torch.npu.nvtx.mark

-

否

-

58

-

torch.cuda.nvtx.range_push

-

torch.npu.nvtx.range_push

-

否

-

59

-

torch.cuda.nvtx.range_pop

-

torch.npu.nvtx.range_pop

-

否

-

60

-

torch.cuda._sleep

-

torch.npu._sleep

-

否

-

61

-

torch.cuda.Stream.priority_range

-

torch.npu.Stream.priority_range

-

否

-

62

-

torch.cuda.get_device_properties

-

torch.npu.get_device_properties

-

否

-

63

-

torch.cuda.amp.GradScaler

-

torch.npu.amp.GradScaler

-

否

-
- ->![](public_sys-resources/icon-note.gif) **说明：** ->torch.npu.set\_device\(\)接口只支持在程序开始的位置通过set\_device进行指定，不支持多次指定和with torch.npu.device\(id\)方式的device切换 - diff --git "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225/public_sys-resources/icon-caution.gif" "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225/public_sys-resources/icon-caution.gif" deleted file mode 100644 index 6e90d7cfc2193e39e10bb58c38d01a23f045d571..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 580 zcmV-K0=xZ3Nk%w1VIu$?0Hp~4{QBgqmQ+MG9K51r{QB&)np^||1PlfQ%(86!{`~yv zv{XhUWKt}AZaiE{EOcHp{O-j3`t;<+eEiycJT4p@77X;(jQsMfB$R?oG%6hQ z+MMLZbQBH@)Vg&1^3?qHb(5!%>3r0+`eq=&V&E}0Dypi0000000000 z00000A^8LW000R9EC2ui03!e$000L5z=Uu}ED8YtqjJd<+B}(9bIOb$3-31_h|V>=0A{ z1Hh0#H30>fNT})^fRU_83uewx9oRr{f{Sx1Ml`t)EQ zGkHZ67&~y{W5Jpq4H_WfuLxp*3<7O}GEl;1ESe36fLNs=B0&LQM1Buf(R)qg(BRd`t1OPjI1m_q4 diff --git "a/docs/zh/PyTorch 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zt0stin4;pC8M{WLSlRH*1pzqEw1}3oOskyNN?j;7HD{BBZ*OEcv4HK!6Bk6beR+04 z&8}k>SkTusVTDmkyOz#5fCA$JTPGJVQvr3uZ?QzzPQFvD0rGf_PdrcF`pMs}p^BcF zKtKTd`0wipR%nKN&Wj+V}pX;WC3SdJV!a_8Qi zE7z`U*|Y^H0^}fB$R?oG%6hQ z+MMLZbQBH@)Vg&1^3?qHb(5!%>3r0+`eq=&V&E}0Dypi0000000000 z00000A^8LW000R9EC2ui03!e$000L5z=Uu}ED8YtqjJd<+B}(9bIOb$3-31_h|V>=0A{ z1Hh0#H30>fNT})^fRU_83uewx9oRr{f{Sx1Ml`t)EQ zGkHZ67&~y{W5Jpq4H_WfuLxp*3<7O}GEl;1ESe36fLNs=B0&LQM1Buf(R)qg(BRd`t1OPjI1m_q4 diff --git "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" index aafb7db50cd..1c6b798ced4 100644 --- "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" +++ "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" @@ -1191,7 +1191,7 @@ torch.npu.set_device()接口只支持在程序开始的位置通过set_device进行指定，不支持多次指定和with torch.npu.device(id)方式的device切换 -## NPU自定义算子清单 +## NPU自定义算子 | 序号 | PyTorch 算子（由昇腾开发） | 昇腾适配算子 | | ---- | ---------------------------------------------- | ---------------------------------------------- | @@ -1296,8 +1296,8 @@ torch.npu.set_device()接口只支持在程序开始的位置通过set_device进 count adam result. - Parameters： - - **beta1_power** (Number) - - - **beta2_power** (Number) - + - **beta1_power** (Number) - power of beta1. + - **beta2_power** (Number) - power of beta2. - **lr** (Number) - learning rate. - **beta1** (Number) - exponential decay rate for the 1st moment estimates. - **beta2** (Number) - exponential decay rate for the 2nd moment estimates. @@ -1305,9 +1305,9 @@ count adam result. - **grad** (Tensor) - the gradient. - **use_locking** (bool) - If `True` use locks for update operations. - **use_nesterov** (bool) -If `True`, uses the nesterov update. - - **var** (Tensor) - - - **m** (Tensor) - - - **v** (Tensor) - + - **var** (Tensor) - variables to be optimized. + - **m** (Tensor) - mean value of variables. + - **v** (Tensor) - variance of variables. - constraints： @@ -1327,12 +1327,12 @@ count adam result in bert. - **beta2** (Number) - exponential decay rate for the 2nd moment estimates. - **epsilon** (Number) - term added to the denominator to improve numerical stability. - **grad** (Tensor) - the gradient. - - **max_grad_norm** (Number) - - - **global_grad_norm** (Number) - + - **max_grad_norm** (Number) - maximum norm for the gradients. + - **global_grad_norm** (Number) - L2_norm for the gradients. - **weight_decay** (Number) - weight decay - - **var** (Tensor) - - - **m** (Tensor) - - - **v** (Tensor) - + - **var** (Tensor) - variables to be optimized. + - **m** (Tensor) -mean value of variables. + - **v** (Tensor) - variance of variables. - constraints： @@ -1340,7 +1340,35 @@ count adam result in bert. - Examples： - None + ```python + >>> var_in = torch.rand(321538).uniform_(-32.,21.).npu() + >>> var_in + tensor([ 0.6119, 5.8193, 3.0683, ..., -28.5832, 12.9402, -24.0488], + device='npu:0') + >>> m_in = torch.zeros(321538).npu() + >>> v_in = torchzeros(321538).npu() + >>> grad = torch.rand(321538).uniform_(-0.05,0.03).npu() + >>> grad + tensor([-0.0315, -0.0113, -0.0132, ..., 0.0106, -0.0226, -0.0252], + device='npu:0') + >>> max_grad_norm = -1. + >>> beta1 = 0.9 + >>> beta2 = 0.99 + >>> weight_decay = 0. + >>> lr = 0.1 + >>> epsilon = 1e-06 + >>> global_grad_norm = 0. + >>> var_out, m_out, v_out = torch.npu_bert_apply_adam(var_in, m_in, v_in, lr, beta1, beta2, epsilon, grad, max_grad_norm, global_grad_norm, weight_decay) + >>> var_out + tensor([ 0.7118, 5.9192, 3.1682, ..., -28.6831, 13.0402, -23.9489], + device='npu:0') + >>> m_out + tensor([-0.0032, -0.0011, -0.0013, ..., 0.0011, -0.0023, -0.0025], + device='npu:0') + >>> v_out + tensor([9.9431e-06, 1.2659e-06, 1.7328e-06, ..., 1.1206e-06, 5.0933e-06, + 6.3495e-06], device='npu:0') + ``` -- Gitee From 70de6408ca751b8232d882254f74039909eb9420 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Fri, 10 Dec 2021 17:05:42 +0800 Subject: [PATCH 24/55] updata --- ...\214\201\346\270\205\345\215\225_1.5.0.md" | 1566 ++++++++--------- ...\214\201\346\270\205\345\215\225_1.8.1.md" | 230 ++- 2 files changed, 894 insertions(+), 902 deletions(-) diff --git "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" index 1c6b798ced4..ec95cc153c0 100644 --- "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" +++ "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" @@ -1,6 +1,6 @@ ## [Tensors](https://pytorch.org/docs/1.5.0/torch.html) -| 序号 | API名称 | PyTorch1.5.0支持情况 | +| 序号 | API名称 | 支持情况 | | ---- | ----------------------------- | -------------------------------------- | | 1 | torch.is_tensor | 是 | | 2 | torch.is_storage | 是 | @@ -53,617 +53,617 @@ ## Generators -| 序号 | API名称 | 是否支持（PyTorch1.5.0） | -| ---- | ------------------------------- | ------------------------ | -| 1 | torch._C.Generator | 是 | -| 2 | torch._C.Generator.device | 是 | -| 3 | torch._C.Generator.get_state | 否 | -| 4 | torch._C.Generator.initial_seed | 是 | -| 5 | torch._C.Generator.manual_seed | 是 | -| 6 | torch._C.Generator.seed | 是 | -| 7 | torch._C.Generator.set_state | 否 | +| 序号 | API名称 | 是否支持 | +| ---- | ------------------------------- | -------- | +| 1 | torch._C.Generator | 是 | +| 2 | torch._C.Generator.device | 是 | +| 3 | torch._C.Generator.get_state | 否 | +| 4 | torch._C.Generator.initial_seed | 是 | +| 5 | torch._C.Generator.manual_seed | 是 | +| 6 | torch._C.Generator.seed | 是 | +| 7 | torch._C.Generator.set_state | 否 | ## Random sampling -| 序号 | API名称 | 是否支持（PyTorch1.5.0） | -| ---- | ------------------------------------------ | ------------------------ | -| 1 | torch.seed | 是 | -| 2 | torch.manual_seed | 是 | -| 3 | torch.initial_seed | 是 | -| 4 | torch.get_rng_state | 是 | -| 5 | torch.set_rng_state | 是 | -| 6 | torch.torch.default_generator | 是 | -| 7 | torch.bernoulli | 是 | -| 8 | torch.multinomial | 是 | -| 9 | torch.normal | 是 | -| 10 | torch.poisson | 否 | -| 11 | torch.rand | 是 | -| 12 | torch.rand_like | 是 | -| 13 | torch.randint | 是 | -| 14 | torch.randint_like | 是 | -| 15 | torch.randn | 是 | -| 16 | torch.randn_like | 是 | -| 17 | torch.randperm | 是 | -| 18 | torch.Tensor.bernoulli_() | 是 | -| 19 | torch.Tensor.bernoulli_() | 是 | -| 20 | torch.Tensor.exponential_() | 否 | -| 21 | torch.Tensor.geometric_() | 否 | -| 22 | torch.Tensor.log_normal_() | 否 | -| 23 | torch.Tensor.normal_() | 是 | -| 24 | torch.Tensor.random_() | 是 | -| 25 | torch.Tensor.uniform_() | 是 | -| 26 | torch.quasirandom.SobolEngine | 是 | -| 27 | torch.quasirandom.SobolEngine.draw | 是 | -| 28 | torch.quasirandom.SobolEngine.fast_forward | 是 | -| 29 | torch.quasirandom.SobolEngine.reset | 是 | +| 序号 | API名称 | 是否支持 | +| ---- | ------------------------------------------ | -------- | +| 1 | torch.seed | 是 | +| 2 | torch.manual_seed | 是 | +| 3 | torch.initial_seed | 是 | +| 4 | torch.get_rng_state | 是 | +| 5 | torch.set_rng_state | 是 | +| 6 | torch.torch.default_generator | 是 | +| 7 | torch.bernoulli | 是 | +| 8 | torch.multinomial | 是 | +| 9 | torch.normal | 是 | +| 10 | torch.poisson | 否 | +| 11 | torch.rand | 是 | +| 12 | torch.rand_like | 是 | +| 13 | torch.randint | 是 | +| 14 | torch.randint_like | 是 | +| 15 | torch.randn | 是 | +| 16 | torch.randn_like | 是 | +| 17 | torch.randperm | 是 | +| 18 | torch.Tensor.bernoulli_() | 是 | +| 19 | torch.Tensor.bernoulli_() | 是 | +| 20 | torch.Tensor.exponential_() | 否 | +| 21 | torch.Tensor.geometric_() | 否 | +| 22 | torch.Tensor.log_normal_() | 否 | +| 23 | torch.Tensor.normal_() | 是 | +| 24 | torch.Tensor.random_() | 是 | +| 25 | torch.Tensor.uniform_() | 是 | +| 26 | torch.quasirandom.SobolEngine | 是 | +| 27 | torch.quasirandom.SobolEngine.draw | 是 | +| 28 | torch.quasirandom.SobolEngine.fast_forward | 是 | +| 29 | torch.quasirandom.SobolEngine.reset | 是 | ## Serialization -| 序号 | API名称 | 是否支持（PyTorch1.5.0） | -| ---- | ---------- | ------------------------ | -| 1 | torch.save | 是 | -| 2 | torch.load | 是 | +| 序号 | API名称 | 是否支持 | +| ---- | ---------- | -------- | +| 1 | torch.save | 是 | +| 2 | torch.load | 是 | ## Math operations -| 序号 | API名称 | 是否支持（PyTorch1.5.0） | -| ---- | ------------------------ | ------------------------ | -| 1 | torch.abs | 是 | -| 2 | torch.acos | 是 | -| 3 | torch.add | 是 | -| 4 | torch.addcdiv | 是 | -| 5 | torch.addcmul | 是 | -| 6 | torch.angle | 否 | -| 7 | torch.asin | 是 | -| 8 | torch.atan | 是 | -| 9 | torch.atan2 | 是 | -| 10 | torch.bitwise_not | 是 | -| 11 | torch.bitwise_and | 是 | -| 12 | torch.bitwise_or | 是 | -| 13 | torch.bitwise_xor | 是 | -| 14 | torch.ceil | 是 | -| 15 | torch.clamp | 是 | -| 16 | torch.conj | 否 | -| 17 | torch.cos | 是 | -| 18 | torch.cosh | 是 | -| 19 | torch.div | 是 | -| 20 | torch.digamma | 否 | -| 21 | torch.erf | 是 | -| 22 | torch.erfc | 是 | -| 23 | torch.erfinv | 是 | -| 24 | torch.exp | 是 | -| 25 | torch.expm1 | 是 | -| 26 | torch.floor | 是 | -| 27 | torch.floor_divide | 是 | -| 28 | torch.fmod | 是 | -| 29 | torch.frac | 是 | -| 30 | torch.imag | 否 | -| 31 | torch.lerp | 是 | -| 32 | torch.lgamma | 否 | -| 33 | torch.log | 是 | -| 34 | torch.log10 | 是 | -| 35 | torch.log1p | 是 | -| 36 | torch.log2 | 是 | -| 37 | torch.logical_and | 是 | -| 38 | torch.logical_not | 是 | -| 39 | torch.logical_or | 是 | -| 40 | torch.logical_xor | 是 | -| 41 | torch.mul | 是 | -| 42 | torch.mvlgamma | 否 | -| 43 | torch.neg | 是 | -| 44 | torch.polygamma | 否 | -| 45 | torch.pow | 是 | -| 46 | torch.real | 是 | -| 47 | torch.reciprocal | 是 | -| 48 | torch.remainder | 是 | -| 49 | torch.round | 是 | -| 50 | torch.rsqrt | 是 | -| 51 | torch.sigmoid | 是 | -| 52 | torch.sign | 是 | -| 53 | torch.sin | 是 | -| 54 | torch.sinh | 是 | -| 55 | torch.sqrt | 是 | -| 56 | torch.square | 是 | -| 57 | torch.tan | 是 | -| 58 | torch.tanh | 是 | -| 59 | torch.true_divide | 是 | -| 60 | torch.trunc | 是 | -| 61 | torch.argmax | 是 | -| 62 | torch.argmin | 是 | -| 63 | torch.dist | 是 | -| 64 | torch.logsumexp | 是 | -| 65 | torch.mean | 是 | -| 66 | torch.median | 是 | -| 67 | torch.mode | 否 | -| 68 | torch.norm | 是 | -| 69 | torch.prod | 是 | -| 70 | torch.std | 是 | -| 71 | torch.std_mean | 是 | -| 72 | torch.sum | 是 | -| 73 | torch.unique | 是 | -| 74 | torch.unique_consecutive | 否 | -| 75 | torch.var | 否 | -| 76 | torch.var_mean | 否 | -| 77 | torch.allclose | 是 | -| 78 | torch.argsort | 是 | -| 79 | torch.eq | 是 | -| 80 | torch.equal | 是 | -| 81 | torch.ge | 是 | -| 82 | torch.gt | 是 | -| 83 | torch.isfinite | 是 | -| 84 | torch.isinf | 是 | -| 85 | torch.isnan | 是 | -| 86 | torch.kthvalue | 是 | -| 87 | torch.le | 是 | -| 88 | torch.lt | 是 | -| 89 | torch.max | 是 | -| 90 | torch.min | 是 | -| 91 | torch.ne | 是 | -| 92 | torch.sort | 是 | -| 93 | torch.topk | 是 | -| 94 | torch.fft | 否 | -| 95 | torch.ifft | 否 | -| 96 | torch.rfft | 否 | -| 97 | torch.irfft | 否 | -| 98 | torch.stft | 否 | -| 99 | torch.bartlett_window | 是 | -| 100 | torch.blackman_window | 是 | -| 101 | torch.hamming_window | 是 | -| 102 | torch.hann_window | 是 | -| 103 | torch.bincount | 是 | -| 104 | torch.broadcast_tensors | 是 | -| 105 | torch.cartesian_prod | 是 | -| 106 | torch.cdist | 是 | -| 107 | torch.combinations | 否 | -| 108 | torch.cross | 是 | -| 109 | torch.cummax | 是 | -| 110 | torch.cummin | 是 | -| 111 | torch.cumprod | 是 | -| 112 | torch.cumsum | 是 | -| 113 | torch.diag | 是 | -| 114 | torch.diag_embed | 是 | -| 115 | torch.diagflat | 是 | -| 116 | torch.diagonal | 是 | -| 117 | torch.einsum | 是 | -| 118 | torch.flatten | 是 | -| 119 | torch.flip | 是 | -| 120 | torch.rot90 | 是 | -| 121 | torch.histc | 否 | -| 122 | torch.meshgrid | 是 | -| 123 | torch.renorm | 是 | -| 124 | torch.repeat_interleave | 是 | -| 125 | torch.roll | 是 | -| 126 | torch.tensordot | 是 | -| 127 | torch.trace | 否 | -| 128 | torch.tril | 是 | -| 129 | torch.tril_indices | 是 | -| 130 | torch.triu | 是 | -| 131 | torch.triu_indices | 是 | -| 132 | torch.addbmm | 是 | -| 133 | torch.addmm | 是 | -| 134 | torch.addmv | 是 | -| 135 | torch.addr | 是 | -| 136 | torch.baddbmm | 是 | -| 137 | torch.bmm | 是 | -| 138 | torch.chain_matmul | 是 | -| 139 | torch.cholesky | 否 | -| 140 | torch.cholesky_inverse | 否 | -| 141 | torch.cholesky_solve | 否 | -| 142 | torch.dot | 是 | -| 143 | torch.eig | 否 | -| 144 | torch.geqrf | 否 | -| 145 | torch.ger | 是 | -| 146 | torch.inverse | 是 | -| 147 | torch.det | 否 | -| 148 | torch.logdet | 否 | -| 149 | torch.slogdet | 是 | -| 150 | torch.lstsq | 否 | -| 151 | torch.lu | 否 | -| 152 | torch.lu_solve | 否 | -| 153 | torch.lu_unpack | 否 | -| 154 | torch.matmul | 是 | -| 155 | torch.matrix_power | 是 | -| 156 | torch.matrix_rank | 是 | -| 157 | torch.mm | 是 | -| 158 | torch.mv | 是 | -| 159 | torch.orgqr | 否 | -| 160 | torch.ormqr | 否 | -| 161 | torch.pinverse | 是 | -| 162 | torch.qr | 是 | -| 163 | torch.solve | 否 | -| 164 | torch.svd | 是 | -| 165 | torch.svd_lowrank | 是 | -| 166 | torch.pca_lowrank | 是 | -| 167 | torch.symeig | 是 | -| 168 | torch.lobpcg | 否 | -| 169 | torch.trapz | 是 | -| 170 | torch.triangular_solve | 是 | +| 序号 | API名称 | 是否支持 | +| ---- | ------------------------ | -------- | +| 1 | torch.abs | 是 | +| 2 | torch.acos | 是 | +| 3 | torch.add | 是 | +| 4 | torch.addcdiv | 是 | +| 5 | torch.addcmul | 是 | +| 6 | torch.angle | 否 | +| 7 | torch.asin | 是 | +| 8 | torch.atan | 是 | +| 9 | torch.atan2 | 是 | +| 10 | torch.bitwise_not | 是 | +| 11 | torch.bitwise_and | 是 | +| 12 | torch.bitwise_or | 是 | +| 13 | torch.bitwise_xor | 是 | +| 14 | torch.ceil | 是 | +| 15 | torch.clamp | 是 | +| 16 | torch.conj | 否 | +| 17 | torch.cos | 是 | +| 18 | torch.cosh | 是 | +| 19 | torch.div | 是 | +| 20 | torch.digamma | 否 | +| 21 | torch.erf | 是 | +| 22 | torch.erfc | 是 | +| 23 | torch.erfinv | 是 | +| 24 | torch.exp | 是 | +| 25 | torch.expm1 | 是 | +| 26 | torch.floor | 是 | +| 27 | torch.floor_divide | 是 | +| 28 | torch.fmod | 是 | +| 29 | torch.frac | 是 | +| 30 | torch.imag | 否 | +| 31 | torch.lerp | 是 | +| 32 | torch.lgamma | 否 | +| 33 | torch.log | 是 | +| 34 | torch.log10 | 是 | +| 35 | torch.log1p | 是 | +| 36 | torch.log2 | 是 | +| 37 | torch.logical_and | 是 | +| 38 | torch.logical_not | 是 | +| 39 | torch.logical_or | 是 | +| 40 | torch.logical_xor | 是 | +| 41 | torch.mul | 是 | +| 42 | torch.mvlgamma | 否 | +| 43 | torch.neg | 是 | +| 44 | torch.polygamma | 否 | +| 45 | torch.pow | 是 | +| 46 | torch.real | 是 | +| 47 | torch.reciprocal | 是 | +| 48 | torch.remainder | 是 | +| 49 | torch.round | 是 | +| 50 | torch.rsqrt | 是 | +| 51 | torch.sigmoid | 是 | +| 52 | torch.sign | 是 | +| 53 | torch.sin | 是 | +| 54 | torch.sinh | 是 | +| 55 | torch.sqrt | 是 | +| 56 | torch.square | 是 | +| 57 | torch.tan | 是 | +| 58 | torch.tanh | 是 | +| 59 | torch.true_divide | 是 | +| 60 | torch.trunc | 是 | +| 61 | torch.argmax | 是 | +| 62 | torch.argmin | 是 | +| 63 | torch.dist | 是 | +| 64 | torch.logsumexp | 是 | +| 65 | torch.mean | 是 | +| 66 | torch.median | 是 | +| 67 | torch.mode | 否 | +| 68 | torch.norm | 是 | +| 69 | torch.prod | 是 | +| 70 | torch.std | 是 | +| 71 | torch.std_mean | 是 | +| 72 | torch.sum | 是 | +| 73 | torch.unique | 是 | +| 74 | torch.unique_consecutive | 否 | +| 75 | torch.var | 否 | +| 76 | torch.var_mean | 否 | +| 77 | torch.allclose | 是 | +| 78 | torch.argsort | 是 | +| 79 | torch.eq | 是 | +| 80 | torch.equal | 是 | +| 81 | torch.ge | 是 | +| 82 | torch.gt | 是 | +| 83 | torch.isfinite | 是 | +| 84 | torch.isinf | 是 | +| 85 | torch.isnan | 是 | +| 86 | torch.kthvalue | 是 | +| 87 | torch.le | 是 | +| 88 | torch.lt | 是 | +| 89 | torch.max | 是 | +| 90 | torch.min | 是 | +| 91 | torch.ne | 是 | +| 92 | torch.sort | 是 | +| 93 | torch.topk | 是 | +| 94 | torch.fft | 否 | +| 95 | torch.ifft | 否 | +| 96 | torch.rfft | 否 | +| 97 | torch.irfft | 否 | +| 98 | torch.stft | 否 | +| 99 | torch.bartlett_window | 是 | +| 100 | torch.blackman_window | 是 | +| 101 | torch.hamming_window | 是 | +| 102 | torch.hann_window | 是 | +| 103 | torch.bincount | 是 | +| 104 | torch.broadcast_tensors | 是 | +| 105 | torch.cartesian_prod | 是 | +| 106 | torch.cdist | 是 | +| 107 | torch.combinations | 否 | +| 108 | torch.cross | 是 | +| 109 | torch.cummax | 是 | +| 110 | torch.cummin | 是 | +| 111 | torch.cumprod | 是 | +| 112 | torch.cumsum | 是 | +| 113 | torch.diag | 是 | +| 114 | torch.diag_embed | 是 | +| 115 | torch.diagflat | 是 | +| 116 | torch.diagonal | 是 | +| 117 | torch.einsum | 是 | +| 118 | torch.flatten | 是 | +| 119 | torch.flip | 是 | +| 120 | torch.rot90 | 是 | +| 121 | torch.histc | 否 | +| 122 | torch.meshgrid | 是 | +| 123 | torch.renorm | 是 | +| 124 | torch.repeat_interleave | 是 | +| 125 | torch.roll | 是 | +| 126 | torch.tensordot | 是 | +| 127 | torch.trace | 否 | +| 128 | torch.tril | 是 | +| 129 | torch.tril_indices | 是 | +| 130 | torch.triu | 是 | +| 131 | torch.triu_indices | 是 | +| 132 | torch.addbmm | 是 | +| 133 | torch.addmm | 是 | +| 134 | torch.addmv | 是 | +| 135 | torch.addr | 是 | +| 136 | torch.baddbmm | 是 | +| 137 | torch.bmm | 是 | +| 138 | torch.chain_matmul | 是 | +| 139 | torch.cholesky | 否 | +| 140 | torch.cholesky_inverse | 否 | +| 141 | torch.cholesky_solve | 否 | +| 142 | torch.dot | 是 | +| 143 | torch.eig | 否 | +| 144 | torch.geqrf | 否 | +| 145 | torch.ger | 是 | +| 146 | torch.inverse | 是 | +| 147 | torch.det | 否 | +| 148 | torch.logdet | 否 | +| 149 | torch.slogdet | 是 | +| 150 | torch.lstsq | 否 | +| 151 | torch.lu | 否 | +| 152 | torch.lu_solve | 否 | +| 153 | torch.lu_unpack | 否 | +| 154 | torch.matmul | 是 | +| 155 | torch.matrix_power | 是 | +| 156 | torch.matrix_rank | 是 | +| 157 | torch.mm | 是 | +| 158 | torch.mv | 是 | +| 159 | torch.orgqr | 否 | +| 160 | torch.ormqr | 否 | +| 161 | torch.pinverse | 是 | +| 162 | torch.qr | 是 | +| 163 | torch.solve | 否 | +| 164 | torch.svd | 是 | +| 165 | torch.svd_lowrank | 是 | +| 166 | torch.pca_lowrank | 是 | +| 167 | torch.symeig | 是 | +| 168 | torch.lobpcg | 否 | +| 169 | torch.trapz | 是 | +| 170 | torch.triangular_solve | 是 | ## Utilities -| 序号 | API名称 | 是否支持（PyTorch1.5.0） | -| ---- | ----------------------------- | ------------------------ | -| 1 | torch.compiled_with_cxx11_abi | 是 | -| 2 | torch.result_type | 是 | -| 3 | torch.can_cast | 是 | -| 4 | torch.promote_types | 是 | +| 序号 | API名称 | 是否支持 | +| ---- | ----------------------------- | -------- | +| 1 | torch.compiled_with_cxx11_abi | 是 | +| 2 | torch.result_type | 是 | +| 3 | torch.can_cast | 是 | +| 4 | torch.promote_types | 是 | ## Other -| 序号 | API名称 | 是否支持（PyTorch1.5.0） | -| ---- | ----------------------------- | ------------------------ | -| 1 | torch.no_grad | 是 | -| 2 | torch.enable_grad | 是 | -| 3 | torch.set_grad_enabled | 是 | -| 4 | torch.get_num_threads | 是 | -| 5 | torch.set_num_threads | 是 | -| 6 | torch.get_num_interop_threads | 是 | -| 7 | torch.set_num_interop_threads | 是 | +| 序号 | API名称 | 是否支持 | +| ---- | ----------------------------- | -------- | +| 1 | torch.no_grad | 是 | +| 2 | torch.enable_grad | 是 | +| 3 | torch.set_grad_enabled | 是 | +| 4 | torch.get_num_threads | 是 | +| 5 | torch.set_num_threads | 是 | +| 6 | torch.get_num_interop_threads | 是 | +| 7 | torch.set_num_interop_threads | 是 | ## torch.Tensor -| 序号 | API名称 | 是否支持（PyTorch1.5.0） | -| ---- | -------------------------------------- | ------------------------ | -| 1 | torch.Tensor | 是 | -| 2 | torch.Tensor.new_tensor | 是 | -| 3 | torch.Tensor.new_full | 是 | -| 4 | torch.Tensor.new_empty | 是 | -| 5 | torch.Tensor.new_ones | 是 | -| 6 | torch.Tensor.new_zeros | 是 | -| 7 | torch.Tensor.is_cuda | 是 | -| 8 | torch.Tensor.is_quantized | 是 | -| 9 | torch.Tensor.device | 是 | -| 10 | torch.Tensor.ndim | 是 | -| 11 | torch.Tensor.T | 是 | -| 12 | torch.Tensor.abs | 是 | -| 13 | torch.Tensor.abs_ | 是 | -| 14 | torch.Tensor.acos | 是 | -| 15 | torch.Tensor.acos_ | 是 | -| 16 | torch.Tensor.add | 是 | -| 17 | torch.Tensor.add_ | 是 | -| 18 | torch.Tensor.addbmm | 是 | -| 19 | torch.Tensor.addbmm_ | 是 | -| 20 | torch.Tensor.addcdiv | 是 | -| 21 | torch.Tensor.addcdiv_ | 是 | -| 22 | torch.Tensor.addcmul | 是 | -| 23 | torch.Tensor.addcmul_ | 是 | -| 24 | torch.Tensor.addmm | 是 | -| 25 | torch.Tensor.addmm_ | 是 | -| 26 | torch.Tensor.addmv | 是 | -| 27 | torch.Tensor.addmv_ | 是 | -| 28 | torch.Tensor.addr | 是 | -| 29 | torch.Tensor.addr_ | 是 | -| 30 | torch.Tensor.allclose | 是 | -| 31 | torch.Tensor.angle | 否 | -| 32 | torch.Tensor.apply_ | 否 | -| 33 | torch.Tensor.argmax | 是 | -| 34 | torch.Tensor.argmin | 是 | -| 35 | torch.Tensor.argsort | 是 | -| 36 | torch.Tensor.asin | 是 | -| 37 | torch.Tensor.asin_ | 是 | -| 38 | torch.Tensor.as_strided | 是 | -| 39 | torch.Tensor.atan | 是 | -| 40 | torch.Tensor.atan2 | 是 | -| 41 | torch.Tensor.atan2_ | 是 | -| 42 | torch.Tensor.atan_ | 是 | -| 43 | torch.Tensor.baddbmm | 是 | -| 44 | torch.Tensor.baddbmm_ | 是 | -| 45 | torch.Tensor.bernoulli | 是 | -| 46 | torch.Tensor.bernoulli_ | 是 | -| 47 | torch.Tensor.bfloat16 | 否 | -| 48 | torch.Tensor.bincount | 是 | -| 49 | torch.Tensor.bitwise_not | 是 | -| 50 | torch.Tensor.bitwise_not_ | 是 | -| 51 | torch.Tensor.bitwise_and | 是 | -| 52 | torch.Tensor.bitwise_and_ | 是 | -| 53 | torch.Tensor.bitwise_or | 是 | -| 54 | torch.Tensor.bitwise_or_ | 是 | -| 55 | torch.Tensor.bitwise_xor | 是 | -| 56 | torch.Tensor.bitwise_xor_ | 是 | -| 57 | torch.Tensor.bmm | 是 | -| 58 | torch.Tensor.bool | 是 | -| 59 | torch.Tensor.byte | 是 | -| 60 | torch.Tensor.cauchy_ | 否 | -| 61 | torch.Tensor.ceil | 是 | -| 62 | torch.Tensor.ceil_ | 是 | -| 63 | torch.Tensor.char | 是 | -| 64 | torch.Tensor.cholesky | 否 | -| 65 | torch.Tensor.cholesky_inverse | 否 | -| 66 | torch.Tensor.cholesky_solve | 否 | -| 67 | torch.Tensor.chunk | 是 | -| 68 | torch.Tensor.clamp | 是 | -| 69 | torch.Tensor.clamp_ | 是 | -| 70 | torch.Tensor.clone | 是 | -| 71 | torch.Tensor.contiguous | 是 | -| 72 | torch.Tensor.copy_ | 是 | -| 73 | torch.Tensor.conj | 否 | -| 74 | torch.Tensor.cos | 是 | -| 75 | torch.Tensor.cos_ | 是 | -| 76 | torch.Tensor.cosh | 是 | -| 77 | torch.Tensor.cosh_ | 是 | -| 78 | torch.Tensor.cpu | 是 | -| 79 | torch.Tensor.cross | 是 | -| 80 | torch.Tensor.cuda | 否 | -| 81 | torch.Tensor.cummax | 是 | -| 82 | torch.Tensor.cummin | 是 | -| 83 | torch.Tensor.cumprod | 是 | -| 84 | torch.Tensor.cumsum | 是 | -| 85 | torch.Tensor.data_ptr | 是 | -| 86 | torch.Tensor.dequantize | 否 | -| 87 | torch.Tensor.det | 否 | -| 88 | torch.Tensor.dense_dim | 否 | -| 89 | torch.Tensor.diag | 是 | -| 90 | torch.Tensor.diag_embed | 是 | -| 91 | torch.Tensor.diagflat | 是 | -| 92 | torch.Tensor.diagonal | 是 | -| 93 | torch.Tensor.fill_diagonal_ | 是 | -| 94 | torch.Tensor.digamma | 否 | -| 95 | torch.Tensor.digamma_ | 否 | -| 96 | torch.Tensor.dim | 是 | -| 97 | torch.Tensor.dist | 是 | -| 98 | torch.Tensor.div | 是 | -| 99 | torch.Tensor.div_ | 是 | -| 100 | torch.Tensor.dot | 是 | -| 101 | torch.Tensor.double | 否 | -| 102 | torch.Tensor.eig | 否 | -| 103 | torch.Tensor.element_size | 是 | -| 104 | torch.Tensor.eq | 是 | -| 105 | torch.Tensor.eq_ | 是 | -| 106 | torch.Tensor.equal | 是 | -| 107 | torch.Tensor.erf | 是 | -| 108 | torch.Tensor.erf_ | 是 | -| 109 | torch.Tensor.erfc | 是 | -| 110 | torch.Tensor.erfc_ | 是 | -| 111 | torch.Tensor.erfinv | 是 | -| 112 | torch.Tensor.erfinv_ | 是 | -| 113 | torch.Tensor.exp | 是 | -| 114 | torch.Tensor.exp_ | 是 | -| 115 | torch.Tensor.expm1 | 是 | -| 116 | torch.Tensor.expm1_ | 是 | -| 117 | torch.Tensor.expand | 是 | -| 118 | torch.Tensor.expand_as | 是 | -| 119 | torch.Tensor.exponential_ | 否 | -| 120 | torch.Tensor.fft | 否 | -| 121 | torch.Tensor.fill_ | 是 | -| 122 | torch.Tensor.flatten | 是 | -| 123 | torch.Tensor.flip | 是 | -| 124 | torch.Tensor.float | 是 | -| 125 | torch.Tensor.floor | 是 | -| 126 | torch.Tensor.floor_ | 是 | -| 127 | torch.Tensor.floor_divide | 是 | -| 128 | torch.Tensor.floor_divide_ | 是 | -| 129 | torch.Tensor.fmod | 是 | -| 130 | torch.Tensor.fmod_ | 是 | -| 131 | torch.Tensor.frac | 是 | -| 132 | torch.Tensor.frac_ | 是 | -| 133 | torch.Tensor.gather | 是 | -| 134 | torch.Tensor.ge | 是 | -| 135 | torch.Tensor.ge_ | 是 | -| 136 | torch.Tensor.geometric_ | 否 | -| 137 | torch.Tensor.geqrf | 否 | -| 138 | torch.Tensor.ger | 是 | -| 139 | torch.Tensor.get_device | 是 | -| 140 | torch.Tensor.gt | 是 | -| 141 | torch.Tensor.gt_ | 是 | -| 142 | torch.Tensor.half | 是 | -| 143 | torch.Tensor.hardshrink | 是 | -| 144 | torch.Tensor.histc | 否 | -| 145 | torch.Tensor.ifft | 否 | -| 146 | torch.Tensor.index_add_ | 是 | -| 147 | torch.Tensor.index_add | 是 | -| 148 | torch.Tensor.index_copy_ | 是 | -| 149 | torch.Tensor.index_copy | 是 | -| 150 | torch.Tensor.index_fill_ | 是 | -| 151 | torch.Tensor.index_fill | 是 | -| 152 | torch.Tensor.index_put_ | 是 | -| 153 | torch.Tensor.index_put | 是 | -| 154 | torch.Tensor.index_select | 是 | -| 155 | torch.Tensor.indices | 否 | -| 156 | torch.Tensor.int | 是 | -| 157 | torch.Tensor.int_repr | 否 | -| 158 | torch.Tensor.inverse | 是 | -| 159 | torch.Tensor.irfft | 否 | -| 160 | torch.Tensor.is_contiguous | 是 | -| 161 | torch.Tensor.is_complex | 是 | -| 162 | torch.Tensor.is_floating_point | 是 | -| 163 | torch.Tensor.is_pinned | 是 | -| 164 | torch.Tensor.is_set_to | 否 | -| 165 | torch.Tensor.is_shared | 是 | -| 166 | torch.Tensor.is_signed | 是 | -| 167 | torch.Tensor.is_sparse | 是 | -| 168 | torch.Tensor.item | 是 | -| 169 | torch.Tensor.kthvalue | 是 | -| 170 | torch.Tensor.le | 是 | -| 171 | torch.Tensor.le_ | 是 | -| 172 | torch.Tensor.lerp | 是 | -| 173 | torch.Tensor.lerp_ | 是 | -| 174 | torch.Tensor.lgamma | 否 | -| 175 | torch.Tensor.lgamma_ | 否 | -| 176 | torch.Tensor.log | 是 | -| 177 | torch.Tensor.log_ | 是 | -| 178 | torch.Tensor.logdet | 否 | -| 179 | torch.Tensor.log10 | 是 | -| 180 | torch.Tensor.log10_ | 是 | -| 181 | torch.Tensor.log1p | 是 | -| 182 | torch.Tensor.log1p_ | 是 | -| 183 | torch.Tensor.log2 | 是 | -| 184 | torch.Tensor.log2_ | 是 | -| 185 | torch.Tensor.log_normal_ | 是 | -| 186 | torch.Tensor.logsumexp | 是 | -| 187 | torch.Tensor.logical_and | 是 | -| 188 | torch.Tensor.logical_and_ | 是 | -| 189 | torch.Tensor.logical_not | 是 | -| 190 | torch.Tensor.logical_not_ | 是 | -| 191 | torch.Tensor.logical_or | 是 | -| 192 | torch.Tensor.logical_or_ | 是 | -| 193 | torch.Tensor.logical_xor | 否 | -| 194 | torch.Tensor.logical_xor_ | 否 | -| 195 | torch.Tensor.long | 是 | -| 196 | torch.Tensor.lstsq | 否 | -| 197 | torch.Tensor.lt | 是 | -| 198 | torch.Tensor.lt_ | 是 | -| 199 | torch.Tensor.lu | 是 | -| 200 | torch.Tensor.lu_solve | 是 | -| 201 | torch.Tensor.map_ | 否 | -| 202 | torch.Tensor.masked_scatter_ | 是 | -| 203 | torch.Tensor.masked_scatter | 是 | -| 204 | torch.Tensor.masked_fill_ | 是 | -| 205 | torch.Tensor.masked_fill | 是 | -| 206 | torch.Tensor.masked_select | 是 | -| 207 | torch.Tensor.matmul | 是 | -| 208 | torch.Tensor.matrix_power | 是 | -| 209 | torch.Tensor.max | 是 | -| 210 | torch.Tensor.mean | 是 | -| 211 | torch.Tensor.median | 是 | -| 212 | torch.Tensor.min | 是 | -| 213 | torch.Tensor.mm | 是 | -| 214 | torch.Tensor.mode | 否 | -| 215 | torch.Tensor.mul | 是 | -| 216 | torch.Tensor.mul_ | 是 | -| 217 | torch.Tensor.multinomial | 是 | -| 218 | torch.Tensor.mv | 是 | -| 219 | torch.Tensor.mvlgamma | 否 | -| 220 | torch.Tensor.mvlgamma_ | 否 | -| 221 | torch.Tensor.narrow | 是 | -| 222 | torch.Tensor.narrow_copy | 是 | -| 223 | torch.Tensor.ndimension | 是 | -| 224 | torch.Tensor.ne | 是 | -| 225 | torch.Tensor.ne_ | 是 | -| 226 | torch.Tensor.neg | 是 | -| 227 | torch.Tensor.neg_ | 是 | -| 228 | torch.Tensor.nelement | 是 | -| 229 | torch.Tensor.nonzero | 是 | -| 230 | torch.Tensor.norm | 是 | -| 231 | torch.Tensor.normal_ | 是 | -| 232 | torch.Tensor.numel | 是 | -| 233 | torch.Tensor.numpy | 否 | -| 234 | torch.Tensor.orgqr | 否 | -| 235 | torch.Tensor.ormqr | 否 | -| 236 | torch.Tensor.permute | 是 | -| 237 | torch.Tensor.pin_memory | 否 | -| 238 | torch.Tensor.pinverse | 是 | -| 239 | torch.Tensor.polygamma | 否 | -| 240 | torch.Tensor.polygamma_ | 否 | -| 241 | torch.Tensor.pow | 是 | -| 242 | torch.Tensor.pow_ | 是 | -| 243 | torch.Tensor.prod | 是 | -| 244 | torch.Tensor.put_ | 是 | -| 245 | torch.Tensor.qr | 是 | -| 246 | torch.Tensor.qscheme | 否 | -| 247 | torch.Tensor.q_scale | 否 | -| 248 | torch.Tensor.q_zero_point | 否 | -| 249 | torch.Tensor.q_per_channel_scales | 否 | -| 250 | torch.Tensor.q_per_channel_zero_points | 否 | -| 251 | torch.Tensor.q_per_channel_axis | 否 | -| 252 | torch.Tensor.random_ | 是 | -| 253 | torch.Tensor.reciprocal | 是 | -| 254 | torch.Tensor.reciprocal_ | 是 | -| 255 | torch.Tensor.record_stream | 是 | -| 256 | torch.Tensor.remainder | 是 | -| 257 | torch.Tensor.remainder_ | 是 | -| 258 | torch.Tensor.renorm | 是 | -| 259 | torch.Tensor.renorm_ | 是 | -| 260 | torch.Tensor.repeat | 是 | -| 261 | torch.Tensor.repeat_interleave | 是 | -| 262 | torch.Tensor.requires_grad_ | 是 | -| 263 | torch.Tensor.reshape | 是 | -| 264 | torch.Tensor.reshape_as | 是 | -| 265 | torch.Tensor.resize_ | 是 | -| 266 | torch.Tensor.resize_as_ | 是 | -| 267 | torch.Tensor.rfft | 否 | -| 268 | torch.Tensor.roll | 是 | -| 269 | torch.Tensor.rot90 | 是 | -| 270 | torch.Tensor.round | 是 | -| 271 | torch.Tensor.round_ | 是 | -| 272 | torch.Tensor.rsqrt | 是 | -| 273 | torch.Tensor.rsqrt_ | 是 | -| 274 | torch.Tensor.scatter | 是 | -| 275 | torch.Tensor.scatter_ | 是 | -| 276 | torch.Tensor.scatter_add_ | 是 | -| 277 | torch.Tensor.scatter_add | 是 | -| 278 | torch.Tensor.select | 是 | -| 279 | torch.Tensor.set_ | 是 | -| 280 | torch.Tensor.share_memory_ | 否 | -| 281 | torch.Tensor.short | 是 | -| 282 | torch.Tensor.sigmoid | 是 | -| 283 | torch.Tensor.sigmoid_ | 是 | -| 284 | torch.Tensor.sign | 是 | -| 285 | torch.Tensor.sign_ | 是 | -| 286 | torch.Tensor.sin | 是 | -| 287 | torch.Tensor.sin_ | 是 | -| 288 | torch.Tensor.sinh | 是 | -| 289 | torch.Tensor.sinh_ | 是 | -| 290 | torch.Tensor.size | 是 | -| 291 | torch.Tensor.slogdet | 是 | -| 292 | torch.Tensor.solve | 否 | -| 293 | torch.Tensor.sort | 是 | -| 294 | torch.Tensor.split | 是 | -| 295 | torch.Tensor.sparse_mask | 否 | -| 296 | torch.Tensor.sparse_dim | 否 | -| 297 | torch.Tensor.sqrt | 是 | -| 298 | torch.Tensor.sqrt_ | 是 | -| 299 | torch.Tensor.square | 是 | -| 300 | torch.Tensor.square_ | 是 | -| 301 | torch.Tensor.squeeze | 是 | -| 302 | torch.Tensor.squeeze_ | 是 | -| 303 | torch.Tensor.std | 是 | -| 304 | torch.Tensor.stft | 否 | -| 305 | torch.Tensor.storage | 是 | -| 306 | torch.Tensor.storage_offset | 是 | -| 307 | torch.Tensor.storage_type | 是 | -| 308 | torch.Tensor.stride | 是 | -| 309 | torch.Tensor.sub | 是 | -| 310 | torch.Tensor.sub_ | 是 | -| 311 | torch.Tensor.sum | 是 | -| 312 | torch.Tensor.sum_to_size | 是 | -| 313 | torch.Tensor.svd | 是 | -| 314 | torch.Tensor.symeig | 是 | -| 315 | torch.Tensor.t | 是 | -| 316 | torch.Tensor.t_ | 是 | -| 317 | torch.Tensor.to | 是 | -| 318 | torch.Tensor.to_mkldnn | 否 | -| 319 | torch.Tensor.take | 是 | -| 320 | torch.Tensor.tan | 是 | -| 321 | torch.Tensor.tan_ | 是 | -| 322 | torch.Tensor.tanh | 是 | -| 323 | torch.Tensor.tanh_ | 是 | -| 324 | torch.Tensor.tolist | 是 | -| 325 | torch.Tensor.topk | 是 | -| 326 | torch.Tensor.to_sparse | 否 | -| 327 | torch.Tensor.trace | 否 | -| 328 | torch.Tensor.transpose | 是 | -| 329 | torch.Tensor.transpose_ | 是 | -| 330 | torch.Tensor.triangular_solve | 是 | -| 331 | torch.Tensor.tril | 是 | -| 332 | torch.Tensor.tril_ | 是 | -| 333 | torch.Tensor.triu | 是 | -| 334 | torch.Tensor.triu_ | 是 | -| 335 | torch.Tensor.true_divide | 是 | -| 336 | torch.Tensor.true_divide_ | 是 | -| 337 | torch.Tensor.trunc | 是 | -| 338 | torch.Tensor.trunc_ | 是 | -| 339 | torch.Tensor.type | 是 | -| 340 | torch.Tensor.type_as | 是 | -| 341 | torch.Tensor.unbind | 是 | -| 342 | torch.Tensor.unfold | 是 | -| 343 | torch.Tensor.uniform_ | 是 | -| 344 | torch.Tensor.unique | 是 | -| 345 | torch.Tensor.unique_consecutive | 否 | -| 346 | torch.Tensor.unsqueeze | 是 | -| 347 | torch.Tensor.unsqueeze_ | 是 | -| 348 | torch.Tensor.values | 否 | -| 349 | torch.Tensor.var | 否 | -| 350 | torch.Tensor.view | 是 | -| 351 | torch.Tensor.view_as | 是 | -| 352 | torch.Tensor.where | 是 | -| 353 | torch.Tensor.zero_ | 是 | -| 354 | torch.BoolTensor | 是 | -| 355 | torch.BoolTensor.all | 是 | -| 356 | torch.BoolTensor.any | 是 | +| 序号 | API名称 | 是否支持 | +| ---- | -------------------------------------- | -------- | +| 1 | torch.Tensor | 是 | +| 2 | torch.Tensor.new_tensor | 是 | +| 3 | torch.Tensor.new_full | 是 | +| 4 | torch.Tensor.new_empty | 是 | +| 5 | torch.Tensor.new_ones | 是 | +| 6 | torch.Tensor.new_zeros | 是 | +| 7 | torch.Tensor.is_cuda | 是 | +| 8 | torch.Tensor.is_quantized | 是 | +| 9 | torch.Tensor.device | 是 | +| 10 | torch.Tensor.ndim | 是 | +| 11 | torch.Tensor.T | 是 | +| 12 | torch.Tensor.abs | 是 | +| 13 | torch.Tensor.abs_ | 是 | +| 14 | torch.Tensor.acos | 是 | +| 15 | torch.Tensor.acos_ | 是 | +| 16 | torch.Tensor.add | 是 | +| 17 | torch.Tensor.add_ | 是 | +| 18 | torch.Tensor.addbmm | 是 | +| 19 | torch.Tensor.addbmm_ | 是 | +| 20 | torch.Tensor.addcdiv | 是 | +| 21 | torch.Tensor.addcdiv_ | 是 | +| 22 | torch.Tensor.addcmul | 是 | +| 23 | torch.Tensor.addcmul_ | 是 | +| 24 | torch.Tensor.addmm | 是 | +| 25 | torch.Tensor.addmm_ | 是 | +| 26 | torch.Tensor.addmv | 是 | +| 27 | torch.Tensor.addmv_ | 是 | +| 28 | torch.Tensor.addr | 是 | +| 29 | torch.Tensor.addr_ | 是 | +| 30 | torch.Tensor.allclose | 是 | +| 31 | torch.Tensor.angle | 否 | +| 32 | torch.Tensor.apply_ | 否 | +| 33 | torch.Tensor.argmax | 是 | +| 34 | torch.Tensor.argmin | 是 | +| 35 | torch.Tensor.argsort | 是 | +| 36 | torch.Tensor.asin | 是 | +| 37 | torch.Tensor.asin_ | 是 | +| 38 | torch.Tensor.as_strided | 是 | +| 39 | torch.Tensor.atan | 是 | +| 40 | torch.Tensor.atan2 | 是 | +| 41 | torch.Tensor.atan2_ | 是 | +| 42 | torch.Tensor.atan_ | 是 | +| 43 | torch.Tensor.baddbmm | 是 | +| 44 | torch.Tensor.baddbmm_ | 是 | +| 45 | torch.Tensor.bernoulli | 是 | +| 46 | torch.Tensor.bernoulli_ | 是 | +| 47 | torch.Tensor.bfloat16 | 否 | +| 48 | torch.Tensor.bincount | 是 | +| 49 | torch.Tensor.bitwise_not | 是 | +| 50 | torch.Tensor.bitwise_not_ | 是 | +| 51 | torch.Tensor.bitwise_and | 是 | +| 52 | torch.Tensor.bitwise_and_ | 是 | +| 53 | torch.Tensor.bitwise_or | 是 | +| 54 | torch.Tensor.bitwise_or_ | 是 | +| 55 | torch.Tensor.bitwise_xor | 是 | +| 56 | torch.Tensor.bitwise_xor_ | 是 | +| 57 | torch.Tensor.bmm | 是 | +| 58 | torch.Tensor.bool | 是 | +| 59 | torch.Tensor.byte | 是 | +| 60 | torch.Tensor.cauchy_ | 否 | +| 61 | torch.Tensor.ceil | 是 | +| 62 | torch.Tensor.ceil_ | 是 | +| 63 | torch.Tensor.char | 是 | +| 64 | torch.Tensor.cholesky | 否 | +| 65 | torch.Tensor.cholesky_inverse | 否 | +| 66 | torch.Tensor.cholesky_solve | 否 | +| 67 | torch.Tensor.chunk | 是 | +| 68 | torch.Tensor.clamp | 是 | +| 69 | torch.Tensor.clamp_ | 是 | +| 70 | torch.Tensor.clone | 是 | +| 71 | torch.Tensor.contiguous | 是 | +| 72 | torch.Tensor.copy_ | 是 | +| 73 | torch.Tensor.conj | 否 | +| 74 | torch.Tensor.cos | 是 | +| 75 | torch.Tensor.cos_ | 是 | +| 76 | torch.Tensor.cosh | 是 | +| 77 | torch.Tensor.cosh_ | 是 | +| 78 | torch.Tensor.cpu | 是 | +| 79 | torch.Tensor.cross | 是 | +| 80 | torch.Tensor.cuda | 否 | +| 81 | torch.Tensor.cummax | 是 | +| 82 | torch.Tensor.cummin | 是 | +| 83 | torch.Tensor.cumprod | 是 | +| 84 | torch.Tensor.cumsum | 是 | +| 85 | torch.Tensor.data_ptr | 是 | +| 86 | torch.Tensor.dequantize | 否 | +| 87 | torch.Tensor.det | 否 | +| 88 | torch.Tensor.dense_dim | 否 | +| 89 | torch.Tensor.diag | 是 | +| 90 | torch.Tensor.diag_embed | 是 | +| 91 | torch.Tensor.diagflat | 是 | +| 92 | torch.Tensor.diagonal | 是 | +| 93 | torch.Tensor.fill_diagonal_ | 是 | +| 94 | torch.Tensor.digamma | 否 | +| 95 | torch.Tensor.digamma_ | 否 | +| 96 | torch.Tensor.dim | 是 | +| 97 | torch.Tensor.dist | 是 | +| 98 | torch.Tensor.div | 是 | +| 99 | torch.Tensor.div_ | 是 | +| 100 | torch.Tensor.dot | 是 | +| 101 | torch.Tensor.double | 否 | +| 102 | torch.Tensor.eig | 否 | +| 103 | torch.Tensor.element_size | 是 | +| 104 | torch.Tensor.eq | 是 | +| 105 | torch.Tensor.eq_ | 是 | +| 106 | torch.Tensor.equal | 是 | +| 107 | torch.Tensor.erf | 是 | +| 108 | torch.Tensor.erf_ | 是 | +| 109 | torch.Tensor.erfc | 是 | +| 110 | torch.Tensor.erfc_ | 是 | +| 111 | torch.Tensor.erfinv | 是 | +| 112 | torch.Tensor.erfinv_ | 是 | +| 113 | torch.Tensor.exp | 是 | +| 114 | torch.Tensor.exp_ | 是 | +| 115 | torch.Tensor.expm1 | 是 | +| 116 | torch.Tensor.expm1_ | 是 | +| 117 | torch.Tensor.expand | 是 | +| 118 | torch.Tensor.expand_as | 是 | +| 119 | torch.Tensor.exponential_ | 否 | +| 120 | torch.Tensor.fft | 否 | +| 121 | torch.Tensor.fill_ | 是 | +| 122 | torch.Tensor.flatten | 是 | +| 123 | torch.Tensor.flip | 是 | +| 124 | torch.Tensor.float | 是 | +| 125 | torch.Tensor.floor | 是 | +| 126 | torch.Tensor.floor_ | 是 | +| 127 | torch.Tensor.floor_divide | 是 | +| 128 | torch.Tensor.floor_divide_ | 是 | +| 129 | torch.Tensor.fmod | 是 | +| 130 | torch.Tensor.fmod_ | 是 | +| 131 | torch.Tensor.frac | 是 | +| 132 | torch.Tensor.frac_ | 是 | +| 133 | torch.Tensor.gather | 是 | +| 134 | torch.Tensor.ge | 是 | +| 135 | torch.Tensor.ge_ | 是 | +| 136 | torch.Tensor.geometric_ | 否 | +| 137 | torch.Tensor.geqrf | 否 | +| 138 | torch.Tensor.ger | 是 | +| 139 | torch.Tensor.get_device | 是 | +| 140 | torch.Tensor.gt | 是 | +| 141 | torch.Tensor.gt_ | 是 | +| 142 | torch.Tensor.half | 是 | +| 143 | torch.Tensor.hardshrink | 是 | +| 144 | torch.Tensor.histc | 否 | +| 145 | torch.Tensor.ifft | 否 | +| 146 | torch.Tensor.index_add_ | 是 | +| 147 | torch.Tensor.index_add | 是 | +| 148 | torch.Tensor.index_copy_ | 是 | +| 149 | torch.Tensor.index_copy | 是 | +| 150 | torch.Tensor.index_fill_ | 是 | +| 151 | torch.Tensor.index_fill | 是 | +| 152 | torch.Tensor.index_put_ | 是 | +| 153 | torch.Tensor.index_put | 是 | +| 154 | torch.Tensor.index_select | 是 | +| 155 | torch.Tensor.indices | 否 | +| 156 | torch.Tensor.int | 是 | +| 157 | torch.Tensor.int_repr | 否 | +| 158 | torch.Tensor.inverse | 是 | +| 159 | torch.Tensor.irfft | 否 | +| 160 | torch.Tensor.is_contiguous | 是 | +| 161 | torch.Tensor.is_complex | 是 | +| 162 | torch.Tensor.is_floating_point | 是 | +| 163 | torch.Tensor.is_pinned | 是 | +| 164 | torch.Tensor.is_set_to | 否 | +| 165 | torch.Tensor.is_shared | 是 | +| 166 | torch.Tensor.is_signed | 是 | +| 167 | torch.Tensor.is_sparse | 是 | +| 168 | torch.Tensor.item | 是 | +| 169 | torch.Tensor.kthvalue | 是 | +| 170 | torch.Tensor.le | 是 | +| 171 | torch.Tensor.le_ | 是 | +| 172 | torch.Tensor.lerp | 是 | +| 173 | torch.Tensor.lerp_ | 是 | +| 174 | torch.Tensor.lgamma | 否 | +| 175 | torch.Tensor.lgamma_ | 否 | +| 176 | torch.Tensor.log | 是 | +| 177 | torch.Tensor.log_ | 是 | +| 178 | torch.Tensor.logdet | 否 | +| 179 | torch.Tensor.log10 | 是 | +| 180 | torch.Tensor.log10_ | 是 | +| 181 | torch.Tensor.log1p | 是 | +| 182 | torch.Tensor.log1p_ | 是 | +| 183 | torch.Tensor.log2 | 是 | +| 184 | torch.Tensor.log2_ | 是 | +| 185 | torch.Tensor.log_normal_ | 是 | +| 186 | torch.Tensor.logsumexp | 是 | +| 187 | torch.Tensor.logical_and | 是 | +| 188 | torch.Tensor.logical_and_ | 是 | +| 189 | torch.Tensor.logical_not | 是 | +| 190 | torch.Tensor.logical_not_ | 是 | +| 191 | torch.Tensor.logical_or | 是 | +| 192 | torch.Tensor.logical_or_ | 是 | +| 193 | torch.Tensor.logical_xor | 否 | +| 194 | torch.Tensor.logical_xor_ | 否 | +| 195 | torch.Tensor.long | 是 | +| 196 | torch.Tensor.lstsq | 否 | +| 197 | torch.Tensor.lt | 是 | +| 198 | torch.Tensor.lt_ | 是 | +| 199 | torch.Tensor.lu | 是 | +| 200 | torch.Tensor.lu_solve | 是 | +| 201 | torch.Tensor.map_ | 否 | +| 202 | torch.Tensor.masked_scatter_ | 是 | +| 203 | torch.Tensor.masked_scatter | 是 | +| 204 | torch.Tensor.masked_fill_ | 是 | +| 205 | torch.Tensor.masked_fill | 是 | +| 206 | torch.Tensor.masked_select | 是 | +| 207 | torch.Tensor.matmul | 是 | +| 208 | torch.Tensor.matrix_power | 是 | +| 209 | torch.Tensor.max | 是 | +| 210 | torch.Tensor.mean | 是 | +| 211 | torch.Tensor.median | 是 | +| 212 | torch.Tensor.min | 是 | +| 213 | torch.Tensor.mm | 是 | +| 214 | torch.Tensor.mode | 否 | +| 215 | torch.Tensor.mul | 是 | +| 216 | torch.Tensor.mul_ | 是 | +| 217 | torch.Tensor.multinomial | 是 | +| 218 | torch.Tensor.mv | 是 | +| 219 | torch.Tensor.mvlgamma | 否 | +| 220 | torch.Tensor.mvlgamma_ | 否 | +| 221 | torch.Tensor.narrow | 是 | +| 222 | torch.Tensor.narrow_copy | 是 | +| 223 | torch.Tensor.ndimension | 是 | +| 224 | torch.Tensor.ne | 是 | +| 225 | torch.Tensor.ne_ | 是 | +| 226 | torch.Tensor.neg | 是 | +| 227 | torch.Tensor.neg_ | 是 | +| 228 | torch.Tensor.nelement | 是 | +| 229 | torch.Tensor.nonzero | 是 | +| 230 | torch.Tensor.norm | 是 | +| 231 | torch.Tensor.normal_ | 是 | +| 232 | torch.Tensor.numel | 是 | +| 233 | torch.Tensor.numpy | 否 | +| 234 | torch.Tensor.orgqr | 否 | +| 235 | torch.Tensor.ormqr | 否 | +| 236 | torch.Tensor.permute | 是 | +| 237 | torch.Tensor.pin_memory | 否 | +| 238 | torch.Tensor.pinverse | 是 | +| 239 | torch.Tensor.polygamma | 否 | +| 240 | torch.Tensor.polygamma_ | 否 | +| 241 | torch.Tensor.pow | 是 | +| 242 | torch.Tensor.pow_ | 是 | +| 243 | torch.Tensor.prod | 是 | +| 244 | torch.Tensor.put_ | 是 | +| 245 | torch.Tensor.qr | 是 | +| 246 | torch.Tensor.qscheme | 否 | +| 247 | torch.Tensor.q_scale | 否 | +| 248 | torch.Tensor.q_zero_point | 否 | +| 249 | torch.Tensor.q_per_channel_scales | 否 | +| 250 | torch.Tensor.q_per_channel_zero_points | 否 | +| 251 | torch.Tensor.q_per_channel_axis | 否 | +| 252 | torch.Tensor.random_ | 是 | +| 253 | torch.Tensor.reciprocal | 是 | +| 254 | torch.Tensor.reciprocal_ | 是 | +| 255 | torch.Tensor.record_stream | 是 | +| 256 | torch.Tensor.remainder | 是 | +| 257 | torch.Tensor.remainder_ | 是 | +| 258 | torch.Tensor.renorm | 是 | +| 259 | torch.Tensor.renorm_ | 是 | +| 260 | torch.Tensor.repeat | 是 | +| 261 | torch.Tensor.repeat_interleave | 是 | +| 262 | torch.Tensor.requires_grad_ | 是 | +| 263 | torch.Tensor.reshape | 是 | +| 264 | torch.Tensor.reshape_as | 是 | +| 265 | torch.Tensor.resize_ | 是 | +| 266 | torch.Tensor.resize_as_ | 是 | +| 267 | torch.Tensor.rfft | 否 | +| 268 | torch.Tensor.roll | 是 | +| 269 | torch.Tensor.rot90 | 是 | +| 270 | torch.Tensor.round | 是 | +| 271 | torch.Tensor.round_ | 是 | +| 272 | torch.Tensor.rsqrt | 是 | +| 273 | torch.Tensor.rsqrt_ | 是 | +| 274 | torch.Tensor.scatter | 是 | +| 275 | torch.Tensor.scatter_ | 是 | +| 276 | torch.Tensor.scatter_add_ | 是 | +| 277 | torch.Tensor.scatter_add | 是 | +| 278 | torch.Tensor.select | 是 | +| 279 | torch.Tensor.set_ | 是 | +| 280 | torch.Tensor.share_memory_ | 否 | +| 281 | torch.Tensor.short | 是 | +| 282 | torch.Tensor.sigmoid | 是 | +| 283 | torch.Tensor.sigmoid_ | 是 | +| 284 | torch.Tensor.sign | 是 | +| 285 | torch.Tensor.sign_ | 是 | +| 286 | torch.Tensor.sin | 是 | +| 287 | torch.Tensor.sin_ | 是 | +| 288 | torch.Tensor.sinh | 是 | +| 289 | torch.Tensor.sinh_ | 是 | +| 290 | torch.Tensor.size | 是 | +| 291 | torch.Tensor.slogdet | 是 | +| 292 | torch.Tensor.solve | 否 | +| 293 | torch.Tensor.sort | 是 | +| 294 | torch.Tensor.split | 是 | +| 295 | torch.Tensor.sparse_mask | 否 | +| 296 | torch.Tensor.sparse_dim | 否 | +| 297 | torch.Tensor.sqrt | 是 | +| 298 | torch.Tensor.sqrt_ | 是 | +| 299 | torch.Tensor.square | 是 | +| 300 | torch.Tensor.square_ | 是 | +| 301 | torch.Tensor.squeeze | 是 | +| 302 | torch.Tensor.squeeze_ | 是 | +| 303 | torch.Tensor.std | 是 | +| 304 | torch.Tensor.stft | 否 | +| 305 | torch.Tensor.storage | 是 | +| 306 | torch.Tensor.storage_offset | 是 | +| 307 | torch.Tensor.storage_type | 是 | +| 308 | torch.Tensor.stride | 是 | +| 309 | torch.Tensor.sub | 是 | +| 310 | torch.Tensor.sub_ | 是 | +| 311 | torch.Tensor.sum | 是 | +| 312 | torch.Tensor.sum_to_size | 是 | +| 313 | torch.Tensor.svd | 是 | +| 314 | torch.Tensor.symeig | 是 | +| 315 | torch.Tensor.t | 是 | +| 316 | torch.Tensor.t_ | 是 | +| 317 | torch.Tensor.to | 是 | +| 318 | torch.Tensor.to_mkldnn | 否 | +| 319 | torch.Tensor.take | 是 | +| 320 | torch.Tensor.tan | 是 | +| 321 | torch.Tensor.tan_ | 是 | +| 322 | torch.Tensor.tanh | 是 | +| 323 | torch.Tensor.tanh_ | 是 | +| 324 | torch.Tensor.tolist | 是 | +| 325 | torch.Tensor.topk | 是 | +| 326 | torch.Tensor.to_sparse | 否 | +| 327 | torch.Tensor.trace | 否 | +| 328 | torch.Tensor.transpose | 是 | +| 329 | torch.Tensor.transpose_ | 是 | +| 330 | torch.Tensor.triangular_solve | 是 | +| 331 | torch.Tensor.tril | 是 | +| 332 | torch.Tensor.tril_ | 是 | +| 333 | torch.Tensor.triu | 是 | +| 334 | torch.Tensor.triu_ | 是 | +| 335 | torch.Tensor.true_divide | 是 | +| 336 | torch.Tensor.true_divide_ | 是 | +| 337 | torch.Tensor.trunc | 是 | +| 338 | torch.Tensor.trunc_ | 是 | +| 339 | torch.Tensor.type | 是 | +| 340 | torch.Tensor.type_as | 是 | +| 341 | torch.Tensor.unbind | 是 | +| 342 | torch.Tensor.unfold | 是 | +| 343 | torch.Tensor.uniform_ | 是 | +| 344 | torch.Tensor.unique | 是 | +| 345 | torch.Tensor.unique_consecutive | 否 | +| 346 | torch.Tensor.unsqueeze | 是 | +| 347 | torch.Tensor.unsqueeze_ | 是 | +| 348 | torch.Tensor.values | 否 | +| 349 | torch.Tensor.var | 否 | +| 350 | torch.Tensor.view | 是 | +| 351 | torch.Tensor.view_as | 是 | +| 352 | torch.Tensor.where | 是 | +| 353 | torch.Tensor.zero_ | 是 | +| 354 | torch.BoolTensor | 是 | +| 355 | torch.BoolTensor.all | 是 | +| 356 | torch.BoolTensor.any | 是 | ## Layers (torch.nn) -| 序号 | API名称 | 是否支持（PyTorch1.5.0） | +| 序号 | API名称 | 是否支持 | | ---- | -------------------------------------------------------- | ---------------------------- | | 1 | torch.nn.Parameter | 是 | | 2 | torch.nn.Module | 是 | @@ -984,7 +984,7 @@ ## Functions(torch.nn.functional) -| 序号 | API名称 | 是否支持（PyTorch1.5.0） | +| 序号 | API名称 | 是否支持 | | ---- | ---------------------------------------------------- | --------------------------- | | 1 | torch.nn.functional.conv1d | 是 | | 2 | torch.nn.functional.conv2d | 是 | @@ -1088,206 +1088,206 @@ ## torch.distributed -| 序号 | API名称 | 是否支持（PyTorch1.5.0） | -| ---- | ------------------------------------- | ------------------------ | -| 1 | torch.distributed.init_process_group | 是 | -| 2 | torch.distributed.Backend | 是 | -| 3 | torch.distributed.get_backend | 是 | -| 4 | torch.distributed.get_rank | 是 | -| 5 | torch.distributed.get_world_size | 是 | -| 6 | torch.distributed.is_initialized | 是 | -| 7 | torch.distributed.is_mpi_available | 是 | -| 8 | torch.distributed.is_nccl_available | 是 | -| 9 | torch.distributed.new_group | 是 | -| 10 | torch.distributed.send | 否 | -| 11 | torch.distributed.recv | 否 | -| 12 | torch.distributed.isend | 否 | -| 13 | torch.distributed.irecv | 否 | -| 14 | is_completed | 是 | -| 15 | wait | 是 | -| 16 | torch.distributed.broadcast | 是 | -| 17 | torch.distributed.all_reduce | 是 | -| 18 | torch.distributed.reduce | 否 | -| 19 | torch.distributed.all_gather | 是 | -| 20 | torch.distributed.gather | 否 | -| 21 | torch.distributed.scatter | 否 | -| 22 | torch.distributed.barrier | 是 | -| 23 | torch.distributed.ReduceOp | 是 | -| 24 | torch.distributed.reduce_op | 是 | -| 25 | torch.distributed.broadcast_multigpu | 否 | -| 26 | torch.distributed.all_reduce_multigpu | 否 | -| 27 | torch.distributed.reduce_multigpu | 否 | -| 28 | torch.distributed.all_gather_multigpu | 否 | -| 29 | torch.distributed.launch | 是 | -| 30 | torch.multiprocessing.spawn | 是 | +| 序号 | API名称 | 是否支持 | +| ---- | ------------------------------------- | -------- | +| 1 | torch.distributed.init_process_group | 是 | +| 2 | torch.distributed.Backend | 是 | +| 3 | torch.distributed.get_backend | 是 | +| 4 | torch.distributed.get_rank | 是 | +| 5 | torch.distributed.get_world_size | 是 | +| 6 | torch.distributed.is_initialized | 是 | +| 7 | torch.distributed.is_mpi_available | 是 | +| 8 | torch.distributed.is_nccl_available | 是 | +| 9 | torch.distributed.new_group | 是 | +| 10 | torch.distributed.send | 否 | +| 11 | torch.distributed.recv | 否 | +| 12 | torch.distributed.isend | 否 | +| 13 | torch.distributed.irecv | 否 | +| 14 | is_completed | 是 | +| 15 | wait | 是 | +| 16 | torch.distributed.broadcast | 是 | +| 17 | torch.distributed.all_reduce | 是 | +| 18 | torch.distributed.reduce | 否 | +| 19 | torch.distributed.all_gather | 是 | +| 20 | torch.distributed.gather | 否 | +| 21 | torch.distributed.scatter | 否 | +| 22 | torch.distributed.barrier | 是 | +| 23 | torch.distributed.ReduceOp | 是 | +| 24 | torch.distributed.reduce_op | 是 | +| 25 | torch.distributed.broadcast_multigpu | 否 | +| 26 | torch.distributed.all_reduce_multigpu | 否 | +| 27 | torch.distributed.reduce_multigpu | 否 | +| 28 | torch.distributed.all_gather_multigpu | 否 | +| 29 | torch.distributed.launch | 是 | +| 30 | torch.multiprocessing.spawn | 是 | ## torch.npu -| 序号 | API名称 | npu对应API名称 | 是否支持（PyTorch1.5.0） | -| ---- | ------------------------------------- | ------------------------------------ | ------------------------ | -| 1 | torch.cuda.current_blas_handle | torch.npu.current_blas_handle | 否 | -| 2 | torch.cuda.current_device | torch.npu.current_device | 是 | -| 3 | torch.cuda.current_stream | torch.npu.current_stream | 是 | -| 4 | torch.cuda.default_stream | torch.npu.default_stream | 是 | -| 5 | torch.cuda.device | torch.npu.device | 是 | -| 6 | torch.cuda.device_count | torch.npu.device_count | 是 | -| 7 | torch.cuda.device_of | torch.npu.device_of | 是 | -| 8 | torch.cuda.get_device_capability | torch.npu.get_device_capability | 否 | -| 9 | torch.cuda.get_device_name | torch.npu.get_device_name | 否 | -| 10 | torch.cuda.init | torch.npu.init | 是 | -| 11 | torch.cuda.ipc_collect | torch.npu.ipc_collect | 否 | -| 12 | torch.cuda.is_available | torch.npu.is_available | 是 | -| 13 | torch.cuda.is_initialized | torch.npu.is_initialized | 是 | -| 14 | torch.cuda.set_device | torch.npu.set_device | 部分支持 | -| 15 | torch.cuda.stream | torch.npu.stream | 是 | -| 16 | torch.cuda.synchronize | torch.npu.synchronize | 是 | -| 17 | torch.cuda.get_rng_state | torch.npu.get_rng_state | 否 | -| 18 | torch.cuda.get_rng_state_all | torch.npu.get_rng_state_all | 否 | -| 19 | torch.cuda.set_rng_state | torch.npu.set_rng_state | 否 | -| 20 | torch.cuda.set_rng_state_all | torch.npu.set_rng_state_all | 否 | -| 21 | torch.cuda.manual_seed | torch.npu.manual_seed | 否 | -| 22 | torch.cuda.manual_seed_all | torch.npu.manual_seed_all | 否 | -| 23 | torch.cuda.seed | torch.npu.seed | 否 | -| 24 | torch.cuda.seed_all | torch.npu.seed_all | 否 | -| 25 | torch.cuda.initial_seed | torch.npu.initial_seed | 否 | -| 26 | torch.cuda.comm.broadcast | torch.npu.comm.broadcast | 否 | -| 27 | torch.cuda.comm.broadcast_coalesced | torch.npu.comm.broadcast_coalesced | 否 | -| 28 | torch.cuda.comm.reduce_add | torch.npu.comm.reduce_add | 否 | -| 29 | torch.cuda.comm.scatter | torch.npu.comm.scatter | 否 | -| 30 | torch.cuda.comm.gather | torch.npu.comm.gather | 否 | -| 31 | torch.cuda.Stream | torch.npu.Stream | 是 | -| 32 | torch.cuda.Stream.query | torch.npu.Stream.query | 是 | -| 33 | torch.cuda.Stream.record_event | torch.npu.Stream.record_event | 是 | -| 34 | torch.cuda.Stream.synchronize | torch.npu.Stream.synchronize | 是 | -| 35 | torch.cuda.Stream.wait_event | torch.npu.Stream.wait_event | 是 | -| 36 | torch.cuda.Stream.wait_stream | torch.npu.Stream.wait_stream | 是 | -| 37 | torch.cuda.Event | torch.npu.Event | 是 | -| 38 | torch.cuda.Event.elapsed_time | torch.npu.Event.elapsed_time | 是 | -| 39 | torch.cuda.Event.from_ipc_handle | torch.npu.Event.from_ipc_handle | 否 | -| 40 | torch.cuda.Event.ipc_handle | torch.npu.Event.ipc_handle | 否 | -| 41 | torch.cuda.Event.query | torch.npu.Event.query | 是 | -| 42 | torch.cuda.Event.record | torch.npu.Event.record | 是 | -| 43 | torch.cuda.Event.synchronize | torch.npu.Event.synchronize | 是 | -| 44 | torch.cuda.Event.wait | torch.npu.Event.wait | 是 | -| 45 | torch.cuda.empty_cache | torch.npu.empty_cache | 是 | -| 46 | torch.cuda.memory_stats | torch.npu.memory_stats | 是 | -| 47 | torch.cuda.memory_summary | torch.npu.memory_summary | 是 | -| 48 | torch.cuda.memory_snapshot | torch.npu.memory_snapshot | 是 | -| 49 | torch.cuda.memory_allocated | torch.npu.memory_allocated | 是 | -| 50 | torch.cuda.max_memory_allocated | torch.npu.max_memory_allocated | 是 | -| 51 | torch.cuda.reset_max_memory_allocated | torch.npu.reset_max_memory_allocated | 是 | -| 52 | torch.cuda.memory_reserved | torch.npu.memory_reserved | 是 | -| 53 | torch.cuda.max_memory_reserved | torch.npu.max_memory_reserved | 是 | -| 54 | torch.cuda.memory_cached | torch.npu.memory_cached | 是 | -| 55 | torch.cuda.max_memory_cached | torch.npu.max_memory_cached | 是 | -| 56 | torch.cuda.reset_max_memory_cached | torch.npu.reset_max_memory_cached | 是 | -| 57 | torch.cuda.nvtx.mark | torch.npu.nvtx.mark | 否 | -| 58 | torch.cuda.nvtx.range_push | torch.npu.nvtx.range_push | 否 | -| 59 | torch.cuda.nvtx.range_pop | torch.npu.nvtx.range_pop | 否 | -| 60 | torch.cuda._sleep | torch.npu._sleep | 否 | -| 61 | torch.cuda.Stream.priority_range | torch.npu.Stream.priority_range | 否 | -| 62 | torch.cuda.get_device_properties | torch.npu.get_device_properties | 否 | -| 63 | torch.cuda.amp.GradScaler | torch.npu.amp.GradScaler | 否 | +| 序号 | API名称 | npu对应API名称 | 是否支持 | +| ---- | ------------------------------------- | ------------------------------------ | -------- | +| 1 | torch.cuda.current_blas_handle | torch.npu.current_blas_handle | 否 | +| 2 | torch.cuda.current_device | torch.npu.current_device | 是 | +| 3 | torch.cuda.current_stream | torch.npu.current_stream | 是 | +| 4 | torch.cuda.default_stream | torch.npu.default_stream | 是 | +| 5 | torch.cuda.device | torch.npu.device | 是 | +| 6 | torch.cuda.device_count | torch.npu.device_count | 是 | +| 7 | torch.cuda.device_of | torch.npu.device_of | 是 | +| 8 | torch.cuda.get_device_capability | torch.npu.get_device_capability | 否 | +| 9 | torch.cuda.get_device_name | torch.npu.get_device_name | 否 | +| 10 | torch.cuda.init | torch.npu.init | 是 | +| 11 | torch.cuda.ipc_collect | torch.npu.ipc_collect | 否 | +| 12 | torch.cuda.is_available | torch.npu.is_available | 是 | +| 13 | torch.cuda.is_initialized | torch.npu.is_initialized | 是 | +| 14 | torch.cuda.set_device | torch.npu.set_device | 部分支持 | +| 15 | torch.cuda.stream | torch.npu.stream | 是 | +| 16 | torch.cuda.synchronize | torch.npu.synchronize | 是 | +| 17 | torch.cuda.get_rng_state | torch.npu.get_rng_state | 否 | +| 18 | torch.cuda.get_rng_state_all | torch.npu.get_rng_state_all | 否 | +| 19 | torch.cuda.set_rng_state | torch.npu.set_rng_state | 否 | +| 20 | torch.cuda.set_rng_state_all | torch.npu.set_rng_state_all | 否 | +| 21 | torch.cuda.manual_seed | torch.npu.manual_seed | 否 | +| 22 | torch.cuda.manual_seed_all | torch.npu.manual_seed_all | 否 | +| 23 | torch.cuda.seed | torch.npu.seed | 否 | +| 24 | torch.cuda.seed_all | torch.npu.seed_all | 否 | +| 25 | torch.cuda.initial_seed | torch.npu.initial_seed | 否 | +| 26 | torch.cuda.comm.broadcast | torch.npu.comm.broadcast | 否 | +| 27 | torch.cuda.comm.broadcast_coalesced | torch.npu.comm.broadcast_coalesced | 否 | +| 28 | torch.cuda.comm.reduce_add | torch.npu.comm.reduce_add | 否 | +| 29 | torch.cuda.comm.scatter | torch.npu.comm.scatter | 否 | +| 30 | torch.cuda.comm.gather | torch.npu.comm.gather | 否 | +| 31 | torch.cuda.Stream | torch.npu.Stream | 是 | +| 32 | torch.cuda.Stream.query | torch.npu.Stream.query | 是 | +| 33 | torch.cuda.Stream.record_event | torch.npu.Stream.record_event | 是 | +| 34 | torch.cuda.Stream.synchronize | torch.npu.Stream.synchronize | 是 | +| 35 | torch.cuda.Stream.wait_event | torch.npu.Stream.wait_event | 是 | +| 36 | torch.cuda.Stream.wait_stream | torch.npu.Stream.wait_stream | 是 | +| 37 | torch.cuda.Event | torch.npu.Event | 是 | +| 38 | torch.cuda.Event.elapsed_time | torch.npu.Event.elapsed_time | 是 | +| 39 | torch.cuda.Event.from_ipc_handle | torch.npu.Event.from_ipc_handle | 否 | +| 40 | torch.cuda.Event.ipc_handle | torch.npu.Event.ipc_handle | 否 | +| 41 | torch.cuda.Event.query | torch.npu.Event.query | 是 | +| 42 | torch.cuda.Event.record | torch.npu.Event.record | 是 | +| 43 | torch.cuda.Event.synchronize | torch.npu.Event.synchronize | 是 | +| 44 | torch.cuda.Event.wait | torch.npu.Event.wait | 是 | +| 45 | torch.cuda.empty_cache | torch.npu.empty_cache | 是 | +| 46 | torch.cuda.memory_stats | torch.npu.memory_stats | 是 | +| 47 | torch.cuda.memory_summary | torch.npu.memory_summary | 是 | +| 48 | torch.cuda.memory_snapshot | torch.npu.memory_snapshot | 是 | +| 49 | torch.cuda.memory_allocated | torch.npu.memory_allocated | 是 | +| 50 | torch.cuda.max_memory_allocated | torch.npu.max_memory_allocated | 是 | +| 51 | torch.cuda.reset_max_memory_allocated | torch.npu.reset_max_memory_allocated | 是 | +| 52 | torch.cuda.memory_reserved | torch.npu.memory_reserved | 是 | +| 53 | torch.cuda.max_memory_reserved | torch.npu.max_memory_reserved | 是 | +| 54 | torch.cuda.memory_cached | torch.npu.memory_cached | 是 | +| 55 | torch.cuda.max_memory_cached | torch.npu.max_memory_cached | 是 | +| 56 | torch.cuda.reset_max_memory_cached | torch.npu.reset_max_memory_cached | 是 | +| 57 | torch.cuda.nvtx.mark | torch.npu.nvtx.mark | 否 | +| 58 | torch.cuda.nvtx.range_push | torch.npu.nvtx.range_push | 否 | +| 59 | torch.cuda.nvtx.range_pop | torch.npu.nvtx.range_pop | 否 | +| 60 | torch.cuda._sleep | torch.npu._sleep | 否 | +| 61 | torch.cuda.Stream.priority_range | torch.npu.Stream.priority_range | 否 | +| 62 | torch.cuda.get_device_properties | torch.npu.get_device_properties | 否 | +| 63 | torch.cuda.amp.GradScaler | torch.npu.amp.GradScaler | 否 | torch.npu.set_device()接口只支持在程序开始的位置通过set_device进行指定，不支持多次指定和with torch.npu.device(id)方式的device切换 ## NPU自定义算子 -| 序号 | PyTorch 算子（由昇腾开发） | 昇腾适配算子 | -| ---- | ---------------------------------------------- | ---------------------------------------------- | -| 1 | npu_convolution_transpose | npu_convolution_transpose | -| 2 | npu_conv_transpose2d | conv_transpose2d_npu | -| 3 | npu_convolution_transpose_backward | npu_convolution_transpose_backward | -| 4 | npu_conv_transpose2d_backward | conv_transpose2d_backward_npu | -| 5 | npu_conv_transpose3d_backward | conv_transpose3d_backward_npu | -| 6 | npu_convolution | npu_convolution | -| 7 | npu_convolution_backward | npu_convolution_backward | -| 8 | npu_convolution_double_backward | npu_convolution_double_backward | -| 9 | npu_conv2d | conv2d_npu | -| 10 | npu_conv2d.out | conv2d_out_npu | -| 11 | npu_conv2d_backward | conv2d_backward_npu | -| 12 | npu_conv3d | conv3d_npu | -| 13 | npu_conv3d.out | conv3d_out_npu | -| 14 | npu_conv3d_backward | conv3d_backward_npu | -| 15 | one_ | one_npu_ | -| 16 | npu_sort_v2.out | sort_without_indices_out_npu | -| 17 | npu_sort_v2 | sort_without_indices_npu | -| 18 | npu_format_cast | format_cast_npu | -| 19 | npu_format_cast_.acl_format | format_cast_npu_ | -| 20 | npu_format_cast_.src | format_cast_npu_ | -| 21 | npu_transpose_to_contiguous | transpose_to_contiguous_npu | -| 22 | npu_transpose | transpose_npu | -| 23 | npu_transpose.out | transpose_out_npu | -| 24 | npu_broadcast | broadcast_npu | -| 25 | npu_broadcast.out | broadcast_out_npu | -| 26 | npu_dtype_cast | dtype_cast_npu | -| 27 | npu_dtype_cast_.Tensor | dtype_cast_npu_ | -| 28 | npu_roi_alignbk | roi_align_backward_npu | -| 29 | empty_with_format | empty_with_format_npu | -| 30 | empty_with_format.names | empty_with_format_npu | -| 31 | copy_memory_ | copy_memory_npu_ | -| 32 | npu_one_hot | one_hot_npu | -| 33 | npu_stride_add | stride_add_npu | -| 34 | npu_softmax_cross_entropy_with_logits | softmax_cross_entropy_with_logits_npu | -| 35 | npu_softmax_cross_entropy_with_logits_backward | softmax_cross_entropy_with_logits_backward_npu | -| 36 | npu_ps_roi_pooling | ps_roi_pooling_npu | -| 37 | npu_ps_roi_pooling_backward | ps_roi_pooling_backward_npu | -| 38 | npu_roi_align | roi_align_npu | -| 39 | npu_nms_v4 | nms_v4_npu | -| 40 | npu_lstm | lstm_npu | -| 41 | npu_lstm_backward | lstm_backward_npu | -| 42 | npu_iou | iou_npu | -| 43 | npu_ptiou | ptiou_npu | -| 44 | npu_nms_with_mask | nms_with_mask_npu | -| 45 | npu_pad | pad_npu | -| 46 | npu_bounding_box_encode | bounding_box_encode_npu | -| 47 | npu_bounding_box_decode | bounding_box_decode_npu | -| 48 | npu_gru | gru_npu | -| 49 | npu_gru_backward | gru_backward_npu | -| 50 | npu_set_.source_Storage_storage_offset_format | set_npu_ | -| 51 | npu_random_choice_with_mask | random_choice_with_mask_npu | -| 52 | npu_batch_nms | batch_nms_npu | -| 53 | npu_slice | slice_npu | -| 54 | npu_slice.out | slice_out_npu | -| 55 | npu_dropoutV2 | dropout_v2_npu | -| 56 | npu_dropoutV2_backward | dropout_v2_backward_npu | -| 57 | _npu_dropout | _dropout_npu | -| 58 | _npu_dropout_inplace | _dropout_npu_inplace | -| 59 | npu_dropout_backward | dropout_backward_npu | -| 60 | npu_indexing | indexing_npu | -| 61 | npu_indexing.out | indexing_out_npu | -| 62 | npu_ifmr | ifmr_npu | -| 63 | npu_max.dim | max_v1_npu | -| 64 | npu_max.names_dim | max_v1_npu | -| 65 | npu_scatter | scatter_npu | -| 66 | npu_max_backward | max_backward_npu | -| 67 | npu_apply_adam | apply_adam_npu | -| 68 | npu_layer_norm_eval | layer_norm_eval_npu | -| 69 | npu_alloc_float_status | alloc_float_status_npu | -| 70 | npu_get_float_status | get_float_status_npu | -| 71 | npu_clear_float_status | clear_float_status_npu | -| 72 | npu_confusion_transpose | confusion_transpose_npu | -| 73 | npu_confusion_transpose_backward | confusion_transpose_backward_npu | -| 74 | npu_bmmV2 | bmm_v2_npu | -| 75 | fast_gelu | fast_gelu_npu | -| 76 | fast_gelu_backward | fast_gelu_backward_npu | -| 77 | npu_sub_sample | sub_sample_npu | -| 78 | npu_deformable_conv2d | deformable_conv2d_npu | -| 79 | npu_deformable_conv2dbk | deformable_conv2d_backward_npu | -| 80 | npu_mish | mish_npu | -| 81 | npu_anchor_response_flags | anchor_response_flags_npu | -| 82 | npu_yolo_boxes_encode | yolo_boxes_encode_npu | -| 83 | npu_grid_assign_positive | grid_assign_positive_npu | -| 84 | npu_mish_backward | mish_backward_npu | -| 85 | npu_normalize_batch | normalize_batch_npu | -| 86 | npu_masked_fill_range | masked_fill_range_npu | -| 87 | npu_linear | linear_npu | -| 88 | npu_linear_backward | linear_backward_npu | -| 89 | npu_bert_apply_adam | bert_apply_adam_npu | -| 90 | npu_giou | giou_npu | -| 91 | npu_giou_backward | giou_backward_npu | +| 序号 | 算子名称 | +| ---- | ---------------------------------------------- | +| 1 | npu_convolution_transpose | +| 2 | npu_conv_transpose2d | +| 3 | npu_convolution_transpose_backward | +| 4 | npu_conv_transpose2d_backward | +| 5 | npu_conv_transpose3d_backward | +| 6 | npu_convolution | +| 7 | npu_convolution_backward | +| 8 | npu_convolution_double_backward | +| 9 | npu_conv2d | +| 10 | npu_conv2d.out | +| 11 | npu_conv2d_backward | +| 12 | npu_conv3d | +| 13 | npu_conv3d.out | +| 14 | npu_conv3d_backward | +| 15 | one_ | +| 16 | npu_sort_v2.out | +| 17 | npu_sort_v2 | +| 18 | npu_format_cast | +| 19 | npu_format_cast_.acl_format | +| 20 | npu_format_cast_.src | +| 21 | npu_transpose_to_contiguous | +| 22 | npu_transpose | +| 23 | npu_transpose.out | +| 24 | npu_broadcast | +| 25 | npu_broadcast.out | +| 26 | npu_dtype_cast | +| 27 | npu_dtype_cast_.Tensor | +| 28 | npu_roi_alignbk | +| 29 | empty_with_format | +| 30 | empty_with_format.names | +| 31 | copy_memory_ | +| 32 | npu_one_hot | +| 33 | npu_stride_add | +| 34 | npu_softmax_cross_entropy_with_logits | +| 35 | npu_softmax_cross_entropy_with_logits_backward | +| 36 | npu_ps_roi_pooling | +| 37 | npu_ps_roi_pooling_backward | +| 38 | npu_roi_align | +| 39 | npu_nms_v4 | +| 40 | npu_lstm | +| 41 | npu_lstm_backward | +| 42 | npu_iou | +| 43 | npu_ptiou | +| 44 | npu_nms_with_mask | +| 45 | npu_pad | +| 46 | npu_bounding_box_encode | +| 47 | npu_bounding_box_decode | +| 48 | npu_gru | +| 49 | npu_gru_backward | +| 50 | npu_set_.source_Storage_storage_offset_format | +| 51 | npu_random_choice_with_mask | +| 52 | npu_batch_nms | +| 53 | npu_slice | +| 54 | npu_slice.out | +| 55 | npu_dropoutV2 | +| 56 | npu_dropoutV2_backward | +| 57 | _npu_dropout | +| 58 | _npu_dropout_inplace | +| 59 | npu_dropout_backward | +| 60 | npu_indexing | +| 61 | npu_indexing.out | +| 62 | npu_ifmr | +| 63 | npu_max.dim | +| 64 | npu_max.names_dim | +| 65 | npu_scatter | +| 66 | npu_max_backward | +| 67 | npu_apply_adam | +| 68 | npu_layer_norm_eval | +| 69 | npu_alloc_float_status | +| 70 | npu_get_float_status | +| 71 | npu_clear_float_status | +| 72 | npu_confusion_transpose | +| 73 | npu_confusion_transpose_backward | +| 74 | npu_bmmV2 | +| 75 | fast_gelu | +| 76 | fast_gelu_backward | +| 77 | npu_sub_sample | +| 78 | npu_deformable_conv2d | +| 79 | npu_deformable_conv2dbk | +| 80 | npu_mish | +| 81 | npu_anchor_response_flags | +| 82 | npu_yolo_boxes_encode | +| 83 | npu_grid_assign_positive | +| 84 | npu_mish_backward | +| 85 | npu_normalize_batch | +| 86 | npu_masked_fill_range | +| 87 | npu_linear | +| 88 | npu_linear_backward | +| 89 | npu_bert_apply_adam | +| 90 | npu_giou | +| 91 | npu_giou_backward | -算子接口说明： +详细算子接口说明： > ``` > npu_apply_adam(beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, use_locking, use_nesterov, out = (var, m, v)) diff --git "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.8.1.md" "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.8.1.md" index dc429ac253c..c280756f98f 100644 --- "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.8.1.md" +++ "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.8.1.md" @@ -448,10 +448,6 @@ | 6 | [are_deterministic_algorithms_enabled](https://pytorch.org/docs/1.8.1/generated/torch.are_deterministic_algorithms_enabled.html#torch.are_deterministic_algorithms_enabled) | 否 | | 7 | [_assert](https://pytorch.org/docs/1.8.1/generated/torch._assert.html#torch._assert) | 否 | -## Other - -## torch.Tensor - # Layers (torch.nn) | 序号 | API名称 | 支持情况 | @@ -774,9 +770,9 @@ From the `torch.nn.utils` module -## Functions(torch.nn.functional) +# Functions(torch.nn.functional) -### [Convolution functions](https://pytorch.org/docs/1.8.1/nn.functional.html#convolution-functions) +## [Convolution functions](https://pytorch.org/docs/1.8.1/nn.functional.html#convolution-functions) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | @@ -789,7 +785,7 @@ From the `torch.nn.utils` module | 7 | [unfold](https://pytorch.org/docs/1.8.1/nn.functional.html#unfold) | 否 | | 8 | [fold](https://pytorch.org/docs/1.8.1/nn.functional.html#fold) | 否 | -### [Pooling functions](https://pytorch.org/docs/1.8.1/nn.functional.html#pooling-functions) +## [Pooling functions](https://pytorch.org/docs/1.8.1/nn.functional.html#pooling-functions) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | @@ -811,7 +807,7 @@ From the `torch.nn.utils` module | 16 | [adaptive_avg_pool2d](https://pytorch.org/docs/1.8.1/nn.functional.html#adaptive-avg-pool2d) | 否 | | 17 | [adaptive_avg_pool3d](https://pytorch.org/docs/1.8.1/nn.functional.html#adaptive-avg-pool3d) | 否 | -### [Non-linear activation functions](https://pytorch.org/docs/1.8.1/nn.functional.html#non-linear-activation-functions) +## [Non-linear activation functions](https://pytorch.org/docs/1.8.1/nn.functional.html#non-linear-activation-functions) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | @@ -843,7 +839,7 @@ From the `torch.nn.utils` module | 26 | [hardsigmoid](https://pytorch.org/docs/1.8.1/nn.functional.html#hardsigmoid) | 否 | | 27 | [silu](https://pytorch.org/docs/1.8.1/nn.functional.html#silu) | 否 | -### [Normalization functions](https://pytorch.org/docs/1.8.1/nn.functional.html#normalization-functions) +## [Normalization functions](https://pytorch.org/docs/1.8.1/nn.functional.html#normalization-functions) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | @@ -853,14 +849,14 @@ From the `torch.nn.utils` module | 4 | [local_response_norm](https://pytorch.org/docs/1.8.1/nn.functional.html#local-response-norm) | 否 | | 5 | [normalize](https://pytorch.org/docs/1.8.1/nn.functional.html#normalize) | 否 | -### [Linear functions](https://pytorch.org/docs/1.8.1/nn.functional.html#linear-functions)[Linear functions](https://pytorch.org/docs/1.8.1/nn.functional.html#linear-functions) +## [Linear functions](https://pytorch.org/docs/1.8.1/nn.functional.html#linear-functions)[Linear functions](https://pytorch.org/docs/1.8.1/nn.functional.html#linear-functions) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | | 1 | [linear](https://pytorch.org/docs/1.8.1/nn.functional.html#linear) | 否 | | 2 | [bilinear](https://pytorch.org/docs/1.8.1/nn.functional.html#bilinear) | 否 | -### [Dropout functions](https://pytorch.org/docs/1.8.1/nn.functional.html#dropout-functions) +## [Dropout functions](https://pytorch.org/docs/1.8.1/nn.functional.html#dropout-functions) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | @@ -870,7 +866,7 @@ From the `torch.nn.utils` module | 4 | [dropout2d](https://pytorch.org/docs/1.8.1/nn.functional.html#dropout2d) | 否 | | 5 | [dropout3d](https://pytorch.org/docs/1.8.1/nn.functional.html#dropout3d) | 否 | -### [Sparse functions](https://pytorch.org/docs/1.8.1/nn.functional.html#sparse-functions) +## [Sparse functions](https://pytorch.org/docs/1.8.1/nn.functional.html#sparse-functions) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | @@ -878,7 +874,7 @@ From the `torch.nn.utils` module | 2 | [embedding_bag](https://pytorch.org/docs/1.8.1/nn.functional.html#embedding-bag) | 否 | | 3 | [one_hot](https://pytorch.org/docs/1.8.1/nn.functional.html#one-hot) | 否 | -### [Distance functions](https://pytorch.org/docs/1.8.1/nn.functional.html#distance-functions) +## [Distance functions](https://pytorch.org/docs/1.8.1/nn.functional.html#distance-functions) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | @@ -886,7 +882,7 @@ From the `torch.nn.utils` module | 2 | [cosine_similarity](https://pytorch.org/docs/1.8.1/nn.functional.html#cosine-similarity) | 否 | | 3 | [pdist](https://pytorch.org/docs/1.8.1/nn.functional.html#pdist) | 否 | -### [Loss functions](https://pytorch.org/docs/1.8.1/nn.functional.html#loss-functions) +## [Loss functions](https://pytorch.org/docs/1.8.1/nn.functional.html#loss-functions) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | @@ -910,7 +906,7 @@ From the `torch.nn.utils` module | 18 | [triplet_margin_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#triplet-margin-loss) | 否 | | 19 | [triplet_margin_with_distance_loss](https://pytorch.org/docs/1.8.1/nn.functional.html#triplet-margin-with-distance-loss) | 否 | -### [Vision functions](https://pytorch.org/docs/1.8.1/nn.functional.html#vision-functions) +## [Vision functions](https://pytorch.org/docs/1.8.1/nn.functional.html#vision-functions) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | @@ -924,17 +920,13 @@ From the `torch.nn.utils` module | 8 | [grid_sample](https://pytorch.org/docs/1.8.1/nn.functional.html#grid-sample) | 否 | | 9 | [affine_grid](https://pytorch.org/docs/1.8.1/nn.functional.html#affine-grid) | 否 | -### [DataParallel functions (multi-GPU, distributed)](https://pytorch.org/docs/1.8.1/nn.functional.html#dataparallel-functions-multi-gpu-distributed) +## [DataParallel functions (multi-GPU, distributed)](https://pytorch.org/docs/1.8.1/nn.functional.html#dataparallel-functions-multi-gpu-distributed) | 序号 | API名称 | 支持情况 | | ---- | ------------------------------------------------------------ | -------- | | 1 | [data_parallel](https://pytorch.org/docs/1.8.1/nn.functional.html#data-parallel) | 否 | - - - - -## [torch.distributed](https://pytorch.org/docs/1.8.1/distributed.html) +# [torch.distributed](https://pytorch.org/docs/1.8.1/distributed.html) | 序号 | API名称 | 支持情况 | | ---- | ----------------------------------------- | -------- | @@ -989,7 +981,7 @@ From the `torch.nn.utils` module | 49 | torch.distributed.launch | 否 | | 50 | torch.multiprocessing.spawn | 否 | -## torch.npu +# torch.npu | 序号 | API名称 | npu对应API名称 | 是否支持 | | ---- | ------------------------------------- | ------------------------------------ | -------- | @@ -1057,103 +1049,103 @@ From the `torch.nn.utils` module | 62 | torch.cuda.get_device_properties | torch.npu.get_device_properties | 否 | | 63 | torch.cuda.amp.GradScaler | torch.npu.amp.GradScaler | 否 | -## NPU自定义算子 - -| 序号 | PyTorch 算子（由昇腾开发） | 昇腾适配算子 | -| ---- | ---------------------------------------------- | ---------------------------------------------- | -| 1 | npu_convolution_transpose | npu_convolution_transpose | -| 2 | npu_conv_transpose2d | conv_transpose2d_npu | -| 3 | npu_convolution_transpose_backward | npu_convolution_transpose_backward | -| 4 | npu_conv_transpose2d_backward | conv_transpose2d_backward_npu | -| 5 | npu_conv_transpose3d_backward | conv_transpose3d_backward_npu | -| 6 | npu_convolution | npu_convolution | -| 7 | npu_convolution_backward | npu_convolution_backward | -| 8 | npu_convolution_double_backward | npu_convolution_double_backward | -| 9 | npu_conv2d | conv2d_npu | -| 10 | npu_conv2d.out | conv2d_out_npu | -| 11 | npu_conv2d_backward | conv2d_backward_npu | -| 12 | npu_conv3d | conv3d_npu | -| 13 | npu_conv3d.out | conv3d_out_npu | -| 14 | npu_conv3d_backward | conv3d_backward_npu | -| 15 | one_ | one_npu_ | -| 16 | npu_sort_v2.out | sort_without_indices_out_npu | -| 17 | npu_sort_v2 | sort_without_indices_npu | -| 18 | npu_format_cast | format_cast_npu | -| 19 | npu_format_cast_.acl_format | format_cast_npu_ | -| 20 | npu_format_cast_.src | format_cast_npu_ | -| 21 | npu_transpose_to_contiguous | transpose_to_contiguous_npu | -| 22 | npu_transpose | transpose_npu | -| 23 | npu_transpose.out | transpose_out_npu | -| 24 | npu_broadcast | broadcast_npu | -| 25 | npu_broadcast.out | broadcast_out_npu | -| 26 | npu_dtype_cast | dtype_cast_npu | -| 27 | npu_dtype_cast_.Tensor | dtype_cast_npu_ | -| 28 | npu_roi_alignbk | roi_align_backward_npu | -| 29 | empty_with_format | empty_with_format_npu | -| 30 | empty_with_format.names | empty_with_format_npu | -| 31 | copy_memory_ | copy_memory_npu_ | -| 32 | npu_one_hot | one_hot_npu | -| 33 | npu_stride_add | stride_add_npu | -| 34 | npu_softmax_cross_entropy_with_logits | softmax_cross_entropy_with_logits_npu | -| 35 | npu_softmax_cross_entropy_with_logits_backward | softmax_cross_entropy_with_logits_backward_npu | -| 36 | npu_ps_roi_pooling | ps_roi_pooling_npu | -| 37 | npu_ps_roi_pooling_backward | ps_roi_pooling_backward_npu | -| 38 | npu_roi_align | roi_align_npu | -| 39 | npu_nms_v4 | nms_v4_npu | -| 40 | npu_lstm | lstm_npu | -| 41 | npu_lstm_backward | lstm_backward_npu | -| 42 | npu_iou | iou_npu | -| 43 | npu_ptiou | ptiou_npu | -| 44 | npu_nms_with_mask | nms_with_mask_npu | -| 45 | npu_pad | pad_npu | -| 46 | npu_bounding_box_encode | bounding_box_encode_npu | -| 47 | npu_bounding_box_decode | bounding_box_decode_npu | -| 48 | npu_gru | gru_npu | -| 49 | npu_gru_backward | gru_backward_npu | -| 50 | npu_set_.source_Storage_storage_offset_format | set_npu_ | -| 51 | npu_random_choice_with_mask | random_choice_with_mask_npu | -| 52 | npu_batch_nms | batch_nms_npu | -| 53 | npu_slice | slice_npu | -| 54 | npu_slice.out | slice_out_npu | -| 55 | npu_dropoutV2 | dropout_v2_npu | -| 56 | npu_dropoutV2_backward | dropout_v2_backward_npu | -| 57 | _npu_dropout | _dropout_npu | -| 58 | _npu_dropout_inplace | _dropout_npu_inplace | -| 59 | npu_dropout_backward | dropout_backward_npu | -| 60 | npu_indexing | indexing_npu | -| 61 | npu_indexing.out | indexing_out_npu | -| 62 | npu_ifmr | ifmr_npu | -| 63 | npu_max.dim | max_v1_npu | -| 64 | npu_max.names_dim | max_v1_npu | -| 65 | npu_scatter | scatter_npu | -| 66 | npu_max_backward | max_backward_npu | -| 67 | npu_apply_adam | apply_adam_npu | -| 68 | npu_layer_norm_eval | layer_norm_eval_npu | -| 69 | npu_alloc_float_status | alloc_float_status_npu | -| 70 | npu_get_float_status | get_float_status_npu | -| 71 | npu_clear_float_status | clear_float_status_npu | -| 72 | npu_confusion_transpose | confusion_transpose_npu | -| 73 | npu_confusion_transpose_backward | confusion_transpose_backward_npu | -| 74 | npu_bmmV2 | bmm_v2_npu | -| 75 | fast_gelu | fast_gelu_npu | -| 76 | fast_gelu_backward | fast_gelu_backward_npu | -| 77 | npu_sub_sample | sub_sample_npu | -| 78 | npu_deformable_conv2d | deformable_conv2d_npu | -| 79 | npu_deformable_conv2dbk | deformable_conv2d_backward_npu | -| 80 | npu_mish | mish_npu | -| 81 | npu_anchor_response_flags | anchor_response_flags_npu | -| 82 | npu_yolo_boxes_encode | yolo_boxes_encode_npu | -| 83 | npu_grid_assign_positive | grid_assign_positive_npu | -| 84 | npu_mish_backward | mish_backward_npu | -| 85 | npu_normalize_batch | normalize_batch_npu | -| 86 | npu_masked_fill_range | masked_fill_range_npu | -| 87 | npu_linear | linear_npu | -| 88 | npu_linear_backward | linear_backward_npu | -| 89 | npu_bert_apply_adam | bert_apply_adam_npu | -| 90 | npu_giou | giou_npu | -| 91 | npu_giou_backward | giou_backward_npu | - -算子接口算子接口说明： +# NPU自定义算子 + +| 序号 | 算子名称 | +| ---- | ---------------------------------------------- | +| 1 | npu_convolution_transpose | +| 2 | npu_conv_transpose2d | +| 3 | npu_convolution_transpose_backward | +| 4 | npu_conv_transpose2d_backward | +| 5 | npu_conv_transpose3d_backward | +| 6 | npu_convolution | +| 7 | npu_convolution_backward | +| 8 | npu_convolution_double_backward | +| 9 | npu_conv2d | +| 10 | npu_conv2d.out | +| 11 | npu_conv2d_backward | +| 12 | npu_conv3d | +| 13 | npu_conv3d.out | +| 14 | npu_conv3d_backward | +| 15 | one_ | +| 16 | npu_sort_v2.out | +| 17 | npu_sort_v2 | +| 18 | npu_format_cast | +| 19 | npu_format_cast_.acl_format | +| 20 | npu_format_cast_.src | +| 21 | npu_transpose_to_contiguous | +| 22 | npu_transpose | +| 23 | npu_transpose.out | +| 24 | npu_broadcast | +| 25 | npu_broadcast.out | +| 26 | npu_dtype_cast | +| 27 | npu_dtype_cast_.Tensor | +| 28 | npu_roi_alignbk | +| 29 | empty_with_format | +| 30 | empty_with_format.names | +| 31 | copy_memory_ | +| 32 | npu_one_hot | +| 33 | npu_stride_add | +| 34 | npu_softmax_cross_entropy_with_logits | +| 35 | npu_softmax_cross_entropy_with_logits_backward | +| 36 | npu_ps_roi_pooling | +| 37 | npu_ps_roi_pooling_backward | +| 38 | npu_roi_align | +| 39 | npu_nms_v4 | +| 40 | npu_lstm | +| 41 | npu_lstm_backward | +| 42 | npu_iou | +| 43 | npu_ptiou | +| 44 | npu_nms_with_mask | +| 45 | npu_pad | +| 46 | npu_bounding_box_encode | +| 47 | npu_bounding_box_decode | +| 48 | npu_gru | +| 49 | npu_gru_backward | +| 50 | npu_set_.source_Storage_storage_offset_format | +| 51 | npu_random_choice_with_mask | +| 52 | npu_batch_nms | +| 53 | npu_slice | +| 54 | npu_slice.out | +| 55 | npu_dropoutV2 | +| 56 | npu_dropoutV2_backward | +| 57 | _npu_dropout | +| 58 | _npu_dropout_inplace | +| 59 | npu_dropout_backward | +| 60 | npu_indexing | +| 61 | npu_indexing.out | +| 62 | npu_ifmr | +| 63 | npu_max.dim | +| 64 | npu_max.names_dim | +| 65 | npu_scatter | +| 66 | npu_max_backward | +| 67 | npu_apply_adam | +| 68 | npu_layer_norm_eval | +| 69 | npu_alloc_float_status | +| 70 | npu_get_float_status | +| 71 | npu_clear_float_status | +| 72 | npu_confusion_transpose | +| 73 | npu_confusion_transpose_backward | +| 74 | npu_bmmV2 | +| 75 | fast_gelu | +| 76 | fast_gelu_backward | +| 77 | npu_sub_sample | +| 78 | npu_deformable_conv2d | +| 79 | npu_deformable_conv2dbk | +| 80 | npu_mish | +| 81 | npu_anchor_response_flags | +| 82 | npu_yolo_boxes_encode | +| 83 | npu_grid_assign_positive | +| 84 | npu_mish_backward | +| 85 | npu_normalize_batch | +| 86 | npu_masked_fill_range | +| 87 | npu_linear | +| 88 | npu_linear_backward | +| 89 | npu_bert_apply_adam | +| 90 | npu_giou | +| 91 | npu_giou_backward | + +详细算子接口说明： > ``` > npu_apply_adam(beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, use_locking, use_nesterov, out = (var, m, v)) -- Gitee From 7ba02260248cb02915f0ec15e7a1933cab899514 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Thu, 16 Dec 2021 17:21:56 +0800 Subject: [PATCH 25/55] quick start --- ...55\347\273\203\346\214\207\345\215\227.md" | 4072 ++++++++--------- 1 file changed, 2017 insertions(+), 2055 deletions(-) diff --git "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" index deb69bcb26c..bd5c1d664df 100644 --- "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" +++ "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" @@ -1,8 +1,11 @@ + + # PyTorch网络模型移植&训练指南 - [概述](#概述md) - [约束与限制](#约束与限制md) - [迁移流程](#迁移流程md) +- [快速上手](#快速上手) - [模型移植评估](#模型移植评估md) - [环境准备](#环境准备md) - [模型迁移](#模型迁移md) @@ -73,7 +76,7 @@

模型选取

-

详情请参见模型选取。

+

选取需要迁移的模型。

模型移植评估

@@ -134,2530 +137,2489 @@ -

模型移植评估

+## 快速上手 -1. 在选取模型时，尽可能选取权威Pytorch模型实现仓作为标杆，包括但不限于Pytorch\([example](https://github.com/pytorch/examples/tree/master/imagenet)/[vision](https://github.com/pytorch/vision)等\)、facebookresearch\([Detectron](https://github.com/facebookresearch/Detectron)/[detectron2](https://github.com/facebookresearch/detectron2)等\)和open-mmlab\([mmdetection](https://github.com/open-mmlab/mmdetection)/[mmpose](https://github.com/open-mmlab/mmpose)等\)。 -2. 查看算子适配情况。将原始模型及训练脚本迁移到昇腾AI处理器上之前，可以将原始模型及训练脚本在CPU上进行训练，使用dump op方法获取算子信息，与《PyTorch适配算子清单》算子进行比较，查看是否支持。dump op方法参见[dump op方法](#dump-op方法md)，当有不支持算子时参见《PyTorch算子开发指南》进行算子开发。 +### 简介 - >![](public_sys-resources/icon-note.gif) **说明：** - >查看算子适配情况也可以先将模型及训练脚本迁移到昇腾AI处理器（迁移方法参见下文）进行训练来查看报错信息。一般会提示不能在昇腾AI处理器的backend下运行某个算子（第一个不支持的算子）。 +对ResNet50模型进行迁移，帮助用户快速了解迁移过程。 +### 模型选取 -

环境准备

+本样例基于PyTorch官网提供的Imagenet数据集训练模型[main.py](https://github.com/pytorch/examples/tree/master/imagenet/main.py)脚本进行适配昇腾910 AI处理器的迁移。 -请参见《PyTorch安装指南》进行PyTorch及混合精度模块安装，并配置环境变量。 +### 模型移植评估 -

模型迁移

+模型是否可以迁移成功主要取决于模型算子是否支持昇腾AI处理器。故需要对模型算子对昇腾AI处理器的支持性进行评估，一般有两种方式评估算子支持性 +- 模型迁移前，使用dump op方法获取算子信息，与《PyTorch适配算子清单》算子进行比较，确定是否支持。 +- 模型迁移后，在昇腾设备上进行运行训练脚本，若存在不支持昇腾AI设备的算子，会提示报错信息。 -

工具迁移

+若存在不支持算子，可以采用修该模型用等价支持的算子替换或者参考《PyTorch算子开发指南》进行算子开发。 -Ascend平台提供了脚本转换工具使用户能通过命令行方式将训练脚本迁移到昇腾AI处理器上进行训练，命令行方式工具详细使用说明参见下文。除命令行方式外，用户也可通过MindStudio中集成的PyTorch GPU2Ascend功能进行迁移，详情请参见《MindStudio 用户指南》。 +ResNet50模型用到的算子已经在昇腾AI处理器上支持。 -

功能介绍

+### 环境准备 -**简介** +请参见《PyTorch安装指南》进行CANN软件安装、PyTorch框架及混合精度模块安装，并配置环境变量。 -昇腾NPU是AI算力的后起之秀，但目前训练和在线推理脚本大多是基于GPU的。由于NPU与GPU的架构差异，基于GPU的训练和在线推理脚本不能直接在NPU上使用，脚本转换工具提供了将基于GPU的脚本转换为基于NPU的脚本的自动化方法，节省了人工手动进行脚本迁移的学习成本与工作量，大幅提升了迁移效率。 +参考PyTorch [examples](https://github.com/pytorch/examples/tree/master/imagenet) 准备模型运行所需要的Python环境及依赖。 ->![](public_sys-resources/icon-note.gif) **说明：** ->- 脚本转换工具根据适配规则，对用户脚本给出修改建议并提供转换功能，大幅度提高了脚本迁移速度，降低了开发者的工作量。除使用[表1](#zh-cn_topic_0000001133095885_table4705239194613)里的脚本转换成功后可直接运行外，其他脚本的转换结果仅供参考，仍需用户根据实际情况做少量适配。 ->- [表1](#zh-cn_topic_0000001133095885_table4705239194613)里的原脚本需要在GPU环境下且基于python3能够跑通。 ->- [表1](#zh-cn_topic_0000001133095885_table4705239194613)里的脚本转换后的执行逻辑与转换前保持一致。 ->- 此脚本转换工具当前仅支持PyTorch训练脚本转换。 +### 模型迁移 -**表 1** 模型支持列表 +在main.py训练脚本的基础上进行修改，实现模型的单卡训练和单机多卡训练迁移。 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

序号

-

模型名称

-

1

-

3D AttentionNet

-

2

-

3D Nested_UNet

-

3

-

Advanced East

-

4

-

AlexNet

-

5

-

DeeplabV3+(Xception-JFT)

-

6

-

DeepMar

-

7

-

Densenet121

-

8

-

DenseNet161

-

9

-

DenseNet169

-

10

-

DenseNet201

-

11

-

EAST

-

12

-

FCN

-

13

-

FD-GAN

-

14

-

FOTS

-

15

-

GENet

-

16

-

GoogleNet

-

17

-

GRU

-

18

-

Inception V4

-

19

-

InceptionV2

-

20

-

LPRNet

-

21

-

LSTM

-

22

-

MNASNet0_5

-

23

-

MNASNet0_75

-

24

-

MNASNet1_0

-

25

-

MNASNet1_3

-

26

-

MobileNetV1

-

27

-

MobileNetV2

-

28

-

PNet

-

29

-

PSENet

-

30

-

RAFT

-

31

-

RecVAE

-

32

-

ResNet101

-

33

-

ResNet152

-

34

-

ResNet18

-

35

-

ResNet34

-

36

-

ResNet50

-

37

-

Resnext101_32x8d

-

38

-

Resnext50

-

39

-

RNet

-

40

-

Shufflenetv2

-

41

-

SqueezeNet1_0

-

42

-

SqueezeNet1_1

-

43

-

U-Net

-

44

-

VAE+GAN

-

45

-

VGG11

-

46

-

VGG11_BN

-

47

-

VGG13

-

48

-

VGG13_BN

-

49

-

VGG16

-

50

-

VGG16_BN

-

51

-

VGG19

-

52

-

VGG19_BN

-

53

-

VIT-base

-

54

-

Wide_ResNet101_2

-

55

-

Wide_ResNet50_2

-
-**系统要求** - -脚本转换工具支持Ubuntu 18.04、CentOS 7.6或EulerOS 2.8。 +#### 单卡训练迁移 -**环境准备** +1. 在main.py脚本中导入torch.npu模块。 -详情请参考《CANN 软件安装指南》安装开发环境。 + ```python + import torch.npu + ``` -

操作指南

+2. 在main.py中定义训练设备。 -**参数说明** + ```python + CALCULATE_DEVICE = "npu:0" + ``` -**表 1** 参数说明 +3. 修改参数以及判断选项，使其只在昇腾910 AI处理器上进行训练。 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

参数

-

参数说明

-

取值示例

-

-i

-

--input

-
  • 要进行转换的原始脚本文件所在文件夹路径或文件路径。
  • 必选。
-
  • /home/username/fmktransplt
  • /home/username/fmktransplt.py
-

-o

-

--output

-
  • 脚本转换结果文件输出路径。会在该路径下输出带有msft后缀的文件夹。
  • 必选。
-

/home/username/fmktransplt_output

-

-r

-

--rule

-
  • 用户自定义通用转换规则的json文件路径，主要分为：函数参数修改、函数名称修改和模块名称修改三部分。
  • 可选。
-

/home/username/fmktransplt_rule.json

-

-s

-

--specify-device

-
  • 可以通过环境变量设置指定device作为高级特性，但有可能导致原本脚本中分布式功能失效。
  • 可选。
-

-

-

-sim

-

--similar

-
  • 用功能相似的API替换某些不支持的API，但有可能导致准确性和性能下降。
  • 可选。
-

-

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distributed

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将GPU单卡脚本转换为NPU多卡脚本，仅支持使用torch.utils.data.DataLoader方式加载数据的场景，指定此参数时，才可以指定以下两个参数。

-
  • -m，--main：训练脚本的入口python文件，必选。
  • -t，--target model：目标模型变量名，默认为'model'，可选。
-

-

-

-h

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--help

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显示帮助信息。

-

-

-
-**自定义规则文件** + 代码位置：main.py文件中的main\_worker\(\)函数： -自定义转换规则样例如下： + ```python + def main_worker(gpu, ngpus_per_node, args): + global best_acc1 + # 原代码为使用GPU进行训练，原代码如下： + # args.gpu = gpu + ############## npu modify begin ############# + args.gpu = None + ############## npu modify end ############# + + if args.gpu is not None: + print("Use GPU: {} for training".format(args.gpu)) + + if args.distributed: + if args.dist_url == "env://" and args.rank == -1: + args.rank = int(os.environ["RANK"]) + if args.multiprocessing_distributed: + # For multiprocessing distributed training, rank needs to be the + # global rank among all the processes + args.rank = args.rank * ngpus_per_node + gpu + dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url, + world_size=args.world_size, rank=args.rank) + # create model + if args.pretrained: + print("=> using pre-trained model '{}'".format(args.arch)) + model = models.__dict__[args.arch](pretrained=True) + else: + print("=> creating model '{}'".format(args.arch)) + model = models.__dict__[args.arch]() + # 原代码中需要判断是否在GPU上进行训练，原代码如下： + # if not torch.cuda.is_available(): + # print('using CPU, this will be slow') + # elif args.distributed: + ############## npu modify begin ############# + # 迁移后为直接判断是否进行分布式训练，去掉判断是否在GPU上进行训练 + if args.distributed: + ############## npu modify end ############# + # For multiprocessing distributed, DistributedDataParallel constructor + # should always set the single device scope, otherwise, + # DistributedDataParallel will use all available devices. + if args.gpu is not None: + ...... + ``` -``` -{ - "rules": { - "ArgsModifyRule": [ - { - "func_name": "name1", - "arg_idx": 0, - "arg_new": "agrs0" - }, - { - "func_name": "name2", - "arg_idx": 0, - "arg_new": "agrs0" - } - ], - "FuncNameModifyRule": [ - { - "old_name": "func", - "new_name": "new_func" - } - ], - "ModuleNameModifyRule": [ - { - "old_name": "module", - "new_name": "new_module", - "parent_module":"parent_module" - } - ] - } -} -``` +4. 将模型以及损失函数迁移到昇腾910 AI处理器上进行计算。 + + 代码位置：main.py文件中的main\_worker\(\)函数： + + ```python + elif args.gpu is not None: + torch.cuda.set_device(args.gpu) + model = model.cuda(args.gpu) + else: + # DataParallel will divide and allocate batch_size to all available GPUs + if args.arch.startswith('alexnet') or args.arch.startswith('vgg'): + model.features = torch.nn.DataParallel(model.features) + model.cuda() + else: + # 原代码使用torch.nn.DataParallel()类来用多个GPU加速训练 + # model = torch.nn.DataParallel(model).cuda() + ############## npu modify begin ############# + # 将模型迁移到NPU上进行训练。 + model = model.to(CALCULATE_DEVICE) + ############## npu modify end ############# + # 原代码中损失函数是在GPU上进行计算 + # # define loss function (criterion) and optimizer + # criterion = nn.CrossEntropyLoss().cuda(args.gpu) + ############## npu modify begin ############# + # 将损失函数迁移到NPU上进行计算。 + criterion = nn.CrossEntropyLoss().to(CALCULATE_DEVICE) + ############## npu modify end ############# + ``` -**表 2** 参数说明 +5. 将数据集目标结果target修改成int32类型解决算子报错问题；将数据集迁移到昇腾910 AI处理器上进行计算。 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

参数

-

说明

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ArgsModifyRule

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函数参数修改

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func_name

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函数名称

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arg_idx

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参数的位置

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arg_new

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新的参数

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FuncNameModifyRule

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函数名称修改

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ModuleNameModifyRule

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模块名称修改

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old_name

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旧名称

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new_name

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新名称

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parent_module

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父级模块全名

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例如torch.cuda.amp，amp的父级模块全名为torch.cuda。

-
-**执行转换** + - 代码位置：main.py文件中的train\(\)函数： -1. 进入脚本转换工具所在路径。 + ```python + for i, (images, target) in enumerate(train_loader): + # measure data loading time + data_time.update(time.time() - end) + + if args.gpu is not None: + images = images.cuda(args.gpu, non_blocking=True) + # 原代码中训练数据集在GPU上进行加载计算，原代码如下： + # if torch.cuda.is_available(): + # target = target.cuda(args.gpu, non_blocking=True) + ############## npu modify begin ############# + # 将数据集迁移到NPU上进行计算并修改target数据类型，以提升性能 + if 'npu' in CALCULATE_DEVICE: + target = target.to(torch.int32) + images, target = images.to(CALCULATE_DEVICE, non_blocking=True), target.to(CALCULATE_DEVICE, non_blocking=True) + ############## npu modify end ############# + ``` - ``` - cd Ascend-cann-toolkit安装目录/ascend-toolkit/{version}/{arch}-linux/toolkit/tools/ms_fmk_transplt - ``` + - 代码位置：main.py文件中的validate\(\)函数： + + ```python + with torch.no_grad(): + end = time.time() + for i, (images, target) in enumerate(val_loader): + if args.gpu is not None: + images = images.cuda(args.gpu, non_blocking=True) + # 原代码中训练数据集在GPU上进行加载计算，原代码如下： + # if torch.cuda.is_available(): + # target = target.cuda(args.gpu, non_blocking=True) + ############## npu modify begin ############# + # 将数据集迁移到NPU上进行计算并修改target数据类型 + if 'npu' in CALCULATE_DEVICE: + target = target.to(torch.int32) + images, target = images.to(CALCULATE_DEVICE, non_blocking=True), target.to(CALCULATE_DEVICE, non_blocking=True) + ############## npu modify end ############# + ``` -2. 执行脚本转换工具。 +6. 设置当前正在使用的device。 - ``` - python3 ms_fmk_transplt.py -i 原始脚本路径 -o 脚本转换结果输出路径 [-r 自定义规则json文件路径] [-s] [-sim] [distributed -m 训练脚本的入口文件 -t 目标模型变量名] - ``` + 代码位置：main.py文件中的主函数入口： - >![](public_sys-resources/icon-note.gif) **说明：** - >distributed及其参数-m、-t在语句最后指定。 + ```python + if __name__ == '__main__': + ############## npu modify begin ############# + if 'npu' in CALCULATE_DEVICE: + torch.npu.set_device(CALCULATE_DEVICE) + ############## npu modify begin ############# + main() + ``` -3. 完成脚本转换。 +#### 单机多卡训练修改 -

结果解析

+1. main.py增加头文件以支持基于PyTorch框架的模型在昇腾910 AI处理器上训练及进行混合精度训练。 -脚本转换完成后，进入脚本转换结果输出路径查看结果文件，以GPU单卡脚本转换为NPU多卡脚本为例。 + ```python + import torch.npu + from apex import amp + ``` -``` -├── xxx_msft // 脚本转换结果输出目录，默认为原始脚本路径。xxx为原始脚本所在文件夹名称。 -│ ├── 生成脚本文件 // 与转换前的脚本文件目录结构一致。 -│ ├── msFmkTranspltlog.txt // 脚本转换过程日志文件。 -│ ├── unsupported_op.xlsx // 不支持算子列表文件。 -│ ├── change_list.csv // 修改记录文件。 -│ ├── run_distributed_npu.sh // 多卡启动shell脚本。 -``` +2. 参数设置增加以下参数，包括指定参与训练的昇腾910 AI处理器以及进行混合精度训练需要的参数。 -

手工迁移

+ ```python + parser.add_argument('--device', default='npu', type=str, help='npu or gpu') + parser.add_argument('--addr', default='10.136.181.115', type=str, help='master addr') + parser.add_argument('--device-list', default='0,1,2,3,4,5,6,7', type=str, help='device id list') + parser.add_argument('--amp', default=False, action='store_true', help='use amp to train the model') + parser.add_argument('--loss-scale', default=1024., type=float, + help='loss scale using in amp, default -1 means dynamic') + parser.add_argument('--opt-level', default='O2', type=str, + help='loss scale using in amp, default -1 means dynamic') + ``` +3. 创建由device\_id到process\_id的映射函数，指定device进行训练。在main.py函数中增加以下接口。 -

单P训练模型迁移

- -当前在线对接方案优点在于保证在昇腾AI处理器上训练与GPU的使用方式和风格保持一致。用户在使用在线对接方案时，**只需在Python侧和Device相关操作中，指定device为昇腾AI处理器**，即可完成用昇腾AI处理器在PyTorch对网络的开发、训练以及调试。针对单P模型训练，主要迁移改动如下。 - -迁移前GPU代码： + ```python + def device_id_to_process_device_map(device_list): + devices = device_list.split(",") + devices = [int(x) for x in devices] + devices.sort() + + process_device_map = dict() + for process_id, device_id in enumerate(devices): + process_device_map[process_id] = device_id + + return process_device_map + ``` -``` - CALCULATE_DEVICE = “gpu:0” - torch.cuda.set_device(CALCULATE_DEVICE) - # 放到device的两种方法 - model = model.cuda() # 写法1 - model = model.to(CALCULATE_DEVICE) # 写法2 - # 将输入也从host放到device - images = images.to(CALCULATE_DEVICE) - target = target.to(CALCULATE_DEVICE) -``` +4. 指定训练服务器的ip和端口。 -迁移到昇腾AI处理器上代码为： + 代码位置：main.py文件中的主函数main\(\)（修改部分为字体加粗部分）。 -``` - CALCULATE_DEVICE = “npu:0” - torch.npu.set_device(CALCULATE_DEVICE) - # 放到device的两种方法 - model = model.npu() # 写法1 - model = model.to(CALCULATE_DEVICE) # 写法2 - # 将输入也从host放到device - images = images.to(CALCULATE_DEVICE) - target = target.to(CALCULATE_DEVICE) -``` + ```python + def main(): + args = parser.parse_args() + ############## npu modify begin ############# + os.environ['MASTER_ADDR'] = args.addr + os.environ['MASTER_PORT'] = '29688' + ############## npu modify end ############# + ``` -更多迁移细节请参见[单P训练修改](#单P训练修改md)。 +5. 创建由device\_id到process\_id的映射参数，获取单节点昇腾910 AI处理器数量。 -

多P训练模型迁移

+ 代码位置：main.py文件中的主函数main\(\)。 -多P训练模型迁移除了需在**Python侧和Device相关操作中，指定device为昇腾AI处理器**外，依然通过PyTorch的DistributedDataParallel方式来进行分布式训练，即在模型初始化阶段执行init\_process\_group，再将模型初始化为DistributedDataParallel模型。但须注意的是在初始化init\_process\_group时需要将**backend**配置为**hccl**并屏蔽掉初始化方式。 + ```python + args.distributed = args.world_size > 1 or args.multiprocessing_distributed + ############## npu modify begin ############# + args.process_device_map = device_id_to_process_device_map(args.device_list) + if args.device == 'npu': + ngpus_per_node = len(args.process_device_map) + else: + ngpus_per_node = torch.cuda.device_count() + ############## npu modify end ############# + # 原代码如下： + # ngpus_per_node = torch.cuda.device_count() + ``` -PyTorch分布式训练代码示例（部分代码省略）： +6. 获取进程process\_id对应的昇腾910 AI处理器编号，指定在对应的昇腾910 AI处理器上进行训练。 -``` -import torch -import torch.distributed as dist -import torch.nn.parallel -def main(): - args = parser.parse_args() - # 需屏蔽掉初始化方式 - dist.init_process_group(backend='hccl',# init_method=args.dist_url, - world_size=args.world_size, rank=args.rank) - model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) # model需要下发到npu上 - train_loader = torch.utils.data.DataLoader( - train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None), - num_workers=args.workers, pin_memory=True, sampler=train_sampler) - for epoch in range(args.start_epoch, args.epochs): - acc1 = train(train_loader, model, criterion, optimizer, epoch, args,ngpus_per_node, - lr_scheduler) -``` + 代码位置：main.py文件中的main\_worker\(\)。 -更多迁移细节请参见[分布式训练修改](#分布式训练修改md)。 + ```python + def main_worker(gpu, ngpus_per_node, args): + global best_acc1 + ############## npu modify begin ############# + args.gpu = args.process_device_map[gpu] + ############## npu modify end ############# + # 原代码如下： + # args.gpu = gpu + ``` -

PyTorch接口替换

+7. 初始化进程组，屏蔽掉初始化方式。 -1. 为了使昇腾AI处理器使用PyTorch框架的能力，需要对原生的PyTorch框架进行一定Device层面的适配，对外呈现是需要将跟cpu和cuda相关的接口进行切换；在进行网络迁移时，需要将某些设备相关的接口转换成跟昇腾AI处理器相关的接口，当前适配的设备相关接口请参见[表1](#table1922064517344)： + 代码位置：main.py文件中的main\_worker\(\)。 - **表 1** 设备接口替换 + ```python + ############## npu modify begin ############# + if args.device == 'npu': + dist.init_process_group(backend=args.dist_backend, #init_method=args.dist_url, + world_size=args.world_size, rank=args.rank) + else: + dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url, + world_size=args.world_size, rank=args.rank) + ############## npu modify begin ############# + # 原代码如下： + # dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url, + # world_size=args.world_size, rank=args.rank) + ``` - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

PyTorch原始接口

-

适配昇腾AI处理器后的接口

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说明

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torch.cuda.is_available()

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torch.npu.is_available()

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判断当前环境上设备是否可用（不代表最后结果）。

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torch.cuda.current_device()

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torch.npu.current_device()

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获取当前正在使用的device。

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torch.cuda.device_count()

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torch.npu.device_count()

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获取当前环境上的设备数量。

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torch.cuda.set_device()

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torch.npu.set_device()

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设置当前正在使用的device。

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torch.tensor([1,2,3]).is_cuda

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torch.tensor([1,2,3]).is_npu

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判断某个tensor是否是cuda/npu设备上的格式。

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torch.tensor([1,2,3]).cuda()

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torch.tensor([1,2,3]).npu()

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将某个tensor转换成cuda/npu格式。

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torch.tensor([1,2,3]).to("cuda")

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torch.tensor([1,2,3]).to('npu')

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将某个tensor转换成cuda/npu格式。

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torch.cuda.synchronize()

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torch.npu.synchronize()

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同步等待事件完成。

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torch.cuda.device

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torch.npu.device

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生成一个device类，可以执行device相关操作。

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torch.cuda.Stream(device)

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torch.npu.Stream(device)

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生成一个stream对象。

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torch.cuda.stream(Stream)

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torch.npu.stream(Stream)

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多用于作用域限定。

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torch.cuda.current_stream()

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torch.npu.current_stream()

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获取当前stream。

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torch.cuda.default_stream()

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torch.npu.default_stream()

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获取默认stream。

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device = torch.device("cuda:0")

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device = torch.device("npu:0")

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指定一个设备。

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torch.autograd.profiler.profile

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(use_cuda=True)

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torch.autograd.profiler.profile

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(use_npu=True)

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指定执行profiler过程中使用cuda/npu。

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torch.cuda.Event()

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torch.npu.Event()

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返回某个设备上的事件。

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+8. 要进行分布式训练且需要引入混合精度模块，并且需要将模型迁移到昇腾AI处理器上，因此需要屏蔽掉原始代码中判断是否为分布式训练以及模型是否在GPU上进行训练的代码部分。 -2. 用户在构建网络或进行网络迁移时，需要创建指定数据类型的tensor，在昇腾AI处理器上创建的tensor如下。 + 代码位置：main.py文件中的main\_worker\(\)。 - **表 2** tensor创建接口替换 + ```python + # create model + if args.pretrained: + print("=> using pre-trained model '{}'".format(args.arch)) + model = models.__dict__[args.arch](pretrained=True) + else: + print("=> creating model '{}'".format(args.arch)) + model = models.__dict__[args.arch]() + ############## npu modify begin ############# + # 代码中添加如下内容 + # 指定训练设备为昇腾AI处理器 + loc = 'npu:{}'.format(args.gpu) + torch.npu.set_device(loc) + # 计算用于训练的batch_size和workers + args.batch_size = int(args.batch_size / ngpus_per_node) + args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node) + ############## npu modify end ############# + # 原始代码如下，需屏蔽掉，已注释 + # if not torch.cuda.is_available(): + # print('using CPU, this will be slow') + # elif args.distributed: + # # For multiprocessing distributed, DistributedDataParallel constructor + # # should always set the single device scope, otherwise, + # # DistributedDataParallel will use all available devices. + # if args.gpu is not None: + # torch.cuda.set_device(args.gpu) + # model.cuda(args.gpu) + # # When using a single GPU per process and per + # # DistributedDataParallel, we need to divide the batch size + # # ourselves based on the total number of GPUs we have + # args.batch_size = int(args.batch_size / ngpus_per_node) + # args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node) + # model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) + # else: + # model.cuda() + # # DistributedDataParallel will divide and allocate batch_size to all + # # available GPUs if device_ids are not set + # model = torch.nn.parallel.DistributedDataParallel(model) + # elif args.gpu is not None: + # torch.cuda.set_device(args.gpu) + # model = model.cuda(args.gpu) + # else: + # # DataParallel will divide and allocate batch_size to all available GPUs + # if args.arch.startswith('alexnet') or args.arch.startswith('vgg'): + # model.features = torch.nn.DataParallel(model.features) + # model.cuda() + # else: + # model = torch.nn.DataParallel(model).cuda() + ``` - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

GPU tensor

-

适配昇腾AI处理器后的接口

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torch.tensor([1,2,3],dtype=torch.long,device='cuda')

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torch.tensor([1,2,3],dtype=torch.long,device='npu')

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torch.tensor([1,2,3],dtype=torch.int,device='cuda')

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torch.tensor([1,2,3],dtype=torch.int,device='npu')

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torch.tensor([1,2,3],dtype=torch.half,device='cuda')

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torch.tensor([1,2,3],dtype=torch.half,device='npu')

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torch.tensor([1,2,3],dtype=torch.float,device='cuda')

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torch.tensor([1,2,3],dtype=torch.float,device='npu')

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torch.tensor([1,2,3],dtype=torch.bool,device='cuda')

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torch.tensor([1,2,3],dtype=torch.bool,device='npu')

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torch.cuda.BoolTensor([1,2,3])

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torch.npu.BoolTensor([1,2,3])

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torch.cuda.FloatTensor([1,2,3])

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torch.npu.FloatTensor([1,2,3])

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torch.cuda.IntTensor([1,2,3])

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torch.npu.IntTensor([1,2,3])

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torch.cuda.LongTensor([1,2,3])

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torch.npu.LongTensor([1,2,3])

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torch.cuda.HalfTensor([1,2,3])

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torch.npu.HalfTensor([1,2,3])

-
+9. 屏蔽掉损失函数、优化器和断点训练部分，将这部分在后面与混合精度训练结合起来。 + 代码位置：main.py文件中的main\_worker\(\)。 -更多接口请参见《PyTorch API支持清单》。 + ```python + # 屏蔽掉原始代码，已注释 + # # define loss function (criterion) and optimizer + # criterion = nn.CrossEntropyLoss().cuda(args.gpu) + # + # optimizer = torch.optim.SGD(model.parameters(), args.lr, + # momentum=args.momentum, + # weight_decay=args.weight_decay) + # + # # optionally resume from a checkpoint + # if args.resume: + # if os.path.isfile(args.resume): + # print("=> loading checkpoint '{}'".format(args.resume)) + # if args.gpu is None: + # checkpoint = torch.load(args.resume) + # else: + # # Map model to be loaded to specified single gpu. + # loc = 'cuda:{}'.format(args.gpu) + # checkpoint = torch.load(args.resume, map_location=loc) + # args.start_epoch = checkpoint['epoch'] + # best_acc1 = checkpoint['best_acc1'] + # if args.gpu is not None: + # # best_acc1 may be from a checkpoint from a different GPU + # best_acc1 = best_acc1.to(args.gpu) + # model.load_state_dict(checkpoint['state_dict']) + # optimizer.load_state_dict(checkpoint['optimizer']) + # print("=> loaded checkpoint '{}' (epoch {})" + # .format(args.resume, checkpoint['epoch'])) + # else: + # print("=> no checkpoint found at '{}'".format(args.resume)) + # + # cudnn.benchmark = True + ``` -

混合精度

+10. 数据加载器，结合了数据集和取样器，并且可以提供多个线程处理数据集。使用昇腾AI处理器进行训练，需要将**pin\_memory**设置为**False**；由于当前仅支持固定shape下的训练，数据流中剩余的样本数可能小于batch大小，因此需要将**drop\_last**设置为**True**；另外需要将验证部分数据集**shuffle**设置为**True**。 -**概述** + 代码位置：main.py文件中的main\_worker\(\)。 -基于NPU芯片的架构特性，会涉及到混合精度训练，即混合使用float16和float32数据类型的应用场景。使用float16代替float32有如下好处： + ```python + ############## npu modify begin ############# + train_loader = torch.utils.data.DataLoader( + train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None), + num_workers=args.workers, pin_memory=False, sampler=train_sampler, drop_last=True) + + val_loader = torch.utils.data.DataLoader( + datasets.ImageFolder(valdir, transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + normalize, + ])), + batch_size=args.batch_size, shuffle=True, + num_workers=args.workers, pin_memory=False, drop_last=True) + ############## npu modify end ############# + ``` -- 对于中间变量的内存占用更少，节省内存的使用。 -- 因内存使用会减少，所以数据传出的时间也会相应减少。 -- float16的计算单元可以提供更快的计算性能。 +11. 进行损失函数及优化器构建，将模型、损失函数迁移到昇腾AI处理器上；将优化器、模型与混合精度模块进行结合以支持混合精度训练；将断点训练部分与混合精度模块结合以支持混合精度训练。 -但是，混合精度训练受限于float16表达的精度范围，单纯将float32转换成float16会影响训练收敛情况，为了保证部分计算使用float16来进行加速的同时能保证训练收敛，这里采用混合精度模块Apex来达到以上效果。混合精度模块Apex是一个集优化性能、精度收敛于一身的综合优化库。 + 代码位置：main.py文件中的main\_worker\(\)中验证数据加载后。 -适配昇腾AI处理器的混合精度模块Apex除了上述优点外，还能提升运算性能。具体如下： + ```python + val_loader = torch.utils.data.DataLoader( + datasets.ImageFolder(valdir, transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + normalize, + ])), + batch_size=args.batch_size, shuffle=True, + num_workers=args.workers, pin_memory=False, drop_last=True) + + ############## npu modify begin ############# + model = model.to(loc) + # define loss function (criterion) and optimizer + criterion = nn.CrossEntropyLoss().to(loc) + optimizer = torch.optim.SGD(model.parameters(), args.lr, + momentum=args.momentum, + weight_decay=args.weight_decay) + + if args.amp: + model, optimizer = amp.initialize(model, optimizer, opt_level=args.opt_level, loss_scale=args.loss_scale) + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) + + # optionally resume from a checkpoint + if args.resume: + if os.path.isfile(args.resume): + print("=> loading checkpoint '{}'".format(args.resume)) + checkpoint = torch.load(args.resume, map_location=loc) + args.start_epoch = checkpoint['epoch'] + best_acc1 = checkpoint['best_acc1'] + model.load_state_dict(checkpoint['state_dict']) + optimizer.load_state_dict(checkpoint['optimizer']) + if args.amp: + amp.load_state_dict(checkpoint['amp']) + print("=> loaded checkpoint '{}' (epoch {})" + .format(args.resume, checkpoint['epoch'])) + else: + print("=> no checkpoint found at '{}'".format(args.resume)) + + cudnn.benchmark = True + ############## npu modify end ############# + ``` -- Apex在混合精度运算过程中，会对模型的grad进行运算。开启combine\_grad开关，可以加速这些运算。具体为将amp.initialize\(\)接口参数combine\_grad设置为True； -- 适配后的Apex针对adadelta/adam/sgd/lamb做了昇腾AI处理器亲和性优化，得到的NPU融合优化器与原生算法保持一致，但运算速度更快。使用时只需将原有优化器替换为apex.optimizers.\*（“\*”为优化器名称，例如NpuFusedSGD）。 +12. 断点checkpoint保存需要与混合精度训练结合，修改如下。 -**特性支持** + 代码位置：main.py文件中的main\_worker\(\)（修改部分为字体加粗部分）。 -混合精度模块功能和优化描述如[表1](#table10717173813332)所示。 + ```python + # remember best acc@1 and save checkpoint + is_best = acc1 > best_acc1 + best_acc1 = max(acc1, best_acc1) + + if not args.multiprocessing_distributed or (args.multiprocessing_distributed + and args.rank % ngpus_per_node == 0): + ############## npu modify begin ############# + if args.amp: + save_checkpoint({ + 'epoch': epoch + 1, + 'arch': args.arch, + 'state_dict': model.state_dict(), + 'best_acc1': best_acc1, + 'optimizer' : optimizer.state_dict(), + 'amp': amp.state_dict(), + }, is_best) + else: + save_checkpoint({ + 'epoch': epoch + 1, + 'arch': args.arch, + 'state_dict': model.state_dict(), + 'best_acc1': best_acc1, + 'optimizer' : optimizer.state_dict(), + }, is_best) + ############## npu modify end ############# + ``` -**表 1** 混合精度模块功能 +13. 训练时，需要将数据集迁移到昇腾AI处理器上，修改如下： - -

功能

+ 代码位置：main.py文件中的train\(\)（修改部分为字体加粗部分）。 + + ```python + for i, (images, target) in enumerate(train_loader): + # measure data loading time + data_time.update(time.time() - end) + ############## npu modify begin ############# + loc = 'npu:{}'.format(args.gpu) + target = target.to(torch.int32) + images, target = images.to(loc, non_blocking=False), target.to(loc, non_blocking=False) + ############## npu modify end ############# + # 原模型代码如下： + # if args.gpu is not None: + # images = images.cuda(args.gpu, non_blocking=True) + # if torch.cuda.is_available(): + # target = target.cuda(args.gpu, non_blocking=True) + ``` + +14. 标记反向传播.backward\(\)发生的位置，这样混合精度模块就可以进行Loss Scaling并清除每次迭代的状态，代码如下： + + 代码位置：main.py文件中的train\(\)（修改部分为字体加粗部分）。 + + ```python + optimizer.zero_grad() + ############## npu modify begin ############# + if args.amp: + with amp.scale_loss(loss, optimizer) as scaled_loss: + scaled_loss.backward() + else: + loss.backward() + ############## npu modify end ############# + # 原代码如下注释部分： + # loss.backward() + optimizer.step() + ``` + +15. 验证时，需要将验证数据集迁移到昇腾AI处理器上，修改如下： + + 代码位置：main.py文件中的validate\(\)。 + + ```python + with torch.no_grad(): + end = time.time() + for i, (images, target) in enumerate(val_loader): + ############## npu modify begin ############# + loc = 'npu:{}'.format(args.gpu) + target = target.to(torch.int32) + images, target = images.to(loc, non_blocking=False), target.to(loc, non_blocking=False) + ############## npu modify end ############# + # 原模型代码如下注释部分： + # if args.gpu is not None: + # images = images.cuda(args.gpu, non_blocking=True) + # if torch.cuda.is_available(): + # target = target.cuda(args.gpu, non_blocking=True) + ``` + +### 模型训练 + +**准备数据集** + +准备数据集并上传到运行环境的目录下，例如：/home/data/resnet50/imagenet。 + +**执行命令** + +单卡训练: + +```shell +python3 main.py /home/data/resnet50/imagenet --batch-size 128 \ # 训练批次大小 + --lr 0.1 \ # 学习率 + --epochs 90 \ # 训练迭代轮数 + --arch resnet50 \ # 模型架构 + --world-size 1 \ + --rank 0 \ + --workers 40 \ # 加载数据进程数 + --momentum 0.9 \ # 动量 + --weight-decay 1e-4 # 权重衰减 +``` + +分布式训练： + +```shell +python3 main.py /home/data/resnet50/imagenet --addr='1.1.1.1' \ # 示例IP地址，请根据实际修改 + --seed 49 \ # 随机种子 + --workers 160 \ # 加载数据进程数 + --lr 0.8 \ + --print-freq 1 \ + --arch resnet50 \ # 模型架构 + --dist-url 'tcp://127.0.0.1:50000' \ + --dist-backend 'hccl' \ + --multiprocessing-distributed \ # 使用多卡训练 + --world-size 1 \ + --batch-size 2048 \ # 训练批次大小 + --epochs 90 \ # 训练迭代轮数 + --rank 0 \ + --device-list '0,1,2,3,4,5,6,7' \ + --amp # 使用混合精度训练 +``` + +>![](public_sys-resources/icon-note.gif) **说明：** +>dist-backend需配置成hccl以支持在昇腾AI设备上进行分布式训练。 + + +

模型移植评估

+ +1. 在选取模型时，尽可能选取权威Pytorch模型实现仓作为标杆，包括但不限于Pytorch\([example](https://github.com/pytorch/examples/tree/master/imagenet)/[vision](https://github.com/pytorch/vision)等\)、facebookresearch\([Detectron](https://github.com/facebookresearch/Detectron)/[detectron2](https://github.com/facebookresearch/detectron2)等\)和open-mmlab\([mmdetection](https://github.com/open-mmlab/mmdetection)/[mmpose](https://github.com/open-mmlab/mmpose)等\)。 +2. 查看算子适配情况。将原始模型及训练脚本迁移到昇腾AI处理器上之前，可以将原始模型及训练脚本在CPU上进行训练，使用dump op方法获取算子信息，与《PyTorch适配算子清单》算子进行比较，查看是否支持。dump op方法参见[dump op方法](#dump-op方法md)，当有不支持算子时参见《PyTorch算子开发指南》进行算子开发。 + + >![](public_sys-resources/icon-note.gif) **说明：** + >查看算子适配情况也可以先将模型及训练脚本迁移到昇腾AI处理器（迁移方法参见下文）进行训练来查看报错信息。一般会提示不能在昇腾AI处理器的backend下运行某个算子（第一个不支持的算子）。 + + +

环境准备

+ +请参见《PyTorch安装指南》进行PyTorch及混合精度模块安装，并配置环境变量。 + +

模型迁移

+ + +

工具迁移

+ +Ascend平台提供了脚本转换工具使用户能通过命令行方式将训练脚本迁移到昇腾AI处理器上进行训练，命令行方式工具详细使用说明参见下文。除命令行方式外，用户也可通过MindStudio中集成的PyTorch GPU2Ascend功能进行迁移，详情请参见《MindStudio 用户指南》。 + +

功能介绍

+ +**简介** + +昇腾NPU是AI算力的后起之秀，但目前训练和在线推理脚本大多是基于GPU的。由于NPU与GPU的架构差异，基于GPU的训练和在线推理脚本不能直接在NPU上使用，脚本转换工具提供了将基于GPU的脚本转换为基于NPU的脚本的自动化方法，节省了人工手动进行脚本迁移的学习成本与工作量，大幅提升了迁移效率。 + +>![](public_sys-resources/icon-note.gif) **说明：** +>- 脚本转换工具根据适配规则，对用户脚本给出修改建议并提供转换功能，大幅度提高了脚本迁移速度，降低了开发者的工作量。除使用[表1](#zh-cn_topic_0000001133095885_table4705239194613)里的脚本转换成功后可直接运行外，其他脚本的转换结果仅供参考，仍需用户根据实际情况做少量适配。 +>- [表1](#zh-cn_topic_0000001133095885_table4705239194613)里的原脚本需要在GPU环境下且基于python3能够跑通。 +>- [表1](#zh-cn_topic_0000001133095885_table4705239194613)里的脚本转换后的执行逻辑与转换前保持一致。 +>- 此脚本转换工具当前仅支持PyTorch训练脚本转换。 + +**表 1** 模型支持列表 + + + - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + +

序号

描述

+

模型名称

O1配置模式

+ +

1

+

3D AttentionNet

+

2

+

3D Nested_UNet

+

3

+

Advanced East

+

4

+

AlexNet

+

5

+

DeeplabV3+(Xception-JFT)

+

6

+

DeepMar

+

7

+

Densenet121

+

8

+

DenseNet161

+

9

+

DenseNet169

+

10

+

DenseNet201

+

11

+

EAST

+

12

+

FCN

+

13

+

FD-GAN

+

14

+

FOTS

+

15

+

GENet

+

16

+

GoogleNet

+

17

+

GRU

+

18

+

Inception V4

+

19

+

InceptionV2

+

20

+

LPRNet

+

21

+

LSTM

+

22

+

MNASNet0_5

+

23

+

MNASNet0_75

+

24

+

MNASNet1_0

+

25

+

MNASNet1_3

+

26

+

MobileNetV1

+

27

+

MobileNetV2

+

28

+

PNet

+

29

+

PSENet

+

30

+

RAFT

+

31

+

RecVAE

+

32

+

ResNet101

+

33

+

ResNet152

+

34

+

ResNet18

+

35

+

ResNet34

+

36

+

ResNet50

+

37

+

Resnext101_32x8d

+

38

+

Resnext50

+

39

+

RNet

+

40

+

Shufflenetv2

+

41

+

SqueezeNet1_0

+

42

+

SqueezeNet1_1

+

43

Conv，Matmul等使用float16计算，其他如Softmax、BN使用float32。

+

U-Net

O2配置模式

+

44

除了BN使用float32外，其他绝大部分使用float16。

+

VAE+GAN

静态Loss Scale功能

+

45

静态设置参数确保混合精度训练收敛。

+

VGG11

动态Loss Scale功能

+

46

动态计算loss Scale值并判断是否溢出。

+

VGG11_BN

+

47

+

VGG13

+

48

+

VGG13_BN

+

49

+

VGG16

+

50

+

VGG16_BN

+

51

+

VGG19

+

52

+

VGG19_BN

+

53

+

VIT-base

+

54

+

Wide_ResNet101_2

+

55

+

Wide_ResNet50_2
+**系统要求** ->![](public_sys-resources/icon-note.gif) **说明：** ->- 当前版本的实现方式主要为python实现，不支持AscendCL或者CUDA优化。 ->- 当前昇腾AI设备暂不支持原始Apex的FusedLayerNorm接口模块，如果模型原始脚本文件使用了FusedLayerNorm接口模块，需要在模型迁移过程中将脚本头文件“from apex.normalization import FusedLayerNorm“替换为“from torch.nn import LayerNorm“。 - -**将混合精度模块集成到PyTorch模型中** - -1. 使用apex混合精度模块需要首先从apex库中导入amp，代码如下： - - ``` - from apex import amp - ``` - -2. 导入amp模块后，需要初始化amp，使其能对模型、优化器以及PyTorch内部函数进行必要的改动，初始化代码如下： - - ``` - model, optimizer = amp.initialize(model, optimizer，combine_grad=True) - ``` - -3. 标记反向传播.backward\(\)发生的位置，这样Amp就可以进行Loss Scaling并清除每次迭代的状态，代码如下： - - 原始代码： - - ``` - loss = criterion(…) - loss.backward() - optimizer.step() - ``` - - 修改以支持loss scaling后的代码： - - ``` - loss = criterion(…) - with amp.scale_loss(loss, optimizer) as scaled_loss: - scaled_loss.backward() - optimizer.step() - ``` - - -

模型训练

- -训练脚本迁移完成后，需要参见[配置环境变量](#zh-cn_topic_0000001144082004md)设置环境变量，然后执行**python3** _xxx_进行模型训练。具体样例请参考[脚本执行](#脚本执行md)。 - ->![](public_sys-resources/icon-note.gif) **说明：** ->执行“python3 xxx“命令时，须将python3软链接到与当前pytorch适配版本的python安装路径。 - -

性能调优和分析

- - -

前提条件

- -1. 参见[样例说明](#样例说明md)改造开源代码，使模型能够正常运行，包括数据预处理，前向计算，loss计算，混合精度，反向计算，参数更新等。 -2. 模型迁移阶段优先关注模型是否能跑通，现有算子是否能满足，如果遇到不满足的算子需参见《PyTorch算子开发指南》进行算子适配开发。 -3. 优先打通单卡功能，再打通多卡功能。 - -

调测过程

- -- **[总体思路](#总体思路md)** - -- **[采集训练过程相关数据](#采集训练过程相关数据md)** - -- **[host侧性能优化](#host侧性能优化md)** - -- **[训练过程性能优化](#训练过程性能优化md)** - - -

总体思路

- -1. 通过训练执行结果，判断吞吐量指标是否达到预期要求。 -2. 当吞吐量指标不达标时，需要找出制约性能瓶颈的原因，主要为以下几个方面： - - 算子瓶颈，在某个算子上执行过慢。 - - copy瓶颈，非连续转连续时进行copy带来的瓶颈。 - - 框架瓶颈，由于算子格式转换带来了额外操作。 - - 编译瓶颈，由于shape或属性来回变化造成反复编译。 - -3. 针对以上制约性能瓶颈的原因进行分析与优化。 - -

采集训练过程相关数据

- -**Profiling数据采集** - -当模型训练过程中吞吐量指标不达标时，可以通过采集训练过程中的profiling数据，分析哪个环节、哪个算子导致的性能消耗。Profiling数据采集分为PyTorch层面和CANN层面的采集，PyTorch层面采集的是PyTorch API的数据，CANN层面采集的是TBE算子的数据。 - -请参见以下方式进行profiling数据的获取，并根据实际情况选择需要的数据采集方式。 - -- PyTorch层面Profiling数据采集。 - 1. 获取chrome\_trace文件。 - - 使用profile接口对原始代码的loss计算和优化过程进行改造。 - - ``` - # 使用ascend-pytorch适配的profile接口，即可获得，推荐只运行一个step - with torch.autograd.profiler.profile(use_npu=True) as prof: - out = model(input_tensor) - loss=loss_func(out) - loss.backward() - optimizer.zero_grad() - optimizer.step() - # 打印profiler结果信息 - print(prof) - # 导出chrome_trace文件到指定路径 - output_path = '/home/HwHiAiUser/profile_data.json' - prof.export_chrome_trace(output_path) - ``` - - 2. 运行成功后会打印出profiler结果信息。 - - 打印结果包含CPU和NPU的耗时等相关信息，详细信息参见表2 。 - - **表2** profiler结果字段表 - - | Name | Self CPU % | Self CPU | CPU total % | CPU total | CPU time avg | Self NPU % | Self NPU | NPU total | NPU time avg | # of Calls | - | ---- | ---------- | -------- | ----------- | --------- | ------------ | ---------- | -------- | --------- | ------------ | :--------: | - - 3. 查看chrome\_trace文件。 - - chrome\_trace文件可以通过以下方式打开查看：在Chrome浏览器 中输入“chrome://tracing“地址，然后将落盘文件拖到空白处即可打开文件内容，通过键盘W、A、S、D键，可以对profiler的结果进行缩放和移动。 - - 4. profiler其他功能。 - - 获取算子输入tensor的shape信息。 - - ```python - # 添加record_shapes参数，获取算子输入tensor的shape信息 - with torch.autograd.profiler.profile(use_npu=True, record_shapes=True) as prof: - # 添加模型计算过程 - print(prof) - ``` - - 打印结果中增加了每个算子的`Input Shape`信息。 - - - 获取使用NPU的内存信息。 - - ```python - # 添加profile参数，获取算子内存占用信息 - with torch.autograd.profiler.profile(use_npu=True, profile_memory=True) as prof: - # 添加模型计算过程 - print(prof) - ``` - - 打印结果中增加了每个算子的`CPU Mem`、`Self CPU Mem`、`NPU Mem`、`Self NPU Mem`信息。 - - >![](public_sys-resources/icon-note.gif) **说明：** - > - >该功能仅支持PyTorch1.8版本以上。 - - - 获取简洁的算子性能信息。 - - 该功能只打印每个算子栈最底层的算子信息，使分析结果更简洁。 - - ```python - # 添加use_npu_simple参数，获取简洁的算子信息 - with torch.autograd.profiler.profile(use_npu=True, use_npu_simple=True) as prof: - # 添加模型计算过程 - # 导出chrome_trace文件到指定路径 - output_path = '/home/HwHiAiUser/profile_data.json' - prof.export_chrome_trace(output_path) - ``` - - 在Chrome浏览器中打开chrome\_trace结果文件，可查看简洁的算子性能信息。 - -- CANN层面Profiling数据采集。 - - 1. 获取性能数据文件。 +脚本转换工具支持Ubuntu 18.04、CentOS 7.6或EulerOS 2.8。 - ``` - profiler_result_path = "/home/profiling_data" # profiling 数据保存的文件夹，需提前手动创建，请根据实际指定。 - with torch.npu.profile(profiler_result_path): - out = model(input_tensor) - loss=loss_func(out,target) - loss.backward() - optimizer.zero_grad() - optimizer.step() - ``` +**环境准备** - >![](public_sys-resources/icon-note.gif) **说明：** - >获取性能数据文件时，model、input\_tensor、target需要下发到npu上。 +详情请参考《CANN 软件安装指南》安装开发环境。 - 2. 解析性能数据文件。 +

操作指南

- 请参见《CANN 开发辅助工具指南》中“Profiling工具使用指南（训练）”章节。 +**参数说明** +**表 1** 参数说明 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

参数

+

参数说明

+

取值示例

+

-i

+

--input

+
  • 要进行转换的原始脚本文件所在文件夹路径或文件路径。
  • 必选。
+
  • /home/username/fmktransplt
  • /home/username/fmktransplt.py
+

-o

+

--output

+
  • 脚本转换结果文件输出路径。会在该路径下输出带有msft后缀的文件夹。
  • 必选。
+

/home/username/fmktransplt_output

+

-r

+

--rule

+
  • 用户自定义通用转换规则的json文件路径，主要分为：函数参数修改、函数名称修改和模块名称修改三部分。
  • 可选。
+

/home/username/fmktransplt_rule.json

+

-s

+

--specify-device

+
  • 可以通过环境变量设置指定device作为高级特性，但有可能导致原本脚本中分布式功能失效。
  • 可选。
+

-

+

-sim

+

--similar

+
  • 用功能相似的API替换某些不支持的API，但有可能导致准确性和性能下降。
  • 可选。
+

-

+

distributed

+

将GPU单卡脚本转换为NPU多卡脚本，仅支持使用torch.utils.data.DataLoader方式加载数据的场景，指定此参数时，才可以指定以下两个参数。

+
  • -m，--main：训练脚本的入口python文件，必选。
  • -t，--target model：目标模型变量名，默认为'model'，可选。
+

-

+

-h

+

--help

+

显示帮助信息。

+

-

+
+**自定义规则文件** -**获取算子信息OP\_INFO** +自定义转换规则样例如下： -网络模型最终是以OP执行的，通过OPInfo日志，我们可以获取实际执行时的算子及其属性。通过get\_ascend\_op\_info.py脚本获取。 +``` +{ + "rules": { + "ArgsModifyRule": [ + { + "func_name": "name1", + "arg_idx": 0, + "arg_new": "agrs0" + }, + { + "func_name": "name2", + "arg_idx": 0, + "arg_new": "agrs0" + } + ], + "FuncNameModifyRule": [ + { + "old_name": "func", + "new_name": "new_func" + } + ], + "ModuleNameModifyRule": [ + { + "old_name": "module", + "new_name": "new_module", + "parent_module":"parent_module" + } + ] + } +} +``` -1. 编写get\_ascend\_op\_info.py脚本获取算子信息，脚本内容如下。 +**表 2** 参数说明 - ``` - # -*- coding: utf-8 -*- - """用于导出OPINFO - """ - import os - import sys - import argparse - - def func(host_log_folder): - """ - :param host_log_folder: where host_log_folder addr is. - :return: - """ - host_log_files = os.listdir(host_log_folder) - result = {} - - for host_log in host_log_files: - if not host_log.endswith('.log') or host_log.endswith('.out'): - continue - with open(os.path.join(host_log_folder, host_log), 'r')as f: - host_log_lines = f.readlines() - for line in host_log_lines: - if line.startswith('[INFO] ASCENDCL') and "aclopCompile::aclOp" in line: - op_info = line.split('OpType: ')[1][:-2] - op_type = op_info.split(',')[0] - op_param = op_info[len(op_type) + 2:] - if op_type not in result.keys(): - result[op_type] = [op_param] - else: - result[op_type].append(op_param) - - with open('ascend_op_info_summary.txt', 'w')as f: - for k, v in result.items(): - v_set = set(v) - for info in v_set: - f.write(k + " " + info + "\n") - - if __name__ == "__main__": - parser = argparse.ArgumentParser(description='trans the log') - parser.add_argument('--host_log_folder', default="./", - help="input the dir name, trans the current dir with default") - ags = parser.parse_args() - func(ags.host_log_folder) - ``` + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

参数

+

说明

+

ArgsModifyRule

+

函数参数修改

+

func_name

+

函数名称

+

arg_idx

+

参数的位置

+

arg_new

+

新的参数

+

FuncNameModifyRule

+

函数名称修改

+

ModuleNameModifyRule

+

模块名称修改

+

old_name

+

旧名称

+

new_name

+

新名称

+

parent_module

+

父级模块全名

+

例如torch.cuda.amp，amp的父级模块全名为torch.cuda。

+
+**执行转换** -2. 设置环境变量，将host日志打屏。 +1. 进入脚本转换工具所在路径。 ``` - export ASCEND_SLOG_PRINT_TO_STDOUT=1 + cd Ascend-cann-toolkit安装目录/ascend-toolkit/{version}/{arch}-linux/toolkit/tools/ms_fmk_transplt ``` -3. 设置日志级别为info，参考《CANN 日志参考》设置日志级别。 -4. 执行训练脚本，进行模型训练，训练完成后获取host侧日志，默认位置为$HOME/ascend/log/plog目录下，$HOME表示Host侧用户根目录。 -5. 解析host侧日志会在当前目录下得到OPInfo信息ascend\_op\_info\_summary.txt。 +2. 执行脚本转换工具。 ``` - python3 get_ascend_op_info.py --host_log_folder $HOME/ascend/log/plog + python3 ms_fmk_transplt.py -i 原始脚本路径 -o 脚本转换结果输出路径 [-r 自定义规则json文件路径] [-s] [-sim] [distributed -m 训练脚本的入口文件 -t 目标模型变量名] ``` -6. 分析TaskInfo中额外的task，尤其关注transdata。 - -

host侧性能优化

- -- **[概述](#概述-0md)** - -- **[修改CPU性能模式（X86服务器）](#修改CPU性能模式X86服务器md)** - -- **[修改CPU性能模式（ARM服务器）](#修改CPU性能模式ARM服务器md)** - -- **[安装高性能pillow库（X86服务器）](#安装高性能pillow库X86服务器md)** - -- **[（可选）安装指定版本OpenCV库](#可选安装指定版本OpenCV库md)** - + >![](public_sys-resources/icon-note.gif) **说明：** + >distributed及其参数-m、-t在语句最后指定。 -
概述
+3. 完成脚本转换。 -在进行PyTorch模型迁移训练时，部分网络模型会出现FPS较低、性能不达标的情况。可以考虑对服务器进行以下优化尝试提高训练性能。 +

结果解析

-- 修改CPU性能模式。 -- 安装高性能pillow库。 -- （可选）安装指定版本OpenCV库。 +脚本转换完成后，进入脚本转换结果输出路径查看结果文件，以GPU单卡脚本转换为NPU多卡脚本为例。 -
修改CPU性能模式（X86服务器）
+``` +├── xxx_msft // 脚本转换结果输出目录，默认为原始脚本路径。xxx为原始脚本所在文件夹名称。 +│ ├── 生成脚本文件 // 与转换前的脚本文件目录结构一致。 +│ ├── msFmkTranspltlog.txt // 脚本转换过程日志文件。 +│ ├── unsupported_op.xlsx // 不支持算子列表文件。 +│ ├── change_list.csv // 修改记录文件。 +│ ├── run_distributed_npu.sh // 多卡启动shell脚本。 +``` -**设置电源策略为高性能模式** +

手工迁移

-提升网络性能需要在X86服务器BIOS设置中将电源策略设为高性能模式，具体操作如下。 -1. 登录iBMC界面，启动虚拟控制台，远程控制选择HTML5集成远程控制台，如[图1](#fig15869135420288)。 +

单P训练模型迁移

- **图 1** 远程登录控制台 - ![](figures/远程登录控制台.png "远程登录控制台") +当前在线对接方案优点在于保证在昇腾AI处理器上训练与GPU的使用方式和风格保持一致。用户在使用在线对接方案时，**只需在Python侧和Device相关操作中，指定device为昇腾AI处理器**，即可完成用昇腾AI处理器在PyTorch对网络的开发、训练以及调试。针对单P模型训练，主要迁移改动如下。 -2. 在虚拟界面工具栏中，单击启动项工具![](figures/zh-cn_image_0000001144241932.png)，弹出启动项配置界面，如[图2](#fig744814574243)。 +迁移前GPU代码： - **图 2** 启动项工具 - ![](figures/启动项工具.png "启动项工具") +``` + CALCULATE_DEVICE = “gpu:0” + torch.cuda.set_device(CALCULATE_DEVICE) + # 放到device的两种方法 + model = model.cuda() # 写法1 + model = model.to(CALCULATE_DEVICE) # 写法2 + # 将输入也从host放到device + images = images.to(CALCULATE_DEVICE) + target = target.to(CALCULATE_DEVICE) +``` -3. 在启动项配置界面选择，选择“BIOS设置”，然后在虚拟界面工具栏中单击重启工具![](figures/zh-cn_image_0000001190201999.png)，重启服务器。 -4. 系统重启后进入BIOS配置界面，依次选择“Advanced”\>“Socket Configuration”，如[图3](#fig4546303814)所示。 +迁移到昇腾AI处理器上代码为： - **图 3** Socket Configuration - ![](figures/Socket-Configuration.png "Socket-Configuration") +``` + CALCULATE_DEVICE = “npu:0” + torch.npu.set_device(CALCULATE_DEVICE) + # 放到device的两种方法 + model = model.npu() # 写法1 + model = model.to(CALCULATE_DEVICE) # 写法2 + # 将输入也从host放到device + images = images.to(CALCULATE_DEVICE) + target = target.to(CALCULATE_DEVICE) +``` -5. 进入Advanced Power Mgmt. Configuration，设置Power Policy为Performance。如[图4](#fig15501111014442)。 +更多迁移细节请参见[单P训练修改](#单P训练修改md)。 - **图 4** 设置电源策略 - ![](figures/设置电源策略.png "设置电源策略") +

多P训练模型迁移

-6. 按下“F10”保存配置并重启服务器。 +多P训练模型迁移除了需在**Python侧和Device相关操作中，指定device为昇腾AI处理器**外，依然通过PyTorch的DistributedDataParallel方式来进行分布式训练，即在模型初始化阶段执行init\_process\_group，再将模型初始化为DistributedDataParallel模型。但须注意的是在初始化init\_process\_group时需要将**backend**配置为**hccl**并屏蔽掉初始化方式。 -**将CPU设置为performance模式** +PyTorch分布式训练代码示例（部分代码省略）： -请使用root用户执行如下操作。 +``` +import torch +import torch.distributed as dist +import torch.nn.parallel +def main(): + args = parser.parse_args() + # 需屏蔽掉初始化方式 + dist.init_process_group(backend='hccl',# init_method=args.dist_url, + world_size=args.world_size, rank=args.rank) + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) # model需要下发到npu上 + train_loader = torch.utils.data.DataLoader( + train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None), + num_workers=args.workers, pin_memory=True, sampler=train_sampler) + for epoch in range(args.start_epoch, args.epochs): + acc1 = train(train_loader, model, criterion, optimizer, epoch, args,ngpus_per_node, + lr_scheduler) +``` -1. 使用如下命令查看当前CPU模式。 +更多迁移细节请参见[分布式训练修改](#分布式训练修改md)。 - ``` - cat /sys/devices/system/cpu/cpu0/cpufreq/scaling_governor - ``` +

PyTorch接口替换

- 执行以上命令会输出当前CPU模式，CPU模式说明请参见[表1](#table354392019384)。如果当前CPU模式不是performance模式，请执行以下操作设置CPU为performance模式。否则请跳过以下步骤。 +1. 为了使昇腾AI处理器使用PyTorch框架的能力，需要对原生的PyTorch框架进行一定Device层面的适配，对外呈现是需要将跟cpu和cuda相关的接口进行切换；在进行网络迁移时，需要将某些设备相关的接口转换成跟昇腾AI处理器相关的接口，当前适配的设备相关接口请参见[表1](#table1922064517344)： - **表 1** CPU模式 + **表 1** 设备接口替换 - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

调速器

+ + - + - - + - - + - - + - - + - - + - - + - -

PyTorch原始接口

描述

+

适配昇腾AI处理器后的接口

+

说明

performance

+

torch.cuda.is_available()

运行于最大频率。

+

torch.npu.is_available()

+

判断当前环境上设备是否可用（不代表最后结果）。

powersave

+

torch.cuda.current_device()

运行于最小频率。

+

torch.npu.current_device()

+

获取当前正在使用的device。

userspace

+

torch.cuda.device_count()

运行于用户指定的频率。

+

torch.npu.device_count()

+

获取当前环境上的设备数量。

ondemand

+

torch.cuda.set_device()

按需快速动态调整CPU频率，一有CPU计算量的任务，就会立即达到最大频率运行，空闲时间增加就降低频率。

+

torch.npu.set_device()

+

设置当前正在使用的device。

conservative

+

torch.tensor([1,2,3]).is_cuda

按需快速动态调整CPU频率，比ondemand的调整更保守。

+

torch.tensor([1,2,3]).is_npu

+

判断某个tensor是否是cuda/npu设备上的格式。

schedutil

+

torch.tensor([1,2,3]).cuda()

基于调度程序调整 CPU 频率。

+

torch.tensor([1,2,3]).npu()

+

将某个tensor转换成cuda/npu格式。
- -2. 安装工具，使用如下命令安装。 - - 以“ubuntu/debian“系统为例。 - - ``` - apt-get install linux-tools-$(uname -r) - ``` - - - 以“centos/bclinux/euler“系统为例： - - ``` - yum install kernel-tools -y - systemctl daemon-reload - systemctl enable cpupower - systemctl start cpupower - ``` - -3. 设置CPU为performance模式。 - - ``` - cpupower frequency-set -g performance - ``` - -4. 再次执行[步骤1](#li158435131344)查看当前CPU模式是否已设置为performance模式。 - -
修改CPU性能模式（ARM服务器）
- -**设置电源策略为高性能模式** - -在某些对Host侧CPU要求较高的模型中，例如目标检测类模型，需要进行较为复杂的图像预处理，开启电源高性能模式能一定程度上提高性能和稳定性。ARM服务器提升网络性能需要在BIOS设置中将电源策略设为高性能模式，具体操作如下。 - -1. 登录ibmc界面，启动虚拟控制台，远程控制选择HTML5集成远程控制台，如[图1](#fig15869135420288)。 - - **图 1** 远程登录控制台 - ![](figures/远程登录控制台-0.png "远程登录控制台-0") - -2. 在虚拟界面工具栏中，单击启动项工具![](figures/zh-cn_image_0000001190202013.png)，弹出启动项配置界面，如[图2](#fig744814574243)。 - - **图 2** 启动项工具 - ![](figures/启动项工具-1.png "启动项工具-1") - -3. 在启动项配置界面选择，选择“BIOS设置”，然后在虚拟界面工具栏中单击重启工具![](figures/zh-cn_image_0000001144082138.png)，重启服务器。 -4. 系统重启后进入BIOS配置界面，依次选择“Advanced”\>“ Performance Config”，如[图3](#fig4546303814)所示。 - - **图 3** Performance Config - ![](figures/Performance-Config.png "Performance-Config") - -5. 进入“Performance Config”，设置Power Policy为Performance。如[图4](#fig15501111014442)。 - - **图 4** 设置电源策略 - ![](figures/设置电源策略-2.png "设置电源策略-2") - -6. 按下“F10”保存配置并重启服务器。 - -
安装高性能pillow库（X86服务器）
- -1. 安装高性能pillow库相关依赖，命令如下。 - - ubuntu/debian： - - ``` - apt-get install libtiff5-dev libjpeg8-dev libopenjp2-7-dev zlib1g-dev libfreetype6-dev liblcms2-dev libwebp-dev tcl8.6-dev tk8.6-dev python3-tk libharfbuzz-dev libfribidi-dev libxcb1-dev - ``` - - centos/bclinux/euler： - - ``` - yum install libtiff-devel libjpeg-devel openjpeg2-devel zlib-devel freetype-devel lcms2-devel libwebp-devel tcl-devel tk-devel harfbuzz-devel fribidi-devel libraqm-devel libimagequant-devel libxcb-devel - ``` - -2. 安装高性能pillow库。 - 1. 执行如下命令卸载原生pillow。 - - ``` - pip3.7 uninstall -y pillow - ``` - - 2. 安装SSE4版本pillow-simd。 - - 使用root用户安装，执行如下命令，若使用非root用户安装，需在命令结尾加上--user。 - - ``` - pip3.7 install pillow-simd - ``` - - >![](public_sys-resources/icon-note.gif) **说明：** - >如果CPU支持AVX2指令集，可安装AVX2版本pillow-simd，命令如下： - >``` - >CC="cc -mavx2" pip3.7 install -U --force-reinstall pillow-simd - >``` - - -3. 修改torchvision代码解决pillow-simd缺少PILLOW\_VERSION问题。torchvision安装参见[样例获取](#样例获取md)。 - - 将/usr/local/python3.x.x/lib/python3.x/site-packages/torchvision/transforms/functional.py第5行代码修改如下： - - ``` - try: - from PIL import Image, ImageOps, ImageEnhance,PILLOW_VERSION - except: - from PIL import Image, ImageOps, ImageEnhance - PILLOW_VERSION="7.0.0" - ``` - - -
（可选）安装指定版本OpenCV库
- -如模型依赖OpenCV，基于训练性能考虑，建议安装OpenCV-3.4.10版本。 - -1. 获取源码：[获取地址](https://opencv.org/releases/)。 -2. 安装指导：[获取地址](https://docs.opencv.org/3.4.10/d7/d9f/tutorial_linux_install.html)。 - -

训练过程性能优化

- -**算子瓶颈优化** - -1. 获取训练过程中的Profiling数据，参见[Profiling数据采集](#采集训练过程相关数据md)。 -2. 分析Profiling数据，得到耗时较大的算子。 -3. 参见[单算子样例编写说明](#单算子样例编写说明md)构建耗时较大算子的单算子样例，通过与CPU或GPU运行单算子样例时间进行对比，若发现性能不足，则有以下两种方案解决。 - - 规避方案：使用同等语义其他高效算子替代。 - - 解决方案：改进算子性能。 +

torch.tensor([1,2,3]).to("cuda")

+

torch.tensor([1,2,3]).to('npu')

+

将某个tensor转换成cuda/npu格式。

+

torch.cuda.synchronize()

+

torch.npu.synchronize()

+

同步等待事件完成。

+

torch.cuda.device

+

torch.npu.device

+

生成一个device类，可以执行device相关操作。

+

torch.cuda.Stream(device)

+

torch.npu.Stream(device)

+

生成一个stream对象。

+

torch.cuda.stream(Stream)

+

torch.npu.stream(Stream)

+

多用于作用域限定。

+

torch.cuda.current_stream()

+

torch.npu.current_stream()

+

获取当前stream。

+

torch.cuda.default_stream()

+

torch.npu.default_stream()

+

获取默认stream。

+

device = torch.device("cuda:0")

+

device = torch.device("npu:0")

+

指定一个设备。

+

torch.autograd.profiler.profile

+

(use_cuda=True)

+

torch.autograd.profiler.profile

+

(use_npu=True)

+

指定执行profiler过程中使用cuda/npu。

+

torch.cuda.Event()

+

torch.npu.Event()

+

返回某个设备上的事件。

+
-**copy瓶颈优化** +2. 用户在构建网络或进行网络迁移时，需要创建指定数据类型的tensor，在昇腾AI处理器上创建的tensor如下。 -1. 获取训练过程中的Profiling数据，参见[Profiling数据采集](#采集训练过程相关数据md)。 -2. 分析Profiling数据分析整网中的D2DCopywithStreamSynchronize/PTCopy/format\_contiguous的耗时。 -3. 若发现耗时较大，则需参照以下两种方案解决。 - - 方案一：（规避方案）PyTorch中View类型框架类算子会导致非连续转连续操作。优化思路为尽量使用计算类算子代替View类框架算子，常见的View类框架算子如View、Permute、Transpose等。更多View类框架算子可参考[https://pytorch.org/docs/stable/tensor\_view.html](https://pytorch.org/docs/stable/tensor_view.html)。 - - 方案二：（解决方案）加速转连续操作。 + **表 2** tensor创建接口替换 -**框架瓶颈优化** + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

GPU tensor

+

适配昇腾AI处理器后的接口

+

torch.tensor([1,2,3],dtype=torch.long,device='cuda')

+

torch.tensor([1,2,3],dtype=torch.long,device='npu')

+

torch.tensor([1,2,3],dtype=torch.int,device='cuda')

+

torch.tensor([1,2,3],dtype=torch.int,device='npu')

+

torch.tensor([1,2,3],dtype=torch.half,device='cuda')

+

torch.tensor([1,2,3],dtype=torch.half,device='npu')

+

torch.tensor([1,2,3],dtype=torch.float,device='cuda')

+

torch.tensor([1,2,3],dtype=torch.float,device='npu')

+

torch.tensor([1,2,3],dtype=torch.bool,device='cuda')

+

torch.tensor([1,2,3],dtype=torch.bool,device='npu')

+

torch.cuda.BoolTensor([1,2,3])

+

torch.npu.BoolTensor([1,2,3])

+

torch.cuda.FloatTensor([1,2,3])

+

torch.npu.FloatTensor([1,2,3])

+

torch.cuda.IntTensor([1,2,3])

+

torch.npu.IntTensor([1,2,3])

+

torch.cuda.LongTensor([1,2,3])

+

torch.npu.LongTensor([1,2,3])

+

torch.cuda.HalfTensor([1,2,3])

+

torch.npu.HalfTensor([1,2,3])

+
-1. 获取训练过程中算子信息OP\_INFO，参见[获取算子信息OP\_INFO](#采集训练过程相关数据md)。 -2. 分析OP\_INFO中算子的规格和调用关系，定位是否插入了多余的算子，重点关注transdata是否合理。 -3. 优化方案：通过指定部分算子初始化格式，对多余的格式转换算子进行消除。 -4. 在pytorch/torch/nn/modules/module.py中，在cast\_weight中指定算子初始化格式，如下图。 - ![](figures/指定算子初始化方式.png) +更多接口请参见《PyTorch API支持清单》。 - 格式设置原则可参考如下规则： +

混合精度

- - Conv2D相关：Weight 可设置为FZ格式，如第424行。 - - Linear相关的参数，可设置为NZ格式，如第409行。 +**概述** -**编译瓶颈优化** +基于NPU芯片的架构特性，会涉及到混合精度训练，即混合使用float16和float32数据类型的应用场景。使用float16代替float32有如下好处： -1. 获取训练过程中算子信息OP\_INFO，参见[获取算子信息OP\_INFO](#采集训练过程相关数据md)。 -2. 查看INFO日志，观察第一个step以后的aclopCompile::aclOp关键字，如果后续接了Match op inputs/type failed或To compile op则说明该算子存在动态编译，需要优化。 -3. 需参照以下两种方案解决。 - - 规避方案：在理解模型语义和相关API基础上，使用固定Shape的方式代替动态Shape。 - - 解决方案：减少编译或不需要编译该算子。 - - 优化算子编译配置请参见[编译选项设置](#编译选项设置md)。 +- 对于中间变量的内存占用更少，节省内存的使用。 +- 因内存使用会减少，所以数据传出的时间也会相应减少。 +- float16的计算单元可以提供更快的计算性能。 +但是，混合精度训练受限于float16表达的精度范围，单纯将float32转换成float16会影响训练收敛情况，为了保证部分计算使用float16来进行加速的同时能保证训练收敛，这里采用混合精度模块Apex来达到以上效果。混合精度模块Apex是一个集优化性能、精度收敛于一身的综合优化库。 -

亲和库

+适配昇腾AI处理器的混合精度模块Apex除了上述优点外，还能提升运算性能。具体如下： +- Apex在混合精度运算过程中，会对模型的grad进行运算。开启combine\_grad开关，可以加速这些运算。具体为将amp.initialize\(\)接口参数combine\_grad设置为True； +- 适配后的Apex针对adadelta/adam/sgd/lamb做了昇腾AI处理器亲和性优化，得到的NPU融合优化器与原生算法保持一致，但运算速度更快。使用时只需将原有优化器替换为apex.optimizers.\*（“\*”为优化器名称，例如NpuFusedSGD）。 -

来源介绍

+**特性支持** -针对公版模型中常见的网络结构和函数，我们针对性地对其进行了优化，使得运算性能大幅度提升，同时，将其集成到Pytorch框架中，便于模型性能调优中使用。 +混合精度模块功能和优化描述如[表1](#table10717173813332)所示。 -

功能介绍

+**表 1** 混合精度模块功能 - - -

函数名

-

位置

+ + - - - - - - - - - - - - -

功能

功能说明

+

描述

pairwise_iou

-

torch.contrib.npu.optimized_lib

+

O1配置模式

计算两个目标框的IOU。

+

Conv，Matmul等使用float16计算，其他如Softmax、BN使用float32。

fast_rcnn_inference_single_image

-

torch.contrib.npu.optimized_lib

+

O2配置模式

Maskrcnn和Fasterrcnn模型的推理接口。

+

除了BN使用float32外，其他绝大部分使用float16。

ChannelShuffle

-

torch.contrib.npu.optimized_lib

+

静态Loss Scale功能

提供NPU亲和的channelshuffle操作，适用于shufflenetv2等模型。

+

静态设置参数确保混合精度训练收敛。

PreLoader

-

torch.contrib.npu.optimized_lib

+

动态Loss Scale功能

提供针对昇腾AI处理器加速的数据加载方法。

+

动态计算loss Scale值并判断是否溢出。
>![](public_sys-resources/icon-note.gif) **说明：** ->该部分调优内容会随着版本不断增强和更新，请以实际PyTorch版本中对应路径下的内容为准。 +>- 当前版本的实现方式主要为python实现，不支持AscendCL或者CUDA优化。 +>- 当前昇腾AI设备暂不支持原始Apex的FusedLayerNorm接口模块，如果模型原始脚本文件使用了FusedLayerNorm接口模块，需要在模型迁移过程中将脚本头文件“from apex.normalization import FusedLayerNorm“替换为“from torch.nn import LayerNorm“。 -

精度调测

+**将混合精度模块集成到PyTorch模型中** +1. 使用apex混合精度模块需要首先从apex库中导入amp，代码如下： -

前提条件

+ ``` + from apex import amp + ``` -优先在同等语义和超参下，跑一定的epoch（推荐完整epoch数的20%），使精度，loss等对齐GPU相应水平，完成后再对齐最终精度。 +2. 导入amp模块后，需要初始化amp，使其能对模型、优化器以及PyTorch内部函数进行必要的改动，初始化代码如下： -

调测过程

+ ``` + model, optimizer = amp.initialize(model, optimizer，combine_grad=True) + ``` -- **[总体思路](#总体思路-4md)** +3. 标记反向传播.backward\(\)发生的位置，这样Amp就可以进行Loss Scaling并清除每次迭代的状态，代码如下： -- **[精度调优方法](#精度调优方法md)** + 原始代码： + + ``` + loss = criterion(…) + loss.backward() + optimizer.step() + ``` + 修改以支持loss scaling后的代码： -

总体思路

+ ``` + loss = criterion(…) + with amp.scale_loss(loss, optimizer) as scaled_loss: + scaled_loss.backward() + optimizer.step() + ``` -精度问题排查需要找出是哪一步出现的问题，主要以下几个方面： -1. 模型网络计算错误。 - - 定位思路：在网络中加入hook进行排查判断是哪个地方有较大嫌疑，然后构建[单算子用例](#单算子样例编写说明md)逐渐缩小错误范围，证明该算子在当前网络场景下计算有误，可以对比CPU或GPU结果证明。 +

模型训练

- - 规避方案：使用同等语义其他算子替代。 +训练脚本迁移完成后，需要参见[配置环境变量](#zh-cn_topic_0000001144082004md)设置环境变量，然后执行**python3** _xxx_进行模型训练。具体样例请参考[脚本执行](#脚本执行md)。 - - 解决方案：改进算子精度或功能问题。 +>![](public_sys-resources/icon-note.gif) **说明：** +>执行“python3 xxx“命令时，须将python3软链接到与当前pytorch适配版本的python安装路径。 -2. loss计算错误。 - - 定位思路：由于Loss的特殊性和可以自定义，在判断Loss计算错误后建议dump网络中的loss的输入来测试而非随机同shape tensor，这样才能更好地复现证明。 +

性能调优和分析

- - 规避方案：使用同等语义其他算子替代。 - - 解决方案：改进算子精度或功能问题（loss也是由算子构成）。 +

前提条件

-3. 参数更新错误。 +1. 参见[样例说明](#样例说明md)改造开源代码，使模型能够正常运行，包括数据预处理，前向计算，loss计算，混合精度，反向计算，参数更新等。 +2. 模型迁移阶段优先关注模型是否能跑通，现有算子是否能满足，如果遇到不满足的算子需参见《PyTorch算子开发指南》进行算子适配开发。 +3. 优先打通单卡功能，再打通多卡功能。 + +

调测过程

+ +- **[总体思路](#总体思路md)** + +- **[采集训练过程相关数据](#采集训练过程相关数据md)** + +- **[host侧性能优化](#host侧性能优化md)** + +- **[训练过程性能优化](#训练过程性能优化md)** + + +

总体思路

+ +1. 通过训练执行结果，判断吞吐量指标是否达到预期要求。 +2. 当吞吐量指标不达标时，需要找出制约性能瓶颈的原因，主要为以下几个方面： + - 算子瓶颈，在某个算子上执行过慢。 + - copy瓶颈，非连续转连续时进行copy带来的瓶颈。 + - 框架瓶颈，由于算子格式转换带来了额外操作。 + - 编译瓶颈，由于shape或属性来回变化造成反复编译。 + +3. 针对以上制约性能瓶颈的原因进行分析与优化。 + +

采集训练过程相关数据

+ +**Profiling数据采集** + +当模型训练过程中吞吐量指标不达标时，可以通过采集训练过程中的profiling数据，分析哪个环节、哪个算子导致的性能消耗。Profiling数据采集分为PyTorch层面和CANN层面的采集，PyTorch层面采集的是PyTorch API的数据，CANN层面采集的是TBE算子的数据。 + +请参见以下方式进行profiling数据的获取，并根据实际情况选择需要的数据采集方式。 + +- PyTorch层面Profiling数据采集。 + 1. 获取chrome\_trace文件。 - - 定位思路：在每个optim.step\(\)前对网络中的参数逐个打印其grad进行排查判断是哪个地方有较大嫌疑，然后构建单算子用例逐渐缩小错误范围，证明该算子在当前网络场景下梯度计算有误，可以对比CPU或GPU结果证明。该项优先级应低于[1.](#li17755175510322)与[2.](#li25281726103316)，因为1与2的错误同样可以造成grad异常。 + 使用profile接口对原始代码的loss计算和优化过程进行改造。 - - 规避方案：使用同等语义其他算子替代。 + ``` + # 使用ascend-pytorch适配的profile接口，即可获得，推荐只运行一个step + with torch.autograd.profiler.profile(use_npu=True) as prof: + out = model(input_tensor) + loss=loss_func(out) + loss.backward() + optimizer.zero_grad() + optimizer.step() + # 打印profiler结果信息 + print(prof) + # 导出chrome_trace文件到指定路径 + output_path = '/home/HwHiAiUser/profile_data.json' + prof.export_chrome_trace(output_path) + ``` - - 解决方案：改进计算grad的算子精度或功能问题。 + 2. 运行成功后会打印出profiler结果信息。 -4. 多卡计算错误。 + 打印结果包含CPU和NPU的耗时等相关信息，详细信息参见表2 。 + + **表2** profiler结果字段表 + + | Name | Self CPU % | Self CPU | CPU total % | CPU total | CPU time avg | Self NPU % | Self NPU | NPU total | NPU time avg | # of Calls | + | ---- | ---------- | -------- | ----------- | --------- | ------------ | ---------- | -------- | --------- | ------------ | :--------: | + + 3. 查看chrome\_trace文件。 - - 定位思路：在保证单卡精度OK的前提下，稳定复现多卡不收敛。 + chrome\_trace文件可以通过以下方式打开查看：在Chrome浏览器 中输入“chrome://tracing“地址，然后将落盘文件拖到空白处即可打开文件内容，通过键盘W、A、S、D键，可以对profiler的结果进行缩放和移动。 - - 解决方案：建议联系华为方支撑人员，提供稳定复现的单P和多P脚本。 + 4. profiler其他功能。 + - 获取算子输入tensor的shape信息。 -

精度调优方法

+ ```python + # 添加record_shapes参数，获取算子输入tensor的shape信息 + with torch.autograd.profiler.profile(use_npu=True, record_shapes=True) as prof: + # 添加模型计算过程 + print(prof) + ``` -模型出现精度问题一般有：因算子溢出导致的训练loss不收敛或者精度不达标问题，整个网络训练引起的性能不达标问题。用户可通过单算子溢出检测和整网调测适度解决模型精度不达标问题。 + 打印结果中增加了每个算子的`Input Shape`信息。 -- **[环境准备](#环境准备md)** -- **[模型算子精度对比](模型算子精度对比)** -- **[单算子溢出检测](#单算子溢出检测md)** -- **[IR与TBE算子映射](IR与TBE算子映射)** -- **[NPU与GPU算子映射](NPU与GPU算子映射)** -- **[整网调测](#整网调测md)** + - 获取使用NPU的内存信息。 -##### 环境准备 + ```python + # 添加profile参数，获取算子内存占用信息 + with torch.autograd.profiler.profile(use_npu=True, profile_memory=True) as prof: + # 添加模型计算过程 + print(prof) + ``` -- 安装hdf5工具以支持算子dump功能，安装详情请参见[编译安装hdf5](#编译安装hdf5md)。 + 打印结果中增加了每个算子的`CPU Mem`、`Self CPU Mem`、`NPU Mem`、`Self NPU Mem`信息。 - 若使用模型算子精度对比功能，需要同时在NPU和GPU环境安装hdf5。否则，仅在NPU环境安装hdf5即可。 + >![](public_sys-resources/icon-note.gif) **说明：** + > + >该功能仅支持PyTorch1.8版本以上。 -- 安装支持dump功能的Ascend PyTorch框架，编译前请修改build.sh脚本，其余操作请参见《PyTorch安装指南》。 + - 获取简洁的算子性能信息。 - - 在NPU环境PyTorch安装 + 该功能只打印每个算子栈最底层的算子信息，使分析结果更简洁。 - 编译前修改build.sh脚本，在脚本中增加`USE_DUMP=1`字段。 + ```python + # 添加use_npu_simple参数，获取简洁的算子信息 + with torch.autograd.profiler.profile(use_npu=True, use_npu_simple=True) as prof: + # 添加模型计算过程 + # 导出chrome_trace文件到指定路径 + output_path = '/home/HwHiAiUser/profile_data.json' + prof.export_chrome_trace(output_path) + ``` - ```bash - DEBUG=0 USE_DISTRIBUTED=1 USE_HCCL=1 USE_MKLDNN=0 USE_CUDA=0 USE_NPU=1 BUILD_TEST=0 USE_NNPACK=0 USE_DUMP=1 python"${PY_VERSION}" setup.py build bdist_wheel - ``` + 在Chrome浏览器中打开chrome\_trace结果文件，可查看简洁的算子性能信息。 - - （可选）在GPU环境PyTorch安装，若对模型算子精度对比，请执行此操作，否则请忽略。 +- CANN层面Profiling数据采集。 - 编译前修改build.sh，在脚本中增加`USE_DUMP=1`、`USE_NCCL=0`字段，将 `USE_HCCL`、`USE_NPU`字段的值修改为0，将`USE_CUDA`字段的值修改为1。 + 1. 获取性能数据文件。 - ```bash - DEBUG=0 USE_DISTRIBUTED=1 USE_HCCL=0 USE_NCCL=0 USE_MKLDNN=0 USE_CUDA=1 USE_NPU=0 BUILD_TEST=0 USE_NNPACK=0 USE_DUMP=1 python"${PY_VERSION}" setup.py build bdist_wheel - ``` + ``` + profiler_result_path = "/home/profiling_data" # profiling 数据保存的文件夹，需提前手动创建，请根据实际指定。 + with torch.npu.profile(profiler_result_path): + out = model(input_tensor) + loss=loss_func(out,target) + loss.backward() + optimizer.zero_grad() + optimizer.step() + ``` -##### 模型算子精度对比 + >![](public_sys-resources/icon-note.gif) **说明：** + >获取性能数据文件时，model、input\_tensor、target需要下发到npu上。 -用户使用精度对比工具，在相同输入的情况下，获取模型在GPU和NPU进行训练时模型内算子输出的精度差异，从而帮助开发者实现算子精度问题定位。 + 2. 解析性能数据文件。 -约束说明： + 请参见《CANN 开发辅助工具指南》中“Profiling工具使用指南（训练）”章节。 -- 建议使用小batchsize，一般设置为8及以下。 - 由于每个算子输入、输出数据会存储在硬盘中，会占用较大空间，故建议使用小batchsize节省硬盘空间。 -- 建议仅dump一个step的数据进行精度对比。 +**获取算子信息OP\_INFO** -- 目前支持精度为fp32、O1或O2训练过程的算子精度对比。 +网络模型最终是以OP执行的，通过OPInfo日志，我们可以获取实际执行时的算子及其属性。通过get\_ascend\_op\_info.py脚本获取。 -对比模式： +1. 编写get\_ascend\_op\_info.py脚本获取算子信息，脚本内容如下。 -- GPU的输入和输出为已知数据，将GPU的输入数据加载到NPU上执行得到输出数据，NPU与GPU输出数据对比。 -- NPU的输入和输出为已知数据，将NPU的输入数据加载到GPU上执行得到输出数据，NPU与GPU输出数据对比。 + ``` + # -*- coding: utf-8 -*- + """用于导出OPINFO + """ + import os + import sys + import argparse + + def func(host_log_folder): + """ + :param host_log_folder: where host_log_folder addr is. + :return: + """ + host_log_files = os.listdir(host_log_folder) + result = {} + + for host_log in host_log_files: + if not host_log.endswith('.log') or host_log.endswith('.out'): + continue + with open(os.path.join(host_log_folder, host_log), 'r')as f: + host_log_lines = f.readlines() + for line in host_log_lines: + if line.startswith('[INFO] ASCENDCL') and "aclopCompile::aclOp" in line: + op_info = line.split('OpType: ')[1][:-2] + op_type = op_info.split(',')[0] + op_param = op_info[len(op_type) + 2:] + if op_type not in result.keys(): + result[op_type] = [op_param] + else: + result[op_type].append(op_param) + + with open('ascend_op_info_summary.txt', 'w')as f: + for k, v in result.items(): + v_set = set(v) + for info in v_set: + f.write(k + " " + info + "\n") + + if __name__ == "__main__": + parser = argparse.ArgumentParser(description='trans the log') + parser.add_argument('--host_log_folder', default="./", + help="input the dir name, trans the current dir with default") + ags = parser.parse_args() + func(ags.host_log_folder) + ``` -操作步骤： +2. 设置环境变量，将host日志打屏。 -1. 在GPU或NPU环境，使用dumper工具获取GPU或NPU的模型输入和算子输出数据。 + ``` + export ASCEND_SLOG_PRINT_TO_STDOUT=1 + ``` - 修改训练代码，增加数据dump功能。在模型训练代码的正向、反向计算位置使用`with`语句增加`torch.utils.dumper()`方法dump数据。例如，在GPU环境下修改示例： +3. 设置日志级别为info，参考《CANN 日志参考》设置日志级别。 +4. 执行训练脚本，进行模型训练，训练完成后获取host侧日志，默认位置为$HOME/ascend/log/plog目录下，$HOME表示Host侧用户根目录。 +5. 解析host侧日志会在当前目录下得到OPInfo信息ascend\_op\_info\_summary.txt。 - ```python - for i, data in enumerate(dataloader): - with torch.utils.dumper(use_dump=True, dump_path="./model_gpu.h5") as dump: - # 模型训练代码 - xxx # forward code - xxx # backward code - exit() - xxx # optimizer code - ``` + ``` + python3 get_ascend_op_info.py --host_log_folder $HOME/ascend/log/plog + ``` - dump_path参数为dump数据保存文件路径及名称。建议仅dump一个step的数据用于精度对比，同时参数更新代码放在with语句外。 +6. 分析TaskInfo中额外的task，尤其关注transdata。 -2. 将在GPU(NPU)环境dump的数据model_gpu.h5拷贝到NPU(GPU)环境。 +

host侧性能优化

-3. 在NPU或NPU环境，使用dumper工具加载已经dump出的数据，并获取算子输出数据。 +- **[概述](#概述-0md)** - 修改训练代码，增加数据load、dump功能。在模型训练代码的正向、反向计算位置使用`with`语句增加`torch.utils.dumper()`方法load、dump数据。例如，在NPU环境下修改示例： +- **[修改CPU性能模式（X86服务器）](#修改CPU性能模式X86服务器md)** - ```python - for i, data in enumerate(dataloader): - with torch.utils.dumper(use_dump=True, load_file_path="./model_gpu.h5", dump_path="./model_npu.h5") as dump: - # 模型训练代码 - xxx # forward code - xxx # backward code - exit() - xxx # optimizer code - ``` +- **[修改CPU性能模式（ARM服务器）](#修改CPU性能模式ARM服务器md)** - load_file_path参数为从GPU或NPU获取的dump数据路径，dump_path参数为dump数据保存文件路径及名称。建议仅dump一个step的数据用于精度对比，同时参数更新代码放在with语句外。 +- **[安装高性能pillow库（X86服务器）](#安装高性能pillow库X86服务器md)** -4. 使用msaccucmp.py对算子输出数据对比。 +- **[（可选）安装指定版本OpenCV库](#可选安装指定版本OpenCV库md)** - 1. ascend-toolkit提供了msaccucmp.py工具脚本用具精度对比。 - - 该脚本路径为："/user/local/Ascend/ascend-toolkit/latest/tools/operator_cmp/compare/msaccucmp.py"， +
概述
- 路径仅供参考，请以ascend-toolkit实际安装路径为准。 +在进行PyTorch模型迁移训练时，部分网络模型会出现FPS较低、性能不达标的情况。可以考虑对服务器进行以下优化尝试提高训练性能。 - - 也可以使用如下命令查找msaccucmp.py路径。 +- 修改CPU性能模式。 +- 安装高性能pillow库。 +- （可选）安装指定版本OpenCV库。 - ```linux - find / -name msaccucmp.py - ``` +
修改CPU性能模式（X86服务器）
- 2. 执行msaccucmp.py脚本，进行精度对比。 +**设置电源策略为高性能模式** - ``` - python3 /user/local/Ascend/ascend-toolkit/latest/tools/operator_cmp/compare/msaccucmp.py compare -m ./model_npu.h5 -g ./model_gpu.h5 - ``` +提升网络性能需要在X86服务器BIOS设置中将电源策略设为高性能模式，具体操作如下。 - 参数说明： +1. 登录iBMC界面，启动虚拟控制台，远程控制选择HTML5集成远程控制台，如[图1](#fig15869135420288)。 - `-g`参数传入使用GPU获得的dump数据文件路径。 + **图 1** 远程登录控制台 + ![](figures/远程登录控制台.png "远程登录控制台") - `-m`参数传入使用NPU获得的dump数据文件路径。 +2. 在虚拟界面工具栏中，单击启动项工具![](figures/zh-cn_image_0000001144241932.png)，弹出启动项配置界面，如[图2](#fig744814574243)。 + **图 2** 启动项工具 + ![](figures/启动项工具.png "启动项工具") -
单算子溢出检测
+3. 在启动项配置界面选择，选择“BIOS设置”，然后在虚拟界面工具栏中单击重启工具![](figures/zh-cn_image_0000001190201999.png)，重启服务器。 +4. 系统重启后进入BIOS配置界面，依次选择“Advanced”\>“Socket Configuration”，如[图3](#fig4546303814)所示。 -用户通过算子溢出检测功能检测算子是否有溢出，然后采集溢出算子的数据，从而帮助开发人员快速定位并解决算子精度问题。 + **图 3** Socket Configuration + ![](figures/Socket-Configuration.png "Socket-Configuration") -约束说明： +5. 进入Advanced Power Mgmt. Configuration，设置Power Policy为Performance。如[图4](#fig15501111014442)。 -- 本功能只提供IR级别的算子溢出检测，且只支持AICORE，不支持Atomic。 -- 使用单算子溢出检测功能时，请不要同时开启apex的动态loss scale模式和使用tensor融合功能。 + **图 4** 设置电源策略 + ![](figures/设置电源策略.png "设置电源策略") -采集溢出算子数据： +6. 按下“F10”保存配置并重启服务器。 -``` -# check_overflow为溢出检测控制开关 -# dump_path为dump文件保存路径 -with torch.utils.dumper(check_overflow=check_overflow, dump_path=dump_path, load_file_path='') as dump: - # 需要检测算子溢出的代码片段 -``` +**将CPU设置为performance模式** -运行一个step，模型运行过程中，如果有算子溢出，会打印出相应IR的名字。 +请使用root用户执行如下操作。 -查看Dump数据： +1. 使用如下命令查看当前CPU模式。 -如果训练过程中采集到了Dump数据，则会在\{dump\_path\}路径下生成dump数据的.h5文件，用户可进入路径自行查看。 + ``` + cat /sys/devices/system/cpu/cpu0/cpufreq/scaling_governor + ``` -解决方法： + 执行以上命令会输出当前CPU模式，CPU模式说明请参见[表1](#table354392019384)。如果当前CPU模式不是performance模式，请执行以下操作设置CPU为performance模式。否则请跳过以下步骤。 -1. 将采集到的.h5文件映射到TBE算子，映射方法请参见[IR与TBE算子映射](#IR与TBE算子映射)。 + **表 1** CPU模式 -2. 请将算子溢出的打印截图及映射后的TBE算子输入输出文件通过Issue附件形式反馈给华为开发人员。 + + + + + + + + + + + + + + + + + + + + + + + + +

调速器

+

描述

+

performance

+

运行于最大频率。

+

powersave

+

运行于最小频率。

+

userspace

+

运行于用户指定的频率。

+

ondemand

+

按需快速动态调整CPU频率，一有CPU计算量的任务，就会立即达到最大频率运行，空闲时间增加就降低频率。

+

conservative

+

按需快速动态调整CPU频率，比ondemand的调整更保守。

+

schedutil

+

基于调度程序调整 CPU 频率。

+
-##### IR与TBE算子映射 +2. 安装工具，使用如下命令安装。 + - 以“ubuntu/debian“系统为例。 -前提条件： + ``` + apt-get install linux-tools-$(uname -r) + ``` -- 设置环境变量`export ACL_DUMP_DATA=0`。 -- 在脚本中避免使用`torch.npu.init.dump()`和`torch.npu.set.dump()`接口。 + - 以“centos/bclinux/euler“系统为例： -操作步骤： + ``` + yum install kernel-tools -y + systemctl daemon-reload + systemctl enable cpupower + systemctl start cpupower + ``` -1. 准备好需要映射的算子.h5文件。 +3. 设置CPU为performance模式。 - - 算子溢出检测场景下，单算子溢出检测已生成需要映射的算子.h5文件。 + ``` + cpupower frequency-set -g performance + ``` - - 精度对比场景下，需根据精度对比结果，参照下面命令提取需要映射的算子.h5文件。 +4. 再次执行[步骤1](#li158435131344)查看当前CPU模式是否已设置为performance模式。 - ``` - h5copy -pv -i "./input.h5" -o "./output.h5" -s "/op1/seqid/" -d "/op1/seqid/" - ``` +
修改CPU性能模式（ARM服务器）
- -i 为输入精度对比文件 +**设置电源策略为高性能模式** - -o 为输出需要映射的算子.h5文件路径 +在某些对Host侧CPU要求较高的模型中，例如目标检测类模型，需要进行较为复杂的图像预处理，开启电源高性能模式能一定程度上提高性能和稳定性。ARM服务器提升网络性能需要在BIOS设置中将电源策略设为高性能模式，具体操作如下。 - -s 为需要提取的源算子名称及seqid +1. 登录ibmc界面，启动虚拟控制台，远程控制选择HTML5集成远程控制台，如[图1](#fig15869135420288)。 - -d 为需要提取的目的算子名称及seqid + **图 1** 远程登录控制台 + ![](figures/远程登录控制台-0.png "远程登录控制台-0") - 若需要提取多个算子，则修改-s、-d参数，多次执行该命令，可以把多算子追加提取到output.h5中。 +2. 在虚拟界面工具栏中，单击启动项工具![](figures/zh-cn_image_0000001190202013.png)，弹出启动项配置界面，如[图2](#fig744814574243)。 - 该命令需-s和-d参数相同。 + **图 2** 启动项工具 + ![](figures/启动项工具-1.png "启动项工具-1") - 示例： +3. 在启动项配置界面选择，选择“BIOS设置”，然后在虚拟界面工具栏中单击重启工具![](figures/zh-cn_image_0000001144082138.png)，重启服务器。 +4. 系统重启后进入BIOS配置界面，依次选择“Advanced”\>“ Performance Config”，如[图3](#fig4546303814)所示。 - ``` - h5copy -pv -i "./dump_npu.h5" -o "./output.h5" -s "/numpy_T/1/" -d "/numpy_T/1/" - ``` + **图 3** Performance Config + ![](figures/Performance-Config.png "Performance-Config") - 该示例表示从“./dump_npu.h5”中抽取seqid为1的numpy_T算子的输入、输出数据到"./output.h5"文件中。 +5. 进入“Performance Config”，设置Power Policy为Performance。如[图4](#fig15501111014442)。 -2. 配置acl.json文件。 + **图 4** 设置电源策略 + ![](figures/设置电源策略-2.png "设置电源策略-2") - 在模型目录下创建acl dump功能所需的的配置文件acl.json +6. 按下“F10”保存配置并重启服务器。 - ``` - { - "dump": - { - "dump_list":[] - "dump_path":"./output_IR2TBE"# 映射结果输出路径 - "dump_mode":"all" - "dump_op_switch":"on" - } - - } - ``` +
安装高性能pillow库（X86服务器）
- 需将`dump_path`修改为结果输出路径，其他字段不需要修改。 +1. 安装高性能pillow库相关依赖，命令如下。 -3. 修改训练脚本。 + ubuntu/debian： - 在训练脚本中添加`with`语句开启IR映射TBE功能。 + ``` + apt-get install libtiff5-dev libjpeg8-dev libopenjp2-7-dev zlib1g-dev libfreetype6-dev liblcms2-dev libwebp-dev tcl8.6-dev tk8.6-dev python3-tk libharfbuzz-dev libfribidi-dev libxcb1-dev + ``` - ```python - with torch.utils.dumper(use_load=True, dump_path="./",load_file_path="./output.h5", load_with_acl_dump=True) as dump: - # 模型计算代码，需用户自己添加 - # x = model(input_data) - ``` + centos/bclinux/euler： -4. 模型运行。 + ``` + yum install libtiff-devel libjpeg-devel openjpeg2-devel zlib-devel freetype-devel lcms2-devel libwebp-devel tcl-devel tk-devel harfbuzz-devel fribidi-devel libraqm-devel libimagequant-devel libxcb-devel + ``` - 运行一步完整的模型计算过程，在计算过程中load遇到output.h5中的数据后，自动开启acl dump功能，执行IR，并dump出IR相对应的TBE算子的输入输出数据，IR执行结束，acl dump结束。 +2. 安装高性能pillow库。 + 1. 执行如下命令卸载原生pillow。 -5. 获得映射文件。 + ``` + pip3.7 uninstall -y pillow + ``` - 运行成功后，在acl.json配置文件中的`dump_path`路径下查看输出结果文件。 + 2. 安装SSE4版本pillow-simd。 -##### NPU与GPU算子映射 + 使用root用户安装，执行如下命令，若使用非root用户安装，需在命令结尾加上--user。 -请参见《开发辅助工具指南》中 ”精度对比工具使用指南（训练）“中 “数据准备章节” 中的 “[准备以PyTorch为原始训练网络的精度比对数据文件](https://support.huawei.com/enterprise/zh/doc/EDOC1100219269/2324edc8#ZH-CN_TOPIC_0000001162580808)”。 + ``` + pip3.7 install pillow-simd + ``` -
整网调测
+ >![](public_sys-resources/icon-note.gif) **说明：** + >如果CPU支持AVX2指令集，可安装AVX2版本pillow-simd，命令如下： + >``` + >CC="cc -mavx2" pip3.7 install -U --force-reinstall pillow-simd + >``` -用户也可通过分析整个网络的方式来进行网络模型的精度调测。 -1. 通过对比CPU和昇腾AI处理器的结果，判断在昇腾AI处理器上计算是否正确。 +3. 修改torchvision代码解决pillow-simd缺少PILLOW\_VERSION问题。torchvision安装参见[样例获取](#样例获取md)。 - 代码样例（本样例只体现基本方法，禁止直接复制）如下： + 将/usr/local/python3.x.x/lib/python3.x/site-packages/torchvision/transforms/functional.py第5行代码修改如下： ``` - # 固定入参，保证模型与输入数据在CPU和昇腾AI处理器上相同 - input_tensor_cpu = torch.Tensor() - model_cpu = build_model() - # 将输入数据迁移到昇腾AI处理器上 - input_tensor_npu = input_tensor_cpu.npu() - # 将模型迁移到昇腾AI处理器上 - model_npu = model_cpu.npu() - - # 运算结果对比 - output_cpu = model_cpu(input_tensor_cpu) - output_npu = model_npu(input_tensor_npu) - compute_result = (output_cpu - output_npu).abs().mean()) - print(compute_result) + try: + from PIL import Image, ImageOps, ImageEnhance,PILLOW_VERSION + except: + from PIL import Image, ImageOps, ImageEnhance + PILLOW_VERSION="7.0.0" ``` - 因昇腾AI处理器硬件架构与cpu不同，计算结果会略有不同。若运算结果较为接近（一般不高于1e-4），则认为运算结果正常。 -2. 通过Pytorch的hook机制来打印正向反向传播中module的输入和输出来分析。 +
（可选）安装指定版本OpenCV库
- 代码样例（本样例只体现基本方法，禁止直接复制）如下： +如模型依赖OpenCV，基于训练性能考虑，建议安装OpenCV-3.4.10版本。 - ``` - # 设置hook func - def hook_func(name, module): - def hook_function(module, inputs, outputs): - print(name+' inputs', inputs) - print(name+' outputs', outputs) - return hook_function - - # 注册正反向hook - for name, module in model.named_modules(): - module.register_forward_hook(hook_func('[forward]: '+name, module)) - module.register_backward_hook(hook_func('[backward]: '+name, module)) - - # 运行 - model(input_tensor) - ``` +1. 获取源码：[获取地址](https://opencv.org/releases/)。 +2. 安装指导：[获取地址](https://docs.opencv.org/3.4.10/d7/d9f/tutorial_linux_install.html)。 - 通过分析打印正向反向传播中的inputs, outputs来确定。 +

训练过程性能优化

-3. 通过直接获取module的grad, running\_mean, running\_var等参数来分析更新量。 +**算子瓶颈优化** - 代码样例（本样例只体现基本方法，禁止直接复制）如下： +1. 获取训练过程中的Profiling数据，参见[Profiling数据采集](#采集训练过程相关数据md)。 +2. 分析Profiling数据，得到耗时较大的算子。 +3. 参见[单算子样例编写说明](#单算子样例编写说明md)构建耗时较大算子的单算子样例，通过与CPU或GPU运行单算子样例时间进行对比，若发现性能不足，则有以下两种方案解决。 + - 规避方案：使用同等语义其他高效算子替代。 + - 解决方案：改进算子性能。 - ``` - # 例如获取梯度和BN的均值方法来排查 - for name, module in model.named_modules(): - if isinstance(module, nn._BatchNorm): - print("[BN_buffer]: "+name, module.running_mean, module.running_var) - print("[grad]: "+name, module.grad) - ``` +**copy瓶颈优化** +1. 获取训练过程中的Profiling数据，参见[Profiling数据采集](#采集训练过程相关数据md)。 +2. 分析Profiling数据分析整网中的D2DCopywithStreamSynchronize/PTCopy/format\_contiguous的耗时。 +3. 若发现耗时较大，则需参照以下两种方案解决。 + - 方案一：（规避方案）PyTorch中View类型框架类算子会导致非连续转连续操作。优化思路为尽量使用计算类算子代替View类框架算子，常见的View类框架算子如View、Permute、Transpose等。更多View类框架算子可参考[https://pytorch.org/docs/stable/tensor\_view.html](https://pytorch.org/docs/stable/tensor_view.html)。 + - 方案二：（解决方案）加速转连续操作。 -

模型保存与转换

+**框架瓶颈优化** -- **[简介](#简介md)** +1. 获取训练过程中算子信息OP\_INFO，参见[获取算子信息OP\_INFO](#采集训练过程相关数据md)。 +2. 分析OP\_INFO中算子的规格和调用关系，定位是否插入了多余的算子，重点关注transdata是否合理。 +3. 优化方案：通过指定部分算子初始化格式，对多余的格式转换算子进行消除。 +4. 在pytorch/torch/nn/modules/module.py中，在cast\_weight中指定算子初始化格式，如下图。 -- **[模型保存](#模型保存md)** + ![](figures/指定算子初始化方式.png) -- **[导出ONNX模型](#导出ONNX模型md)** + 格式设置原则可参考如下规则： + - Conv2D相关：Weight 可设置为FZ格式，如第424行。 + - Linear相关的参数，可设置为NZ格式，如第409行。 -

简介

+**编译瓶颈优化** -模型训练完成后，通过Pytorch提供的接口保存模型文件并导出ONNX模型，然后通过ATC工具将其转换为适配昇腾AI处理器的.om文件用于离线推理。 +1. 获取训练过程中算子信息OP\_INFO，参见[获取算子信息OP\_INFO](#采集训练过程相关数据md)。 +2. 查看INFO日志，观察第一个step以后的aclopCompile::aclOp关键字，如果后续接了Match op inputs/type failed或To compile op则说明该算子存在动态编译，需要优化。 +3. 需参照以下两种方案解决。 + - 规避方案：在理解模型语义和相关API基础上，使用固定Shape的方式代替动态Shape。 + - 解决方案：减少编译或不需要编译该算子。 + - 优化算子编译配置请参见[编译选项设置](#编译选项设置md)。 -本章主要介绍如何将训练好的pth文件pth.tar文件转换为ONNX模型，将ONNX模型转换为适配昇腾AI处理器的.om文件流程请参考《CANN 开发辅助工具指南》手册中“ATC工具使用指南”章节。 -如果想使用Auto Tune优化功能，请参考《CANN 开发辅助工具指南》手册中“Auto Tune工具使用指导”章节。 +

亲和库

-离线推理应用构建请参考《CANN 应用软件开发指南\(C&C++, 推理\)》。整体流程如下： -![](figures/zh-cn_image_0000001144082132.png) +

来源介绍

-

模型保存

+针对公版模型中常见的网络结构和函数，我们针对性地对其进行了优化，使得运算性能大幅度提升，同时，将其集成到Pytorch框架中，便于模型性能调优中使用。 -Pytorch在训练过程中，通常使用torch.save\(\)来保存Checkpoint文件，根据模型文件的后续用途会保存为两种格式的模型文件： +

功能介绍

-- .pth或.pt扩展名的文件：用于在线推理或导出ONNX格式模型，仅保存模型参数，不保存模型结构，以便压缩文件的体积，可以用Netron等可视化工具打开，一般如[图1 .pth文件](#fig315704722610)所示。 + + + + + + + + + + + + + + + + + + + + + + + +

函数名

+

位置

+

功能说明

+

pairwise_iou

+

torch.contrib.npu.optimized_lib

+

计算两个目标框的IOU。

+

fast_rcnn_inference_single_image

+

torch.contrib.npu.optimized_lib

+

Maskrcnn和Fasterrcnn模型的推理接口。

+

ChannelShuffle

+

torch.contrib.npu.optimized_lib

+

提供NPU亲和的channelshuffle操作，适用于shufflenetv2等模型。

+

PreLoader

+

torch.contrib.npu.optimized_lib

+

提供针对昇腾AI处理器加速的数据加载方法。

+
- **图 1** .pth文件 - ![](figures/pth文件.jpg "pth文件") +>![](public_sys-resources/icon-note.gif) **说明：** +>该部分调优内容会随着版本不断增强和更新，请以实际PyTorch版本中对应路径下的内容为准。 - 通过**state\_dict**来保存和加载模型，示例如下： +

精度调测

- 1. 保存模型。 - ``` - # 创建保存路径 - PATH = "state_dict_model.pt" - # 保存模型 - torch.save(net.state_dict(), PATH) - ``` +

前提条件

- 2. 加载模型以用于在线推理，示例如下，详情请参见《PyTorch在线推理指南》。 +优先在同等语义和超参下，跑一定的epoch（推荐完整epoch数的20%），使精度，loss等对齐GPU相应水平，完成后再对齐最终精度。 - ``` - # 模型文件保存路径 - PATH = "state_dict_model.pt" - model = TheModelClass(*args, **kwargs) - # 加载模型 - model.load_state_dict(torch.load(PATH)) - model.eval() - ``` +

调测过程

- >![](public_sys-resources/icon-notice.gif) **须知：** - >保存.pth或.pt文件扩展名的文件时要提供模型定义文件，否则无法部署。 +- **[总体思路](#总体思路-4md)** -- .pth.tar扩展名的文件：可用于在线推理或重新加载后继续训练。保存多个组件，以字典形式保存，常见的组件包括模型和优化器的state\_dict、停止时的epoch、最新记录的训练损失以及外部的torch.nn.Embedding层等。如果仅用于部署推理模型，推荐只在.pth.tar扩展名的文件中保存权重信息即模型的state\_dict。 +- **[精度调优方法](#精度调优方法md)** - 保存和加载模型示例如下： - 1. 保存模型。 +

总体思路

- ``` - PATH = "checkpoint.pth.tar" - torch.save({ - 'epoch': epoch, - 'loss': loss, - 'state_dict': model.state_dict(), - 'optimizer' : optimizer.state_dict(), - ... - }, PATH) - ``` +精度问题排查需要找出是哪一步出现的问题，主要以下几个方面： - 2. 加载模型用于推理或恢复训练。 +1. 模型网络计算错误。 + - 定位思路：在网络中加入hook进行排查判断是哪个地方有较大嫌疑，然后构建[单算子用例](#单算子样例编写说明md)逐渐缩小错误范围，证明该算子在当前网络场景下计算有误，可以对比CPU或GPU结果证明。 - ``` - model = TheModelClass(*args, **kwargs) - optimizer = TheOptimizerClass(*args, **kwargs) - - checkpoint = torch.load(PATH) - model.load_state_dict(checkpoint['model_state_dict']) - optimizer.load_state_dict(checkpoint['optimizer_state_dict']) - epoch = checkpoint['epoch'] - loss = checkpoint['loss'] - - model.eval() - # - or - - model.train() - ``` + - 规避方案：使用同等语义其他算子替代。 + - 解决方案：改进算子精度或功能问题。 +2. loss计算错误。 + - 定位思路：由于Loss的特殊性和可以自定义，在判断Loss计算错误后建议dump网络中的loss的输入来测试而非随机同shape tensor，这样才能更好地复现证明。 ->![](public_sys-resources/icon-notice.gif) **须知：** ->通常情况下，训练图和推理图中对同一个算子处理方式不同（例如BatchNorm和dropout等算子），在输入格式上也有差别，因此在运行推理或导出ONNX模型之前，必须调用model.eval\(\) 来将dropout和batch normalization层设置为推理模式。 + - 规避方案：使用同等语义其他算子替代。 -

导出ONNX模型

+ - 解决方案：改进算子精度或功能问题（loss也是由算子构成）。 -**简介** +3. 参数更新错误。 -昇腾AI处理器Pytorch模型的部署策略是基于Pytorch官方支持的ONNX模块实现的。ONNX是业内目前比较主流的模型格式，广泛用于模型交流及部署。本节主要介绍如何将Checkpoint文件通过torch.onnx.export\(\)接口导出为ONNX模型。 + - 定位思路：在每个optim.step\(\)前对网络中的参数逐个打印其grad进行排查判断是哪个地方有较大嫌疑，然后构建单算子用例逐渐缩小错误范围，证明该算子在当前网络场景下梯度计算有误，可以对比CPU或GPU结果证明。该项优先级应低于[1.](#li17755175510322)与[2.](#li25281726103316)，因为1与2的错误同样可以造成grad异常。 -**.pth或.pt文件导出ONNX模型** + - 规避方案：使用同等语义其他算子替代。 -保存的.pth或.pt文件可以通过Pytorch构建模型再加载权重的方法恢复，然后导出ONNX模型，样例如下。 + - 解决方案：改进计算grad的算子精度或功能问题。 -``` -import torch -import torch.onnx -import torchvision.models as models -# 设置使用CPU导出模型 -device = torch.device("cpu") - -def convert(): - # 模型定义来自于torchvision，样例生成的模型文件是基于resnet50模型 - model = models.resnet50(pretrained = False) - resnet50_model = torch.load('resnet50.pth', map_location='cpu') - model.load_state_dict(resnet50_model) - - batch_size = 1 #批处理大小 - input_shape = (3, 224, 224) #输入数据,改成自己的输入shape +4. 多卡计算错误。 - # 模型设置为推理模式 - model.eval() + - 定位思路：在保证单卡精度OK的前提下，稳定复现多卡不收敛。 - dummy_input = torch.randn(batch_size, *input_shape) # 定义输入shape - torch.onnx.export(model, - dummy_input, - "resnet50_official.onnx", - input_names = ["input"], # 构造输入名 - output_names = ["output"], # 构造输出名 - opset_version=11， # ATC工具目前仅支持opset_version=11 - dynamic_axes={"input":{0:"batch_size"}, "output":{0:"batch_size"}}) #支持输出动态轴 - ) - -if __name__ == "__main__": - convert() -``` + - 解决方案：建议联系华为方支撑人员，提供稳定复现的单P和多P脚本。 ->![](public_sys-resources/icon-note.gif) **说明：** ->- 在导出ONNX模型之前，必须调用model.eval\(\) 来将dropout和batch normalization层设置为推理模式。 ->- 样例脚本中的model来自于torchvision模块中的定义，用户使用自己的模型时需自行指定。 ->- 构造输入输出需要对应训练时的输入输出，否则无法正常推理。 +

精度调优方法

-**.pth.tar文件导出ONNX模型** +模型出现精度问题一般有：因算子溢出导致的训练loss不收敛或者精度不达标问题，整个网络训练引起的性能不达标问题。用户可通过单算子溢出检测和整网调测适度解决模型精度不达标问题。 -.pth.tar在导出ONNX模型时需要先确定保存时的信息，有时保存的节点名称和模型定义中的节点会有差异，例如会多出前缀和后缀。在进行转换的时候，可以对节点名称进行修改。转换代码样例如下。 +- **[环境准备](#环境准备md)** +- **[模型算子精度对比](模型算子精度对比)** +- **[单算子溢出检测](#单算子溢出检测md)** +- **[IR与TBE算子映射](IR与TBE算子映射)** +- **[NPU与GPU算子映射](NPU与GPU算子映射)** +- **[整网调测](#整网调测md)** -``` -import torch -import torch.onnx -from collections import OrderedDict -import mobilenet +##### 环境准备 -# 本样例中的pth.tar文件保存时节点名加了前缀module，通过遍历删除 -def proc_nodes_module(checkpoint, AttrName): - new_state_dict = OrderedDict() - for key, value in checkpoint[AttrName].items(): - if key == "module.features.0.0.weight": - print(value) - if(key[0:7] == "module."): - name = key[7:] - else: - name = key[0:] +- 安装hdf5工具以支持算子dump功能，安装详情请参见[编译安装hdf5](#编译安装hdf5md)。 - new_state_dict[name] = value - return new_state_dict + 若使用模型算子精度对比功能，需要同时在NPU和GPU环境安装hdf5。否则，仅在NPU环境安装hdf5即可。 -def convert(): - checkpoint = torch.load("./mobilenet_cpu.pth.tar", map_location=torch.device('cpu')) - checkpoint['state_dict'] = proc_nodes_module(checkpoint,'state_dict') - model = mobilenet.mobilenet_v2(pretrained = False) - model.load_state_dict(checkpoint['state_dict']) - model.eval() - input_names = ["actual_input_1"] - output_names = ["output1"] - dummy_input = torch.randn(1, 3, 224, 224) - torch.onnx.export(model, dummy_input, "mobilenetV2_npu.onnx", input_names = input_names, output_names = output_names, opset_version=11) +- 安装支持dump功能的Ascend PyTorch框架，编译前请修改build.sh脚本，其余操作请参见《PyTorch安装指南》。 -if __name__ == "__main__": - convert() -``` + - 在NPU环境PyTorch安装 -

样例说明

+ 编译前修改build.sh脚本，在脚本中增加`USE_DUMP=1`字段。 -- **[ResNet50模型迁移示例](#ResNet50模型迁移示例md)** + ```bash + DEBUG=0 USE_DISTRIBUTED=1 USE_HCCL=1 USE_MKLDNN=0 USE_CUDA=0 USE_NPU=1 BUILD_TEST=0 USE_NNPACK=0 USE_DUMP=1 python"${PY_VERSION}" setup.py build bdist_wheel + ``` -- **[ShuffleNet模型调优示例](#ShuffleNet模型调优示例md)** + - （可选）在GPU环境PyTorch安装，若对模型算子精度对比，请执行此操作，否则请忽略。 + 编译前修改build.sh，在脚本中增加`USE_DUMP=1`、`USE_NCCL=0`字段，将 `USE_HCCL`、`USE_NPU`字段的值修改为0，将`USE_CUDA`字段的值修改为1。 -

ResNet50模型迁移示例

+ ```bash + DEBUG=0 USE_DISTRIBUTED=1 USE_HCCL=0 USE_NCCL=0 USE_MKLDNN=0 USE_CUDA=1 USE_NPU=0 BUILD_TEST=0 USE_NNPACK=0 USE_DUMP=1 python"${PY_VERSION}" setup.py build bdist_wheel + ``` -- **[样例获取](#样例获取md)** +##### 模型算子精度对比 -- **[训练脚本迁移](#训练脚本迁移md)** +用户使用精度对比工具，在相同输入的情况下，获取模型在GPU和NPU进行训练时模型内算子输出的精度差异，从而帮助开发者实现算子精度问题定位。 -- **[脚本执行](#脚本执行md)** +约束说明： +- 建议使用小batchsize，一般设置为8及以下。 -

样例获取

+ 由于每个算子输入、输出数据会存储在硬盘中，会占用较大空间，故建议使用小batchsize节省硬盘空间。 -样例获取 +- 建议仅dump一个step的数据进行精度对比。 -1. 本样例基于PyTorch官网提供的Imagenet数据集训练模型进行适配昇腾910 AI处理器的迁移改造，样例获取路径为[https://github.com/pytorch/examples/tree/master/imagenet](https://github.com/pytorch/examples/tree/master/imagenet)。 -2. 本样例依赖torchvision，需要安装torchvision依赖，如果使用非root用户安装， 则需在命令末尾加上**--user**。 +- 目前支持精度为fp32、O1或O2训练过程的算子精度对比。 - 当服务器运行环境为X86架构时，安装命令如下： +对比模式： - ``` - pip3.7 install torchvision==0.6.0 --no-deps - ``` +- GPU的输入和输出为已知数据，将GPU的输入数据加载到NPU上执行得到输出数据，NPU与GPU输出数据对比。 +- NPU的输入和输出为已知数据，将NPU的输入数据加载到GPU上执行得到输出数据，NPU与GPU输出数据对比。 - 当服务器运行环境为ARM架构时，安装命令如下： +操作步骤： - ``` - pip3.7 install torchvision==0.2.2.post3 --no-deps - ``` +1. 在GPU或NPU环境，使用dumper工具获取GPU或NPU的模型输入和算子输出数据。 -3. Resnet50模型参考PyTorch官网模型[https://pytorch.org/hub/pytorch\_vision\_resnet/](https://pytorch.org/hub/pytorch_vision_resnet/)，实际使用有如下两种方式。 - 1. 直接调用对应接口，例如： + 修改训练代码，增加数据dump功能。在模型训练代码的正向、反向计算位置使用`with`语句增加`torch.utils.dumper()`方法dump数据。例如，在GPU环境下修改示例： - ``` - import torchvision.models as models - model = models.resnet50() - ``` + ```python + for i, data in enumerate(dataloader): + with torch.utils.dumper(use_dump=True, dump_path="./model_gpu.h5") as dump: + # 模型训练代码 + xxx # forward code + xxx # backward code + exit() + xxx # optimizer code + ``` - >![](public_sys-resources/icon-note.gif) **说明：** - >Resnet50为PyTorch内置模型，了解更多内置模型请前往[Pytorch官网](https://pytorch.org/)。 + dump_path参数为dump数据保存文件路径及名称。建议仅dump一个step的数据用于精度对比，同时参数更新代码放在with语句外。 - 2. 在脚本执行中直接指定参数arch为resnet50，内容如下，本样例迁移采用该种方式，请参见[脚本执行](#脚本执行md)。 +2. 将在GPU(NPU)环境dump的数据model_gpu.h5拷贝到NPU(GPU)环境。 - ``` - --arch resnet50 - ``` +3. 在NPU或NPU环境，使用dumper工具加载已经dump出的数据，并获取算子输出数据。 + 修改训练代码，增加数据load、dump功能。在模型训练代码的正向、反向计算位置使用`with`语句增加`torch.utils.dumper()`方法load、dump数据。例如，在NPU环境下修改示例： + ```python + for i, data in enumerate(dataloader): + with torch.utils.dumper(use_dump=True, load_file_path="./model_gpu.h5", dump_path="./model_npu.h5") as dump: + # 模型训练代码 + xxx # forward code + xxx # backward code + exit() + xxx # optimizer code + ``` -目录结构 + load_file_path参数为从GPU或NPU获取的dump数据路径，dump_path参数为dump数据保存文件路径及名称。建议仅dump一个step的数据用于精度对比，同时参数更新代码放在with语句外。 -主要文件目录结构如下所示： +4. 使用msaccucmp.py对算子输出数据对比。 -``` -├──main.py -``` + 1. ascend-toolkit提供了msaccucmp.py工具脚本用具精度对比。 -

训练脚本迁移

+ - 该脚本路径为："/user/local/Ascend/ascend-toolkit/latest/tools/operator_cmp/compare/msaccucmp.py"， -- **[单P训练修改](#单P训练修改md)** + 路径仅供参考，请以ascend-toolkit实际安装路径为准。 -- **[分布式训练修改](#分布式训练修改md)** + - 也可以使用如下命令查找msaccucmp.py路径。 + ```linux + find / -name msaccucmp.py + ``` -
单P训练修改
+ 2. 执行msaccucmp.py脚本，进行精度对比。 -1. main.py增加头文件以支持基于PyTorch框架的模型在昇腾910 AI处理器上训练： + ``` + python3 /user/local/Ascend/ascend-toolkit/latest/tools/operator_cmp/compare/msaccucmp.py compare -m ./model_npu.h5 -g ./model_gpu.h5 + ``` - ``` - import torch.npu - ``` + 参数说明： -2. 在main.py文件中头文件后添加参数以指定使用昇腾910 AI处理器进行训练： + `-g`参数传入使用GPU获得的dump数据文件路径。 - ``` - CALCULATE_DEVICE = "npu:1" - ``` + `-m`参数传入使用NPU获得的dump数据文件路径。 -3. 修改参数以及判断选项，使其只在昇腾910 AI处理器上进行训练。 - 代码位置：main.py文件中的main\_worker\(\)函数（修改部分为字体加粗部分）： +
单算子溢出检测
- ``` - def main_worker(gpu, ngpus_per_node, args): - global best_acc1 - # 原代码为使用GPU进行训练，原代码如下： - # args.gpu = gpu - ############## npu modify begin ############# - args.gpu = None - ############## npu modify end ############# - if args.gpu is not None: - print("Use GPU: {} for training".format(args.gpu)) - - if args.distributed: - if args.dist_url == "env://" and args.rank == -1: - args.rank = int(os.environ["RANK"]) - if args.multiprocessing_distributed: - # For multiprocessing distributed training, rank needs to be the - # global rank among all the processes - args.rank = args.rank * ngpus_per_node + gpu - dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url, - world_size=args.world_size, rank=args.rank) - # create model - if args.pretrained: - print("=> using pre-trained model '{}'".format(args.arch)) - model = models.__dict__[args.arch](pretrained=True) - else: - print("=> creating model '{}'".format(args.arch)) - model = models.__dict__[args.arch]() - # 原代码中需要判断是否在GPU上进行训练，原代码如下： - # if not torch.cuda.is_available(): - # print('using CPU, this will be slow') - # elif args.distributed: - ############## npu modify begin ############# - # 迁移后为直接判断是否进行分布式训练，去掉判断是否在GPU上进行训练 - if args.distributed: - ############## npu modify end ############# - # For multiprocessing distributed, DistributedDataParallel constructor - # should always set the single device scope, otherwise, - # DistributedDataParallel will use all available devices. - if args.gpu is not None: - ...... - ``` +用户通过算子溢出检测功能检测算子是否有溢出，然后采集溢出算子的数据，从而帮助开发人员快速定位并解决算子精度问题。 -4. 将模型以及损失函数迁移到昇腾910 AI处理器上进行计算。 +约束说明： - 代码位置：main.py文件中的main\_worker\(\)函数（修改部分为字体加粗部分）： +- 本功能只提供IR级别的算子溢出检测，且只支持AICORE，不支持Atomic。 +- 使用单算子溢出检测功能时，请不要同时开启apex的动态loss scale模式和使用tensor融合功能。 - ``` - elif args.gpu is not None: - torch.cuda.set_device(args.gpu) - model = model.cuda(args.gpu) - else: - # DataParallel will divide and allocate batch_size to all available GPUs - if args.arch.startswith('alexnet') or args.arch.startswith('vgg'): - model.features = torch.nn.DataParallel(model.features) - model.cuda() - else: - # 原代码使用torch.nn.DataParallel()类来用多个GPU加速训练 - # model = torch.nn.DataParallel(model).cuda() - ############## npu modify begin ############# - # 将模型迁移到NPU上进行训练。 - model = model.to(CALCULATE_DEVICE) - ############## npu modify end ############# - # 原代码中损失函数是在GPU上进行计算 - # # define loss function (criterion) and optimizer - # criterion = nn.CrossEntropyLoss().cuda(args.gpu) - ############## npu modify begin ############# - # 将损失函数迁移到NPU上进行计算。 - criterion = nn.CrossEntropyLoss().to(CALCULATE_DEVICE) - ############## npu modify end ############# - ``` +采集溢出算子数据： -5. 将数据集目标结果target修改成int32类型解决算子报错问题；将数据集迁移到昇腾910 AI处理器上进行计算。 - - 代码位置：main.py文件中的train\(\)函数（修改部分为字体加粗部分）： +``` +# check_overflow为溢出检测控制开关 +# dump_path为dump文件保存路径 +with torch.utils.dumper(check_overflow=check_overflow, dump_path=dump_path, load_file_path='') as dump: + # 需要检测算子溢出的代码片段 +``` - ``` - for i, (images, target) in enumerate(train_loader): - # measure data loading time - data_time.update(time.time() - end) - - if args.gpu is not None: - images = images.cuda(args.gpu, non_blocking=True) - # 原代码中训练数据集在GPU上进行加载计算，原代码如下： - # if torch.cuda.is_available(): - # target = target.cuda(args.gpu, non_blocking=True) - ############## npu modify begin ############# - # 将数据集迁移到NPU上进行计算并修改target数据类型，以提升性能 - if 'npu' in CALCULATE_DEVICE: - target = target.to(torch.int32) - images, target = images.to(CALCULATE_DEVICE, non_blocking=True), target.to(CALCULATE_DEVICE, non_blocking=True) - ############## npu modify end ############# - ``` +运行一个step，模型运行过程中，如果有算子溢出，会打印出相应IR的名字。 - - 代码位置：main.py文件中的validate\(\)函数（修改部分为字体加粗部分）： +查看Dump数据： - ``` - with torch.no_grad(): - end = time.time() - for i, (images, target) in enumerate(val_loader): - if args.gpu is not None: - images = images.cuda(args.gpu, non_blocking=True) - # 原代码中训练数据集在GPU上进行加载计算，原代码如下： - # if torch.cuda.is_available(): - # target = target.cuda(args.gpu, non_blocking=True) - ############## npu modify begin ############# - # 将数据集迁移到NPU上进行计算并修改target数据类型 - if 'npu' in CALCULATE_DEVICE: - target = target.to(torch.int32) - images, target = images.to(CALCULATE_DEVICE, non_blocking=True), target.to(CALCULATE_DEVICE, non_blocking=True) - ############## npu modify end ############# - ``` +如果训练过程中采集到了Dump数据，则会在\{dump\_path\}路径下生成dump数据的.h5文件，用户可进入路径自行查看。 -6. 设置当前正在使用的device。 +解决方法： - 代码位置：main.py文件中的主函数入口（修改部分为字体加粗部分）： +1. 将采集到的.h5文件映射到TBE算子，映射方法请参见[IR与TBE算子映射](#IR与TBE算子映射)。 - ``` - if __name__ == '__main__': - ############## npu modify begin ############# - if 'npu' in CALCULATE_DEVICE: - torch.npu.set_device(CALCULATE_DEVICE) - ############## npu modify begin ############# - main() - ``` +2. 请将算子溢出的打印截图及映射后的TBE算子输入输出文件通过Issue附件形式反馈给华为开发人员。 +##### IR与TBE算子映射 -
分布式训练修改
+前提条件： -1. main.py增加头文件以支持基于PyTorch框架的模型在昇腾910 AI处理器上训练及进行混合精度训练。 +- 设置环境变量`export ACL_DUMP_DATA=0`。 +- 在脚本中避免使用`torch.npu.init.dump()`和`torch.npu.set.dump()`接口。 - ``` - import torch.npu - from apex import amp - ``` +操作步骤： -2. 参数设置增加以下参数，包括指定参与训练的昇腾910 AI处理器以及进行混合精度训练需要的参数。 +1. 准备好需要映射的算子.h5文件。 - ``` - parser.add_argument('--device', default='npu', type=str, help='npu or gpu') - parser.add_argument('--addr', default='10.136.181.115', type=str, help='master addr') - parser.add_argument('--device-list', default='0,1,2,3,4,5,6,7', type=str, help='device id list') - parser.add_argument('--amp', default=False, action='store_true', help='use amp to train the model') - parser.add_argument('--loss-scale', default=1024., type=float, - help='loss scale using in amp, default -1 means dynamic') - parser.add_argument('--opt-level', default='O2', type=str, - help='loss scale using in amp, default -1 means dynamic') - ``` + - 算子溢出检测场景下，单算子溢出检测已生成需要映射的算子.h5文件。 -3. 创建由device\_id到process\_id的映射函数，指定device进行训练。在main.py函数中增加以下接口。 + - 精度对比场景下，需根据精度对比结果，参照下面命令提取需要映射的算子.h5文件。 - ``` - def device_id_to_process_device_map(device_list): - devices = device_list.split(",") - devices = [int(x) for x in devices] - devices.sort() - - process_device_map = dict() - for process_id, device_id in enumerate(devices): - process_device_map[process_id] = device_id - - return process_device_map - ``` + ``` + h5copy -pv -i "./input.h5" -o "./output.h5" -s "/op1/seqid/" -d "/op1/seqid/" + ``` -4. 指定训练服务器的ip和端口。 + -i 为输入精度对比文件 - 代码位置：main.py文件中的主函数main\(\)（修改部分为字体加粗部分）。 + -o 为输出需要映射的算子.h5文件路径 - ``` - def main(): - args = parser.parse_args() - ############## npu modify begin ############# - os.environ['MASTER_ADDR'] = args.addr - os.environ['MASTER_PORT'] = '29688' - ############## npu modify end ############# - ``` + -s 为需要提取的源算子名称及seqid -5. 创建由device\_id到process\_id的映射参数，获取单节点昇腾910 AI处理器数量。 + -d 为需要提取的目的算子名称及seqid - 代码位置：main.py文件中的主函数main\(\)（修改部分为字体加粗部分）。 + 若需要提取多个算子，则修改-s、-d参数，多次执行该命令，可以把多算子追加提取到output.h5中。 - ``` - args.distributed = args.world_size > 1 or args.multiprocessing_distributed - ############## npu modify begin ############# - args.process_device_map = device_id_to_process_device_map(args.device_list) - if args.device == 'npu': - ngpus_per_node = len(args.process_device_map) - else: - ngpus_per_node = torch.cuda.device_count() - ############## npu modify end ############# - # 原代码如下： - # ngpus_per_node = torch.cuda.device_count() - ``` + 该命令需-s和-d参数相同。 -6. 获取进程process\_id对应的昇腾910 AI处理器编号，指定在对应的昇腾910 AI处理器上进行训练。 + 示例： - 代码位置：main.py文件中的main\_worker\(\)（修改部分为字体加粗部分）。 + ``` + h5copy -pv -i "./dump_npu.h5" -o "./output.h5" -s "/numpy_T/1/" -d "/numpy_T/1/" + ``` - ``` - def main_worker(gpu, ngpus_per_node, args): - global best_acc1 - ############## npu modify begin ############# - args.gpu = args.process_device_map[gpu] - ############## npu modify end ############# - # 原代码如下： - # args.gpu = gpu - ``` + 该示例表示从“./dump_npu.h5”中抽取seqid为1的numpy_T算子的输入、输出数据到"./output.h5"文件中。 -7. 初始化进程组，屏蔽掉初始化方式。 +2. 配置acl.json文件。 - 代码位置：main.py文件中的main\_worker\(\)（修改部分为字体加粗部分）。 + 在模型目录下创建acl dump功能所需的的配置文件acl.json - ``` - ############## npu modify begin ############# - if args.device == 'npu': - dist.init_process_group(backend=args.dist_backend, #init_method=args.dist_url, - world_size=args.world_size, rank=args.rank) - else: - dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url, - world_size=args.world_size, rank=args.rank) - ############## npu modify begin ############# - # 原代码如下： - # dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url, - world_size=args.world_size, rank=args.rank) - ``` + ``` + { + "dump": + { + "dump_list":[] + "dump_path":"./output_IR2TBE"# 映射结果输出路径 + "dump_mode":"all" + "dump_op_switch":"on" + } + + } + ``` -8. 要进行分布式训练且需要引入混合精度模块，并且需要将模型迁移到昇腾AI处理器上，因此需要屏蔽掉原始代码中判断是否为分布式训练以及模型是否在GPU上进行训练的代码部分。 + 需将`dump_path`修改为结果输出路径，其他字段不需要修改。 - 代码位置：main.py文件中的main\_worker\(\)（修改部分为字体加粗部分）。 +3. 修改训练脚本。 - ``` - # create model - if args.pretrained: - print("=> using pre-trained model '{}'".format(args.arch)) - model = models.__dict__[args.arch](pretrained=True) - else: - print("=> creating model '{}'".format(args.arch)) - model = models.__dict__[args.arch]() - ############## npu modify begin ############# - # 代码中添加如下内容 - # 指定训练设备为昇腾AI处理器 - loc = 'npu:{}'.format(args.gpu) - torch.npu.set_device(loc) - # 计算用于训练的batch_size和workers - args.batch_size = int(args.batch_size / ngpus_per_node) - args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node) - ############## npu modify end ############# - # 原始代码如下，需屏蔽掉，已注释 - # if not torch.cuda.is_available(): - # print('using CPU, this will be slow') - # elif args.distributed: - # # For multiprocessing distributed, DistributedDataParallel constructor - # # should always set the single device scope, otherwise, - # # DistributedDataParallel will use all available devices. - # if args.gpu is not None: - # torch.cuda.set_device(args.gpu) - # model.cuda(args.gpu) - # # When using a single GPU per process and per - # # DistributedDataParallel, we need to divide the batch size - # # ourselves based on the total number of GPUs we have - # args.batch_size = int(args.batch_size / ngpus_per_node) - # args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node) - # model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) - # else: - # model.cuda() - # # DistributedDataParallel will divide and allocate batch_size to all - # # available GPUs if device_ids are not set - # model = torch.nn.parallel.DistributedDataParallel(model) - # elif args.gpu is not None: - # torch.cuda.set_device(args.gpu) - # model = model.cuda(args.gpu) - # else: - # # DataParallel will divide and allocate batch_size to all available GPUs - # if args.arch.startswith('alexnet') or args.arch.startswith('vgg'): - # model.features = torch.nn.DataParallel(model.features) - # model.cuda() - # else: - # model = torch.nn.DataParallel(model).cuda() - ``` + 在训练脚本中添加`with`语句开启IR映射TBE功能。 -9. 屏蔽掉损失函数、优化器和断点训练部分，将这部分在后面与混合精度训练结合起来。 + ```python + with torch.utils.dumper(use_load=True, dump_path="./",load_file_path="./output.h5", load_with_acl_dump=True) as dump: + # 模型计算代码，需用户自己添加 + # x = model(input_data) + ``` - 代码位置：main.py文件中的main\_worker\(\)（修改部分为字体加粗部分）。 +4. 模型运行。 - ``` - # 屏蔽掉原始代码，已注释 - # # define loss function (criterion) and optimizer - # criterion = nn.CrossEntropyLoss().cuda(args.gpu) - # - # optimizer = torch.optim.SGD(model.parameters(), args.lr, - # momentum=args.momentum, - # weight_decay=args.weight_decay) - # - # # optionally resume from a checkpoint - # if args.resume: - # if os.path.isfile(args.resume): - # print("=> loading checkpoint '{}'".format(args.resume)) - # if args.gpu is None: - # checkpoint = torch.load(args.resume) - # else: - # # Map model to be loaded to specified single gpu. - # loc = 'cuda:{}'.format(args.gpu) - # checkpoint = torch.load(args.resume, map_location=loc) - # args.start_epoch = checkpoint['epoch'] - # best_acc1 = checkpoint['best_acc1'] - # if args.gpu is not None: - # # best_acc1 may be from a checkpoint from a different GPU - # best_acc1 = best_acc1.to(args.gpu) - # model.load_state_dict(checkpoint['state_dict']) - # optimizer.load_state_dict(checkpoint['optimizer']) - # print("=> loaded checkpoint '{}' (epoch {})" - # .format(args.resume, checkpoint['epoch'])) - # else: - # print("=> no checkpoint found at '{}'".format(args.resume)) - # - # cudnn.benchmark = True - ``` + 运行一步完整的模型计算过程，在计算过程中load遇到output.h5中的数据后，自动开启acl dump功能，执行IR，并dump出IR相对应的TBE算子的输入输出数据，IR执行结束，acl dump结束。 -10. 数据加载器，结合了数据集和取样器，并且可以提供多个线程处理数据集。使用昇腾AI处理器进行训练，需要将**pin\_memory**设置为**False**；由于当前仅支持固定shape下的训练，数据流中剩余的样本数可能小于batch大小，因此需要将**drop\_last**设置为**True**；另外需要将验证部分数据集**shuffle**设置为**True**。 +5. 获得映射文件。 - 代码位置：main.py文件中的main\_worker\(\)（修改部分为字体加粗部分）。 + 运行成功后，在acl.json配置文件中的`dump_path`路径下查看输出结果文件。 - ``` - ############## npu modify begin ############# - train_loader = torch.utils.data.DataLoader( - train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None), - num_workers=args.workers, pin_memory=False, sampler=train_sampler, drop_last=True) - - val_loader = torch.utils.data.DataLoader( - datasets.ImageFolder(valdir, transforms.Compose([ - transforms.Resize(256), - transforms.CenterCrop(224), - transforms.ToTensor(), - normalize, - ])), - batch_size=args.batch_size, shuffle=True, - num_workers=args.workers, pin_memory=False, drop_last=True) - ############## npu modify end ############# - ``` +##### NPU与GPU算子映射 -11. 进行损失函数及优化器构建，将模型、损失函数迁移到昇腾AI处理器上；将优化器、模型与混合精度模块进行结合以支持混合精度训练；将断点训练部分与混合精度模块结合以支持混合精度训练。 +请参见《开发辅助工具指南》中 ”精度对比工具使用指南（训练）“中 “数据准备章节” 中的 “[准备以PyTorch为原始训练网络的精度比对数据文件](https://support.huawei.com/enterprise/zh/doc/EDOC1100219269/2324edc8#ZH-CN_TOPIC_0000001162580808)”。 - 代码位置：main.py文件中的main\_worker\(\)中验证数据加载**后**（修改部分为字体加粗部分）。 +
整网调测
- ``` - val_loader = torch.utils.data.DataLoader( - datasets.ImageFolder(valdir, transforms.Compose([ - transforms.Resize(256), - transforms.CenterCrop(224), - transforms.ToTensor(), - normalize, - ])), - batch_size=args.batch_size, shuffle=True, - num_workers=args.workers, pin_memory=False, drop_last=True) - - ############## npu modify begin ############# - model = model.to(loc) - # define loss function (criterion) and optimizer - criterion = nn.CrossEntropyLoss().to(loc) - optimizer = torch.optim.SGD(model.parameters(), args.lr, - momentum=args.momentum, - weight_decay=args.weight_decay) - - if args.amp: - model, optimizer = amp.initialize(model, optimizer, opt_level=args.opt_level, loss_scale=args.loss_scale) - model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) - - # optionally resume from a checkpoint - if args.resume: - if os.path.isfile(args.resume): - print("=> loading checkpoint '{}'".format(args.resume)) - checkpoint = torch.load(args.resume, map_location=loc) - args.start_epoch = checkpoint['epoch'] - best_acc1 = checkpoint['best_acc1'] - model.load_state_dict(checkpoint['state_dict']) - optimizer.load_state_dict(checkpoint['optimizer']) - if args.amp: - amp.load_state_dict(checkpoint['amp']) - print("=> loaded checkpoint '{}' (epoch {})" - .format(args.resume, checkpoint['epoch'])) - else: - print("=> no checkpoint found at '{}'".format(args.resume)) - - cudnn.benchmark = True - ############## npu modify end ############# - ``` +用户也可通过分析整个网络的方式来进行网络模型的精度调测。 -12. 断点checkpoint保存需要与混合精度训练结合，修改如下。 +1. 通过对比CPU和昇腾AI处理器的结果，判断在昇腾AI处理器上计算是否正确。 - 代码位置：main.py文件中的main\_worker\(\)（修改部分为字体加粗部分）。 + 代码样例（本样例只体现基本方法，禁止直接复制）如下： ``` - # remember best acc@1 and save checkpoint - is_best = acc1 > best_acc1 - best_acc1 = max(acc1, best_acc1) + # 固定入参，保证模型与输入数据在CPU和昇腾AI处理器上相同 + input_tensor_cpu = torch.Tensor() + model_cpu = build_model() + # 将输入数据迁移到昇腾AI处理器上 + input_tensor_npu = input_tensor_cpu.npu() + # 将模型迁移到昇腾AI处理器上 + model_npu = model_cpu.npu() - if not args.multiprocessing_distributed or (args.multiprocessing_distributed - and args.rank % ngpus_per_node == 0): - ############## npu modify begin ############# - if args.amp: - save_checkpoint({ - 'epoch': epoch + 1, - 'arch': args.arch, - 'state_dict': model.state_dict(), - 'best_acc1': best_acc1, - 'optimizer' : optimizer.state_dict(), - 'amp': amp.state_dict(), - }, is_best) - else: - save_checkpoint({ - 'epoch': epoch + 1, - 'arch': args.arch, - 'state_dict': model.state_dict(), - 'best_acc1': best_acc1, - 'optimizer' : optimizer.state_dict(), - }, is_best) - ############## npu modify end ############# + # 运算结果对比 + output_cpu = model_cpu(input_tensor_cpu) + output_npu = model_npu(input_tensor_npu) + compute_result = (output_cpu - output_npu).abs().mean()) + print(compute_result) ``` -13. 训练时，需要将数据集迁移到昇腾AI处理器上，修改如下： + 因昇腾AI处理器硬件架构与cpu不同，计算结果会略有不同。若运算结果较为接近（一般不高于1e-4），则认为运算结果正常。 + +2. 通过Pytorch的hook机制来打印正向反向传播中module的输入和输出来分析。 - 代码位置：main.py文件中的train\(\)（修改部分为字体加粗部分）。 + 代码样例（本样例只体现基本方法，禁止直接复制）如下： ``` - for i, (images, target) in enumerate(train_loader): - # measure data loading time - data_time.update(time.time() - end) - ############## npu modify begin ############# - loc = 'npu:{}'.format(args.gpu) - target = target.to(torch.int32) - images, target = images.to(loc, non_blocking=False), target.to(loc, non_blocking=False) - ############## npu modify end ############# - # 原模型代码如下： - # if args.gpu is not None: - # images = images.cuda(args.gpu, non_blocking=True) - # if torch.cuda.is_available(): - # target = target.cuda(args.gpu, non_blocking=True) + # 设置hook func + def hook_func(name, module): + def hook_function(module, inputs, outputs): + print(name+' inputs', inputs) + print(name+' outputs', outputs) + return hook_function + + # 注册正反向hook + for name, module in model.named_modules(): + module.register_forward_hook(hook_func('[forward]: '+name, module)) + module.register_backward_hook(hook_func('[backward]: '+name, module)) + + # 运行 + model(input_tensor) ``` -14. 标记反向传播.backward\(\)发生的位置，这样混合精度模块就可以进行Loss Scaling并清除每次迭代的状态，代码如下： + 通过分析打印正向反向传播中的inputs, outputs来确定。 - 代码位置：main.py文件中的train\(\)（修改部分为字体加粗部分）。 +3. 通过直接获取module的grad, running\_mean, running\_var等参数来分析更新量。 + + 代码样例（本样例只体现基本方法，禁止直接复制）如下： ``` - optimizer.zero_grad() - ############## npu modify begin ############# - if args.amp: - with amp.scale_loss(loss, optimizer) as scaled_loss: - scaled_loss.backward() - else: - loss.backward() - # 原代码如下注释部分： - # loss.backward() - ############## npu modify end ############# - optimizer.step() + # 例如获取梯度和BN的均值方法来排查 + for name, module in model.named_modules(): + if isinstance(module, nn._BatchNorm): + print("[BN_buffer]: "+name, module.running_mean, module.running_var) + print("[grad]: "+name, module.grad) ``` -15. 验证时，需要将验证数据集迁移到昇腾AI处理器上，修改如下： - 代码位置：main.py文件中的validate\(\)（修改部分为字体加粗部分）。 +

模型保存与转换

- ``` - with torch.no_grad(): - end = time.time() - for i, (images, target) in enumerate(val_loader): - ############## npu modify begin ############# - loc = 'npu:{}'.format(args.gpu) - target = target.to(torch.int32) - images, target = images.to(loc, non_blocking=False), target.to(loc, non_blocking=False) - ############## npu modify end ############# - # 原模型代码如下注释部分： - # if args.gpu is not None: - # images = images.cuda(args.gpu, non_blocking=True) - # if torch.cuda.is_available(): - # target = target.cuda(args.gpu, non_blocking=True) - ``` +- **[简介](#简介md)** + +- **[模型保存](#模型保存md)** + +- **[导出ONNX模型](#导出ONNX模型md)** + + +

简介

+ +模型训练完成后，通过Pytorch提供的接口保存模型文件并导出ONNX模型，然后通过ATC工具将其转换为适配昇腾AI处理器的.om文件用于离线推理。 + +本章主要介绍如何将训练好的pth文件pth.tar文件转换为ONNX模型，将ONNX模型转换为适配昇腾AI处理器的.om文件流程请参考《CANN 开发辅助工具指南》手册中“ATC工具使用指南”章节。 + +如果想使用Auto Tune优化功能，请参考《CANN 开发辅助工具指南》手册中“Auto Tune工具使用指导”章节。 + +离线推理应用构建请参考《CANN 应用软件开发指南\(C&C++, 推理\)》。整体流程如下： + +![](figures/zh-cn_image_0000001144082132.png) + +

模型保存

+ +Pytorch在训练过程中，通常使用torch.save\(\)来保存Checkpoint文件，根据模型文件的后续用途会保存为两种格式的模型文件： + +- .pth或.pt扩展名的文件：用于在线推理或导出ONNX格式模型，仅保存模型参数，不保存模型结构，以便压缩文件的体积，可以用Netron等可视化工具打开，一般如[图1 .pth文件](#fig315704722610)所示。 + + **图 1** .pth文件 + ![](figures/pth文件.jpg "pth文件") + + 通过**state\_dict**来保存和加载模型，示例如下： + + 1. 保存模型。 + + ``` + # 创建保存路径 + PATH = "state_dict_model.pt" + # 保存模型 + torch.save(net.state_dict(), PATH) + ``` + + 2. 加载模型以用于在线推理，示例如下，详情请参见《PyTorch在线推理指南》。 + + ``` + # 模型文件保存路径 + PATH = "state_dict_model.pt" + model = TheModelClass(*args, **kwargs) + # 加载模型 + model.load_state_dict(torch.load(PATH)) + model.eval() + ``` + + >![](public_sys-resources/icon-notice.gif) **须知：** + >保存.pth或.pt文件扩展名的文件时要提供模型定义文件，否则无法部署。 + +- .pth.tar扩展名的文件：可用于在线推理或重新加载后继续训练。保存多个组件，以字典形式保存，常见的组件包括模型和优化器的state\_dict、停止时的epoch、最新记录的训练损失以及外部的torch.nn.Embedding层等。如果仅用于部署推理模型，推荐只在.pth.tar扩展名的文件中保存权重信息即模型的state\_dict。 + + 保存和加载模型示例如下： + + 1. 保存模型。 + + ``` + PATH = "checkpoint.pth.tar" + torch.save({ + 'epoch': epoch, + 'loss': loss, + 'state_dict': model.state_dict(), + 'optimizer' : optimizer.state_dict(), + ... + }, PATH) + ``` + + 2. 加载模型用于推理或恢复训练。 + ``` + model = TheModelClass(*args, **kwargs) + optimizer = TheOptimizerClass(*args, **kwargs) + + checkpoint = torch.load(PATH) + model.load_state_dict(checkpoint['model_state_dict']) + optimizer.load_state_dict(checkpoint['optimizer_state_dict']) + epoch = checkpoint['epoch'] + loss = checkpoint['loss'] + + model.eval() + # - or - + model.train() + ``` -

脚本执行

-**准备数据集** -准备数据集并上传到运行环境的目录下，例如：/home/data/resnet50/imagenet +>![](public_sys-resources/icon-notice.gif) **须知：** +>通常情况下，训练图和推理图中对同一个算子处理方式不同（例如BatchNorm和dropout等算子），在输入格式上也有差别，因此在运行推理或导出ONNX模型之前，必须调用model.eval\(\) 来将dropout和batch normalization层设置为推理模式。 -**配置环境变量** +

导出ONNX模型

-请参考[配置环境变量](#zh-cn_topic_0000001144082004md)配置环境变量。 +**简介** -**执行命令** +昇腾AI处理器Pytorch模型的部署策略是基于Pytorch官方支持的ONNX模块实现的。ONNX是业内目前比较主流的模型格式，广泛用于模型交流及部署。本节主要介绍如何将Checkpoint文件通过torch.onnx.export\(\)接口导出为ONNX模型。 -例如： +**.pth或.pt文件导出ONNX模型** -单卡: +保存的.pth或.pt文件可以通过Pytorch构建模型再加载权重的方法恢复，然后导出ONNX模型，样例如下。 ``` -python3 main.py /home/data/resnet50/imagenet --batch-size 128 \ # 训练批次大小 - --lr 0.1 \ # 学习率 - --epochs 90 \ # 训练迭代轮数 - --arch resnet50 \ # 模型架构 - --world-size 1 \ - --rank 0 \ - --workers 40 \ # 加载数据进程数 - --momentum 0.9 \ # 动量 - --weight-decay 1e-4 # 权重衰减 +import torch +import torch.onnx +import torchvision.models as models +# 设置使用CPU导出模型 +device = torch.device("cpu") + +def convert(): + # 模型定义来自于torchvision，样例生成的模型文件是基于resnet50模型 + model = models.resnet50(pretrained = False) + resnet50_model = torch.load('resnet50.pth', map_location='cpu') + model.load_state_dict(resnet50_model) + + batch_size = 1 #批处理大小 + input_shape = (3, 224, 224) #输入数据,改成自己的输入shape + + # 模型设置为推理模式 + model.eval() + + dummy_input = torch.randn(batch_size, *input_shape) # 定义输入shape + torch.onnx.export(model, + dummy_input, + "resnet50_official.onnx", + input_names = ["input"], # 构造输入名 + output_names = ["output"], # 构造输出名 + opset_version=11， # ATC工具目前仅支持opset_version=11 + dynamic_axes={"input":{0:"batch_size"}, "output":{0:"batch_size"}}) #支持输出动态轴 + ) + +if __name__ == "__main__": + convert() ``` -分布式： +>![](public_sys-resources/icon-note.gif) **说明：** +>- 在导出ONNX模型之前，必须调用model.eval\(\) 来将dropout和batch normalization层设置为推理模式。 +>- 样例脚本中的model来自于torchvision模块中的定义，用户使用自己的模型时需自行指定。 +>- 构造输入输出需要对应训练时的输入输出，否则无法正常推理。 + +**.pth.tar文件导出ONNX模型** + +.pth.tar在导出ONNX模型时需要先确定保存时的信息，有时保存的节点名称和模型定义中的节点会有差异，例如会多出前缀和后缀。在进行转换的时候，可以对节点名称进行修改。转换代码样例如下。 ``` -python3 main.py /home/data/resnet50/imagenet --addr='1.1.1.1' \ # 示例IP地址，请根据实际修改 - --seed 49 \ # 随机种子 - --workers 160 \ # 加载数据进程数 - --lr 0.8 \ - --print-freq 1 \ - --arch resnet50 \ # 模型架构 - --dist-url 'tcp://127.0.0.1:50000' \ - --dist-backend 'hccl' \ - --multiprocessing-distributed \ # 使用多卡训练 - --world-size 1 \ - --batch-size 2048 \ # 训练批次大小 - --epochs 90 \ # 训练迭代轮数 - --rank 0 \ - --device-list '0,1,2,3,4,5,6,7' \ - --amp # 使用混合精度训练 +import torch +import torch.onnx +from collections import OrderedDict +import mobilenet + +# 本样例中的pth.tar文件保存时节点名加了前缀module，通过遍历删除 +def proc_nodes_module(checkpoint, AttrName): + new_state_dict = OrderedDict() + for key, value in checkpoint[AttrName].items(): + if key == "module.features.0.0.weight": + print(value) + if(key[0:7] == "module."): + name = key[7:] + else: + name = key[0:] + + new_state_dict[name] = value + return new_state_dict + +def convert(): + checkpoint = torch.load("./mobilenet_cpu.pth.tar", map_location=torch.device('cpu')) + checkpoint['state_dict'] = proc_nodes_module(checkpoint,'state_dict') + model = mobilenet.mobilenet_v2(pretrained = False) + model.load_state_dict(checkpoint['state_dict']) + model.eval() + input_names = ["actual_input_1"] + output_names = ["output1"] + dummy_input = torch.randn(1, 3, 224, 224) + torch.onnx.export(model, dummy_input, "mobilenetV2_npu.onnx", input_names = input_names, output_names = output_names, opset_version=11) + +if __name__ == "__main__": + convert() ``` ->![](public_sys-resources/icon-note.gif) **说明：** ->dist-backend需配置成hccl以支持在昇腾AI设备上进行分布式训练。 +

模型调优样例

ShuffleNet模型调优示例

-- Gitee From 6f3525fe77c9ba994118a4209d56fe36e1043376 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Wed, 29 Dec 2021 11:52:37 +0800 Subject: [PATCH 26/55] modified: "zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" --- ...55\347\273\203\346\214\207\345\215\227.md" | 32 ++++++++++++++++--- 1 file changed, 28 insertions(+), 4 deletions(-) diff --git "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" index bd5c1d664df..477a9d9c353 100644 --- "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" +++ "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" @@ -13,9 +13,7 @@ - [性能调优和分析](#性能调优和分析md) - [精度调测](#精度调测md) - [模型保存与转换](#模型保存与转换md) -- [样例说明](#样例说明md) - - [ResNet50模型迁移示例](#ResNet50模型迁移示例md) - - [ShuffleNet模型调优示例](#ShuffleNet模型调优示例md) +- [ShuffleNet模型调优示例](#ShuffleNet模型调优示例md) - [参考信息](#参考信息md) - [FAQ](#FAQmd) - [软件安装常见问题](#软件安装常见问题md) @@ -2050,8 +2048,34 @@ def main(): - 解决方案：减少编译或不需要编译该算子。 - 优化算子编译配置请参见[编译选项设置](#编译选项设置md)。 +### 端到端性能工具（E2E prof）使用说明 -

亲和库

+#### E2E prof工具介绍 + +E2E prof工具是一个将pytorch框架的profiling工具和cann prof工具获取到的框架层面数据和算子性能数据统一集成，实现端到端的模型和算子性能分析工具。 + +#### E2E prof使用教程 + +添加with语句使能E2E prof功能 + +with torch.npu.profile(profiler_result_path="./result",use_e2e_profiler=Ture): + +​ model_train() + +- profiler_result_path表示prof结果保存路径，默认为当前路径。 +- use_e2e_profiler表示是否开启E2E prof功能，默认为False（仅开启CANN prof功能）。 + +（因NUP算子需要编译后才能执行，为保证数据的准确性，建议先运行10个step，在第十个step后再进行E2E prof操作。） + +#### E2E prof结果解析 + +通过E2E prof工具获得的结果为原始数据，需要通过解析后查看。以使用教程中路径为例，工具会在profiler_result_path路径下创建文件夹以保存原始数据。 + + + + + +### 亲和库

来源介绍

-- Gitee From 8fcc88fec79fe42d331e823d2efbdb491dd2353a Mon Sep 17 00:00:00 2001 From: pengzirong Date: Mon, 20 Dec 2021 23:24:47 +0800 Subject: [PATCH 27/55] =?UTF-8?q?=E5=90=8C=E6=AD=A51.5.0=2052e8bc75379dc30?= =?UTF-8?q?79e31ccbeb65ccebc3a80f191=20=20=20=20=20!2897=20embedding=5Fbag?= =?UTF-8?q?=5Fbackward=5Fnpu=20hostcpu=E9=80=82=E9=85=8D=E5=AE=9E=E7=8E=B0?= =?UTF-8?q?=20=20=20=20=20Merge=20pull=20request=20!2897=20from=20?= =?UTF-8?q?=E7=89=9B=E4=BD=9C=E4=B8=9C/master=20--=20=20=20=20=20!2901=20?= =?UTF-8?q?=E5=90=88=E5=B9=B6dev=E5=88=86=E6=94=AF=20=20=20=20=20Merge=20p?= =?UTF-8?q?ull=20request=20!2901=20from=20weili10/dev=20--=20=20=20=20=20!?= =?UTF-8?q?2904=20python3.9=E7=8E=AF=E5=A2=83jit=20index=20parsing?= =?UTF-8?q?=E5=91=8A=E8=AD=A6=E8=A7=A3=E9=99=A4=20=20=20=20=20Merge=20pull?= =?UTF-8?q?=20request=20!2904=20from=20=E6=9D=8E=E8=B6=85/master=20--=20?= =?UTF-8?q?=20=20=20=20!2902=20=E4=BF=AE=E5=A4=8DE2E=20prof=E5=A4=9A?= =?UTF-8?q?=E6=AC=A1=E8=B0=83=E7=94=A8=E7=9A=84=E9=97=AE=E9=A2=98=20=20=20?= =?UTF-8?q?=20=20Merge=20pull=20request=20!2902=20from=20shibo/e2e=5Fbugfi?= =?UTF-8?q?x=20=E5=90=8C=E6=AD=A51.8.1=20f9f494b14ca91bbf236f99f78aef3927c?= =?UTF-8?q?7c3cdab?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- patch/pytorch1.5.0_npu.patch | 717 +++++++++--------- patch/pytorch1.8.1_npu.patch | 184 ++--- .../src/ATen/native/native_functions.yaml | 2 + .../src/ATen/native/npu/ArangeKernelNpu.cpp | 6 +- .../npu/EmbeddingBagBackwardKernelNpu.cpp | 58 ++ .../src/ATen/native/npu/frame/NPUDefine.cpp | 3 +- .../ATen/native/npu/frame/OpParamMaker.cpp | 52 +- .../ATen/native/npu/nputools/E2eProfiler.cpp | 7 +- .../src/THNPU/THNPUCachingHostAllocator.cpp | 2 +- .../src/c10/npu/NPUCachingAllocator.cpp | 55 +- .../src/c10/npu/NPUCachingAllocator.h | 4 +- pytorch1.5.0/src/c10/npu/NPUQueue.cpp | 14 +- pytorch1.5.0/src/c10/npu/NPUQueue.h | 7 +- pytorch1.5.0/src/c10/npu/NPUStream.cpp | 48 +- pytorch1.5.0/src/c10/npu/NPUStream.h | 2 + .../npu/interface/AsyncTaskQueueInterface.cpp | 76 +- .../npu/interface/AsyncTaskQueueInterface.h | 25 +- .../test_npu_tools/test_npu_profiler.py | 76 +- 18 files changed, 806 insertions(+), 532 deletions(-) create mode 100644 pytorch1.5.0/src/aten/src/ATen/native/npu/EmbeddingBagBackwardKernelNpu.cpp diff --git a/patch/pytorch1.5.0_npu.patch b/patch/pytorch1.5.0_npu.patch index 085ab421df8..168a35fd587 100644 --- a/patch/pytorch1.5.0_npu.patch +++ b/patch/pytorch1.5.0_npu.patch @@ -1,6 +1,6 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/CMakeLists.txt pytorch-develop-150/aten/CMakeLists.txt --- pytorch-v1.5.0/aten/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/CMakeLists.txt 2021-12-18 23:01:06.049043092 +0800 ++++ pytorch-develop-150/aten/CMakeLists.txt 2021-12-20 23:24:40.275378243 +0800 @@ -22,8 +22,10 @@ set(ATen_CPU_INCLUDE) set(ATen_THIRD_PARTY_INCLUDE) @@ -51,7 +51,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_CPU_DEPENDENCY_LIBS ${ATen_CPU_DEPENDENCY_LIBS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/CMakeLists.txt pytorch-develop-150/aten/src/ATen/CMakeLists.txt --- pytorch-v1.5.0/aten/src/ATen/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/CMakeLists.txt 2021-12-18 23:01:06.049043092 +0800 ++++ pytorch-develop-150/aten/src/ATen/CMakeLists.txt 2021-12-20 23:24:40.275378243 +0800 @@ -67,6 +67,9 @@ FILE(GLOB native_quantized_h "native/quantized/*.h" "native/quantized/cpu/*.h") FILE(GLOB native_cpu_h "native/cpu/*.h") @@ -129,7 +129,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_QUANTIZED_SRCS ${ATen_QUANTIZED_SRCS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/core/dispatch/DispatchTable.h pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h --- pytorch-v1.5.0/aten/src/ATen/core/dispatch/DispatchTable.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h 2021-12-18 23:01:06.053043123 +0800 ++++ pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h 2021-12-20 23:24:40.279378293 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -170,7 +170,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/function_wrapper.py pytorch-develop-150/aten/src/ATen/function_wrapper.py --- pytorch-v1.5.0/aten/src/ATen/function_wrapper.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/function_wrapper.py 2021-12-18 23:01:06.053043123 +0800 ++++ pytorch-develop-150/aten/src/ATen/function_wrapper.py 2021-12-20 23:24:40.279378293 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -354,7 +354,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for option in declaration['options']: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/gen.py pytorch-develop-150/aten/src/ATen/gen.py --- pytorch-v1.5.0/aten/src/ATen/gen.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/gen.py 2021-12-18 23:01:06.053043123 +0800 ++++ pytorch-develop-150/aten/src/ATen/gen.py 2021-12-20 23:24:40.279378293 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -512,7 +512,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= generate_outputs() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/BatchLinearAlgebra.cpp pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp --- pytorch-v1.5.0/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-12-18 23:01:06.053043123 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-12-20 23:24:40.279378293 +0800 @@ -680,7 +680,7 @@ std::tuple triangular_solve_out(Tensor& result, Tensor& clone_A, const Tensor& self, const Tensor& A, bool upper, bool transpose, bool unitriangular) { @@ -524,7 +524,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return std::tuple(result, clone_A); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/cpu/Activation.cpp pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp --- pytorch-v1.5.0/aten/src/ATen/native/cpu/Activation.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp 2021-12-18 23:01:06.057043153 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp 2021-12-20 23:24:40.283378342 +0800 @@ -339,20 +339,20 @@ void hardsigmoid_backward_kernel(TensorIterator& iter) { @@ -552,7 +552,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/Memory.cpp pytorch-develop-150/aten/src/ATen/native/Memory.cpp --- pytorch-v1.5.0/aten/src/ATen/native/Memory.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/Memory.cpp 2021-12-18 23:01:06.057043153 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/Memory.cpp 2021-12-20 23:24:40.283378342 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -614,7 +614,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= detail::computeStorageSize(self.sizes(), self.strides()), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/native_functions.yaml pytorch-develop-150/aten/src/ATen/native/native_functions.yaml --- pytorch-v1.5.0/aten/src/ATen/native/native_functions.yaml 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/native_functions.yaml 2021-12-18 23:01:06.065043215 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/native_functions.yaml 2021-12-20 23:24:40.287378391 +0800 @@ -1,6 +1,5 @@ # See README.md in this directory for more guidance @@ -1663,7 +1663,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: embedding_sparse_backward(Tensor grad, Tensor indices, int num_weights, int padding_idx, bool scale_grad_by_freq) -> Tensor use_c10_dispatcher: full -@@ -1099,6 +1412,8 @@ +@@ -1099,8 +1412,12 @@ dispatch: CPU: _embedding_bag_cpu CUDA: _embedding_bag_cuda @@ -1671,8 +1671,12 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + NPU: _embedding_bag_npu - func: _embedding_bag_backward(Tensor grad, Tensor indices, Tensor offsets, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, int num_weights, bool scale_grad_by_freq, int mode, bool sparse, Tensor? per_sample_weights) -> Tensor ++ npu_dispatch: ++ NPU: _embedding_bag_backward_npu + + - func: _embedding_bag_sparse_backward(Tensor grad, Tensor indices, Tensor offsets, Tensor offset2bag, Tensor bag_size, int num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights) -> Tensor -@@ -1125,6 +1440,8 @@ +@@ -1125,6 +1442,8 @@ MkldnnCPU: empty_mkldnn SparseCPU: empty_sparse SparseCUDA: empty_sparse @@ -1681,7 +1685,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: new_empty(Tensor self, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor variants: method -@@ -1154,6 +1471,8 @@ +@@ -1154,6 +1473,8 @@ supports_named_tensor: True variants: method device_guard: False @@ -1690,7 +1694,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: empty.out(int[] size, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) device_guard: False -@@ -1161,16 +1480,22 @@ +@@ -1161,16 +1482,22 @@ - func: empty_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor device_guard: False supports_named_tensor: True @@ -1713,7 +1717,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: erf_(Tensor(a!) self) -> Tensor(a!) supports_named_tensor: True -@@ -1178,17 +1503,25 @@ +@@ -1178,17 +1505,25 @@ dispatch: CPU: _erf__cpu CUDA: _erf__cuda @@ -1739,7 +1743,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: erfc_(Tensor(a!) self) -> Tensor(a!) supports_named_tensor: True -@@ -1196,17 +1529,23 @@ +@@ -1196,17 +1531,23 @@ dispatch: CPU: _erfc__cpu CUDA: _erfc__cuda @@ -1763,7 +1767,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: exp_(Tensor(a!) self) -> Tensor(a!) supports_named_tensor: True -@@ -1214,51 +1553,69 @@ +@@ -1214,51 +1555,69 @@ dispatch: CPU: _exp__cpu CUDA: _exp__cuda @@ -1835,7 +1839,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -> Tensor use_c10_dispatcher: full -@@ -1280,25 +1637,35 @@ +@@ -1280,25 +1639,35 @@ - func: fill_.Scalar(Tensor(a!) self, Scalar value) -> Tensor(a!) supports_named_tensor: True variants: function, method @@ -1871,7 +1875,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: floor_divide(Tensor self, Tensor other) -> Tensor variants: function, method -@@ -1308,6 +1675,8 @@ +@@ -1308,6 +1677,8 @@ SparseCPU: floor_divide_sparse SparseCUDA: floor_divide_sparse supports_named_tensor: True @@ -1880,7 +1884,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: floor_divide_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) variants: method -@@ -1317,6 +1686,8 @@ +@@ -1317,6 +1688,8 @@ SparseCPU: floor_divide_sparse_ SparseCUDA: floor_divide_sparse_ supports_named_tensor: True @@ -1889,7 +1893,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: floor_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) dispatch: -@@ -1325,33 +1696,56 @@ +@@ -1325,33 +1698,56 @@ SparseCPU: floor_divide_out_sparse_zerodim SparseCUDA: floor_divide_out_sparse_zerodim supports_named_tensor: True @@ -1946,7 +1950,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: full_like(Tensor self, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor supports_named_tensor: True -@@ -1373,40 +1767,62 @@ +@@ -1373,40 +1769,62 @@ # `align_corners = True`. - func: grid_sampler(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor use_c10_dispatcher: full @@ -2009,7 +2013,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: hinge_embedding_loss(Tensor self, Tensor target, float margin=1.0, int reduction=Mean) -> Tensor use_c10_dispatcher: full -@@ -1414,8 +1830,13 @@ +@@ -1414,8 +1832,13 @@ - func: ger(Tensor self, Tensor vec2) -> Tensor use_c10_dispatcher: full variants: function, method @@ -2023,7 +2027,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: group_norm(Tensor input, int num_groups, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enabled=True) -> Tensor -@@ -1460,6 +1881,8 @@ +@@ -1460,6 +1883,8 @@ # NB: The following functions are declared in aten/src/ATen/templates/TensorBody.h and defined in aten/src/ATen/TensorIndexing.cpp: # - Tensor Tensor::index(ArrayRef indices) # - Tensor Tensor::index(std::initializer_list indices) @@ -2032,7 +2036,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: index_copy_(Tensor(a!) self, int dim, Tensor index, Tensor source) -> Tensor(a!) variants: method -@@ -1476,17 +1899,23 @@ +@@ -1476,17 +1901,23 @@ - func: index_put_(Tensor(a!) self, Tensor?[] indices, Tensor values, bool accumulate=False) -> Tensor(a!) variants: function, method @@ -2057,7 +2061,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: instance_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool use_input_stats, float momentum, float eps, bool cudnn_enabled) -> Tensor variants: function -@@ -1494,8 +1923,12 @@ +@@ -1494,8 +1925,12 @@ - func: inverse(Tensor self) -> Tensor use_c10_dispatcher: full variants: function, method @@ -2070,7 +2074,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _inverse_helper(Tensor self) -> Tensor use_c10_dispatcher: full -@@ -1507,6 +1940,8 @@ +@@ -1507,6 +1942,8 @@ - func: isclose(Tensor self, Tensor other, float rtol=1e-05, float atol=1e-08, bool equal_nan=False) -> Tensor use_c10_dispatcher: full variants: function, method @@ -2079,7 +2083,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: isnan(Tensor self) -> Tensor use_c10_dispatcher: full -@@ -1518,6 +1953,8 @@ +@@ -1518,6 +1955,8 @@ CUDA: isnan SparseCPU: isnan_sparse SparseCUDA: isnan_sparse @@ -2088,7 +2092,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: is_distributed(Tensor self) -> bool use_c10_dispatcher: full -@@ -1541,6 +1978,8 @@ +@@ -1541,6 +1980,8 @@ variants: function, method device_guard: False supports_named_tensor: True @@ -2097,7 +2101,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: is_same_size(Tensor self, Tensor other) -> bool use_c10_dispatcher: full -@@ -1556,29 +1995,41 @@ +@@ -1556,29 +1997,41 @@ - func: kl_div(Tensor self, Tensor target, int reduction=Mean) -> Tensor use_c10_dispatcher: full @@ -2139,7 +2143,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: layer_norm(Tensor input, int[] normalized_shape, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enable=True) -> Tensor -@@ -1586,11 +2037,15 @@ +@@ -1586,11 +2039,15 @@ dispatch: CPU: layer_norm_cpu CUDA: layer_norm_cuda @@ -2155,7 +2159,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: linear(Tensor input, Tensor weight, Tensor? bias=None) -> Tensor python_module: nn -@@ -1622,46 +2077,64 @@ +@@ -1622,46 +2079,64 @@ use_c10_dispatcher: full - func: linspace(Scalar start, Scalar end, int steps=100, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor @@ -2220,7 +2224,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: log1p_(Tensor(a!) self) -> Tensor(a!) supports_named_tensor: True -@@ -1671,6 +2144,8 @@ +@@ -1671,6 +2146,8 @@ CUDA: log1p_ SparseCPU: log1p_sparse_ SparseCUDA: log1p_sparse_ @@ -2229,7 +2233,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True -@@ -1679,67 +2154,95 @@ +@@ -1679,67 +2156,95 @@ CUDA: log1p_out SparseCPU: log1p_out_sparse SparseCUDA: log1p_out_sparse @@ -2325,7 +2329,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: margin_ranking_loss(Tensor input1, Tensor input2, Tensor target, float margin=0.0, int reduction=Mean) -> Tensor use_c10_dispatcher: full -@@ -1748,9 +2251,13 @@ +@@ -1748,9 +2253,13 @@ use_c10_dispatcher: full variants: function, method supports_named_tensor: True @@ -2339,7 +2343,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: matrix_rank.tol(Tensor self, float tol, bool symmetric=False) -> Tensor use_c10_dispatcher: full -@@ -1765,22 +2272,34 @@ +@@ -1765,22 +2274,34 @@ - func: max.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices) variants: function, method supports_named_tensor: True @@ -2374,7 +2378,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Return: (Tensor output, Tensor indices) - func: max_pool1d_with_indices(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor) -@@ -1791,6 +2310,8 @@ +@@ -1791,6 +2312,8 @@ - func: max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor supports_named_tensor: True @@ -2383,7 +2387,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: mkldnn_max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor requires_tensor: True -@@ -1814,6 +2335,8 @@ +@@ -1814,6 +2337,8 @@ CPU: mean_cpu_gpu CUDA: mean_cpu_gpu QuantizedCPU: quantized_mean_cpu @@ -2392,7 +2396,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: mean.dim(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor variants: function, method -@@ -1822,6 +2345,8 @@ +@@ -1822,6 +2347,8 @@ CPU: mean_cpu_gpu CUDA: mean_cpu_gpu QuantizedCPU: quantized_mean_cpu @@ -2401,7 +2405,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: mean.out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True -@@ -1829,47 +2354,73 @@ +@@ -1829,47 +2356,73 @@ CPU: mean_out_cpu_gpu CUDA: mean_out_cpu_gpu QuantizedCPU: quantized_mean_out_cpu @@ -2475,7 +2479,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: mkldnn_convolution(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups) -> Tensor -@@ -1958,6 +2509,8 @@ +@@ -1958,6 +2511,8 @@ CUDA: legacy::cuda::_th_mm SparseCPU: _sparse_mm SparseCUDA: _sparse_mm @@ -2484,7 +2488,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) -@@ -1966,6 +2519,8 @@ +@@ -1966,6 +2521,8 @@ CUDA: legacy::cuda::_th_mm_out SparseCPU: _sparse_mm_out SparseCUDA: _sparse_mm_out @@ -2493,7 +2497,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: _sparse_mm(Tensor sparse, Tensor dense) -> Tensor -@@ -1994,6 +2549,8 @@ +@@ -1994,6 +2551,8 @@ SparseCPU: mul_sparse SparseCUDA: mul_sparse MkldnnCPU: mkldnn_mul @@ -2502,7 +2506,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: mul_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) -@@ -2004,6 +2561,8 @@ +@@ -2004,6 +2563,8 @@ SparseCPU: mul_sparse_ SparseCUDA: mul_sparse_ MkldnnCPU: mkldnn_mul_ @@ -2511,7 +2515,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: mul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) -@@ -2013,15 +2572,21 @@ +@@ -2013,15 +2574,21 @@ SparseCPU: mul_out_sparse_cpu SparseCUDA: mul_out_sparse_cuda MkldnnCPU: mkldnn_mul_out @@ -2533,7 +2537,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: mv(Tensor self, Tensor vec) -> Tensor use_c10_dispatcher: full -@@ -2030,12 +2595,16 @@ +@@ -2030,12 +2597,16 @@ CPU: mv_cpu CUDA: legacy::cuda::_th_mv supports_named_tensor: True @@ -2550,7 +2554,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: mvlgamma(Tensor self, int p) -> Tensor use_c10_dispatcher: full -@@ -2052,6 +2621,8 @@ +@@ -2052,6 +2623,8 @@ CUDA: narrow_copy_dense SparseCPU: narrow_copy_sparse SparseCUDA: narrow_copy_sparse @@ -2559,7 +2563,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: narrow(Tensor(a) self, int dim, int start, int length) -> Tensor(a) variants: function, method -@@ -2068,6 +2639,8 @@ +@@ -2068,6 +2641,8 @@ CPU: batch_norm_cpu CUDA: batch_norm_cuda MkldnnCPU: mkldnn_batch_norm @@ -2568,7 +2572,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: native_batch_norm.out(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, *, Tensor(a!) out, Tensor(b!) save_mean, Tensor(c!) save_invstd) -> (Tensor(a!), Tensor(b!), Tensor(c!)) dispatch: -@@ -2076,14 +2649,20 @@ +@@ -2076,14 +2651,20 @@ - func: batch_norm_stats(Tensor input, float eps) -> (Tensor, Tensor) dispatch: CUDA: batch_norm_stats_cuda @@ -2589,7 +2593,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # for backward compatibility - func: batch_norm_gather_stats(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int count) -> (Tensor, Tensor) -@@ -2093,19 +2672,27 @@ +@@ -2093,19 +2674,27 @@ - func: batch_norm_gather_stats_with_counts(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int[] counts) -> (Tensor, Tensor) dispatch: CUDA: batch_norm_gather_stats_with_counts_cuda @@ -2617,7 +2621,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: batch_norm_update_stats(Tensor input, Tensor? running_mean, Tensor? running_var, float momentum) -> (Tensor, Tensor) dispatch: -@@ -2117,6 +2704,8 @@ +@@ -2117,6 +2706,8 @@ - func: _nnpack_spatial_convolution(Tensor input, Tensor weight, Tensor? bias, int[2] padding, int[2] stride=1) -> Tensor variants: function @@ -2626,7 +2630,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _nnpack_spatial_convolution_backward(Tensor input, Tensor grad_output, Tensor weight, int[2] padding, bool[3] output_mask) -> (Tensor, Tensor, Tensor) variants: function -@@ -2129,42 +2718,60 @@ +@@ -2129,42 +2720,60 @@ - func: ones.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor device_guard: False @@ -2689,7 +2693,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Only exposed from C++ -- in Python, # we expose it as an attribute `T`, not a function. -@@ -2253,54 +2860,82 @@ +@@ -2253,54 +2862,82 @@ supports_named_tensor: True - func: randperm(int n, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor @@ -2773,7 +2777,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: repeat_interleave.Tensor(Tensor repeats) -> Tensor use_c10_dispatcher: full -@@ -2316,6 +2951,8 @@ +@@ -2316,6 +2953,8 @@ - func: repeat_interleave.self_int(Tensor self, int repeats, int? dim=None) -> Tensor use_c10_dispatcher: full variants: function, method @@ -2782,7 +2786,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: reshape(Tensor self, int[] shape) -> Tensor variants: function, method -@@ -2337,16 +2974,22 @@ +@@ -2337,16 +2976,22 @@ use_c10_dispatcher: full supports_named_tensor: True variants: function, method @@ -2805,7 +2809,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: rrelu(Tensor self, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor -@@ -2360,6 +3003,8 @@ +@@ -2360,6 +3005,8 @@ CUDA: relu MkldnnCPU: mkldnn_relu QuantizedCPU: quantized_relu @@ -2814,7 +2818,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: relu_(Tensor(a!) self) -> Tensor(a!) -@@ -2370,6 +3015,8 @@ +@@ -2370,6 +3017,8 @@ CUDA: relu_ MkldnnCPU: mkldnn_relu_ QuantizedCPU: quantized_relu_ @@ -2823,7 +2827,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: prelu(Tensor self, Tensor weight) -> Tensor use_c10_dispatcher: full -@@ -2377,12 +3024,16 @@ +@@ -2377,12 +3026,16 @@ dispatch: CPU: prelu_cpu CUDA: prelu_cuda @@ -2840,7 +2844,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: gelu(Tensor self) -> Tensor use_c10_dispatcher: full -@@ -2390,6 +3041,8 @@ +@@ -2390,6 +3043,8 @@ dispatch: CPU: gelu_cpu CUDA: gelu_cuda @@ -2849,7 +2853,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: gelu_backward(Tensor grad, Tensor self) -> Tensor use_c10_dispatcher: full -@@ -2397,29 +3050,41 @@ +@@ -2397,29 +3052,41 @@ dispatch: CPU: gelu_backward_cpu CUDA: gelu_backward_cuda @@ -2891,7 +2895,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: select.Dimname(Tensor(a) self, Dimname dim, int index) -> Tensor(a) variants: function, method -@@ -2433,14 +3098,21 @@ +@@ -2433,14 +3100,21 @@ - func: selu(Tensor self) -> Tensor use_c10_dispatcher: full @@ -2914,7 +2918,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: sigmoid(Tensor self) -> Tensor use_c10_dispatcher: full -@@ -2451,6 +3123,8 @@ +@@ -2451,6 +3125,8 @@ CUDA: sigmoid QuantizedCPU: quantized_sigmoid MkldnnCPU: mkldnn_sigmoid @@ -2923,7 +2927,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: sigmoid_(Tensor(a!) self) -> Tensor(a!) supports_named_tensor: True -@@ -2459,36 +3133,52 @@ +@@ -2459,36 +3135,52 @@ CPU: sigmoid_ CUDA: sigmoid_ MkldnnCPU: mkldnn_sigmoid_ @@ -2976,7 +2980,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Returns a copy of this `Variable` that is detached from its autograd graph. # This method is OK to call if the `Variable` is a view. -@@ -2533,6 +3223,8 @@ +@@ -2533,6 +3225,8 @@ - func: slogdet(Tensor self) -> (Tensor sign, Tensor logabsdet) variants: function, method @@ -2985,7 +2989,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: smm(Tensor self, Tensor mat2) -> Tensor use_c10_dispatcher: full -@@ -2542,10 +3234,14 @@ +@@ -2542,10 +3236,14 @@ - func: softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor variants: function, method supports_named_tensor: True @@ -3000,7 +3004,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _softmax(Tensor self, int dim, bool half_to_float) -> Tensor use_c10_dispatcher: full -@@ -2553,12 +3249,16 @@ +@@ -2553,12 +3251,16 @@ CPU: softmax_cpu CUDA: softmax_cuda MkldnnCPU: mkldnn_softmax @@ -3017,7 +3021,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: split.Tensor(Tensor(a) self, int split_size, int dim=0) -> Tensor(a)[] variants: function, method -@@ -2609,8 +3309,12 @@ +@@ -2609,8 +3311,12 @@ SparseCUDA: _sspaddmm_out_cuda - func: stack(Tensor[] tensors, int dim=0) -> Tensor @@ -3030,7 +3034,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # The signature is designed to be consistent with librosa except that it is # missing the `pad_mode` and `center` arguments, which are taken care of at -@@ -2633,20 +3337,30 @@ +@@ -2633,20 +3339,30 @@ - func: sum(Tensor self, *, ScalarType? dtype=None) -> Tensor variants: function, method supports_named_tensor: True @@ -3061,7 +3065,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: sum_to_size(Tensor self, int[] size) -> Tensor variants: method -@@ -2656,13 +3370,19 @@ +@@ -2656,13 +3372,19 @@ use_c10_dispatcher: full supports_named_tensor: True variants: function, method @@ -3081,7 +3085,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: square(Tensor self) -> Tensor use_c10_dispatcher: full -@@ -2677,51 +3397,81 @@ +@@ -2677,51 +3399,81 @@ use_c10_dispatcher: full variants: function, method supports_named_tensor: True @@ -3164,7 +3168,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: t(Tensor(a) self) -> Tensor(a) device_guard: False -@@ -2736,6 +3486,8 @@ +@@ -2736,6 +3488,8 @@ use_c10_dispatcher: full supports_named_tensor: True variants: function, method @@ -3173,7 +3177,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: tan_(Tensor(a!) self) -> Tensor(a!) supports_named_tensor: True -@@ -2743,12 +3495,16 @@ +@@ -2743,12 +3497,16 @@ dispatch: CPU: _tan__cpu CUDA: _tan__cuda @@ -3190,7 +3194,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: tanh(Tensor self) -> Tensor use_c10_dispatcher: full -@@ -2758,6 +3514,8 @@ +@@ -2758,6 +3516,8 @@ CPU: tanh CUDA: tanh QuantizedCPU: quantized_tanh @@ -3199,7 +3203,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: tanh_(Tensor(a!) self) -> Tensor(a!) supports_named_tensor: True -@@ -2765,12 +3523,16 @@ +@@ -2765,12 +3525,16 @@ dispatch: CPU: _tanh__cpu CUDA: _tanh__cuda @@ -3216,7 +3220,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: tensordot(Tensor self, Tensor other, int[] dims_self, int[] dims_other) -> Tensor variants: function -@@ -2783,6 +3545,8 @@ +@@ -2783,6 +3547,8 @@ dispatch: CPU: threshold CUDA: threshold_cuda @@ -3225,7 +3229,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: threshold_(Tensor(a!) self, Scalar threshold, Scalar value) -> Tensor(a!) variants: function -@@ -2790,12 +3554,16 @@ +@@ -2790,12 +3556,16 @@ dispatch: CPU: threshold_ CUDA: threshold__cuda @@ -3242,7 +3246,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: threshold_backward(Tensor grad_output, Tensor self, Scalar threshold) -> Tensor use_c10_dispatcher: full -@@ -2803,6 +3571,8 @@ +@@ -2803,6 +3573,8 @@ dispatch: CPU: threshold_backward CUDA: threshold_backward_cuda @@ -3251,7 +3255,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: transpose.int(Tensor(a) self, int dim0, int dim1) -> Tensor(a) variants: function, method -@@ -2835,18 +3605,24 @@ +@@ -2835,18 +3607,24 @@ use_c10_dispatcher: full python_module: nn variants: function @@ -3276,7 +3280,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # default int[] value [0,1] should not add space after comma, since native_parse.py uses ', ' to split args -@@ -2872,6 +3648,8 @@ +@@ -2872,6 +3650,8 @@ CUDA: true_divide SparseCPU: true_divide_sparse SparseCUDA: true_divide_sparse @@ -3285,7 +3289,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: true_divide_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) -@@ -2881,6 +3659,8 @@ +@@ -2881,6 +3661,8 @@ CUDA: true_divide_ SparseCPU: true_divide_sparse_ SparseCUDA: true_divide_sparse_ @@ -3294,7 +3298,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: true_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) -@@ -2889,31 +3669,43 @@ +@@ -2889,31 +3671,43 @@ CUDA: true_divide_out SparseCPU: true_divide_out_sparse_zerodim SparseCUDA: true_divide_out_sparse_zerodim @@ -3338,7 +3342,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: type_as(Tensor self, Tensor other) -> Tensor use_c10_dispatcher: full -@@ -2956,6 +3748,8 @@ +@@ -2956,6 +3750,8 @@ dispatch: CPU: _unique2_cpu CUDA: _unique2_cuda @@ -3347,7 +3351,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _unsafe_view(Tensor self, int[] size) -> Tensor -@@ -2971,32 +3765,48 @@ +@@ -2971,32 +3767,48 @@ use_c10_dispatcher: full variants: function, method supports_named_tensor: True @@ -3396,7 +3400,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: view_as(Tensor self, Tensor other) -> Tensor use_c10_dispatcher: full -@@ -3009,13 +3819,19 @@ +@@ -3009,13 +3821,19 @@ - func: where.self(Tensor condition, Tensor self, Tensor other) -> Tensor use_c10_dispatcher: full variants: function, method @@ -3416,7 +3420,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: norm_except_dim(Tensor v, int pow=2, int dim=0) -> Tensor variants: function -@@ -3041,13 +3857,21 @@ +@@ -3041,13 +3859,21 @@ - func: zeros.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor device_guard: False @@ -3438,7 +3442,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _standard_gamma_grad(Tensor self, Tensor output) -> Tensor use_c10_dispatcher: full -@@ -3100,25 +3924,37 @@ +@@ -3100,25 +3926,37 @@ - func: _sparse_sum_backward(Tensor grad, Tensor self, int[] dim) -> Tensor dispatch: @@ -3478,7 +3482,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: norm.names_ScalarOpt_dim_dtype(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim, *, ScalarType dtype) -> Tensor variants: function, method -@@ -3162,12 +3998,16 @@ +@@ -3162,12 +4000,16 @@ SparseCUDA: clone_sparse MkldnnCPU: mkldnn_clone QuantizedCPU: quantized_clone @@ -3495,7 +3499,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: pow.Tensor_Scalar_out(Tensor self, Scalar exponent, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True -@@ -3176,6 +4016,8 @@ +@@ -3176,6 +4018,8 @@ CUDA: pow_out SparseCPU: pow_out_sparse_scalar SparseCUDA: pow_out_sparse_scalar @@ -3504,7 +3508,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: pow.Tensor_Scalar(Tensor self, Scalar exponent) -> Tensor use_c10_dispatcher: full -@@ -3186,6 +4028,8 @@ +@@ -3186,6 +4030,8 @@ CUDA: pow SparseCPU: pow_sparse_scalar SparseCUDA: pow_sparse_scalar @@ -3513,7 +3517,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: zero_(Tensor(a!) self) -> Tensor(a!) supports_named_tensor: True -@@ -3196,6 +4040,14 @@ +@@ -3196,6 +4042,14 @@ SparseCPU: zero_sparse_ SparseCUDA: zero_sparse_ MkldnnCPU: mkldnn_zero_ @@ -3528,7 +3532,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: sub.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) dispatch: -@@ -3204,6 +4056,8 @@ +@@ -3204,6 +4058,8 @@ SparseCPU: sub_out_sparse SparseCUDA: sub_out_sparse supports_named_tensor: True @@ -3537,7 +3541,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: sub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor use_c10_dispatcher: full -@@ -3213,6 +4067,8 @@ +@@ -3213,6 +4069,8 @@ CUDA: sub SparseCPU: sub_sparse SparseCUDA: sub_sparse @@ -3546,7 +3550,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: sub_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!) -@@ -3222,6 +4078,8 @@ +@@ -3222,6 +4080,8 @@ CUDA: sub_ SparseCPU: sub_sparse_ SparseCUDA: sub_sparse_ @@ -3555,7 +3559,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True # For C++ only, until we have conversion from C++ numbers to Tensor -@@ -3229,21 +4087,29 @@ +@@ -3229,21 +4089,29 @@ use_c10_dispatcher: full variants: function, method supports_named_tensor: True @@ -3585,7 +3589,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Functionally the same as addmm, but we give it a different derivative formula # that doesn't propagate gradients to non-present entries on sparse. -@@ -3257,6 +4123,8 @@ +@@ -3257,6 +4125,8 @@ CUDA: legacy::cuda::_th_addmm_out SparseCPU: addmm_out_sparse_dense_cpu SparseCUDA: addmm_out_sparse_dense_cuda @@ -3594,7 +3598,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor -@@ -3267,6 +4135,8 @@ +@@ -3267,6 +4137,8 @@ CUDA: legacy::cuda::_th_addmm SparseCPU: addmm_sparse_dense_cpu SparseCUDA: addmm_sparse_dense_cuda @@ -3603,7 +3607,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: addmm_(Tensor(a!) self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!) -@@ -3278,9 +4148,10 @@ +@@ -3278,9 +4150,10 @@ # broadcasting SparseCPU: s_addmm_sparse_dense_cpu_ SparseCUDA: s_addmm_sparse_dense_cuda_ @@ -3615,7 +3619,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # NOTE [ Sparse: autograd and API ] # # -@@ -3396,7 +4267,6 @@ +@@ -3396,7 +4269,6 @@ # shared. In other words, their outputs are non-differentiable views of the # sparse tensor. @@ -3623,7 +3627,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # FIXME: would be nicer if TensorOptions was optional based; not adding default arguments for options given # the default would never make sense. - func: sparse_coo_tensor.size(int[] size, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False) -> Tensor -@@ -3433,7 +4303,6 @@ +@@ -3433,7 +4305,6 @@ SparseCUDA: sparse_resize_and_clear_ requires_tensor: True @@ -3631,7 +3635,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: sparse_mask(Tensor self, Tensor mask) -> Tensor use_c10_dispatcher: full variants: method -@@ -3442,7 +4311,6 @@ +@@ -3442,7 +4313,6 @@ SparseCUDA: sparse_mask_cuda requires_tensor: True @@ -3639,7 +3643,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: to_dense(Tensor self) -> Tensor use_c10_dispatcher: full variants: method -@@ -3474,7 +4342,6 @@ +@@ -3474,7 +4344,6 @@ requires_tensor: True device_guard: False @@ -3647,7 +3651,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: dense_dim(Tensor self) -> int use_c10_dispatcher: full variants: method -@@ -3494,7 +4361,6 @@ +@@ -3494,7 +4363,6 @@ requires_tensor: True device_guard: False @@ -3655,7 +3659,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _nnz(Tensor self) -> int use_c10_dispatcher: full variants: method -@@ -3504,7 +4370,6 @@ +@@ -3504,7 +4372,6 @@ requires_tensor: True device_guard: False @@ -3663,7 +3667,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: coalesce(Tensor self) -> Tensor use_c10_dispatcher: full variants: method -@@ -3513,7 +4378,6 @@ +@@ -3513,7 +4380,6 @@ SparseCUDA: coalesce_sparse_cuda requires_tensor: True @@ -3671,7 +3675,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: is_coalesced(Tensor self) -> bool use_c10_dispatcher: full variants: method -@@ -3524,7 +4388,6 @@ +@@ -3524,7 +4390,6 @@ device_guard: False supports_named_tensor: True @@ -3679,7 +3683,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _indices(Tensor(a) self) -> Tensor(a) variants: method dispatch: -@@ -3568,7 +4431,6 @@ +@@ -3568,7 +4433,6 @@ requires_tensor: True device_guard: False @@ -3687,7 +3691,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: hspmm.out(Tensor mat1, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) dispatch: SparseCPU: hspmm_out_sparse_cpu -@@ -3630,11 +4492,15 @@ +@@ -3630,11 +4494,15 @@ variants: function dispatch: CPU: quantize_per_tensor_cpu @@ -3703,7 +3707,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: dequantize(Tensor self) -> Tensor use_c10_dispatcher: full -@@ -3713,20 +4579,28 @@ +@@ -3713,20 +4581,28 @@ variants: method device_guard: False supports_named_tensor: True @@ -3732,7 +3736,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: meshgrid(Tensor[] tensors) -> Tensor[] -@@ -3765,6 +4639,8 @@ +@@ -3765,6 +4641,8 @@ dispatch: CPU: _local_scalar_dense_cpu CUDA: _local_scalar_dense_cuda @@ -3741,7 +3745,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= variants: function supports_named_tensor: True -@@ -3791,10 +4667,16 @@ +@@ -3791,10 +4669,16 @@ # RNN cells and layers - func: lstm.input(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor, Tensor) @@ -3758,7 +3762,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: gru.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor) -@@ -3839,10 +4721,14 @@ +@@ -3839,10 +4723,14 @@ # PackedSequence utilities - func: _pack_padded_sequence(Tensor input, Tensor lengths, bool batch_first) -> (Tensor, Tensor) @@ -3773,7 +3777,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # wrappers for legacy TH methods -@@ -3852,6 +4738,8 @@ +@@ -3852,6 +4740,8 @@ dispatch: CPU: set_ CUDA: set_ @@ -3782,7 +3786,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: set_.source_Storage_storage_offset(Tensor(a!) self, Storage source, int storage_offset, int[] size, int[] stride=[]) -> Tensor(a!) variants: method -@@ -3860,6 +4748,8 @@ +@@ -3860,6 +4750,8 @@ CPU: legacy::cpu::_th_set_ CUDA: legacy::cuda::_th_set_ QuantizedCPU: set_storage @@ -3791,7 +3795,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: set_.source_Tensor(Tensor(a!) self, Tensor source) -> Tensor(a!) variants: method -@@ -3867,12 +4757,16 @@ +@@ -3867,12 +4759,16 @@ dispatch: CPU: set_tensor_ CUDA: set_tensor_ @@ -3808,7 +3812,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: set_quantizer_(Tensor(a!) self, ConstQuantizerPtr quantizer) -> Tensor(a!) variants: method -@@ -3892,6 +4786,8 @@ +@@ -3892,6 +4788,8 @@ dispatch: CPU: masked_fill__cpu CUDA: masked_fill__cuda @@ -3817,7 +3821,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: masked_fill.Scalar(Tensor self, Tensor mask, Scalar value) -> Tensor -@@ -3904,6 +4800,8 @@ +@@ -3904,6 +4802,8 @@ dispatch: CPU: masked_fill__cpu CUDA: masked_fill__cuda @@ -3826,7 +3830,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: masked_fill.Tensor(Tensor self, Tensor mask, Tensor value) -> Tensor -@@ -3916,6 +4814,8 @@ +@@ -3916,6 +4816,8 @@ dispatch: CPU: masked_scatter__cpu CUDA: masked_scatter__cuda @@ -3835,7 +3839,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: masked_scatter(Tensor self, Tensor mask, Tensor source) -> Tensor use_c10_dispatcher: full -@@ -3929,25 +4829,35 @@ +@@ -3929,25 +4831,35 @@ CUDA: view MkldnnCPU: mkldnn_view QuantizedCPU: view @@ -3871,7 +3875,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: index_fill_.int_Scalar(Tensor(a!) self, int dim, Tensor index, Scalar value) -> Tensor(a!) variants: method -@@ -3955,11 +4865,15 @@ +@@ -3955,11 +4867,15 @@ dispatch: CPU: legacy::cpu::_th_index_fill_ CUDA: legacy::cuda::_th_index_fill_ @@ -3887,7 +3891,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: index_fill_.int_Tensor(Tensor(a!) self, int dim, Tensor index, Tensor value) -> Tensor(a!) variants: method -@@ -3967,11 +4881,15 @@ +@@ -3967,11 +4883,15 @@ CPU: index_fill_ CUDA: index_fill_ supports_named_tensor: True @@ -3903,7 +3907,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: index_fill_.Dimname_Scalar(Tensor(a!) self, Dimname dim, Tensor index, Scalar value) -> Tensor(a!) variants: method -@@ -3994,6 +4912,8 @@ +@@ -3994,6 +4914,8 @@ dispatch: CPU: scatter_cpu_ CUDA: legacy::cuda::_th_scatter_ @@ -3912,7 +3916,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: scatter.src(Tensor self, int dim, Tensor index, Tensor src) -> Tensor use_c10_dispatcher: full -@@ -4004,6 +4924,8 @@ +@@ -4004,6 +4926,8 @@ dispatch: CPU: scatter_fill_cpu_ CUDA: legacy::cuda::_th_scatter_ @@ -3921,7 +3925,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: scatter.value(Tensor self, int dim, Tensor index, Scalar value) -> Tensor use_c10_dispatcher: full -@@ -4020,81 +4942,127 @@ +@@ -4020,81 +4944,127 @@ dispatch: CPU: scatter_add_cpu_ CUDA: legacy::cuda::_th_scatter_add_ @@ -4049,7 +4053,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: __iand__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) variants: method -@@ -4107,70 +5075,106 @@ +@@ -4107,70 +5077,106 @@ dispatch: CPU: bitwise_or_out CUDA: bitwise_or_out @@ -4156,7 +4160,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: __ixor__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) variants: method -@@ -4184,6 +5188,8 @@ +@@ -4184,6 +5190,8 @@ dispatch: CPU: __lshift__ CUDA: __lshift__ @@ -4165,7 +4169,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: __lshift__.Tensor(Tensor self, Tensor other) -> Tensor use_c10_dispatcher: full -@@ -4191,18 +5197,24 @@ +@@ -4191,18 +5199,24 @@ dispatch: CPU: __lshift__ CUDA: __lshift__ @@ -4190,7 +4194,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: __rshift__.Scalar(Tensor self, Scalar other) -> Tensor use_c10_dispatcher: full -@@ -4210,6 +5222,8 @@ +@@ -4210,6 +5224,8 @@ dispatch: CPU: __rshift__ CUDA: __rshift__ @@ -4199,7 +4203,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: __rshift__.Tensor(Tensor self, Tensor other) -> Tensor use_c10_dispatcher: full -@@ -4217,18 +5231,24 @@ +@@ -4217,18 +5233,24 @@ dispatch: CPU: __rshift__ CUDA: __rshift__ @@ -4224,7 +4228,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: lgamma_(Tensor(a!) self) -> Tensor(a!) supports_named_tensor: True -@@ -4240,18 +5260,24 @@ +@@ -4240,18 +5262,24 @@ - func: atan2_(Tensor(a!) self, Tensor other) -> Tensor(a!) supports_named_tensor: True variants: method @@ -4249,7 +4253,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: digamma_(Tensor(a!) self) -> Tensor(a!) supports_named_tensor: True -@@ -4266,6 +5292,8 @@ +@@ -4266,6 +5294,8 @@ dispatch: CPU: legacy::cpu::_th_renorm_ CUDA: legacy::cuda::_th_renorm_ @@ -4258,7 +4262,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: pow_.Scalar(Tensor(a!) self, Scalar exponent) -> Tensor(a!) supports_named_tensor: True -@@ -4273,6 +5301,8 @@ +@@ -4273,6 +5303,8 @@ dispatch: CPU: pow_ CUDA: pow_ @@ -4267,7 +4271,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: pow_.Tensor(Tensor(a!) self, Tensor exponent) -> Tensor(a!) supports_named_tensor: True -@@ -4280,53 +5310,71 @@ +@@ -4280,53 +5312,71 @@ dispatch: CPU: pow_ CUDA: pow_ @@ -4339,7 +4343,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: addbmm(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor use_c10_dispatcher: full -@@ -4334,28 +5382,40 @@ +@@ -4334,28 +5384,40 @@ dispatch: CPU: legacy::cpu::_th_addbmm CUDA: legacy::cuda::_th_addbmm @@ -4380,7 +4384,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: cauchy_(Tensor(a!) self, float median=0, float sigma=1, *, Generator? generator=None) -> Tensor(a!) -@@ -4380,6 +5440,8 @@ +@@ -4380,6 +5442,8 @@ dispatch: CPU: legacy::cpu::_th_diag_out CUDA: legacy::cuda::_th_diag_out @@ -4389,7 +4393,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: diag(Tensor self, int diagonal=0) -> Tensor use_c10_dispatcher: full -@@ -4387,40 +5449,58 @@ +@@ -4387,40 +5451,58 @@ dispatch: CPU: legacy::cpu::_th_diag CUDA: legacy::cuda::_th_diag @@ -4448,7 +4452,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: trace(Tensor self) -> Tensor use_c10_dispatcher: full -@@ -4435,6 +5515,8 @@ +@@ -4435,6 +5517,8 @@ CPU: ne_out CUDA: ne_out QuantizedCPU: ne_out_quantized_cpu @@ -4457,7 +4461,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: ne.Scalar(Tensor self, Scalar other) -> Tensor supports_named_tensor: True -@@ -4444,6 +5526,8 @@ +@@ -4444,6 +5528,8 @@ CPU: ne CUDA: ne QuantizedCPU: ne_quantized_cpu @@ -4466,7 +4470,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: ne.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True -@@ -4451,6 +5535,8 @@ +@@ -4451,6 +5537,8 @@ CPU: ne_out CUDA: ne_out QuantizedCPU: ne_out_quantized_cpu @@ -4475,7 +4479,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: ne.Tensor(Tensor self, Tensor other) -> Tensor supports_named_tensor: True -@@ -4460,6 +5546,8 @@ +@@ -4460,6 +5548,8 @@ CPU: ne CUDA: ne QuantizedCPU: ne_quantized_cpu @@ -4484,7 +4488,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: eq.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True -@@ -4467,6 +5555,8 @@ +@@ -4467,6 +5557,8 @@ CPU: eq_out CUDA: eq_out QuantizedCPU: eq_out_quantized_cpu @@ -4493,7 +4497,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: eq.Scalar(Tensor self, Scalar other) -> Tensor supports_named_tensor: True -@@ -4476,6 +5566,8 @@ +@@ -4476,6 +5568,8 @@ CPU: eq CUDA: eq QuantizedCPU: eq_quantized_cpu @@ -4502,7 +4506,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: eq.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True -@@ -4483,6 +5575,8 @@ +@@ -4483,6 +5577,8 @@ CPU: eq_out CUDA: eq_out QuantizedCPU: eq_out_quantized_cpu @@ -4511,7 +4515,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: eq.Tensor(Tensor self, Tensor other) -> Tensor supports_named_tensor: True -@@ -4492,6 +5586,8 @@ +@@ -4492,6 +5588,8 @@ CPU: eq CUDA: eq QuantizedCPU: eq_quantized_cpu @@ -4520,7 +4524,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: ge.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True -@@ -4499,6 +5595,8 @@ +@@ -4499,6 +5597,8 @@ CPU: ge_out CUDA: ge_out QuantizedCPU: ge_out_quantized_cpu @@ -4529,7 +4533,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: ge.Scalar(Tensor self, Scalar other) -> Tensor supports_named_tensor: True -@@ -4508,6 +5606,8 @@ +@@ -4508,6 +5608,8 @@ CPU: ge CUDA: ge QuantizedCPU: ge_quantized_cpu @@ -4538,7 +4542,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: ge.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True -@@ -4515,6 +5615,8 @@ +@@ -4515,6 +5617,8 @@ CPU: ge_out CUDA: ge_out QuantizedCPU: ge_out_quantized_cpu @@ -4547,7 +4551,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: ge.Tensor(Tensor self, Tensor other) -> Tensor supports_named_tensor: True -@@ -4524,6 +5626,8 @@ +@@ -4524,6 +5628,8 @@ CPU: ge CUDA: ge QuantizedCPU: ge_quantized_cpu @@ -4556,7 +4560,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: le.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True -@@ -4531,6 +5635,8 @@ +@@ -4531,6 +5637,8 @@ CPU: le_out CUDA: le_out QuantizedCPU: le_out_quantized_cpu @@ -4565,7 +4569,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: le.Scalar(Tensor self, Scalar other) -> Tensor supports_named_tensor: True -@@ -4540,6 +5646,8 @@ +@@ -4540,6 +5648,8 @@ CPU: le CUDA: le QuantizedCPU: le_quantized_cpu @@ -4574,7 +4578,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: le.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True -@@ -4547,6 +5655,8 @@ +@@ -4547,6 +5657,8 @@ CPU: le_out CUDA: le_out QuantizedCPU: le_out_quantized_cpu @@ -4583,7 +4587,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: le.Tensor(Tensor self, Tensor other) -> Tensor supports_named_tensor: True -@@ -4556,6 +5666,8 @@ +@@ -4556,6 +5668,8 @@ CPU: le CUDA: le QuantizedCPU: le_quantized_cpu @@ -4592,7 +4596,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: gt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True -@@ -4563,6 +5675,8 @@ +@@ -4563,6 +5677,8 @@ CPU: gt_out CUDA: gt_out QuantizedCPU: gt_out_quantized_cpu @@ -4601,7 +4605,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: gt.Scalar(Tensor self, Scalar other) -> Tensor supports_named_tensor: True -@@ -4572,6 +5686,8 @@ +@@ -4572,6 +5688,8 @@ CPU: gt CUDA: gt QuantizedCPU: gt_quantized_cpu @@ -4610,7 +4614,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: gt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True -@@ -4579,6 +5695,8 @@ +@@ -4579,6 +5697,8 @@ CPU: gt_out CUDA: gt_out QuantizedCPU: gt_out_quantized_cpu @@ -4619,7 +4623,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: gt.Tensor(Tensor self, Tensor other) -> Tensor supports_named_tensor: True -@@ -4588,6 +5706,8 @@ +@@ -4588,6 +5708,8 @@ CPU: gt CUDA: gt QuantizedCPU: gt_quantized_cpu @@ -4628,7 +4632,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: lt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True -@@ -4595,6 +5715,8 @@ +@@ -4595,6 +5717,8 @@ CPU: lt_out CUDA: lt_out QuantizedCPU: lt_out_quantized_cpu @@ -4637,7 +4641,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: lt.Scalar(Tensor self, Scalar other) -> Tensor supports_named_tensor: True -@@ -4604,6 +5726,8 @@ +@@ -4604,6 +5728,8 @@ CPU: lt CUDA: lt QuantizedCPU: lt_quantized_cpu @@ -4646,7 +4650,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: lt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True -@@ -4611,6 +5735,8 @@ +@@ -4611,6 +5737,8 @@ CPU: lt_out CUDA: lt_out QuantizedCPU: lt_out_quantized_cpu @@ -4655,7 +4659,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: lt.Tensor(Tensor self, Tensor other) -> Tensor supports_named_tensor: True -@@ -4620,11 +5746,16 @@ +@@ -4620,11 +5748,16 @@ CPU: lt CUDA: lt QuantizedCPU: lt_quantized_cpu @@ -4672,7 +4676,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: take(Tensor self, Tensor index) -> Tensor use_c10_dispatcher: full -@@ -4632,11 +5763,16 @@ +@@ -4632,11 +5765,16 @@ dispatch: CPU: legacy::cpu::_th_take CUDA: legacy::cuda::_th_take @@ -4689,7 +4693,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: index_select(Tensor self, int dim, Tensor index) -> Tensor use_c10_dispatcher: full -@@ -4646,17 +5782,25 @@ +@@ -4646,17 +5784,25 @@ CUDA: legacy::cuda::_th_index_select SparseCPU: index_select_sparse SparseCUDA: index_select_sparse @@ -4715,7 +4719,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: masked_select(Tensor self, Tensor mask) -> Tensor use_c10_dispatcher: full -@@ -4665,11 +5809,15 @@ +@@ -4665,11 +5811,15 @@ CPU: masked_select_cpu CUDA: masked_select_cuda supports_named_tensor: True @@ -4731,7 +4735,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: nonzero(Tensor self) -> Tensor use_c10_dispatcher: full -@@ -4677,6 +5825,8 @@ +@@ -4677,6 +5827,8 @@ dispatch: CPU: legacy::cpu::_th_nonzero CUDA: legacy::cuda::_th_nonzero @@ -4740,7 +4744,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: nonzero_numpy(Tensor self) -> Tensor[] variants: method, function -@@ -4685,6 +5835,8 @@ +@@ -4685,6 +5837,8 @@ dispatch: CPU: gather_out_cpu CUDA: gather_out_cuda @@ -4749,7 +4753,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: gather(Tensor self, int dim, Tensor index, *, bool sparse_grad=False) -> Tensor use_c10_dispatcher: full -@@ -4692,34 +5844,50 @@ +@@ -4692,34 +5846,50 @@ dispatch: CPU: gather_cpu CUDA: gather_cuda @@ -4800,7 +4804,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: lstsq.X(Tensor self, Tensor A, *, Tensor(a!) X, Tensor(b!) qr) -> (Tensor(a!) solution, Tensor(b!) QR) dispatch: -@@ -4742,6 +5910,8 @@ +@@ -4742,6 +5912,8 @@ dispatch: CPU: _triangular_solve_helper_cpu CUDA: _triangular_solve_helper_cuda @@ -4809,7 +4813,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: symeig.e(Tensor self, bool eigenvectors=False, bool upper=True, *, Tensor(a!) e, Tensor(b!) V) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) -@@ -4753,6 +5923,8 @@ +@@ -4753,6 +5925,8 @@ dispatch: CPU: _symeig_helper_cpu CUDA: _symeig_helper_cuda @@ -4818,7 +4822,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: eig.e(Tensor self, bool eigenvectors=False, *, Tensor(a!) e, Tensor(b!) v) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) dispatch: -@@ -4775,6 +5947,8 @@ +@@ -4775,6 +5949,8 @@ dispatch: CPU: _svd_helper_cpu CUDA: _svd_helper_cuda @@ -4827,7 +4831,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: cholesky.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!) -@@ -4826,9 +6000,13 @@ +@@ -4826,9 +6002,13 @@ CUDA: legacy::cuda::_th_potri - func: qr.Q(Tensor self, bool some=True, *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R) @@ -4841,7 +4845,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _qr_helper(Tensor self, bool some) -> (Tensor, Tensor) variants: function -@@ -4891,12 +6069,16 @@ +@@ -4891,12 +6071,16 @@ dispatch: CPU: multinomial_out CUDA: multinomial_out @@ -4858,7 +4862,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _multinomial_alias_setup(Tensor probs) -> (Tensor, Tensor) variants: function -@@ -4947,6 +6129,8 @@ +@@ -4947,6 +6131,8 @@ dispatch: CPU: erfinv CUDA: erfinv @@ -4867,7 +4871,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: erfinv_(Tensor(a!) self) -> Tensor(a!) supports_named_tensor: True -@@ -4954,26 +6138,36 @@ +@@ -4954,26 +6140,36 @@ dispatch: CPU: _erfinv__cpu CUDA: _erfinv__cuda @@ -4904,7 +4908,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: dist(Tensor self, Tensor other, Scalar p=2) -> Tensor use_c10_dispatcher: full -@@ -4981,21 +6175,29 @@ +@@ -4981,21 +6177,29 @@ - func: atan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) supports_named_tensor: True @@ -4934,7 +4938,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: lerp.Scalar(Tensor self, Tensor end, Scalar weight) -> Tensor use_c10_dispatcher: full -@@ -5003,6 +6205,8 @@ +@@ -5003,6 +6207,8 @@ dispatch: CPU: lerp_cpu_scalar CUDA: lerp_cuda_scalar @@ -4943,7 +4947,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: lerp.Tensor(Tensor self, Tensor end, Tensor weight) -> Tensor use_c10_dispatcher: full -@@ -5010,6 +6214,8 @@ +@@ -5010,6 +6216,8 @@ dispatch: CPU: lerp_cpu_tensor CUDA: lerp_cuda_tensor @@ -4952,7 +4956,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: histc.out(Tensor self, int bins=100, Scalar min=0, Scalar max=0, *, Tensor(a!) out) -> Tensor(a!) dispatch: -@@ -5027,6 +6233,8 @@ +@@ -5027,6 +6235,8 @@ dispatch: CPU: fmod_out CUDA: legacy::cuda::_th_fmod_out @@ -4961,7 +4965,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: fmod.Scalar(Tensor self, Scalar other) -> Tensor use_c10_dispatcher: full -@@ -5034,11 +6242,15 @@ +@@ -5034,11 +6244,15 @@ dispatch: CPU: fmod CUDA: legacy::cuda::_th_fmod @@ -4977,7 +4981,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: fmod.Tensor(Tensor self, Tensor other) -> Tensor use_c10_dispatcher: full -@@ -5046,11 +6258,15 @@ +@@ -5046,11 +6260,15 @@ dispatch: CPU: fmod CUDA: legacy::cuda::_th_fmod @@ -4993,7 +4997,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: remainder.Scalar(Tensor self, Scalar other) -> Tensor use_c10_dispatcher: full -@@ -5058,11 +6274,15 @@ +@@ -5058,11 +6276,15 @@ dispatch: CPU: remainder CUDA: remainder @@ -5009,7 +5013,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: remainder.Tensor(Tensor self, Tensor other) -> Tensor use_c10_dispatcher: full -@@ -5070,12 +6290,18 @@ +@@ -5070,12 +6292,18 @@ dispatch: CPU: remainder CUDA: remainder @@ -5028,7 +5032,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: min(Tensor self) -> Tensor use_c10_dispatcher: full -@@ -5084,13 +6310,19 @@ +@@ -5084,13 +6312,19 @@ CPU: min CUDA: legacy::cuda::_th_min QuantizedCPU: min_quant @@ -5048,7 +5052,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: max(Tensor self) -> Tensor use_c10_dispatcher: full -@@ -5099,6 +6331,8 @@ +@@ -5099,6 +6333,8 @@ CPU: max CUDA: legacy::cuda::_th_max QuantizedCPU: max_quant @@ -5057,7 +5061,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: median(Tensor self) -> Tensor -@@ -5107,12 +6341,16 @@ +@@ -5107,12 +6343,16 @@ dispatch: CPU: median_cpu CUDA: median_cuda @@ -5074,7 +5078,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: sort(Tensor self, int dim=-1, bool descending=False) -> (Tensor values, Tensor indices) variants: method, function -@@ -5120,23 +6358,45 @@ +@@ -5120,23 +6360,45 @@ CPU: legacy::cpu::_th_sort CUDA: legacy::cuda::_th_sort QuantizedCPU: sort_quant @@ -5120,7 +5124,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: topk(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices) variants: method, function -@@ -5144,11 +6404,15 @@ +@@ -5144,11 +6406,15 @@ CPU: topk CUDA: topk QuantizedCPU: quantized_topk_cpu @@ -5136,7 +5140,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: any(Tensor self) -> Tensor use_c10_dispatcher: full -@@ -5159,11 +6423,15 @@ +@@ -5159,11 +6425,15 @@ CUDA: any SparseCPU: any_sparse SparseCUDA: any_sparse @@ -5152,7 +5156,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: renorm(Tensor self, Scalar p, int dim, Scalar maxnorm) -> Tensor use_c10_dispatcher: full -@@ -5171,6 +6439,8 @@ +@@ -5171,6 +6441,8 @@ dispatch: CPU: legacy::cpu::_th_renorm CUDA: legacy::cuda::_th_renorm @@ -5161,7 +5165,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: unfold(Tensor(a) self, int dimension, int size, int step) -> Tensor(a) variants: method -@@ -5178,6 +6448,8 @@ +@@ -5178,6 +6450,8 @@ dispatch: CPU: unfold CUDA: unfold @@ -5170,7 +5174,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: equal(Tensor self, Tensor other) -> bool use_c10_dispatcher: full -@@ -5186,6 +6458,8 @@ +@@ -5186,6 +6460,8 @@ CPU: legacy::cpu::_th_equal CUDA: legacy::cuda::_th_equal QuantizedCPU: quantized_equal @@ -5179,7 +5183,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: pow.Tensor_Tensor_out(Tensor self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!) -@@ -5193,6 +6467,8 @@ +@@ -5193,6 +6469,8 @@ dispatch: CPU: pow_out CUDA: pow_out @@ -5188,7 +5192,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: pow.Tensor_Tensor(Tensor self, Tensor exponent) -> Tensor use_c10_dispatcher: full -@@ -5201,12 +6477,16 @@ +@@ -5201,12 +6479,16 @@ dispatch: CPU: pow CUDA: pow @@ -5205,7 +5209,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: pow.Scalar(Scalar self, Tensor exponent) -> Tensor use_c10_dispatcher: full -@@ -5214,6 +6494,8 @@ +@@ -5214,6 +6496,8 @@ dispatch: CPU: pow CUDA: pow @@ -5214,7 +5218,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: normal_(Tensor(a!) self, float mean=0, float std=1, *, Generator? generator=None) -> Tensor(a!) variants: method -@@ -5221,40 +6503,58 @@ +@@ -5221,40 +6505,58 @@ CPU: normal_cpu_ CUDA: normal_cuda_ supports_named_tensor: True @@ -5273,7 +5277,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: alias(Tensor(a) self) -> Tensor(a) variants: method, function -@@ -5265,43 +6565,59 @@ +@@ -5265,43 +6567,59 @@ dispatch: CPU: legacy::cpu::_th_addr CUDA: legacy::cuda::_th_addr @@ -5333,7 +5337,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _var(Tensor self, bool unbiased=True) -> Tensor use_c10_dispatcher: full -@@ -5309,6 +6625,8 @@ +@@ -5309,6 +6627,8 @@ CPU: legacy::cpu::_th_var CUDA: legacy::cuda::_th_var supports_named_tensor: True @@ -5342,7 +5346,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _std(Tensor self, bool unbiased=True) -> Tensor use_c10_dispatcher: full -@@ -5321,6 +6639,8 @@ +@@ -5321,6 +6641,8 @@ variants: function dispatch: CUDA: _amp_non_finite_check_and_unscale_cuda_ @@ -5351,7 +5355,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _amp_update_scale(Tensor(a!) growth_tracker, Tensor current_scale, Tensor found_inf, float scale_growth_factor, float scale_backoff_factor, int growth_interval) -> Tensor variants: function -@@ -5332,12 +6652,16 @@ +@@ -5332,12 +6654,16 @@ CPU: _cat_cpu CUDA: cat_cuda QuantizedCPU: quantized_cat @@ -5368,7 +5372,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _mode(Tensor self, int dim=-1, bool keepdim=False) -> (Tensor, Tensor) dispatch: -@@ -5353,36 +6677,50 @@ +@@ -5353,36 +6679,50 @@ dispatch: CPU: legacy::cpu::_th_max CUDA: legacy::cuda::_th_max @@ -5419,7 +5423,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: mse_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction) -> Tensor use_c10_dispatcher: full -@@ -5390,23 +6728,33 @@ +@@ -5390,23 +6730,33 @@ dispatch: CPU: mse_loss_backward CUDA: mse_loss_backward @@ -5453,7 +5457,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: multi_margin_loss.out(Tensor self, Tensor target, Scalar p=1, Scalar margin=1, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) python_module: nn -@@ -5434,22 +6782,30 @@ +@@ -5434,22 +6784,30 @@ - func: multilabel_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) python_module: nn @@ -5484,7 +5488,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: multilabel_margin_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, Tensor is_target, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn -@@ -5466,97 +6822,137 @@ +@@ -5466,97 +6824,137 @@ - func: nll_loss.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!) python_module: nn @@ -5622,7 +5626,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: elu.out(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1, *, Tensor(a!) out) -> Tensor(a!) python_module: nn -@@ -5564,6 +6960,8 @@ +@@ -5564,6 +6962,8 @@ CPU: elu_out CUDA: elu_out QuantizedCPU: quantized_elu_out @@ -5631,7 +5635,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: elu(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor use_c10_dispatcher: full -@@ -5572,16 +6970,22 @@ +@@ -5572,16 +6972,22 @@ CPU: elu CUDA: elu QuantizedCPU: quantized_elu @@ -5654,7 +5658,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: elu_(Tensor(a!) self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor(a!) python_module: nn -@@ -5589,12 +6993,16 @@ +@@ -5589,12 +6995,16 @@ CPU: elu_ CUDA: elu_ QuantizedCPU: quantized_elu_ @@ -5671,7 +5675,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: glu(Tensor self, int dim=-1) -> Tensor use_c10_dispatcher: full -@@ -5602,12 +7010,16 @@ +@@ -5602,12 +7012,16 @@ dispatch: CPU: glu CUDA: legacy::cuda::_thnn_glu_forward @@ -5688,7 +5692,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: glu_backward(Tensor grad_output, Tensor self, int dim) -> Tensor use_c10_dispatcher: full -@@ -5615,20 +7027,30 @@ +@@ -5615,20 +7029,30 @@ dispatch: CPU: glu_backward CUDA: legacy::cuda::_thnn_glu_backward @@ -5719,7 +5723,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: hardtanh.out(Tensor self, Scalar min_val=-1, Scalar max_val=1, *, Tensor(a!) out) -> Tensor(a!) python_module: nn -@@ -5636,6 +7058,8 @@ +@@ -5636,6 +7060,8 @@ CPU: hardtanh_out CUDA: hardtanh_out QuantizedCPU: quantized_hardtanh_out @@ -5728,7 +5732,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -> Tensor use_c10_dispatcher: full -@@ -5644,16 +7068,22 @@ +@@ -5644,16 +7070,22 @@ CPU: hardtanh CUDA: hardtanh QuantizedCPU: quantized_hardtanh @@ -5751,7 +5755,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: hardtanh_(Tensor(a!) self, Scalar min_val=-1, Scalar max_val=1) -> Tensor(a!) python_module: nn -@@ -5661,6 +7091,8 @@ +@@ -5661,6 +7093,8 @@ CPU: hardtanh_ CUDA: hardtanh_ QuantizedCPU: quantized_hardtanh_ @@ -5760,7 +5764,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: leaky_relu.out(Tensor self, Scalar negative_slope=0.01, *, Tensor(a!) out) -> Tensor(a!) python_module: nn -@@ -5668,6 +7100,8 @@ +@@ -5668,6 +7102,8 @@ CPU: leaky_relu_out CUDA: leaky_relu_out QuantizedCPU: quantized_leaky_relu_out @@ -5769,7 +5773,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: leaky_relu(Tensor self, Scalar negative_slope=0.01) -> Tensor use_c10_dispatcher: full -@@ -5676,10 +7110,14 @@ +@@ -5676,10 +7112,14 @@ CPU: leaky_relu CUDA: leaky_relu QuantizedCPU: quantized_leaky_relu @@ -5784,7 +5788,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: leaky_relu_(Tensor(a!) self, Scalar negative_slope=0.01) -> Tensor(a!) python_module: nn -@@ -5687,31 +7125,44 @@ +@@ -5687,31 +7127,44 @@ CPU: leaky_relu_ CUDA: leaky_relu_ QuantizedCPU: quantized_leaky_relu_ @@ -5829,7 +5833,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: log_sigmoid_backward(Tensor grad_output, Tensor self, Tensor buffer) -> Tensor use_c10_dispatcher: full -@@ -5719,62 +7170,88 @@ +@@ -5719,62 +7172,88 @@ dispatch: CPU: log_sigmoid_backward_cpu CUDA: legacy::cuda::_thnn_log_sigmoid_backward @@ -5918,7 +5922,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: adaptive_avg_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!) python_module: nn -@@ -5782,9 +7259,13 @@ +@@ -5782,9 +7261,13 @@ CPU: adaptive_avg_pool2d_out_cpu CUDA: adaptive_avg_pool2d_out_cuda MkldnnCPU: mkldnn_adaptive_avg_pool2d_out @@ -5932,7 +5936,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: mkldnn_adaptive_avg_pool2d(Tensor self, int[2] output_size) -> Tensor dispatch: -@@ -5796,6 +7277,8 @@ +@@ -5796,6 +7279,8 @@ CPU: adaptive_avg_pool2d_cpu CUDA: adaptive_avg_pool2d_cuda QuantizedCPU: quantized_adaptive_avg_pool2d @@ -5941,7 +5945,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: _adaptive_avg_pool2d_backward(Tensor grad_output, Tensor self) -> Tensor use_c10_dispatcher: full -@@ -5803,24 +7286,32 @@ +@@ -5803,24 +7288,32 @@ dispatch: CPU: adaptive_avg_pool2d_backward_cpu CUDA: adaptive_avg_pool2d_backward_cuda @@ -5974,7 +5978,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: adaptive_avg_pool3d_backward(Tensor grad_output, Tensor self) -> Tensor use_c10_dispatcher: full -@@ -5828,6 +7319,8 @@ +@@ -5828,6 +7321,8 @@ dispatch: CPU: adaptive_avg_pool3d_backward_cpu CUDA: adaptive_avg_pool3d_backward_cuda @@ -5983,7 +5987,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Return: (Tensor output, Tensor indices) - func: adaptive_max_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) -@@ -5835,6 +7328,8 @@ +@@ -5835,6 +7330,8 @@ dispatch: CPU: adaptive_max_pool2d_out_cpu CUDA: adaptive_max_pool2d_out_cuda @@ -5992,7 +5996,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Return: (Tensor output, Tensor indices) - func: adaptive_max_pool2d(Tensor self, int[2] output_size) -> (Tensor, Tensor) -@@ -5842,12 +7337,16 @@ +@@ -5842,12 +7339,16 @@ dispatch: CPU: adaptive_max_pool2d_cpu CUDA: adaptive_max_pool2d_cuda @@ -6009,7 +6013,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: adaptive_max_pool2d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor use_c10_dispatcher: full -@@ -5855,6 +7354,8 @@ +@@ -5855,6 +7356,8 @@ dispatch: CPU: adaptive_max_pool2d_backward_cpu CUDA: adaptive_max_pool2d_backward_cuda @@ -6018,7 +6022,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Return: (Tensor output, Tensor indices) - func: adaptive_max_pool3d.out(Tensor self, int[3] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) -@@ -5889,6 +7390,8 @@ +@@ -5889,6 +7392,8 @@ CPU: avg_pool2d_out_cpu CUDA: avg_pool2d_out_cuda MkldnnCPU: mkldnn_avg_pool2d_out @@ -6027,7 +6031,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: avg_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> Tensor python_module: nn -@@ -5897,24 +7400,32 @@ +@@ -5897,24 +7402,32 @@ CUDA: avg_pool2d_cuda MkldnnCPU: mkldnn_avg_pool2d QuantizedCPU: quantized_avg_pool2d @@ -6060,7 +6064,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: avg_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> Tensor python_module: nn -@@ -5922,18 +7433,24 @@ +@@ -5922,18 +7435,24 @@ CPU: avg_pool3d_cpu CUDA: avg_pool3d_cuda QuantizedCPU: quantized_avg_pool3d @@ -6085,7 +6089,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Return: (Tensor output, Tensor indices) - func: fractional_max_pool2d.output(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) -@@ -5993,6 +7510,8 @@ +@@ -5993,6 +7512,8 @@ dispatch: CPU: max_pool2d_with_indices_out_cpu CUDA: max_pool2d_with_indices_out_cuda @@ -6094,7 +6098,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Return: (Tensor output, Tensor indices) - func: max_pool2d_with_indices(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor) -@@ -6000,6 +7519,8 @@ +@@ -6000,6 +7521,8 @@ dispatch: CPU: max_pool2d_with_indices_cpu CUDA: max_pool2d_with_indices_cuda @@ -6103,7 +6107,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: max_pool2d_with_indices_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, int[2] dilation, bool ceil_mode, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) -@@ -6007,12 +7528,16 @@ +@@ -6007,12 +7530,16 @@ dispatch: CPU: max_pool2d_with_indices_backward_out_cpu CUDA: max_pool2d_with_indices_backward_out_cuda @@ -6120,7 +6124,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Return: (Tensor output, Tensor indices) - func: max_pool3d_with_indices.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) -@@ -6020,6 +7545,8 @@ +@@ -6020,6 +7547,8 @@ dispatch: CPU: max_pool3d_with_indices_out_cpu CUDA: max_pool3d_with_indices_out_cuda @@ -6129,7 +6133,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Return: (Tensor output, Tensor indices) - func: max_pool3d_with_indices(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor) -@@ -6027,6 +7554,8 @@ +@@ -6027,6 +7556,8 @@ dispatch: CPU: max_pool3d_with_indices_cpu CUDA: max_pool3d_with_indices_cuda @@ -6138,7 +6142,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= supports_named_tensor: True - func: max_pool3d_with_indices_backward.grad_input(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, int[3] dilation, bool ceil_mode, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) -@@ -6034,60 +7563,81 @@ +@@ -6034,60 +7565,81 @@ dispatch: CPU: max_pool3d_with_indices_backward_out_cpu CUDA: max_pool3d_with_indices_backward_out_cuda @@ -6220,7 +6224,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: reflection_pad1d.out(Tensor self, int[2] padding, *, Tensor(a!) out) -> Tensor(a!) python_module: nn -@@ -6118,24 +7668,32 @@ +@@ -6118,24 +7670,32 @@ dispatch: CPU: reflection_pad2d_out_cpu CUDA: reflection_pad2d_out_cuda @@ -6253,7 +6257,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: replication_pad1d.out(Tensor self, int[2] padding, *, Tensor(a!) out) -> Tensor(a!) python_module: nn -@@ -6166,24 +7724,32 @@ +@@ -6166,24 +7726,32 @@ dispatch: CPU: replication_pad2d_out_cpu CUDA: replication_pad2d_out_cuda @@ -6286,7 +6290,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: replication_pad3d.out(Tensor self, int[6] padding, *, Tensor(a!) out) -> Tensor(a!) python_module: nn -@@ -6214,12 +7780,16 @@ +@@ -6214,12 +7782,16 @@ dispatch: CPU: upsample_linear1d_out_cpu CUDA: upsample_linear1d_out_cuda @@ -6303,7 +6307,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: upsample_linear1d_backward.grad_input(Tensor grad_output, int[1] output_size, int[3] input_size, bool align_corners, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn -@@ -6232,12 +7802,16 @@ +@@ -6232,12 +7804,16 @@ dispatch: CPU: upsample_linear1d_backward_cpu CUDA: upsample_linear1d_backward_cuda @@ -6320,7 +6324,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: upsample_bilinear2d(Tensor self, int[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor python_module: nn -@@ -6245,96 +7819,128 @@ +@@ -6245,96 +7821,128 @@ CPU: upsample_bilinear2d_cpu CUDA: upsample_bilinear2d_cuda QuantizedCPU: quantized_upsample_bilinear2d_cpu @@ -6449,7 +6453,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: upsample_nearest2d(Tensor self, int[2] output_size, float? scales_h=None, float? scales_w=None) -> Tensor python_module: nn -@@ -6342,24 +7948,32 @@ +@@ -6342,24 +7950,32 @@ CPU: upsample_nearest2d_cpu CUDA: upsample_nearest2d_cuda QuantizedCPU: quantized_upsample_nearest2d_cpu @@ -6482,7 +6486,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: upsample_nearest3d(Tensor self, int[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor python_module: nn -@@ -6367,38 +7981,52 @@ +@@ -6367,38 +7983,52 @@ CPU: upsample_nearest3d_cpu CUDA: upsample_nearest3d_cuda QuantizedCPU: quantized_upsample_nearest3d_cpu @@ -6535,7 +6539,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # What's a thnn_conv_ versus a slow_conv_? # -@@ -6423,24 +8051,32 @@ +@@ -6423,24 +8053,32 @@ dispatch: CPU: slow_conv_transpose2d_out_cpu CUDA: slow_conv_transpose2d_out_cuda @@ -6568,7 +6572,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: slow_conv_transpose3d.out(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] output_padding=0, int[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!) python_module: nn -@@ -6468,21 +8104,29 @@ +@@ -6468,21 +8106,29 @@ - func: thnn_conv2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, *, Tensor(a!) out) -> Tensor(a!) python_module: nn @@ -6598,7 +6602,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: thnn_conv2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor weight, int[2] kernel_size, int[2] stride, int[2] padding, Tensor finput, Tensor fgrad_input, *, Tensor(a!)? grad_input, Tensor(b!)? grad_weight, Tensor(c!)? grad_bias) -> (Tensor(a!), Tensor(b!), Tensor(c!)) python_module: nn -@@ -6495,48 +8139,70 @@ +@@ -6495,48 +8141,70 @@ dispatch: CPU: slow_conv2d_backward_cpu CUDA: legacy::cuda::_thnn_conv2d_backward @@ -6669,7 +6673,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: slow_conv3d_backward.grad_input(Tensor grad_output, Tensor self, Tensor weight, int[3] kernel_size, int[3] stride, int[3] padding, Tensor finput, Tensor fgrad_input, *, Tensor(a!)? grad_input, Tensor(b!)? grad_weight, Tensor(c!)? grad_bias) -> (Tensor(a!), Tensor(b!), Tensor(c!)) python_module: nn -@@ -6553,12 +8219,16 @@ +@@ -6553,12 +8221,16 @@ dispatch: CPU: slow_conv_dilated2d_cpu CUDA: slow_conv_dilated2d_cuda @@ -6686,7 +6690,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - func: slow_conv_dilated3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1) -> Tensor python_module: nn -@@ -6577,57 +8247,475 @@ +@@ -6577,57 +8249,475 @@ dispatch: CPU: col2im_out_cpu CUDA: col2im_out_cuda @@ -7165,7 +7169,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S --- pytorch-v1.5.0/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-12-18 23:01:06.077043308 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-12-20 23:24:40.303378589 +0800 @@ -659,14 +659,14 @@ SUB x1, x1, 4 @@ -7191,7 +7195,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CMP x1, 2 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorCompare.cpp pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorCompare.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp 2021-12-18 23:01:06.057043153 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp 2021-12-20 23:24:40.283378342 +0800 @@ -64,7 +64,7 @@ Tensor isinf(const Tensor &self) { @@ -7203,7 +7207,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return AT_DISPATCH_FLOATING_TYPES_AND_HALF(self.scalar_type(), "isinf", [&]() { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorFactories.cpp pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorFactories.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp 2021-12-18 23:01:06.057043153 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp 2021-12-20 23:24:40.283378342 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7248,7 +7252,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorProperties.cpp pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorProperties.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp 2021-12-18 23:01:06.057043153 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp 2021-12-20 23:24:40.283378342 +0800 @@ -87,6 +87,7 @@ if (self.is_contiguous(memory_format)) { return self; @@ -7259,7 +7263,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= "preserve memory format is unsupported by the contiguous operator"); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/UpSampleBicubic2d.cpp pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp --- pytorch-v1.5.0/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-12-18 23:01:06.057043153 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-12-20 23:24:40.283378342 +0800 @@ -26,7 +26,7 @@ const scalar_t* in = &idata[output_y * input_width + output_x]; scalar_t* out = &odata[output_y * output_width + output_x]; @@ -7271,7 +7275,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= out += output_width * output_height; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native_parse.py pytorch-develop-150/aten/src/ATen/native_parse.py --- pytorch-v1.5.0/aten/src/ATen/native_parse.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native_parse.py 2021-12-18 23:01:06.085043369 +0800 ++++ pytorch-develop-150/aten/src/ATen/native_parse.py 2021-12-20 23:24:40.311378688 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -7309,7 +7313,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= msg = '''Exception raised in processing function: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/preprocess_declarations.py pytorch-develop-150/aten/src/ATen/preprocess_declarations.py --- pytorch-v1.5.0/aten/src/ATen/preprocess_declarations.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/preprocess_declarations.py 2021-12-18 23:01:06.085043369 +0800 ++++ pytorch-develop-150/aten/src/ATen/preprocess_declarations.py 2021-12-20 23:24:40.311378688 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -7341,7 +7345,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/templates/TensorBody.h pytorch-develop-150/aten/src/ATen/templates/TensorBody.h --- pytorch-v1.5.0/aten/src/ATen/templates/TensorBody.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/templates/TensorBody.h 2021-12-18 23:01:06.085043369 +0800 ++++ pytorch-develop-150/aten/src/ATen/templates/TensorBody.h 2021-12-20 23:24:40.311378688 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7374,7 +7378,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/templates/TensorMethods.h pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h --- pytorch-v1.5.0/aten/src/ATen/templates/TensorMethods.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h 2021-12-18 23:01:06.085043369 +0800 ++++ pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h 2021-12-20 23:24:40.311378688 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7408,7 +7412,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/CMakeLists.txt pytorch-develop-150/aten/src/TH/CMakeLists.txt --- pytorch-v1.5.0/aten/src/TH/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/CMakeLists.txt 2021-12-18 23:01:06.085043369 +0800 ++++ pytorch-develop-150/aten/src/TH/CMakeLists.txt 2021-12-20 23:24:40.311378688 +0800 @@ -48,6 +48,11 @@ ${CMAKE_CURRENT_SOURCE_DIR} PARENT_SCOPE) @@ -7423,7 +7427,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/generic/THStorage.cpp pytorch-develop-150/aten/src/TH/generic/THStorage.cpp --- pytorch-v1.5.0/aten/src/TH/generic/THStorage.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/generic/THStorage.cpp 2021-12-18 23:01:06.089043399 +0800 ++++ pytorch-develop-150/aten/src/TH/generic/THStorage.cpp 2021-12-20 23:24:40.311378688 +0800 @@ -1,9 +1,32 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7532,7 +7536,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/generic/THStorage.h pytorch-develop-150/aten/src/TH/generic/THStorage.h --- pytorch-v1.5.0/aten/src/TH/generic/THStorage.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/generic/THStorage.h 2021-12-18 23:01:06.089043399 +0800 ++++ pytorch-develop-150/aten/src/TH/generic/THStorage.h 2021-12-20 23:24:40.311378688 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7571,7 +7575,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/CMakeLists.txt pytorch-develop-150/c10/CMakeLists.txt --- pytorch-v1.5.0/c10/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/CMakeLists.txt 2021-12-18 23:01:06.093043431 +0800 ++++ pytorch-develop-150/c10/CMakeLists.txt 2021-12-20 23:24:40.319378786 +0800 @@ -63,6 +63,14 @@ message(STATUS "don't use NUMA") endif() @@ -7600,7 +7604,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # not checked in diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Backend.h pytorch-develop-150/c10/core/Backend.h --- pytorch-v1.5.0/c10/core/Backend.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Backend.h 2021-12-18 23:01:06.093043431 +0800 ++++ pytorch-develop-150/c10/core/Backend.h 2021-12-20 23:24:40.319378786 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7697,7 +7701,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case Backend::CUDA: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Device.cpp pytorch-develop-150/c10/core/Device.cpp --- pytorch-v1.5.0/c10/core/Device.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Device.cpp 2021-12-18 23:01:06.093043431 +0800 ++++ pytorch-develop-150/c10/core/Device.cpp 2021-12-20 23:24:40.319378786 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7737,7 +7741,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= types.begin(), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Device.h pytorch-develop-150/c10/core/Device.h --- pytorch-v1.5.0/c10/core/Device.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Device.h 2021-12-18 23:01:06.093043431 +0800 ++++ pytorch-develop-150/c10/core/Device.h 2021-12-20 23:24:40.319378786 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7772,7 +7776,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return type_ == DeviceType::CPU; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DeviceType.cpp pytorch-develop-150/c10/core/DeviceType.cpp --- pytorch-v1.5.0/c10/core/DeviceType.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DeviceType.cpp 2021-12-18 23:01:06.093043431 +0800 ++++ pytorch-develop-150/c10/core/DeviceType.cpp 2021-12-20 23:24:40.319378786 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7812,7 +7816,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return false; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DeviceType.h pytorch-develop-150/c10/core/DeviceType.h --- pytorch-v1.5.0/c10/core/DeviceType.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DeviceType.h 2021-12-18 23:01:06.093043431 +0800 ++++ pytorch-develop-150/c10/core/DeviceType.h 2021-12-20 23:24:40.319378786 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7855,7 +7859,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= constexpr DeviceType kXLA = DeviceType::XLA; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DispatchKey.cpp pytorch-develop-150/c10/core/DispatchKey.cpp --- pytorch-v1.5.0/c10/core/DispatchKey.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DispatchKey.cpp 2021-12-18 23:01:06.097043462 +0800 ++++ pytorch-develop-150/c10/core/DispatchKey.cpp 2021-12-20 23:24:40.319378786 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7887,7 +7891,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::SparseCPUTensorId: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DispatchKey.h pytorch-develop-150/c10/core/DispatchKey.h --- pytorch-v1.5.0/c10/core/DispatchKey.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DispatchKey.h 2021-12-18 23:01:06.097043462 +0800 ++++ pytorch-develop-150/c10/core/DispatchKey.h 2021-12-20 23:24:40.319378786 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7919,7 +7923,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Storage.h pytorch-develop-150/c10/core/Storage.h --- pytorch-v1.5.0/c10/core/Storage.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Storage.h 2021-12-18 23:01:06.097043462 +0800 ++++ pytorch-develop-150/c10/core/Storage.h 2021-12-20 23:24:40.319378786 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7953,7 +7957,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/StorageImpl.cpp pytorch-develop-150/c10/core/StorageImpl.cpp --- pytorch-v1.5.0/c10/core/StorageImpl.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/StorageImpl.cpp 2021-12-18 23:01:06.097043462 +0800 ++++ pytorch-develop-150/c10/core/StorageImpl.cpp 2021-12-20 23:24:40.319378786 +0800 @@ -1 +1,18 @@ #include + @@ -7975,7 +7979,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +} // namespace c10 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/StorageImpl.h pytorch-develop-150/c10/core/StorageImpl.h --- pytorch-v1.5.0/c10/core/StorageImpl.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/StorageImpl.h 2021-12-18 23:01:06.097043462 +0800 ++++ pytorch-develop-150/c10/core/StorageImpl.h 2021-12-20 23:24:40.319378786 +0800 @@ -1,11 +1,55 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8102,7 +8106,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/TensorImpl.h pytorch-develop-150/c10/core/TensorImpl.h --- pytorch-v1.5.0/c10/core/TensorImpl.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/TensorImpl.h 2021-12-18 23:01:06.097043462 +0800 ++++ pytorch-develop-150/c10/core/TensorImpl.h 2021-12-20 23:24:40.323378836 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8170,7 +8174,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/TensorOptions.h pytorch-develop-150/c10/core/TensorOptions.h --- pytorch-v1.5.0/c10/core/TensorOptions.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/TensorOptions.h 2021-12-18 23:01:06.097043462 +0800 ++++ pytorch-develop-150/c10/core/TensorOptions.h 2021-12-20 23:24:40.323378836 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8211,7 +8215,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } else if (tid == DispatchKey::HIPTensorId) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/cuda/CMakeLists.txt pytorch-develop-150/c10/cuda/CMakeLists.txt --- pytorch-v1.5.0/c10/cuda/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/cuda/CMakeLists.txt 2021-12-18 23:01:06.097043462 +0800 ++++ pytorch-develop-150/c10/cuda/CMakeLists.txt 2021-12-20 23:24:40.323378836 +0800 @@ -24,6 +24,7 @@ CUDACachingAllocator.cpp impl/CUDAGuardImpl.cpp @@ -8230,7 +8234,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= torch_cuda_based_add_library(c10_cuda ${C10_CUDA_SRCS} ${C10_CUDA_HEADERS}) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/macros/Export.h pytorch-develop-150/c10/macros/Export.h --- pytorch-v1.5.0/c10/macros/Export.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/macros/Export.h 2021-12-18 23:01:06.097043462 +0800 ++++ pytorch-develop-150/c10/macros/Export.h 2021-12-20 23:24:40.323378836 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8357,7 +8361,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/caffe2/CMakeLists.txt pytorch-develop-150/caffe2/CMakeLists.txt --- pytorch-v1.5.0/caffe2/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/caffe2/CMakeLists.txt 2021-12-18 23:01:06.101043493 +0800 ++++ pytorch-develop-150/caffe2/CMakeLists.txt 2021-12-20 23:24:40.327378885 +0800 @@ -32,6 +32,7 @@ # Add source, includes, and libs to lists list(APPEND Caffe2_CPU_SRCS ${ATen_CPU_SRCS}) @@ -8512,7 +8516,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Call again since Caffe2_HIP_INCLUDE is extended with ATen include dirs. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/.clang-format pytorch-develop-150/.clang-format --- pytorch-v1.5.0/.clang-format 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/.clang-format 2021-12-18 23:01:06.045043060 +0800 ++++ pytorch-develop-150/.clang-format 2021-12-20 23:24:40.271378194 +0800 @@ -84,5 +84,4 @@ SpacesInSquareBrackets: false Standard: Cpp11 @@ -8523,7 +8527,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/BuildVariables.cmake pytorch-develop-150/cmake/BuildVariables.cmake --- pytorch-v1.5.0/cmake/BuildVariables.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/BuildVariables.cmake 2021-12-18 23:01:06.157043924 +0800 ++++ pytorch-develop-150/cmake/BuildVariables.cmake 2021-12-20 23:24:40.383379577 +0800 @@ -11,6 +11,7 @@ # CMakeLists.txt files under each folder respectively. set(Caffe2_CPU_SRCS) @@ -8550,7 +8554,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # symbols. However, if the lib is whole linked in caffe2 lib, we don't want diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Codegen.cmake pytorch-develop-150/cmake/Codegen.cmake --- pytorch-v1.5.0/cmake/Codegen.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Codegen.cmake 2021-12-18 23:01:06.157043924 +0800 ++++ pytorch-develop-150/cmake/Codegen.cmake 2021-12-20 23:24:40.383379577 +0800 @@ -191,13 +191,14 @@ file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt generated_cpp) file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt-cuda cuda_generated_cpp) @@ -8581,7 +8585,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endif() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Dependencies.cmake pytorch-develop-150/cmake/Dependencies.cmake --- pytorch-v1.5.0/cmake/Dependencies.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Dependencies.cmake 2021-12-18 23:01:06.157043924 +0800 ++++ pytorch-develop-150/cmake/Dependencies.cmake 2021-12-20 23:24:40.383379577 +0800 @@ -1509,6 +1509,13 @@ ENDIF(NOT C_HAS_THREAD) endif() @@ -8598,7 +8602,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Summary.cmake pytorch-develop-150/cmake/Summary.cmake --- pytorch-v1.5.0/cmake/Summary.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Summary.cmake 2021-12-18 23:01:06.157043924 +0800 ++++ pytorch-develop-150/cmake/Summary.cmake 2021-12-20 23:24:40.383379577 +0800 @@ -134,6 +134,7 @@ if(NOT "${SELECTED_OP_LIST}" STREQUAL "") message(STATUS " SELECTED_OP_LIST : ${SELECTED_OP_LIST}") @@ -8609,7 +8613,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endfunction() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/TorchConfig.cmake.in pytorch-develop-150/cmake/TorchConfig.cmake.in --- pytorch-v1.5.0/cmake/TorchConfig.cmake.in 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/TorchConfig.cmake.in 2021-12-18 23:01:06.157043924 +0800 ++++ pytorch-develop-150/cmake/TorchConfig.cmake.in 2021-12-20 23:24:40.383379577 +0800 @@ -112,6 +112,11 @@ list(APPEND TORCH_LIBRARIES ${TORCH_CUDA_LIBRARIES}) endif() @@ -8624,7 +8628,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(TORCH_CXX_FLAGS "-D_GLIBCXX_USE_CXX11_ABI=@GLIBCXX_USE_CXX11_ABI@") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/CMakeLists.txt pytorch-develop-150/CMakeLists.txt --- pytorch-v1.5.0/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/CMakeLists.txt 2021-12-18 23:01:06.045043060 +0800 ++++ pytorch-develop-150/CMakeLists.txt 2021-12-20 23:24:40.271378194 +0800 @@ -205,6 +205,10 @@ option(USE_TBB "Use TBB" OFF) option(ONNX_ML "Enable traditional ONNX ML API." ON) @@ -8691,7 +8695,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-missing-braces") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/CONTRIBUTING.zh.md pytorch-develop-150/CONTRIBUTING.zh.md --- pytorch-v1.5.0/CONTRIBUTING.zh.md 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-150/CONTRIBUTING.zh.md 2021-12-18 23:01:06.045043060 +0800 ++++ pytorch-develop-150/CONTRIBUTING.zh.md 2021-12-20 23:24:40.271378194 +0800 @@ -0,0 +1,228 @@ +# PyTorch贡献指南 +- [贡献者许可协议](#贡献者许可协议.md) @@ -8923,7 +8927,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/.dockerignore pytorch-develop-150/.dockerignore --- pytorch-v1.5.0/.dockerignore 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/.dockerignore 2021-12-18 23:01:06.045043060 +0800 ++++ pytorch-develop-150/.dockerignore 2021-12-20 23:24:40.271378194 +0800 @@ -1,257 +1 @@ -# READ THIS BEFORE YOU REFACTOR ME -# @@ -9199,7 +9203,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/requirements.txt pytorch-develop-150/requirements.txt --- pytorch-v1.5.0/requirements.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/requirements.txt 2021-12-18 23:01:06.045043060 +0800 ++++ pytorch-develop-150/requirements.txt 2021-12-20 23:24:40.271378194 +0800 @@ -4,4 +4,13 @@ requests setuptools @@ -9219,7 +9223,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/setup.py pytorch-develop-150/setup.py --- pytorch-v1.5.0/setup.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/setup.py 2021-12-18 23:01:06.045043060 +0800 ++++ pytorch-develop-150/setup.py 2021-12-20 23:24:40.271378194 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -9322,7 +9326,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= 'python/serialized_test/data/operator_test/*.zip', diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/test/distributed/test_c10d.py pytorch-develop-150/test/distributed/test_c10d.py --- pytorch-v1.5.0/test/distributed/test_c10d.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/test/distributed/test_c10d.py 2021-12-18 23:01:06.173044047 +0800 ++++ pytorch-develop-150/test/distributed/test_c10d.py 2021-12-20 23:24:40.395379725 +0800 @@ -3049,8 +3049,8 @@ model = self._create_mixed_precision_model() reducer = self._create_reducer_for_models([model]) @@ -9336,7 +9340,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Check that the grad of fc3 is not set. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/test/run_test.py pytorch-develop-150/test/run_test.py --- pytorch-v1.5.0/test/run_test.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/test/run_test.py 2021-12-18 23:01:06.169044017 +0800 ++++ pytorch-develop-150/test/run_test.py 2021-12-20 23:24:40.399379774 +0800 @@ -11,6 +11,8 @@ import subprocess import sys @@ -9506,7 +9510,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= shell(['coverage', 'html']) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/test/test_autograd.py pytorch-develop-150/test/test_autograd.py --- pytorch-v1.5.0/test/test_autograd.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/test/test_autograd.py 2021-12-18 23:01:06.169044017 +0800 ++++ pytorch-develop-150/test/test_autograd.py 2021-12-20 23:24:40.399379774 +0800 @@ -24,7 +24,7 @@ from torch.autograd.function import once_differentiable from torch.autograd.profiler import (profile, format_time, EventList, @@ -9537,7 +9541,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/test/test_nn.py pytorch-develop-150/test/test_nn.py --- pytorch-v1.5.0/test/test_nn.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/test/test_nn.py 2021-12-18 23:01:06.173044047 +0800 ++++ pytorch-develop-150/test/test_nn.py 2021-12-20 23:24:40.399379774 +0800 @@ -3535,14 +3535,17 @@ # earlier versions or no versions, it should provide default value of 0. bn = nn.BatchNorm2d(3) @@ -9560,7 +9564,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= @unittest.skipIf(not PY3, 'Python 2.7 generates cyclic trash') diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/test/test_torch.py pytorch-develop-150/test/test_torch.py --- pytorch-v1.5.0/test/test_torch.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/test/test_torch.py 2021-12-18 23:01:06.173044047 +0800 ++++ pytorch-develop-150/test/test_torch.py 2021-12-20 23:24:40.403379824 +0800 @@ -4087,6 +4087,9 @@ def test_print(self): default_type = torch.Tensor().type() @@ -9609,7 +9613,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= @onlyCPU diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/test/test_utils.py pytorch-develop-150/test/test_utils.py --- pytorch-v1.5.0/test/test_utils.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/test/test_utils.py 2021-12-18 23:01:06.173044047 +0800 ++++ pytorch-develop-150/test/test_utils.py 2021-12-20 23:24:40.403379824 +0800 @@ -6,6 +6,7 @@ import random import tempfile @@ -9628,7 +9632,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= load_tests = load_tests diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/derivatives.yaml pytorch-develop-150/tools/autograd/derivatives.yaml --- pytorch-v1.5.0/tools/autograd/derivatives.yaml 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/derivatives.yaml 2021-12-18 23:01:06.761048578 +0800 ++++ pytorch-develop-150/tools/autograd/derivatives.yaml 2021-12-20 23:24:40.979386934 +0800 @@ -107,6 +107,10 @@ # # NB: The parameter names here MUST be consistent with the parameter names @@ -9753,7 +9757,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/dump_utils.py pytorch-develop-150/tools/autograd/dump_utils.py --- pytorch-v1.5.0/tools/autograd/dump_utils.py 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/dump_utils.py 2021-12-18 23:01:06.761048578 +0800 ++++ pytorch-develop-150/tools/autograd/dump_utils.py 2021-12-20 23:24:40.979386934 +0800 @@ -0,0 +1,312 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# All rights reserved. @@ -10069,7 +10073,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + return prepare_to_check_overflow, overflow_dump_inputs diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_autograd_functions.py pytorch-develop-150/tools/autograd/gen_autograd_functions.py --- pytorch-v1.5.0/tools/autograd/gen_autograd_functions.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_autograd_functions.py 2021-12-18 23:01:06.761048578 +0800 ++++ pytorch-develop-150/tools/autograd/gen_autograd_functions.py 2021-12-20 23:24:40.979386934 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -10275,7 +10279,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_python_functions.py pytorch-develop-150/tools/autograd/gen_python_functions.py --- pytorch-v1.5.0/tools/autograd/gen_python_functions.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_python_functions.py 2021-12-18 23:01:06.761048578 +0800 ++++ pytorch-develop-150/tools/autograd/gen_python_functions.py 2021-12-20 23:24:40.979386934 +0800 @@ -1,3 +1,20 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -10317,7 +10321,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= 'value': argname, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_variable_type.py pytorch-develop-150/tools/autograd/gen_variable_type.py --- pytorch-v1.5.0/tools/autograd/gen_variable_type.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_variable_type.py 2021-12-18 23:01:06.761048578 +0800 ++++ pytorch-develop-150/tools/autograd/gen_variable_type.py 2021-12-20 23:24:40.979386934 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -10498,7 +10502,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/Functions.cpp pytorch-develop-150/tools/autograd/templates/Functions.cpp --- pytorch-v1.5.0/tools/autograd/templates/Functions.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/Functions.cpp 2021-12-18 23:01:06.761048578 +0800 ++++ pytorch-develop-150/tools/autograd/templates/Functions.cpp 2021-12-20 23:24:40.979386934 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2021 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10579,7 +10583,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto sparse = sparse_.coalesce(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/python_torch_functions.cpp pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp --- pytorch-v1.5.0/tools/autograd/templates/python_torch_functions.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp 2021-12-18 23:01:06.761048578 +0800 ++++ pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp 2021-12-20 23:24:40.979386934 +0800 @@ -22,7 +22,7 @@ #include "torch/csrc/autograd/generated/variable_factories.h" #include "torch/csrc/utils/structseq.h" @@ -10663,7 +10667,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/python_variable_methods.cpp pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp --- pytorch-v1.5.0/tools/autograd/templates/python_variable_methods.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp 2021-12-18 23:01:06.761048578 +0800 ++++ pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp 2021-12-20 23:24:40.979386934 +0800 @@ -15,7 +15,13 @@ #include "torch/csrc/cuda/Stream.h" #include "torch/csrc/cuda/Event.h" @@ -10758,7 +10762,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"has_names", (PyCFunction)THPVariable_has_names, METH_NOARGS, NULL}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/VariableType.cpp pytorch-develop-150/tools/autograd/templates/VariableType.cpp --- pytorch-v1.5.0/tools/autograd/templates/VariableType.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/VariableType.cpp 2021-12-18 23:01:06.761048578 +0800 ++++ pytorch-develop-150/tools/autograd/templates/VariableType.cpp 2021-12-20 23:24:40.979386934 +0800 @@ -1,7 +1,29 @@ +// Copyright (c) 2021 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10791,7 +10795,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/VariableType.h pytorch-develop-150/tools/autograd/templates/VariableType.h --- pytorch-v1.5.0/tools/autograd/templates/VariableType.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/VariableType.h 2021-12-18 23:01:06.761048578 +0800 ++++ pytorch-develop-150/tools/autograd/templates/VariableType.h 2021-12-20 23:24:40.979386934 +0800 @@ -1,3 +1,20 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10823,7 +10827,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= const at::Tensor & unpack(const Tensor & t, const char * name, int pos); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/build_variables.bzl pytorch-develop-150/tools/build_variables.bzl --- pytorch-v1.5.0/tools/build_variables.bzl 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/build_variables.bzl 2021-12-18 23:01:06.757048547 +0800 ++++ pytorch-develop-150/tools/build_variables.bzl 2021-12-20 23:24:40.979386934 +0800 @@ -46,6 +46,7 @@ "torch/csrc/autograd/functions/utils.cpp", "torch/csrc/autograd/input_buffer.cpp", @@ -10909,7 +10913,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def grad(outputs: _TensorOrTensors, inputs: _TensorOrTensors, grad_outputs: Optional[_TensorOrTensors]=..., retain_graph: Optional[bool]=..., create_graph: bool=..., only_inputs: bool=..., allow_unused: bool=...) -> Tuple[Tensor, ...]: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/autograd/profiler.py pytorch-develop-150/torch/autograd/profiler.py --- pytorch-v1.5.0/torch/autograd/profiler.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/autograd/profiler.py 2021-12-18 23:01:06.761048578 +0800 ++++ pytorch-develop-150/torch/autograd/profiler.py 2021-12-20 23:24:40.983386984 +0800 @@ -1,8 +1,25 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -11386,7 +11390,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ''.join(result) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/CMakeLists.txt pytorch-develop-150/torch/CMakeLists.txt --- pytorch-v1.5.0/torch/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/CMakeLists.txt 2021-12-18 23:01:06.761048578 +0800 ++++ pytorch-develop-150/torch/CMakeLists.txt 2021-12-20 23:24:40.979386934 +0800 @@ -97,6 +97,7 @@ ${TORCH_SRC_DIR}/csrc/tensor/python_tensor.cpp ${TORCH_SRC_DIR}/csrc/utils.cpp @@ -11418,7 +11422,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endif() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/engine.cpp pytorch-develop-150/torch/csrc/autograd/engine.cpp --- pytorch-v1.5.0/torch/csrc/autograd/engine.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/engine.cpp 2021-12-18 23:01:06.769048640 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/engine.cpp 2021-12-20 23:24:40.987387033 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11570,7 +11574,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto event = c10::Event{c10::DeviceType::CUDA}; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/function.h pytorch-develop-150/torch/csrc/autograd/function.h --- pytorch-v1.5.0/torch/csrc/autograd/function.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/function.h 2021-12-18 23:01:06.769048640 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/function.h 2021-12-20 23:24:40.987387033 +0800 @@ -11,6 +11,7 @@ #include @@ -11592,7 +11596,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // probably operate with names. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/functions/tensor.cpp pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp --- pytorch-v1.5.0/torch/csrc/autograd/functions/tensor.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp 2021-12-18 23:01:06.769048640 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp 2021-12-20 23:24:40.991387082 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11624,7 +11628,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= /*non_blocking=*/false, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/init.cpp pytorch-develop-150/torch/csrc/autograd/init.cpp --- pytorch-v1.5.0/torch/csrc/autograd/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/init.cpp 2021-12-18 23:01:06.769048640 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/init.cpp 2021-12-20 23:24:40.991387082 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11667,7 +11671,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= m.def("_enable_profiler", enableProfiler); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/input_buffer.cpp pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp --- pytorch-v1.5.0/torch/csrc/autograd/input_buffer.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp 2021-12-18 23:01:06.769048640 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp 2021-12-20 23:24:40.991387082 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11719,7 +11723,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto& old_var = buffer[pos]; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/profiler.cpp pytorch-develop-150/torch/csrc/autograd/profiler.cpp --- pytorch-v1.5.0/torch/csrc/autograd/profiler.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/profiler.cpp 2021-12-18 23:01:06.769048640 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/profiler.cpp 2021-12-20 23:24:40.991387082 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11959,7 +11963,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CUDAStubs::~CUDAStubs() = default; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/profiler.h pytorch-develop-150/torch/csrc/autograd/profiler.h --- pytorch-v1.5.0/torch/csrc/autograd/profiler.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/profiler.h 2021-12-18 23:01:06.769048640 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/profiler.h 2021-12-20 23:24:40.991387082 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12095,7 +12099,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/python_variable.cpp pytorch-develop-150/torch/csrc/autograd/python_variable.cpp --- pytorch-v1.5.0/torch/csrc/autograd/python_variable.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/python_variable.cpp 2021-12-18 23:01:06.769048640 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/python_variable.cpp 2021-12-20 23:24:40.991387082 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12149,7 +12153,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"is_complex", (getter)THPVariable_is_complex, nullptr, nullptr, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/python_variable_indexing.cpp pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp --- pytorch-v1.5.0/torch/csrc/autograd/python_variable_indexing.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-18 23:01:06.769048640 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-20 23:24:40.991387082 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12180,7 +12184,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= at::Device self_device = self_.device(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/record_function.cpp pytorch-develop-150/torch/csrc/autograd/record_function.cpp --- pytorch-v1.5.0/torch/csrc/autograd/record_function.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/record_function.cpp 2021-12-18 23:01:06.769048640 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/record_function.cpp 2021-12-20 23:24:40.991387082 +0800 @@ -154,6 +154,12 @@ } } @@ -12214,7 +12218,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/record_function.h pytorch-develop-150/torch/csrc/autograd/record_function.h --- pytorch-v1.5.0/torch/csrc/autograd/record_function.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/record_function.h 2021-12-18 23:01:06.769048640 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/record_function.h 2021-12-20 23:24:40.991387082 +0800 @@ -44,6 +44,9 @@ // Default constructor is used with before function called afterwards RecordFunction() {} @@ -12278,7 +12282,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (torch::autograd::profiler::needsInputs()) { \ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/utils/wrap_outputs.h pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h --- pytorch-v1.5.0/torch/csrc/autograd/utils/wrap_outputs.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h 2021-12-18 23:01:06.769048640 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h 2021-12-20 23:24:40.991387082 +0800 @@ -168,6 +168,45 @@ return r.release(); } @@ -12327,7 +12331,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (!r) throw python_error(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/VariableTypeManual.cpp pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp --- pytorch-v1.5.0/torch/csrc/autograd/VariableTypeManual.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp 2021-12-18 23:01:06.769048640 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp 2021-12-20 23:24:40.987387033 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12361,7 +12365,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (!t.defined()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/comm.cpp pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/comm.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp 2021-12-18 23:01:06.773048671 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp 2021-12-20 23:24:40.991387082 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12467,7 +12471,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= while (!in_flight.empty()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/init.cpp pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp 2021-12-18 23:01:06.773048671 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp 2021-12-20 23:24:40.991387082 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12524,7 +12528,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= .def("is_success", &::c10d::ProcessGroup::Work::isSuccess) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/reducer.cpp pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/reducer.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp 2021-12-18 23:01:06.773048671 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp 2021-12-20 23:24:40.991387082 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12659,7 +12663,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/DynamicTypes.cpp pytorch-develop-150/torch/csrc/DynamicTypes.cpp --- pytorch-v1.5.0/torch/csrc/DynamicTypes.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/DynamicTypes.cpp 2021-12-18 23:01:06.765048609 +0800 ++++ pytorch-develop-150/torch/csrc/DynamicTypes.cpp 2021-12-20 23:24:40.983386984 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12708,7 +12712,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return it->second; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/Generator.cpp pytorch-develop-150/torch/csrc/Generator.cpp --- pytorch-v1.5.0/torch/csrc/Generator.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/Generator.cpp 2021-12-18 23:01:06.765048609 +0800 ++++ pytorch-develop-150/torch/csrc/Generator.cpp 2021-12-20 23:24:40.983386984 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12776,7 +12780,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #endif diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/serialization.cpp pytorch-develop-150/torch/csrc/generic/serialization.cpp --- pytorch-v1.5.0/torch/csrc/generic/serialization.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/serialization.cpp 2021-12-18 23:01:06.773048671 +0800 ++++ pytorch-develop-150/torch/csrc/generic/serialization.cpp 2021-12-20 23:24:40.991387082 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12876,7 +12880,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/Storage.cpp pytorch-develop-150/torch/csrc/generic/Storage.cpp --- pytorch-v1.5.0/torch/csrc/generic/Storage.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/Storage.cpp 2021-12-18 23:01:06.773048671 +0800 ++++ pytorch-develop-150/torch/csrc/generic/Storage.cpp 2021-12-20 23:24:40.991387082 +0800 @@ -1,7 +1,25 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12955,7 +12959,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for (Py_ssize_t i = 0; i < length; i++) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/StorageMethods.cpp pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp --- pytorch-v1.5.0/torch/csrc/generic/StorageMethods.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp 2021-12-18 23:01:06.773048671 +0800 ++++ pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp 2021-12-20 23:24:40.991387082 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13003,7 +13007,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"_write_file", (PyCFunction)THPStorage_(writeFile), METH_VARARGS, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/Module.cpp pytorch-develop-150/torch/csrc/Module.cpp --- pytorch-v1.5.0/torch/csrc/Module.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/Module.cpp 2021-12-18 23:01:06.765048609 +0800 ++++ pytorch-develop-150/torch/csrc/Module.cpp 2021-12-20 23:24:40.983386984 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13164,7 +13168,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto set_module_attr = [&](const char* name, PyObject* v, bool incref = true) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/tensor/python_tensor.cpp pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp --- pytorch-v1.5.0/torch/csrc/tensor/python_tensor.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp 2021-12-18 23:01:06.781048733 +0800 ++++ pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp 2021-12-20 23:24:41.003387231 +0800 @@ -1,18 +1,35 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13541,7 +13545,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +} // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/init.cpp pytorch-develop-150/torch/csrc/utils/init.cpp --- pytorch-v1.5.0/torch/csrc/utils/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/init.cpp 2021-12-18 23:01:06.781048733 +0800 ++++ pytorch-develop-150/torch/csrc/utils/init.cpp 2021-12-20 23:24:41.003387231 +0800 @@ -1,7 +1,13 @@ #include #include @@ -13706,7 +13710,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/init.h pytorch-develop-150/torch/csrc/utils/init.h --- pytorch-v1.5.0/torch/csrc/utils/init.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/init.h 2021-12-18 23:01:06.781048733 +0800 ++++ pytorch-develop-150/torch/csrc/utils/init.h 2021-12-20 23:24:41.003387231 +0800 @@ -8,4 +8,7 @@ void initThroughputBenchmarkBindings(PyObject* module); @@ -13717,7 +13721,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/python_arg_parser.h pytorch-develop-150/torch/csrc/utils/python_arg_parser.h --- pytorch-v1.5.0/torch/csrc/utils/python_arg_parser.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/python_arg_parser.h 2021-12-18 23:01:06.781048733 +0800 ++++ pytorch-develop-150/torch/csrc/utils/python_arg_parser.h 2021-12-20 23:24:41.003387231 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13752,7 +13756,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return at::Device(device_str); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_layouts.cpp pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_layouts.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp 2021-12-18 23:01:06.781048733 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp 2021-12-20 23:24:41.003387231 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13783,7 +13787,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= registerLayoutObject((THPLayout*)strided_layout, at::Backend::QuantizedCPU); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_new.cpp pytorch-develop-150/torch/csrc/utils/tensor_new.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_new.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_new.cpp 2021-12-18 23:01:06.781048733 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_new.cpp 2021-12-20 23:24:41.003387231 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13919,7 +13923,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= " or ", c10::DispatchKey::XLATensorId, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_types.cpp pytorch-develop-150/torch/csrc/utils/tensor_types.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_types.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_types.cpp 2021-12-18 23:01:06.781048733 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_types.cpp 2021-12-20 23:24:41.003387231 +0800 @@ -1,58 +1,91 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -14132,7 +14136,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def get_rng_state(): ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/distributed/distributed_c10d.py pytorch-develop-150/torch/distributed/distributed_c10d.py --- pytorch-v1.5.0/torch/distributed/distributed_c10d.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/distributed/distributed_c10d.py 2021-12-18 23:01:06.785048763 +0800 ++++ pytorch-develop-150/torch/distributed/distributed_c10d.py 2021-12-20 23:24:41.003387231 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14213,7 +14217,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/__init__.py pytorch-develop-150/torch/__init__.py --- pytorch-v1.5.0/torch/__init__.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/__init__.py 2021-12-18 23:01:06.761048578 +0800 ++++ pytorch-develop-150/torch/__init__.py 2021-12-20 23:24:40.979386934 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14254,9 +14258,30 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +#register npu shutdown hook on exit +atexit.register(_npu_shutdown) \ No newline at end of file +diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/jit/frontend.py pytorch-develop-150/torch/jit/frontend.py +--- pytorch-v1.5.0/torch/jit/frontend.py 2021-04-10 18:39:32.000000000 +0800 ++++ pytorch-develop-150/torch/jit/frontend.py 2021-12-20 23:24:41.003387231 +0800 +@@ -616,6 +616,17 @@ + return Subscript(base, [build_SliceExpr(ctx, base, expr.slice)]) + elif sub_type is ast.ExtSlice: + return Subscript(base, build_ExtSlice(ctx, base, expr.slice)) ++ elif sys.version_info >= (3, 9): # In Python3.9 array indicies are not wrapped in ast.Index ++ if sub_type is ast.Tuple: ++ # N-dimensional indexing using Tuple: x[(i, j, k)] is equivalent to x[i, j, k] ++ indices = [] ++ for index_expr in expr.slice.elts: ++ if isinstance(index_expr, ast.Slice): ++ indices.append(build_SliceExpr(ctx, base, index_expr)) ++ else: ++ indices.append(build_expr(ctx, index_expr)) ++ return Subscript(base, indices) ++ return Subscript(base, [build_expr(ctx, expr.slice)]) + else: # Ellipsis (can only happen in Python 2) + raise NotSupportedError(base.range(), "ellipsis is not supported") + diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/lib/c10d/CMakeLists.txt pytorch-develop-150/torch/lib/c10d/CMakeLists.txt --- pytorch-v1.5.0/torch/lib/c10d/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/lib/c10d/CMakeLists.txt 2021-12-18 23:01:06.785048763 +0800 ++++ pytorch-develop-150/torch/lib/c10d/CMakeLists.txt 2021-12-20 23:24:41.003387231 +0800 @@ -28,6 +28,10 @@ option(USE_C10D_NCCL "USE C10D NCCL" ON) endif() @@ -14309,7 +14334,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= copy_header(ProcessGroupMPI.hpp) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/lib/libshm/CMakeLists.txt pytorch-develop-150/torch/lib/libshm/CMakeLists.txt --- pytorch-v1.5.0/torch/lib/libshm/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/lib/libshm/CMakeLists.txt 2021-12-18 23:01:06.785048763 +0800 ++++ pytorch-develop-150/torch/lib/libshm/CMakeLists.txt 2021-12-20 23:24:41.007387280 +0800 @@ -37,8 +37,11 @@ SET_TARGET_PROPERTIES(shm PROPERTIES PREFIX "lib" @@ -14366,7 +14391,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -_maybe_indices_t = _scalar_or_tuple_2_t[Tensor] diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/functional.py pytorch-develop-150/torch/nn/functional.py --- pytorch-v1.5.0/torch/nn/functional.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/functional.py 2021-12-18 23:01:06.785048763 +0800 ++++ pytorch-develop-150/torch/nn/functional.py 2021-12-20 23:24:41.007387280 +0800 @@ -1611,7 +1611,7 @@ else: output = input.matmul(weight.t()) @@ -14389,7 +14414,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -from . import parallel as parallel diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/batchnorm.py pytorch-develop-150/torch/nn/modules/batchnorm.py --- pytorch-v1.5.0/torch/nn/modules/batchnorm.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/batchnorm.py 2021-12-18 23:01:06.789048794 +0800 ++++ pytorch-develop-150/torch/nn/modules/batchnorm.py 2021-12-20 23:24:41.007387280 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14421,7 +14446,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= self.register_parameter('running_var', None) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/module.py pytorch-develop-150/torch/nn/modules/module.py --- pytorch-v1.5.0/torch/nn/modules/module.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/module.py 2021-12-18 23:01:06.789048794 +0800 ++++ pytorch-develop-150/torch/nn/modules/module.py 2021-12-20 23:24:41.007387280 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14581,7 +14606,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return t.to(device, dtype if t.is_floating_point() else None, non_blocking, memory_format=convert_to_format) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/normalization.py pytorch-develop-150/torch/nn/modules/normalization.py --- pytorch-v1.5.0/torch/nn/modules/normalization.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/normalization.py 2021-12-18 23:01:06.789048794 +0800 ++++ pytorch-develop-150/torch/nn/modules/normalization.py 2021-12-20 23:24:41.007387280 +0800 @@ -128,13 +128,14 @@ """ __constants__ = ['normalized_shape', 'eps', 'elementwise_affine'] @@ -14650,7 +14675,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - module_kwargs: Optional[Any] = ...) -> Tensor: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/parallel/distributed.py pytorch-develop-150/torch/nn/parallel/distributed.py --- pytorch-v1.5.0/torch/nn/parallel/distributed.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/parallel/distributed.py 2021-12-18 23:01:06.789048794 +0800 ++++ pytorch-develop-150/torch/nn/parallel/distributed.py 2021-12-20 23:24:41.007387280 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15005,7 +15030,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def remove_weight_norm(module: T_module, name: str = ...) -> T_module: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/onnx/symbolic_opset9.py pytorch-develop-150/torch/onnx/symbolic_opset9.py --- pytorch-v1.5.0/torch/onnx/symbolic_opset9.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/onnx/symbolic_opset9.py 2021-12-18 23:01:06.789048794 +0800 ++++ pytorch-develop-150/torch/onnx/symbolic_opset9.py 2021-12-20 23:24:41.011387329 +0800 @@ -1621,14 +1621,23 @@ slices = [sym_help._slice_helper(g, w, axes=[0], starts=[x * n], ends=[y * n]) for x, y in intervals] return g.op('Concat', *slices, axis_i=0) @@ -15083,7 +15108,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, params: _params_t, lr: float=..., lr_decay: float=..., weight_decay: float=..., initial_accumulator_value: float=..., eps: float=...) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/optim/adamax.py pytorch-develop-150/torch/optim/adamax.py --- pytorch-v1.5.0/torch/optim/adamax.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/optim/adamax.py 2021-12-18 23:01:06.789048794 +0800 ++++ pytorch-develop-150/torch/optim/adamax.py 2021-12-20 23:24:41.011387329 +0800 @@ -80,8 +80,8 @@ exp_inf.mul_(beta2).unsqueeze(0), grad.abs().add_(eps).unsqueeze_(0) @@ -15260,7 +15285,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, params: _params_t, lr: float=..., betas: Tuple[float, float]=..., eps: float=...) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/serialization.py pytorch-develop-150/torch/serialization.py --- pytorch-v1.5.0/torch/serialization.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/serialization.py 2021-12-18 23:01:06.761048578 +0800 ++++ pytorch-develop-150/torch/serialization.py 2021-12-20 23:24:40.983386984 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15344,7 +15369,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def location_tag(storage): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/storage.py pytorch-develop-150/torch/storage.py --- pytorch-v1.5.0/torch/storage.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/storage.py 2021-12-18 23:01:06.761048578 +0800 ++++ pytorch-develop-150/torch/storage.py 2021-12-20 23:24:40.983386984 +0800 @@ -7,6 +7,7 @@ class _StorageBase(object): @@ -15364,7 +15389,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= else: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/tensor.py pytorch-develop-150/torch/tensor.py --- pytorch-v1.5.0/torch/tensor.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/tensor.py 2021-12-18 23:01:06.761048578 +0800 ++++ pytorch-develop-150/torch/tensor.py 2021-12-20 23:24:40.983386984 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15426,7 +15451,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def __reversed__(self): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/_tensor_str.py pytorch-develop-150/torch/_tensor_str.py --- pytorch-v1.5.0/torch/_tensor_str.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/_tensor_str.py 2021-12-18 23:01:06.761048578 +0800 ++++ pytorch-develop-150/torch/_tensor_str.py 2021-12-20 23:24:40.983386984 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15480,7 +15505,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= has_default_dtype = self.dtype in (torch.get_default_dtype(), torch.int64, torch.bool) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/data/dataloader.py pytorch-develop-150/torch/utils/data/dataloader.py --- pytorch-v1.5.0/torch/utils/data/dataloader.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/data/dataloader.py 2021-12-18 23:01:06.793048825 +0800 ++++ pytorch-develop-150/torch/utils/data/dataloader.py 2021-12-20 23:24:41.011387329 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15689,7 +15714,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, sampler: Sampler[int], batch_size: int, drop_last: bool) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/data/_utils/pin_memory.py pytorch-develop-150/torch/utils/data/_utils/pin_memory.py --- pytorch-v1.5.0/torch/utils/data/_utils/pin_memory.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/data/_utils/pin_memory.py 2021-12-18 23:01:06.793048825 +0800 ++++ pytorch-develop-150/torch/utils/data/_utils/pin_memory.py 2021-12-20 23:24:41.011387329 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15750,7 +15775,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/__init__.py pytorch-develop-150/torch/utils/__init__.py --- pytorch-v1.5.0/torch/utils/__init__.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/__init__.py 2021-12-18 23:01:06.793048825 +0800 ++++ pytorch-develop-150/torch/utils/__init__.py 2021-12-20 23:24:41.011387329 +0800 @@ -1,6 +1,9 @@ from __future__ import absolute_import, division, print_function, unicode_literals @@ -15763,7 +15788,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def set_module(obj, mod): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/_utils.py pytorch-develop-150/torch/_utils.py --- pytorch-v1.5.0/torch/_utils.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/_utils.py 2021-12-18 23:01:06.761048578 +0800 ++++ pytorch-develop-150/torch/_utils.py 2021-12-20 23:24:40.983386984 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. diff --git a/patch/pytorch1.8.1_npu.patch b/patch/pytorch1.8.1_npu.patch index a674923f004..d6bf75c6311 100644 --- a/patch/pytorch1.8.1_npu.patch +++ b/patch/pytorch1.8.1_npu.patch @@ -1,6 +1,6 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/CMakeLists.txt pytorch-develop-181/aten/CMakeLists.txt --- pytorch-v1.8.1/aten/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/CMakeLists.txt 2021-12-18 23:01:10.905080428 +0800 ++++ pytorch-develop-181/aten/CMakeLists.txt 2021-12-20 23:24:45.107437788 +0800 @@ -22,9 +22,11 @@ set(ATen_CPU_INCLUDE) set(ATen_THIRD_PARTY_INCLUDE) @@ -52,7 +52,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_CPU_DEPENDENCY_LIBS ${ATen_CPU_DEPENDENCY_LIBS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/autocast_mode.h pytorch-develop-181/aten/src/ATen/autocast_mode.h --- pytorch-v1.8.1/aten/src/ATen/autocast_mode.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/autocast_mode.h 2021-12-18 23:01:10.909080458 +0800 ++++ pytorch-develop-181/aten/src/ATen/autocast_mode.h 2021-12-20 23:24:45.111437836 +0800 @@ -5,7 +5,7 @@ namespace { @@ -64,7 +64,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/CMakeLists.txt pytorch-develop-181/aten/src/ATen/CMakeLists.txt --- pytorch-v1.8.1/aten/src/ATen/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/CMakeLists.txt 2021-12-18 23:01:10.905080428 +0800 ++++ pytorch-develop-181/aten/src/ATen/CMakeLists.txt 2021-12-20 23:24:45.107437788 +0800 @@ -85,6 +85,10 @@ file(GLOB native_quantized_h "native/quantized/*.h" "native/quantized/cpu/*.h") file(GLOB native_cpu_h "native/cpu/*.h") @@ -115,7 +115,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_HIP_SRCS ${ATen_HIP_SRCS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/Dispatcher.h pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/Dispatcher.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h 2021-12-18 23:01:10.913080489 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h 2021-12-20 23:24:45.115437886 +0800 @@ -417,6 +417,11 @@ } } @@ -130,7 +130,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.cpp pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-12-18 23:01:10.913080489 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-12-20 23:24:45.115437886 +0800 @@ -6,6 +6,7 @@ namespace c10 { @@ -141,7 +141,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= std::unordered_set not_observed_ops = { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.h pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-12-18 23:01:10.913080489 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-12-20 23:24:45.115437886 +0800 @@ -1,12 +1,16 @@ #pragma once @@ -161,7 +161,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/OperatorEntry.h pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-12-18 23:01:10.913080489 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-12-20 23:24:45.115437886 +0800 @@ -10,6 +10,7 @@ #include #include @@ -172,7 +172,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #include diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/VariableFallbackKernel.cpp pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp --- pytorch-v1.8.1/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-12-18 23:01:10.909080458 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-12-20 23:24:45.115437886 +0800 @@ -48,4 +48,8 @@ m.fallback(torch::CppFunction::makeFallthrough()); } @@ -184,7 +184,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/Convolution.cpp pytorch-develop-181/aten/src/ATen/native/Convolution.cpp --- pytorch-v1.8.1/aten/src/ATen/native/Convolution.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/Convolution.cpp 2021-12-18 23:01:10.921080550 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/Convolution.cpp 2021-12-20 23:24:45.127438033 +0800 @@ -603,7 +603,9 @@ const Tensor& input, const Tensor& weight, const Tensor& bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, @@ -198,7 +198,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= at::Tensor _convolution( diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/Memory.cpp pytorch-develop-181/aten/src/ATen/native/Memory.cpp --- pytorch-v1.8.1/aten/src/ATen/native/Memory.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/Memory.cpp 2021-12-18 23:01:10.925080581 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/Memory.cpp 2021-12-20 23:24:45.131438083 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -249,7 +249,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= detail::computeStorageNbytes( diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/native_functions.yaml pytorch-develop-181/aten/src/ATen/native/native_functions.yaml --- pytorch-v1.8.1/aten/src/ATen/native/native_functions.yaml 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/native_functions.yaml 2021-12-18 23:01:10.961080857 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/native_functions.yaml 2021-12-20 23:24:45.167438525 +0800 @@ -2073,6 +2073,8 @@ dispatch: CPU, CUDA: isnan @@ -480,7 +480,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +- func: npu_apply_adam.out(Scalar beta1_power, Scalar beta2_power, Scalar lr, Scalar beta1, Scalar beta2, Scalar epsilon, Tensor grad, bool? use_locking, bool? use_nesterov, *, Tensor(a!) var, Tensor(b!) m, Tensor(c!) v) -> (Tensor(a!), Tensor(b!), Tensor(c!)) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/NPUVariableMethodStubs.cpp pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp --- pytorch-v1.8.1/aten/src/ATen/native/NPUVariableMethodStubs.cpp 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp 2021-12-18 23:01:10.925080581 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp 2021-12-20 23:24:45.131438083 +0800 @@ -0,0 +1,464 @@ +#include "ATen/native/npu/common/FormatCastHelper.h" +#include "ATen/native/npu/frame/FormatHelper.h" @@ -1864,7 +1864,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -} diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/TensorFactories.cpp pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp --- pytorch-v1.8.1/aten/src/ATen/native/TensorFactories.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp 2021-12-18 23:01:10.941080704 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp 2021-12-20 23:24:45.143438230 +0800 @@ -1,3 +1,20 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -1897,7 +1897,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= namespace { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h --- pytorch-v1.8.1/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-12-18 23:01:10.997081133 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-12-20 23:24:45.199438919 +0800 @@ -1,19229 +1,19229 @@ -// -// Copyright (c) 2017-2020 Advanced Micro Devices, Inc. All rights reserved. @@ -40359,7 +40359,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +#endif // #ifdef VMA_IMPLEMENTATION diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/record_function.cpp pytorch-develop-181/aten/src/ATen/record_function.cpp --- pytorch-v1.8.1/aten/src/ATen/record_function.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/record_function.cpp 2021-12-18 23:01:11.001081164 +0800 ++++ pytorch-develop-181/aten/src/ATen/record_function.cpp 2021-12-20 23:24:45.203438968 +0800 @@ -400,6 +400,9 @@ rf_tls_.tls_record_function_enabled_ = enable; } @@ -40372,7 +40372,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (rf_tls_ptr->tls_record_function_enabled_) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/record_function.h pytorch-develop-181/aten/src/ATen/record_function.h --- pytorch-v1.8.1/aten/src/ATen/record_function.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/record_function.h 2021-12-18 23:01:11.001081164 +0800 ++++ pytorch-develop-181/aten/src/ATen/record_function.h 2021-12-20 23:24:45.203438968 +0800 @@ -2,6 +2,7 @@ #include @@ -40408,7 +40408,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // scope - record scope that this function tracks diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/templates/TensorBody.h pytorch-develop-181/aten/src/ATen/templates/TensorBody.h --- pytorch-v1.8.1/aten/src/ATen/templates/TensorBody.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/templates/TensorBody.h 2021-12-18 23:01:11.001081164 +0800 ++++ pytorch-develop-181/aten/src/ATen/templates/TensorBody.h 2021-12-20 23:24:45.203438968 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40441,7 +40441,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= bool is_xpu() const; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/templates/TensorMethods.cpp pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp --- pytorch-v1.8.1/aten/src/ATen/templates/TensorMethods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp 2021-12-18 23:01:11.001081164 +0800 ++++ pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp 2021-12-20 23:24:45.203438968 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40486,7 +40486,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/Utils.cpp pytorch-develop-181/aten/src/ATen/Utils.cpp --- pytorch-v1.8.1/aten/src/ATen/Utils.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/Utils.cpp 2021-12-18 23:01:10.909080458 +0800 ++++ pytorch-develop-181/aten/src/ATen/Utils.cpp 2021-12-20 23:24:45.111437836 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40530,7 +40530,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/TH/generic/THStorage.cpp pytorch-develop-181/aten/src/TH/generic/THStorage.cpp --- pytorch-v1.8.1/aten/src/TH/generic/THStorage.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/TH/generic/THStorage.cpp 2021-12-18 23:01:11.005081195 +0800 ++++ pytorch-develop-181/aten/src/TH/generic/THStorage.cpp 2021-12-20 23:24:45.207439017 +0800 @@ -1,9 +1,32 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40643,7 +40643,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/TH/generic/THStorage.h pytorch-develop-181/aten/src/TH/generic/THStorage.h --- pytorch-v1.8.1/aten/src/TH/generic/THStorage.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/TH/generic/THStorage.h 2021-12-18 23:01:11.005081195 +0800 ++++ pytorch-develop-181/aten/src/TH/generic/THStorage.h 2021-12-20 23:24:45.207439017 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40682,7 +40682,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/CMakeLists.txt pytorch-develop-181/c10/CMakeLists.txt --- pytorch-v1.8.1/c10/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/CMakeLists.txt 2021-12-18 23:01:11.017081287 +0800 ++++ pytorch-develop-181/c10/CMakeLists.txt 2021-12-20 23:24:45.223439214 +0800 @@ -79,6 +79,14 @@ message(STATUS "don't use NUMA") endif() @@ -40711,7 +40711,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # not checked in diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Backend.h pytorch-develop-181/c10/core/Backend.h --- pytorch-v1.8.1/c10/core/Backend.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Backend.h 2021-12-18 23:01:11.017081287 +0800 ++++ pytorch-develop-181/c10/core/Backend.h 2021-12-20 23:24:45.223439214 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40805,7 +40805,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Device.cpp pytorch-develop-181/c10/core/Device.cpp --- pytorch-v1.8.1/c10/core/Device.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Device.cpp 2021-12-18 23:01:11.017081287 +0800 ++++ pytorch-develop-181/c10/core/Device.cpp 2021-12-20 23:24:45.223439214 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40836,7 +40836,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= types.begin(), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Device.h pytorch-develop-181/c10/core/Device.h --- pytorch-v1.8.1/c10/core/Device.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Device.h 2021-12-18 23:01:11.017081287 +0800 ++++ pytorch-develop-181/c10/core/Device.h 2021-12-20 23:24:45.223439214 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40871,7 +40871,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= bool is_cpu() const noexcept { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DeviceType.cpp pytorch-develop-181/c10/core/DeviceType.cpp --- pytorch-v1.8.1/c10/core/DeviceType.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DeviceType.cpp 2021-12-18 23:01:11.017081287 +0800 ++++ pytorch-develop-181/c10/core/DeviceType.cpp 2021-12-20 23:24:45.223439214 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40911,7 +40911,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return false; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DeviceType.h pytorch-develop-181/c10/core/DeviceType.h --- pytorch-v1.8.1/c10/core/DeviceType.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DeviceType.h 2021-12-18 23:01:11.017081287 +0800 ++++ pytorch-develop-181/c10/core/DeviceType.h 2021-12-20 23:24:45.223439214 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40953,7 +40953,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= constexpr DeviceType kMSNPU = DeviceType::MSNPU; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKey.cpp pytorch-develop-181/c10/core/DispatchKey.cpp --- pytorch-v1.8.1/c10/core/DispatchKey.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKey.cpp 2021-12-18 23:01:11.017081287 +0800 ++++ pytorch-develop-181/c10/core/DispatchKey.cpp 2021-12-20 23:24:45.223439214 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41002,7 +41002,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::PrivateUse1: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKey.h pytorch-develop-181/c10/core/DispatchKey.h --- pytorch-v1.8.1/c10/core/DispatchKey.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKey.h 2021-12-18 23:01:11.017081287 +0800 ++++ pytorch-develop-181/c10/core/DispatchKey.h 2021-12-20 23:24:45.223439214 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41049,7 +41049,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= PrivateUse3_PreAutograd = AutogradPrivateUse3, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKeySet.cpp pytorch-develop-181/c10/core/DispatchKeySet.cpp --- pytorch-v1.8.1/c10/core/DispatchKeySet.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKeySet.cpp 2021-12-18 23:01:11.017081287 +0800 ++++ pytorch-develop-181/c10/core/DispatchKeySet.cpp 2021-12-20 23:24:45.223439214 +0800 @@ -11,6 +11,7 @@ DispatchKey::XLA, DispatchKey::NestedTensor, @@ -41069,7 +41069,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::AutogradXPU: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKeySet.h pytorch-develop-181/c10/core/DispatchKeySet.h --- pytorch-v1.8.1/c10/core/DispatchKeySet.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKeySet.h 2021-12-18 23:01:11.017081287 +0800 ++++ pytorch-develop-181/c10/core/DispatchKeySet.h 2021-12-20 23:24:45.223439214 +0800 @@ -193,6 +193,7 @@ DispatchKey::AutogradCPU, DispatchKey::AutogradCUDA, @@ -41080,7 +41080,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= DispatchKey::AutogradPrivateUse1, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Storage.h pytorch-develop-181/c10/core/Storage.h --- pytorch-v1.8.1/c10/core/Storage.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Storage.h 2021-12-18 23:01:11.021081318 +0800 ++++ pytorch-develop-181/c10/core/Storage.h 2021-12-20 23:24:45.223439214 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41114,7 +41114,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/StorageImpl.h pytorch-develop-181/c10/core/StorageImpl.h --- pytorch-v1.8.1/c10/core/StorageImpl.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/StorageImpl.h 2021-12-18 23:01:11.021081318 +0800 ++++ pytorch-develop-181/c10/core/StorageImpl.h 2021-12-20 23:24:45.223439214 +0800 @@ -1,12 +1,42 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41174,7 +41174,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/TensorImpl.h pytorch-develop-181/c10/core/TensorImpl.h --- pytorch-v1.8.1/c10/core/TensorImpl.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/TensorImpl.h 2021-12-18 23:01:11.021081318 +0800 ++++ pytorch-develop-181/c10/core/TensorImpl.h 2021-12-20 23:24:45.223439214 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41217,7 +41217,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ts.has(DispatchKey::SparseCPU) || diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/TensorOptions.h pytorch-develop-181/c10/core/TensorOptions.h --- pytorch-v1.8.1/c10/core/TensorOptions.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/TensorOptions.h 2021-12-18 23:01:11.021081318 +0800 ++++ pytorch-develop-181/c10/core/TensorOptions.h 2021-12-20 23:24:45.223439214 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41258,7 +41258,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } else if (tid == DispatchKey::QuantizedXPU) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/macros/Export.h pytorch-develop-181/c10/macros/Export.h --- pytorch-v1.8.1/c10/macros/Export.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/macros/Export.h 2021-12-18 23:01:11.021081318 +0800 ++++ pytorch-develop-181/c10/macros/Export.h 2021-12-20 23:24:45.227439262 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41327,7 +41327,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #define C10_API_ENUM C10_API diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/caffe2/CMakeLists.txt pytorch-develop-181/caffe2/CMakeLists.txt --- pytorch-v1.8.1/caffe2/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/caffe2/CMakeLists.txt 2021-12-18 23:01:11.029081378 +0800 ++++ pytorch-develop-181/caffe2/CMakeLists.txt 2021-12-20 23:24:45.231439312 +0800 @@ -76,6 +76,7 @@ # Add source, includes, and libs to lists list(APPEND Caffe2_CPU_SRCS ${ATen_CPU_SRCS}) @@ -41452,7 +41452,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Call again since Caffe2_HIP_INCLUDE is extended with ATen include dirs. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/BuildVariables.cmake pytorch-develop-181/cmake/BuildVariables.cmake --- pytorch-v1.8.1/cmake/BuildVariables.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/BuildVariables.cmake 2021-12-18 23:01:11.105081961 +0800 ++++ pytorch-develop-181/cmake/BuildVariables.cmake 2021-12-20 23:24:45.311440296 +0800 @@ -11,6 +11,7 @@ # CMakeLists.txt files under each folder respectively. set(Caffe2_CPU_SRCS) @@ -41476,7 +41476,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # symbols. However, if the lib is whole linked in caffe2 lib, we don't want diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Codegen.cmake pytorch-develop-181/cmake/Codegen.cmake --- pytorch-v1.8.1/cmake/Codegen.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Codegen.cmake 2021-12-18 23:01:11.105081961 +0800 ++++ pytorch-develop-181/cmake/Codegen.cmake 2021-12-20 23:24:45.311440296 +0800 @@ -208,13 +208,14 @@ file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt generated_cpp) file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt-cuda cuda_generated_cpp) @@ -41509,7 +41509,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= function(append_filelist name outputvar) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Dependencies.cmake pytorch-develop-181/cmake/Dependencies.cmake --- pytorch-v1.8.1/cmake/Dependencies.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Dependencies.cmake 2021-12-18 23:01:11.105081961 +0800 ++++ pytorch-develop-181/cmake/Dependencies.cmake 2021-12-20 23:24:45.311440296 +0800 @@ -1771,6 +1771,13 @@ endif(NOT C_HAS_THREAD) endif() @@ -41526,7 +41526,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Summary.cmake pytorch-develop-181/cmake/Summary.cmake --- pytorch-v1.8.1/cmake/Summary.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Summary.cmake 2021-12-18 23:01:11.105081961 +0800 ++++ pytorch-develop-181/cmake/Summary.cmake 2021-12-20 23:24:45.311440296 +0800 @@ -127,6 +127,7 @@ message(STATUS " USE_MKLDNN_CBLAS : ${USE_MKLDNN_CBLAS}") endif() @@ -41545,7 +41545,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= message(STATUS " Private Dependencies : ${Caffe2_DEPENDENCY_LIBS}") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/TorchConfig.cmake.in pytorch-develop-181/cmake/TorchConfig.cmake.in --- pytorch-v1.8.1/cmake/TorchConfig.cmake.in 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/TorchConfig.cmake.in 2021-12-18 23:01:11.105081961 +0800 ++++ pytorch-develop-181/cmake/TorchConfig.cmake.in 2021-12-20 23:24:45.311440296 +0800 @@ -158,6 +158,11 @@ list(APPEND TORCH_LIBRARIES ${TORCH_CUDA_LIBRARIES}) endif() @@ -41560,7 +41560,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(TORCH_CXX_FLAGS "-D_GLIBCXX_USE_CXX11_ABI=@GLIBCXX_USE_CXX11_ABI@") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/CMakeLists.txt pytorch-develop-181/CMakeLists.txt --- pytorch-v1.8.1/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/CMakeLists.txt 2021-12-18 23:01:10.893080335 +0800 ++++ pytorch-develop-181/CMakeLists.txt 2021-12-20 23:24:45.095437640 +0800 @@ -261,6 +261,10 @@ "USE_DISTRIBUTED" OFF) option(USE_TBB "Use TBB" OFF) @@ -41604,7 +41604,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if(APPLE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/setup.py pytorch-develop-181/setup.py --- pytorch-v1.8.1/setup.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/setup.py 2021-12-18 23:01:11.121082083 +0800 ++++ pytorch-develop-181/setup.py 2021-12-20 23:24:45.323440443 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -41673,7 +41673,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= 'utils/benchmark/utils/valgrind_wrapper/*.h', diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/derivatives.yaml pytorch-develop-181/tools/autograd/derivatives.yaml --- pytorch-v1.8.1/tools/autograd/derivatives.yaml 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/derivatives.yaml 2021-12-18 23:01:11.577085578 +0800 ++++ pytorch-develop-181/tools/autograd/derivatives.yaml 2021-12-20 23:24:45.783446098 +0800 @@ -1976,3 +1976,7 @@ - name: nonzero(Tensor self) -> Tensor @@ -41684,7 +41684,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + mat2: npu_bmm_v2_mat2_backward(grad, self, mat2, mat2.sizes()) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/gen_python_functions.py pytorch-develop-181/tools/autograd/gen_python_functions.py --- pytorch-v1.8.1/tools/autograd/gen_python_functions.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/gen_python_functions.py 2021-12-18 23:01:11.577085578 +0800 ++++ pytorch-develop-181/tools/autograd/gen_python_functions.py 2021-12-20 23:24:45.783446098 +0800 @@ -1,3 +1,20 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -41708,7 +41708,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # The bindings are generated as methods on python_variable or functions on the diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/Functions.cpp pytorch-develop-181/tools/autograd/templates/Functions.cpp --- pytorch-v1.8.1/tools/autograd/templates/Functions.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/Functions.cpp 2021-12-18 23:01:11.577085578 +0800 ++++ pytorch-develop-181/tools/autograd/templates/Functions.cpp 2021-12-20 23:24:45.783446098 +0800 @@ -14,6 +14,35 @@ namespace torch { namespace autograd { namespace generated { @@ -41747,7 +41747,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }}} // namespace torch::autograd::generated diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/python_torch_functions.cpp pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp --- pytorch-v1.8.1/tools/autograd/templates/python_torch_functions.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp 2021-12-18 23:01:11.577085578 +0800 ++++ pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp 2021-12-20 23:24:45.787446147 +0800 @@ -30,7 +30,7 @@ #include "torch/csrc/autograd/generated/variable_factories.h" #include "torch/csrc/utils/structseq.h" @@ -41783,7 +41783,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return torch::range(start, end, step, options); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/python_variable_methods.cpp pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp --- pytorch-v1.8.1/tools/autograd/templates/python_variable_methods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp 2021-12-18 23:01:11.577085578 +0800 ++++ pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp 2021-12-20 23:24:45.787446147 +0800 @@ -20,7 +20,13 @@ #ifdef USE_CUDA #include "torch/csrc/cuda/Event.h" @@ -41851,7 +41851,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"has_names", THPVariable_has_names, METH_NOARGS, NULL}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/build_variables.bzl pytorch-develop-181/tools/build_variables.bzl --- pytorch-v1.8.1/tools/build_variables.bzl 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/build_variables.bzl 2021-12-18 23:01:11.577085578 +0800 ++++ pytorch-develop-181/tools/build_variables.bzl 2021-12-20 23:24:45.787446147 +0800 @@ -362,6 +362,7 @@ libtorch_cuda_core_sources = [ "torch/csrc/CudaIPCTypes.cpp", @@ -41870,7 +41870,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= "torch/csrc/utils/python_arg_parser.cpp", diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/codegen/gen.py pytorch-develop-181/tools/codegen/gen.py --- pytorch-v1.8.1/tools/codegen/gen.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/codegen/gen.py 2021-12-18 23:01:11.581085608 +0800 ++++ pytorch-develop-181/tools/codegen/gen.py 2021-12-20 23:24:45.787446147 +0800 @@ -815,7 +815,7 @@ core_fm = make_file_manager(core_install_dir) cpu_fm = make_file_manager(options.install_dir) @@ -41918,7 +41918,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= main() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/codegen/model.py pytorch-develop-181/tools/codegen/model.py --- pytorch-v1.8.1/tools/codegen/model.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/codegen/model.py 2021-12-18 23:01:11.581085608 +0800 ++++ pytorch-develop-181/tools/codegen/model.py 2021-12-20 23:24:45.791446196 +0800 @@ -79,6 +79,7 @@ SparseHIP = auto() SparseXPU = auto() @@ -42060,7 +42060,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/autograd/profiler.py pytorch-develop-181/torch/autograd/profiler.py --- pytorch-v1.8.1/torch/autograd/profiler.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/autograd/profiler.py 2021-12-18 23:01:11.585085639 +0800 ++++ pytorch-develop-181/torch/autograd/profiler.py 2021-12-20 23:24:45.791446196 +0800 @@ -37,14 +37,17 @@ class EventList(list): """A list of Events (for pretty printing)""" @@ -42739,7 +42739,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ''.join(result) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_C/_autograd.pyi pytorch-develop-181/torch/_C/_autograd.pyi --- pytorch-v1.8.1/torch/_C/_autograd.pyi 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_C/_autograd.pyi 2021-12-18 23:01:11.581085608 +0800 ++++ pytorch-develop-181/torch/_C/_autograd.pyi 2021-12-20 23:24:45.791446196 +0800 @@ -9,14 +9,17 @@ CUDA = ... NVTX = ... @@ -42760,7 +42760,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= class ProfilerConfig: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/CMakeLists.txt pytorch-develop-181/torch/CMakeLists.txt --- pytorch-v1.8.1/torch/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/CMakeLists.txt 2021-12-18 23:01:11.581085608 +0800 ++++ pytorch-develop-181/torch/CMakeLists.txt 2021-12-20 23:24:45.791446196 +0800 @@ -131,6 +131,20 @@ endif() @@ -42784,7 +42784,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= list(APPEND TORCH_PYTHON_SRCS ${GENERATED_THNN_CXX_CUDA}) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/engine.cpp pytorch-develop-181/torch/csrc/autograd/engine.cpp --- pytorch-v1.8.1/torch/csrc/autograd/engine.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/engine.cpp 2021-12-18 23:01:11.589085670 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/engine.cpp 2021-12-20 23:24:45.799446295 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -42881,7 +42881,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto outputs = call_function(graph_task, func, inputs); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/functions/tensor.cpp pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp --- pytorch-v1.8.1/torch/csrc/autograd/functions/tensor.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp 2021-12-18 23:01:11.589085670 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp 2021-12-20 23:24:45.799446295 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -42913,7 +42913,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= /*non_blocking=*/false, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/init.cpp pytorch-develop-181/torch/csrc/autograd/init.cpp --- pytorch-v1.8.1/torch/csrc/autograd/init.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/init.cpp 2021-12-18 23:01:11.589085670 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/init.cpp 2021-12-20 23:24:45.799446295 +0800 @@ -52,6 +52,7 @@ .value("Disabled", ProfilerState::Disabled) .value("CPU", ProfilerState::CPU) @@ -42956,7 +42956,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= .value("OPENCL", c10::DeviceType::OPENCL) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/input_buffer.cpp pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp --- pytorch-v1.8.1/torch/csrc/autograd/input_buffer.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp 2021-12-18 23:01:11.589085670 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp 2021-12-20 23:24:45.799446295 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43008,7 +43008,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto& old_var = buffer[pos]; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.cpp pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp --- pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp 2021-12-18 23:01:11.589085670 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp 2021-12-20 23:24:45.799446295 +0800 @@ -147,7 +147,7 @@ constexpr const CUDAStubs* default_stubs_addr = &default_stubs; // Constant initialization, so it is guaranteed to be initialized before @@ -43251,7 +43251,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CUDAStubs::~CUDAStubs() = default; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.h pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h --- pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h 2021-12-18 23:01:11.589085670 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h 2021-12-20 23:24:45.799446295 +0800 @@ -19,6 +19,8 @@ #include // for gettimeofday() #endif @@ -43440,7 +43440,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= std::vector&& remoteProfiledEvents); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/python_variable.cpp pytorch-develop-181/torch/csrc/autograd/python_variable.cpp --- pytorch-v1.8.1/torch/csrc/autograd/python_variable.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/python_variable.cpp 2021-12-18 23:01:11.593085701 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/python_variable.cpp 2021-12-20 23:24:45.799446295 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43494,7 +43494,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"is_mkldnn", (getter)THPVariable_is_mkldnn, nullptr, nullptr, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/python_variable_indexing.cpp pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp --- pytorch-v1.8.1/torch/csrc/autograd/python_variable_indexing.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-18 23:01:11.593085701 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-20 23:24:45.799446295 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43527,7 +43527,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/distributed/c10d/init.cpp pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp --- pytorch-v1.8.1/torch/csrc/distributed/c10d/init.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp 2021-12-18 23:01:11.593085701 +0800 ++++ pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp 2021-12-20 23:24:45.803446344 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43694,7 +43694,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= torch::class_<::c10d::DistributedC10d>("dist_c10d", "frontend") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/DynamicTypes.cpp pytorch-develop-181/torch/csrc/DynamicTypes.cpp --- pytorch-v1.8.1/torch/csrc/DynamicTypes.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/DynamicTypes.cpp 2021-12-18 23:01:11.585085639 +0800 ++++ pytorch-develop-181/torch/csrc/DynamicTypes.cpp 2021-12-20 23:24:45.795446245 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43743,7 +43743,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return it->second; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/Generator.cpp pytorch-develop-181/torch/csrc/Generator.cpp --- pytorch-v1.8.1/torch/csrc/Generator.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/Generator.cpp 2021-12-18 23:01:11.585085639 +0800 ++++ pytorch-develop-181/torch/csrc/Generator.cpp 2021-12-20 23:24:45.795446245 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43790,7 +43790,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= " is not supported for torch.Generator() api."); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/serialization.cpp pytorch-develop-181/torch/csrc/generic/serialization.cpp --- pytorch-v1.8.1/torch/csrc/generic/serialization.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/serialization.cpp 2021-12-18 23:01:11.597085731 +0800 ++++ pytorch-develop-181/torch/csrc/generic/serialization.cpp 2021-12-20 23:24:45.807446393 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43888,7 +43888,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/Storage.cpp pytorch-develop-181/torch/csrc/generic/Storage.cpp --- pytorch-v1.8.1/torch/csrc/generic/Storage.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/Storage.cpp 2021-12-18 23:01:11.597085731 +0800 ++++ pytorch-develop-181/torch/csrc/generic/Storage.cpp 2021-12-20 23:24:45.807446393 +0800 @@ -1,7 +1,25 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43968,7 +43968,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for (Py_ssize_t i = 0; i < length; i++) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/StorageMethods.cpp pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp --- pytorch-v1.8.1/torch/csrc/generic/StorageMethods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp 2021-12-18 23:01:11.597085731 +0800 ++++ pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp 2021-12-20 23:24:45.807446393 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44048,7 +44048,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/Module.cpp pytorch-develop-181/torch/csrc/Module.cpp --- pytorch-v1.8.1/torch/csrc/Module.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/Module.cpp 2021-12-18 23:01:11.585085639 +0800 ++++ pytorch-develop-181/torch/csrc/Module.cpp 2021-12-20 23:24:45.795446245 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44174,7 +44174,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (incref) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/tensor/python_tensor.cpp pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp --- pytorch-v1.8.1/torch/csrc/tensor/python_tensor.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp 2021-12-18 23:01:11.625085946 +0800 ++++ pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp 2021-12-20 23:24:45.831446688 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44212,7 +44212,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/python_arg_parser.h pytorch-develop-181/torch/csrc/utils/python_arg_parser.h --- pytorch-v1.8.1/torch/csrc/utils/python_arg_parser.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/python_arg_parser.h 2021-12-18 23:01:11.625085946 +0800 ++++ pytorch-develop-181/torch/csrc/utils/python_arg_parser.h 2021-12-20 23:24:45.831446688 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44247,7 +44247,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return at::Device(device_str); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_layouts.cpp pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_layouts.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp 2021-12-18 23:01:11.625085946 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp 2021-12-20 23:24:45.835446737 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44278,7 +44278,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= PyObject *sparse_coo_layout = THPLayout_New(at::Layout::Sparse, "torch.sparse_coo"); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_new.cpp pytorch-develop-181/torch/csrc/utils/tensor_new.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_new.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_new.cpp 2021-12-18 23:01:11.625085946 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_new.cpp 2021-12-20 23:24:45.835446737 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44410,7 +44410,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= dispatch_key); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_types.cpp pytorch-develop-181/torch/csrc/utils/tensor_types.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_types.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_types.cpp 2021-12-18 23:01:11.625085946 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_types.cpp 2021-12-20 23:24:45.835446737 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44460,7 +44460,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/distributed/distributed_c10d.py pytorch-develop-181/torch/distributed/distributed_c10d.py --- pytorch-v1.8.1/torch/distributed/distributed_c10d.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/distributed/distributed_c10d.py 2021-12-18 23:01:11.625085946 +0800 ++++ pytorch-develop-181/torch/distributed/distributed_c10d.py 2021-12-20 23:24:45.835446737 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -44551,7 +44551,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= prefix_store, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/__init__.py pytorch-develop-181/torch/__init__.py --- pytorch-v1.8.1/torch/__init__.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/__init__.py 2021-12-18 23:01:11.581085608 +0800 ++++ pytorch-develop-181/torch/__init__.py 2021-12-20 23:24:45.791446196 +0800 @@ -675,3 +675,11 @@ # class usage. We add these lines here to preserve backward compatibility. quantized_lstm = torch.ops.aten.quantized_lstm @@ -44567,7 +44567,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/CMakeLists.txt pytorch-develop-181/torch/lib/c10d/CMakeLists.txt --- pytorch-v1.8.1/torch/lib/c10d/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/CMakeLists.txt 2021-12-18 23:01:11.633086007 +0800 ++++ pytorch-develop-181/torch/lib/c10d/CMakeLists.txt 2021-12-20 23:24:45.843446835 +0800 @@ -27,6 +27,10 @@ option(USE_C10D_NCCL "USE C10D NCCL" ON) endif() @@ -44620,7 +44620,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= copy_header(ProcessGroupMPI.hpp) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/comm.cpp pytorch-develop-181/torch/lib/c10d/comm.cpp --- pytorch-v1.8.1/torch/lib/c10d/comm.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/comm.cpp 2021-12-18 23:01:11.633086007 +0800 ++++ pytorch-develop-181/torch/lib/c10d/comm.cpp 2021-12-20 23:24:45.843446835 +0800 @@ -12,6 +12,26 @@ class BroadcastWork { @@ -44709,7 +44709,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= while (!in_flight.empty()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/reducer.cpp pytorch-develop-181/torch/lib/c10d/reducer.cpp --- pytorch-v1.8.1/torch/lib/c10d/reducer.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/reducer.cpp 2021-12-18 23:01:11.633086007 +0800 ++++ pytorch-develop-181/torch/lib/c10d/reducer.cpp 2021-12-20 23:24:45.843446835 +0800 @@ -18,6 +18,18 @@ namespace c10d { namespace { @@ -44901,7 +44901,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // A bucket with one or more dense tensors needs to be unflattened. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/libshm/CMakeLists.txt pytorch-develop-181/torch/lib/libshm/CMakeLists.txt --- pytorch-v1.8.1/torch/lib/libshm/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/libshm/CMakeLists.txt 2021-12-18 23:01:11.637086037 +0800 ++++ pytorch-develop-181/torch/lib/libshm/CMakeLists.txt 2021-12-20 23:24:45.843446835 +0800 @@ -41,8 +41,11 @@ set_target_properties(shm PROPERTIES PREFIX "lib" @@ -44917,7 +44917,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # https://github.com/libgit2/libgit2/issues/2128#issuecomment-35649830 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/batchnorm.py pytorch-develop-181/torch/nn/modules/batchnorm.py --- pytorch-v1.8.1/torch/nn/modules/batchnorm.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/batchnorm.py 2021-12-18 23:01:11.637086037 +0800 ++++ pytorch-develop-181/torch/nn/modules/batchnorm.py 2021-12-20 23:24:45.847446885 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -44939,7 +44939,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= from ._functions import SyncBatchNorm as sync_batch_norm diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/module.py pytorch-develop-181/torch/nn/modules/module.py --- pytorch-v1.8.1/torch/nn/modules/module.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/module.py 2021-12-18 23:01:11.637086037 +0800 ++++ pytorch-develop-181/torch/nn/modules/module.py 2021-12-20 23:24:45.847446885 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45065,7 +45065,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if convert_to_format is not None and t.dim() == 4: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/normalization.py pytorch-develop-181/torch/nn/modules/normalization.py --- pytorch-v1.8.1/torch/nn/modules/normalization.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/normalization.py 2021-12-18 23:01:11.637086037 +0800 ++++ pytorch-develop-181/torch/nn/modules/normalization.py 2021-12-20 23:24:45.847446885 +0800 @@ -167,8 +167,11 @@ init.zeros_(self.bias) @@ -45082,7 +45082,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return '{normalized_shape}, eps={eps}, ' \ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/parallel/distributed.py pytorch-develop-181/torch/nn/parallel/distributed.py --- pytorch-v1.8.1/torch/nn/parallel/distributed.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/parallel/distributed.py 2021-12-18 23:01:11.641086068 +0800 ++++ pytorch-develop-181/torch/nn/parallel/distributed.py 2021-12-20 23:24:45.847446885 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45114,7 +45114,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= output = self.module(*inputs[0], **kwargs[0]) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/serialization.py pytorch-develop-181/torch/serialization.py --- pytorch-v1.8.1/torch/serialization.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/serialization.py 2021-12-18 23:01:11.649086129 +0800 ++++ pytorch-develop-181/torch/serialization.py 2021-12-20 23:24:45.855446983 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45203,7 +45203,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= See also: `saving-loading-tensors` diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/storage.py pytorch-develop-181/torch/storage.py --- pytorch-v1.8.1/torch/storage.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/storage.py 2021-12-18 23:01:11.649086129 +0800 ++++ pytorch-develop-181/torch/storage.py 2021-12-20 23:24:45.855446983 +0800 @@ -8,6 +8,7 @@ class _StorageBase(object): _cdata: Any @@ -45223,7 +45223,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= else: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/tensor.py pytorch-develop-181/torch/tensor.py --- pytorch-v1.8.1/torch/tensor.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/tensor.py 2021-12-18 23:01:11.649086129 +0800 ++++ pytorch-develop-181/torch/tensor.py 2021-12-20 23:24:45.855446983 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45285,7 +45285,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def __reversed__(self): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_tensor_str.py pytorch-develop-181/torch/_tensor_str.py --- pytorch-v1.8.1/torch/_tensor_str.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_tensor_str.py 2021-12-18 23:01:11.585085639 +0800 ++++ pytorch-develop-181/torch/_tensor_str.py 2021-12-20 23:24:45.791446196 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45329,7 +45329,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # TODO: add an API to map real -> complex dtypes diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/utils/data/dataloader.py pytorch-develop-181/torch/utils/data/dataloader.py --- pytorch-v1.8.1/torch/utils/data/dataloader.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/utils/data/dataloader.py 2021-12-18 23:01:11.653086160 +0800 ++++ pytorch-develop-181/torch/utils/data/dataloader.py 2021-12-20 23:24:45.863447082 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45390,7 +45390,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= pin_memory_thread.start() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/utils/data/_utils/pin_memory.py pytorch-develop-181/torch/utils/data/_utils/pin_memory.py --- pytorch-v1.8.1/torch/utils/data/_utils/pin_memory.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/utils/data/_utils/pin_memory.py 2021-12-18 23:01:07.749056184 +0800 ++++ pytorch-develop-181/torch/utils/data/_utils/pin_memory.py 2021-12-20 23:24:41.951398924 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45436,7 +45436,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # logic of this function. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_utils.py pytorch-develop-181/torch/_utils.py --- pytorch-v1.8.1/torch/_utils.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_utils.py 2021-12-18 23:01:11.585085639 +0800 ++++ pytorch-develop-181/torch/_utils.py 2021-12-20 23:24:45.791446196 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. diff --git a/pytorch1.5.0/src/aten/src/ATen/native/native_functions.yaml b/pytorch1.5.0/src/aten/src/ATen/native/native_functions.yaml index 7f9eaf7216f..6beef2106ea 100644 --- a/pytorch1.5.0/src/aten/src/ATen/native/native_functions.yaml +++ b/pytorch1.5.0/src/aten/src/ATen/native/native_functions.yaml @@ -1416,6 +1416,8 @@ NPU: _embedding_bag_npu - func: _embedding_bag_backward(Tensor grad, Tensor indices, Tensor offsets, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, int num_weights, bool scale_grad_by_freq, int mode, bool sparse, Tensor? per_sample_weights) -> Tensor + npu_dispatch: + NPU: _embedding_bag_backward_npu - func: _embedding_bag_sparse_backward(Tensor grad, Tensor indices, Tensor offsets, Tensor offset2bag, Tensor bag_size, int num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights) -> Tensor diff --git a/pytorch1.5.0/src/aten/src/ATen/native/npu/ArangeKernelNpu.cpp b/pytorch1.5.0/src/aten/src/ATen/native/npu/ArangeKernelNpu.cpp index a1b41a1ecbd..bab2a7d9eb6 100644 --- a/pytorch1.5.0/src/aten/src/ATen/native/npu/ArangeKernelNpu.cpp +++ b/pytorch1.5.0/src/aten/src/ATen/native/npu/ArangeKernelNpu.cpp @@ -40,9 +40,9 @@ Tensor& arange_out_npu_nocheck( Scalar step) { OpCommand cmd; cmd.Name("Range") - .Input(start, result.scalar_type()) // start - .Input(end, result.scalar_type()) // limit - .Input(step, result.scalar_type()) // delta + .Input(start, result.scalar_type(), CompileType::MEMORY_HOST_COMPILE_DEPENDENT) + .Input(end, result.scalar_type(), CompileType::MEMORY_HOST_COMPILE_DEPENDENT) + .Input(step, result.scalar_type(), CompileType::MEMORY_HOST_COMPILE_DEPENDENT) .Output(result) .Run(); diff --git a/pytorch1.5.0/src/aten/src/ATen/native/npu/EmbeddingBagBackwardKernelNpu.cpp b/pytorch1.5.0/src/aten/src/ATen/native/npu/EmbeddingBagBackwardKernelNpu.cpp new file mode 100644 index 00000000000..3e31219374c --- /dev/null +++ b/pytorch1.5.0/src/aten/src/ATen/native/npu/EmbeddingBagBackwardKernelNpu.cpp @@ -0,0 +1,58 @@ +// Copyright (c) 2021 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. +// All rights reserved. +// +// Licensed under the BSD 3-Clause License (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// https://opensource.org/licenses/BSD-3-Clause +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "ATen/native/npu/utils/OpAdapter.h" + +namespace at { +namespace native { +using namespace at::native::npu; + +Tensor _embedding_bag_backward_npu( + const Tensor& grad, + const Tensor& indices, + const Tensor& offsets, + const Tensor& offset2bag, + const Tensor& bag_size, + const Tensor& maximum_indices, + int64_t num_weights, + bool scale_grad_by_freq, + int64_t mode, + bool sparse, + const Tensor& per_sample_weights) { + + Tensor grad_cpu = grad.to("cpu"); + Tensor indices_cpu = indices.to("cpu"); + Tensor offsets_cpu = offsets.to("cpu"); + Tensor offset2bag_cpu = offset2bag.to("cpu"); + Tensor bag_size_cpu = bag_size.to("cpu"); + Tensor maximum_indices_cpu = maximum_indices.to("cpu"); + Tensor per_sample_weights_cpu = per_sample_weights; + if (per_sample_weights_cpu.defined()) { + Tensor per_sample_weights_cpu = per_sample_weights_cpu.to("cpu"); + } + + Tensor result = at::_embedding_bag_backward( + grad_cpu, indices_cpu, offsets_cpu, offset2bag_cpu, bag_size_cpu, + maximum_indices_cpu, num_weights, scale_grad_by_freq, mode, sparse, per_sample_weights_cpu); + + result = at::native::sparse_to_dense(result); + result = result.to(indices.device()); + + return result; +} + +} // namespace native +} // namespace at \ No newline at end of file diff --git a/pytorch1.5.0/src/aten/src/ATen/native/npu/frame/NPUDefine.cpp b/pytorch1.5.0/src/aten/src/ATen/native/npu/frame/NPUDefine.cpp index c4f381cde49..c2fd13906c9 100644 --- a/pytorch1.5.0/src/aten/src/ATen/native/npu/frame/NPUDefine.cpp +++ b/pytorch1.5.0/src/aten/src/ATen/native/npu/frame/NPUDefine.cpp @@ -64,6 +64,7 @@ void ExecuteParas::Copy(ExecuteParas& other) { } this->hostMemory = other.hostMemory; this->isFuzzy = other.isFuzzy; + this->isCompiling = other.isCompiling; } void ExecuteParas::CopyEx(ExecuteParas& other) @@ -71,11 +72,11 @@ void ExecuteParas::CopyEx(ExecuteParas& other) this->paras = other.paras; this->attr = other.attr; this->constParams = other.constParams; + this->isCompiling = other.isCompiling; if (other.opDynamicType != "") { this->dynamicCompileAttr = other.dynamicCompileAttr; this->dynamicRunAttr = other.dynamicRunAttr; this->dynamicParam = other.dynamicParam; - this->isCompiling = other.isCompiling; } } diff --git a/pytorch1.5.0/src/aten/src/ATen/native/npu/frame/OpParamMaker.cpp b/pytorch1.5.0/src/aten/src/ATen/native/npu/frame/OpParamMaker.cpp index c5a6a896150..fe9fd6da0dd 100644 --- a/pytorch1.5.0/src/aten/src/ATen/native/npu/frame/OpParamMaker.cpp +++ b/pytorch1.5.0/src/aten/src/ATen/native/npu/frame/OpParamMaker.cpp @@ -18,6 +18,7 @@ #include #include "c10/npu/NPUQueue.h" #include "c10/npu/NPUCachingAllocator.h" +#include "c10/npu/NPUEventManager.h" #include "c10/npu/interface/AsyncTaskQueueInterface.h" #include "c10/npu/NPUQueue.h" #include @@ -246,7 +247,6 @@ int ExecFunc(QueueParas* in, aclrtStream stream) { } } RECORD_HOST_FUNCTION("aclopCompileAndExecute: " + cur_paras->opType, std::vector({})); - E2E_RECORD_FUNCTION(cur_paras->opType); ret = aclopCompileAndExecute( (cur_paras->opType).c_str(), cur_paras->paras.input_num, @@ -293,7 +293,32 @@ int RecordEventFunc(QueueParas* in, aclrtStream stream) { if (ret != ACL_ERROR_NONE) { C10_NPU_SHOW_ERR_MSG(); } - THNPUCachingHostAllocator_insertCompleteEvent(cur_paras->event); + // Temporary modification to avoid problem that + // event must be recorded before query + if (cur_paras->eventAllocatorType == HOST_ALLOCATOR_EVENT) { + THNPUCachingHostAllocator_insertCompleteEvent(cur_paras->event); + } else if (cur_paras->eventAllocatorType == NPU_ALLOCATOR_EVENT) { + c10::npu::NPUCachingAllocator::NpuAllocatorInsertRecordedEvent(cur_paras->event); + } + + return ret; +} + +int WaitEventFunc(QueueParas* in, aclrtStream stream) { + auto cur_paras = static_cast(in->paramVal); + aclError ret = aclrtStreamWaitEvent(stream, cur_paras->event); + if (ret != ACL_ERROR_NONE) { + C10_NPU_SHOW_ERR_MSG(); + } + return ret; +} + +int LazyDestroyEventFunc(QueueParas* in, aclrtStream stream) { + auto cur_paras = static_cast(in->paramVal); + aclError ret = c10::npu::NPUEventManager::GetInstance().LazyDestroy(cur_paras->event); + if (ret != ACL_ERROR_NONE) { + C10_NPU_SHOW_ERR_MSG(); + } return ret; } @@ -314,6 +339,7 @@ void CopyFunc(void* dst, void* src, SmallVector& needClearVec, uint3 } else if (dstPtr->paramType == ASYNC_MEMCPY_EX) { needClearVec.swap((static_cast(dstPtr->paramVal))->pinMem); } + dstPtr->paramStream = srcPtr->paramStream; dstPtr->paramType = srcPtr->paramType; dstPtr->paramLen = srcPtr->paramLen; size_t maxSize = GetMaxLen(sizeof(ExecuteParas), sizeof(CopyParas), sizeof(EventParas)); @@ -322,25 +348,14 @@ void CopyFunc(void* dst, void* src, SmallVector& needClearVec, uint3 (static_cast(dstPtr->paramVal))->Copy(*(static_cast(srcPtr->paramVal))); } else if ((srcPtr->paramType == ASYNC_MEMCPY) || (srcPtr->paramType == ASYNC_MEMCPY_EX)) { (static_cast(dstPtr->paramVal))->Copy(*(static_cast(srcPtr->paramVal))); - } else { + } else if (srcPtr->paramType == RECORD_EVENT || + srcPtr->paramType == WAIT_EVENT || + srcPtr->paramType == LAZY_DESTROY_EVENT) { (static_cast(dstPtr->paramVal))->Copy(*(static_cast(srcPtr->paramVal))); } } void ReleaseFunc(void* ptr, c10::npu::ReleaseQueue& releaseQueue) { - auto queueParam = static_cast(ptr); - auto type = queueParam->paramType; - if (type == COMPILE_AND_EXECUTE) { - auto cur_paras = static_cast(queueParam->paramVal); - if (!cur_paras->opDynamicType.empty()) { - cur_paras->DynamicRelease(); - cur_paras->opDynamicType = ""; - } - cur_paras->Release(); - } -} - -void ReleaseFunc_(void* ptr, c10::npu::ReleaseQueue& releaseQueue) { releaseQueue.PushToReleaseQueue(ptr); } @@ -363,12 +378,15 @@ AsyncFuncMap funcMap = { {ASYNC_MEMCPY, MemcopyAsyncFunc}, {ASYNC_MEMCPY_EX, MemcopyAsyncFunc}, {RECORD_EVENT, RecordEventFunc}, + {WAIT_EVENT, WaitEventFunc}, + {LAZY_DESTROY_EVENT, LazyDestroyEventFunc}, }; -int AsncExecFunc(void* data, aclrtStream stream, uint32_t queueLen) { +int AsncExecFunc(void* data, uint32_t queueLen) { RECORD_HOST_FUNCTION("Dequeue queue_len: " + to_string(queueLen), std::vector({})); auto queueParam = static_cast(data); auto type = queueParam->paramType; + aclrtStream stream = queueParam->paramStream; auto ret = funcMap[type](queueParam, stream); return ret; } diff --git a/pytorch1.5.0/src/aten/src/ATen/native/npu/nputools/E2eProfiler.cpp b/pytorch1.5.0/src/aten/src/ATen/native/npu/nputools/E2eProfiler.cpp index 7888dc79066..d9c97f53c5e 100644 --- a/pytorch1.5.0/src/aten/src/ATen/native/npu/nputools/E2eProfiler.cpp +++ b/pytorch1.5.0/src/aten/src/ATen/native/npu/nputools/E2eProfiler.cpp @@ -69,7 +69,9 @@ void pushCallback( } void popCallback() { - manager().popCallback(); + if (hasCallbacks()) { + manager().popCallback(); + } } bool hasCallbacks() { @@ -126,6 +128,8 @@ void initMsPorf(const std::string dump_path, uint64_t npu_event, void init_e2e_profiler(const std::string dump_path, uint64_t npu_event, uint64_t aicore_metrics) { + + popCallback(); initMsPorf(dump_path, npu_event, aicore_metrics); pushCallback( [](E2ERecordFunction& fn) { @@ -144,6 +148,7 @@ void finalize_e2e_profiler() { C10_NPU_SHOW_ERR_MSG(); } c10::npu::acl::AclProfilingFinalize(); + popCallback(); } /* static */ diff --git a/pytorch1.5.0/src/aten/src/THNPU/THNPUCachingHostAllocator.cpp b/pytorch1.5.0/src/aten/src/THNPU/THNPUCachingHostAllocator.cpp index c9f388977a2..260bbf17df8 100644 --- a/pytorch1.5.0/src/aten/src/THNPU/THNPUCachingHostAllocator.cpp +++ b/pytorch1.5.0/src/aten/src/THNPU/THNPUCachingHostAllocator.cpp @@ -306,7 +306,7 @@ struct HostAllocator { if (err != ACL_ERROR_NONE) break; - err = c10::npu::queue::LaunchRecordEventTask(event, *it, needClearVec); + err = c10::npu::queue::HostAllocatorLaunchRecordEventTask(event, *it, needClearVec); if (err != ACL_ERROR_NONE) break; diff --git a/pytorch1.5.0/src/c10/npu/NPUCachingAllocator.cpp b/pytorch1.5.0/src/c10/npu/NPUCachingAllocator.cpp index a4a97d6ffac..5e0a9d7817f 100644 --- a/pytorch1.5.0/src/c10/npu/NPUCachingAllocator.cpp +++ b/pytorch1.5.0/src/c10/npu/NPUCachingAllocator.cpp @@ -16,6 +16,7 @@ #include #include +#include #include #include #include @@ -233,6 +234,8 @@ struct THNCachingAllocator { // lock around calls to aclFree (to prevent deadlocks with NCCL) mutable std::mutex npu_free_mutex; + mutable std::mutex recorded_event_mutex; + // cached blocks larger than 1 MB BlockPool large_blocks; @@ -245,6 +248,8 @@ struct THNCachingAllocator { // outstanding acl events std::deque> npu_events; + std::set recorded_events; + THNCachingAllocator() : large_blocks(BlockComparator), small_blocks(BlockComparator) {} @@ -824,6 +829,15 @@ struct THNCachingAllocator { for (auto& e : npu_events) { aclrtEvent event = e.first; + { + std::lock_guard lock(recorded_event_mutex); + auto it = recorded_events.begin(); + it = recorded_events.find(event); + if (c10::npu::OptionsManager::CheckQueueEnable() && + it == recorded_events.end()) { + break; + } + } Block* block = e.second; if (device.has_value() && block->device != *device) { remaining_events.push_back(e); @@ -831,8 +845,14 @@ struct THNCachingAllocator { } C10_NPU_CHECK(aclrtSynchronizeEvent(event)); + { + std::lock_guard lock(recorded_event_mutex); + auto it = recorded_events.find(event); + if (it != recorded_events.end()) { + recorded_events.erase(it); + } + } C10_NPU_CHECK(aclrtDestroyEvent(event)); - block->event_count--; if (block->event_count == 0) { free_block(block); @@ -850,6 +870,11 @@ struct THNCachingAllocator { return it->second; } + void insertRecordedEvent(aclrtEvent event) { + std::lock_guard lock(recorded_event_mutex); + recorded_events.insert(event); + } + void insert_events(Block* block) { int prev_device = 0; C10_NPU_CHECK(aclrtGetDevice(&prev_device)); @@ -866,8 +891,9 @@ struct THNCachingAllocator { } aclrtEvent event = nullptr; - aclrtCreateEvent(&event); - aclrtRecordEvent(event, it->stream()); + C10_NPU_CHECK(c10::npu::acl::AclrtCreateEventWithFlag(&event, ACL_EVENT_TIME_LINE)); + + c10::npu::queue::NpuAllocatorLaunchRecordEventTask(event, *it); block->event_count++; npu_events.emplace_back(event, block); @@ -893,6 +919,16 @@ struct THNCachingAllocator { aclrtEvent event = e.first; Block* block = e.second; + { + std::lock_guard lock(recorded_event_mutex); + auto it = recorded_events.begin(); + it = recorded_events.find(event); + if (c10::npu::OptionsManager::CheckQueueEnable() && + it == recorded_events.end()) { + break; + } + } + aclrtEventStatus status = ACL_EVENT_STATUS_RESERVED; aclError err = aclrtQueryEvent(event, &status); if (err != ACL_ERROR_NONE) { @@ -902,7 +938,14 @@ struct THNCachingAllocator { break; } - aclrtDestroyEvent(event); + { + std::lock_guard lock(recorded_event_mutex); + auto it = recorded_events.find(event); + if (it != recorded_events.end()) { + recorded_events.erase(it); + } + } + C10_NPU_CHECK(aclrtDestroyEvent(event)); block->event_count--; if (block->event_count == 0) { @@ -1083,6 +1126,10 @@ std::vector snapshot() { return caching_allocator.snapshot(); } +void NpuAllocatorInsertRecordedEvent(aclrtEvent event) { + return caching_allocator.insertRecordedEvent(event); +} + uint64_t currentMemoryAllocated(int device) { assertValidDevice(device); return caching_allocator.get_stats_for_device(device).amount_allocated; diff --git a/pytorch1.5.0/src/c10/npu/NPUCachingAllocator.h b/pytorch1.5.0/src/c10/npu/NPUCachingAllocator.h index 5388f7bb5ec..b212fe6835a 100644 --- a/pytorch1.5.0/src/c10/npu/NPUCachingAllocator.h +++ b/pytorch1.5.0/src/c10/npu/NPUCachingAllocator.h @@ -21,7 +21,7 @@ #include #include #include - +#include #include namespace c10 { @@ -144,6 +144,8 @@ C10_NPU_API std::mutex* getFreeMutex(); C10_NPU_API std::shared_ptr getIpcDevPtr(std::string handle); C10_NPU_API void FreeDeviceCachedMemory(int device); + +C10_NPU_API void NpuAllocatorInsertRecordedEvent(aclrtEvent event); } // namespace NPUCachingAllocator } // namespace npu diff --git a/pytorch1.5.0/src/c10/npu/NPUQueue.cpp b/pytorch1.5.0/src/c10/npu/NPUQueue.cpp index 6051bcbc021..eaf57cb5f46 100644 --- a/pytorch1.5.0/src/c10/npu/NPUQueue.cpp +++ b/pytorch1.5.0/src/c10/npu/NPUQueue.cpp @@ -78,10 +78,10 @@ public: this->deleteFunc = func; } - int Call(void* head, int offset, aclrtStream stream, uint32_t queueLen) { + int Call(void* head, int offset, uint32_t queueLen) { TORCH_CHECK(this->execFunc, "Failed to find execution function."); auto dstPtr = (uint8_t*)head + sizePerParams * offset; - return this->execFunc(dstPtr, stream, queueLen); + return this->execFunc(dstPtr, queueLen); } void Copy(void* dstHead, int offset, void* src, SmallVector& needClearVec, uint32_t queueLen) { @@ -284,7 +284,7 @@ bool Repository::ReadQueue() { } uint32_t queueLen = (write_idx.idx - read_idx.idx + kQueueCapacity) % kQueueCapacity; - auto ret = manager().Call(datas, read_idx.idx, calcu_stream_, queueLen); + auto ret = manager().Call(datas, read_idx.idx, queueLen); if (ret != 0) { while (!IsEmptyQueue()) { // ignore other tasks @@ -491,7 +491,7 @@ void StartConsume(Repository* repo, DeviceIndex device_id) { return; } -void Repository::InitRepo(DeviceIndex device_id, aclrtStream calcu_stream) { +void Repository::InitRepo(DeviceIndex device_id) { struct timeval tv; gettimeofday(&tv, NULL); QUEUE_COUT( @@ -503,11 +503,7 @@ void Repository::InitRepo(DeviceIndex device_id, aclrtStream calcu_stream) { if (datas == nullptr) { datas = manager().Init(kQueueCapacity); } - if (calcu_stream == nullptr) { - NPU_LOGE("stream should not be null when init task queue."); - return; - } - calcu_stream_ = calcu_stream; + efd_read = eventfd(0, 0); efd_write = eventfd(0, 0); efd_empty = eventfd(0, 0); diff --git a/pytorch1.5.0/src/c10/npu/NPUQueue.h b/pytorch1.5.0/src/c10/npu/NPUQueue.h index e937046c9d8..7b4ad1dbbc0 100644 --- a/pytorch1.5.0/src/c10/npu/NPUQueue.h +++ b/pytorch1.5.0/src/c10/npu/NPUQueue.h @@ -87,7 +87,7 @@ class NPUQueueBase { virtual void Enqueue(void* cur_paras, SmallVector& needClearVec) = 0; virtual void Dequeue() = 0; virtual NPUStatus MakeSureQueueEmpty() = 0; - virtual void InitRepo(DeviceIndex device_id, aclrtStream calcu_stream) = 0; + virtual void InitRepo(DeviceIndex device_id) = 0; virtual bool CheckInit() const = 0; }; @@ -107,7 +107,7 @@ class Repository : public NPUQueueBase { void Enqueue(void* cur_paras, SmallVector& needClearVec) override; void Dequeue() override; NPUStatus MakeSureQueueEmpty() override; - void InitRepo(DeviceIndex device_id, aclrtStream calcu_stream) override; + void InitRepo(DeviceIndex device_id) override; bool CheckInit() const override; private: @@ -139,11 +139,10 @@ class Repository : public NPUQueueBase { // The logic is ensured by original pytorch, but this is added here just in // case. std::mutex mu_enqueue; - aclrtStream calcu_stream_; ReleaseQueue releaseQueue; }; -using ACL_EXEC_FUNC = std::function; +using ACL_EXEC_FUNC = std::function; using ACL_COPY_FUNC = std::function&, uint32_t)>; using ACL_RELEASE_FUNC = std::function; using ACL_NEW_FUNC = std::function; diff --git a/pytorch1.5.0/src/c10/npu/NPUStream.cpp b/pytorch1.5.0/src/c10/npu/NPUStream.cpp index 57222edd2aa..0a0154e97d7 100644 --- a/pytorch1.5.0/src/c10/npu/NPUStream.cpp +++ b/pytorch1.5.0/src/c10/npu/NPUStream.cpp @@ -19,7 +19,7 @@ #include #include #include - +#include #include #include @@ -170,15 +170,12 @@ static void initGlobalStreamState() { auto& default_streamsi = default_streams[device_id]; C10_NPU_CHECK(aclrtCreateStream(&default_streamsi.stream)); if (OptionsManager::CheckQueueEnable()) { - default_streamsi.repo->InitRepo(device_id, default_streamsi.stream); + default_streamsi.repo->InitRepo(device_id); } // Initializes secondary streams secondary_streams[device_id].device_index = device_id; auto& secondary_streamsi = secondary_streams[device_id]; C10_NPU_CHECK(aclrtCreateStream(&secondary_streamsi.stream)); - if (OptionsManager::CheckQueueEnable()) { - secondary_streamsi.repo->InitRepo(device_id, secondary_streamsi.stream); - } } static void initDeviceStreamState(DeviceIndex device_index) { @@ -191,10 +188,6 @@ static void initDeviceStreamState(DeviceIndex device_index) { npu_streami.device_index = device_index; C10_NPU_CHECK(aclrtCreateStream(&npu_streami.stream)); - - if (OptionsManager::CheckQueueEnable()) { - npu_streami.repo->InitRepo(device_index, npu_streami.stream); - } } } @@ -357,8 +350,7 @@ NPUStatus emptyAllNPUStream() { NPUStatus ret; for (auto i = decltype(num_npus){0}; i < num_npus; ++i) { auto& default_streamsi = default_streams[i]; - auto& secondary_streamsi = secondary_streams[i]; - if (default_streamsi.stream == nullptr && secondary_streamsi.stream == nullptr) { + if (default_streamsi.stream == nullptr) { continue; } NPUGuard device_guard{i}; @@ -368,29 +360,6 @@ NPUStatus emptyAllNPUStream() { return ret; } } - if (secondary_streamsi.stream != nullptr && secondary_streamsi.repo->CheckInit()) { - ret = secondary_streamsi.repo->MakeSureQueueEmpty(); - if (ret != SUCCESS) { - return ret; - } - } - - } - - for (auto i = decltype(num_npus){0}; i < num_npus; ++i) { - for (auto j = decltype(kStreamsPerPool){0}; j < kStreamsPerPool; ++j) { - auto& npu_streamj = npu_streams[i][j]; - if (npu_streamj.stream == nullptr) { - continue; - } - NPUGuard device_guard{i}; - if (npu_streamj.repo->CheckInit()) { - ret = npu_streamj.repo->MakeSureQueueEmpty(); - if (ret != SUCCESS) { - return ret; - } - } - } } return SUCCESS; } @@ -415,10 +384,13 @@ void enCurrentNPUStream( device_index = current_device(); } check_npu(device_index); - current_streams[device_index]->repo->Enqueue(cur_paras, needClearVec); - if (current_streams[device_index]->repo->GetStatus() == RepoStatus::INIT) { - current_streams[device_index]->repo->MakeSureQueueEmpty(); - current_streams[device_index]->repo->ChangeStatus(RepoStatus::INIT, RepoStatus::RUN); + + c10::npu::queue::QueueParas* queueParam = static_cast(cur_paras); + queueParam->paramStream = current_streams[device_index]->stream; + default_streams[device_index].repo->Enqueue(cur_paras, needClearVec); + if (default_streams[device_index].repo->GetStatus() == RepoStatus::INIT) { + default_streams[device_index].repo->MakeSureQueueEmpty(); + default_streams[device_index].repo->ChangeStatus(RepoStatus::INIT, RepoStatus::RUN); } } diff --git a/pytorch1.5.0/src/c10/npu/NPUStream.h b/pytorch1.5.0/src/c10/npu/NPUStream.h index 702303120e9..f9de9b81d34 100644 --- a/pytorch1.5.0/src/c10/npu/NPUStream.h +++ b/pytorch1.5.0/src/c10/npu/NPUStream.h @@ -123,6 +123,8 @@ CAFFE2_API NPUStream getCurrentSecondaryStream(DeviceIndex device_index = -1); CAFFE2_API aclrtStream getCurrentNPUStreamNoWait(DeviceIndex device_index = -1); +CAFFE2_API NPUStatus emptyAllNPUStream(); + CAFFE2_API void npuSynchronizeDevice(); CAFFE2_API void enCurrentNPUStream( diff --git a/pytorch1.5.0/src/c10/npu/interface/AsyncTaskQueueInterface.cpp b/pytorch1.5.0/src/c10/npu/interface/AsyncTaskQueueInterface.cpp index 6e3bd9df171..c52c09e7541 100644 --- a/pytorch1.5.0/src/c10/npu/interface/AsyncTaskQueueInterface.cpp +++ b/pytorch1.5.0/src/c10/npu/interface/AsyncTaskQueueInterface.cpp @@ -15,7 +15,7 @@ #include "AsyncTaskQueueInterface.h" #include "c10/npu/OptionsManager.h" - +#include "c10/npu/NPUEventManager.h" namespace c10 { namespace npu { namespace queue { @@ -30,9 +30,9 @@ void CopyParas::Copy(CopyParas& other) { } } -void EventParas::Copy(EventParas& other) -{ +void EventParas::Copy(EventParas& other) { this->event = other.event; + this->eventAllocatorType = other.eventAllocatorType; } class AsyncCopyTask { @@ -49,10 +49,12 @@ private: class EventTask { public: - explicit EventTask(aclrtEvent event); + explicit EventTask(aclrtEvent event, EventAllocatorType allocatorType = RESERVED) : + eventParam_(event, allocatorType) {}; ~EventTask() = default; void LaunchRecordTask(at::npu::NPUStream npuStream, SmallVector& needClearVec); - + void LaunchWaitTask(at::npu::NPUStream npuStream); + void LaunchLazyDestroyTask(); private: EventParas eventParam_; }; @@ -132,11 +134,6 @@ aclError LaunchAsyncCopyTask(void* dst, size_t dstLen, void* src, size_t srcLen, return ACL_ERROR_NONE; } -EventTask::EventTask(aclrtEvent event) -{ - eventParam_.event = event; -} - void EventTask::LaunchRecordTask(at::npu::NPUStream npuStream, SmallVector& needClearVec) { if (c10::npu::OptionsManager::CheckQueueEnable()) { @@ -152,10 +149,65 @@ void EventTask::LaunchRecordTask(at::npu::NPUStream npuStream, SmallVector& needClearVec) -{ +aclError HostAllocatorLaunchRecordEventTask(aclrtEvent event, + at::npu::NPUStream npuStream, + SmallVector& needClearVec) { + EventTask recordTask(event, HOST_ALLOCATOR_EVENT); + recordTask.LaunchRecordTask(npuStream, needClearVec); + return ACL_ERROR_NONE; +} + +aclError NpuAllocatorLaunchRecordEventTask(aclrtEvent event, + at::npu::NPUStream npuStream) { + EventTask recordTask(event, NPU_ALLOCATOR_EVENT); + SmallVector needClearVec; + recordTask.LaunchRecordTask(npuStream, needClearVec); + needClearVec.clear(); + return ACL_ERROR_NONE; +} + +aclError LaunchRecordEventTask(aclrtEvent event, at::npu::NPUStream npuStream) { EventTask recordTask(event); + SmallVector needClearVec; recordTask.LaunchRecordTask(npuStream, needClearVec); + needClearVec.clear(); + return ACL_ERROR_NONE; +} + +void EventTask::LaunchWaitTask(at::npu::NPUStream npuStream) { + if (c10::npu::OptionsManager::CheckQueueEnable()) { + at::npu::NPUStream currentStream = c10::npu::getCurrentNPUStream(); + c10::npu::setCurrentNPUStream(npuStream); + QueueParas params(WAIT_EVENT, sizeof(EventParas), &eventParam_); + SmallVector needClearVec; + c10::npu::enCurrentNPUStream(¶ms, needClearVec); + c10::npu::setCurrentNPUStream(currentStream); + needClearVec.clear(); + } else { + AT_NPU_CHECK(aclrtStreamWaitEvent(npuStream, eventParam_.event)); + } +} + +aclError LaunchWaitEventTask(aclrtEvent event, at::npu::NPUStream npuStream) { + EventTask waitTask(event); + waitTask.LaunchWaitTask(npuStream); + return ACL_ERROR_NONE; +} + +void EventTask::LaunchLazyDestroyTask() { + if (c10::npu::OptionsManager::CheckQueueEnable()) { + QueueParas params(LAZY_DESTROY_EVENT, sizeof(EventParas), &eventParam_); + SmallVector needClearVec; + c10::npu::enCurrentNPUStream(¶ms, needClearVec); + needClearVec.clear(); + } else { + AT_NPU_CHECK(c10::npu::NPUEventManager::GetInstance().LazyDestroy(eventParam_.event)); + } +} + +aclError LaunchLazyDestroyEventTask(aclrtEvent event) { + EventTask lazyDestroyTask(event); + lazyDestroyTask.LaunchLazyDestroyTask(); return ACL_ERROR_NONE; } } // namespace queue diff --git a/pytorch1.5.0/src/c10/npu/interface/AsyncTaskQueueInterface.h b/pytorch1.5.0/src/c10/npu/interface/AsyncTaskQueueInterface.h index 58801ddb9b3..09e87df101e 100644 --- a/pytorch1.5.0/src/c10/npu/interface/AsyncTaskQueueInterface.h +++ b/pytorch1.5.0/src/c10/npu/interface/AsyncTaskQueueInterface.h @@ -33,9 +33,18 @@ struct CopyParas { void Copy(CopyParas& other); }; +enum EventAllocatorType { + HOST_ALLOCATOR_EVENT = 1, + NPU_ALLOCATOR_EVENT = 2, + RESERVED = -1, +}; + struct EventParas { + explicit EventParas(aclrtEvent aclEvent, EventAllocatorType allocatorType) : + event(aclEvent), eventAllocatorType(allocatorType) {} aclrtEvent event = nullptr; void Copy(EventParas& other); + EventAllocatorType eventAllocatorType = RESERVED; }; enum QueueParamType { @@ -43,10 +52,13 @@ enum QueueParamType { ASYNC_MEMCPY = 2, ASYNC_MEMCPY_EX = 3, RECORD_EVENT = 4, + WAIT_EVENT = 5, + LAZY_DESTROY_EVENT = 6, }; struct QueueParas { QueueParas(QueueParamType type, size_t len, void *val) : paramType(type), paramLen(len), paramVal(val) {} + aclrtStream paramStream = nullptr; QueueParamType paramType = COMPILE_AND_EXECUTE; size_t paramLen = 0; void* paramVal = nullptr; @@ -57,7 +69,18 @@ aclError LaunchAsyncCopyTask(void* dst, size_t dstLen, void* src, size_t srcLen, aclError LaunchAsyncCopyTask(void* dst, size_t dstLen, void* src, size_t srcLen, aclrtMemcpyKind kind, Storage& st, bool isPinMem); -aclError LaunchRecordEventTask(aclrtEvent event, at::npu::NPUStream npuStream, SmallVector& needClearVec); +aclError HostAllocatorLaunchRecordEventTask(aclrtEvent event, + at::npu::NPUStream npuStream, + SmallVector& needClearVec); + +aclError NpuAllocatorLaunchRecordEventTask(aclrtEvent event, + at::npu::NPUStream npuStream); + +aclError LaunchRecordEventTask(aclrtEvent event, at::npu::NPUStream npuStream); + +aclError LaunchWaitEventTask(aclrtEvent event, at::npu::NPUStream npuStream); + +aclError LaunchLazyDestroyEventTask(aclrtEvent event); } // namespace queue } // namespace npu } // namespace c10 diff --git a/pytorch1.5.0/test/test_npu/test_npu_tools/test_npu_profiler.py b/pytorch1.5.0/test/test_npu/test_npu_tools/test_npu_profiler.py index d4f18687068..0681e8cdc24 100644 --- a/pytorch1.5.0/test/test_npu/test_npu_tools/test_npu_profiler.py +++ b/pytorch1.5.0/test/test_npu/test_npu_tools/test_npu_profiler.py @@ -85,18 +85,90 @@ class TestCannProfiler(TestCase): optimizer.zero_grad() optimizer.step() - def _test_e2e_ops(self, *args, **kwargs): + def _test_cann_ops(self, *args, **kwargs): config = torch.npu.profileConfig(**kwargs) torch.npu.prof_start(config.NpuEventConfig, config.AiCoreMetricsConfig) self._run_ops() torch.npu.prof_stop() - def _test_e2e_model(self, *args, **kwargs): + def _test_cann_model(self, *args, **kwargs): config = torch.npu.profileConfig(**kwargs) torch.npu.prof_start(config.NpuEventConfig, config.AiCoreMetricsConfig) self._run_small_model() torch.npu.prof_stop() + def test_with_ops(self): + for events in TestCannProfiler.enevtTypeResults: + for i in range(5): + self._test_cann_ops(**events, aiCoreMetricsType=i) + + def test_with_small_model(self): + for events in TestCannProfiler.enevtTypeResults: + for i in range(5): + self._test_cann_model(**events, aiCoreMetricsType=i) + + +class TestE2EProfiler(TestCase): + enevtTypeResults = [] + results_path = "./results" + + @classmethod + def setUpClass(cls): + if not os.path.exists(TestE2EProfiler.results_path): + os.makedirs(TestE2EProfiler.results_path) + tensor = torch.rand(2,3).npu() + + enevtTypes = [{"ACL_PROF_ACL_API":False}, {"ACL_PROF_TASK_TIME":False}, + {"ACL_PROF_AICORE_METRICS":False}, {"ACL_PROF_AICPU":False}, + {"ACL_PROF_L2CACHE":False}, {"ACL_PROF_HCCL_TRACE":False}, + {"ACL_PROF_TRAINING_TRACE":False}] + + enevtTypeCombinations = list(combinations(enevtTypes, 1)) + list(combinations(enevtTypes, 2)) + \ + list(combinations(enevtTypes, 3)) + list(combinations(enevtTypes, 4)) + \ + list(combinations(enevtTypes, 5)) + list(combinations(enevtTypes, 6)) + for events in enevtTypeCombinations: + temp_events = {} + for event in events: + temp_events.update(event) + TestE2EProfiler.enevtTypeResults.append(temp_events) + + @classmethod + def tearDownClass(cls): + if os.path.exists(TestCannProfiler.results_path): + shutil.rmtree(TestCannProfiler.results_path) + + def _run_ops(self): + input_1 = torch.rand(10, 10).npu() + input_2 = torch.rand(10, 10).npu() + out = input_1*input_2 + + def _run_small_model(self): + input_shape = (4, 3, 24, 24) + out_shape = (4, 12, 24, 24) + device = "npu" + model = SmallModel(input_shape[1], out_shape[1]).to(device) + criterion = torch.nn.MSELoss() + optimizer = torch.optim.SGD(model.parameters(), lr=0.0001) + for i in range(10): + inputs = torch.rand(input_shape).to(device) + target = torch.rand(out_shape).reshape(out_shape[0], -1).to(device) + output = model(inputs) + loss = criterion(output, target) + loss.backward() + optimizer.zero_grad() + optimizer.step() + + def _test_e2e_ops(self, *args, **kwargs): + config = torch.npu.profileConfig(**kwargs) + with torch.npu.profile(TestE2EProfiler.results_path, True, config): + self._run_ops() + + + def _test_e2e_model(self, *args, **kwargs): + config = torch.npu.profileConfig(**kwargs) + with torch.npu.profile(TestE2EProfiler.results_path, True, config): + self._run_small_model() + def test_with_ops(self): for events in TestCannProfiler.enevtTypeResults: for i in range(5): -- Gitee From c741840b09b4c4955284afb5b750c30997a5e23a Mon Sep 17 00:00:00 2001 From: pengzirong Date: Tue, 21 Dec 2021 12:00:51 +0800 Subject: [PATCH 28/55] =?UTF-8?q?=E5=90=8C=E6=AD=A51.5.0=2095f1549a1eadb6c?= =?UTF-8?q?7c7e55033aaaa75810b5ce3aa=20=20=20=20=20!2896=20UT=E5=85=A8?= =?UTF-8?q?=E9=87=8F=E6=B5=8B=E8=AF=95=E5=8A=A0=E5=85=A5test=5Fgraph=5Fmod?= =?UTF-8?q?e=20test=5Fdynamic=5Fops=E7=AD=89=E7=9B=AE=E5=BD=95=E4=B8=8B?= =?UTF-8?q?=E7=9A=84=E7=94=A8=E4=BE=8B=20=20=20=20=20Merge=20pull=20reques?= =?UTF-8?q?t=20!2896=20from=20=E9=BE=9A=E4=BF=8A=E5=BC=BA/master=20?= =?UTF-8?q?=E5=90=8C=E6=AD=A51.8.1=20de23fbe927c2cf47ea37da06a001735b3cc65?= =?UTF-8?q?904=20=20=20=20=20!2890=201.8.1=20=5F=5FRshift=5F=5F=E7=AE=97?= =?UTF-8?q?=E5=AD=90=E9=80=82=E9=85=8D=20=20=20=20=20Merge=20pull=20reques?= =?UTF-8?q?t=20!2890=20from=20=E5=AD=99=E5=B9=B8/1.8=5FRshift?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- patch/pytorch1.5.0_npu.patch | 232 +++++++++--------- patch/pytorch1.8.1_npu.patch | 184 +++++++------- pytorch1.5.0/access_control_test.py | 4 + pytorch1.5.0/test/test_npu/graph_utils.py | 11 + pytorch1.5.0/test/test_npu/run_tests.py | 34 ++- .../test_dynamic_ops/common_device_type.py | 4 +- .../test_npu/test_dynamic_ops/common_utils.py | 4 +- .../test_npu/test_dynamic_ops/util_test.py | 4 +- .../test_network_ops/common_device_type.py | 2 +- .../test_npu/test_network_ops/common_utils.py | 2 +- .../test_npu/test_network_ops/util_test.py | 2 +- .../test_npu/test_npu_tools/common_utils.py | 2 +- .../test_trans_contiguous/__init__.py | 0 .../common_device_type.py | 2 +- .../test_trans_contiguous/common_utils.py | 2 +- .../test_trans_contiguous/util_test.py | 2 +- pytorch1.5.0/test/test_npu/util_test.py | 18 +- .../ATen/native/npu/__Rshift__KernelNpu.cpp | 76 ++++++ .../test_network_ops/test___rshift__.py | 68 +++++ 19 files changed, 411 insertions(+), 242 deletions(-) create mode 100644 pytorch1.5.0/test/test_npu/graph_utils.py create mode 100644 pytorch1.5.0/test/test_npu/test_trans_contiguous/__init__.py create mode 100644 pytorch1.8.1/src/aten/src/ATen/native/npu/__Rshift__KernelNpu.cpp create mode 100644 pytorch1.8.1/test/test_npu/test_network_ops/test___rshift__.py diff --git a/patch/pytorch1.5.0_npu.patch b/patch/pytorch1.5.0_npu.patch index 168a35fd587..a9f10255f5a 100644 --- a/patch/pytorch1.5.0_npu.patch +++ b/patch/pytorch1.5.0_npu.patch @@ -1,6 +1,6 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/CMakeLists.txt pytorch-develop-150/aten/CMakeLists.txt --- pytorch-v1.5.0/aten/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/CMakeLists.txt 2021-12-20 23:24:40.275378243 +0800 ++++ pytorch-develop-150/aten/CMakeLists.txt 2021-12-21 12:00:44.614901109 +0800 @@ -22,8 +22,10 @@ set(ATen_CPU_INCLUDE) set(ATen_THIRD_PARTY_INCLUDE) @@ -51,7 +51,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_CPU_DEPENDENCY_LIBS ${ATen_CPU_DEPENDENCY_LIBS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/CMakeLists.txt pytorch-develop-150/aten/src/ATen/CMakeLists.txt --- pytorch-v1.5.0/aten/src/ATen/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/CMakeLists.txt 2021-12-20 23:24:40.275378243 +0800 ++++ pytorch-develop-150/aten/src/ATen/CMakeLists.txt 2021-12-21 12:00:44.614901109 +0800 @@ -67,6 +67,9 @@ FILE(GLOB native_quantized_h "native/quantized/*.h" "native/quantized/cpu/*.h") FILE(GLOB native_cpu_h "native/cpu/*.h") @@ -129,7 +129,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_QUANTIZED_SRCS ${ATen_QUANTIZED_SRCS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/core/dispatch/DispatchTable.h pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h --- pytorch-v1.5.0/aten/src/ATen/core/dispatch/DispatchTable.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h 2021-12-20 23:24:40.279378293 +0800 ++++ pytorch-develop-150/aten/src/ATen/core/dispatch/DispatchTable.h 2021-12-21 12:00:44.618901141 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -170,7 +170,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/function_wrapper.py pytorch-develop-150/aten/src/ATen/function_wrapper.py --- pytorch-v1.5.0/aten/src/ATen/function_wrapper.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/function_wrapper.py 2021-12-20 23:24:40.279378293 +0800 ++++ pytorch-develop-150/aten/src/ATen/function_wrapper.py 2021-12-21 12:00:44.618901141 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -354,7 +354,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for option in declaration['options']: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/gen.py pytorch-develop-150/aten/src/ATen/gen.py --- pytorch-v1.5.0/aten/src/ATen/gen.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/gen.py 2021-12-20 23:24:40.279378293 +0800 ++++ pytorch-develop-150/aten/src/ATen/gen.py 2021-12-21 12:00:44.618901141 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -512,7 +512,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= generate_outputs() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/BatchLinearAlgebra.cpp pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp --- pytorch-v1.5.0/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-12-20 23:24:40.279378293 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/BatchLinearAlgebra.cpp 2021-12-21 12:00:44.618901141 +0800 @@ -680,7 +680,7 @@ std::tuple triangular_solve_out(Tensor& result, Tensor& clone_A, const Tensor& self, const Tensor& A, bool upper, bool transpose, bool unitriangular) { @@ -524,7 +524,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return std::tuple(result, clone_A); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/cpu/Activation.cpp pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp --- pytorch-v1.5.0/aten/src/ATen/native/cpu/Activation.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp 2021-12-20 23:24:40.283378342 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/cpu/Activation.cpp 2021-12-21 12:00:44.622901173 +0800 @@ -339,20 +339,20 @@ void hardsigmoid_backward_kernel(TensorIterator& iter) { @@ -552,7 +552,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/Memory.cpp pytorch-develop-150/aten/src/ATen/native/Memory.cpp --- pytorch-v1.5.0/aten/src/ATen/native/Memory.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/Memory.cpp 2021-12-20 23:24:40.283378342 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/Memory.cpp 2021-12-21 12:00:44.622901173 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -614,7 +614,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= detail::computeStorageSize(self.sizes(), self.strides()), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/native_functions.yaml pytorch-develop-150/aten/src/ATen/native/native_functions.yaml --- pytorch-v1.5.0/aten/src/ATen/native/native_functions.yaml 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/native_functions.yaml 2021-12-20 23:24:40.287378391 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/native_functions.yaml 2021-12-21 12:00:44.630901236 +0800 @@ -1,6 +1,5 @@ # See README.md in this directory for more guidance @@ -7169,7 +7169,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S --- pytorch-v1.5.0/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-12-20 23:24:40.303378589 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/quantized/cpu/qnnpack/src/q8gemm/8x8-dq-aarch64-neon.S 2021-12-21 12:00:44.646901363 +0800 @@ -659,14 +659,14 @@ SUB x1, x1, 4 @@ -7195,7 +7195,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CMP x1, 2 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorCompare.cpp pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorCompare.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp 2021-12-20 23:24:40.283378342 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorCompare.cpp 2021-12-21 12:00:44.622901173 +0800 @@ -64,7 +64,7 @@ Tensor isinf(const Tensor &self) { @@ -7207,7 +7207,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return AT_DISPATCH_FLOATING_TYPES_AND_HALF(self.scalar_type(), "isinf", [&]() { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorFactories.cpp pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorFactories.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp 2021-12-20 23:24:40.283378342 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorFactories.cpp 2021-12-21 12:00:44.622901173 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7252,7 +7252,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/TensorProperties.cpp pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp --- pytorch-v1.5.0/aten/src/ATen/native/TensorProperties.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp 2021-12-20 23:24:40.283378342 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/TensorProperties.cpp 2021-12-21 12:00:44.622901173 +0800 @@ -87,6 +87,7 @@ if (self.is_contiguous(memory_format)) { return self; @@ -7263,7 +7263,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= "preserve memory format is unsupported by the contiguous operator"); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native/UpSampleBicubic2d.cpp pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp --- pytorch-v1.5.0/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-12-20 23:24:40.283378342 +0800 ++++ pytorch-develop-150/aten/src/ATen/native/UpSampleBicubic2d.cpp 2021-12-21 12:00:44.622901173 +0800 @@ -26,7 +26,7 @@ const scalar_t* in = &idata[output_y * input_width + output_x]; scalar_t* out = &odata[output_y * output_width + output_x]; @@ -7275,7 +7275,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= out += output_width * output_height; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/native_parse.py pytorch-develop-150/aten/src/ATen/native_parse.py --- pytorch-v1.5.0/aten/src/ATen/native_parse.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/native_parse.py 2021-12-20 23:24:40.311378688 +0800 ++++ pytorch-develop-150/aten/src/ATen/native_parse.py 2021-12-21 12:00:44.654901427 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -7313,7 +7313,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= msg = '''Exception raised in processing function: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/preprocess_declarations.py pytorch-develop-150/aten/src/ATen/preprocess_declarations.py --- pytorch-v1.5.0/aten/src/ATen/preprocess_declarations.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/preprocess_declarations.py 2021-12-20 23:24:40.311378688 +0800 ++++ pytorch-develop-150/aten/src/ATen/preprocess_declarations.py 2021-12-21 12:00:44.654901427 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -7345,7 +7345,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/templates/TensorBody.h pytorch-develop-150/aten/src/ATen/templates/TensorBody.h --- pytorch-v1.5.0/aten/src/ATen/templates/TensorBody.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/templates/TensorBody.h 2021-12-20 23:24:40.311378688 +0800 ++++ pytorch-develop-150/aten/src/ATen/templates/TensorBody.h 2021-12-21 12:00:44.654901427 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7378,7 +7378,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/ATen/templates/TensorMethods.h pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h --- pytorch-v1.5.0/aten/src/ATen/templates/TensorMethods.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h 2021-12-20 23:24:40.311378688 +0800 ++++ pytorch-develop-150/aten/src/ATen/templates/TensorMethods.h 2021-12-21 12:00:44.654901427 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7412,7 +7412,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/CMakeLists.txt pytorch-develop-150/aten/src/TH/CMakeLists.txt --- pytorch-v1.5.0/aten/src/TH/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/CMakeLists.txt 2021-12-20 23:24:40.311378688 +0800 ++++ pytorch-develop-150/aten/src/TH/CMakeLists.txt 2021-12-21 12:00:44.658901459 +0800 @@ -48,6 +48,11 @@ ${CMAKE_CURRENT_SOURCE_DIR} PARENT_SCOPE) @@ -7427,7 +7427,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/generic/THStorage.cpp pytorch-develop-150/aten/src/TH/generic/THStorage.cpp --- pytorch-v1.5.0/aten/src/TH/generic/THStorage.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/generic/THStorage.cpp 2021-12-20 23:24:40.311378688 +0800 ++++ pytorch-develop-150/aten/src/TH/generic/THStorage.cpp 2021-12-21 12:00:44.658901459 +0800 @@ -1,9 +1,32 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7536,7 +7536,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/aten/src/TH/generic/THStorage.h pytorch-develop-150/aten/src/TH/generic/THStorage.h --- pytorch-v1.5.0/aten/src/TH/generic/THStorage.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/aten/src/TH/generic/THStorage.h 2021-12-20 23:24:40.311378688 +0800 ++++ pytorch-develop-150/aten/src/TH/generic/THStorage.h 2021-12-21 12:00:44.658901459 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7575,7 +7575,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/CMakeLists.txt pytorch-develop-150/c10/CMakeLists.txt --- pytorch-v1.5.0/c10/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/CMakeLists.txt 2021-12-20 23:24:40.319378786 +0800 ++++ pytorch-develop-150/c10/CMakeLists.txt 2021-12-21 12:00:44.666901522 +0800 @@ -63,6 +63,14 @@ message(STATUS "don't use NUMA") endif() @@ -7604,7 +7604,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # not checked in diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Backend.h pytorch-develop-150/c10/core/Backend.h --- pytorch-v1.5.0/c10/core/Backend.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Backend.h 2021-12-20 23:24:40.319378786 +0800 ++++ pytorch-develop-150/c10/core/Backend.h 2021-12-21 12:00:44.666901522 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7701,7 +7701,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case Backend::CUDA: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Device.cpp pytorch-develop-150/c10/core/Device.cpp --- pytorch-v1.5.0/c10/core/Device.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Device.cpp 2021-12-20 23:24:40.319378786 +0800 ++++ pytorch-develop-150/c10/core/Device.cpp 2021-12-21 12:00:44.666901522 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7741,7 +7741,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= types.begin(), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Device.h pytorch-develop-150/c10/core/Device.h --- pytorch-v1.5.0/c10/core/Device.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Device.h 2021-12-20 23:24:40.319378786 +0800 ++++ pytorch-develop-150/c10/core/Device.h 2021-12-21 12:00:44.666901522 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7776,7 +7776,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return type_ == DeviceType::CPU; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DeviceType.cpp pytorch-develop-150/c10/core/DeviceType.cpp --- pytorch-v1.5.0/c10/core/DeviceType.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DeviceType.cpp 2021-12-20 23:24:40.319378786 +0800 ++++ pytorch-develop-150/c10/core/DeviceType.cpp 2021-12-21 12:00:44.666901522 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7816,7 +7816,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return false; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DeviceType.h pytorch-develop-150/c10/core/DeviceType.h --- pytorch-v1.5.0/c10/core/DeviceType.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DeviceType.h 2021-12-20 23:24:40.319378786 +0800 ++++ pytorch-develop-150/c10/core/DeviceType.h 2021-12-21 12:00:44.666901522 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7859,7 +7859,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= constexpr DeviceType kXLA = DeviceType::XLA; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DispatchKey.cpp pytorch-develop-150/c10/core/DispatchKey.cpp --- pytorch-v1.5.0/c10/core/DispatchKey.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DispatchKey.cpp 2021-12-20 23:24:40.319378786 +0800 ++++ pytorch-develop-150/c10/core/DispatchKey.cpp 2021-12-21 12:00:44.666901522 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7891,7 +7891,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::SparseCPUTensorId: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/DispatchKey.h pytorch-develop-150/c10/core/DispatchKey.h --- pytorch-v1.5.0/c10/core/DispatchKey.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/DispatchKey.h 2021-12-20 23:24:40.319378786 +0800 ++++ pytorch-develop-150/c10/core/DispatchKey.h 2021-12-21 12:00:44.666901522 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7923,7 +7923,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/Storage.h pytorch-develop-150/c10/core/Storage.h --- pytorch-v1.5.0/c10/core/Storage.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/Storage.h 2021-12-20 23:24:40.319378786 +0800 ++++ pytorch-develop-150/c10/core/Storage.h 2021-12-21 12:00:44.666901522 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -7957,7 +7957,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/StorageImpl.cpp pytorch-develop-150/c10/core/StorageImpl.cpp --- pytorch-v1.5.0/c10/core/StorageImpl.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/StorageImpl.cpp 2021-12-20 23:24:40.319378786 +0800 ++++ pytorch-develop-150/c10/core/StorageImpl.cpp 2021-12-21 12:00:44.666901522 +0800 @@ -1 +1,18 @@ #include + @@ -7979,7 +7979,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +} // namespace c10 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/StorageImpl.h pytorch-develop-150/c10/core/StorageImpl.h --- pytorch-v1.5.0/c10/core/StorageImpl.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/StorageImpl.h 2021-12-20 23:24:40.319378786 +0800 ++++ pytorch-develop-150/c10/core/StorageImpl.h 2021-12-21 12:00:44.666901522 +0800 @@ -1,11 +1,55 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8106,7 +8106,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/TensorImpl.h pytorch-develop-150/c10/core/TensorImpl.h --- pytorch-v1.5.0/c10/core/TensorImpl.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/TensorImpl.h 2021-12-20 23:24:40.323378836 +0800 ++++ pytorch-develop-150/c10/core/TensorImpl.h 2021-12-21 12:00:44.666901522 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8174,7 +8174,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/core/TensorOptions.h pytorch-develop-150/c10/core/TensorOptions.h --- pytorch-v1.5.0/c10/core/TensorOptions.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/core/TensorOptions.h 2021-12-20 23:24:40.323378836 +0800 ++++ pytorch-develop-150/c10/core/TensorOptions.h 2021-12-21 12:00:44.666901522 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8215,7 +8215,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } else if (tid == DispatchKey::HIPTensorId) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/cuda/CMakeLists.txt pytorch-develop-150/c10/cuda/CMakeLists.txt --- pytorch-v1.5.0/c10/cuda/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/cuda/CMakeLists.txt 2021-12-20 23:24:40.323378836 +0800 ++++ pytorch-develop-150/c10/cuda/CMakeLists.txt 2021-12-21 12:00:44.670901554 +0800 @@ -24,6 +24,7 @@ CUDACachingAllocator.cpp impl/CUDAGuardImpl.cpp @@ -8234,7 +8234,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= torch_cuda_based_add_library(c10_cuda ${C10_CUDA_SRCS} ${C10_CUDA_HEADERS}) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/c10/macros/Export.h pytorch-develop-150/c10/macros/Export.h --- pytorch-v1.5.0/c10/macros/Export.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/c10/macros/Export.h 2021-12-20 23:24:40.323378836 +0800 ++++ pytorch-develop-150/c10/macros/Export.h 2021-12-21 12:00:44.670901554 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -8361,7 +8361,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/caffe2/CMakeLists.txt pytorch-develop-150/caffe2/CMakeLists.txt --- pytorch-v1.5.0/caffe2/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/caffe2/CMakeLists.txt 2021-12-20 23:24:40.327378885 +0800 ++++ pytorch-develop-150/caffe2/CMakeLists.txt 2021-12-21 12:00:44.674901586 +0800 @@ -32,6 +32,7 @@ # Add source, includes, and libs to lists list(APPEND Caffe2_CPU_SRCS ${ATen_CPU_SRCS}) @@ -8516,7 +8516,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Call again since Caffe2_HIP_INCLUDE is extended with ATen include dirs. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/.clang-format pytorch-develop-150/.clang-format --- pytorch-v1.5.0/.clang-format 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/.clang-format 2021-12-20 23:24:40.271378194 +0800 ++++ pytorch-develop-150/.clang-format 2021-12-21 12:00:44.610901077 +0800 @@ -84,5 +84,4 @@ SpacesInSquareBrackets: false Standard: Cpp11 @@ -8527,7 +8527,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/BuildVariables.cmake pytorch-develop-150/cmake/BuildVariables.cmake --- pytorch-v1.5.0/cmake/BuildVariables.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/BuildVariables.cmake 2021-12-20 23:24:40.383379577 +0800 ++++ pytorch-develop-150/cmake/BuildVariables.cmake 2021-12-21 12:00:44.742902125 +0800 @@ -11,6 +11,7 @@ # CMakeLists.txt files under each folder respectively. set(Caffe2_CPU_SRCS) @@ -8554,7 +8554,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # symbols. However, if the lib is whole linked in caffe2 lib, we don't want diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Codegen.cmake pytorch-develop-150/cmake/Codegen.cmake --- pytorch-v1.5.0/cmake/Codegen.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Codegen.cmake 2021-12-20 23:24:40.383379577 +0800 ++++ pytorch-develop-150/cmake/Codegen.cmake 2021-12-21 12:00:44.742902125 +0800 @@ -191,13 +191,14 @@ file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt generated_cpp) file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt-cuda cuda_generated_cpp) @@ -8585,7 +8585,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endif() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Dependencies.cmake pytorch-develop-150/cmake/Dependencies.cmake --- pytorch-v1.5.0/cmake/Dependencies.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Dependencies.cmake 2021-12-20 23:24:40.383379577 +0800 ++++ pytorch-develop-150/cmake/Dependencies.cmake 2021-12-21 12:00:44.742902125 +0800 @@ -1509,6 +1509,13 @@ ENDIF(NOT C_HAS_THREAD) endif() @@ -8602,7 +8602,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/Summary.cmake pytorch-develop-150/cmake/Summary.cmake --- pytorch-v1.5.0/cmake/Summary.cmake 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/Summary.cmake 2021-12-20 23:24:40.383379577 +0800 ++++ pytorch-develop-150/cmake/Summary.cmake 2021-12-21 12:00:44.742902125 +0800 @@ -134,6 +134,7 @@ if(NOT "${SELECTED_OP_LIST}" STREQUAL "") message(STATUS " SELECTED_OP_LIST : ${SELECTED_OP_LIST}") @@ -8613,7 +8613,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endfunction() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/cmake/TorchConfig.cmake.in pytorch-develop-150/cmake/TorchConfig.cmake.in --- pytorch-v1.5.0/cmake/TorchConfig.cmake.in 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/cmake/TorchConfig.cmake.in 2021-12-20 23:24:40.383379577 +0800 ++++ pytorch-develop-150/cmake/TorchConfig.cmake.in 2021-12-21 12:00:44.742902125 +0800 @@ -112,6 +112,11 @@ list(APPEND TORCH_LIBRARIES ${TORCH_CUDA_LIBRARIES}) endif() @@ -8628,7 +8628,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(TORCH_CXX_FLAGS "-D_GLIBCXX_USE_CXX11_ABI=@GLIBCXX_USE_CXX11_ABI@") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/CMakeLists.txt pytorch-develop-150/CMakeLists.txt --- pytorch-v1.5.0/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/CMakeLists.txt 2021-12-20 23:24:40.271378194 +0800 ++++ pytorch-develop-150/CMakeLists.txt 2021-12-21 12:00:44.610901077 +0800 @@ -205,6 +205,10 @@ option(USE_TBB "Use TBB" OFF) option(ONNX_ML "Enable traditional ONNX ML API." ON) @@ -8695,7 +8695,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-missing-braces") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/CONTRIBUTING.zh.md pytorch-develop-150/CONTRIBUTING.zh.md --- pytorch-v1.5.0/CONTRIBUTING.zh.md 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-150/CONTRIBUTING.zh.md 2021-12-20 23:24:40.271378194 +0800 ++++ pytorch-develop-150/CONTRIBUTING.zh.md 2021-12-21 12:00:44.610901077 +0800 @@ -0,0 +1,228 @@ +# PyTorch贡献指南 +- [贡献者许可协议](#贡献者许可协议.md) @@ -8927,7 +8927,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/.dockerignore pytorch-develop-150/.dockerignore --- pytorch-v1.5.0/.dockerignore 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/.dockerignore 2021-12-20 23:24:40.271378194 +0800 ++++ pytorch-develop-150/.dockerignore 2021-12-21 12:00:44.610901077 +0800 @@ -1,257 +1 @@ -# READ THIS BEFORE YOU REFACTOR ME -# @@ -9203,7 +9203,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/requirements.txt pytorch-develop-150/requirements.txt --- pytorch-v1.5.0/requirements.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/requirements.txt 2021-12-20 23:24:40.271378194 +0800 ++++ pytorch-develop-150/requirements.txt 2021-12-21 12:00:44.610901077 +0800 @@ -4,4 +4,13 @@ requests setuptools @@ -9223,7 +9223,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/setup.py pytorch-develop-150/setup.py --- pytorch-v1.5.0/setup.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/setup.py 2021-12-20 23:24:40.271378194 +0800 ++++ pytorch-develop-150/setup.py 2021-12-21 12:00:44.610901077 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -9326,7 +9326,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= 'python/serialized_test/data/operator_test/*.zip', diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/test/distributed/test_c10d.py pytorch-develop-150/test/distributed/test_c10d.py --- pytorch-v1.5.0/test/distributed/test_c10d.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/test/distributed/test_c10d.py 2021-12-20 23:24:40.395379725 +0800 ++++ pytorch-develop-150/test/distributed/test_c10d.py 2021-12-21 12:00:44.758902252 +0800 @@ -3049,8 +3049,8 @@ model = self._create_mixed_precision_model() reducer = self._create_reducer_for_models([model]) @@ -9340,7 +9340,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Check that the grad of fc3 is not set. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/test/run_test.py pytorch-develop-150/test/run_test.py --- pytorch-v1.5.0/test/run_test.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/test/run_test.py 2021-12-20 23:24:40.399379774 +0800 ++++ pytorch-develop-150/test/run_test.py 2021-12-21 12:00:44.762902284 +0800 @@ -11,6 +11,8 @@ import subprocess import sys @@ -9510,7 +9510,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= shell(['coverage', 'html']) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/test/test_autograd.py pytorch-develop-150/test/test_autograd.py --- pytorch-v1.5.0/test/test_autograd.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/test/test_autograd.py 2021-12-20 23:24:40.399379774 +0800 ++++ pytorch-develop-150/test/test_autograd.py 2021-12-21 12:00:44.762902284 +0800 @@ -24,7 +24,7 @@ from torch.autograd.function import once_differentiable from torch.autograd.profiler import (profile, format_time, EventList, @@ -9541,7 +9541,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/test/test_nn.py pytorch-develop-150/test/test_nn.py --- pytorch-v1.5.0/test/test_nn.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/test/test_nn.py 2021-12-20 23:24:40.399379774 +0800 ++++ pytorch-develop-150/test/test_nn.py 2021-12-21 12:00:44.766902316 +0800 @@ -3535,14 +3535,17 @@ # earlier versions or no versions, it should provide default value of 0. bn = nn.BatchNorm2d(3) @@ -9564,7 +9564,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= @unittest.skipIf(not PY3, 'Python 2.7 generates cyclic trash') diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/test/test_torch.py pytorch-develop-150/test/test_torch.py --- pytorch-v1.5.0/test/test_torch.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/test/test_torch.py 2021-12-20 23:24:40.403379824 +0800 ++++ pytorch-develop-150/test/test_torch.py 2021-12-21 12:00:44.782902443 +0800 @@ -4087,6 +4087,9 @@ def test_print(self): default_type = torch.Tensor().type() @@ -9613,7 +9613,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= @onlyCPU diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/test/test_utils.py pytorch-develop-150/test/test_utils.py --- pytorch-v1.5.0/test/test_utils.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/test/test_utils.py 2021-12-20 23:24:40.403379824 +0800 ++++ pytorch-develop-150/test/test_utils.py 2021-12-21 12:00:44.782902443 +0800 @@ -6,6 +6,7 @@ import random import tempfile @@ -9632,7 +9632,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= load_tests = load_tests diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/derivatives.yaml pytorch-develop-150/tools/autograd/derivatives.yaml --- pytorch-v1.5.0/tools/autograd/derivatives.yaml 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/derivatives.yaml 2021-12-20 23:24:40.979386934 +0800 ++++ pytorch-develop-150/tools/autograd/derivatives.yaml 2021-12-21 12:00:45.318906694 +0800 @@ -107,6 +107,10 @@ # # NB: The parameter names here MUST be consistent with the parameter names @@ -9757,7 +9757,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/dump_utils.py pytorch-develop-150/tools/autograd/dump_utils.py --- pytorch-v1.5.0/tools/autograd/dump_utils.py 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/dump_utils.py 2021-12-20 23:24:40.979386934 +0800 ++++ pytorch-develop-150/tools/autograd/dump_utils.py 2021-12-21 12:00:45.318906694 +0800 @@ -0,0 +1,312 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# All rights reserved. @@ -10073,7 +10073,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + return prepare_to_check_overflow, overflow_dump_inputs diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_autograd_functions.py pytorch-develop-150/tools/autograd/gen_autograd_functions.py --- pytorch-v1.5.0/tools/autograd/gen_autograd_functions.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_autograd_functions.py 2021-12-20 23:24:40.979386934 +0800 ++++ pytorch-develop-150/tools/autograd/gen_autograd_functions.py 2021-12-21 12:00:45.318906694 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -10279,7 +10279,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_python_functions.py pytorch-develop-150/tools/autograd/gen_python_functions.py --- pytorch-v1.5.0/tools/autograd/gen_python_functions.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_python_functions.py 2021-12-20 23:24:40.979386934 +0800 ++++ pytorch-develop-150/tools/autograd/gen_python_functions.py 2021-12-21 12:00:45.318906694 +0800 @@ -1,3 +1,20 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -10321,7 +10321,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= 'value': argname, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/gen_variable_type.py pytorch-develop-150/tools/autograd/gen_variable_type.py --- pytorch-v1.5.0/tools/autograd/gen_variable_type.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/gen_variable_type.py 2021-12-20 23:24:40.979386934 +0800 ++++ pytorch-develop-150/tools/autograd/gen_variable_type.py 2021-12-21 12:00:45.318906694 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2021 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -10502,7 +10502,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/Functions.cpp pytorch-develop-150/tools/autograd/templates/Functions.cpp --- pytorch-v1.5.0/tools/autograd/templates/Functions.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/Functions.cpp 2021-12-20 23:24:40.979386934 +0800 ++++ pytorch-develop-150/tools/autograd/templates/Functions.cpp 2021-12-21 12:00:45.318906694 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2021 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10583,7 +10583,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto sparse = sparse_.coalesce(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/python_torch_functions.cpp pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp --- pytorch-v1.5.0/tools/autograd/templates/python_torch_functions.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp 2021-12-20 23:24:40.979386934 +0800 ++++ pytorch-develop-150/tools/autograd/templates/python_torch_functions.cpp 2021-12-21 12:00:45.318906694 +0800 @@ -22,7 +22,7 @@ #include "torch/csrc/autograd/generated/variable_factories.h" #include "torch/csrc/utils/structseq.h" @@ -10667,7 +10667,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/python_variable_methods.cpp pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp --- pytorch-v1.5.0/tools/autograd/templates/python_variable_methods.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp 2021-12-20 23:24:40.979386934 +0800 ++++ pytorch-develop-150/tools/autograd/templates/python_variable_methods.cpp 2021-12-21 12:00:45.318906694 +0800 @@ -15,7 +15,13 @@ #include "torch/csrc/cuda/Stream.h" #include "torch/csrc/cuda/Event.h" @@ -10762,7 +10762,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"has_names", (PyCFunction)THPVariable_has_names, METH_NOARGS, NULL}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/VariableType.cpp pytorch-develop-150/tools/autograd/templates/VariableType.cpp --- pytorch-v1.5.0/tools/autograd/templates/VariableType.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/VariableType.cpp 2021-12-20 23:24:40.979386934 +0800 ++++ pytorch-develop-150/tools/autograd/templates/VariableType.cpp 2021-12-21 12:00:45.318906694 +0800 @@ -1,7 +1,29 @@ +// Copyright (c) 2021 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10795,7 +10795,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/autograd/templates/VariableType.h pytorch-develop-150/tools/autograd/templates/VariableType.h --- pytorch-v1.5.0/tools/autograd/templates/VariableType.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/autograd/templates/VariableType.h 2021-12-20 23:24:40.979386934 +0800 ++++ pytorch-develop-150/tools/autograd/templates/VariableType.h 2021-12-21 12:00:45.318906694 +0800 @@ -1,3 +1,20 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -10827,7 +10827,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= const at::Tensor & unpack(const Tensor & t, const char * name, int pos); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/tools/build_variables.bzl pytorch-develop-150/tools/build_variables.bzl --- pytorch-v1.5.0/tools/build_variables.bzl 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/tools/build_variables.bzl 2021-12-20 23:24:40.979386934 +0800 ++++ pytorch-develop-150/tools/build_variables.bzl 2021-12-21 12:00:45.318906694 +0800 @@ -46,6 +46,7 @@ "torch/csrc/autograd/functions/utils.cpp", "torch/csrc/autograd/input_buffer.cpp", @@ -10913,7 +10913,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def grad(outputs: _TensorOrTensors, inputs: _TensorOrTensors, grad_outputs: Optional[_TensorOrTensors]=..., retain_graph: Optional[bool]=..., create_graph: bool=..., only_inputs: bool=..., allow_unused: bool=...) -> Tuple[Tensor, ...]: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/autograd/profiler.py pytorch-develop-150/torch/autograd/profiler.py --- pytorch-v1.5.0/torch/autograd/profiler.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/autograd/profiler.py 2021-12-20 23:24:40.983386984 +0800 ++++ pytorch-develop-150/torch/autograd/profiler.py 2021-12-21 12:00:45.318906694 +0800 @@ -1,8 +1,25 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -11390,7 +11390,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ''.join(result) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/CMakeLists.txt pytorch-develop-150/torch/CMakeLists.txt --- pytorch-v1.5.0/torch/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/CMakeLists.txt 2021-12-20 23:24:40.979386934 +0800 ++++ pytorch-develop-150/torch/CMakeLists.txt 2021-12-21 12:00:45.318906694 +0800 @@ -97,6 +97,7 @@ ${TORCH_SRC_DIR}/csrc/tensor/python_tensor.cpp ${TORCH_SRC_DIR}/csrc/utils.cpp @@ -11422,7 +11422,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= endif() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/engine.cpp pytorch-develop-150/torch/csrc/autograd/engine.cpp --- pytorch-v1.5.0/torch/csrc/autograd/engine.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/engine.cpp 2021-12-20 23:24:40.987387033 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/engine.cpp 2021-12-21 12:00:45.326906758 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11574,7 +11574,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto event = c10::Event{c10::DeviceType::CUDA}; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/function.h pytorch-develop-150/torch/csrc/autograd/function.h --- pytorch-v1.5.0/torch/csrc/autograd/function.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/function.h 2021-12-20 23:24:40.987387033 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/function.h 2021-12-21 12:00:45.326906758 +0800 @@ -11,6 +11,7 @@ #include @@ -11596,7 +11596,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // probably operate with names. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/functions/tensor.cpp pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp --- pytorch-v1.5.0/torch/csrc/autograd/functions/tensor.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp 2021-12-20 23:24:40.991387082 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/functions/tensor.cpp 2021-12-21 12:00:45.326906758 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11628,7 +11628,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= /*non_blocking=*/false, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/init.cpp pytorch-develop-150/torch/csrc/autograd/init.cpp --- pytorch-v1.5.0/torch/csrc/autograd/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/init.cpp 2021-12-20 23:24:40.991387082 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/init.cpp 2021-12-21 12:00:45.326906758 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11671,7 +11671,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= m.def("_enable_profiler", enableProfiler); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/input_buffer.cpp pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp --- pytorch-v1.5.0/torch/csrc/autograd/input_buffer.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp 2021-12-20 23:24:40.991387082 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/input_buffer.cpp 2021-12-21 12:00:45.326906758 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11723,7 +11723,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto& old_var = buffer[pos]; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/profiler.cpp pytorch-develop-150/torch/csrc/autograd/profiler.cpp --- pytorch-v1.5.0/torch/csrc/autograd/profiler.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/profiler.cpp 2021-12-20 23:24:40.991387082 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/profiler.cpp 2021-12-21 12:00:45.326906758 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -11963,7 +11963,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CUDAStubs::~CUDAStubs() = default; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/profiler.h pytorch-develop-150/torch/csrc/autograd/profiler.h --- pytorch-v1.5.0/torch/csrc/autograd/profiler.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/profiler.h 2021-12-20 23:24:40.991387082 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/profiler.h 2021-12-21 12:00:45.326906758 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12099,7 +12099,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/python_variable.cpp pytorch-develop-150/torch/csrc/autograd/python_variable.cpp --- pytorch-v1.5.0/torch/csrc/autograd/python_variable.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/python_variable.cpp 2021-12-20 23:24:40.991387082 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/python_variable.cpp 2021-12-21 12:00:45.326906758 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12153,7 +12153,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"is_complex", (getter)THPVariable_is_complex, nullptr, nullptr, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/python_variable_indexing.cpp pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp --- pytorch-v1.5.0/torch/csrc/autograd/python_variable_indexing.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-20 23:24:40.991387082 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-21 12:00:45.326906758 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12184,7 +12184,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= at::Device self_device = self_.device(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/record_function.cpp pytorch-develop-150/torch/csrc/autograd/record_function.cpp --- pytorch-v1.5.0/torch/csrc/autograd/record_function.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/record_function.cpp 2021-12-20 23:24:40.991387082 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/record_function.cpp 2021-12-21 12:00:45.326906758 +0800 @@ -154,6 +154,12 @@ } } @@ -12218,7 +12218,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/record_function.h pytorch-develop-150/torch/csrc/autograd/record_function.h --- pytorch-v1.5.0/torch/csrc/autograd/record_function.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/record_function.h 2021-12-20 23:24:40.991387082 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/record_function.h 2021-12-21 12:00:45.326906758 +0800 @@ -44,6 +44,9 @@ // Default constructor is used with before function called afterwards RecordFunction() {} @@ -12282,7 +12282,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (torch::autograd::profiler::needsInputs()) { \ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/utils/wrap_outputs.h pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h --- pytorch-v1.5.0/torch/csrc/autograd/utils/wrap_outputs.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h 2021-12-20 23:24:40.991387082 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/utils/wrap_outputs.h 2021-12-21 12:00:45.326906758 +0800 @@ -168,6 +168,45 @@ return r.release(); } @@ -12331,7 +12331,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (!r) throw python_error(); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/autograd/VariableTypeManual.cpp pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp --- pytorch-v1.5.0/torch/csrc/autograd/VariableTypeManual.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp 2021-12-20 23:24:40.987387033 +0800 ++++ pytorch-develop-150/torch/csrc/autograd/VariableTypeManual.cpp 2021-12-21 12:00:45.326906758 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12365,7 +12365,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (!t.defined()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/comm.cpp pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/comm.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp 2021-12-20 23:24:40.991387082 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/comm.cpp 2021-12-21 12:00:45.330906789 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12471,7 +12471,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= while (!in_flight.empty()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/init.cpp pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp 2021-12-20 23:24:40.991387082 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/init.cpp 2021-12-21 12:00:45.330906789 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12528,7 +12528,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= .def("is_success", &::c10d::ProcessGroup::Work::isSuccess) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/distributed/c10d/reducer.cpp pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp --- pytorch-v1.5.0/torch/csrc/distributed/c10d/reducer.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp 2021-12-20 23:24:40.991387082 +0800 ++++ pytorch-develop-150/torch/csrc/distributed/c10d/reducer.cpp 2021-12-21 12:00:45.330906789 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12663,7 +12663,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/DynamicTypes.cpp pytorch-develop-150/torch/csrc/DynamicTypes.cpp --- pytorch-v1.5.0/torch/csrc/DynamicTypes.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/DynamicTypes.cpp 2021-12-20 23:24:40.983386984 +0800 ++++ pytorch-develop-150/torch/csrc/DynamicTypes.cpp 2021-12-21 12:00:45.322906726 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12712,7 +12712,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return it->second; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/Generator.cpp pytorch-develop-150/torch/csrc/Generator.cpp --- pytorch-v1.5.0/torch/csrc/Generator.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/Generator.cpp 2021-12-20 23:24:40.983386984 +0800 ++++ pytorch-develop-150/torch/csrc/Generator.cpp 2021-12-21 12:00:45.322906726 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12780,7 +12780,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #endif diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/serialization.cpp pytorch-develop-150/torch/csrc/generic/serialization.cpp --- pytorch-v1.5.0/torch/csrc/generic/serialization.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/serialization.cpp 2021-12-20 23:24:40.991387082 +0800 ++++ pytorch-develop-150/torch/csrc/generic/serialization.cpp 2021-12-21 12:00:45.330906789 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12880,7 +12880,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/Storage.cpp pytorch-develop-150/torch/csrc/generic/Storage.cpp --- pytorch-v1.5.0/torch/csrc/generic/Storage.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/Storage.cpp 2021-12-20 23:24:40.991387082 +0800 ++++ pytorch-develop-150/torch/csrc/generic/Storage.cpp 2021-12-21 12:00:45.330906789 +0800 @@ -1,7 +1,25 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -12959,7 +12959,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for (Py_ssize_t i = 0; i < length; i++) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/generic/StorageMethods.cpp pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp --- pytorch-v1.5.0/torch/csrc/generic/StorageMethods.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp 2021-12-20 23:24:40.991387082 +0800 ++++ pytorch-develop-150/torch/csrc/generic/StorageMethods.cpp 2021-12-21 12:00:45.330906789 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13007,7 +13007,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"_write_file", (PyCFunction)THPStorage_(writeFile), METH_VARARGS, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/Module.cpp pytorch-develop-150/torch/csrc/Module.cpp --- pytorch-v1.5.0/torch/csrc/Module.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/Module.cpp 2021-12-20 23:24:40.983386984 +0800 ++++ pytorch-develop-150/torch/csrc/Module.cpp 2021-12-21 12:00:45.322906726 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13168,7 +13168,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto set_module_attr = [&](const char* name, PyObject* v, bool incref = true) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/tensor/python_tensor.cpp pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp --- pytorch-v1.5.0/torch/csrc/tensor/python_tensor.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp 2021-12-20 23:24:41.003387231 +0800 ++++ pytorch-develop-150/torch/csrc/tensor/python_tensor.cpp 2021-12-21 12:00:45.338906853 +0800 @@ -1,18 +1,35 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13545,7 +13545,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +} // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/init.cpp pytorch-develop-150/torch/csrc/utils/init.cpp --- pytorch-v1.5.0/torch/csrc/utils/init.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/init.cpp 2021-12-20 23:24:41.003387231 +0800 ++++ pytorch-develop-150/torch/csrc/utils/init.cpp 2021-12-21 12:00:45.338906853 +0800 @@ -1,7 +1,13 @@ #include #include @@ -13710,7 +13710,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/init.h pytorch-develop-150/torch/csrc/utils/init.h --- pytorch-v1.5.0/torch/csrc/utils/init.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/init.h 2021-12-20 23:24:41.003387231 +0800 ++++ pytorch-develop-150/torch/csrc/utils/init.h 2021-12-21 12:00:45.338906853 +0800 @@ -8,4 +8,7 @@ void initThroughputBenchmarkBindings(PyObject* module); @@ -13721,7 +13721,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } // namespace torch diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/python_arg_parser.h pytorch-develop-150/torch/csrc/utils/python_arg_parser.h --- pytorch-v1.5.0/torch/csrc/utils/python_arg_parser.h 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/python_arg_parser.h 2021-12-20 23:24:41.003387231 +0800 ++++ pytorch-develop-150/torch/csrc/utils/python_arg_parser.h 2021-12-21 12:00:45.338906853 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13756,7 +13756,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return at::Device(device_str); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_layouts.cpp pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_layouts.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp 2021-12-20 23:24:41.003387231 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_layouts.cpp 2021-12-21 12:00:45.338906853 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13787,7 +13787,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= registerLayoutObject((THPLayout*)strided_layout, at::Backend::QuantizedCPU); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_new.cpp pytorch-develop-150/torch/csrc/utils/tensor_new.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_new.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_new.cpp 2021-12-20 23:24:41.003387231 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_new.cpp 2021-12-21 12:00:45.338906853 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -13923,7 +13923,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= " or ", c10::DispatchKey::XLATensorId, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/csrc/utils/tensor_types.cpp pytorch-develop-150/torch/csrc/utils/tensor_types.cpp --- pytorch-v1.5.0/torch/csrc/utils/tensor_types.cpp 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/csrc/utils/tensor_types.cpp 2021-12-20 23:24:41.003387231 +0800 ++++ pytorch-develop-150/torch/csrc/utils/tensor_types.cpp 2021-12-21 12:00:45.338906853 +0800 @@ -1,58 +1,91 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -14136,7 +14136,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def get_rng_state(): ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/distributed/distributed_c10d.py pytorch-develop-150/torch/distributed/distributed_c10d.py --- pytorch-v1.5.0/torch/distributed/distributed_c10d.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/distributed/distributed_c10d.py 2021-12-20 23:24:41.003387231 +0800 ++++ pytorch-develop-150/torch/distributed/distributed_c10d.py 2021-12-21 12:00:45.338906853 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14217,7 +14217,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/__init__.py pytorch-develop-150/torch/__init__.py --- pytorch-v1.5.0/torch/__init__.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/__init__.py 2021-12-20 23:24:40.979386934 +0800 ++++ pytorch-develop-150/torch/__init__.py 2021-12-21 12:00:45.318906694 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14260,7 +14260,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/jit/frontend.py pytorch-develop-150/torch/jit/frontend.py --- pytorch-v1.5.0/torch/jit/frontend.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/jit/frontend.py 2021-12-20 23:24:41.003387231 +0800 ++++ pytorch-develop-150/torch/jit/frontend.py 2021-12-21 12:00:45.342906884 +0800 @@ -616,6 +616,17 @@ return Subscript(base, [build_SliceExpr(ctx, base, expr.slice)]) elif sub_type is ast.ExtSlice: @@ -14281,7 +14281,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/lib/c10d/CMakeLists.txt pytorch-develop-150/torch/lib/c10d/CMakeLists.txt --- pytorch-v1.5.0/torch/lib/c10d/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/lib/c10d/CMakeLists.txt 2021-12-20 23:24:41.003387231 +0800 ++++ pytorch-develop-150/torch/lib/c10d/CMakeLists.txt 2021-12-21 12:00:45.342906884 +0800 @@ -28,6 +28,10 @@ option(USE_C10D_NCCL "USE C10D NCCL" ON) endif() @@ -14334,7 +14334,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= copy_header(ProcessGroupMPI.hpp) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/lib/libshm/CMakeLists.txt pytorch-develop-150/torch/lib/libshm/CMakeLists.txt --- pytorch-v1.5.0/torch/lib/libshm/CMakeLists.txt 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/lib/libshm/CMakeLists.txt 2021-12-20 23:24:41.007387280 +0800 ++++ pytorch-develop-150/torch/lib/libshm/CMakeLists.txt 2021-12-21 12:00:45.342906884 +0800 @@ -37,8 +37,11 @@ SET_TARGET_PROPERTIES(shm PROPERTIES PREFIX "lib" @@ -14391,7 +14391,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -_maybe_indices_t = _scalar_or_tuple_2_t[Tensor] diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/functional.py pytorch-develop-150/torch/nn/functional.py --- pytorch-v1.5.0/torch/nn/functional.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/functional.py 2021-12-20 23:24:41.007387280 +0800 ++++ pytorch-develop-150/torch/nn/functional.py 2021-12-21 12:00:45.342906884 +0800 @@ -1611,7 +1611,7 @@ else: output = input.matmul(weight.t()) @@ -14414,7 +14414,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -from . import parallel as parallel diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/batchnorm.py pytorch-develop-150/torch/nn/modules/batchnorm.py --- pytorch-v1.5.0/torch/nn/modules/batchnorm.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/batchnorm.py 2021-12-20 23:24:41.007387280 +0800 ++++ pytorch-develop-150/torch/nn/modules/batchnorm.py 2021-12-21 12:00:45.342906884 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14446,7 +14446,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= self.register_parameter('running_var', None) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/module.py pytorch-develop-150/torch/nn/modules/module.py --- pytorch-v1.5.0/torch/nn/modules/module.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/module.py 2021-12-20 23:24:41.007387280 +0800 ++++ pytorch-develop-150/torch/nn/modules/module.py 2021-12-21 12:00:45.342906884 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -14606,7 +14606,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return t.to(device, dtype if t.is_floating_point() else None, non_blocking, memory_format=convert_to_format) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/modules/normalization.py pytorch-develop-150/torch/nn/modules/normalization.py --- pytorch-v1.5.0/torch/nn/modules/normalization.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/modules/normalization.py 2021-12-20 23:24:41.007387280 +0800 ++++ pytorch-develop-150/torch/nn/modules/normalization.py 2021-12-21 12:00:45.342906884 +0800 @@ -128,13 +128,14 @@ """ __constants__ = ['normalized_shape', 'eps', 'elementwise_affine'] @@ -14675,7 +14675,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - module_kwargs: Optional[Any] = ...) -> Tensor: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/nn/parallel/distributed.py pytorch-develop-150/torch/nn/parallel/distributed.py --- pytorch-v1.5.0/torch/nn/parallel/distributed.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/nn/parallel/distributed.py 2021-12-20 23:24:41.007387280 +0800 ++++ pytorch-develop-150/torch/nn/parallel/distributed.py 2021-12-21 12:00:45.346906916 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15030,7 +15030,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -def remove_weight_norm(module: T_module, name: str = ...) -> T_module: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/onnx/symbolic_opset9.py pytorch-develop-150/torch/onnx/symbolic_opset9.py --- pytorch-v1.5.0/torch/onnx/symbolic_opset9.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/onnx/symbolic_opset9.py 2021-12-20 23:24:41.011387329 +0800 ++++ pytorch-develop-150/torch/onnx/symbolic_opset9.py 2021-12-21 12:00:45.346906916 +0800 @@ -1621,14 +1621,23 @@ slices = [sym_help._slice_helper(g, w, axes=[0], starts=[x * n], ends=[y * n]) for x, y in intervals] return g.op('Concat', *slices, axis_i=0) @@ -15108,7 +15108,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, params: _params_t, lr: float=..., lr_decay: float=..., weight_decay: float=..., initial_accumulator_value: float=..., eps: float=...) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/optim/adamax.py pytorch-develop-150/torch/optim/adamax.py --- pytorch-v1.5.0/torch/optim/adamax.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/optim/adamax.py 2021-12-20 23:24:41.011387329 +0800 ++++ pytorch-develop-150/torch/optim/adamax.py 2021-12-21 12:00:45.346906916 +0800 @@ -80,8 +80,8 @@ exp_inf.mul_(beta2).unsqueeze(0), grad.abs().add_(eps).unsqueeze_(0) @@ -15285,7 +15285,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, params: _params_t, lr: float=..., betas: Tuple[float, float]=..., eps: float=...) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/serialization.py pytorch-develop-150/torch/serialization.py --- pytorch-v1.5.0/torch/serialization.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/serialization.py 2021-12-20 23:24:40.983386984 +0800 ++++ pytorch-develop-150/torch/serialization.py 2021-12-21 12:00:45.318906694 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15369,7 +15369,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def location_tag(storage): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/storage.py pytorch-develop-150/torch/storage.py --- pytorch-v1.5.0/torch/storage.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/storage.py 2021-12-20 23:24:40.983386984 +0800 ++++ pytorch-develop-150/torch/storage.py 2021-12-21 12:00:45.318906694 +0800 @@ -7,6 +7,7 @@ class _StorageBase(object): @@ -15389,7 +15389,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= else: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/tensor.py pytorch-develop-150/torch/tensor.py --- pytorch-v1.5.0/torch/tensor.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/tensor.py 2021-12-20 23:24:40.983386984 +0800 ++++ pytorch-develop-150/torch/tensor.py 2021-12-21 12:00:45.318906694 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15451,7 +15451,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def __reversed__(self): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/_tensor_str.py pytorch-develop-150/torch/_tensor_str.py --- pytorch-v1.5.0/torch/_tensor_str.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/_tensor_str.py 2021-12-20 23:24:40.983386984 +0800 ++++ pytorch-develop-150/torch/_tensor_str.py 2021-12-21 12:00:45.318906694 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15505,7 +15505,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= has_default_dtype = self.dtype in (torch.get_default_dtype(), torch.int64, torch.bool) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/data/dataloader.py pytorch-develop-150/torch/utils/data/dataloader.py --- pytorch-v1.5.0/torch/utils/data/dataloader.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/data/dataloader.py 2021-12-20 23:24:41.011387329 +0800 ++++ pytorch-develop-150/torch/utils/data/dataloader.py 2021-12-21 12:00:45.346906916 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15714,7 +15714,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - def __init__(self, sampler: Sampler[int], batch_size: int, drop_last: bool) -> None: ... diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/data/_utils/pin_memory.py pytorch-develop-150/torch/utils/data/_utils/pin_memory.py --- pytorch-v1.5.0/torch/utils/data/_utils/pin_memory.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/data/_utils/pin_memory.py 2021-12-20 23:24:41.011387329 +0800 ++++ pytorch-develop-150/torch/utils/data/_utils/pin_memory.py 2021-12-21 12:00:45.346906916 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -15775,7 +15775,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= - diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/utils/__init__.py pytorch-develop-150/torch/utils/__init__.py --- pytorch-v1.5.0/torch/utils/__init__.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/utils/__init__.py 2021-12-20 23:24:41.011387329 +0800 ++++ pytorch-develop-150/torch/utils/__init__.py 2021-12-21 12:00:45.346906916 +0800 @@ -1,6 +1,9 @@ from __future__ import absolute_import, division, print_function, unicode_literals @@ -15788,7 +15788,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def set_module(obj, mod): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.5.0/torch/_utils.py pytorch-develop-150/torch/_utils.py --- pytorch-v1.5.0/torch/_utils.py 2021-04-10 18:39:32.000000000 +0800 -+++ pytorch-develop-150/torch/_utils.py 2021-12-20 23:24:40.983386984 +0800 ++++ pytorch-develop-150/torch/_utils.py 2021-12-21 12:00:45.318906694 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. diff --git a/patch/pytorch1.8.1_npu.patch b/patch/pytorch1.8.1_npu.patch index d6bf75c6311..885db57c483 100644 --- a/patch/pytorch1.8.1_npu.patch +++ b/patch/pytorch1.8.1_npu.patch @@ -1,6 +1,6 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/CMakeLists.txt pytorch-develop-181/aten/CMakeLists.txt --- pytorch-v1.8.1/aten/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/CMakeLists.txt 2021-12-20 23:24:45.107437788 +0800 ++++ pytorch-develop-181/aten/CMakeLists.txt 2021-12-21 12:00:49.426939248 +0800 @@ -22,9 +22,11 @@ set(ATen_CPU_INCLUDE) set(ATen_THIRD_PARTY_INCLUDE) @@ -52,7 +52,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_CPU_DEPENDENCY_LIBS ${ATen_CPU_DEPENDENCY_LIBS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/autocast_mode.h pytorch-develop-181/aten/src/ATen/autocast_mode.h --- pytorch-v1.8.1/aten/src/ATen/autocast_mode.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/autocast_mode.h 2021-12-20 23:24:45.111437836 +0800 ++++ pytorch-develop-181/aten/src/ATen/autocast_mode.h 2021-12-21 12:00:49.430939280 +0800 @@ -5,7 +5,7 @@ namespace { @@ -64,7 +64,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/CMakeLists.txt pytorch-develop-181/aten/src/ATen/CMakeLists.txt --- pytorch-v1.8.1/aten/src/ATen/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/CMakeLists.txt 2021-12-20 23:24:45.107437788 +0800 ++++ pytorch-develop-181/aten/src/ATen/CMakeLists.txt 2021-12-21 12:00:49.426939248 +0800 @@ -85,6 +85,10 @@ file(GLOB native_quantized_h "native/quantized/*.h" "native/quantized/cpu/*.h") file(GLOB native_cpu_h "native/cpu/*.h") @@ -115,7 +115,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(ATen_HIP_SRCS ${ATen_HIP_SRCS} PARENT_SCOPE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/Dispatcher.h pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/Dispatcher.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h 2021-12-20 23:24:45.115437886 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/Dispatcher.h 2021-12-21 12:00:49.434939312 +0800 @@ -417,6 +417,11 @@ } } @@ -130,7 +130,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.cpp pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-12-20 23:24:45.115437886 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.cpp 2021-12-21 12:00:49.434939312 +0800 @@ -6,6 +6,7 @@ namespace c10 { @@ -141,7 +141,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= std::unordered_set not_observed_ops = { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.h pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-12-20 23:24:45.115437886 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/ObservedOperators.h 2021-12-21 12:00:49.434939312 +0800 @@ -1,12 +1,16 @@ #pragma once @@ -161,7 +161,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/dispatch/OperatorEntry.h pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h --- pytorch-v1.8.1/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-12-20 23:24:45.115437886 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/dispatch/OperatorEntry.h 2021-12-21 12:00:49.434939312 +0800 @@ -10,6 +10,7 @@ #include #include @@ -172,7 +172,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #include diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/core/VariableFallbackKernel.cpp pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp --- pytorch-v1.8.1/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-12-20 23:24:45.115437886 +0800 ++++ pytorch-develop-181/aten/src/ATen/core/VariableFallbackKernel.cpp 2021-12-21 12:00:49.434939312 +0800 @@ -48,4 +48,8 @@ m.fallback(torch::CppFunction::makeFallthrough()); } @@ -184,7 +184,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/Convolution.cpp pytorch-develop-181/aten/src/ATen/native/Convolution.cpp --- pytorch-v1.8.1/aten/src/ATen/native/Convolution.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/Convolution.cpp 2021-12-20 23:24:45.127438033 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/Convolution.cpp 2021-12-21 12:00:49.446939407 +0800 @@ -603,7 +603,9 @@ const Tensor& input, const Tensor& weight, const Tensor& bias, IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation, @@ -198,7 +198,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= at::Tensor _convolution( diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/Memory.cpp pytorch-develop-181/aten/src/ATen/native/Memory.cpp --- pytorch-v1.8.1/aten/src/ATen/native/Memory.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/Memory.cpp 2021-12-20 23:24:45.131438083 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/Memory.cpp 2021-12-21 12:00:49.450939438 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -249,7 +249,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= detail::computeStorageNbytes( diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/native_functions.yaml pytorch-develop-181/aten/src/ATen/native/native_functions.yaml --- pytorch-v1.8.1/aten/src/ATen/native/native_functions.yaml 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/native_functions.yaml 2021-12-20 23:24:45.167438525 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/native_functions.yaml 2021-12-21 12:00:49.486939723 +0800 @@ -2073,6 +2073,8 @@ dispatch: CPU, CUDA: isnan @@ -480,7 +480,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +- func: npu_apply_adam.out(Scalar beta1_power, Scalar beta2_power, Scalar lr, Scalar beta1, Scalar beta2, Scalar epsilon, Tensor grad, bool? use_locking, bool? use_nesterov, *, Tensor(a!) var, Tensor(b!) m, Tensor(c!) v) -> (Tensor(a!), Tensor(b!), Tensor(c!)) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/NPUVariableMethodStubs.cpp pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp --- pytorch-v1.8.1/aten/src/ATen/native/NPUVariableMethodStubs.cpp 1970-01-01 08:00:00.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp 2021-12-20 23:24:45.131438083 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/NPUVariableMethodStubs.cpp 2021-12-21 12:00:49.450939438 +0800 @@ -0,0 +1,464 @@ +#include "ATen/native/npu/common/FormatCastHelper.h" +#include "ATen/native/npu/frame/FormatHelper.h" @@ -1864,7 +1864,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -} diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/TensorFactories.cpp pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp --- pytorch-v1.8.1/aten/src/ATen/native/TensorFactories.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp 2021-12-20 23:24:45.143438230 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/TensorFactories.cpp 2021-12-21 12:00:49.466939565 +0800 @@ -1,3 +1,20 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -1897,7 +1897,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= namespace { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h --- pytorch-v1.8.1/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-12-20 23:24:45.199438919 +0800 ++++ pytorch-develop-181/aten/src/ATen/native/vulkan/api/vk_mem_alloc.h 2021-12-21 12:00:49.518939977 +0800 @@ -1,19229 +1,19229 @@ -// -// Copyright (c) 2017-2020 Advanced Micro Devices, Inc. All rights reserved. @@ -40359,7 +40359,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= +#endif // #ifdef VMA_IMPLEMENTATION diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/record_function.cpp pytorch-develop-181/aten/src/ATen/record_function.cpp --- pytorch-v1.8.1/aten/src/ATen/record_function.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/record_function.cpp 2021-12-20 23:24:45.203438968 +0800 ++++ pytorch-develop-181/aten/src/ATen/record_function.cpp 2021-12-21 12:00:49.522940009 +0800 @@ -400,6 +400,9 @@ rf_tls_.tls_record_function_enabled_ = enable; } @@ -40372,7 +40372,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (rf_tls_ptr->tls_record_function_enabled_) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/record_function.h pytorch-develop-181/aten/src/ATen/record_function.h --- pytorch-v1.8.1/aten/src/ATen/record_function.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/record_function.h 2021-12-20 23:24:45.203438968 +0800 ++++ pytorch-develop-181/aten/src/ATen/record_function.h 2021-12-21 12:00:49.522940009 +0800 @@ -2,6 +2,7 @@ #include @@ -40408,7 +40408,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // scope - record scope that this function tracks diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/templates/TensorBody.h pytorch-develop-181/aten/src/ATen/templates/TensorBody.h --- pytorch-v1.8.1/aten/src/ATen/templates/TensorBody.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/templates/TensorBody.h 2021-12-20 23:24:45.203438968 +0800 ++++ pytorch-develop-181/aten/src/ATen/templates/TensorBody.h 2021-12-21 12:00:49.522940009 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40441,7 +40441,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= bool is_xpu() const; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/templates/TensorMethods.cpp pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp --- pytorch-v1.8.1/aten/src/ATen/templates/TensorMethods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp 2021-12-20 23:24:45.203438968 +0800 ++++ pytorch-develop-181/aten/src/ATen/templates/TensorMethods.cpp 2021-12-21 12:00:49.522940009 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40486,7 +40486,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/ATen/Utils.cpp pytorch-develop-181/aten/src/ATen/Utils.cpp --- pytorch-v1.8.1/aten/src/ATen/Utils.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/ATen/Utils.cpp 2021-12-20 23:24:45.111437836 +0800 ++++ pytorch-develop-181/aten/src/ATen/Utils.cpp 2021-12-21 12:00:49.430939280 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40530,7 +40530,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/TH/generic/THStorage.cpp pytorch-develop-181/aten/src/TH/generic/THStorage.cpp --- pytorch-v1.8.1/aten/src/TH/generic/THStorage.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/TH/generic/THStorage.cpp 2021-12-20 23:24:45.207439017 +0800 ++++ pytorch-develop-181/aten/src/TH/generic/THStorage.cpp 2021-12-21 12:00:49.526940041 +0800 @@ -1,9 +1,32 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40643,7 +40643,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/aten/src/TH/generic/THStorage.h pytorch-develop-181/aten/src/TH/generic/THStorage.h --- pytorch-v1.8.1/aten/src/TH/generic/THStorage.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/aten/src/TH/generic/THStorage.h 2021-12-20 23:24:45.207439017 +0800 ++++ pytorch-develop-181/aten/src/TH/generic/THStorage.h 2021-12-21 12:00:49.526940041 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40682,7 +40682,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/CMakeLists.txt pytorch-develop-181/c10/CMakeLists.txt --- pytorch-v1.8.1/c10/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/CMakeLists.txt 2021-12-20 23:24:45.223439214 +0800 ++++ pytorch-develop-181/c10/CMakeLists.txt 2021-12-21 12:00:49.538940136 +0800 @@ -79,6 +79,14 @@ message(STATUS "don't use NUMA") endif() @@ -40711,7 +40711,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # not checked in diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Backend.h pytorch-develop-181/c10/core/Backend.h --- pytorch-v1.8.1/c10/core/Backend.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Backend.h 2021-12-20 23:24:45.223439214 +0800 ++++ pytorch-develop-181/c10/core/Backend.h 2021-12-21 12:00:49.538940136 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40805,7 +40805,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Device.cpp pytorch-develop-181/c10/core/Device.cpp --- pytorch-v1.8.1/c10/core/Device.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Device.cpp 2021-12-20 23:24:45.223439214 +0800 ++++ pytorch-develop-181/c10/core/Device.cpp 2021-12-21 12:00:49.538940136 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40836,7 +40836,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= types.begin(), diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Device.h pytorch-develop-181/c10/core/Device.h --- pytorch-v1.8.1/c10/core/Device.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Device.h 2021-12-20 23:24:45.223439214 +0800 ++++ pytorch-develop-181/c10/core/Device.h 2021-12-21 12:00:49.538940136 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40871,7 +40871,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= bool is_cpu() const noexcept { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DeviceType.cpp pytorch-develop-181/c10/core/DeviceType.cpp --- pytorch-v1.8.1/c10/core/DeviceType.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DeviceType.cpp 2021-12-20 23:24:45.223439214 +0800 ++++ pytorch-develop-181/c10/core/DeviceType.cpp 2021-12-21 12:00:49.538940136 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40911,7 +40911,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return false; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DeviceType.h pytorch-develop-181/c10/core/DeviceType.h --- pytorch-v1.8.1/c10/core/DeviceType.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DeviceType.h 2021-12-20 23:24:45.223439214 +0800 ++++ pytorch-develop-181/c10/core/DeviceType.h 2021-12-21 12:00:49.538940136 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -40953,7 +40953,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= constexpr DeviceType kMSNPU = DeviceType::MSNPU; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKey.cpp pytorch-develop-181/c10/core/DispatchKey.cpp --- pytorch-v1.8.1/c10/core/DispatchKey.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKey.cpp 2021-12-20 23:24:45.223439214 +0800 ++++ pytorch-develop-181/c10/core/DispatchKey.cpp 2021-12-21 12:00:49.538940136 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41002,7 +41002,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::PrivateUse1: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKey.h pytorch-develop-181/c10/core/DispatchKey.h --- pytorch-v1.8.1/c10/core/DispatchKey.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKey.h 2021-12-20 23:24:45.223439214 +0800 ++++ pytorch-develop-181/c10/core/DispatchKey.h 2021-12-21 12:00:49.538940136 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41049,7 +41049,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= PrivateUse3_PreAutograd = AutogradPrivateUse3, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKeySet.cpp pytorch-develop-181/c10/core/DispatchKeySet.cpp --- pytorch-v1.8.1/c10/core/DispatchKeySet.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKeySet.cpp 2021-12-20 23:24:45.223439214 +0800 ++++ pytorch-develop-181/c10/core/DispatchKeySet.cpp 2021-12-21 12:00:49.538940136 +0800 @@ -11,6 +11,7 @@ DispatchKey::XLA, DispatchKey::NestedTensor, @@ -41069,7 +41069,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= case DispatchKey::AutogradXPU: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/DispatchKeySet.h pytorch-develop-181/c10/core/DispatchKeySet.h --- pytorch-v1.8.1/c10/core/DispatchKeySet.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/DispatchKeySet.h 2021-12-20 23:24:45.223439214 +0800 ++++ pytorch-develop-181/c10/core/DispatchKeySet.h 2021-12-21 12:00:49.542940167 +0800 @@ -193,6 +193,7 @@ DispatchKey::AutogradCPU, DispatchKey::AutogradCUDA, @@ -41080,7 +41080,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= DispatchKey::AutogradPrivateUse1, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/Storage.h pytorch-develop-181/c10/core/Storage.h --- pytorch-v1.8.1/c10/core/Storage.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/Storage.h 2021-12-20 23:24:45.223439214 +0800 ++++ pytorch-develop-181/c10/core/Storage.h 2021-12-21 12:00:49.542940167 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41114,7 +41114,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/StorageImpl.h pytorch-develop-181/c10/core/StorageImpl.h --- pytorch-v1.8.1/c10/core/StorageImpl.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/StorageImpl.h 2021-12-20 23:24:45.223439214 +0800 ++++ pytorch-develop-181/c10/core/StorageImpl.h 2021-12-21 12:00:49.542940167 +0800 @@ -1,12 +1,42 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41174,7 +41174,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/TensorImpl.h pytorch-develop-181/c10/core/TensorImpl.h --- pytorch-v1.8.1/c10/core/TensorImpl.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/TensorImpl.h 2021-12-20 23:24:45.223439214 +0800 ++++ pytorch-develop-181/c10/core/TensorImpl.h 2021-12-21 12:00:49.542940167 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41217,7 +41217,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ts.has(DispatchKey::SparseCPU) || diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/core/TensorOptions.h pytorch-develop-181/c10/core/TensorOptions.h --- pytorch-v1.8.1/c10/core/TensorOptions.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/core/TensorOptions.h 2021-12-20 23:24:45.223439214 +0800 ++++ pytorch-develop-181/c10/core/TensorOptions.h 2021-12-21 12:00:49.542940167 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41258,7 +41258,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } else if (tid == DispatchKey::QuantizedXPU) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/c10/macros/Export.h pytorch-develop-181/c10/macros/Export.h --- pytorch-v1.8.1/c10/macros/Export.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/c10/macros/Export.h 2021-12-20 23:24:45.227439262 +0800 ++++ pytorch-develop-181/c10/macros/Export.h 2021-12-21 12:00:49.542940167 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -41327,7 +41327,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= #define C10_API_ENUM C10_API diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/caffe2/CMakeLists.txt pytorch-develop-181/caffe2/CMakeLists.txt --- pytorch-v1.8.1/caffe2/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/caffe2/CMakeLists.txt 2021-12-20 23:24:45.231439312 +0800 ++++ pytorch-develop-181/caffe2/CMakeLists.txt 2021-12-21 12:00:49.550940231 +0800 @@ -76,6 +76,7 @@ # Add source, includes, and libs to lists list(APPEND Caffe2_CPU_SRCS ${ATen_CPU_SRCS}) @@ -41452,7 +41452,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # Call again since Caffe2_HIP_INCLUDE is extended with ATen include dirs. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/BuildVariables.cmake pytorch-develop-181/cmake/BuildVariables.cmake --- pytorch-v1.8.1/cmake/BuildVariables.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/BuildVariables.cmake 2021-12-20 23:24:45.311440296 +0800 ++++ pytorch-develop-181/cmake/BuildVariables.cmake 2021-12-21 12:00:49.622940800 +0800 @@ -11,6 +11,7 @@ # CMakeLists.txt files under each folder respectively. set(Caffe2_CPU_SRCS) @@ -41476,7 +41476,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # symbols. However, if the lib is whole linked in caffe2 lib, we don't want diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Codegen.cmake pytorch-develop-181/cmake/Codegen.cmake --- pytorch-v1.8.1/cmake/Codegen.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Codegen.cmake 2021-12-20 23:24:45.311440296 +0800 ++++ pytorch-develop-181/cmake/Codegen.cmake 2021-12-21 12:00:49.622940800 +0800 @@ -208,13 +208,14 @@ file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt generated_cpp) file(READ ${CMAKE_BINARY_DIR}/aten/src/ATen/generated_cpp.txt-cuda cuda_generated_cpp) @@ -41509,7 +41509,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= function(append_filelist name outputvar) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Dependencies.cmake pytorch-develop-181/cmake/Dependencies.cmake --- pytorch-v1.8.1/cmake/Dependencies.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Dependencies.cmake 2021-12-20 23:24:45.311440296 +0800 ++++ pytorch-develop-181/cmake/Dependencies.cmake 2021-12-21 12:00:49.626940832 +0800 @@ -1771,6 +1771,13 @@ endif(NOT C_HAS_THREAD) endif() @@ -41526,7 +41526,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/Summary.cmake pytorch-develop-181/cmake/Summary.cmake --- pytorch-v1.8.1/cmake/Summary.cmake 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/Summary.cmake 2021-12-20 23:24:45.311440296 +0800 ++++ pytorch-develop-181/cmake/Summary.cmake 2021-12-21 12:00:49.626940832 +0800 @@ -127,6 +127,7 @@ message(STATUS " USE_MKLDNN_CBLAS : ${USE_MKLDNN_CBLAS}") endif() @@ -41545,7 +41545,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= message(STATUS " Private Dependencies : ${Caffe2_DEPENDENCY_LIBS}") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/cmake/TorchConfig.cmake.in pytorch-develop-181/cmake/TorchConfig.cmake.in --- pytorch-v1.8.1/cmake/TorchConfig.cmake.in 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/cmake/TorchConfig.cmake.in 2021-12-20 23:24:45.311440296 +0800 ++++ pytorch-develop-181/cmake/TorchConfig.cmake.in 2021-12-21 12:00:49.626940832 +0800 @@ -158,6 +158,11 @@ list(APPEND TORCH_LIBRARIES ${TORCH_CUDA_LIBRARIES}) endif() @@ -41560,7 +41560,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= set(TORCH_CXX_FLAGS "-D_GLIBCXX_USE_CXX11_ABI=@GLIBCXX_USE_CXX11_ABI@") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/CMakeLists.txt pytorch-develop-181/CMakeLists.txt --- pytorch-v1.8.1/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/CMakeLists.txt 2021-12-20 23:24:45.095437640 +0800 ++++ pytorch-develop-181/CMakeLists.txt 2021-12-21 12:00:49.418939185 +0800 @@ -261,6 +261,10 @@ "USE_DISTRIBUTED" OFF) option(USE_TBB "Use TBB" OFF) @@ -41604,7 +41604,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if(APPLE) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/setup.py pytorch-develop-181/setup.py --- pytorch-v1.8.1/setup.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/setup.py 2021-12-20 23:24:45.323440443 +0800 ++++ pytorch-develop-181/setup.py 2021-12-21 12:00:49.638940927 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -41673,7 +41673,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= 'utils/benchmark/utils/valgrind_wrapper/*.h', diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/derivatives.yaml pytorch-develop-181/tools/autograd/derivatives.yaml --- pytorch-v1.8.1/tools/autograd/derivatives.yaml 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/derivatives.yaml 2021-12-20 23:24:45.783446098 +0800 ++++ pytorch-develop-181/tools/autograd/derivatives.yaml 2021-12-21 12:00:50.090944505 +0800 @@ -1976,3 +1976,7 @@ - name: nonzero(Tensor self) -> Tensor @@ -41684,7 +41684,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= + mat2: npu_bmm_v2_mat2_backward(grad, self, mat2, mat2.sizes()) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/gen_python_functions.py pytorch-develop-181/tools/autograd/gen_python_functions.py --- pytorch-v1.8.1/tools/autograd/gen_python_functions.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/gen_python_functions.py 2021-12-20 23:24:45.783446098 +0800 ++++ pytorch-develop-181/tools/autograd/gen_python_functions.py 2021-12-21 12:00:50.090944505 +0800 @@ -1,3 +1,20 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -41708,7 +41708,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # The bindings are generated as methods on python_variable or functions on the diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/Functions.cpp pytorch-develop-181/tools/autograd/templates/Functions.cpp --- pytorch-v1.8.1/tools/autograd/templates/Functions.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/Functions.cpp 2021-12-20 23:24:45.783446098 +0800 ++++ pytorch-develop-181/tools/autograd/templates/Functions.cpp 2021-12-21 12:00:50.090944505 +0800 @@ -14,6 +14,35 @@ namespace torch { namespace autograd { namespace generated { @@ -41747,7 +41747,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }}} // namespace torch::autograd::generated diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/python_torch_functions.cpp pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp --- pytorch-v1.8.1/tools/autograd/templates/python_torch_functions.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp 2021-12-20 23:24:45.787446147 +0800 ++++ pytorch-develop-181/tools/autograd/templates/python_torch_functions.cpp 2021-12-21 12:00:50.090944505 +0800 @@ -30,7 +30,7 @@ #include "torch/csrc/autograd/generated/variable_factories.h" #include "torch/csrc/utils/structseq.h" @@ -41783,7 +41783,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return torch::range(start, end, step, options); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/autograd/templates/python_variable_methods.cpp pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp --- pytorch-v1.8.1/tools/autograd/templates/python_variable_methods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp 2021-12-20 23:24:45.787446147 +0800 ++++ pytorch-develop-181/tools/autograd/templates/python_variable_methods.cpp 2021-12-21 12:00:50.090944505 +0800 @@ -20,7 +20,13 @@ #ifdef USE_CUDA #include "torch/csrc/cuda/Event.h" @@ -41851,7 +41851,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"has_names", THPVariable_has_names, METH_NOARGS, NULL}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/build_variables.bzl pytorch-develop-181/tools/build_variables.bzl --- pytorch-v1.8.1/tools/build_variables.bzl 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/build_variables.bzl 2021-12-20 23:24:45.787446147 +0800 ++++ pytorch-develop-181/tools/build_variables.bzl 2021-12-21 12:00:50.090944505 +0800 @@ -362,6 +362,7 @@ libtorch_cuda_core_sources = [ "torch/csrc/CudaIPCTypes.cpp", @@ -41870,7 +41870,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= "torch/csrc/utils/python_arg_parser.cpp", diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/codegen/gen.py pytorch-develop-181/tools/codegen/gen.py --- pytorch-v1.8.1/tools/codegen/gen.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/codegen/gen.py 2021-12-20 23:24:45.787446147 +0800 ++++ pytorch-develop-181/tools/codegen/gen.py 2021-12-21 12:00:50.094944536 +0800 @@ -815,7 +815,7 @@ core_fm = make_file_manager(core_install_dir) cpu_fm = make_file_manager(options.install_dir) @@ -41918,7 +41918,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= main() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/tools/codegen/model.py pytorch-develop-181/tools/codegen/model.py --- pytorch-v1.8.1/tools/codegen/model.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/tools/codegen/model.py 2021-12-20 23:24:45.791446196 +0800 ++++ pytorch-develop-181/tools/codegen/model.py 2021-12-21 12:00:50.094944536 +0800 @@ -79,6 +79,7 @@ SparseHIP = auto() SparseXPU = auto() @@ -42060,7 +42060,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= -) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/autograd/profiler.py pytorch-develop-181/torch/autograd/profiler.py --- pytorch-v1.8.1/torch/autograd/profiler.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/autograd/profiler.py 2021-12-20 23:24:45.791446196 +0800 ++++ pytorch-develop-181/torch/autograd/profiler.py 2021-12-21 12:00:50.098944568 +0800 @@ -37,14 +37,17 @@ class EventList(list): """A list of Events (for pretty printing)""" @@ -42739,7 +42739,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return ''.join(result) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_C/_autograd.pyi pytorch-develop-181/torch/_C/_autograd.pyi --- pytorch-v1.8.1/torch/_C/_autograd.pyi 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_C/_autograd.pyi 2021-12-20 23:24:45.791446196 +0800 ++++ pytorch-develop-181/torch/_C/_autograd.pyi 2021-12-21 12:00:50.098944568 +0800 @@ -9,14 +9,17 @@ CUDA = ... NVTX = ... @@ -42760,7 +42760,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= class ProfilerConfig: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/CMakeLists.txt pytorch-develop-181/torch/CMakeLists.txt --- pytorch-v1.8.1/torch/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/CMakeLists.txt 2021-12-20 23:24:45.791446196 +0800 ++++ pytorch-develop-181/torch/CMakeLists.txt 2021-12-21 12:00:50.098944568 +0800 @@ -131,6 +131,20 @@ endif() @@ -42784,7 +42784,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= list(APPEND TORCH_PYTHON_SRCS ${GENERATED_THNN_CXX_CUDA}) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/engine.cpp pytorch-develop-181/torch/csrc/autograd/engine.cpp --- pytorch-v1.8.1/torch/csrc/autograd/engine.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/engine.cpp 2021-12-20 23:24:45.799446295 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/engine.cpp 2021-12-21 12:00:50.102944600 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -42881,7 +42881,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto outputs = call_function(graph_task, func, inputs); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/functions/tensor.cpp pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp --- pytorch-v1.8.1/torch/csrc/autograd/functions/tensor.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp 2021-12-20 23:24:45.799446295 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/functions/tensor.cpp 2021-12-21 12:00:50.106944631 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -42913,7 +42913,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= /*non_blocking=*/false, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/init.cpp pytorch-develop-181/torch/csrc/autograd/init.cpp --- pytorch-v1.8.1/torch/csrc/autograd/init.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/init.cpp 2021-12-20 23:24:45.799446295 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/init.cpp 2021-12-21 12:00:50.106944631 +0800 @@ -52,6 +52,7 @@ .value("Disabled", ProfilerState::Disabled) .value("CPU", ProfilerState::CPU) @@ -42956,7 +42956,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= .value("OPENCL", c10::DeviceType::OPENCL) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/input_buffer.cpp pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp --- pytorch-v1.8.1/torch/csrc/autograd/input_buffer.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp 2021-12-20 23:24:45.799446295 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/input_buffer.cpp 2021-12-21 12:00:50.106944631 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43008,7 +43008,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= auto& old_var = buffer[pos]; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.cpp pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp --- pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp 2021-12-20 23:24:45.799446295 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.cpp 2021-12-21 12:00:50.106944631 +0800 @@ -147,7 +147,7 @@ constexpr const CUDAStubs* default_stubs_addr = &default_stubs; // Constant initialization, so it is guaranteed to be initialized before @@ -43251,7 +43251,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= CUDAStubs::~CUDAStubs() = default; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.h pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h --- pytorch-v1.8.1/torch/csrc/autograd/profiler_legacy.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h 2021-12-20 23:24:45.799446295 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/profiler_legacy.h 2021-12-21 12:00:50.106944631 +0800 @@ -19,6 +19,8 @@ #include // for gettimeofday() #endif @@ -43440,7 +43440,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= std::vector&& remoteProfiledEvents); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/python_variable.cpp pytorch-develop-181/torch/csrc/autograd/python_variable.cpp --- pytorch-v1.8.1/torch/csrc/autograd/python_variable.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/python_variable.cpp 2021-12-20 23:24:45.799446295 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/python_variable.cpp 2021-12-21 12:00:50.106944631 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43494,7 +43494,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= {"is_mkldnn", (getter)THPVariable_is_mkldnn, nullptr, nullptr, nullptr}, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/autograd/python_variable_indexing.cpp pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp --- pytorch-v1.8.1/torch/csrc/autograd/python_variable_indexing.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-20 23:24:45.799446295 +0800 ++++ pytorch-develop-181/torch/csrc/autograd/python_variable_indexing.cpp 2021-12-21 12:00:50.106944631 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43527,7 +43527,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/distributed/c10d/init.cpp pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp --- pytorch-v1.8.1/torch/csrc/distributed/c10d/init.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp 2021-12-20 23:24:45.803446344 +0800 ++++ pytorch-develop-181/torch/csrc/distributed/c10d/init.cpp 2021-12-21 12:00:50.110944664 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43694,7 +43694,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= torch::class_<::c10d::DistributedC10d>("dist_c10d", "frontend") diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/DynamicTypes.cpp pytorch-develop-181/torch/csrc/DynamicTypes.cpp --- pytorch-v1.8.1/torch/csrc/DynamicTypes.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/DynamicTypes.cpp 2021-12-20 23:24:45.795446245 +0800 ++++ pytorch-develop-181/torch/csrc/DynamicTypes.cpp 2021-12-21 12:00:50.098944568 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43743,7 +43743,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return it->second; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/Generator.cpp pytorch-develop-181/torch/csrc/Generator.cpp --- pytorch-v1.8.1/torch/csrc/Generator.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/Generator.cpp 2021-12-20 23:24:45.795446245 +0800 ++++ pytorch-develop-181/torch/csrc/Generator.cpp 2021-12-21 12:00:50.098944568 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43790,7 +43790,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= " is not supported for torch.Generator() api."); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/serialization.cpp pytorch-develop-181/torch/csrc/generic/serialization.cpp --- pytorch-v1.8.1/torch/csrc/generic/serialization.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/serialization.cpp 2021-12-20 23:24:45.807446393 +0800 ++++ pytorch-develop-181/torch/csrc/generic/serialization.cpp 2021-12-21 12:00:50.110944664 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43888,7 +43888,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/Storage.cpp pytorch-develop-181/torch/csrc/generic/Storage.cpp --- pytorch-v1.8.1/torch/csrc/generic/Storage.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/Storage.cpp 2021-12-20 23:24:45.807446393 +0800 ++++ pytorch-develop-181/torch/csrc/generic/Storage.cpp 2021-12-21 12:00:50.110944664 +0800 @@ -1,7 +1,25 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -43968,7 +43968,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= for (Py_ssize_t i = 0; i < length; i++) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/generic/StorageMethods.cpp pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp --- pytorch-v1.8.1/torch/csrc/generic/StorageMethods.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp 2021-12-20 23:24:45.807446393 +0800 ++++ pytorch-develop-181/torch/csrc/generic/StorageMethods.cpp 2021-12-21 12:00:50.110944664 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44048,7 +44048,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= }; diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/Module.cpp pytorch-develop-181/torch/csrc/Module.cpp --- pytorch-v1.8.1/torch/csrc/Module.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/Module.cpp 2021-12-20 23:24:45.795446245 +0800 ++++ pytorch-develop-181/torch/csrc/Module.cpp 2021-12-21 12:00:50.098944568 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44174,7 +44174,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if (incref) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/tensor/python_tensor.cpp pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp --- pytorch-v1.8.1/torch/csrc/tensor/python_tensor.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp 2021-12-20 23:24:45.831446688 +0800 ++++ pytorch-develop-181/torch/csrc/tensor/python_tensor.cpp 2021-12-21 12:00:50.138944885 +0800 @@ -1,3 +1,18 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44212,7 +44212,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/python_arg_parser.h pytorch-develop-181/torch/csrc/utils/python_arg_parser.h --- pytorch-v1.8.1/torch/csrc/utils/python_arg_parser.h 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/python_arg_parser.h 2021-12-20 23:24:45.831446688 +0800 ++++ pytorch-develop-181/torch/csrc/utils/python_arg_parser.h 2021-12-21 12:00:50.138944885 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44247,7 +44247,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return at::Device(device_str); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_layouts.cpp pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_layouts.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp 2021-12-20 23:24:45.835446737 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_layouts.cpp 2021-12-21 12:00:50.138944885 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44278,7 +44278,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= PyObject *sparse_coo_layout = THPLayout_New(at::Layout::Sparse, "torch.sparse_coo"); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_new.cpp pytorch-develop-181/torch/csrc/utils/tensor_new.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_new.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_new.cpp 2021-12-20 23:24:45.835446737 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_new.cpp 2021-12-21 12:00:50.138944885 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44410,7 +44410,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= dispatch_key); diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/csrc/utils/tensor_types.cpp pytorch-develop-181/torch/csrc/utils/tensor_types.cpp --- pytorch-v1.8.1/torch/csrc/utils/tensor_types.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/csrc/utils/tensor_types.cpp 2021-12-20 23:24:45.835446737 +0800 ++++ pytorch-develop-181/torch/csrc/utils/tensor_types.cpp 2021-12-21 12:00:50.138944885 +0800 @@ -1,3 +1,19 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. @@ -44460,7 +44460,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= } diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/distributed/distributed_c10d.py pytorch-develop-181/torch/distributed/distributed_c10d.py --- pytorch-v1.8.1/torch/distributed/distributed_c10d.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/distributed/distributed_c10d.py 2021-12-20 23:24:45.835446737 +0800 ++++ pytorch-develop-181/torch/distributed/distributed_c10d.py 2021-12-21 12:00:50.142944917 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -44551,7 +44551,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= prefix_store, diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/__init__.py pytorch-develop-181/torch/__init__.py --- pytorch-v1.8.1/torch/__init__.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/__init__.py 2021-12-20 23:24:45.791446196 +0800 ++++ pytorch-develop-181/torch/__init__.py 2021-12-21 12:00:50.098944568 +0800 @@ -675,3 +675,11 @@ # class usage. We add these lines here to preserve backward compatibility. quantized_lstm = torch.ops.aten.quantized_lstm @@ -44567,7 +44567,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= \ No newline at end of file diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/CMakeLists.txt pytorch-develop-181/torch/lib/c10d/CMakeLists.txt --- pytorch-v1.8.1/torch/lib/c10d/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/CMakeLists.txt 2021-12-20 23:24:45.843446835 +0800 ++++ pytorch-develop-181/torch/lib/c10d/CMakeLists.txt 2021-12-21 12:00:50.146944948 +0800 @@ -27,6 +27,10 @@ option(USE_C10D_NCCL "USE C10D NCCL" ON) endif() @@ -44620,7 +44620,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= copy_header(ProcessGroupMPI.hpp) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/comm.cpp pytorch-develop-181/torch/lib/c10d/comm.cpp --- pytorch-v1.8.1/torch/lib/c10d/comm.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/comm.cpp 2021-12-20 23:24:45.843446835 +0800 ++++ pytorch-develop-181/torch/lib/c10d/comm.cpp 2021-12-21 12:00:50.150944980 +0800 @@ -12,6 +12,26 @@ class BroadcastWork { @@ -44709,7 +44709,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= while (!in_flight.empty()) { diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/c10d/reducer.cpp pytorch-develop-181/torch/lib/c10d/reducer.cpp --- pytorch-v1.8.1/torch/lib/c10d/reducer.cpp 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/c10d/reducer.cpp 2021-12-20 23:24:45.843446835 +0800 ++++ pytorch-develop-181/torch/lib/c10d/reducer.cpp 2021-12-21 12:00:50.150944980 +0800 @@ -18,6 +18,18 @@ namespace c10d { namespace { @@ -44901,7 +44901,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= // A bucket with one or more dense tensors needs to be unflattened. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/lib/libshm/CMakeLists.txt pytorch-develop-181/torch/lib/libshm/CMakeLists.txt --- pytorch-v1.8.1/torch/lib/libshm/CMakeLists.txt 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/lib/libshm/CMakeLists.txt 2021-12-20 23:24:45.843446835 +0800 ++++ pytorch-develop-181/torch/lib/libshm/CMakeLists.txt 2021-12-21 12:00:50.150944980 +0800 @@ -41,8 +41,11 @@ set_target_properties(shm PROPERTIES PREFIX "lib" @@ -44917,7 +44917,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # https://github.com/libgit2/libgit2/issues/2128#issuecomment-35649830 diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/batchnorm.py pytorch-develop-181/torch/nn/modules/batchnorm.py --- pytorch-v1.8.1/torch/nn/modules/batchnorm.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/batchnorm.py 2021-12-20 23:24:45.847446885 +0800 ++++ pytorch-develop-181/torch/nn/modules/batchnorm.py 2021-12-21 12:00:50.154945012 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -44939,7 +44939,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= from ._functions import SyncBatchNorm as sync_batch_norm diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/module.py pytorch-develop-181/torch/nn/modules/module.py --- pytorch-v1.8.1/torch/nn/modules/module.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/module.py 2021-12-20 23:24:45.847446885 +0800 ++++ pytorch-develop-181/torch/nn/modules/module.py 2021-12-21 12:00:50.154945012 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45065,7 +45065,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= if convert_to_format is not None and t.dim() == 4: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/modules/normalization.py pytorch-develop-181/torch/nn/modules/normalization.py --- pytorch-v1.8.1/torch/nn/modules/normalization.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/modules/normalization.py 2021-12-20 23:24:45.847446885 +0800 ++++ pytorch-develop-181/torch/nn/modules/normalization.py 2021-12-21 12:00:50.154945012 +0800 @@ -167,8 +167,11 @@ init.zeros_(self.bias) @@ -45082,7 +45082,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= return '{normalized_shape}, eps={eps}, ' \ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/nn/parallel/distributed.py pytorch-develop-181/torch/nn/parallel/distributed.py --- pytorch-v1.8.1/torch/nn/parallel/distributed.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/nn/parallel/distributed.py 2021-12-20 23:24:45.847446885 +0800 ++++ pytorch-develop-181/torch/nn/parallel/distributed.py 2021-12-21 12:00:50.154945012 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45114,7 +45114,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= output = self.module(*inputs[0], **kwargs[0]) diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/serialization.py pytorch-develop-181/torch/serialization.py --- pytorch-v1.8.1/torch/serialization.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/serialization.py 2021-12-20 23:24:45.855446983 +0800 ++++ pytorch-develop-181/torch/serialization.py 2021-12-21 12:00:50.162945075 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45203,7 +45203,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= See also: `saving-loading-tensors` diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/storage.py pytorch-develop-181/torch/storage.py --- pytorch-v1.8.1/torch/storage.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/storage.py 2021-12-20 23:24:45.855446983 +0800 ++++ pytorch-develop-181/torch/storage.py 2021-12-21 12:00:50.162945075 +0800 @@ -8,6 +8,7 @@ class _StorageBase(object): _cdata: Any @@ -45223,7 +45223,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= else: diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/tensor.py pytorch-develop-181/torch/tensor.py --- pytorch-v1.8.1/torch/tensor.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/tensor.py 2021-12-20 23:24:45.855446983 +0800 ++++ pytorch-develop-181/torch/tensor.py 2021-12-21 12:00:50.162945075 +0800 @@ -1,3 +1,18 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45285,7 +45285,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= def __reversed__(self): diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_tensor_str.py pytorch-develop-181/torch/_tensor_str.py --- pytorch-v1.8.1/torch/_tensor_str.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_tensor_str.py 2021-12-20 23:24:45.791446196 +0800 ++++ pytorch-develop-181/torch/_tensor_str.py 2021-12-21 12:00:50.098944568 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45329,7 +45329,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # TODO: add an API to map real -> complex dtypes diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/utils/data/dataloader.py pytorch-develop-181/torch/utils/data/dataloader.py --- pytorch-v1.8.1/torch/utils/data/dataloader.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/utils/data/dataloader.py 2021-12-20 23:24:45.863447082 +0800 ++++ pytorch-develop-181/torch/utils/data/dataloader.py 2021-12-21 12:00:50.166945107 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45390,7 +45390,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= pin_memory_thread.start() diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/utils/data/_utils/pin_memory.py pytorch-develop-181/torch/utils/data/_utils/pin_memory.py --- pytorch-v1.8.1/torch/utils/data/_utils/pin_memory.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/utils/data/_utils/pin_memory.py 2021-12-20 23:24:41.951398924 +0800 ++++ pytorch-develop-181/torch/utils/data/_utils/pin_memory.py 2021-12-21 12:00:46.274914276 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. @@ -45436,7 +45436,7 @@ diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude= # logic of this function. diff -Nur '--exclude=.git*' '--exclude=.jenkins' '--exclude=android' '--exclude=OWNERS' '--exclude=third_party' '--exclude=README*' -Nur pytorch-v1.8.1/torch/_utils.py pytorch-develop-181/torch/_utils.py --- pytorch-v1.8.1/torch/_utils.py 2021-03-24 10:28:21.000000000 +0800 -+++ pytorch-develop-181/torch/_utils.py 2021-12-20 23:24:45.791446196 +0800 ++++ pytorch-develop-181/torch/_utils.py 2021-12-21 12:00:50.098944568 +0800 @@ -1,3 +1,19 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. diff --git a/pytorch1.5.0/access_control_test.py b/pytorch1.5.0/access_control_test.py index 060d531828b..845d1926103 100644 --- a/pytorch1.5.0/access_control_test.py +++ b/pytorch1.5.0/access_control_test.py @@ -112,6 +112,10 @@ class TestMgr(): if os.path.exists(changed_file): exist_ut_file.append(changed_file) self.ut_files = exist_ut_file + + for ut in self.ut_files: + if ut.split('/')[-1] == 'run_tests.py': + self.ut_files.remove(ut) if len(self.ut_files) == 0: self.ut_files.append(DEFAULT_UT_FILE) diff --git a/pytorch1.5.0/test/test_npu/graph_utils.py b/pytorch1.5.0/test/test_npu/graph_utils.py new file mode 100644 index 00000000000..24cbc3bb19d --- /dev/null +++ b/pytorch1.5.0/test/test_npu/graph_utils.py @@ -0,0 +1,11 @@ +import torch + +def graph_mode(func): + print("graph mode on") + def wrapper(*args, **kw): + print("runing: ", func.__name__) + torch.npu.enable_graph_mode() + func(*args, **kw) + print("graph mode off") + torch.npu.disable_graph_mode() + return wrapper diff --git a/pytorch1.5.0/test/test_npu/run_tests.py b/pytorch1.5.0/test/test_npu/run_tests.py index c1789f4da98..40fe97adab5 100644 --- a/pytorch1.5.0/test/test_npu/run_tests.py +++ b/pytorch1.5.0/test/test_npu/run_tests.py @@ -67,14 +67,12 @@ def load_local_case(test_case_path): return discover def run_tests(): - - test_case_path='./' - test_report_path=test_case_path+'ReportResult' - + test_case_path = './' + test_report_path = test_case_path+'ReportResult' ENABLE_HTML = bool(os.environ.get('ENABLE_HTML')) - ENABLE_HTML_MX=bool(os.environ.get('ENABLE_HTML_MX')) - ENABLE_CASE_PATH=os.environ.get('ENABLE_CASE_PATH') - ENABLE_OUTPUT_PATH=os.environ.get('ENABLE_OUTPUT_PATH') + ENABLE_HTML_MX = bool(os.environ.get('ENABLE_HTML_MX')) + ENABLE_CASE_PATH = os.environ.get('ENABLE_CASE_PATH') + ENABLE_OUTPUT_PATH = os.environ.get('ENABLE_OUTPUT_PATH') WHITE_LIST_PATH = os.environ.get('WHITE_LIST_PATH') if WHITE_LIST_PATH and os.path.exists(WHITE_LIST_PATH): global FAILURE_FILE_NAME @@ -86,28 +84,28 @@ def run_tests(): if not os.path.exists(ENABLE_CASE_PATH): print('path is not exists: ', ENABLE_CASE_PATH) else: - test_case_path=ENABLE_CASE_PATH - test_report_path=test_case_path+'ReportResult' + test_case_path = ENABLE_CASE_PATH + test_report_path = test_case_path+'ReportResult' if ENABLE_OUTPUT_PATH is not None: if not os.path.exists(ENABLE_OUTPUT_PATH): print('path is not exists: ', ENABLE_OUTPUT_PATH) else: - test_report_path=ENABLE_OUTPUT_PATH + test_report_path = ENABLE_OUTPUT_PATH if not os.path.exists(test_report_path): os.mkdir(test_report_path) print(test_report_path) - now=time.strftime("%Y_%m_%d_%H_%M_%S") - htmlFileName=os.path.join(test_report_path, 'pytorch-unittest-report-'+now+'.html') - txtFileName=os.path.join(test_report_path, 'pytorch-unittest-report-'+now+'.txt') + now = time.strftime("%Y_%m_%d_%H_%M_%S") + htmlFileName = os.path.join(test_report_path, 'pytorch-unittest-report-' + now + '.html') + txtFileName = os.path.join(test_report_path, 'pytorch-unittest-report-' + now + '.txt') if ENABLE_HTML: print('start pytorch HTML unittest testset...') import HTMLTestRunner with open(htmlFileName, "wb") as report_file: - runner=HTMLTestRunner.HTMLTestRunner(stream=report_file, title='AllTest', description='all npu test case', verbosity=2) + runner = HTMLTestRunner.HTMLTestRunner(stream=report_file, title='AllTest', description='all npu test case', verbosity=2) result = runner.run(load_local_case(test_case_path)) new_failures, new_errors = analyse_failure_error_cases(result) if len(new_failures) + len(new_errors) > 0: @@ -116,15 +114,15 @@ def run_tests(): elif ENABLE_HTML_MX: print('start pytorch Multi HTML unittest testset...') import HtmlTestRunner - runner=HtmlTestRunner.HTMLTESTRunner(output=test_report_path, verbosity=2) - result=runner.run(load_local_case(test_case_path)) + runner = HtmlTestRunner.HTMLTESTRunner(output=test_report_path, verbosity=2) + result = runner.run(load_local_case(test_case_path)) if not result.wasSuccessful(): raise RuntimeError("Some cases of Multi HTML unittest testset failed") else: print('start pytorch TEXT unittest testset...') with open(txtFileName, "a") as report_file: - runner=unittest.TextTestRunner(stream=report_file, verbosity=2) - result=runner.run(load_local_case(test_case_path)) + runner = unittest.TextTestRunner(stream=report_file, verbosity=2) + result = runner.run(load_local_case(test_case_path)) if not result.wasSuccessful(): raise RuntimeError("Some cases TEXT unittest failed") print('report files path', txtFileName) diff --git a/pytorch1.5.0/test/test_npu/test_dynamic_ops/common_device_type.py b/pytorch1.5.0/test/test_npu/test_dynamic_ops/common_device_type.py index 585d0dfd083..e177c0fa6c5 100644 --- a/pytorch1.5.0/test/test_npu/test_dynamic_ops/common_device_type.py +++ b/pytorch1.5.0/test/test_npu/test_dynamic_ops/common_device_type.py @@ -16,7 +16,7 @@ import os import sys -common_path = os.path.dirname("../common/") +common_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) + "/common/" if common_path not in sys.path: sys.path.append(common_path) -from common_device_type_new import * \ No newline at end of file +from common_device_type_new import dtypes, instantiate_device_type_tests \ No newline at end of file diff --git a/pytorch1.5.0/test/test_npu/test_dynamic_ops/common_utils.py b/pytorch1.5.0/test/test_npu/test_dynamic_ops/common_utils.py index 82c8de523da..ec154dcfc09 100644 --- a/pytorch1.5.0/test/test_npu/test_dynamic_ops/common_utils.py +++ b/pytorch1.5.0/test/test_npu/test_dynamic_ops/common_utils.py @@ -22,7 +22,7 @@ torch.testing._internal.common_cuda.py can freely initialize CUDA context when i """ import os import sys -common_path = os.path.dirname("../common/") +common_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) + "/common/" if common_path not in sys.path: sys.path.append(common_path) -from common_utils_new import * \ No newline at end of file +from common_utils_new import TestCase, run_tests \ No newline at end of file diff --git a/pytorch1.5.0/test/test_npu/test_dynamic_ops/util_test.py b/pytorch1.5.0/test/test_npu/test_dynamic_ops/util_test.py index b758a013f48..cdda40d7dfe 100644 --- a/pytorch1.5.0/test/test_npu/test_dynamic_ops/util_test.py +++ b/pytorch1.5.0/test/test_npu/test_dynamic_ops/util_test.py @@ -15,8 +15,8 @@ # limitations under the License. import os import sys -common_path = os.path.dirname("../common/") +common_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) + "/common/" if common_path not in sys.path: sys.path.append(common_path) -from util_test_new import * +from util_test_new import create_common_tensor diff --git a/pytorch1.5.0/test/test_npu/test_network_ops/common_device_type.py b/pytorch1.5.0/test/test_npu/test_network_ops/common_device_type.py index e8f7023b019..5713d199ec7 100644 --- a/pytorch1.5.0/test/test_npu/test_network_ops/common_device_type.py +++ b/pytorch1.5.0/test/test_npu/test_network_ops/common_device_type.py @@ -16,7 +16,7 @@ import os import sys -common_path = os.path.dirname("../common/") +common_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) + "/common/" if common_path not in sys.path: sys.path.append(common_path) from common_device_type_new import dtypes, instantiate_device_type_tests, formats \ No newline at end of file diff --git a/pytorch1.5.0/test/test_npu/test_network_ops/common_utils.py b/pytorch1.5.0/test/test_npu/test_network_ops/common_utils.py index 6df722dc300..9108e8c1313 100644 --- a/pytorch1.5.0/test/test_npu/test_network_ops/common_utils.py +++ b/pytorch1.5.0/test/test_npu/test_network_ops/common_utils.py @@ -22,7 +22,7 @@ torch.testing._internal.common_cuda.py can freely initialize CUDA context when i """ import os import sys -common_path = os.path.dirname("../common/") +common_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) + "/common/" if common_path not in sys.path: sys.path.append(common_path) from common_utils_new import TestCase, run_tests diff --git a/pytorch1.5.0/test/test_npu/test_network_ops/util_test.py b/pytorch1.5.0/test/test_npu/test_network_ops/util_test.py index fd0682982f9..aae37df5c60 100755 --- a/pytorch1.5.0/test/test_npu/test_network_ops/util_test.py +++ b/pytorch1.5.0/test/test_npu/test_network_ops/util_test.py @@ -15,7 +15,7 @@ # limitations under the License. import os import sys -common_path = os.path.dirname("../common/") +common_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) + "/common/" if common_path not in sys.path: sys.path.append(common_path) from util_test_new import create_common_tensor, test_2args_broadcast, create_dtype_tensor, UT_FAST_MODE diff --git a/pytorch1.5.0/test/test_npu/test_npu_tools/common_utils.py b/pytorch1.5.0/test/test_npu/test_npu_tools/common_utils.py index 6df722dc300..9108e8c1313 100644 --- a/pytorch1.5.0/test/test_npu/test_npu_tools/common_utils.py +++ b/pytorch1.5.0/test/test_npu/test_npu_tools/common_utils.py @@ -22,7 +22,7 @@ torch.testing._internal.common_cuda.py can freely initialize CUDA context when i """ import os import sys -common_path = os.path.dirname("../common/") +common_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) + "/common/" if common_path not in sys.path: sys.path.append(common_path) from common_utils_new import TestCase, run_tests diff --git a/pytorch1.5.0/test/test_npu/test_trans_contiguous/__init__.py b/pytorch1.5.0/test/test_npu/test_trans_contiguous/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/pytorch1.5.0/test/test_npu/test_trans_contiguous/common_device_type.py b/pytorch1.5.0/test/test_npu/test_trans_contiguous/common_device_type.py index ba98bd7394f..cba7e80cd86 100644 --- a/pytorch1.5.0/test/test_npu/test_trans_contiguous/common_device_type.py +++ b/pytorch1.5.0/test/test_npu/test_trans_contiguous/common_device_type.py @@ -17,7 +17,7 @@ import os import sys -common_path = os.path.dirname("../common/") +common_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) + "/common/" if common_path not in sys.path: sys.path.append(common_path) from common_device_type_new import dtypes, instantiate_device_type_tests \ No newline at end of file diff --git a/pytorch1.5.0/test/test_npu/test_trans_contiguous/common_utils.py b/pytorch1.5.0/test/test_npu/test_trans_contiguous/common_utils.py index f5bd133f8cf..dc5ef99619b 100644 --- a/pytorch1.5.0/test/test_npu/test_trans_contiguous/common_utils.py +++ b/pytorch1.5.0/test/test_npu/test_trans_contiguous/common_utils.py @@ -23,7 +23,7 @@ torch.testing._internal.common_cuda.py can freely initialize CUDA context when i import os import sys -common_path = os.path.dirname("../common/") +common_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) + "/common/" if common_path not in sys.path: sys.path.append(common_path) from common_utils_new import TestCase, run_tests \ No newline at end of file diff --git a/pytorch1.5.0/test/test_npu/test_trans_contiguous/util_test.py b/pytorch1.5.0/test/test_npu/test_trans_contiguous/util_test.py index d68b5e51f58..dea0dcfffde 100644 --- a/pytorch1.5.0/test/test_npu/test_trans_contiguous/util_test.py +++ b/pytorch1.5.0/test/test_npu/test_trans_contiguous/util_test.py @@ -18,7 +18,7 @@ import sys import numpy as np import torch -common_path = os.path.dirname("../common/") +common_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) + "/common/" if common_path not in sys.path: sys.path.append(common_path) from util_test_new import create_common_tensor, test_2args_broadcast, create_dtype_tensor, UT_FAST_MODE diff --git a/pytorch1.5.0/test/test_npu/util_test.py b/pytorch1.5.0/test/test_npu/util_test.py index 747c011970d..98c69948fab 100644 --- a/pytorch1.5.0/test/test_npu/util_test.py +++ b/pytorch1.5.0/test/test_npu/util_test.py @@ -27,13 +27,13 @@ import numpy as np # 29 ：FORMAT_FRACTAL_NZ def create_common_tensor(item, minValue, maxValue): dtype = item[0] - format = item[1] + format_tensor = item[1] shape = item[2] input1 = np.random.uniform(minValue, maxValue, shape).astype(dtype) cpu_input = torch.from_numpy(input1) npu_input = torch.from_numpy(input1).to("npu") - if format != -1: - npu_input = npu_input.npu_format_cast(format) + if format_tensor != -1: + npu_input = npu_input.npu_format_cast(format_tensor) return cpu_input, npu_input @@ -64,3 +64,15 @@ def compare_res_new(cpu_output, npu_output, testcase_name): return print('testcase_name={0}, npu datatype={1} shape={2} fails!'.format( testcase_name, npu_output.dtype, npu_output.shape)) print('testcase_name={0}, datatype={1} shape={2} pass!'.format(testcase_name,cpu_output.dtype, cpu_output.shape)) + + +def create_common_tensor_for_broadcast(item, minValue, maxValue): + dtype = item[0] + npu_format = item[1] + shape = item[2] + input1 = np.random.uniform(minValue, maxValue, shape[0]).astype(dtype) + cpu_input = torch.from_numpy(input1) + npu_input = torch.from_numpy(input1).to("npu") + if npu_format != -1: + npu_input = npu_input.npu_format_cast(npu_format) + return cpu_input, npu_input diff --git a/pytorch1.8.1/src/aten/src/ATen/native/npu/__Rshift__KernelNpu.cpp b/pytorch1.8.1/src/aten/src/ATen/native/npu/__Rshift__KernelNpu.cpp new file mode 100644 index 00000000000..192fc46aa79 --- /dev/null +++ b/pytorch1.8.1/src/aten/src/ATen/native/npu/__Rshift__KernelNpu.cpp @@ -0,0 +1,76 @@ +// Copyright (c) 2020 Huawei Technologies Co., Ltd +// Copyright (c) 2019, Facebook CORPORATION. +// All rights reserved. +// +// Licensed under the BSD 3-Clause License (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// https://opensource.org/licenses/BSD-3-Clause +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "ATen/native/npu/utils/OpAdapter.h" + +namespace at { +namespace native { +using namespace at::native::npu; + +Tensor& __rshift___out_npu_nocheck( + const Tensor& self, + Scalar other, + Tensor& result) { + OpCommand cmd; + cmd.Name("RightShift") + .Input(self) + .Input(other,self.scalar_type()) + .Output(result) + .Run(); + + return result; +} + +Tensor& __rshift___out_npu_nocheck( + const Tensor& self, + const Tensor& other, + Tensor& result) { + OpCommand cmd; + cmd.Name("RightShift") + .Input(self) + .Input(other) + .Output(result) + .Run(); + + return result; +} + +Tensor __rshift___tensor_npu(const Tensor& self, const Tensor& other) { + // calculate the output size + auto outputSize = input_same_output_size(self); + // construct the output tensor of the NPU + Tensor result = OpPreparation::ApplyTensor(self); + __rshift___out_npu_nocheck( self, other,result); + + return result; +} + +Tensor __rshift___scalar_npu(const Tensor& self, Scalar other) { + // calculate the output size + auto outputSize = input_same_output_size(self); + // construct the output tensor of the NPU + Tensor result = OpPreparation::ApplyTensor(self); + + __rshift___out_npu_nocheck( self, other,result); + + return result; +} +TORCH_LIBRARY_IMPL(aten, NPU, m) { + m.impl("__rshift__.Tensor", TORCH_FN(__rshift___tensor_npu)); + m.impl("__rshift__.Scalar", TORCH_FN(__rshift___scalar_npu)); +} +} // namespace native +} // namespace at \ No newline at end of file diff --git a/pytorch1.8.1/test/test_npu/test_network_ops/test___rshift__.py b/pytorch1.8.1/test/test_npu/test_network_ops/test___rshift__.py new file mode 100644 index 00000000000..dbc6b3b6850 --- /dev/null +++ b/pytorch1.8.1/test/test_npu/test_network_ops/test___rshift__.py @@ -0,0 +1,68 @@ +# Copyright (c) 2020 Huawei Technologies Co., Ltd +# Copyright (c) 2019, Facebook CORPORATION. +# All rights reserved. +# +# Licensed under the BSD 3-Clause License (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://opensource.org/licenses/BSD-3-Clause +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +import numpy as np +from common_utils import TestCase, run_tests +from common_device_type import dtypes, instantiate_device_type_tests +from util_test import create_common_tensor + + +class TestRshift(TestCase): + def cpu_op_exec(self, input1, input2): + output = input1.__rshift__(input2) + output = output.numpy() + return output + + def npu_op_exec(self, input1, input2): + output = input1.__rshift__(input2) + output = output.to("cpu") + output = output.numpy() + return output + + def test_rshift_tensor(self, device): + format_list = [0] + shape_list = [(256, 32, 56)] + shape_format = [ + [np.int32, i, j] for i in format_list for j in shape_list + ] + for item in shape_format: + cpu_input1, npu_input1 = create_common_tensor(item, 0, 100) + cpu_input2 = torch.tensor([1]).to(torch.int32) + npu_input2 = cpu_input2.npu() + cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2) + npu_output = self.npu_op_exec(npu_input1, npu_input2) + cpu_output = cpu_output.astype(npu_output.dtype) + self.assertRtolEqual(cpu_output, npu_output) + + def test_rshift_scalar(self, device): + format_list = [0] + shape_list = [(256, 32, 56)] + shape_format = [ + [np.int32, i, j] for i in format_list for j in shape_list + ] + for item in shape_format: + cpu_input1, npu_input1 = create_common_tensor(item, 0, 100) + cpu_input2 = torch.tensor(1).to(torch.int32) + npu_input2 = cpu_input2.npu() + cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2) + npu_output = self.npu_op_exec(npu_input1, npu_input2) + cpu_output = cpu_output.astype(npu_output.dtype) + self.assertRtolEqual(cpu_output, npu_output) + +instantiate_device_type_tests(TestRshift, globals(), except_for='cpu') +if __name__ == "__main__": + run_tests() \ No newline at end of file -- Gitee From bf1c6850ac3d1d25a0dc0ed9df9662237ee2e4d9 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Wed, 29 Dec 2021 03:44:59 +0000 Subject: [PATCH 29/55] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20=E4=B8=B4=E6=97=B6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- "docs/\344\270\264\346\227\266/.keep" | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 "docs/\344\270\264\346\227\266/.keep" diff --git "a/docs/\344\270\264\346\227\266/.keep" "b/docs/\344\270\264\346\227\266/.keep" new file mode 100644 index 00000000000..e69de29bb2d -- Gitee From f5556e67633494c29d9330c31e6041d8ab8e0869 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Wed, 29 Dec 2021 03:45:22 +0000 Subject: [PATCH 30/55] e2e --- "docs/\344\270\264\346\227\266/e2e prof.md" | 77 +++++++++++++++++++++ 1 file changed, 77 insertions(+) create mode 100644 "docs/\344\270\264\346\227\266/e2e prof.md" diff --git "a/docs/\344\270\264\346\227\266/e2e prof.md" "b/docs/\344\270\264\346\227\266/e2e prof.md" new file mode 100644 index 00000000000..3de2c63de99 --- /dev/null +++ "b/docs/\344\270\264\346\227\266/e2e prof.md" @@ -0,0 +1,77 @@ +E2E prof 资料（初稿） + + + +**1. E2E prof****工具介绍** + + 在pytorch模型调优中，一般使用pytorch 的profiling工具分析模型性能瓶颈，然后有针对性的进行优化。但是pytorch的profiling工具仅能获取pytorch框架层面的性能数据， + +无法获取算子在device（cuda等）上的执行耗时数据，因此pytorch引入了nvtx工具可以同时获取pytorch框架和device硬件上算子性能数据，可以更方便、端到端的分析模型/算子性能瓶颈。 + +对于NPU设备来讲，每个算子在pytorch框架进行下发后，需要经过ACL/GE/FE等一系列编译流程后才能在device上执行。pytorch的profiling工具仅能获取pytorch框架层面的耗时数据，而前面介绍的 + +cann prof工具仅能获取CANN（ACL/GE/FE）和device上的耗时数据，无法整体上分析模型的性能瓶颈。因此，E2E Prof是一个端到端的性能分析工具，将pytorch框架和CANN（ACL/FE/GE和device） + +数据统一集成，实现端到端的分析模型/算子性能瓶颈。 + +**2.** **使用教程** + +添加with 语句进行使能E2E prof功能，profiler_result_path表示prof结果保存路径，默认为当前路径，use_e2e_profiler表示是否开启E2E Prof功能，默认为False（则仅开启CANN prof功能）。因NPU算子需要编译才能执行，为了保证数据的准确性，建议模型先运行10个step，在第10个step之后在进行e2e prof操作。 + +with torch.npu.profile(profiler_result_path="./results", use_e2e_profiler=True): + +​ model_train() + +**3.** **结果解析** + + E2E prof获得结果是原始数据，需要通过解析才能查看。以上述路径为例，会在profiler_result_path路径下创建文件夹用于保存原始数据。 + +![img](file:///C:/Users/PWX110~1/AppData/Local/Temp/msohtmlclip1/01/clip_image002.jpg) + + 切换到该路径下（如上述的PROF_000001_20211209183006186_APCOHQPPQLPFMNGC，进入该目录）执行脚本，/usr/local/Ascend/ascend-toolkit/latest/toolkit/tools/profiler/bin/msprof --export=on --output=./， 其中output表示 + + 原始数据的路径。完成后，会在原始数据路径下有timeline目录。 + +![img](file:///C:/Users/PWX110~1/AppData/Local/Temp/msohtmlclip1/01/clip_image004.jpg) + +Timeline路径下则是解析得到的性能数据，可以在chrome://tracing/中打开，示例如下： + +![img](file:///C:/Users/PWX110~1/AppData/Local/Temp/msohtmlclip1/01/clip_image006.jpg) + +目前分为四个层次（从上到下），第一层为pytorch框架的数据，第二面为ACL层面的数据，第三个层面为device的数据，第四个层面为AICPU的数据。后续还会增加更多层次的数据，如RTS/GE层面的数据。 + +**4.****高级用法** + +目前E2E prof默认配置将所有层面数据全部获取，即上述的四个层面。获取数据本身就会影响性能，如果获取数据过多，可能会导致的性能数据不具有参考价值。因此，提供了可配置选项，用于精细化控制获取哪些层面的数据。 + +with torch.npu.profile(profiler_result_path="./results", use_e2e_profiler=True，config=torch.npu. profileConfig(ACL_PROF_ACL_API=True, ACL_PROF_TASK_TIME=True, ACL_PROF_AICORE_METRICS=True,ACL_PROF_AICPU=True, ACL_PROF_L2CACHE=True, ACL_PROF_HCCL_TRACE=True, ACL_PROF_TRAINING_TRACE=True, aiCoreMetricsType=0)): + + + +· ACL_PROF_ACL_API：表示采集AscendCL接口的性能数据，默认True + +· ACL_PROF_TASK_TIME：采集AI Core算子的执行时间，默认True + +· ACL_PROF_AICORE_METRICS：表示采集AI Core性能指标数据，aicore_metrics入参处配置的性能指标采集项才有效，默认为True + +· ACL_PROF_AICPU：0x0008，集AI CPU任务的开始、结束轨迹数据，默认为True + +· ACL_PROF_L2CACHE：表示采集L2 Cache数据，默认True + +· ACL_PROF_HCCL_TRACE：表示采集HCCL数据，默认为True + +· ACL_PROF_TRAINING_TRACE：未知，待解释，默认为True + +其中aiCoreMetricsType的取值和定义如下，默认为0： + +​ ACL_AICORE_ARITHMETIC_UTILIZATION = 0, //各种计算类指标占比统计，包括采集项mac_fp16_ratio、mac_int8_ratio、vec_fp32_ratio、vec_fp16_ratio、vec_int32_ratio、vec_misc_ratio + +​ ACL_AICORE_PIPE_UTILIZATION = 1, //计算单元和搬运单元耗时占比，包括采集项vec_ratio、mac_ratio、scalar_ratio、mte1_ratio、mte2_ratio、mte3_ratio、icache_miss_rate + +​ ACL_AICORE_MEMORY_BANDWIDTH = 2, //外部内存读写类指令占比，包括采集项ub_read_bw、ub_write_bw、l1_read_bw、l1_write_bw、l2_read_bw、l2_write_bw、main_mem_read_bw、main_mem_write_bw + +​ ACL_AICORE_L0B_AND_WIDTH = 3, 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z!HouarA29l{Lju>2>x$E-;mlMvCSI2C)C2qfA76SyDyTS0B`!t(ksD*GdK7fQ=Zm-l9kyM zBx~=mKrVxLJX5vQ?Xo>d@(sw1IaZkQd|^ODPeMTeP9j_P^wYveFy@MWL*2*KP+pCK z@$8)&2A!KI@P_qMcW0YgH&-ako${pWI)|Ogd6S0eY2q~zhv@A7m^|`El_-YZzN-oz zO8qOCV|H0R$c?+y-!Yvg%YmVc6_Uv5+1K=-5zC?kMYGwD`OIDE*#S|vciEVIjWn-| zbxiwC4pmh4(J!_gxSM&f zhSts5rzi4a^L|J0?yJnlA$14RuTEZ0g2Zr;boA>C74l1n!vdoIPiP>88zUwcP#nZ( zNcS12VBmI`9Gcqnf%@TAlrwPXA20#vpZz9?gw6-V_0C%62c2XmHc2M6Wj(JY+v|44Y&Z#t)gJj;TAnSb(1BrwgM=xV8Qg1{MBJL2B_2m_|Jmz@BisBe_c-S z!0vBJy38(9?tLU70YvB5XTVPOe?xNLTZUM_0s7+y;Dg=&5ukV!`=8$){B;8)*AL4H o?tkwMDCPd=|I03*pk+WH(2%9g#q)jRgq~6Uwu(&lO+%mm1 Date: Wed, 29 Dec 2021 14:47:23 +0800 Subject: [PATCH 32/55] 12.29.pytorch --- ...55\347\273\203\346\214\207\345\215\227.md" | 65 ++++++++++++++++++- 1 file changed, 64 insertions(+), 1 deletion(-) diff --git "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" index 477a9d9c353..75d77ded928 100644 --- "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" +++ "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" @@ -2058,9 +2058,11 @@ E2E prof工具是一个将pytorch框架的profiling工具和cann prof工具获 添加with语句使能E2E prof功能 +``` with torch.npu.profile(profiler_result_path="./result",use_e2e_profiler=Ture): -​ model_train() + model_train() +``` - profiler_result_path表示prof结果保存路径，默认为当前路径。 - use_e2e_profiler表示是否开启E2E prof功能，默认为False（仅开启CANN prof功能）。 @@ -2071,9 +2073,70 @@ with torch.npu.profile(profiler_result_path="./result",use_e2e_profiler=Ture): 通过E2E prof工具获得的结果为原始数据，需要通过解析后查看。以使用教程中路径为例，工具会在profiler_result_path路径下创建文件夹以保存原始数据。 +![](D:\projects\pzr\pytorch\docs\zh\PyTorch网络模型移植&训练指南\figures\1.png) + +切换至如上图路径后，执行脚本。 + +``` +/usr/local/Ascend/ascend-toolkit/latest/toolkit/tools/profiler/bin/msprof --export=on --output=./ +``` + +- output：原始数据路径。 + +运行完成后，在原始数据路径下输出timeline目录。如下图： + +![](D:\projects\pzr\pytorch\docs\zh\PyTorch网络模型移植&训练指南\figures\2.png) + +timeline路径下为解析得到的性能数据，可以在chrome://tracing/中打开。 + +内容示例如下图： + + + +该示例分为4个层次，由上到下，第一层（MsprofTx）为Pytorch框架数据，第二层（AscendCL）为ACL层面数据，第三层（Task Scheduler）为device数据，第四层（AI CPU）为AICPU数据。 + +#### E2E prof高级设置 + +E2E prof工具默认配置获取上述所有层面数据。获取数据过程亦会影响性能，若获取数据过多，会导致性能数据不具备参考价值。因此，E2E prof工具提供了可配置选项，用于精细化控制获取部分层面数据。 + +``` +with torch.npu.profile(profiler_result_path="./results", use_e2e_profiler=True，config=torch.npu. profileConfig(ACL_PROF_ACL_API=True, ACL_PROF_TASK_TIME=True, ACL_PROF_AICORE_METRICS=True,ACL_PROF_AICPU=True, ACL_PROF_L2CACHE=True, ACL_PROF_HCCL_TRACE=True, ACL_PROF_TRAINING_TRACE=True, aiCoreMetricsType=0)): +``` +- ACL_PROF_ACL_API：表示采集AscendCL接口的性能数据，默认True +- ACL_PROF_TASK_TIME：采集AI Core算子的执行时间，默认True + + +- ·ACL_PROF_AICORE_METRICS：表示采集AI Core性能指标数据，aicore_metrics入参处配置的性能指标采集项才有效，默认为True + + +- ACL_PROF_AICPU：0x0008，集AI CPU任务的开始、结束轨迹数据，默认为True + +- · ACL_PROF_L2CACHE：表示采集L2 Cache数据，默认True + +- ACL_PROF_HCCL_TRACE：表示采集HCCL数据，默认为True + +- ACL_PROF_TRAINING_TRACE：未知，待解释，默认为True + +其中，aiCoreMetricsType的取值和定义如下，默认为0： + +``` +ACL_AICORE_ARITHMETIC_UTILIZATION = 0, //各种计算类指标占比统计，包括采集项mac_fp16_ratio、mac_int8_ratio、vec_fp32_ratio、vec_fp16_ratio、vec_int32_ratio、vec_misc_ratio + + ACL_AICORE_PIPE_UTILIZATION = 1, //计算单元和搬运单元耗时占比，包括采集项vec_ratio、mac_ratio、scalar_ratio、mte1_ratio、mte2_ratio、mte3_ratio、icache_miss_rate + + ACL_AICORE_MEMORY_BANDWIDTH = 2, //外部内存读写类指令占比，包括采集项ub_read_bw、ub_write_bw、l1_read_bw、l1_write_bw、l2_read_bw、l2_write_bw、main_mem_read_bw、main_mem_write_bw + + ACL_AICORE_L0B_AND_WIDTH = 3, //内部内存读写类指令占比，包括采集项scalar_ld_ratio、scalar_st_ratio、l0a_read_bw、l0a_write_bw、l0b_read_bw、l0b_write_bw、l0c_read_bw、l0c_write_bw + + ACL_AICORE_RESOURCE_CONFLICT_RATIO = 4, //流水线队列类指令占比，包括采集项vec_bankgroup_cflt_ratio、vec_bank_cflt_ratio、vec_resc_cflt_ratio、mte1_iq_full_ratio、mte2_iq_full_ratio、mte3_iq_full_ratio、cube_iq_full_ratio、vec_iq_full_ratio、iq_full_ratio + + ACL_AICORE_NONE = 0xFF 不采集 +``` + +​ ### 亲和库 -- Gitee From 204ed0fa335f5090dc07f1ad1780217de6b8ad10 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Wed, 29 Dec 2021 14:55:21 +0800 Subject: [PATCH 33/55] pic --- .../figures/1.png" | Bin 0 -> 8207 bytes .../figures/2.png" | Bin 0 -> 10834 bytes .../figures/3.png" | Bin 0 -> 62533 bytes 3 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 "docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/figures/1.png" create mode 100644 "docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/figures/2.png" create mode 100644 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zO8qOCV|H0R$c?+y-!Yvg%YmVc6_Uv5+1K=-5zC?kMYGwD`OIDE*#S|vciEVIjWn-| zbxiwC4pmh4(J!_gxSM&f zhSts5rzi4a^L|J0?yJnlA$14RuTEZ0g2Zr;boA>C74l1n!vdoIPiP>88zUwcP#nZ( zNcS12VBmI`9Gcqnf%@TAlrwPXA20#vpZz9?gw6-V_0C%62c2XmHc2M6Wj(JY+v|44Y&Z#t)gJj;TAnSb(1BrwgM=xV8Qg1{MBJL2B_2m_|Jmz@BisBe_c-S z!0vBJy38(9?tLU70YvB5XTVPOe?xNLTZUM_0s7+y;Dg=&5ukV!`=8$){B;8)*AL4H o?tkwMDCPd=|I03*pk+WH(2%9g#q)jRgq~6Uwu(&lO+%mm1 Date: Thu, 30 Dec 2021 15:04:28 +0800 Subject: [PATCH 34/55] new pic --- ...55\347\273\203\346\214\207\345\215\227.md" | 46 ++++++++++--------- 1 file changed, 25 insertions(+), 21 deletions(-) diff --git "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" index 75d77ded928..9882e75b209 100644 --- "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" +++ "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" @@ -2071,29 +2071,35 @@ with torch.npu.profile(profiler_result_path="./result",use_e2e_profiler=Ture): #### E2E prof结果解析 -通过E2E prof工具获得的结果为原始数据，需要通过解析后查看。以使用教程中路径为例，工具会在profiler_result_path路径下创建文件夹以保存原始数据。 +通过E2E prof工具获得的结果为原始数据，需要通过解析后查看。 -![](D:\projects\pzr\pytorch\docs\zh\PyTorch网络模型移植&训练指南\figures\1.png) +1. 以使用教程中路径为例，工具会在profiler_result_path路径下创建文件夹以保存原始数据。![](/figures/1.png) -切换至如上图路径后，执行脚本。 +2. 切换至如上图./result路径后，执行脚本。 -``` -/usr/local/Ascend/ascend-toolkit/latest/toolkit/tools/profiler/bin/msprof --export=on --output=./ -``` + ``` + /usr/local/Ascend/ascend-toolkit/latest/toolkit/tools/profiler/bin/msprof --export=on --output=./ + ``` + + - output：原始数据路径。 -- output：原始数据路径。 +3. 运行完成后，在原始数据路径下输出timeline目录。如下图： -运行完成后，在原始数据路径下输出timeline目录。如下图： + ![](/figures/2.png) -![](D:\projects\pzr\pytorch\docs\zh\PyTorch网络模型移植&训练指南\figures\2.png) +4. timeline路径下为解析得到的性能数据，可以通过chrome://tracing/中打开。 -timeline路径下为解析得到的性能数据，可以在chrome://tracing/中打开。 + 1. 浏览器进入chrome://tracing/。 -内容示例如下图： + 2. 点击load，上传文件查看。 - + -该示例分为4个层次，由上到下，第一层（MsprofTx）为Pytorch框架数据，第二层（AscendCL）为ACL层面数据，第三层（Task Scheduler）为device数据，第四层（AI CPU）为AICPU数据。 + 内容示例如下图： + + + + 该示例分为4个层次，由上到下，第一层（MsprofTx）为Pytorch框架数据，第二层（AscendCL）为ACL层面数据，第三层（Task Scheduler）为device数据，第四层（AI CPU）为AICPU数据。 #### E2E prof高级设置 @@ -2122,19 +2128,17 @@ with torch.npu.profile(profiler_result_path="./results", use_e2e_profiler=True 其中，aiCoreMetricsType的取值和定义如下，默认为0： -``` -ACL_AICORE_ARITHMETIC_UTILIZATION = 0, //各种计算类指标占比统计，包括采集项mac_fp16_ratio、mac_int8_ratio、vec_fp32_ratio、vec_fp16_ratio、vec_int32_ratio、vec_misc_ratio +- ACL_AICORE_ARITHMETIC_UTILIZATION = 0：表示各种计算类指标占比统计，包括采集项mac_fp16_ratio、mac_int8_ratio、vec_fp32_ratio、vec_fp16_ratio、vec_int32_ratio、vec_misc_ratio - ACL_AICORE_PIPE_UTILIZATION = 1, //计算单元和搬运单元耗时占比，包括采集项vec_ratio、mac_ratio、scalar_ratio、mte1_ratio、mte2_ratio、mte3_ratio、icache_miss_rate +- ACL_AICORE_PIPE_UTILIZATION = 1：表示计算单元和搬运单元耗时占比，包括采集项vec_ratio、mac_ratio、scalar_ratio、mte1_ratio、mte2_ratio、mte3_ratio、icache_miss_rate - ACL_AICORE_MEMORY_BANDWIDTH = 2, //外部内存读写类指令占比，包括采集项ub_read_bw、ub_write_bw、l1_read_bw、l1_write_bw、l2_read_bw、l2_write_bw、main_mem_read_bw、main_mem_write_bw +- ACL_AICORE_MEMORY_BANDWIDTH = 2：表示外部内存读写类指令占比，包括采集项ub_read_bw、ub_write_bw、l1_read_bw、l1_write_bw、l2_read_bw、l2_write_bw、main_mem_read_bw、main_mem_write_bw - ACL_AICORE_L0B_AND_WIDTH = 3, //内部内存读写类指令占比，包括采集项scalar_ld_ratio、scalar_st_ratio、l0a_read_bw、l0a_write_bw、l0b_read_bw、l0b_write_bw、l0c_read_bw、l0c_write_bw +- ACL_AICORE_L0B_AND_WIDTH ：表示内部内存读写类指令占比，包括采集项scalar_ld_ratio、scalar_st_ratio、l0a_read_bw、l0a_write_bw、l0b_read_bw、l0b_write_bw、l0c_read_bw、l0c_write_bw - ACL_AICORE_RESOURCE_CONFLICT_RATIO = 4, //流水线队列类指令占比，包括采集项vec_bankgroup_cflt_ratio、vec_bank_cflt_ratio、vec_resc_cflt_ratio、mte1_iq_full_ratio、mte2_iq_full_ratio、mte3_iq_full_ratio、cube_iq_full_ratio、vec_iq_full_ratio、iq_full_ratio +- ACL_AICORE_RESOURCE_CONFLICT_RATIO ：表示流水线队列类指令占比，包括采集项vec_bankgroup_cflt_ratio、vec_bank_cflt_ratio、vec_resc_cflt_ratio、mte1_iq_full_ratio、mte2_iq_full_ratio、mte3_iq_full_ratio、cube_iq_full_ratio、vec_iq_full_ratio、iq_full_ratio - ACL_AICORE_NONE = 0xFF 不采集 -``` +- ACL_AICORE_NONE = 0xFF：表示不采集 ​ -- Gitee From a86aea2e6a61e8ea6089864706161ba22d34cef5 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Thu, 30 Dec 2021 15:09:38 +0800 Subject: [PATCH 35/55] new pic --- .../figures/chrometracing.png" | Bin 0 -> 22612 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 "docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/figures/chrometracing.png" diff --git "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/figures/chrometracing.png" 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zM7}Efb4w=LnBmGO#-zHC|Clkxy~ySdxW%Ddtjk2jkp9?P@)55hXQN66>w!zl^Y$nL zy^Gl?E~&S|2D)`E88>}lKi`^zawc$z~_n`Su83zmV3l%jG>WPf|zSS6zQTHZKO#SXzb6GA)Iioo5 zOpS<&{e?^(+M%bK;DseX33uI)Y)?9yY!xM%9?pXY6pS}HoxTcJ$IavTaS3n>h~A zpf>BA8sdC!+Wf*di?j~=b&l$q^c%3oGzx)Q_9k#4)q}z8;o%LIeB?de(t`G%Z7hbp zO{7~12YSLAw=xK|xrog75+V2y7TO+&;66k;|$S>LRA1DlMia1eX&rk>;7Z1ZTKgY_&+P| z1Hu&Kir>GCobB!xFJ6=({`WfMkU6G;pR#yo0E3(Q&ILPu4Z0)A6<>` zoc_W4?|;c%->@2bGuXWv2->%ry50EKYG5hz=f= Date: Thu, 30 Dec 2021 15:11:46 +0800 Subject: [PATCH 36/55] show pic --- ...5&\350\256\255\347\273\203\346\214\207\345\215\227.md" | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" index 9882e75b209..3412514c30e 100644 --- "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" +++ "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" @@ -2073,7 +2073,7 @@ with torch.npu.profile(profiler_result_path="./result",use_e2e_profiler=Ture): 通过E2E prof工具获得的结果为原始数据，需要通过解析后查看。 -1. 以使用教程中路径为例，工具会在profiler_result_path路径下创建文件夹以保存原始数据。![](/figures/1.png) +1. 以使用教程中路径为例，工具会在profiler_result_path路径下创建文件夹以保存原始数据。![](figures/1.png) 2. 切换至如上图./result路径后，执行脚本。 @@ -2085,7 +2085,7 @@ with torch.npu.profile(profiler_result_path="./result",use_e2e_profiler=Ture): 3. 运行完成后，在原始数据路径下输出timeline目录。如下图： - ![](/figures/2.png) + ![](figures/2.png) 4. timeline路径下为解析得到的性能数据，可以通过chrome://tracing/中打开。 @@ -2093,11 +2093,11 @@ with torch.npu.profile(profiler_result_path="./result",use_e2e_profiler=Ture): 2. 点击load，上传文件查看。 - + 内容示例如下图： - + 该示例分为4个层次，由上到下，第一层（MsprofTx）为Pytorch框架数据，第二层（AscendCL）为ACL层面数据，第三层（Task Scheduler）为device数据，第四层（AI CPU）为AICPU数据。 -- Gitee From c2381add74b004791fa972dfc6e56d79e1a0a6b2 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Thu, 30 Dec 2021 15:20:33 +0800 Subject: [PATCH 37/55] text update --- ...4\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" index 3412514c30e..176c1f806ab 100644 --- "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" +++ "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" @@ -2067,7 +2067,7 @@ with torch.npu.profile(profiler_result_path="./result",use_e2e_profiler=Ture): - profiler_result_path表示prof结果保存路径，默认为当前路径。 - use_e2e_profiler表示是否开启E2E prof功能，默认为False（仅开启CANN prof功能）。 -（因NUP算子需要编译后才能执行，为保证数据的准确性，建议先运行10个step，在第十个step后再进行E2E prof操作。） +（因NUP算子需要编译后才能执行，为保证数据的准确性，建议先运行10个step，在第十个step后再进行E2E prof操作，并且一般只需要profiling1个或者2个setp即可。） #### E2E prof结果解析 @@ -2124,7 +2124,7 @@ with torch.npu.profile(profiler_result_path="./results", use_e2e_profiler=True - ACL_PROF_HCCL_TRACE：表示采集HCCL数据，默认为True -- ACL_PROF_TRAINING_TRACE：未知，待解释，默认为True +- ACL_PROF_TRAINING_TRACE：表示迭代轨迹数据，记录模型正向和反向等步骤，默认为True 其中，aiCoreMetricsType的取值和定义如下，默认为0： -- Gitee From c4136c455ce02276afaede7eda447d7e12fa3bb6 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Thu, 30 Dec 2021 16:20:10 +0800 Subject: [PATCH 38/55] new api support --- ...PI\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" index ec95cc153c0..b789bb23e4e 100644 --- "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" +++ "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" @@ -787,7 +787,7 @@ | 120 | torch.nn.BatchNorm2d | 是 | | 121 | torch.nn.BatchNorm3d | 是 | | 122 | torch.nn.GroupNorm | 是 | -| 123 | torch.nn.SyncBatchNorm | 否 | +| 123 | torch.nn.SyncBatchNorm | 是 | | 124 | torch.nn.SyncBatchNorm.convert_sync_batchnorm | 否 | | 125 | torch.nn.InstanceNorm1d | 是 | | 126 | torch.nn.InstanceNorm2d | 是 | -- Gitee From 92fb47acfca890d930539bd5612cc6b0672f720b Mon Sep 17 00:00:00 2001 From: pengzirong Date: Thu, 30 Dec 2021 16:21:04 +0800 Subject: [PATCH 39/55] new api support --- ...PI\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" index b789bb23e4e..c8b6a8113c6 100644 --- "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" +++ "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" @@ -788,7 +788,7 @@ | 121 | torch.nn.BatchNorm3d | 是 | | 122 | torch.nn.GroupNorm | 是 | | 123 | torch.nn.SyncBatchNorm | 是 | -| 124 | torch.nn.SyncBatchNorm.convert_sync_batchnorm | 否 | +| 124 | torch.nn.SyncBatchNorm.convert_sync_batchnorm | 是 | | 125 | torch.nn.InstanceNorm1d | 是 | | 126 | torch.nn.InstanceNorm2d | 是 | | 127 | torch.nn.InstanceNorm3d | 是 | -- Gitee From 1df6dd2cc6b32927ca405eb1956ef075952e10c8 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Fri, 31 Dec 2021 15:20:49 +0800 Subject: [PATCH 40/55] update --- ...\214\201\346\270\205\345\215\225_1.5.0.md" | 1374 ----------------- 1 file changed, 1374 deletions(-) delete mode 100644 "docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" diff --git "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" deleted file mode 100644 index c8b6a8113c6..00000000000 --- "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" +++ /dev/null @@ -1,1374 +0,0 @@ -## [Tensors](https://pytorch.org/docs/1.5.0/torch.html) - -| 序号 | API名称 | 支持情况 | -| ---- | ----------------------------- | -------------------------------------- | -| 1 | torch.is_tensor | 是 | -| 2 | torch.is_storage | 是 | -| 3 | torch.is_complex | 是，支持判断，但当前硬件限制不支持复数 | -| 4 | torch.is_floating_point | 是 | -| 5 | torch.set_default_dtype | 是 | -| 6 | torch.get_default_dtype | 是 | -| 7 | torch.set_default_tensor_type | 是 | -| 8 | torch.numel | 是 | -| 9 | torch.set_printoptions | 是 | -| 10 | torch.set_flush_denormal | 是 | -| 11 | torch.tensor | 是 | -| 12 | torch.sparse_coo_tensor | 否 | -| 13 | torch.as_tensor | 是 | -| 14 | torch.as_strided | 是 | -| 15 | torch.from_numpy | 是 | -| 16 | torch.zeros | 是 | -| 17 | torch.zeros_like | 是 | -| 18 | torch.ones | 是 | -| 19 | torch.ones_like | 是 | -| 20 | torch.arange | 是 | -| 21 | torch.range | 是 | -| 22 | torch.linspace | 是 | -| 23 | torch.logspace | 是 | -| 24 | torch.eye | 是 | -| 25 | torch.empty | 是 | -| 26 | torch.empty_like | 是 | -| 27 | torch.empty_strided | 是 | -| 28 | torch.full | 是 | -| 29 | torch.full_like | 是 | -| 30 | torch.quantize_per_tensor | 是 | -| 31 | torch.quantize_per_channel | 是 | -| 32 | torch.cat | 是 | -| 33 | torch.chunk | 是 | -| 34 | torch.gather | 是 | -| 35 | torch.index_select | 是 | -| 36 | torch.masked_select | 是 | -| 37 | torch.narrow | 是 | -| 38 | torch.nonzero | 是 | -| 39 | torch.reshape | 是 | -| 40 | torch.split | 是 | -| 41 | torch.squeeze | 是 | -| 42 | torch.stack | 是 | -| 43 | torch.t | 是 | -| 44 | torch.take | 是 | -| 45 | torch.transpose | 是 | -| 46 | torch.unbind | 是 | -| 47 | torch.unsqueeze | 是 | -| 48 | torch.where | 是 | - -## Generators - -| 序号 | API名称 | 是否支持 | -| ---- | ------------------------------- | -------- | -| 1 | torch._C.Generator | 是 | -| 2 | torch._C.Generator.device | 是 | -| 3 | torch._C.Generator.get_state | 否 | -| 4 | torch._C.Generator.initial_seed | 是 | -| 5 | torch._C.Generator.manual_seed | 是 | -| 6 | torch._C.Generator.seed | 是 | -| 7 | torch._C.Generator.set_state | 否 | - -## Random sampling - -| 序号 | API名称 | 是否支持 | -| ---- | ------------------------------------------ | -------- | -| 1 | torch.seed | 是 | -| 2 | torch.manual_seed | 是 | -| 3 | torch.initial_seed | 是 | -| 4 | torch.get_rng_state | 是 | -| 5 | torch.set_rng_state | 是 | -| 6 | torch.torch.default_generator | 是 | -| 7 | torch.bernoulli | 是 | -| 8 | torch.multinomial | 是 | -| 9 | torch.normal | 是 | -| 10 | torch.poisson | 否 | -| 11 | torch.rand | 是 | -| 12 | torch.rand_like | 是 | -| 13 | torch.randint | 是 | -| 14 | torch.randint_like | 是 | -| 15 | torch.randn | 是 | -| 16 | torch.randn_like | 是 | -| 17 | torch.randperm | 是 | -| 18 | torch.Tensor.bernoulli_() | 是 | -| 19 | torch.Tensor.bernoulli_() | 是 | -| 20 | torch.Tensor.exponential_() | 否 | -| 21 | torch.Tensor.geometric_() | 否 | -| 22 | torch.Tensor.log_normal_() | 否 | -| 23 | torch.Tensor.normal_() | 是 | -| 24 | torch.Tensor.random_() | 是 | -| 25 | torch.Tensor.uniform_() | 是 | -| 26 | torch.quasirandom.SobolEngine | 是 | -| 27 | torch.quasirandom.SobolEngine.draw | 是 | -| 28 | torch.quasirandom.SobolEngine.fast_forward | 是 | -| 29 | torch.quasirandom.SobolEngine.reset | 是 | - -## Serialization - -| 序号 | API名称 | 是否支持 | -| ---- | ---------- | -------- | -| 1 | torch.save | 是 | -| 2 | torch.load | 是 | - -## Math operations - -| 序号 | API名称 | 是否支持 | -| ---- | ------------------------ | -------- | -| 1 | torch.abs | 是 | -| 2 | torch.acos | 是 | -| 3 | torch.add | 是 | -| 4 | torch.addcdiv | 是 | -| 5 | torch.addcmul | 是 | -| 6 | torch.angle | 否 | -| 7 | torch.asin | 是 | -| 8 | torch.atan | 是 | -| 9 | torch.atan2 | 是 | -| 10 | torch.bitwise_not | 是 | -| 11 | torch.bitwise_and | 是 | -| 12 | torch.bitwise_or | 是 | -| 13 | torch.bitwise_xor | 是 | -| 14 | torch.ceil | 是 | -| 15 | torch.clamp | 是 | -| 16 | torch.conj | 否 | -| 17 | torch.cos | 是 | -| 18 | torch.cosh | 是 | -| 19 | torch.div | 是 | -| 20 | torch.digamma | 否 | -| 21 | torch.erf | 是 | -| 22 | torch.erfc | 是 | -| 23 | torch.erfinv | 是 | -| 24 | torch.exp | 是 | -| 25 | torch.expm1 | 是 | -| 26 | torch.floor | 是 | -| 27 | torch.floor_divide | 是 | -| 28 | torch.fmod | 是 | -| 29 | torch.frac | 是 | -| 30 | torch.imag | 否 | -| 31 | torch.lerp | 是 | -| 32 | torch.lgamma | 否 | -| 33 | torch.log | 是 | -| 34 | torch.log10 | 是 | -| 35 | torch.log1p | 是 | -| 36 | torch.log2 | 是 | -| 37 | torch.logical_and | 是 | -| 38 | torch.logical_not | 是 | -| 39 | torch.logical_or | 是 | -| 40 | torch.logical_xor | 是 | -| 41 | torch.mul | 是 | -| 42 | torch.mvlgamma | 否 | -| 43 | torch.neg | 是 | -| 44 | torch.polygamma | 否 | -| 45 | torch.pow | 是 | -| 46 | torch.real | 是 | -| 47 | torch.reciprocal | 是 | -| 48 | torch.remainder | 是 | -| 49 | torch.round | 是 | -| 50 | torch.rsqrt | 是 | -| 51 | torch.sigmoid | 是 | -| 52 | torch.sign | 是 | -| 53 | torch.sin | 是 | -| 54 | torch.sinh | 是 | -| 55 | torch.sqrt | 是 | -| 56 | torch.square | 是 | -| 57 | torch.tan | 是 | -| 58 | torch.tanh | 是 | -| 59 | torch.true_divide | 是 | -| 60 | torch.trunc | 是 | -| 61 | torch.argmax | 是 | -| 62 | torch.argmin | 是 | -| 63 | torch.dist | 是 | -| 64 | torch.logsumexp | 是 | -| 65 | torch.mean | 是 | -| 66 | torch.median | 是 | -| 67 | torch.mode | 否 | -| 68 | torch.norm | 是 | -| 69 | torch.prod | 是 | -| 70 | torch.std | 是 | -| 71 | torch.std_mean | 是 | -| 72 | torch.sum | 是 | -| 73 | torch.unique | 是 | -| 74 | torch.unique_consecutive | 否 | -| 75 | torch.var | 否 | -| 76 | torch.var_mean | 否 | -| 77 | torch.allclose | 是 | -| 78 | torch.argsort | 是 | -| 79 | torch.eq | 是 | -| 80 | torch.equal | 是 | -| 81 | torch.ge | 是 | -| 82 | torch.gt | 是 | -| 83 | torch.isfinite | 是 | -| 84 | torch.isinf | 是 | -| 85 | torch.isnan | 是 | -| 86 | torch.kthvalue | 是 | -| 87 | torch.le | 是 | -| 88 | torch.lt | 是 | -| 89 | torch.max | 是 | -| 90 | torch.min | 是 | -| 91 | torch.ne | 是 | -| 92 | torch.sort | 是 | -| 93 | torch.topk | 是 | -| 94 | torch.fft | 否 | -| 95 | torch.ifft | 否 | -| 96 | torch.rfft | 否 | -| 97 | torch.irfft | 否 | -| 98 | torch.stft | 否 | -| 99 | torch.bartlett_window | 是 | -| 100 | torch.blackman_window | 是 | -| 101 | torch.hamming_window | 是 | -| 102 | torch.hann_window | 是 | -| 103 | torch.bincount | 是 | -| 104 | torch.broadcast_tensors | 是 | -| 105 | torch.cartesian_prod | 是 | -| 106 | torch.cdist | 是 | -| 107 | torch.combinations | 否 | -| 108 | torch.cross | 是 | -| 109 | torch.cummax | 是 | -| 110 | torch.cummin | 是 | -| 111 | torch.cumprod | 是 | -| 112 | torch.cumsum | 是 | -| 113 | torch.diag | 是 | -| 114 | torch.diag_embed | 是 | -| 115 | torch.diagflat | 是 | -| 116 | torch.diagonal | 是 | -| 117 | torch.einsum | 是 | -| 118 | torch.flatten | 是 | -| 119 | torch.flip | 是 | -| 120 | torch.rot90 | 是 | -| 121 | torch.histc | 否 | -| 122 | torch.meshgrid | 是 | -| 123 | torch.renorm | 是 | -| 124 | torch.repeat_interleave | 是 | -| 125 | torch.roll | 是 | -| 126 | torch.tensordot | 是 | -| 127 | torch.trace | 否 | -| 128 | torch.tril | 是 | -| 129 | torch.tril_indices | 是 | -| 130 | torch.triu | 是 | -| 131 | torch.triu_indices | 是 | -| 132 | torch.addbmm | 是 | -| 133 | torch.addmm | 是 | -| 134 | torch.addmv | 是 | -| 135 | torch.addr | 是 | -| 136 | torch.baddbmm | 是 | -| 137 | torch.bmm | 是 | -| 138 | torch.chain_matmul | 是 | -| 139 | torch.cholesky | 否 | -| 140 | torch.cholesky_inverse | 否 | -| 141 | torch.cholesky_solve | 否 | -| 142 | torch.dot | 是 | -| 143 | torch.eig | 否 | -| 144 | torch.geqrf | 否 | -| 145 | torch.ger | 是 | -| 146 | torch.inverse | 是 | -| 147 | torch.det | 否 | -| 148 | torch.logdet | 否 | -| 149 | torch.slogdet | 是 | -| 150 | torch.lstsq | 否 | -| 151 | torch.lu | 否 | -| 152 | torch.lu_solve | 否 | -| 153 | torch.lu_unpack | 否 | -| 154 | torch.matmul | 是 | -| 155 | torch.matrix_power | 是 | -| 156 | torch.matrix_rank | 是 | -| 157 | torch.mm | 是 | -| 158 | torch.mv | 是 | -| 159 | torch.orgqr | 否 | -| 160 | torch.ormqr | 否 | -| 161 | torch.pinverse | 是 | -| 162 | torch.qr | 是 | -| 163 | torch.solve | 否 | -| 164 | torch.svd | 是 | -| 165 | torch.svd_lowrank | 是 | -| 166 | torch.pca_lowrank | 是 | -| 167 | torch.symeig | 是 | -| 168 | torch.lobpcg | 否 | -| 169 | torch.trapz | 是 | -| 170 | torch.triangular_solve | 是 | - -## Utilities - -| 序号 | API名称 | 是否支持 | -| ---- | ----------------------------- | -------- | -| 1 | torch.compiled_with_cxx11_abi | 是 | -| 2 | torch.result_type | 是 | -| 3 | torch.can_cast | 是 | -| 4 | torch.promote_types | 是 | - -## Other - -| 序号 | API名称 | 是否支持 | -| ---- | ----------------------------- | -------- | -| 1 | torch.no_grad | 是 | -| 2 | torch.enable_grad | 是 | -| 3 | torch.set_grad_enabled | 是 | -| 4 | torch.get_num_threads | 是 | -| 5 | torch.set_num_threads | 是 | -| 6 | torch.get_num_interop_threads | 是 | -| 7 | torch.set_num_interop_threads | 是 | - -## torch.Tensor - -| 序号 | API名称 | 是否支持 | -| ---- | -------------------------------------- | -------- | -| 1 | torch.Tensor | 是 | -| 2 | torch.Tensor.new_tensor | 是 | -| 3 | torch.Tensor.new_full | 是 | -| 4 | torch.Tensor.new_empty | 是 | -| 5 | torch.Tensor.new_ones | 是 | -| 6 | torch.Tensor.new_zeros | 是 | -| 7 | torch.Tensor.is_cuda | 是 | -| 8 | torch.Tensor.is_quantized | 是 | -| 9 | torch.Tensor.device | 是 | -| 10 | torch.Tensor.ndim | 是 | -| 11 | torch.Tensor.T | 是 | -| 12 | torch.Tensor.abs | 是 | -| 13 | torch.Tensor.abs_ | 是 | -| 14 | torch.Tensor.acos | 是 | -| 15 | torch.Tensor.acos_ | 是 | -| 16 | torch.Tensor.add | 是 | -| 17 | torch.Tensor.add_ | 是 | -| 18 | torch.Tensor.addbmm | 是 | -| 19 | torch.Tensor.addbmm_ | 是 | -| 20 | torch.Tensor.addcdiv | 是 | -| 21 | torch.Tensor.addcdiv_ | 是 | -| 22 | torch.Tensor.addcmul | 是 | -| 23 | torch.Tensor.addcmul_ | 是 | -| 24 | torch.Tensor.addmm | 是 | -| 25 | torch.Tensor.addmm_ | 是 | -| 26 | torch.Tensor.addmv | 是 | -| 27 | torch.Tensor.addmv_ | 是 | -| 28 | torch.Tensor.addr | 是 | -| 29 | torch.Tensor.addr_ | 是 | -| 30 | torch.Tensor.allclose | 是 | -| 31 | torch.Tensor.angle | 否 | -| 32 | torch.Tensor.apply_ | 否 | -| 33 | torch.Tensor.argmax | 是 | -| 34 | torch.Tensor.argmin | 是 | -| 35 | torch.Tensor.argsort | 是 | -| 36 | torch.Tensor.asin | 是 | -| 37 | torch.Tensor.asin_ | 是 | -| 38 | torch.Tensor.as_strided | 是 | -| 39 | torch.Tensor.atan | 是 | -| 40 | torch.Tensor.atan2 | 是 | -| 41 | torch.Tensor.atan2_ | 是 | -| 42 | torch.Tensor.atan_ | 是 | -| 43 | torch.Tensor.baddbmm | 是 | -| 44 | torch.Tensor.baddbmm_ | 是 | -| 45 | torch.Tensor.bernoulli | 是 | -| 46 | torch.Tensor.bernoulli_ | 是 | -| 47 | torch.Tensor.bfloat16 | 否 | -| 48 | torch.Tensor.bincount | 是 | -| 49 | torch.Tensor.bitwise_not | 是 | -| 50 | torch.Tensor.bitwise_not_ | 是 | -| 51 | torch.Tensor.bitwise_and | 是 | -| 52 | torch.Tensor.bitwise_and_ | 是 | -| 53 | torch.Tensor.bitwise_or | 是 | -| 54 | torch.Tensor.bitwise_or_ | 是 | -| 55 | torch.Tensor.bitwise_xor | 是 | -| 56 | torch.Tensor.bitwise_xor_ | 是 | -| 57 | torch.Tensor.bmm | 是 | -| 58 | torch.Tensor.bool | 是 | -| 59 | torch.Tensor.byte | 是 | -| 60 | torch.Tensor.cauchy_ | 否 | -| 61 | torch.Tensor.ceil | 是 | -| 62 | torch.Tensor.ceil_ | 是 | -| 63 | torch.Tensor.char | 是 | -| 64 | torch.Tensor.cholesky | 否 | -| 65 | torch.Tensor.cholesky_inverse | 否 | -| 66 | torch.Tensor.cholesky_solve | 否 | -| 67 | torch.Tensor.chunk | 是 | -| 68 | torch.Tensor.clamp | 是 | -| 69 | torch.Tensor.clamp_ | 是 | -| 70 | torch.Tensor.clone | 是 | -| 71 | torch.Tensor.contiguous | 是 | -| 72 | torch.Tensor.copy_ | 是 | -| 73 | torch.Tensor.conj | 否 | -| 74 | torch.Tensor.cos | 是 | -| 75 | torch.Tensor.cos_ | 是 | -| 76 | torch.Tensor.cosh | 是 | -| 77 | torch.Tensor.cosh_ | 是 | -| 78 | torch.Tensor.cpu | 是 | -| 79 | torch.Tensor.cross | 是 | -| 80 | torch.Tensor.cuda | 否 | -| 81 | torch.Tensor.cummax | 是 | -| 82 | torch.Tensor.cummin | 是 | -| 83 | torch.Tensor.cumprod | 是 | -| 84 | torch.Tensor.cumsum | 是 | -| 85 | torch.Tensor.data_ptr | 是 | -| 86 | torch.Tensor.dequantize | 否 | -| 87 | torch.Tensor.det | 否 | -| 88 | torch.Tensor.dense_dim | 否 | -| 89 | torch.Tensor.diag | 是 | -| 90 | torch.Tensor.diag_embed | 是 | -| 91 | torch.Tensor.diagflat | 是 | -| 92 | torch.Tensor.diagonal | 是 | -| 93 | torch.Tensor.fill_diagonal_ | 是 | -| 94 | torch.Tensor.digamma | 否 | -| 95 | torch.Tensor.digamma_ | 否 | -| 96 | torch.Tensor.dim | 是 | -| 97 | torch.Tensor.dist | 是 | -| 98 | torch.Tensor.div | 是 | -| 99 | torch.Tensor.div_ | 是 | -| 100 | torch.Tensor.dot | 是 | -| 101 | torch.Tensor.double | 否 | -| 102 | torch.Tensor.eig | 否 | -| 103 | torch.Tensor.element_size | 是 | -| 104 | torch.Tensor.eq | 是 | -| 105 | torch.Tensor.eq_ | 是 | -| 106 | torch.Tensor.equal | 是 | -| 107 | torch.Tensor.erf | 是 | -| 108 | torch.Tensor.erf_ | 是 | -| 109 | torch.Tensor.erfc | 是 | -| 110 | torch.Tensor.erfc_ | 是 | -| 111 | torch.Tensor.erfinv | 是 | -| 112 | torch.Tensor.erfinv_ | 是 | -| 113 | torch.Tensor.exp | 是 | -| 114 | torch.Tensor.exp_ | 是 | -| 115 | torch.Tensor.expm1 | 是 | -| 116 | torch.Tensor.expm1_ | 是 | -| 117 | torch.Tensor.expand | 是 | -| 118 | torch.Tensor.expand_as | 是 | -| 119 | torch.Tensor.exponential_ | 否 | -| 120 | torch.Tensor.fft | 否 | -| 121 | torch.Tensor.fill_ | 是 | -| 122 | torch.Tensor.flatten | 是 | -| 123 | torch.Tensor.flip | 是 | -| 124 | torch.Tensor.float | 是 | -| 125 | torch.Tensor.floor | 是 | -| 126 | torch.Tensor.floor_ | 是 | -| 127 | torch.Tensor.floor_divide | 是 | -| 128 | torch.Tensor.floor_divide_ | 是 | -| 129 | torch.Tensor.fmod | 是 | -| 130 | torch.Tensor.fmod_ | 是 | -| 131 | torch.Tensor.frac | 是 | -| 132 | torch.Tensor.frac_ | 是 | -| 133 | torch.Tensor.gather | 是 | -| 134 | torch.Tensor.ge | 是 | -| 135 | torch.Tensor.ge_ | 是 | -| 136 | torch.Tensor.geometric_ | 否 | -| 137 | torch.Tensor.geqrf | 否 | -| 138 | torch.Tensor.ger | 是 | -| 139 | torch.Tensor.get_device | 是 | -| 140 | torch.Tensor.gt | 是 | -| 141 | torch.Tensor.gt_ | 是 | -| 142 | torch.Tensor.half | 是 | -| 143 | torch.Tensor.hardshrink | 是 | -| 144 | torch.Tensor.histc | 否 | -| 145 | torch.Tensor.ifft | 否 | -| 146 | torch.Tensor.index_add_ | 是 | -| 147 | torch.Tensor.index_add | 是 | -| 148 | torch.Tensor.index_copy_ | 是 | -| 149 | torch.Tensor.index_copy | 是 | -| 150 | torch.Tensor.index_fill_ | 是 | -| 151 | torch.Tensor.index_fill | 是 | -| 152 | torch.Tensor.index_put_ | 是 | -| 153 | torch.Tensor.index_put | 是 | -| 154 | torch.Tensor.index_select | 是 | -| 155 | torch.Tensor.indices | 否 | -| 156 | torch.Tensor.int | 是 | -| 157 | torch.Tensor.int_repr | 否 | -| 158 | torch.Tensor.inverse | 是 | -| 159 | torch.Tensor.irfft | 否 | -| 160 | torch.Tensor.is_contiguous | 是 | -| 161 | torch.Tensor.is_complex | 是 | -| 162 | torch.Tensor.is_floating_point | 是 | -| 163 | torch.Tensor.is_pinned | 是 | -| 164 | torch.Tensor.is_set_to | 否 | -| 165 | torch.Tensor.is_shared | 是 | -| 166 | torch.Tensor.is_signed | 是 | -| 167 | torch.Tensor.is_sparse | 是 | -| 168 | torch.Tensor.item | 是 | -| 169 | torch.Tensor.kthvalue | 是 | -| 170 | torch.Tensor.le | 是 | -| 171 | torch.Tensor.le_ | 是 | -| 172 | torch.Tensor.lerp | 是 | -| 173 | torch.Tensor.lerp_ | 是 | -| 174 | torch.Tensor.lgamma | 否 | -| 175 | torch.Tensor.lgamma_ | 否 | -| 176 | torch.Tensor.log | 是 | -| 177 | torch.Tensor.log_ | 是 | -| 178 | torch.Tensor.logdet | 否 | -| 179 | torch.Tensor.log10 | 是 | -| 180 | torch.Tensor.log10_ | 是 | -| 181 | torch.Tensor.log1p | 是 | -| 182 | torch.Tensor.log1p_ | 是 | -| 183 | torch.Tensor.log2 | 是 | -| 184 | torch.Tensor.log2_ | 是 | -| 185 | torch.Tensor.log_normal_ | 是 | -| 186 | torch.Tensor.logsumexp | 是 | -| 187 | torch.Tensor.logical_and | 是 | -| 188 | torch.Tensor.logical_and_ | 是 | -| 189 | torch.Tensor.logical_not | 是 | -| 190 | torch.Tensor.logical_not_ | 是 | -| 191 | torch.Tensor.logical_or | 是 | -| 192 | torch.Tensor.logical_or_ | 是 | -| 193 | torch.Tensor.logical_xor | 否 | -| 194 | torch.Tensor.logical_xor_ | 否 | -| 195 | torch.Tensor.long | 是 | -| 196 | torch.Tensor.lstsq | 否 | -| 197 | torch.Tensor.lt | 是 | -| 198 | torch.Tensor.lt_ | 是 | -| 199 | torch.Tensor.lu | 是 | -| 200 | torch.Tensor.lu_solve | 是 | -| 201 | torch.Tensor.map_ | 否 | -| 202 | torch.Tensor.masked_scatter_ | 是 | -| 203 | torch.Tensor.masked_scatter | 是 | -| 204 | torch.Tensor.masked_fill_ | 是 | -| 205 | torch.Tensor.masked_fill | 是 | -| 206 | torch.Tensor.masked_select | 是 | -| 207 | torch.Tensor.matmul | 是 | -| 208 | torch.Tensor.matrix_power | 是 | -| 209 | torch.Tensor.max | 是 | -| 210 | torch.Tensor.mean | 是 | -| 211 | torch.Tensor.median | 是 | -| 212 | torch.Tensor.min | 是 | -| 213 | torch.Tensor.mm | 是 | -| 214 | torch.Tensor.mode | 否 | -| 215 | torch.Tensor.mul | 是 | -| 216 | torch.Tensor.mul_ | 是 | -| 217 | torch.Tensor.multinomial | 是 | -| 218 | torch.Tensor.mv | 是 | -| 219 | torch.Tensor.mvlgamma | 否 | -| 220 | torch.Tensor.mvlgamma_ | 否 | -| 221 | torch.Tensor.narrow | 是 | -| 222 | torch.Tensor.narrow_copy | 是 | -| 223 | torch.Tensor.ndimension | 是 | -| 224 | torch.Tensor.ne | 是 | -| 225 | torch.Tensor.ne_ | 是 | -| 226 | torch.Tensor.neg | 是 | -| 227 | torch.Tensor.neg_ | 是 | -| 228 | torch.Tensor.nelement | 是 | -| 229 | torch.Tensor.nonzero | 是 | -| 230 | torch.Tensor.norm | 是 | -| 231 | torch.Tensor.normal_ | 是 | -| 232 | torch.Tensor.numel | 是 | -| 233 | torch.Tensor.numpy | 否 | -| 234 | torch.Tensor.orgqr | 否 | -| 235 | torch.Tensor.ormqr | 否 | -| 236 | torch.Tensor.permute | 是 | -| 237 | torch.Tensor.pin_memory | 否 | -| 238 | torch.Tensor.pinverse | 是 | -| 239 | torch.Tensor.polygamma | 否 | -| 240 | torch.Tensor.polygamma_ | 否 | -| 241 | torch.Tensor.pow | 是 | -| 242 | torch.Tensor.pow_ | 是 | -| 243 | torch.Tensor.prod | 是 | -| 244 | torch.Tensor.put_ | 是 | -| 245 | torch.Tensor.qr | 是 | -| 246 | torch.Tensor.qscheme | 否 | -| 247 | torch.Tensor.q_scale | 否 | -| 248 | torch.Tensor.q_zero_point | 否 | -| 249 | torch.Tensor.q_per_channel_scales | 否 | -| 250 | torch.Tensor.q_per_channel_zero_points | 否 | -| 251 | torch.Tensor.q_per_channel_axis | 否 | -| 252 | torch.Tensor.random_ | 是 | -| 253 | torch.Tensor.reciprocal | 是 | -| 254 | torch.Tensor.reciprocal_ | 是 | -| 255 | torch.Tensor.record_stream | 是 | -| 256 | torch.Tensor.remainder | 是 | -| 257 | torch.Tensor.remainder_ | 是 | -| 258 | torch.Tensor.renorm | 是 | -| 259 | torch.Tensor.renorm_ | 是 | -| 260 | torch.Tensor.repeat | 是 | -| 261 | torch.Tensor.repeat_interleave | 是 | -| 262 | torch.Tensor.requires_grad_ | 是 | -| 263 | torch.Tensor.reshape | 是 | -| 264 | torch.Tensor.reshape_as | 是 | -| 265 | torch.Tensor.resize_ | 是 | -| 266 | torch.Tensor.resize_as_ | 是 | -| 267 | torch.Tensor.rfft | 否 | -| 268 | torch.Tensor.roll | 是 | -| 269 | torch.Tensor.rot90 | 是 | -| 270 | torch.Tensor.round | 是 | -| 271 | torch.Tensor.round_ | 是 | -| 272 | torch.Tensor.rsqrt | 是 | -| 273 | torch.Tensor.rsqrt_ | 是 | -| 274 | torch.Tensor.scatter | 是 | -| 275 | torch.Tensor.scatter_ | 是 | -| 276 | torch.Tensor.scatter_add_ | 是 | -| 277 | torch.Tensor.scatter_add | 是 | -| 278 | torch.Tensor.select | 是 | -| 279 | torch.Tensor.set_ | 是 | -| 280 | torch.Tensor.share_memory_ | 否 | -| 281 | torch.Tensor.short | 是 | -| 282 | torch.Tensor.sigmoid | 是 | -| 283 | torch.Tensor.sigmoid_ | 是 | -| 284 | torch.Tensor.sign | 是 | -| 285 | torch.Tensor.sign_ | 是 | -| 286 | torch.Tensor.sin | 是 | -| 287 | torch.Tensor.sin_ | 是 | -| 288 | torch.Tensor.sinh | 是 | -| 289 | torch.Tensor.sinh_ | 是 | -| 290 | torch.Tensor.size | 是 | -| 291 | torch.Tensor.slogdet | 是 | -| 292 | torch.Tensor.solve | 否 | -| 293 | torch.Tensor.sort | 是 | -| 294 | torch.Tensor.split | 是 | -| 295 | torch.Tensor.sparse_mask | 否 | -| 296 | torch.Tensor.sparse_dim | 否 | -| 297 | torch.Tensor.sqrt | 是 | -| 298 | torch.Tensor.sqrt_ | 是 | -| 299 | torch.Tensor.square | 是 | -| 300 | torch.Tensor.square_ | 是 | -| 301 | torch.Tensor.squeeze | 是 | -| 302 | torch.Tensor.squeeze_ | 是 | -| 303 | torch.Tensor.std | 是 | -| 304 | torch.Tensor.stft | 否 | -| 305 | torch.Tensor.storage | 是 | -| 306 | torch.Tensor.storage_offset | 是 | -| 307 | torch.Tensor.storage_type | 是 | -| 308 | torch.Tensor.stride | 是 | -| 309 | torch.Tensor.sub | 是 | -| 310 | torch.Tensor.sub_ | 是 | -| 311 | torch.Tensor.sum | 是 | -| 312 | torch.Tensor.sum_to_size | 是 | -| 313 | torch.Tensor.svd | 是 | -| 314 | torch.Tensor.symeig | 是 | -| 315 | torch.Tensor.t | 是 | -| 316 | torch.Tensor.t_ | 是 | -| 317 | torch.Tensor.to | 是 | -| 318 | torch.Tensor.to_mkldnn | 否 | -| 319 | torch.Tensor.take | 是 | -| 320 | torch.Tensor.tan | 是 | -| 321 | torch.Tensor.tan_ | 是 | -| 322 | torch.Tensor.tanh | 是 | -| 323 | torch.Tensor.tanh_ | 是 | -| 324 | torch.Tensor.tolist | 是 | -| 325 | torch.Tensor.topk | 是 | -| 326 | torch.Tensor.to_sparse | 否 | -| 327 | torch.Tensor.trace | 否 | -| 328 | torch.Tensor.transpose | 是 | -| 329 | torch.Tensor.transpose_ | 是 | -| 330 | torch.Tensor.triangular_solve | 是 | -| 331 | torch.Tensor.tril | 是 | -| 332 | torch.Tensor.tril_ | 是 | -| 333 | torch.Tensor.triu | 是 | -| 334 | torch.Tensor.triu_ | 是 | -| 335 | torch.Tensor.true_divide | 是 | -| 336 | torch.Tensor.true_divide_ | 是 | -| 337 | torch.Tensor.trunc | 是 | -| 338 | torch.Tensor.trunc_ | 是 | -| 339 | torch.Tensor.type | 是 | -| 340 | torch.Tensor.type_as | 是 | -| 341 | torch.Tensor.unbind | 是 | -| 342 | torch.Tensor.unfold | 是 | -| 343 | torch.Tensor.uniform_ | 是 | -| 344 | torch.Tensor.unique | 是 | -| 345 | torch.Tensor.unique_consecutive | 否 | -| 346 | torch.Tensor.unsqueeze | 是 | -| 347 | torch.Tensor.unsqueeze_ | 是 | -| 348 | torch.Tensor.values | 否 | -| 349 | torch.Tensor.var | 否 | -| 350 | torch.Tensor.view | 是 | -| 351 | torch.Tensor.view_as | 是 | -| 352 | torch.Tensor.where | 是 | -| 353 | torch.Tensor.zero_ | 是 | -| 354 | torch.BoolTensor | 是 | -| 355 | torch.BoolTensor.all | 是 | -| 356 | torch.BoolTensor.any | 是 | - -## Layers (torch.nn) - -| 序号 | API名称 | 是否支持 | -| ---- | -------------------------------------------------------- | ---------------------------- | -| 1 | torch.nn.Parameter | 是 | -| 2 | torch.nn.Module | 是 | -| 3 | torch.nn.Module.add_module | 是 | -| 4 | torch.nn.Module.apply | 是 | -| 5 | torch.nn.Module.bfloat16 | 否 | -| 6 | torch.nn.Module.buffers | 是 | -| 7 | torch.nn.Module.children | 是 | -| 8 | torch.nn.Module.cpu | 是 | -| 9 | torch.nn.Module.cuda | 否 | -| 10 | torch.nn.Module.double | 否 | -| 11 | torch.nn.Module.dump_patches | 是 | -| 12 | torch.nn.Module.eval | 是 | -| 13 | torch.nn.Module.extra_repr | 是 | -| 14 | torch.nn.Module.float | 是 | -| 15 | torch.nn.Module.forward | 是 | -| 16 | torch.nn.Module.half | 是 | -| 17 | torch.nn.Module.load_state_dict | 是 | -| 18 | torch.nn.Module.modules | 是 | -| 19 | torch.nn.Module.named_buffers | 是 | -| 20 | torch.nn.Module.named_children | 是 | -| 21 | torch.nn.Module.named_modules | 是 | -| 22 | torch.nn.Module.named_parameters | 是 | -| 23 | torch.nn.Module.parameters | 是 | -| 24 | torch.nn.Module.register_backward_hook | 是 | -| 25 | torch.nn.Module.register_buffer | 是 | -| 26 | torch.nn.Module.register_forward_hook | 是 | -| 27 | torch.nn.Module.register_forward_pre_hook | 是 | -| 28 | torch.nn.Module.register_parameter | 是 | -| 29 | torch.nn.Module.requires_grad_ | 是 | -| 30 | torch.nn.Module.state_dict | 是 | -| 31 | torch.nn.Module.to | 是 | -| 32 | torch.nn.Module.train | 是 | -| 33 | torch.nn.Module.type | 是 | -| 34 | torch.nn.Module.zero_grad | 是 | -| 35 | torch.nn.Sequential | 是 | -| 36 | torch.nn.ModuleList | 是 | -| 37 | torch.nn.ModuleList.append | 是 | -| 38 | torch.nn.ModuleList.extend | 是 | -| 39 | torch.nn.ModuleList.insert | 是 | -| 40 | torch.nn.ModuleDict | 是 | -| 41 | torch.nn.ModuleDict.clear | 是 | -| 42 | torch.nn.ModuleDict.items | 是 | -| 43 | torch.nn.ModuleDict.keys | 是 | -| 44 | torch.nn.ModuleDict.pop | 是 | -| 45 | torch.nn.ModuleDict.update | 是 | -| 46 | torch.nn.ModuleDict.values | 是 | -| 47 | torch.nn.ParameterList | 是 | -| 48 | torch.nn.ParameterList.append | 是 | -| 49 | torch.nn.ParameterList.extend | 是 | -| 50 | torch.nn.ParameterDict | 是 | -| 51 | torch.nn.ParameterDict.clear | 是 | -| 52 | torch.nn.ParameterDict.items | 是 | -| 53 | torch.nn.ParameterDict.keys | 是 | -| 54 | torch.nn.ParameterDict.pop | 是 | -| 55 | torch.nn.ParameterDict.update | 是 | -| 56 | torch.nn.ParameterDict.values | 是 | -| 57 | torch.nn.Conv1d | 是 | -| 58 | torch.nn.Conv2d | 是 | -| 59 | torch.nn.Conv3d | 是 | -| 60 | torch.nn.ConvTranspose1d | 是 | -| 61 | torch.nn.ConvTranspose2d | 是 | -| 62 | torch.nn.ConvTranspose3d | 是 | -| 63 | torch.nn.Unfold | 是 | -| 64 | torch.nn.Fold | 是 | -| 65 | torch.nn.MaxPool1d | 是 | -| 66 | torch.nn.MaxPool2d | 是 | -| 67 | torch.nn.MaxPool3d | 是 | -| 68 | torch.nn.MaxUnpool1d | 是 | -| 69 | torch.nn.MaxUnpool2d | 是 | -| 70 | torch.nn.MaxUnpool3d | 是 | -| 71 | torch.nn.AvgPool1d | 是 | -| 72 | torch.nn.AvgPool2d | 是 | -| 73 | torch.nn.AvgPool3d | 是 | -| 74 | torch.nn.FractionalMaxPool2d | 否 | -| 75 | torch.nn.LPPool1d | 是 | -| 76 | torch.nn.LPPool2d | 是 | -| 77 | torch.nn.AdaptiveMaxPool1d | 是 | -| 78 | torch.nn.AdaptiveMaxPool2d | 是 | -| 79 | torch.nn.AdaptiveMaxPool3d | 否 | -| 80 | torch.nn.AdaptiveAvgPool1d | 是 | -| 81 | torch.nn.AdaptiveAvgPool2d | 是 | -| 82 | torch.nn.AdaptiveAvgPool3d | 是，仅支持D=1，H=1，W=1场景 | -| 83 | torch.nn.ReflectionPad1d | 否 | -| 84 | torch.nn.ReflectionPad2d | 是 | -| 85 | torch.nn.ReplicationPad1d | 否 | -| 86 | torch.nn.ReplicationPad2d | 是 | -| 87 | torch.nn.ReplicationPad3d | 否 | -| 88 | torch.nn.ZeroPad2d | 是 | -| 89 | torch.nn.ConstantPad1d | 是 | -| 90 | torch.nn.ConstantPad2d | 是 | -| 91 | torch.nn.ConstantPad3d | 是 | -| 92 | torch.nn.ELU | 是 | -| 93 | torch.nn.Hardshrink | 是 | -| 94 | torch.nn.Hardtanh | 是 | -| 95 | torch.nn.LeakyReLU | 是 | -| 96 | torch.nn.LogSigmoid | 是 | -| 97 | torch.nn.MultiheadAttention | 是 | -| 98 | torch.nn.PReLU | 是 | -| 99 | torch.nn.ReLU | 是 | -| 100 | torch.nn.ReLU6 | 是 | -| 101 | torch.nn.RReLU | 是 | -| 102 | torch.nn.SELU | 是 | -| 103 | torch.nn.CELU | 是 | -| 104 | torch.nn.GELU | 是 | -| 105 | torch.nn.Sigmoid | 是 | -| 106 | torch.nn.Softplus | 是 | -| 107 | torch.nn.Softshrink | 是，SoftShrink场景暂不支持 | -| 108 | torch.nn.Softsign | 是 | -| 109 | torch.nn.Tanh | 是 | -| 110 | torch.nn.Tanhshrink | 是 | -| 111 | torch.nn.Threshold | 是 | -| 112 | torch.nn.Softmin | 是 | -| 113 | torch.nn.Softmax | 是 | -| 114 | torch.nn.Softmax2d | 是 | -| 115 | torch.nn.LogSoftmax | 是 | -| 116 | torch.nn.AdaptiveLogSoftmaxWithLoss | 否 | -| 117 | torch.nn.AdaptiveLogSoftmaxWithLoss.log_prob | 否 | -| 118 | torch.nn.AdaptiveLogSoftmaxWithLoss.predict | 否 | -| 119 | torch.nn.BatchNorm1d | 是 | -| 120 | torch.nn.BatchNorm2d | 是 | -| 121 | torch.nn.BatchNorm3d | 是 | -| 122 | torch.nn.GroupNorm | 是 | -| 123 | torch.nn.SyncBatchNorm | 是 | -| 124 | torch.nn.SyncBatchNorm.convert_sync_batchnorm | 是 | -| 125 | torch.nn.InstanceNorm1d | 是 | -| 126 | torch.nn.InstanceNorm2d | 是 | -| 127 | torch.nn.InstanceNorm3d | 是 | -| 128 | torch.nn.LayerNorm | 是 | -| 129 | torch.nn.LocalResponseNorm | 是 | -| 130 | torch.nn.RNNBase | 是 | -| 131 | torch.nn.RNNBase.flatten_parameters | 是 | -| 132 | torch.nn.RNN | 是 | -| 133 | torch.nn.LSTM | 是 | -| 134 | torch.nn.GRU | 是，DynamicGRUV2场景暂不支持 | -| 135 | torch.nn.RNNCell | 是 | -| 136 | torch.nn.LSTMCell | 是 | -| 137 | torch.nn.GRUCell | 是 | -| 138 | torch.nn.Transformer | 是 | -| 139 | torch.nn.Transformer.forward | 是 | -| 140 | torch.nn.Transformer.generate_square_subsequent_mask | 是 | -| 141 | torch.nn.TransformerEncoder | 是 | -| 142 | torch.nn.TransformerEncoder.forward | 是 | -| 143 | torch.nn.TransformerDecoder | 是 | -| 144 | torch.nn.TransformerDecoder.forward | 是 | -| 145 | torch.nn.TransformerEncoderLayer | 是 | -| 146 | torch.nn.TransformerEncoderLayer.forward | 是 | -| 147 | torch.nn.TransformerDecoderLayer | 是 | -| 148 | torch.nn.TransformerDecoderLayer.forward | 是 | -| 149 | torch.nn.Identity | 是 | -| 150 | torch.nn.Linear | 是 | -| 151 | torch.nn.Bilinear | 是 | -| 152 | torch.nn.Dropout | 是 | -| 153 | torch.nn.Dropout2d | 是 | -| 154 | torch.nn.Dropout3d | 是 | -| 155 | torch.nn.AlphaDropout | 是 | -| 156 | torch.nn.Embedding | 是 | -| 157 | torch.nn.Embedding.from_pretrained | 是 | -| 158 | torch.nn.EmbeddingBag | 是 | -| 159 | torch.nn.EmbeddingBag.from_pretrained | 是 | -| 160 | torch.nn.CosineSimilarity | 是 | -| 161 | torch.nn.PairwiseDistance | 是 | -| 162 | torch.nn.L1Loss | 是 | -| 163 | torch.nn.MSELoss | 是 | -| 164 | torch.nn.CrossEntropyLoss | 是 | -| 165 | torch.nn.CTCLoss | 是 | -| 166 | torch.nn.NLLLoss | 是 | -| 167 | torch.nn.PoissonNLLLoss | 是 | -| 168 | torch.nn.KLDivLoss | 是 | -| 169 | torch.nn.BCELoss | 是 | -| 170 | torch.nn.BCEWithLogitsLoss | 是 | -| 171 | torch.nn.MarginRankingLoss | 是 | -| 172 | torch.nn.HingeEmbeddingLoss | 是 | -| 173 | torch.nn.MultiLabelMarginLoss | 是 | -| 174 | torch.nn.SmoothL1Loss | 是 | -| 175 | torch.nn.SoftMarginLoss | 是 | -| 176 | torch.nn.MultiLabelSoftMarginLoss | 是 | -| 177 | torch.nn.CosineEmbeddingLoss | 是 | -| 178 | torch.nn.MultiMarginLoss | 否 | -| 179 | torch.nn.TripletMarginLoss | 是 | -| 180 | torch.nn.PixelShuffle | 是 | -| 181 | torch.nn.Upsample | 是 | -| 182 | torch.nn.UpsamplingNearest2d | 是 | -| 183 | torch.nn.UpsamplingBilinear2d | 是 | -| 184 | torch.nn.DataParallel | 否 | -| 185 | torch.nn.parallel.DistributedDataParallel | 是 | -| 186 | torch.nn.parallel.DistributedDataParallel.no_sync | 是 | -| 187 | torch.nn.utils.clip_grad_norm_ | 是 | -| 188 | torch.nn.utils.clip_grad_value_ | 是 | -| 189 | torch.nn.utils.parameters_to_vector | 是 | -| 190 | torch.nn.utils.vector_to_parameters | 是 | -| 197 | torch.nn.utils.prune.PruningContainer | 是 | -| 198 | torch.nn.utils.prune.PruningContainer.add_pruning_method | 是 | -| 199 | torch.nn.utils.prune.PruningContainer.apply | 是 | -| 200 | torch.nn.utils.prune.PruningContainer.apply_mask | 是 | -| 201 | torch.nn.utils.prune.PruningContainer.compute_mask | 是 | -| 202 | torch.nn.utils.prune.PruningContainer.prune | 是 | -| 203 | torch.nn.utils.prune.PruningContainer.remove | 是 | -| 204 | torch.nn.utils.prune.Identity | 是 | -| 205 | torch.nn.utils.prune.Identity.apply | 是 | -| 206 | torch.nn.utils.prune.Identity.apply_mask | 是 | -| 207 | torch.nn.utils.prune.Identity.prune | 是 | -| 208 | torch.nn.utils.prune.Identity.remove | 是 | -| 209 | torch.nn.utils.prune.RandomUnstructured | 是 | -| 210 | torch.nn.utils.prune.RandomUnstructured.apply | 是 | -| 211 | torch.nn.utils.prune.RandomUnstructured.apply_mask | 是 | -| 212 | torch.nn.utils.prune.RandomUnstructured.prune | 是 | -| 213 | torch.nn.utils.prune.RandomUnstructured.remove | 是 | -| 214 | torch.nn.utils.prune.L1Unstructured | 是 | -| 215 | torch.nn.utils.prune.L1Unstructured.apply | 是 | -| 216 | torch.nn.utils.prune.L1Unstructured.apply_mask | 是 | -| 217 | torch.nn.utils.prune.L1Unstructured.prune | 是 | -| 218 | torch.nn.utils.prune.L1Unstructured.remove | 是 | -| 219 | torch.nn.utils.prune.RandomStructured | 是 | -| 220 | torch.nn.utils.prune.RandomStructured.apply | 是 | -| 221 | torch.nn.utils.prune.RandomStructured.apply_mask | 是 | -| 222 | torch.nn.utils.prune.RandomStructured.compute_mask | 是 | -| 223 | torch.nn.utils.prune.RandomStructured.prune | 是 | -| 224 | torch.nn.utils.prune.RandomStructured.remove | 是 | -| 225 | torch.nn.utils.prune.LnStructured | 是 | -| 226 | torch.nn.utils.prune.LnStructured.apply | 是 | -| 227 | torch.nn.utils.prune.LnStructured.apply_mask | 是 | -| 228 | torch.nn.utils.prune.LnStructured.compute_mask | 是 | -| 229 | torch.nn.utils.prune.LnStructured.prune | 是 | -| 230 | torch.nn.utils.prune.LnStructured.remove | 是 | -| 231 | torch.nn.utils.prune.CustomFromMask | 是 | -| 232 | torch.nn.utils.prune.CustomFromMask.apply | 是 | -| 233 | torch.nn.utils.prune.CustomFromMask.apply_mask | 是 | -| 234 | torch.nn.utils.prune.CustomFromMask.prune | 是 | -| 235 | torch.nn.utils.prune.CustomFromMask.remove | 是 | -| 236 | torch.nn.utils.prune.identity | 是 | -| 237 | torch.nn.utils.prune.random_unstructured | 是 | -| 238 | torch.nn.utils.prune.l1_unstructured | 是 | -| 239 | torch.nn.utils.prune.random_structured | 是 | -| 240 | torch.nn.utils.prune.ln_structured | 是 | -| 241 | torch.nn.utils.prune.global_unstructured | 是 | -| 242 | torch.nn.utils.prune.custom_from_mask | 是 | -| 243 | torch.nn.utils.prune.remove | 是 | -| 244 | torch.nn.utils.prune.is_pruned | 是 | -| 245 | torch.nn.utils.weight_norm | 是 | -| 246 | torch.nn.utils.remove_weight_norm | 是 | -| 247 | torch.nn.utils.spectral_norm | 是 | -| 248 | torch.nn.utils.remove_spectral_norm | 是 | -| 249 | torch.nn.utils.rnn.PackedSequence | 是 | -| 250 | torch.nn.utils.rnn.pack_padded_sequence | 是 | -| 251 | torch.nn.utils.rnn.pad_packed_sequence | 否 | -| 252 | torch.nn.utils.rnn.pad_sequence | 是 | -| 253 | torch.nn.utils.rnn.pack_sequence | 否 | -| 254 | torch.nn.Flatten | 是 | -| 255 | torch.quantization.quantize | 否 | -| 256 | torch.quantization.quantize_dynamic | 否 | -| 257 | torch.quantization.quantize_qat | 否 | -| 258 | torch.quantization.prepare | 是 | -| 259 | torch.quantization.prepare_qat | 否 | -| 260 | torch.quantization.convert | 否 | -| 261 | torch.quantization.QConfig | 是 | -| 262 | torch.quantization.QConfigDynamic | 是 | -| 263 | torch.quantization.fuse_modules | 是 | -| 264 | torch.quantization.QuantStub | 是 | -| 265 | torch.quantization.DeQuantStub | 是 | -| 266 | torch.quantization.QuantWrapper | 是 | -| 267 | torch.quantization.add_quant_dequant | 是 | -| 268 | torch.quantization.add_observer_ | 是 | -| 269 | torch.quantization.swap_module | 是 | -| 270 | torch.quantization.propagate_qconfig_ | 是 | -| 271 | torch.quantization.default_eval_fn | 是 | -| 272 | torch.quantization.MinMaxObserver | 是 | -| 273 | torch.quantization.MovingAverageMinMaxObserver | 是 | -| 274 | torch.quantization.PerChannelMinMaxObserver | 是 | -| 275 | torch.quantization.MovingAveragePerChannelMinMaxObserver | 是 | -| 276 | torch.quantization.HistogramObserver | 否 | -| 277 | torch.quantization.FakeQuantize | 否 | -| 278 | torch.quantization.NoopObserver | 是 | -| 279 | torch.quantization.get_observer_dict | 是 | -| 280 | torch.quantization.RecordingObserver | 是 | -| 281 | torch.nn.intrinsic.ConvBn2d | 是 | -| 282 | torch.nn.intrinsic.ConvBnReLU2d | 是 | -| 283 | torch.nn.intrinsic.ConvReLU2d | 是 | -| 284 | torch.nn.intrinsic.ConvReLU3d | 是 | -| 285 | torch.nn.intrinsic.LinearReLU | 是 | -| 286 | torch.nn.intrinsic.qat.ConvBn2d | 否 | -| 287 | torch.nn.intrinsic.qat.ConvBnReLU2d | 否 | -| 288 | torch.nn.intrinsic.qat.ConvReLU2d | 否 | -| 289 | torch.nn.intrinsic.qat.LinearReLU | 否 | -| 290 | torch.nn.intrinsic.quantized.ConvReLU2d | 否 | -| 291 | torch.nn.intrinsic.quantized.ConvReLU3d | 否 | -| 292 | torch.nn.intrinsic.quantized.LinearReLU | 否 | -| 293 | torch.nn.qat.Conv2d | 否 | -| 294 | torch.nn.qat.Conv2d.from_float | 否 | -| 295 | torch.nn.qat.Linear | 否 | -| 296 | torch.nn.qat.Linear.from_float | 否 | -| 297 | torch.nn.quantized.functional.relu | 否 | -| 298 | torch.nn.quantized.functional.linear | 否 | -| 299 | torch.nn.quantized.functional.conv2d | 否 | -| 300 | torch.nn.quantized.functional.conv3d | 否 | -| 301 | torch.nn.quantized.functional.max_pool2d | 否 | -| 302 | torch.nn.quantized.functional.adaptive_avg_pool2d | 否 | -| 303 | torch.nn.quantized.functional.avg_pool2d | 否 | -| 304 | torch.nn.quantized.functional.interpolate | 否 | -| 305 | torch.nn.quantized.functional.upsample | 否 | -| 306 | torch.nn.quantized.functional.upsample_bilinear | 否 | -| 307 | torch.nn.quantized.functional.upsample_nearest | 否 | -| 308 | torch.nn.quantized.ReLU | 否 | -| 309 | torch.nn.quantized.ReLU6 | 否 | -| 310 | torch.nn.quantized.Conv2d | 否 | -| 311 | torch.nn.quantized.Conv2d.from_float | 否 | -| 312 | torch.nn.quantized.Conv3d | 否 | -| 313 | torch.nn.quantized.Conv3d.from_float | 否 | -| 314 | torch.nn.quantized.FloatFunctional | 是 | -| 315 | torch.nn.quantized.QFunctional | 否 | -| 316 | torch.nn.quantized.Quantize | 是 | -| 317 | torch.nn.quantized.DeQuantize | 否 | -| 318 | torch.nn.quantized.Linear | 否 | -| 319 | torch.nn.quantized.Linear.from_float | 否 | -| 320 | torch.nn.quantized.dynamic.Linear | 否 | -| 321 | torch.nn.quantized.dynamic.Linear.from_float | 否 | -| 322 | torch.nn.quantized.dynamic.LSTM | 否 | - -## Functions(torch.nn.functional) - -| 序号 | API名称 | 是否支持 | -| ---- | ---------------------------------------------------- | --------------------------- | -| 1 | torch.nn.functional.conv1d | 是 | -| 2 | torch.nn.functional.conv2d | 是 | -| 3 | torch.nn.functional.conv3d | 是 | -| 4 | torch.nn.functional.conv_transpose1d | 是 | -| 5 | torch.nn.functional.conv_transpose2d | 是 | -| 6 | torch.nn.functional.conv_transpose3d | 是 | -| 7 | torch.nn.functional.unfold | 是 | -| 8 | torch.nn.functional.fold | 是 | -| 9 | torch.nn.functional.avg_pool1d | 是 | -| 10 | torch.nn.functional.avg_pool2d | 是 | -| 11 | torch.nn.functional.avg_pool3d | 是 | -| 12 | torch.nn.functional.max_pool1d | 是 | -| 13 | torch.nn.functional.max_pool2d | 是 | -| 14 | torch.nn.functional.max_pool3d | 是 | -| 15 | torch.nn.functional.max_unpool1d | 是 | -| 16 | torch.nn.functional.max_unpool2d | 是 | -| 17 | torch.nn.functional.max_unpool3d | 是 | -| 18 | torch.nn.functional.lp_pool1d | 是 | -| 19 | torch.nn.functional.lp_pool2d | 是 | -| 20 | torch.nn.functional.adaptive_max_pool1d | 是 | -| 21 | torch.nn.functional.adaptive_max_pool2d | 是 | -| 22 | torch.nn.functional.adaptive_max_pool3d | 否 | -| 23 | torch.nn.functional.adaptive_avg_pool1d | 是 | -| 24 | torch.nn.functional.adaptive_avg_pool2d | 是 | -| 25 | torch.nn.functional.adaptive_avg_pool3d | 是，仅支持D=1，H=1，W=1场景 | -| 26 | torch.nn.functional.threshold | 是 | -| 27 | torch.nn.functional.threshold_ | 是 | -| 28 | torch.nn.functional.relu | 是 | -| 29 | torch.nn.functional.relu_ | 是 | -| 30 | torch.nn.functional.hardtanh | 是 | -| 31 | torch.nn.functional.hardtanh_ | 是 | -| 32 | torch.nn.functional.relu6 | 是 | -| 33 | torch.nn.functional.elu | 是 | -| 34 | torch.nn.functional.elu_ | 是 | -| 35 | torch.nn.functional.selu | 是 | -| 36 | torch.nn.functional.celu | 是 | -| 37 | torch.nn.functional.leaky_relu | 是 | -| 38 | torch.nn.functional.leaky_relu_ | 是 | -| 39 | torch.nn.functional.prelu | 是 | -| 40 | torch.nn.functional.rrelu | 是 | -| 41 | torch.nn.functional.rrelu_ | 是 | -| 42 | torch.nn.functional.glu | 是 | -| 43 | torch.nn.functional.gelu | 是 | -| 44 | torch.nn.functional.logsigmoid | 是 | -| 45 | torch.nn.functional.hardshrink | 是 | -| 46 | torch.nn.functional.tanhshrink | 是 | -| 47 | torch.nn.functional.softsign | 是 | -| 48 | torch.nn.functional.softplus | 是 | -| 49 | torch.nn.functional.softmin | 是 | -| 50 | torch.nn.functional.softmax | 是 | -| 51 | torch.nn.functional.softshrink | 是 | -| 52 | torch.nn.functional.gumbel_softmax | 否 | -| 53 | torch.nn.functional.log_softmax | 是 | -| 54 | torch.nn.functional.tanh | 是 | -| 55 | torch.nn.functional.sigmoid | 是 | -| 56 | torch.nn.functional.batch_norm | 是 | -| 57 | torch.nn.functional.instance_norm | 是 | -| 58 | torch.nn.functional.layer_norm | 是 | -| 59 | torch.nn.functional.local_response_norm | 是 | -| 60 | torch.nn.functional.normalize | 是 | -| 61 | torch.nn.functional.linear | 是 | -| 62 | torch.nn.functional.bilinear | 是 | -| 63 | torch.nn.functional.dropout | 是 | -| 64 | torch.nn.functional.alpha_dropout | 是 | -| 65 | torch.nn.functional.dropout2d | 是 | -| 66 | torch.nn.functional.dropout3d | 是 | -| 67 | torch.nn.functional.embedding | 是 | -| 68 | torch.nn.functional.embedding_bag | 是 | -| 69 | torch.nn.functional.one_hot | 是 | -| 70 | torch.nn.functional.pairwise_distance | 是 | -| 71 | torch.nn.functional.cosine_similarity | 是 | -| 72 | torch.nn.functional.pdist | 是 | -| 73 | torch.nn.functional.binary_cross_entropy | 是 | -| 74 | torch.nn.functional.binary_cross_entropy_with_logits | 是 | -| 75 | torch.nn.functional.poisson_nll_loss | 是 | -| 76 | torch.nn.functional.cosine_embedding_loss | 是 | -| 77 | torch.nn.functional.cross_entropy | 是 | -| 78 | torch.nn.functional.ctc_loss | 是（仅支持2维输入） | -| 79 | torch.nn.functional.hinge_embedding_loss | 是 | -| 80 | torch.nn.functional.kl_div | 是 | -| 81 | torch.nn.functional.l1_loss | 是 | -| 82 | torch.nn.functional.mse_loss | 是 | -| 83 | torch.nn.functional.margin_ranking_loss | 是 | -| 84 | torch.nn.functional.multilabel_margin_loss | 是 | -| 85 | torch.nn.functional.multilabel_soft_margin_loss | 是 | -| 86 | torch.nn.functional.multi_margin_loss | 否 | -| 87 | torch.nn.functional.nll_loss | 是 | -| 88 | torch.nn.functional.smooth_l1_loss | 是 | -| 89 | torch.nn.functional.soft_margin_loss | 是 | -| 90 | torch.nn.functional.triplet_margin_loss | 是 | -| 91 | torch.nn.functional.pixel_shuffle | 是 | -| 92 | torch.nn.functional.pad | 是 | -| 93 | torch.nn.functional.interpolate | 是 | -| 94 | torch.nn.functional.upsample | 是 | -| 95 | torch.nn.functional.upsample_nearest | 是 | -| 96 | torch.nn.functional.upsample_bilinear | 是 | -| 97 | torch.nn.functional.grid_sample | 是 | -| 98 | torch.nn.functional.affine_grid | 是 | -| 99 | torch.nn.parallel.data_parallel | 否 | - -## torch.distributed - -| 序号 | API名称 | 是否支持 | -| ---- | ------------------------------------- | -------- | -| 1 | torch.distributed.init_process_group | 是 | -| 2 | torch.distributed.Backend | 是 | -| 3 | torch.distributed.get_backend | 是 | -| 4 | torch.distributed.get_rank | 是 | -| 5 | torch.distributed.get_world_size | 是 | -| 6 | torch.distributed.is_initialized | 是 | -| 7 | torch.distributed.is_mpi_available | 是 | -| 8 | torch.distributed.is_nccl_available | 是 | -| 9 | torch.distributed.new_group | 是 | -| 10 | torch.distributed.send | 否 | -| 11 | torch.distributed.recv | 否 | -| 12 | torch.distributed.isend | 否 | -| 13 | torch.distributed.irecv | 否 | -| 14 | is_completed | 是 | -| 15 | wait | 是 | -| 16 | torch.distributed.broadcast | 是 | -| 17 | torch.distributed.all_reduce | 是 | -| 18 | torch.distributed.reduce | 否 | -| 19 | torch.distributed.all_gather | 是 | -| 20 | torch.distributed.gather | 否 | -| 21 | torch.distributed.scatter | 否 | -| 22 | torch.distributed.barrier | 是 | -| 23 | torch.distributed.ReduceOp | 是 | -| 24 | torch.distributed.reduce_op | 是 | -| 25 | torch.distributed.broadcast_multigpu | 否 | -| 26 | torch.distributed.all_reduce_multigpu | 否 | -| 27 | torch.distributed.reduce_multigpu | 否 | -| 28 | torch.distributed.all_gather_multigpu | 否 | -| 29 | torch.distributed.launch | 是 | -| 30 | torch.multiprocessing.spawn | 是 | - -## torch.npu - -| 序号 | API名称 | npu对应API名称 | 是否支持 | -| ---- | ------------------------------------- | ------------------------------------ | -------- | -| 1 | torch.cuda.current_blas_handle | torch.npu.current_blas_handle | 否 | -| 2 | torch.cuda.current_device | torch.npu.current_device | 是 | -| 3 | torch.cuda.current_stream | torch.npu.current_stream | 是 | -| 4 | torch.cuda.default_stream | torch.npu.default_stream | 是 | -| 5 | torch.cuda.device | torch.npu.device | 是 | -| 6 | torch.cuda.device_count | torch.npu.device_count | 是 | -| 7 | torch.cuda.device_of | torch.npu.device_of | 是 | -| 8 | torch.cuda.get_device_capability | torch.npu.get_device_capability | 否 | -| 9 | torch.cuda.get_device_name | torch.npu.get_device_name | 否 | -| 10 | torch.cuda.init | torch.npu.init | 是 | -| 11 | torch.cuda.ipc_collect | torch.npu.ipc_collect | 否 | -| 12 | torch.cuda.is_available | torch.npu.is_available | 是 | -| 13 | torch.cuda.is_initialized | torch.npu.is_initialized | 是 | -| 14 | torch.cuda.set_device | torch.npu.set_device | 部分支持 | -| 15 | torch.cuda.stream | torch.npu.stream | 是 | -| 16 | torch.cuda.synchronize | torch.npu.synchronize | 是 | -| 17 | torch.cuda.get_rng_state | torch.npu.get_rng_state | 否 | -| 18 | torch.cuda.get_rng_state_all | torch.npu.get_rng_state_all | 否 | -| 19 | torch.cuda.set_rng_state | torch.npu.set_rng_state | 否 | -| 20 | torch.cuda.set_rng_state_all | torch.npu.set_rng_state_all | 否 | -| 21 | torch.cuda.manual_seed | torch.npu.manual_seed | 否 | -| 22 | torch.cuda.manual_seed_all | torch.npu.manual_seed_all | 否 | -| 23 | torch.cuda.seed | torch.npu.seed | 否 | -| 24 | torch.cuda.seed_all | torch.npu.seed_all | 否 | -| 25 | torch.cuda.initial_seed | torch.npu.initial_seed | 否 | -| 26 | torch.cuda.comm.broadcast | torch.npu.comm.broadcast | 否 | -| 27 | torch.cuda.comm.broadcast_coalesced | torch.npu.comm.broadcast_coalesced | 否 | -| 28 | torch.cuda.comm.reduce_add | torch.npu.comm.reduce_add | 否 | -| 29 | torch.cuda.comm.scatter | torch.npu.comm.scatter | 否 | -| 30 | torch.cuda.comm.gather | torch.npu.comm.gather | 否 | -| 31 | torch.cuda.Stream | torch.npu.Stream | 是 | -| 32 | torch.cuda.Stream.query | torch.npu.Stream.query | 是 | -| 33 | torch.cuda.Stream.record_event | torch.npu.Stream.record_event | 是 | -| 34 | torch.cuda.Stream.synchronize | torch.npu.Stream.synchronize | 是 | -| 35 | torch.cuda.Stream.wait_event | torch.npu.Stream.wait_event | 是 | -| 36 | torch.cuda.Stream.wait_stream | torch.npu.Stream.wait_stream | 是 | -| 37 | torch.cuda.Event | torch.npu.Event | 是 | -| 38 | torch.cuda.Event.elapsed_time | torch.npu.Event.elapsed_time | 是 | -| 39 | torch.cuda.Event.from_ipc_handle | torch.npu.Event.from_ipc_handle | 否 | -| 40 | torch.cuda.Event.ipc_handle | torch.npu.Event.ipc_handle | 否 | -| 41 | torch.cuda.Event.query | torch.npu.Event.query | 是 | -| 42 | torch.cuda.Event.record | torch.npu.Event.record | 是 | -| 43 | torch.cuda.Event.synchronize | torch.npu.Event.synchronize | 是 | -| 44 | torch.cuda.Event.wait | torch.npu.Event.wait | 是 | -| 45 | torch.cuda.empty_cache | torch.npu.empty_cache | 是 | -| 46 | torch.cuda.memory_stats | torch.npu.memory_stats | 是 | -| 47 | torch.cuda.memory_summary | torch.npu.memory_summary | 是 | -| 48 | torch.cuda.memory_snapshot | torch.npu.memory_snapshot | 是 | -| 49 | torch.cuda.memory_allocated | torch.npu.memory_allocated | 是 | -| 50 | torch.cuda.max_memory_allocated | torch.npu.max_memory_allocated | 是 | -| 51 | torch.cuda.reset_max_memory_allocated | torch.npu.reset_max_memory_allocated | 是 | -| 52 | torch.cuda.memory_reserved | torch.npu.memory_reserved | 是 | -| 53 | torch.cuda.max_memory_reserved | torch.npu.max_memory_reserved | 是 | -| 54 | torch.cuda.memory_cached | torch.npu.memory_cached | 是 | -| 55 | torch.cuda.max_memory_cached | torch.npu.max_memory_cached | 是 | -| 56 | torch.cuda.reset_max_memory_cached | torch.npu.reset_max_memory_cached | 是 | -| 57 | torch.cuda.nvtx.mark | torch.npu.nvtx.mark | 否 | -| 58 | torch.cuda.nvtx.range_push | torch.npu.nvtx.range_push | 否 | -| 59 | torch.cuda.nvtx.range_pop | torch.npu.nvtx.range_pop | 否 | -| 60 | torch.cuda._sleep | torch.npu._sleep | 否 | -| 61 | torch.cuda.Stream.priority_range | torch.npu.Stream.priority_range | 否 | -| 62 | torch.cuda.get_device_properties | torch.npu.get_device_properties | 否 | -| 63 | torch.cuda.amp.GradScaler | torch.npu.amp.GradScaler | 否 | - -torch.npu.set_device()接口只支持在程序开始的位置通过set_device进行指定，不支持多次指定和with torch.npu.device(id)方式的device切换 - -## NPU自定义算子 - -| 序号 | 算子名称 | -| ---- | ---------------------------------------------- | -| 1 | npu_convolution_transpose | -| 2 | npu_conv_transpose2d | -| 3 | npu_convolution_transpose_backward | -| 4 | npu_conv_transpose2d_backward | -| 5 | npu_conv_transpose3d_backward | -| 6 | npu_convolution | -| 7 | npu_convolution_backward | -| 8 | npu_convolution_double_backward | -| 9 | npu_conv2d | -| 10 | npu_conv2d.out | -| 11 | npu_conv2d_backward | -| 12 | npu_conv3d | -| 13 | npu_conv3d.out | -| 14 | npu_conv3d_backward | -| 15 | one_ | -| 16 | npu_sort_v2.out | -| 17 | npu_sort_v2 | -| 18 | npu_format_cast | -| 19 | npu_format_cast_.acl_format | -| 20 | npu_format_cast_.src | -| 21 | npu_transpose_to_contiguous | -| 22 | npu_transpose | -| 23 | npu_transpose.out | -| 24 | npu_broadcast | -| 25 | npu_broadcast.out | -| 26 | npu_dtype_cast | -| 27 | npu_dtype_cast_.Tensor | -| 28 | npu_roi_alignbk | -| 29 | empty_with_format | -| 30 | empty_with_format.names | -| 31 | copy_memory_ | -| 32 | npu_one_hot | -| 33 | npu_stride_add | -| 34 | npu_softmax_cross_entropy_with_logits | -| 35 | npu_softmax_cross_entropy_with_logits_backward | -| 36 | npu_ps_roi_pooling | -| 37 | npu_ps_roi_pooling_backward | -| 38 | npu_roi_align | -| 39 | npu_nms_v4 | -| 40 | npu_lstm | -| 41 | npu_lstm_backward | -| 42 | npu_iou | -| 43 | npu_ptiou | -| 44 | npu_nms_with_mask | -| 45 | npu_pad | -| 46 | npu_bounding_box_encode | -| 47 | npu_bounding_box_decode | -| 48 | npu_gru | -| 49 | npu_gru_backward | -| 50 | npu_set_.source_Storage_storage_offset_format | -| 51 | npu_random_choice_with_mask | -| 52 | npu_batch_nms | -| 53 | npu_slice | -| 54 | npu_slice.out | -| 55 | npu_dropoutV2 | -| 56 | npu_dropoutV2_backward | -| 57 | _npu_dropout | -| 58 | _npu_dropout_inplace | -| 59 | npu_dropout_backward | -| 60 | npu_indexing | -| 61 | npu_indexing.out | -| 62 | npu_ifmr | -| 63 | npu_max.dim | -| 64 | npu_max.names_dim | -| 65 | npu_scatter | -| 66 | npu_max_backward | -| 67 | npu_apply_adam | -| 68 | npu_layer_norm_eval | -| 69 | npu_alloc_float_status | -| 70 | npu_get_float_status | -| 71 | npu_clear_float_status | -| 72 | npu_confusion_transpose | -| 73 | npu_confusion_transpose_backward | -| 74 | npu_bmmV2 | -| 75 | fast_gelu | -| 76 | fast_gelu_backward | -| 77 | npu_sub_sample | -| 78 | npu_deformable_conv2d | -| 79 | npu_deformable_conv2dbk | -| 80 | npu_mish | -| 81 | npu_anchor_response_flags | -| 82 | npu_yolo_boxes_encode | -| 83 | npu_grid_assign_positive | -| 84 | npu_mish_backward | -| 85 | npu_normalize_batch | -| 86 | npu_masked_fill_range | -| 87 | npu_linear | -| 88 | npu_linear_backward | -| 89 | npu_bert_apply_adam | -| 90 | npu_giou | -| 91 | npu_giou_backward | - -详细算子接口说明： - -> ``` -> npu_apply_adam(beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, use_locking, use_nesterov, out = (var, m, v)) -> ``` - -count adam result. - -- Parameters： - - **beta1_power** (Number) - power of beta1. - - **beta2_power** (Number) - power of beta2. - - **lr** (Number) - learning rate. - - **beta1** (Number) - exponential decay rate for the 1st moment estimates. - - **beta2** (Number) - exponential decay rate for the 2nd moment estimates. - - **epsilon** (Number) - term added to the denominator to improve numerical stability. - - **grad** (Tensor) - the gradient. - - **use_locking** (bool) - If `True` use locks for update operations. - - **use_nesterov** (bool) -If `True`, uses the nesterov update. - - **var** (Tensor) - variables to be optimized. - - **m** (Tensor) - mean value of variables. - - **v** (Tensor) - variance of variables. - -- constraints： - - None - -- Examples： - - None - -> npu_bert_apply_adam(lr, beta1, beta2, epsilon, grad, max_grad_norm, global_grad_norm, weight_decay, out = (var, m, v)) - -count adam result in bert. - -- Parameters： - - **lr** (Number) - learning rate. - - **beta1** (Number) - exponential decay rate for the 1st moment estimates. - - **beta2** (Number) - exponential decay rate for the 2nd moment estimates. - - **epsilon** (Number) - term added to the denominator to improve numerical stability. - - **grad** (Tensor) - the gradient. - - **max_grad_norm** (Number) - maximum norm for the gradients. - - **global_grad_norm** (Number) - L2_norm for the gradients. - - **weight_decay** (Number) - weight decay - - **var** (Tensor) - variables to be optimized. - - **m** (Tensor) -mean value of variables. - - **v** (Tensor) - variance of variables. - -- constraints： - - None - -- Examples： - - ```python - >>> var_in = torch.rand(321538).uniform_(-32.,21.).npu() - >>> var_in - tensor([ 0.6119, 5.8193, 3.0683, ..., -28.5832, 12.9402, -24.0488], - device='npu:0') - >>> m_in = torch.zeros(321538).npu() - >>> v_in = torchzeros(321538).npu() - >>> grad = torch.rand(321538).uniform_(-0.05,0.03).npu() - >>> grad - tensor([-0.0315, -0.0113, -0.0132, ..., 0.0106, -0.0226, -0.0252], - device='npu:0') - >>> max_grad_norm = -1. - >>> beta1 = 0.9 - >>> beta2 = 0.99 - >>> weight_decay = 0. - >>> lr = 0.1 - >>> epsilon = 1e-06 - >>> global_grad_norm = 0. - >>> var_out, m_out, v_out = torch.npu_bert_apply_adam(var_in, m_in, v_in, lr, beta1, beta2, epsilon, grad, max_grad_norm, global_grad_norm, weight_decay) - >>> var_out - tensor([ 0.7118, 5.9192, 3.1682, ..., -28.6831, 13.0402, -23.9489], - device='npu:0') - >>> m_out - tensor([-0.0032, -0.0011, -0.0013, ..., 0.0011, -0.0023, -0.0025], - device='npu:0') - >>> v_out - tensor([9.9431e-06, 1.2659e-06, 1.7328e-06, ..., 1.1206e-06, 5.0933e-06, - 6.3495e-06], device='npu:0') - ``` - - - -- Gitee From 35d866848b1c6fe8cdb833266abdfeaf545160d8 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Fri, 31 Dec 2021 15:24:22 +0800 Subject: [PATCH 41/55] new file --- ...\214\201\346\270\205\345\215\225_1.5.0.md" | 3224 +++++++++++++++++ 1 file changed, 3224 insertions(+) create mode 100644 "docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" diff --git "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" new file mode 100644 index 00000000000..28ebfbe84af --- /dev/null +++ "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" @@ -0,0 +1,3224 @@ +## [Tensors](https://pytorch.org/docs/1.5.0/torch.html) + +| 序号 | API名称 | 支持情况 | +| ---- | ----------------------------- | -------------------------------------- | +| 1 | torch.is_tensor | 是 | +| 2 | torch.is_storage | 是 | +| 3 | torch.is_complex | 是，支持判断，但当前硬件限制不支持复数 | +| 4 | torch.is_floating_point | 是 | +| 5 | torch.set_default_dtype | 是 | +| 6 | torch.get_default_dtype | 是 | +| 7 | torch.set_default_tensor_type | 是 | +| 8 | torch.numel | 是 | +| 9 | torch.set_printoptions | 是 | +| 10 | torch.set_flush_denormal | 是 | +| 11 | torch.tensor | 是 | +| 12 | torch.sparse_coo_tensor | 否 | +| 13 | torch.as_tensor | 是 | +| 14 | torch.as_strided | 是 | +| 15 | torch.from_numpy | 是 | +| 16 | torch.zeros | 是 | +| 17 | torch.zeros_like | 是 | +| 18 | torch.ones | 是 | +| 19 | torch.ones_like | 是 | +| 20 | torch.arange | 是 | +| 21 | torch.range | 是 | +| 22 | torch.linspace | 是 | +| 23 | torch.logspace | 是 | +| 24 | torch.eye | 是 | +| 25 | torch.empty | 是 | +| 26 | torch.empty_like | 是 | +| 27 | torch.empty_strided | 是 | +| 28 | torch.full | 是 | +| 29 | torch.full_like | 是 | +| 30 | torch.quantize_per_tensor | 是 | +| 31 | torch.quantize_per_channel | 是 | +| 32 | torch.cat | 是 | +| 33 | torch.chunk | 是 | +| 34 | torch.gather | 是 | +| 35 | torch.index_select | 是 | +| 36 | torch.masked_select | 是 | +| 37 | torch.narrow | 是 | +| 38 | torch.nonzero | 是 | +| 39 | torch.reshape | 是 | +| 40 | torch.split | 是 | +| 41 | torch.squeeze | 是 | +| 42 | torch.stack | 是 | +| 43 | torch.t | 是 | +| 44 | torch.take | 是 | +| 45 | torch.transpose | 是 | +| 46 | torch.unbind | 是 | +| 47 | torch.unsqueeze | 是 | +| 48 | torch.where | 是 | + +## Generators + +| 序号 | API名称 | 是否支持 | +| ---- | ------------------------------- | -------- | +| 1 | torch._C.Generator | 是 | +| 2 | torch._C.Generator.device | 是 | +| 3 | torch._C.Generator.get_state | 否 | +| 4 | torch._C.Generator.initial_seed | 是 | +| 5 | torch._C.Generator.manual_seed | 是 | +| 6 | torch._C.Generator.seed | 是 | +| 7 | torch._C.Generator.set_state | 否 | + +## Random sampling + +| 序号 | API名称 | 是否支持 | +| ---- | ------------------------------------------ | -------- | +| 1 | torch.seed | 是 | +| 2 | torch.manual_seed | 是 | +| 3 | torch.initial_seed | 是 | +| 4 | torch.get_rng_state | 是 | +| 5 | torch.set_rng_state | 是 | +| 6 | torch.torch.default_generator | 是 | +| 7 | torch.bernoulli | 是 | +| 8 | torch.multinomial | 是 | +| 9 | torch.normal | 是 | +| 10 | torch.poisson | 否 | +| 11 | torch.rand | 是 | +| 12 | torch.rand_like | 是 | +| 13 | torch.randint | 是 | +| 14 | torch.randint_like | 是 | +| 15 | torch.randn | 是 | +| 16 | torch.randn_like | 是 | +| 17 | torch.randperm | 是 | +| 18 | torch.Tensor.bernoulli_() | 是 | +| 19 | torch.Tensor.bernoulli_() | 是 | +| 20 | torch.Tensor.exponential_() | 否 | +| 21 | torch.Tensor.geometric_() | 否 | +| 22 | torch.Tensor.log_normal_() | 否 | +| 23 | torch.Tensor.normal_() | 是 | +| 24 | torch.Tensor.random_() | 是 | +| 25 | torch.Tensor.uniform_() | 是 | +| 26 | torch.quasirandom.SobolEngine | 是 | +| 27 | torch.quasirandom.SobolEngine.draw | 是 | +| 28 | torch.quasirandom.SobolEngine.fast_forward | 是 | +| 29 | torch.quasirandom.SobolEngine.reset | 是 | + +## Serialization + +| 序号 | API名称 | 是否支持 | +| ---- | ---------- | -------- | +| 1 | torch.save | 是 | +| 2 | torch.load | 是 | + +## Math operations + +| 序号 | API名称 | 是否支持 | +| ---- | ------------------------ | -------- | +| 1 | torch.abs | 是 | +| 2 | torch.acos | 是 | +| 3 | torch.add | 是 | +| 4 | torch.addcdiv | 是 | +| 5 | torch.addcmul | 是 | +| 6 | torch.angle | 否 | +| 7 | torch.asin | 是 | +| 8 | torch.atan | 是 | +| 9 | torch.atan2 | 是 | +| 10 | torch.bitwise_not | 是 | +| 11 | torch.bitwise_and | 是 | +| 12 | torch.bitwise_or | 是 | +| 13 | torch.bitwise_xor | 是 | +| 14 | torch.ceil | 是 | +| 15 | torch.clamp | 是 | +| 16 | torch.conj | 否 | +| 17 | torch.cos | 是 | +| 18 | torch.cosh | 是 | +| 19 | torch.div | 是 | +| 20 | torch.digamma | 否 | +| 21 | torch.erf | 是 | +| 22 | torch.erfc | 是 | +| 23 | torch.erfinv | 是 | +| 24 | torch.exp | 是 | +| 25 | torch.expm1 | 是 | +| 26 | torch.floor | 是 | +| 27 | torch.floor_divide | 是 | +| 28 | torch.fmod | 是 | +| 29 | torch.frac | 是 | +| 30 | torch.imag | 否 | +| 31 | torch.lerp | 是 | +| 32 | torch.lgamma | 否 | +| 33 | torch.log | 是 | +| 34 | torch.log10 | 是 | +| 35 | torch.log1p | 是 | +| 36 | torch.log2 | 是 | +| 37 | torch.logical_and | 是 | +| 38 | torch.logical_not | 是 | +| 39 | torch.logical_or | 是 | +| 40 | torch.logical_xor | 是 | +| 41 | torch.mul | 是 | +| 42 | torch.mvlgamma | 否 | +| 43 | torch.neg | 是 | +| 44 | torch.polygamma | 否 | +| 45 | torch.pow | 是 | +| 46 | torch.real | 是 | +| 47 | torch.reciprocal | 是 | +| 48 | torch.remainder | 是 | +| 49 | torch.round | 是 | +| 50 | torch.rsqrt | 是 | +| 51 | torch.sigmoid | 是 | +| 52 | torch.sign | 是 | +| 53 | torch.sin | 是 | +| 54 | torch.sinh | 是 | +| 55 | torch.sqrt | 是 | +| 56 | torch.square | 是 | +| 57 | torch.tan | 是 | +| 58 | torch.tanh | 是 | +| 59 | torch.true_divide | 是 | +| 60 | torch.trunc | 是 | +| 61 | torch.argmax | 是 | +| 62 | torch.argmin | 是 | +| 63 | torch.dist | 是 | +| 64 | torch.logsumexp | 是 | +| 65 | torch.mean | 是 | +| 66 | torch.median | 是 | +| 67 | torch.mode | 否 | +| 68 | torch.norm | 是 | +| 69 | torch.prod | 是 | +| 70 | torch.std | 是 | +| 71 | torch.std_mean | 是 | +| 72 | torch.sum | 是 | +| 73 | torch.unique | 是 | +| 74 | torch.unique_consecutive | 否 | +| 75 | torch.var | 否 | +| 76 | torch.var_mean | 否 | +| 77 | torch.allclose | 是 | +| 78 | torch.argsort | 是 | +| 79 | torch.eq | 是 | +| 80 | torch.equal | 是 | +| 81 | torch.ge | 是 | +| 82 | torch.gt | 是 | +| 83 | torch.isfinite | 是 | +| 84 | torch.isinf | 是 | +| 85 | torch.isnan | 是 | +| 86 | torch.kthvalue | 是 | +| 87 | torch.le | 是 | +| 88 | torch.lt | 是 | +| 89 | torch.max | 是 | +| 90 | torch.min | 是 | +| 91 | torch.ne | 是 | +| 92 | torch.sort | 是 | +| 93 | torch.topk | 是 | +| 94 | torch.fft | 否 | +| 95 | torch.ifft | 否 | +| 96 | torch.rfft | 否 | +| 97 | torch.irfft | 否 | +| 98 | torch.stft | 否 | +| 99 | torch.bartlett_window | 是 | +| 100 | torch.blackman_window | 是 | +| 101 | torch.hamming_window | 是 | +| 102 | torch.hann_window | 是 | +| 103 | torch.bincount | 是 | +| 104 | torch.broadcast_tensors | 是 | +| 105 | torch.cartesian_prod | 是 | +| 106 | torch.cdist | 是 | +| 107 | torch.combinations | 否 | +| 108 | torch.cross | 是 | +| 109 | torch.cummax | 是 | +| 110 | torch.cummin | 是 | +| 111 | torch.cumprod | 是 | +| 112 | torch.cumsum | 是 | +| 113 | torch.diag | 是 | +| 114 | torch.diag_embed | 是 | +| 115 | torch.diagflat | 是 | +| 116 | torch.diagonal | 是 | +| 117 | torch.einsum | 是 | +| 118 | torch.flatten | 是 | +| 119 | torch.flip | 是 | +| 120 | torch.rot90 | 是 | +| 121 | torch.histc | 否 | +| 122 | torch.meshgrid | 是 | +| 123 | torch.renorm | 是 | +| 124 | torch.repeat_interleave | 是 | +| 125 | torch.roll | 是 | +| 126 | torch.tensordot | 是 | +| 127 | torch.trace | 否 | +| 128 | torch.tril | 是 | +| 129 | torch.tril_indices | 是 | +| 130 | torch.triu | 是 | +| 131 | torch.triu_indices | 是 | +| 132 | torch.addbmm | 是 | +| 133 | torch.addmm | 是 | +| 134 | torch.addmv | 是 | +| 135 | torch.addr | 是 | +| 136 | torch.baddbmm | 是 | +| 137 | torch.bmm | 是 | +| 138 | torch.chain_matmul | 是 | +| 139 | torch.cholesky | 否 | +| 140 | torch.cholesky_inverse | 否 | +| 141 | torch.cholesky_solve | 否 | +| 142 | torch.dot | 是 | +| 143 | torch.eig | 否 | +| 144 | torch.geqrf | 否 | +| 145 | torch.ger | 是 | +| 146 | torch.inverse | 是 | +| 147 | torch.det | 否 | +| 148 | torch.logdet | 否 | +| 149 | torch.slogdet | 是 | +| 150 | torch.lstsq | 否 | +| 151 | torch.lu | 否 | +| 152 | torch.lu_solve | 否 | +| 153 | torch.lu_unpack | 否 | +| 154 | torch.matmul | 是 | +| 155 | torch.matrix_power | 是 | +| 156 | torch.matrix_rank | 是 | +| 157 | torch.mm | 是 | +| 158 | torch.mv | 是 | +| 159 | torch.orgqr | 否 | +| 160 | torch.ormqr | 否 | +| 161 | torch.pinverse | 是 | +| 162 | torch.qr | 是 | +| 163 | torch.solve | 否 | +| 164 | torch.svd | 是 | +| 165 | torch.svd_lowrank | 是 | +| 166 | torch.pca_lowrank | 是 | +| 167 | torch.symeig | 是 | +| 168 | torch.lobpcg | 否 | +| 169 | torch.trapz | 是 | +| 170 | torch.triangular_solve | 是 | + +## Utilities + +| 序号 | API名称 | 是否支持 | +| ---- | ----------------------------- | -------- | +| 1 | torch.compiled_with_cxx11_abi | 是 | +| 2 | torch.result_type | 是 | +| 3 | torch.can_cast | 是 | +| 4 | torch.promote_types | 是 | + +## Other + +| 序号 | API名称 | 是否支持 | +| ---- | ----------------------------- | -------- | +| 1 | torch.no_grad | 是 | +| 2 | torch.enable_grad | 是 | +| 3 | torch.set_grad_enabled | 是 | +| 4 | torch.get_num_threads | 是 | +| 5 | torch.set_num_threads | 是 | +| 6 | torch.get_num_interop_threads | 是 | +| 7 | torch.set_num_interop_threads | 是 | + +## torch.Tensor + +| 序号 | API名称 | 是否支持 | +| ---- | -------------------------------------- | -------- | +| 1 | torch.Tensor | 是 | +| 2 | torch.Tensor.new_tensor | 是 | +| 3 | torch.Tensor.new_full | 是 | +| 4 | torch.Tensor.new_empty | 是 | +| 5 | torch.Tensor.new_ones | 是 | +| 6 | torch.Tensor.new_zeros | 是 | +| 7 | torch.Tensor.is_cuda | 是 | +| 8 | torch.Tensor.is_quantized | 是 | +| 9 | torch.Tensor.device | 是 | +| 10 | torch.Tensor.ndim | 是 | +| 11 | torch.Tensor.T | 是 | +| 12 | torch.Tensor.abs | 是 | +| 13 | torch.Tensor.abs_ | 是 | +| 14 | torch.Tensor.acos | 是 | +| 15 | torch.Tensor.acos_ | 是 | +| 16 | torch.Tensor.add | 是 | +| 17 | torch.Tensor.add_ | 是 | +| 18 | torch.Tensor.addbmm | 是 | +| 19 | torch.Tensor.addbmm_ | 是 | +| 20 | torch.Tensor.addcdiv | 是 | +| 21 | torch.Tensor.addcdiv_ | 是 | +| 22 | torch.Tensor.addcmul | 是 | +| 23 | torch.Tensor.addcmul_ | 是 | +| 24 | torch.Tensor.addmm | 是 | +| 25 | torch.Tensor.addmm_ | 是 | +| 26 | torch.Tensor.addmv | 是 | +| 27 | torch.Tensor.addmv_ | 是 | +| 28 | torch.Tensor.addr | 是 | +| 29 | torch.Tensor.addr_ | 是 | +| 30 | torch.Tensor.allclose | 是 | +| 31 | torch.Tensor.angle | 否 | +| 32 | torch.Tensor.apply_ | 否 | +| 33 | torch.Tensor.argmax | 是 | +| 34 | torch.Tensor.argmin | 是 | +| 35 | torch.Tensor.argsort | 是 | +| 36 | torch.Tensor.asin | 是 | +| 37 | torch.Tensor.asin_ | 是 | +| 38 | torch.Tensor.as_strided | 是 | +| 39 | torch.Tensor.atan | 是 | +| 40 | torch.Tensor.atan2 | 是 | +| 41 | torch.Tensor.atan2_ | 是 | +| 42 | torch.Tensor.atan_ | 是 | +| 43 | torch.Tensor.baddbmm | 是 | +| 44 | torch.Tensor.baddbmm_ | 是 | +| 45 | torch.Tensor.bernoulli | 是 | +| 46 | torch.Tensor.bernoulli_ | 是 | +| 47 | torch.Tensor.bfloat16 | 否 | +| 48 | torch.Tensor.bincount | 是 | +| 49 | torch.Tensor.bitwise_not | 是 | +| 50 | torch.Tensor.bitwise_not_ | 是 | +| 51 | torch.Tensor.bitwise_and | 是 | +| 52 | torch.Tensor.bitwise_and_ | 是 | +| 53 | torch.Tensor.bitwise_or | 是 | +| 54 | torch.Tensor.bitwise_or_ | 是 | +| 55 | torch.Tensor.bitwise_xor | 是 | +| 56 | torch.Tensor.bitwise_xor_ | 是 | +| 57 | torch.Tensor.bmm | 是 | +| 58 | torch.Tensor.bool | 是 | +| 59 | torch.Tensor.byte | 是 | +| 60 | torch.Tensor.cauchy_ | 否 | +| 61 | torch.Tensor.ceil | 是 | +| 62 | torch.Tensor.ceil_ | 是 | +| 63 | torch.Tensor.char | 是 | +| 64 | torch.Tensor.cholesky | 否 | +| 65 | torch.Tensor.cholesky_inverse | 否 | +| 66 | torch.Tensor.cholesky_solve | 否 | +| 67 | torch.Tensor.chunk | 是 | +| 68 | torch.Tensor.clamp | 是 | +| 69 | torch.Tensor.clamp_ | 是 | +| 70 | torch.Tensor.clone | 是 | +| 71 | torch.Tensor.contiguous | 是 | +| 72 | torch.Tensor.copy_ | 是 | +| 73 | torch.Tensor.conj | 否 | +| 74 | torch.Tensor.cos | 是 | +| 75 | torch.Tensor.cos_ | 是 | +| 76 | torch.Tensor.cosh | 是 | +| 77 | torch.Tensor.cosh_ | 是 | +| 78 | torch.Tensor.cpu | 是 | +| 79 | torch.Tensor.cross | 是 | +| 80 | torch.Tensor.cuda | 否 | +| 81 | torch.Tensor.cummax | 是 | +| 82 | torch.Tensor.cummin | 是 | +| 83 | torch.Tensor.cumprod | 是 | +| 84 | torch.Tensor.cumsum | 是 | +| 85 | torch.Tensor.data_ptr | 是 | +| 86 | torch.Tensor.dequantize | 否 | +| 87 | torch.Tensor.det | 否 | +| 88 | torch.Tensor.dense_dim | 否 | +| 89 | torch.Tensor.diag | 是 | +| 90 | torch.Tensor.diag_embed | 是 | +| 91 | torch.Tensor.diagflat | 是 | +| 92 | torch.Tensor.diagonal | 是 | +| 93 | torch.Tensor.fill_diagonal_ | 是 | +| 94 | torch.Tensor.digamma | 否 | +| 95 | torch.Tensor.digamma_ | 否 | +| 96 | torch.Tensor.dim | 是 | +| 97 | torch.Tensor.dist | 是 | +| 98 | torch.Tensor.div | 是 | +| 99 | torch.Tensor.div_ | 是 | +| 100 | torch.Tensor.dot | 是 | +| 101 | torch.Tensor.double | 否 | +| 102 | torch.Tensor.eig | 否 | +| 103 | torch.Tensor.element_size | 是 | +| 104 | torch.Tensor.eq | 是 | +| 105 | torch.Tensor.eq_ | 是 | +| 106 | torch.Tensor.equal | 是 | +| 107 | torch.Tensor.erf | 是 | +| 108 | torch.Tensor.erf_ | 是 | +| 109 | torch.Tensor.erfc | 是 | +| 110 | torch.Tensor.erfc_ | 是 | +| 111 | torch.Tensor.erfinv | 是 | +| 112 | torch.Tensor.erfinv_ | 是 | +| 113 | torch.Tensor.exp | 是 | +| 114 | torch.Tensor.exp_ | 是 | +| 115 | torch.Tensor.expm1 | 是 | +| 116 | torch.Tensor.expm1_ | 是 | +| 117 | torch.Tensor.expand | 是 | +| 118 | torch.Tensor.expand_as | 是 | +| 119 | torch.Tensor.exponential_ | 否 | +| 120 | torch.Tensor.fft | 否 | +| 121 | torch.Tensor.fill_ | 是 | +| 122 | torch.Tensor.flatten | 是 | +| 123 | torch.Tensor.flip | 是 | +| 124 | torch.Tensor.float | 是 | +| 125 | torch.Tensor.floor | 是 | +| 126 | torch.Tensor.floor_ | 是 | +| 127 | torch.Tensor.floor_divide | 是 | +| 128 | torch.Tensor.floor_divide_ | 是 | +| 129 | torch.Tensor.fmod | 是 | +| 130 | torch.Tensor.fmod_ | 是 | +| 131 | torch.Tensor.frac | 是 | +| 132 | torch.Tensor.frac_ | 是 | +| 133 | torch.Tensor.gather | 是 | +| 134 | torch.Tensor.ge | 是 | +| 135 | torch.Tensor.ge_ | 是 | +| 136 | torch.Tensor.geometric_ | 否 | +| 137 | torch.Tensor.geqrf | 否 | +| 138 | torch.Tensor.ger | 是 | +| 139 | torch.Tensor.get_device | 是 | +| 140 | torch.Tensor.gt | 是 | +| 141 | torch.Tensor.gt_ | 是 | +| 142 | torch.Tensor.half | 是 | +| 143 | torch.Tensor.hardshrink | 是 | +| 144 | torch.Tensor.histc | 否 | +| 145 | torch.Tensor.ifft | 否 | +| 146 | torch.Tensor.index_add_ | 是 | +| 147 | torch.Tensor.index_add | 是 | +| 148 | torch.Tensor.index_copy_ | 是 | +| 149 | torch.Tensor.index_copy | 是 | +| 150 | torch.Tensor.index_fill_ | 是 | +| 151 | torch.Tensor.index_fill | 是 | +| 152 | torch.Tensor.index_put_ | 是 | +| 153 | torch.Tensor.index_put | 是 | +| 154 | torch.Tensor.index_select | 是 | +| 155 | torch.Tensor.indices | 否 | +| 156 | torch.Tensor.int | 是 | +| 157 | torch.Tensor.int_repr | 否 | +| 158 | torch.Tensor.inverse | 是 | +| 159 | torch.Tensor.irfft | 否 | +| 160 | torch.Tensor.is_contiguous | 是 | +| 161 | torch.Tensor.is_complex | 是 | +| 162 | torch.Tensor.is_floating_point | 是 | +| 163 | torch.Tensor.is_pinned | 是 | +| 164 | torch.Tensor.is_set_to | 否 | +| 165 | torch.Tensor.is_shared | 是 | +| 166 | torch.Tensor.is_signed | 是 | +| 167 | torch.Tensor.is_sparse | 是 | +| 168 | torch.Tensor.item | 是 | +| 169 | torch.Tensor.kthvalue | 是 | +| 170 | torch.Tensor.le | 是 | +| 171 | torch.Tensor.le_ | 是 | +| 172 | torch.Tensor.lerp | 是 | +| 173 | torch.Tensor.lerp_ | 是 | +| 174 | torch.Tensor.lgamma | 否 | +| 175 | torch.Tensor.lgamma_ | 否 | +| 176 | torch.Tensor.log | 是 | +| 177 | torch.Tensor.log_ | 是 | +| 178 | torch.Tensor.logdet | 否 | +| 179 | torch.Tensor.log10 | 是 | +| 180 | torch.Tensor.log10_ | 是 | +| 181 | torch.Tensor.log1p | 是 | +| 182 | torch.Tensor.log1p_ | 是 | +| 183 | torch.Tensor.log2 | 是 | +| 184 | torch.Tensor.log2_ | 是 | +| 185 | torch.Tensor.log_normal_ | 是 | +| 186 | torch.Tensor.logsumexp | 是 | +| 187 | torch.Tensor.logical_and | 是 | +| 188 | torch.Tensor.logical_and_ | 是 | +| 189 | torch.Tensor.logical_not | 是 | +| 190 | torch.Tensor.logical_not_ | 是 | +| 191 | torch.Tensor.logical_or | 是 | +| 192 | torch.Tensor.logical_or_ | 是 | +| 193 | torch.Tensor.logical_xor | 否 | +| 194 | torch.Tensor.logical_xor_ | 否 | +| 195 | torch.Tensor.long | 是 | +| 196 | torch.Tensor.lstsq | 否 | +| 197 | torch.Tensor.lt | 是 | +| 198 | torch.Tensor.lt_ | 是 | +| 199 | torch.Tensor.lu | 是 | +| 200 | torch.Tensor.lu_solve | 是 | +| 201 | torch.Tensor.map_ | 否 | +| 202 | torch.Tensor.masked_scatter_ | 是 | +| 203 | torch.Tensor.masked_scatter | 是 | +| 204 | torch.Tensor.masked_fill_ | 是 | +| 205 | torch.Tensor.masked_fill | 是 | +| 206 | torch.Tensor.masked_select | 是 | +| 207 | torch.Tensor.matmul | 是 | +| 208 | torch.Tensor.matrix_power | 是 | +| 209 | torch.Tensor.max | 是 | +| 210 | torch.Tensor.mean | 是 | +| 211 | torch.Tensor.median | 是 | +| 212 | torch.Tensor.min | 是 | +| 213 | torch.Tensor.mm | 是 | +| 214 | torch.Tensor.mode | 否 | +| 215 | torch.Tensor.mul | 是 | +| 216 | torch.Tensor.mul_ | 是 | +| 217 | torch.Tensor.multinomial | 是 | +| 218 | torch.Tensor.mv | 是 | +| 219 | torch.Tensor.mvlgamma | 否 | +| 220 | torch.Tensor.mvlgamma_ | 否 | +| 221 | torch.Tensor.narrow | 是 | +| 222 | torch.Tensor.narrow_copy | 是 | +| 223 | torch.Tensor.ndimension | 是 | +| 224 | torch.Tensor.ne | 是 | +| 225 | torch.Tensor.ne_ | 是 | +| 226 | torch.Tensor.neg | 是 | +| 227 | torch.Tensor.neg_ | 是 | +| 228 | torch.Tensor.nelement | 是 | +| 229 | torch.Tensor.nonzero | 是 | +| 230 | torch.Tensor.norm | 是 | +| 231 | torch.Tensor.normal_ | 是 | +| 232 | torch.Tensor.numel | 是 | +| 233 | torch.Tensor.numpy | 否 | +| 234 | torch.Tensor.orgqr | 否 | +| 235 | torch.Tensor.ormqr | 否 | +| 236 | torch.Tensor.permute | 是 | +| 237 | torch.Tensor.pin_memory | 否 | +| 238 | torch.Tensor.pinverse | 是 | +| 239 | torch.Tensor.polygamma | 否 | +| 240 | torch.Tensor.polygamma_ | 否 | +| 241 | torch.Tensor.pow | 是 | +| 242 | torch.Tensor.pow_ | 是 | +| 243 | torch.Tensor.prod | 是 | +| 244 | torch.Tensor.put_ | 是 | +| 245 | torch.Tensor.qr | 是 | +| 246 | torch.Tensor.qscheme | 否 | +| 247 | torch.Tensor.q_scale | 否 | +| 248 | torch.Tensor.q_zero_point | 否 | +| 249 | torch.Tensor.q_per_channel_scales | 否 | +| 250 | torch.Tensor.q_per_channel_zero_points | 否 | +| 251 | torch.Tensor.q_per_channel_axis | 否 | +| 252 | torch.Tensor.random_ | 是 | +| 253 | torch.Tensor.reciprocal | 是 | +| 254 | torch.Tensor.reciprocal_ | 是 | +| 255 | torch.Tensor.record_stream | 是 | +| 256 | torch.Tensor.remainder | 是 | +| 257 | torch.Tensor.remainder_ | 是 | +| 258 | torch.Tensor.renorm | 是 | +| 259 | torch.Tensor.renorm_ | 是 | +| 260 | torch.Tensor.repeat | 是 | +| 261 | torch.Tensor.repeat_interleave | 是 | +| 262 | torch.Tensor.requires_grad_ | 是 | +| 263 | torch.Tensor.reshape | 是 | +| 264 | torch.Tensor.reshape_as | 是 | +| 265 | torch.Tensor.resize_ | 是 | +| 266 | torch.Tensor.resize_as_ | 是 | +| 267 | torch.Tensor.rfft | 否 | +| 268 | torch.Tensor.roll | 是 | +| 269 | torch.Tensor.rot90 | 是 | +| 270 | torch.Tensor.round | 是 | +| 271 | torch.Tensor.round_ | 是 | +| 272 | torch.Tensor.rsqrt | 是 | +| 273 | torch.Tensor.rsqrt_ | 是 | +| 274 | torch.Tensor.scatter | 是 | +| 275 | torch.Tensor.scatter_ | 是 | +| 276 | torch.Tensor.scatter_add_ | 是 | +| 277 | torch.Tensor.scatter_add | 是 | +| 278 | torch.Tensor.select | 是 | +| 279 | torch.Tensor.set_ | 是 | +| 280 | torch.Tensor.share_memory_ | 否 | +| 281 | torch.Tensor.short | 是 | +| 282 | torch.Tensor.sigmoid | 是 | +| 283 | torch.Tensor.sigmoid_ | 是 | +| 284 | torch.Tensor.sign | 是 | +| 285 | torch.Tensor.sign_ | 是 | +| 286 | torch.Tensor.sin | 是 | +| 287 | torch.Tensor.sin_ | 是 | +| 288 | torch.Tensor.sinh | 是 | +| 289 | torch.Tensor.sinh_ | 是 | +| 290 | torch.Tensor.size | 是 | +| 291 | torch.Tensor.slogdet | 是 | +| 292 | torch.Tensor.solve | 否 | +| 293 | torch.Tensor.sort | 是 | +| 294 | torch.Tensor.split | 是 | +| 295 | torch.Tensor.sparse_mask | 否 | +| 296 | torch.Tensor.sparse_dim | 否 | +| 297 | torch.Tensor.sqrt | 是 | +| 298 | torch.Tensor.sqrt_ | 是 | +| 299 | torch.Tensor.square | 是 | +| 300 | torch.Tensor.square_ | 是 | +| 301 | torch.Tensor.squeeze | 是 | +| 302 | torch.Tensor.squeeze_ | 是 | +| 303 | torch.Tensor.std | 是 | +| 304 | torch.Tensor.stft | 否 | +| 305 | torch.Tensor.storage | 是 | +| 306 | torch.Tensor.storage_offset | 是 | +| 307 | torch.Tensor.storage_type | 是 | +| 308 | torch.Tensor.stride | 是 | +| 309 | torch.Tensor.sub | 是 | +| 310 | torch.Tensor.sub_ | 是 | +| 311 | torch.Tensor.sum | 是 | +| 312 | torch.Tensor.sum_to_size | 是 | +| 313 | torch.Tensor.svd | 是 | +| 314 | torch.Tensor.symeig | 是 | +| 315 | torch.Tensor.t | 是 | +| 316 | torch.Tensor.t_ | 是 | +| 317 | torch.Tensor.to | 是 | +| 318 | torch.Tensor.to_mkldnn | 否 | +| 319 | torch.Tensor.take | 是 | +| 320 | torch.Tensor.tan | 是 | +| 321 | torch.Tensor.tan_ | 是 | +| 322 | torch.Tensor.tanh | 是 | +| 323 | torch.Tensor.tanh_ | 是 | +| 324 | torch.Tensor.tolist | 是 | +| 325 | torch.Tensor.topk | 是 | +| 326 | torch.Tensor.to_sparse | 否 | +| 327 | torch.Tensor.trace | 否 | +| 328 | torch.Tensor.transpose | 是 | +| 329 | torch.Tensor.transpose_ | 是 | +| 330 | torch.Tensor.triangular_solve | 是 | +| 331 | torch.Tensor.tril | 是 | +| 332 | torch.Tensor.tril_ | 是 | +| 333 | torch.Tensor.triu | 是 | +| 334 | torch.Tensor.triu_ | 是 | +| 335 | torch.Tensor.true_divide | 是 | +| 336 | torch.Tensor.true_divide_ | 是 | +| 337 | torch.Tensor.trunc | 是 | +| 338 | torch.Tensor.trunc_ | 是 | +| 339 | torch.Tensor.type | 是 | +| 340 | torch.Tensor.type_as | 是 | +| 341 | torch.Tensor.unbind | 是 | +| 342 | torch.Tensor.unfold | 是 | +| 343 | torch.Tensor.uniform_ | 是 | +| 344 | torch.Tensor.unique | 是 | +| 345 | torch.Tensor.unique_consecutive | 否 | +| 346 | torch.Tensor.unsqueeze | 是 | +| 347 | torch.Tensor.unsqueeze_ | 是 | +| 348 | torch.Tensor.values | 否 | +| 349 | torch.Tensor.var | 否 | +| 350 | torch.Tensor.view | 是 | +| 351 | torch.Tensor.view_as | 是 | +| 352 | torch.Tensor.where | 是 | +| 353 | torch.Tensor.zero_ | 是 | +| 354 | torch.BoolTensor | 是 | +| 355 | torch.BoolTensor.all | 是 | +| 356 | torch.BoolTensor.any | 是 | + +## Layers (torch.nn) + +| 序号 | API名称 | 是否支持 | +| ---- | -------------------------------------------------------- | ---------------------------- | +| 1 | torch.nn.Parameter | 是 | +| 2 | torch.nn.Module | 是 | +| 3 | torch.nn.Module.add_module | 是 | +| 4 | torch.nn.Module.apply | 是 | +| 5 | torch.nn.Module.bfloat16 | 否 | +| 6 | torch.nn.Module.buffers | 是 | +| 7 | torch.nn.Module.children | 是 | +| 8 | torch.nn.Module.cpu | 是 | +| 9 | torch.nn.Module.cuda | 否 | +| 10 | torch.nn.Module.double | 否 | +| 11 | torch.nn.Module.dump_patches | 是 | +| 12 | torch.nn.Module.eval | 是 | +| 13 | torch.nn.Module.extra_repr | 是 | +| 14 | torch.nn.Module.float | 是 | +| 15 | torch.nn.Module.forward | 是 | +| 16 | torch.nn.Module.half | 是 | +| 17 | torch.nn.Module.load_state_dict | 是 | +| 18 | torch.nn.Module.modules | 是 | +| 19 | torch.nn.Module.named_buffers | 是 | +| 20 | torch.nn.Module.named_children | 是 | +| 21 | torch.nn.Module.named_modules | 是 | +| 22 | torch.nn.Module.named_parameters | 是 | +| 23 | torch.nn.Module.parameters | 是 | +| 24 | torch.nn.Module.register_backward_hook | 是 | +| 25 | torch.nn.Module.register_buffer | 是 | +| 26 | torch.nn.Module.register_forward_hook | 是 | +| 27 | torch.nn.Module.register_forward_pre_hook | 是 | +| 28 | torch.nn.Module.register_parameter | 是 | +| 29 | torch.nn.Module.requires_grad_ | 是 | +| 30 | torch.nn.Module.state_dict | 是 | +| 31 | torch.nn.Module.to | 是 | +| 32 | torch.nn.Module.train | 是 | +| 33 | torch.nn.Module.type | 是 | +| 34 | torch.nn.Module.zero_grad | 是 | +| 35 | torch.nn.Sequential | 是 | +| 36 | torch.nn.ModuleList | 是 | +| 37 | torch.nn.ModuleList.append | 是 | +| 38 | torch.nn.ModuleList.extend | 是 | +| 39 | torch.nn.ModuleList.insert | 是 | +| 40 | torch.nn.ModuleDict | 是 | +| 41 | torch.nn.ModuleDict.clear | 是 | +| 42 | torch.nn.ModuleDict.items | 是 | +| 43 | torch.nn.ModuleDict.keys | 是 | +| 44 | torch.nn.ModuleDict.pop | 是 | +| 45 | torch.nn.ModuleDict.update | 是 | +| 46 | torch.nn.ModuleDict.values | 是 | +| 47 | torch.nn.ParameterList | 是 | +| 48 | torch.nn.ParameterList.append | 是 | +| 49 | torch.nn.ParameterList.extend | 是 | +| 50 | torch.nn.ParameterDict | 是 | +| 51 | torch.nn.ParameterDict.clear | 是 | +| 52 | torch.nn.ParameterDict.items | 是 | +| 53 | torch.nn.ParameterDict.keys | 是 | +| 54 | torch.nn.ParameterDict.pop | 是 | +| 55 | torch.nn.ParameterDict.update | 是 | +| 56 | torch.nn.ParameterDict.values | 是 | +| 57 | torch.nn.Conv1d | 是 | +| 58 | torch.nn.Conv2d | 是 | +| 59 | torch.nn.Conv3d | 是 | +| 60 | torch.nn.ConvTranspose1d | 是 | +| 61 | torch.nn.ConvTranspose2d | 是 | +| 62 | torch.nn.ConvTranspose3d | 是 | +| 63 | torch.nn.Unfold | 是 | +| 64 | torch.nn.Fold | 是 | +| 65 | torch.nn.MaxPool1d | 是 | +| 66 | torch.nn.MaxPool2d | 是 | +| 67 | torch.nn.MaxPool3d | 是 | +| 68 | torch.nn.MaxUnpool1d | 是 | +| 69 | torch.nn.MaxUnpool2d | 是 | +| 70 | torch.nn.MaxUnpool3d | 是 | +| 71 | torch.nn.AvgPool1d | 是 | +| 72 | torch.nn.AvgPool2d | 是 | +| 73 | torch.nn.AvgPool3d | 是 | +| 74 | torch.nn.FractionalMaxPool2d | 否 | +| 75 | torch.nn.LPPool1d | 是 | +| 76 | torch.nn.LPPool2d | 是 | +| 77 | torch.nn.AdaptiveMaxPool1d | 是 | +| 78 | torch.nn.AdaptiveMaxPool2d | 是 | +| 79 | torch.nn.AdaptiveMaxPool3d | 否 | +| 80 | torch.nn.AdaptiveAvgPool1d | 是 | +| 81 | torch.nn.AdaptiveAvgPool2d | 是 | +| 82 | torch.nn.AdaptiveAvgPool3d | 是，仅支持D=1，H=1，W=1场景 | +| 83 | torch.nn.ReflectionPad1d | 否 | +| 84 | torch.nn.ReflectionPad2d | 是 | +| 85 | torch.nn.ReplicationPad1d | 否 | +| 86 | torch.nn.ReplicationPad2d | 是 | +| 87 | torch.nn.ReplicationPad3d | 否 | +| 88 | torch.nn.ZeroPad2d | 是 | +| 89 | torch.nn.ConstantPad1d | 是 | +| 90 | torch.nn.ConstantPad2d | 是 | +| 91 | torch.nn.ConstantPad3d | 是 | +| 92 | torch.nn.ELU | 是 | +| 93 | torch.nn.Hardshrink | 是 | +| 94 | torch.nn.Hardtanh | 是 | +| 95 | torch.nn.LeakyReLU | 是 | +| 96 | torch.nn.LogSigmoid | 是 | +| 97 | torch.nn.MultiheadAttention | 是 | +| 98 | torch.nn.PReLU | 是 | +| 99 | torch.nn.ReLU | 是 | +| 100 | torch.nn.ReLU6 | 是 | +| 101 | torch.nn.RReLU | 是 | +| 102 | torch.nn.SELU | 是 | +| 103 | torch.nn.CELU | 是 | +| 104 | torch.nn.GELU | 是 | +| 105 | torch.nn.Sigmoid | 是 | +| 106 | torch.nn.Softplus | 是 | +| 107 | torch.nn.Softshrink | 是，SoftShrink场景暂不支持 | +| 108 | torch.nn.Softsign | 是 | +| 109 | torch.nn.Tanh | 是 | +| 110 | torch.nn.Tanhshrink | 是 | +| 111 | torch.nn.Threshold | 是 | +| 112 | torch.nn.Softmin | 是 | +| 113 | torch.nn.Softmax | 是 | +| 114 | torch.nn.Softmax2d | 是 | +| 115 | torch.nn.LogSoftmax | 是 | +| 116 | torch.nn.AdaptiveLogSoftmaxWithLoss | 否 | +| 117 | torch.nn.AdaptiveLogSoftmaxWithLoss.log_prob | 否 | +| 118 | torch.nn.AdaptiveLogSoftmaxWithLoss.predict | 否 | +| 119 | torch.nn.BatchNorm1d | 是 | +| 120 | torch.nn.BatchNorm2d | 是 | +| 121 | torch.nn.BatchNorm3d | 是 | +| 122 | torch.nn.GroupNorm | 是 | +| 123 | torch.nn.SyncBatchNorm | 是 | +| 124 | torch.nn.SyncBatchNorm.convert_sync_batchnorm | 是 | +| 125 | torch.nn.InstanceNorm1d | 是 | +| 126 | torch.nn.InstanceNorm2d | 是 | +| 127 | torch.nn.InstanceNorm3d | 是 | +| 128 | torch.nn.LayerNorm | 是 | +| 129 | torch.nn.LocalResponseNorm | 是 | +| 130 | torch.nn.RNNBase | 是 | +| 131 | torch.nn.RNNBase.flatten_parameters | 是 | +| 132 | torch.nn.RNN | 是 | +| 133 | torch.nn.LSTM | 是 | +| 134 | torch.nn.GRU | 是，DynamicGRUV2场景暂不支持 | +| 135 | torch.nn.RNNCell | 是 | +| 136 | torch.nn.LSTMCell | 是 | +| 137 | torch.nn.GRUCell | 是 | +| 138 | torch.nn.Transformer | 是 | +| 139 | torch.nn.Transformer.forward | 是 | +| 140 | torch.nn.Transformer.generate_square_subsequent_mask | 是 | +| 141 | torch.nn.TransformerEncoder | 是 | +| 142 | torch.nn.TransformerEncoder.forward | 是 | +| 143 | torch.nn.TransformerDecoder | 是 | +| 144 | torch.nn.TransformerDecoder.forward | 是 | +| 145 | torch.nn.TransformerEncoderLayer | 是 | +| 146 | torch.nn.TransformerEncoderLayer.forward | 是 | +| 147 | torch.nn.TransformerDecoderLayer | 是 | +| 148 | torch.nn.TransformerDecoderLayer.forward | 是 | +| 149 | torch.nn.Identity | 是 | +| 150 | torch.nn.Linear | 是 | +| 151 | torch.nn.Bilinear | 是 | +| 152 | torch.nn.Dropout | 是 | +| 153 | torch.nn.Dropout2d | 是 | +| 154 | torch.nn.Dropout3d | 是 | +| 155 | torch.nn.AlphaDropout | 是 | +| 156 | torch.nn.Embedding | 是 | +| 157 | torch.nn.Embedding.from_pretrained | 是 | +| 158 | torch.nn.EmbeddingBag | 是 | +| 159 | torch.nn.EmbeddingBag.from_pretrained | 是 | +| 160 | torch.nn.CosineSimilarity | 是 | +| 161 | torch.nn.PairwiseDistance | 是 | +| 162 | torch.nn.L1Loss | 是 | +| 163 | torch.nn.MSELoss | 是 | +| 164 | torch.nn.CrossEntropyLoss | 是 | +| 165 | torch.nn.CTCLoss | 是 | +| 166 | torch.nn.NLLLoss | 是 | +| 167 | torch.nn.PoissonNLLLoss | 是 | +| 168 | torch.nn.KLDivLoss | 是 | +| 169 | torch.nn.BCELoss | 是 | +| 170 | torch.nn.BCEWithLogitsLoss | 是 | +| 171 | torch.nn.MarginRankingLoss | 是 | +| 172 | torch.nn.HingeEmbeddingLoss | 是 | +| 173 | torch.nn.MultiLabelMarginLoss | 是 | +| 174 | torch.nn.SmoothL1Loss | 是 | +| 175 | torch.nn.SoftMarginLoss | 是 | +| 176 | torch.nn.MultiLabelSoftMarginLoss | 是 | +| 177 | torch.nn.CosineEmbeddingLoss | 是 | +| 178 | torch.nn.MultiMarginLoss | 否 | +| 179 | torch.nn.TripletMarginLoss | 是 | +| 180 | torch.nn.PixelShuffle | 是 | +| 181 | torch.nn.Upsample | 是 | +| 182 | torch.nn.UpsamplingNearest2d | 是 | +| 183 | torch.nn.UpsamplingBilinear2d | 是 | +| 184 | torch.nn.DataParallel | 否 | +| 185 | torch.nn.parallel.DistributedDataParallel | 是 | +| 186 | torch.nn.parallel.DistributedDataParallel.no_sync | 是 | +| 187 | torch.nn.utils.clip_grad_norm_ | 是 | +| 188 | torch.nn.utils.clip_grad_value_ | 是 | +| 189 | torch.nn.utils.parameters_to_vector | 是 | +| 190 | torch.nn.utils.vector_to_parameters | 是 | +| 197 | torch.nn.utils.prune.PruningContainer | 是 | +| 198 | torch.nn.utils.prune.PruningContainer.add_pruning_method | 是 | +| 199 | torch.nn.utils.prune.PruningContainer.apply | 是 | +| 200 | torch.nn.utils.prune.PruningContainer.apply_mask | 是 | +| 201 | torch.nn.utils.prune.PruningContainer.compute_mask | 是 | +| 202 | torch.nn.utils.prune.PruningContainer.prune | 是 | +| 203 | torch.nn.utils.prune.PruningContainer.remove | 是 | +| 204 | torch.nn.utils.prune.Identity | 是 | +| 205 | torch.nn.utils.prune.Identity.apply | 是 | +| 206 | torch.nn.utils.prune.Identity.apply_mask | 是 | +| 207 | torch.nn.utils.prune.Identity.prune | 是 | +| 208 | torch.nn.utils.prune.Identity.remove | 是 | +| 209 | torch.nn.utils.prune.RandomUnstructured | 是 | +| 210 | torch.nn.utils.prune.RandomUnstructured.apply | 是 | +| 211 | torch.nn.utils.prune.RandomUnstructured.apply_mask | 是 | +| 212 | torch.nn.utils.prune.RandomUnstructured.prune | 是 | +| 213 | torch.nn.utils.prune.RandomUnstructured.remove | 是 | +| 214 | torch.nn.utils.prune.L1Unstructured | 是 | +| 215 | torch.nn.utils.prune.L1Unstructured.apply | 是 | +| 216 | torch.nn.utils.prune.L1Unstructured.apply_mask | 是 | +| 217 | torch.nn.utils.prune.L1Unstructured.prune | 是 | +| 218 | torch.nn.utils.prune.L1Unstructured.remove | 是 | +| 219 | torch.nn.utils.prune.RandomStructured | 是 | +| 220 | torch.nn.utils.prune.RandomStructured.apply | 是 | +| 221 | torch.nn.utils.prune.RandomStructured.apply_mask | 是 | +| 222 | torch.nn.utils.prune.RandomStructured.compute_mask | 是 | +| 223 | torch.nn.utils.prune.RandomStructured.prune | 是 | +| 224 | torch.nn.utils.prune.RandomStructured.remove | 是 | +| 225 | torch.nn.utils.prune.LnStructured | 是 | +| 226 | torch.nn.utils.prune.LnStructured.apply | 是 | +| 227 | torch.nn.utils.prune.LnStructured.apply_mask | 是 | +| 228 | torch.nn.utils.prune.LnStructured.compute_mask | 是 | +| 229 | torch.nn.utils.prune.LnStructured.prune | 是 | +| 230 | torch.nn.utils.prune.LnStructured.remove | 是 | +| 231 | torch.nn.utils.prune.CustomFromMask | 是 | +| 232 | torch.nn.utils.prune.CustomFromMask.apply | 是 | +| 233 | torch.nn.utils.prune.CustomFromMask.apply_mask | 是 | +| 234 | torch.nn.utils.prune.CustomFromMask.prune | 是 | +| 235 | torch.nn.utils.prune.CustomFromMask.remove | 是 | +| 236 | torch.nn.utils.prune.identity | 是 | +| 237 | torch.nn.utils.prune.random_unstructured | 是 | +| 238 | torch.nn.utils.prune.l1_unstructured | 是 | +| 239 | torch.nn.utils.prune.random_structured | 是 | +| 240 | torch.nn.utils.prune.ln_structured | 是 | +| 241 | torch.nn.utils.prune.global_unstructured | 是 | +| 242 | torch.nn.utils.prune.custom_from_mask | 是 | +| 243 | torch.nn.utils.prune.remove | 是 | +| 244 | torch.nn.utils.prune.is_pruned | 是 | +| 245 | torch.nn.utils.weight_norm | 是 | +| 246 | torch.nn.utils.remove_weight_norm | 是 | +| 247 | torch.nn.utils.spectral_norm | 是 | +| 248 | torch.nn.utils.remove_spectral_norm | 是 | +| 249 | torch.nn.utils.rnn.PackedSequence | 是 | +| 250 | torch.nn.utils.rnn.pack_padded_sequence | 是 | +| 251 | torch.nn.utils.rnn.pad_packed_sequence | 否 | +| 252 | torch.nn.utils.rnn.pad_sequence | 是 | +| 253 | torch.nn.utils.rnn.pack_sequence | 否 | +| 254 | torch.nn.Flatten | 是 | +| 255 | torch.quantization.quantize | 否 | +| 256 | torch.quantization.quantize_dynamic | 否 | +| 257 | torch.quantization.quantize_qat | 否 | +| 258 | torch.quantization.prepare | 是 | +| 259 | torch.quantization.prepare_qat | 否 | +| 260 | torch.quantization.convert | 否 | +| 261 | torch.quantization.QConfig | 是 | +| 262 | torch.quantization.QConfigDynamic | 是 | +| 263 | torch.quantization.fuse_modules | 是 | +| 264 | torch.quantization.QuantStub | 是 | +| 265 | torch.quantization.DeQuantStub | 是 | +| 266 | torch.quantization.QuantWrapper | 是 | +| 267 | torch.quantization.add_quant_dequant | 是 | +| 268 | torch.quantization.add_observer_ | 是 | +| 269 | torch.quantization.swap_module | 是 | +| 270 | torch.quantization.propagate_qconfig_ | 是 | +| 271 | torch.quantization.default_eval_fn | 是 | +| 272 | torch.quantization.MinMaxObserver | 是 | +| 273 | torch.quantization.MovingAverageMinMaxObserver | 是 | +| 274 | torch.quantization.PerChannelMinMaxObserver | 是 | +| 275 | torch.quantization.MovingAveragePerChannelMinMaxObserver | 是 | +| 276 | torch.quantization.HistogramObserver | 否 | +| 277 | torch.quantization.FakeQuantize | 否 | +| 278 | torch.quantization.NoopObserver | 是 | +| 279 | torch.quantization.get_observer_dict | 是 | +| 280 | torch.quantization.RecordingObserver | 是 | +| 281 | torch.nn.intrinsic.ConvBn2d | 是 | +| 282 | torch.nn.intrinsic.ConvBnReLU2d | 是 | +| 283 | torch.nn.intrinsic.ConvReLU2d | 是 | +| 284 | torch.nn.intrinsic.ConvReLU3d | 是 | +| 285 | torch.nn.intrinsic.LinearReLU | 是 | +| 286 | torch.nn.intrinsic.qat.ConvBn2d | 否 | +| 287 | torch.nn.intrinsic.qat.ConvBnReLU2d | 否 | +| 288 | torch.nn.intrinsic.qat.ConvReLU2d | 否 | +| 289 | torch.nn.intrinsic.qat.LinearReLU | 否 | +| 290 | torch.nn.intrinsic.quantized.ConvReLU2d | 否 | +| 291 | torch.nn.intrinsic.quantized.ConvReLU3d | 否 | +| 292 | torch.nn.intrinsic.quantized.LinearReLU | 否 | +| 293 | torch.nn.qat.Conv2d | 否 | +| 294 | torch.nn.qat.Conv2d.from_float | 否 | +| 295 | torch.nn.qat.Linear | 否 | +| 296 | torch.nn.qat.Linear.from_float | 否 | +| 297 | torch.nn.quantized.functional.relu | 否 | +| 298 | torch.nn.quantized.functional.linear | 否 | +| 299 | torch.nn.quantized.functional.conv2d | 否 | +| 300 | torch.nn.quantized.functional.conv3d | 否 | +| 301 | torch.nn.quantized.functional.max_pool2d | 否 | +| 302 | torch.nn.quantized.functional.adaptive_avg_pool2d | 否 | +| 303 | torch.nn.quantized.functional.avg_pool2d | 否 | +| 304 | torch.nn.quantized.functional.interpolate | 否 | +| 305 | torch.nn.quantized.functional.upsample | 否 | +| 306 | torch.nn.quantized.functional.upsample_bilinear | 否 | +| 307 | torch.nn.quantized.functional.upsample_nearest | 否 | +| 308 | torch.nn.quantized.ReLU | 否 | +| 309 | torch.nn.quantized.ReLU6 | 否 | +| 310 | torch.nn.quantized.Conv2d | 否 | +| 311 | torch.nn.quantized.Conv2d.from_float | 否 | +| 312 | torch.nn.quantized.Conv3d | 否 | +| 313 | torch.nn.quantized.Conv3d.from_float | 否 | +| 314 | torch.nn.quantized.FloatFunctional | 是 | +| 315 | torch.nn.quantized.QFunctional | 否 | +| 316 | torch.nn.quantized.Quantize | 是 | +| 317 | torch.nn.quantized.DeQuantize | 否 | +| 318 | torch.nn.quantized.Linear | 否 | +| 319 | torch.nn.quantized.Linear.from_float | 否 | +| 320 | torch.nn.quantized.dynamic.Linear | 否 | +| 321 | torch.nn.quantized.dynamic.Linear.from_float | 否 | +| 322 | torch.nn.quantized.dynamic.LSTM | 否 | + +## Functions(torch.nn.functional) + +| 序号 | API名称 | 是否支持 | +| ---- | ---------------------------------------------------- | --------------------------- | +| 1 | torch.nn.functional.conv1d | 是 | +| 2 | torch.nn.functional.conv2d | 是 | +| 3 | torch.nn.functional.conv3d | 是 | +| 4 | torch.nn.functional.conv_transpose1d | 是 | +| 5 | torch.nn.functional.conv_transpose2d | 是 | +| 6 | torch.nn.functional.conv_transpose3d | 是 | +| 7 | torch.nn.functional.unfold | 是 | +| 8 | torch.nn.functional.fold | 是 | +| 9 | torch.nn.functional.avg_pool1d | 是 | +| 10 | torch.nn.functional.avg_pool2d | 是 | +| 11 | torch.nn.functional.avg_pool3d | 是 | +| 12 | torch.nn.functional.max_pool1d | 是 | +| 13 | torch.nn.functional.max_pool2d | 是 | +| 14 | torch.nn.functional.max_pool3d | 是 | +| 15 | torch.nn.functional.max_unpool1d | 是 | +| 16 | torch.nn.functional.max_unpool2d | 是 | +| 17 | torch.nn.functional.max_unpool3d | 是 | +| 18 | torch.nn.functional.lp_pool1d | 是 | +| 19 | torch.nn.functional.lp_pool2d | 是 | +| 20 | torch.nn.functional.adaptive_max_pool1d | 是 | +| 21 | torch.nn.functional.adaptive_max_pool2d | 是 | +| 22 | torch.nn.functional.adaptive_max_pool3d | 否 | +| 23 | torch.nn.functional.adaptive_avg_pool1d | 是 | +| 24 | torch.nn.functional.adaptive_avg_pool2d | 是 | +| 25 | torch.nn.functional.adaptive_avg_pool3d | 是，仅支持D=1，H=1，W=1场景 | +| 26 | torch.nn.functional.threshold | 是 | +| 27 | torch.nn.functional.threshold_ | 是 | +| 28 | torch.nn.functional.relu | 是 | +| 29 | torch.nn.functional.relu_ | 是 | +| 30 | torch.nn.functional.hardtanh | 是 | +| 31 | torch.nn.functional.hardtanh_ | 是 | +| 32 | torch.nn.functional.relu6 | 是 | +| 33 | torch.nn.functional.elu | 是 | +| 34 | torch.nn.functional.elu_ | 是 | +| 35 | torch.nn.functional.selu | 是 | +| 36 | torch.nn.functional.celu | 是 | +| 37 | torch.nn.functional.leaky_relu | 是 | +| 38 | torch.nn.functional.leaky_relu_ | 是 | +| 39 | torch.nn.functional.prelu | 是 | +| 40 | torch.nn.functional.rrelu | 是 | +| 41 | torch.nn.functional.rrelu_ | 是 | +| 42 | torch.nn.functional.glu | 是 | +| 43 | torch.nn.functional.gelu | 是 | +| 44 | torch.nn.functional.logsigmoid | 是 | +| 45 | torch.nn.functional.hardshrink | 是 | +| 46 | torch.nn.functional.tanhshrink | 是 | +| 47 | torch.nn.functional.softsign | 是 | +| 48 | torch.nn.functional.softplus | 是 | +| 49 | torch.nn.functional.softmin | 是 | +| 50 | torch.nn.functional.softmax | 是 | +| 51 | torch.nn.functional.softshrink | 是 | +| 52 | torch.nn.functional.gumbel_softmax | 否 | +| 53 | torch.nn.functional.log_softmax | 是 | +| 54 | torch.nn.functional.tanh | 是 | +| 55 | torch.nn.functional.sigmoid | 是 | +| 56 | torch.nn.functional.batch_norm | 是 | +| 57 | torch.nn.functional.instance_norm | 是 | +| 58 | torch.nn.functional.layer_norm | 是 | +| 59 | torch.nn.functional.local_response_norm | 是 | +| 60 | torch.nn.functional.normalize | 是 | +| 61 | torch.nn.functional.linear | 是 | +| 62 | torch.nn.functional.bilinear | 是 | +| 63 | torch.nn.functional.dropout | 是 | +| 64 | torch.nn.functional.alpha_dropout | 是 | +| 65 | torch.nn.functional.dropout2d | 是 | +| 66 | torch.nn.functional.dropout3d | 是 | +| 67 | torch.nn.functional.embedding | 是 | +| 68 | torch.nn.functional.embedding_bag | 是 | +| 69 | torch.nn.functional.one_hot | 是 | +| 70 | torch.nn.functional.pairwise_distance | 是 | +| 71 | torch.nn.functional.cosine_similarity | 是 | +| 72 | torch.nn.functional.pdist | 是 | +| 73 | torch.nn.functional.binary_cross_entropy | 是 | +| 74 | torch.nn.functional.binary_cross_entropy_with_logits | 是 | +| 75 | torch.nn.functional.poisson_nll_loss | 是 | +| 76 | torch.nn.functional.cosine_embedding_loss | 是 | +| 77 | torch.nn.functional.cross_entropy | 是 | +| 78 | torch.nn.functional.ctc_loss | 是（仅支持2维输入） | +| 79 | torch.nn.functional.hinge_embedding_loss | 是 | +| 80 | torch.nn.functional.kl_div | 是 | +| 81 | torch.nn.functional.l1_loss | 是 | +| 82 | torch.nn.functional.mse_loss | 是 | +| 83 | torch.nn.functional.margin_ranking_loss | 是 | +| 84 | torch.nn.functional.multilabel_margin_loss | 是 | +| 85 | torch.nn.functional.multilabel_soft_margin_loss | 是 | +| 86 | torch.nn.functional.multi_margin_loss | 否 | +| 87 | torch.nn.functional.nll_loss | 是 | +| 88 | torch.nn.functional.smooth_l1_loss | 是 | +| 89 | torch.nn.functional.soft_margin_loss | 是 | +| 90 | torch.nn.functional.triplet_margin_loss | 是 | +| 91 | torch.nn.functional.pixel_shuffle | 是 | +| 92 | torch.nn.functional.pad | 是 | +| 93 | torch.nn.functional.interpolate | 是 | +| 94 | torch.nn.functional.upsample | 是 | +| 95 | torch.nn.functional.upsample_nearest | 是 | +| 96 | torch.nn.functional.upsample_bilinear | 是 | +| 97 | torch.nn.functional.grid_sample | 是 | +| 98 | torch.nn.functional.affine_grid | 是 | +| 99 | torch.nn.parallel.data_parallel | 否 | + +## torch.distributed + +| 序号 | API名称 | 是否支持 | +| ---- | ------------------------------------- | -------- | +| 1 | torch.distributed.init_process_group | 是 | +| 2 | torch.distributed.Backend | 是 | +| 3 | torch.distributed.get_backend | 是 | +| 4 | torch.distributed.get_rank | 是 | +| 5 | torch.distributed.get_world_size | 是 | +| 6 | torch.distributed.is_initialized | 是 | +| 7 | torch.distributed.is_mpi_available | 是 | +| 8 | torch.distributed.is_nccl_available | 是 | +| 9 | torch.distributed.new_group | 是 | +| 10 | torch.distributed.send | 否 | +| 11 | torch.distributed.recv | 否 | +| 12 | torch.distributed.isend | 否 | +| 13 | torch.distributed.irecv | 否 | +| 14 | is_completed | 是 | +| 15 | wait | 是 | +| 16 | torch.distributed.broadcast | 是 | +| 17 | torch.distributed.all_reduce | 是 | +| 18 | torch.distributed.reduce | 否 | +| 19 | torch.distributed.all_gather | 是 | +| 20 | torch.distributed.gather | 否 | +| 21 | torch.distributed.scatter | 否 | +| 22 | torch.distributed.barrier | 是 | +| 23 | torch.distributed.ReduceOp | 是 | +| 24 | torch.distributed.reduce_op | 是 | +| 25 | torch.distributed.broadcast_multigpu | 否 | +| 26 | torch.distributed.all_reduce_multigpu | 否 | +| 27 | torch.distributed.reduce_multigpu | 否 | +| 28 | torch.distributed.all_gather_multigpu | 否 | +| 29 | torch.distributed.launch | 是 | +| 30 | torch.multiprocessing.spawn | 是 | + +## torch.npu + +| 序号 | API名称 | npu对应API名称 | 是否支持 | +| ---- | ------------------------------------- | ------------------------------------ | -------- | +| 1 | torch.cuda.current_blas_handle | torch.npu.current_blas_handle | 否 | +| 2 | torch.cuda.current_device | torch.npu.current_device | 是 | +| 3 | torch.cuda.current_stream | torch.npu.current_stream | 是 | +| 4 | torch.cuda.default_stream | torch.npu.default_stream | 是 | +| 5 | torch.cuda.device | torch.npu.device | 是 | +| 6 | torch.cuda.device_count | torch.npu.device_count | 是 | +| 7 | torch.cuda.device_of | torch.npu.device_of | 是 | +| 8 | torch.cuda.get_device_capability | torch.npu.get_device_capability | 否 | +| 9 | torch.cuda.get_device_name | torch.npu.get_device_name | 否 | +| 10 | torch.cuda.init | torch.npu.init | 是 | +| 11 | torch.cuda.ipc_collect | torch.npu.ipc_collect | 否 | +| 12 | torch.cuda.is_available | torch.npu.is_available | 是 | +| 13 | torch.cuda.is_initialized | torch.npu.is_initialized | 是 | +| 14 | torch.cuda.set_device | torch.npu.set_device | 部分支持 | +| 15 | torch.cuda.stream | torch.npu.stream | 是 | +| 16 | torch.cuda.synchronize | torch.npu.synchronize | 是 | +| 17 | torch.cuda.get_rng_state | torch.npu.get_rng_state | 否 | +| 18 | torch.cuda.get_rng_state_all | torch.npu.get_rng_state_all | 否 | +| 19 | torch.cuda.set_rng_state | torch.npu.set_rng_state | 否 | +| 20 | torch.cuda.set_rng_state_all | torch.npu.set_rng_state_all | 否 | +| 21 | torch.cuda.manual_seed | torch.npu.manual_seed | 否 | +| 22 | torch.cuda.manual_seed_all | torch.npu.manual_seed_all | 否 | +| 23 | torch.cuda.seed | torch.npu.seed | 否 | +| 24 | torch.cuda.seed_all | torch.npu.seed_all | 否 | +| 25 | torch.cuda.initial_seed | torch.npu.initial_seed | 否 | +| 26 | torch.cuda.comm.broadcast | torch.npu.comm.broadcast | 否 | +| 27 | torch.cuda.comm.broadcast_coalesced | torch.npu.comm.broadcast_coalesced | 否 | +| 28 | torch.cuda.comm.reduce_add | torch.npu.comm.reduce_add | 否 | +| 29 | torch.cuda.comm.scatter | torch.npu.comm.scatter | 否 | +| 30 | torch.cuda.comm.gather | torch.npu.comm.gather | 否 | +| 31 | torch.cuda.Stream | torch.npu.Stream | 是 | +| 32 | torch.cuda.Stream.query | torch.npu.Stream.query | 是 | +| 33 | torch.cuda.Stream.record_event | torch.npu.Stream.record_event | 是 | +| 34 | torch.cuda.Stream.synchronize | torch.npu.Stream.synchronize | 是 | +| 35 | torch.cuda.Stream.wait_event | torch.npu.Stream.wait_event | 是 | +| 36 | torch.cuda.Stream.wait_stream | torch.npu.Stream.wait_stream | 是 | +| 37 | torch.cuda.Event | torch.npu.Event | 是 | +| 38 | torch.cuda.Event.elapsed_time | torch.npu.Event.elapsed_time | 是 | +| 39 | torch.cuda.Event.from_ipc_handle | torch.npu.Event.from_ipc_handle | 否 | +| 40 | torch.cuda.Event.ipc_handle | torch.npu.Event.ipc_handle | 否 | +| 41 | torch.cuda.Event.query | torch.npu.Event.query | 是 | +| 42 | torch.cuda.Event.record | torch.npu.Event.record | 是 | +| 43 | torch.cuda.Event.synchronize | torch.npu.Event.synchronize | 是 | +| 44 | torch.cuda.Event.wait | torch.npu.Event.wait | 是 | +| 45 | torch.cuda.empty_cache | torch.npu.empty_cache | 是 | +| 46 | torch.cuda.memory_stats | torch.npu.memory_stats | 是 | +| 47 | torch.cuda.memory_summary | torch.npu.memory_summary | 是 | +| 48 | torch.cuda.memory_snapshot | torch.npu.memory_snapshot | 是 | +| 49 | torch.cuda.memory_allocated | torch.npu.memory_allocated | 是 | +| 50 | torch.cuda.max_memory_allocated | torch.npu.max_memory_allocated | 是 | +| 51 | torch.cuda.reset_max_memory_allocated | torch.npu.reset_max_memory_allocated | 是 | +| 52 | torch.cuda.memory_reserved | torch.npu.memory_reserved | 是 | +| 53 | torch.cuda.max_memory_reserved | torch.npu.max_memory_reserved | 是 | +| 54 | torch.cuda.memory_cached | torch.npu.memory_cached | 是 | +| 55 | torch.cuda.max_memory_cached | torch.npu.max_memory_cached | 是 | +| 56 | torch.cuda.reset_max_memory_cached | torch.npu.reset_max_memory_cached | 是 | +| 57 | torch.cuda.nvtx.mark | torch.npu.nvtx.mark | 否 | +| 58 | torch.cuda.nvtx.range_push | torch.npu.nvtx.range_push | 否 | +| 59 | torch.cuda.nvtx.range_pop | torch.npu.nvtx.range_pop | 否 | +| 60 | torch.cuda._sleep | torch.npu._sleep | 否 | +| 61 | torch.cuda.Stream.priority_range | torch.npu.Stream.priority_range | 否 | +| 62 | torch.cuda.get_device_properties | torch.npu.get_device_properties | 否 | +| 63 | torch.cuda.amp.GradScaler | torch.npu.amp.GradScaler | 否 | + +torch.npu.set_device()接口只支持在程序开始的位置通过set_device进行指定，不支持多次指定和with torch.npu.device(id)方式的device切换 + +## NPU自定义算子 + +| 序号 | 算子名称 | +| ---- | ---------------------------------------------- | +| 1 | npu_convolution_transpose | +| 2 | npu_conv_transpose2d | +| 3 | npu_convolution_transpose_backward | +| 4 | npu_conv_transpose2d_backward | +| 5 | npu_conv_transpose3d_backward | +| 6 | npu_convolution | +| 7 | npu_convolution_backward | +| 8 | npu_convolution_double_backward | +| 9 | npu_conv2d | +| 10 | npu_conv2d.out | +| 11 | npu_conv2d_backward | +| 12 | npu_conv3d | +| 13 | npu_conv3d.out | +| 14 | npu_conv3d_backward | +| 15 | one_ | +| 16 | npu_sort_v2.out | +| 17 | npu_sort_v2 | +| 18 | npu_format_cast | +| 19 | npu_format_cast_.acl_format | +| 20 | npu_format_cast_.src | +| 21 | npu_transpose_to_contiguous | +| 22 | npu_transpose | +| 23 | npu_transpose.out | +| 24 | npu_broadcast | +| 25 | npu_broadcast.out | +| 26 | npu_dtype_cast | +| 27 | npu_dtype_cast_.Tensor | +| 28 | npu_roi_alignbk | +| 29 | empty_with_format | +| 30 | empty_with_format.names | +| 31 | copy_memory_ | +| 32 | npu_one_hot | +| 33 | npu_stride_add | +| 34 | npu_softmax_cross_entropy_with_logits | +| 35 | npu_softmax_cross_entropy_with_logits_backward | +| 36 | npu_ps_roi_pooling | +| 37 | npu_ps_roi_pooling_backward | +| 38 | npu_roi_align | +| 39 | npu_nms_v4 | +| 40 | npu_lstm | +| 41 | npu_lstm_backward | +| 42 | npu_iou | +| 43 | npu_ptiou | +| 44 | npu_nms_with_mask | +| 45 | npu_pad | +| 46 | npu_bounding_box_encode | +| 47 | npu_bounding_box_decode | +| 48 | npu_gru | +| 49 | npu_gru_backward | +| 50 | npu_set_.source_Storage_storage_offset_format | +| 51 | npu_random_choice_with_mask | +| 52 | npu_batch_nms | +| 53 | npu_slice | +| 54 | npu_slice.out | +| 55 | npu_dropoutV2 | +| 56 | npu_dropoutV2_backward | +| 57 | _npu_dropout | +| 58 | _npu_dropout_inplace | +| 59 | npu_dropout_backward | +| 60 | npu_indexing | +| 61 | npu_indexing.out | +| 62 | npu_ifmr | +| 63 | npu_max.dim | +| 64 | npu_max.names_dim | +| 65 | npu_scatter | +| 66 | npu_max_backward | +| 67 | npu_apply_adam | +| 68 | npu_layer_norm_eval | +| 69 | npu_alloc_float_status | +| 70 | npu_get_float_status | +| 71 | npu_clear_float_status | +| 72 | npu_confusion_transpose | +| 73 | npu_confusion_transpose_backward | +| 74 | npu_bmmV2 | +| 75 | fast_gelu | +| 76 | fast_gelu_backward | +| 77 | npu_sub_sample | +| 78 | npu_deformable_conv2d | +| 79 | npu_deformable_conv2dbk | +| 80 | npu_mish | +| 81 | npu_anchor_response_flags | +| 82 | npu_yolo_boxes_encode | +| 83 | npu_grid_assign_positive | +| 84 | npu_mish_backward | +| 85 | npu_normalize_batch | +| 86 | npu_masked_fill_range | +| 87 | npu_linear | +| 88 | npu_linear_backward | +| 89 | npu_bert_apply_adam | +| 90 | npu_giou | +| 91 | npu_giou_backward | + +详细算子接口说明： + +> npu_apply_adam(beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, use_locking, use_nesterov, out = (var, m, v)) + +count adam result. + +- Parameters： + - **beta1_power** (Number) - power of beta1. + - **beta2_power** (Number) - power of beta2. + - **lr** (Number) - learning rate. + - **beta1** (Number) - exponential decay rate for the 1st moment estimates. + - **beta2** (Number) - exponential decay rate for the 2nd moment estimates. + - **epsilon** (Number) - term added to the denominator to improve numerical stability. + - **grad** (Tensor) - the gradient. + - **use_locking** (bool) - If `True` use locks for update operations. + - **use_nesterov** (bool) -If `True`, uses the nesterov update. + - **var** (Tensor) - variables to be optimized. + - **m** (Tensor) - mean value of variables. + - **v** (Tensor) - variance of variables. + +- constraints： + + None + +- Examples： + + None + +> npu_convolution_transpose(input, weight, bias, padding, output_padding, stride, dilation, groups) -> Tensor + +Applies a 2D or 3D transposed convolution operator over an input image composed of several input planes, sometimes also called “deconvolution”. + +- Parameters： + - **input** (Tensor) - input tensor of shape(minibatch, in_channels, iH, iW) or (minibatch, in_channels, iT, iH, iW) + - **weight** (Tensor) - filters of shape(in_channels, out_channels/groups, kH, kW) or (in_channels, out_channels/groups, kT, kH, kW) + - **bias** (Tensor, optional) - optional bias of shape(out_channels) + - **padding** (ListInt) - (dilation * (kernel_size - 1) - padding) zero-padding will be added to both sides of each dimension in the input + - **output_padding** (ListInt) - additional size added to one side of each dimension in the output shape. + - **stride** (ListInt) - the stride of the convolving kernel + - **dilation** (ListInt) - the spacing between kernel elements + - **groups** (Number) - split input into groups, in_channels should be divisible by the number of groups + +- constraints： + + None + +- Examples： + + None + +> npu_conv_transpose2d(input, weight, bias, padding, output_padding, stride, dilation, groups) -> Tensor + +Applies a 2D transposed convolution operator over an input image composed of several input planes, sometimes also called “deconvolution”. + +- Parameters： + - **input** (Tensor) - input tensor of shape(minibatch, in_channels, iH, iW) + - **weight** (Tensor) - filters of shape(in_channels, out_channels/groups, kH, kW) + - **bias** (Tensor, optional) - optional bias of shape(out_channels) + - **padding** (ListInt) - (dilation * (kernel_size - 1) - padding) zero-padding will be added to both sides of each dimension in the input + - **output_padding** (ListInt) - additional size added to one side of each dimension in the output shape. + - **stride** (ListInt) - the stride of the convolving kernel + - **dilation** (ListInt) - the spacing between kernel elements + - **groups** (Number) - split input into groups, in_channels should be divisible by the number of groups + +- constraints： + + None + +- Examples： + + None + +> npu_convolution(input, weight, bias, stride, padding, dilation, groups) -> Tensor + +Applies a 2D or 3D convolution over an input image composed of several input planes. + +- Parameters： + - **input** (Tensor) - input tensor of shape(minibatch, in_channels, iH, iW) or (minibatch, in_channels, iT, iH, iW) + - **weight** (Tensor) - filters of shape(out_channels, in_channels/groups, kH, kW) or (out_channels, in_channels/groups, kT, kH, kW) + - **bias** (Tensor, optional) - optional bias of shape(out_channels) + - **stride** (ListInt) - the stride of the convolving kernel + - **padding** (ListInt) - implicit paddings on both sides of the input + - **dilation** (ListInt) - the spacing between kernel elements + - **groups** (ListInt) - split input into groups, in_channels should be divisible by the number of groups + +- constraints： + + None + +- Examples： + + None + +> npu_conv2d(input, weight, bias, stride, padding, dilation, groups) -> Tensor + +Applies a 2D convolution over an input image composed of several input planes. + +- Parameters： + - **input** (Tensor) - input tensor of shape(minibatch, in_channels, iH, iW) + - **weight** (Tensor) - filters of shape(out_channels, in_channels/groups, kH, kW) + - **bias** (Tensor, optional) - optional bias of shape(out_channels) + - **stride** (ListInt) - the stride of the convolving kernel + - **padding** (ListInt) - implicit paddings on both sides of the input + - **dilation** (ListInt) - the spacing between kernel elements + - **groups** (ListInt) - split input into groups, in_channels should be divisible by the number of groups + +- constraints： + + None + +- Examples： + + None + +> npu_conv3d(input, weight, bias, stride, padding, dilation, groups) -> Tensor + +Applies a 3D convolution over an input image composed of several input planes. + +- Parameters： + - **input** (Tensor) - input tensor of shape(minibatch, in_channels, iT, iH, iW) + - **weight** (Tensor) - filters of shape(out_channels, in_channels/groups, kT, kH, kW) + - **bias** (Tensor, optional) - optional bias of shape(out_channels) + - **stride** (ListInt) - the stride of the convolving kernel + - **padding** (ListInt) - implicit paddings on both sides of the input + - **dilation** (ListInt) - the spacing between kernel elements + - **groups** (ListInt) - split input into groups, in_channels should be divisible by the number of groups + +- constraints： + + None + +- Examples： + + None + +> one_(self) -> Tensor + +Fills self tensor with ones. + +- Parameters： + +- **self** (Tensor) - input tensor + +- constraints： + + None + +- Examples： + + ```python + >>> x = torch.rand(2, 3).npu() + >>> x + tensor([[0.6072, 0.9726, 0.3475], + [0.3717, 0.6135, 0.6788]], device='npu:0') + >>> x.one_() + tensor([[1., 1., 1.], + [1., 1., 1.]], device='npu:0') + ``` + +> npu_sort_v2(self, dim=-1, descending=False, out=None) -> Tensor + +Sorts the elements of the input tensor along a given dimension in ascending order by value without indices. +If dim is not given, the last dimension of the input is chosen. +If descending is True then the elements are sorted in descending order by value. + +- Parameters： + - **self** (Tensor) - the input tensor + - **dim** (int, optional) - the dimension to sort along + - **descending** (bool, optional) - controls the sorting order (ascending or descending) + - **out** (Tensor, optional) - the output that can be optionally given to be used as output buffers + +- constraints： + + At present only support the last dim(-1). + +- Examples： + + ```python + >>> x = torch.randn(3, 4).npu() + >>> x + tensor([[-0.0067, 1.7790, 0.5031, -1.7217], + [ 1.1685, -1.0486, -0.2938, 1.3241], + [ 0.1880, -2.7447, 1.3976, 0.7380]], device='npu:0') + >>> sorted_x = torch.npu_sort_v2(x) + >>> sorted_x + tensor([[-1.7217, -0.0067, 0.5029, 1.7793], + [-1.0488, -0.2937, 1.1689, 1.3242], + [-2.7441, 0.1880, 0.7378, 1.3975]], device='npu:0') + ``` + +> npu_format_cast(self, acl_format) -> Tensor + +Change the format of a npu tensor. + +- Parameters： + - **self** (Tensor) - the input tensor + - **acl_format** (int) - the target format to transform + +- constraints： + + None + +- Examples： + + ```python + >>> x = torch.rand(2, 3, 4, 5).npu() + >>> x.storage().npu_format() + 0 + >>> x1 = x.npu_format_cast(29) + >>> x1.storage().npu_format() + 29 + ``` + +> npu_format_cast_ + +> npu_format_cast_.acl_format(self, acl_format) -> Tensor + + In-place version of npu_format_cast() + +> npu_format_cast_.src(self, src) -> Tensor + + In-place Change the format of self, with the same format as src. + + - Parameters： + - **self** (Tensor) - the input tensor + - **src** (Tensor) - the target format to transform + + - constraints： + + None + + - Examples： + + ```python + >>> x = torch.rand(2, 3, 4, 5).npu() + >>> x.storage().npu_format() + 0 + >>> x.npu_format_cast_(29).storage().npu_format() + 29 + ``` + +> npu_transpose(self, perm) -> Tensor + +Returns a view of the original tensor with its dimensions permuted, and make the result contiguous. + +- Parameters： + - **self** (Tensor) - the input tensor + - **perm** (ListInt) - The desired ordering of dimensions + +- constraints： + + None + +- Examples： + + ```python + >>> x = torch.randn(2, 3, 5).npu() + >>> x.shape + torch.Size([2, 3, 5]) + >>> x1 = torch.npu_transpose(x, (2, 0, 1)) + >>> x1.shape + torch.Size([5, 2, 3]) + >>> x2 = x.npu_transpose(2, 0, 1) + >>> x2.shape + torch.Size([5, 2, 3]) + ``` + +> npu_broadcast(self, perm) -> Tensor + +Returns a new view of the self tensor with singleton dimensions expanded to a larger size, and make the result contiguous. + +Tensor can be also expanded to a larger number of dimensions, and the new ones will be appended at the front. + +- Parameters： + - **self** (Tensor) - the input tensor + - **perm** (ListInt) - the desired expanded size + +- constraints： + + None + +- Examples： + + ```python + >>> x = torch.tensor([[1], [2], [3]]).npu() + >>> x.shape + torch.Size([3, 1]) + >>> x.npu_broadcast(3, 4) + tensor([[1, 1, 1, 1], + [2, 2, 2, 2], + [3, 3, 3, 3]], device='npu:0') + ``` + +> npu_dtype_cast(input, dtype) -> Tensor + +Performs Tensor dtype conversion. + +- Parameters： + - **input** (Tensor) - the input tensor. + - **dtype** (torch.dtype) - the desired data type of returned Tensor. + +- constraints： + + None + +- Examples： + + ```python + >>> torch. npu_dtype_cast (torch.tensor([0, 0.5, -1.]).npu(), dtype=torch.int) + tensor([ 0, 0, -1], device='npu:0', dtype=torch.int32) + ``` + +> empty_with_format(size, dtype, layout, device, pin_memory, acl_format) -> Tensor + +Returns a tensor filled with uninitialized data. The shape of the tensor is defined by the variable argument size. The format of the tensor is defined by the variable argument acl_format. + +- Parameters： + + - **size** (int...) – a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple. + + - **dtype** (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, uses a global default (see torch.set_default_tensor_type()). + + - **layout** (torch.layout, optional) – the desired layout of returned Tensor. Default: None. + + - **device** (torch.device, optional) – the desired device of returned tensor. Default: None + + - **pin_memory** (bool, optional) – If set, returned tensor would be allocated in the pinned memory. Default: None. + + - **acl_format** (Number) – the desired memory format of returned Tensor. Default: 2. + +- constraints： + + None + +- Examples： + ```python + >>> torch.empty_with_format((2, 3), dtype=torch.float32, device="npu") + tensor([[1., 1., 1.], + [1., 1., 1.]], device='npu:0') + ``` + +> copy_memory_(dst, src, non_blocking=False) -> Tensor + +Copies the elements from src into self tensor and returns self. + +- Parameters： + - **dst** (Tensor) - the source tensor to copy from. + - **src** (Tensor) - the desired data type of returned Tensor. + - **non_blocking** (bool) - if True and this copy is between CPU and NPU, the copy may occur asynchronously with respect to the host. For other cases, this argument has no effect. + +- constraints： + + copy_memory_ only support npu tensor. + input tensors of copy_memory_ should have same dtype. + input tensors of copy_memory_ should have same device index. + +- Examples： + + ```python + >>> a=torch.IntTensor([0, 0, -1]).npu() + >>> b=torch.IntTensor([1, 1, 1]).npu() + >>> a.copy_memory_(b) + tensor([1, 1, 1], device='npu:0', dtype=torch.int32) + ``` + +> npu_one_hot(input, num_classes=-1, depth=1, on_value=1, off_value=0) -> Tensor + +Returns a one-hot tensor. The locations represented by index in "x" take value "on_value", while all other locations take value "off_value". + +- Parameters： + - **input** (Tensor) - class values of any shape. + - **num_classes** (Tensor) - The axis to fill. Defaults to "-1". + - **depth** (Number) - The depth of the one hot dimension. + - **on_value** (Number) - The value to fill in output when indices[j] = i. + - **off_value** (Number) - The value to fill in output when indices[j] != i. + +- constraints： + + None + +- Examples： + ```python + >>> a=torch.IntTensor([5, 3, 2, 1]).npu() + >>> b=torch.npu_one_hot(a, depth=5) + >>> b + tensor([[0., 0., 0., 0., 0.], + [0., 0., 0., 1., 0.], + [0., 0., 1., 0., 0.], + [0., 1., 0., 0., 0.]], device='npu:0') + ``` + +> npu_stride_add(x1, x2, offset1, offset2, c1_len) -> Tensor + +Add the partial values of two tensors in format NC1HWC0. + +- Parameters： + - **x1** (Tensor) - A Tensor in 5HD. + - **x2** (Tensor) - A Tensor of the same type as "x1", and the same shape as "x1", except for the C1 value. + - **offset1** (Number) - A required int. Offset value of C1 in "x1". + - **offset2** (Number) - A required int. Offset value of C1 in "x2". + - **c1_len** (Number) - A required int. C1 len of "y". The value must be less than the difference between C1 and offset in "x1" and "x2". + +- constraints： + + None + +- Examples： + ```python + >>> a=torch.tensor([[[[[1.]]]]]).npu() + >>> b=torch.npu_stride_add(a, a, 0, 0, 1) + >>> b + tensor([[[[[2.]]], + [[[0.]]], + [[[0.]]], + [[[0.]]], + [[[0.]]], + [[[0.]]], + [[[0.]]], + [[[0.]]], + [[[0.]]], + [[[0.]]], + [[[0.]]], + [[[0.]]], + [[[0.]]], + [[[0.]]], + [[[0.]]], + [[[0.]]]]], device='npu:0') + ``` + +> npu_softmax_cross_entropy_with_logits(features, labels) -> Tensor + +Computes softmax cross entropy cost. + +- Parameters： + - **features** (Tensor) - A Tensor. A "batch_size * num_classes" matrix. + - **labels** (Tensor) - A Tensor of the same type as "features". A "batch_size * num_classes" matrix. + +- constraints： + + None + +- Examples： + + None + +> npu_ps_roi_pooling(x, rois, spatial_scale, group_size, output_dim) -> Tensor + +Performs Position Sensitive PS ROI Pooling. + +- Parameters： + - **x** (Tensor) - An NC1HWC0 tensor, describing the feature map, dimension C1 must be equal to (int(output_dim+15)/C0))*group_size*group_size. + - **rois** (Tensor) - A tensor with shape [batch, 5, rois_num], describing the ROIs, each ROI consists of five elements: "batch_id", "x1", "y1", "x2", and "y2", which "batch_id" indicates the index of the input feature map, "x1", "y1", "x2", or "y2" must be greater than or equal to "0.0". + - **spatial_scale** (Number) - A required float32, scaling factor for mapping the input coordinates to the ROI coordinates . + - **group_size** (Number) - A required int32, specifying the number of groups to encode position-sensitive score maps, must be within the range (0, 128). + - **output_dim** (Number) - A required int32, specifying the number of output channels, must be greater than 0. + +- constraints： + + None + +- Examples： + ```python + >>> roi = torch.tensor([[[1], [2], [3], [4], [5]], + [[6], [7], [8], [9], [10]]], dtype = torch.float16).npu() + >>> x = torch.tensor([[[[ 1]], [[ 2]], [[ 3]], [[ 4]], + [[ 5]], [[ 6]], [[ 7]], [[ 8]]], + [[[ 9]], [[10]], [[11]], [[12]], + [[13]], [[14]], [[15]], [[16]]]], dtype = torch.float16).npu() + >>> out = torch.npu_ps_roi_pooling(x, roi, 0.5, 2, 2) + >>> out + tensor([[[[0., 0.], + [0., 0.]], + [[0., 0.], + [0., 0.]]], + [[[0., 0.], + [0., 0.]], + [[0., 0.], + [0., 0.]]]], device='npu:0', dtype=torch.float16) + ``` + +> npu_roi_align(features, rois, spatial_scale, pooled_height, pooled_width, sample_num, roi_end_mode) -> Tensor + +Obtains the ROI feature matrix from the feature map. It is a customized FasterRcnn operator. + +- Parameters： + - **features** (Tensor) - A Tensor in 5HD. + - **rois** (Tensor) - ROI position. A 2D Tensor with shape (N, 5). "N" indicates the number of ROIs, the value "5" indicates the indexes of images where the ROIs are located, "x0", "y0", "x1", and "y1". + - **spatial_scale** (Number) - A required attribute of type float32, specifying the scaling ratio of "features" to the original image. + - **pooled_height** (Number) - A required attribute of type int32, specifying the H dimension. + - **pooled_width** (Number) - A required attribute of type int32, specifying the W dimension. + - **sample_num** (Number) - An optional attribute of type int32, specifying the horizontal and vertical sampling frequency of each output. If this attribute is set to "0", the sampling frequency is equal to the rounded up value of "rois", which is a floating point number. Defaults to "2". + - **roi_end_mode** (Number) - An optional attribute of type int32. Defaults to "1". + +- constraints： + + None + +- Examples： + ```python + >>> x = torch.FloatTensor([[[[1, 2, 3 , 4, 5, 6], + [7, 8, 9, 10, 11, 12], + [13, 14, 15, 16, 17, 18], + [19, 20, 21, 22, 23, 24], + [25, 26, 27, 28, 29, 30], + [31, 32, 33, 34, 35, 36]]]]).npu() + >>> rois = torch.tensor([[0, -2.0, -2.0, 22.0, 22.0]]).npu() + >>> out = torch.npu_roi_align(x, rois, 0.25, 3, 3, 2, 0) + >>> out + tensor([[[[ 4.5000, 6.5000, 8.5000], + [16.5000, 18.5000, 20.5000], + [28.5000, 30.5000, 32.5000]]]], device='npu:0') + ``` + +> npu_nms_v4(boxes, scores, max_output_size, iou_threshold, scores_threshold, pad_to_max_output_size=False) -> (Tensor, Tensor) + +Greedily selects a subset of bounding boxes in descending order of score. + +- Parameters： + - **boxes** (Tensor) - A 2-D float tensor of shape [num_boxes, 4]. + - **scores** (Tensor) - A 1-D float tensor of shape [num_boxes] representing a single score corresponding to each box (each row of boxes). + - **max_output_size** (Number) - A scalar representing the maximum number of boxes to be selected by non max suppression. + - **iou_threshold** (Tensor) - A 0-D float tensor representing the threshold for deciding whether boxes overlap too much with respect to IOU. + - **scores_threshold** (Tensor) - A 0-D float tensor representing the threshold for deciding when to remove boxes based on score. + - **pad_to_max_output_size** (bool) - If true, the output selected_indices is padded to be of length max_output_size. Defaults to false. + +- Returns: + - **selected_indices** - A 1-D integer tensor of shape [M] representing the selected indices from the boxes tensor, where M <= max_output_size. + - **valid_outputs** - A 0-D integer tensor representing the number of valid elements in selected_indices, with the valid elements appearing first. + +- constraints： + + None + +- Examples： + ```python + >>> boxes=torch.randn(100,4).npu() + >>> scores=torch.randn(100).npu() + >>> boxes.uniform_(0,100) + >>> scores.uniform_(0,1) + >>> max_output_size = 20 + >>> iou_threshold = torch.tensor(0.5).npu() + >>> scores_threshold = torch.tensor(0.3).npu() + >>> npu_output = torch.npu_nms_v4(boxes, scores, max_output_size, iou_threshold, scores_threshold) + >>> npu_output + (tensor([57, 65, 25, 45, 43, 12, 52, 91, 23, 78, 53, 11, 24, 62, 22, 67, 9, 94, + 54, 92], device='npu:0', dtype=torch.int32), tensor(20, device='npu:0', dtype=torch.int32)) + ``` + +> npu_nms_rotated(dets, scores, iou_threshold, scores_threshold=0, max_output_size=-1, mode=0) -> (Tensor, Tensor) + +Greedy selects a subset of the rotated bounding boxes in descending fractional order. + +- Parameters： + - **dets** (Tensor) - A 2-D float tensor of shape [num_boxes, 5]. + - **scores** (Tensor) - A 1-D float tensor of shape [num_boxes] representing a single score corresponding to each box (each row of boxes). + - **iou_threshold** (Number) - A scalar representing the threshold for deciding whether boxes overlap too much with respect to IOU. + - **scores_threshold** (Number) - A scalar representing the threshold for deciding when to remove boxes based on score. Defaults to "0". + - **max_output_size** (Number) - A scalar integer tensor representing the maximum number of boxes to be selected by non max suppression. Defaults to "-1", that is, no constraint is imposed. + - **mode** (Number) - This parameter specifies the layout type of the dets. The default value is 0. If mode is set to 0, the input values of dets are x, y, w, h, and angle. If mode is set to 1, the input values of dets are x1, y1, x2, y2, and angle. Defaults to "0". + +- Returns: + - **selected_index** - A 1-D integer tensor of shape [M] representing the selected indices from the dets tensor, where M <= max_output_size. + - **selected_num** - A 0-D integer tensor representing the number of valid elements in selected_indices. + +- constraints： + + None + +- Examples： + ```python + >>> dets=torch.randn(100,5).npu() + >>> scores=torch.randn(100).npu() + >>> dets.uniform_(0,100) + >>> scores.uniform_(0,1) + >>> output1, output2 = torch.npu_nms_rotated(dets, scores, 0.2, 0, -1, 1) + >>> output1 + tensor([76, 48, 15, 65, 91, 82, 21, 96, 62, 90, 13, 59, 0, 18, 47, 23, 8, 56, + 55, 63, 72, 39, 97, 81, 16, 38, 17, 25, 74, 33, 79, 44, 36, 88, 83, 37, + 64, 45, 54, 41, 22, 28, 98, 40, 30, 20, 1, 86, 69, 57, 43, 9, 42, 27, + 71, 46, 19, 26, 78, 66, 3, 52], device='npu:0', dtype=torch.int32) + >>> output2 + tensor([62], device='npu:0', dtype=torch.int32) + ``` + +> npu_lstm(x, weight, bias, seq_len, h, c, has_biases, num_layers, dropout, train, bidirectional, batch_first, flag_seq, direction) + +DynamicRNN calculation. + +- Parameters： + - **x** (Tensor) - A required 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. + - **weight** (Tensor) - A required 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_ZN_LSTM. + - **bias** (Tensor) - A required 1D Tensor. Must be one of the following types: float16, float32. The format must be ND. + - **seq_len** (Tensor) - A optional Tensor. Only Support float16 in FRACTAL_NZ and int32 in ND. + - **h** (Tensor) - A optional 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. + - **c** (Tensor) - A optional 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. + - **has_biases** (bool) - If the value is true, bias exists. + - **num_layers** (Number) - Number of recurrent layers. Only Support single layer currently. + - **dropout** (Number) - If non-zero, introduces a Dropout layer on the outputs of each LSTM layer except the last layer, with dropout probability equal to dropout. unsupport currently. + - **train** (bool) - An bool identifying is training in the op. Default to true . + - **bidirectional** (bool) - If True, becomes a bidirectional LSTM. unsupport currently. + - **batch_first** (bool) - If True, then the input and output tensors are provided as (batch, seq, feature). unsupport currently. + - **flag_seq** (bool) - If True, then the input is PackSequnce. unsupport currently. + - **direction** (bool) - If True, then the direction is "REDIRECTIONAL", otherwise is "UNIDIRECTIONAL". + +- Returns: + - **y** - A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. + - **output_h** - A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. + - **output_c** - A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. + - **i** - A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. + - **j** - A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. + - **f** - A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. + - **o** - A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. + - **tanhct** - A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. + +- constraints： + + None + +- Examples： + + None + +>npu_iou(bboxes, gtboxes, mode=0) -> Tensor +>npu_ptiou(bboxes, gtboxes, mode=0) -> Tensor + +Computes the intersection over union (iou) or the intersection over. foreground (iof) based on the ground-truth and predicted regions. + +- Parameters： + - **bboxes** (Tensor) - the input tensor. + - **gtboxes** (Tensor) - the input tensor. + - **mode** (Number) - 0 1 corresponds to two modes iou iof. + +- constraints： + + None + +- Examples： + + ```python + >>> bboxes = torch.tensor([[0, 0, 10, 10], + [10, 10, 20, 20], + [32, 32, 38, 42]], dtype=torch.float16).to("npu") + >>> gtboxes = torch.tensor([[0, 0, 10, 20], + [0, 10, 10, 10], + [10, 10, 20, 20]], dtype=torch.float16).to("npu") + >>> output_iou = torch.npu_iou(bboxes, gtboxes, 0) + >>> output_iou + tensor([[0.4985, 0.0000, 0.0000], + [0.0000, 0.0000, 0.0000], + [0.0000, 0.9961, 0.0000]], device='npu:0', dtype=torch.float16) + ``` + +>npu_pad(input, paddings) -> Tensor + +Pads a tensor + +- Parameters： + - **input** (Tensor) - the input tensor. + - **paddings** (ListInt) - type int32 or int64. + +- constraints： + + None + +- Examples： + + ```python + >>> input = torch.tensor([[20, 20, 10, 10]], dtype=torch.float16).to("npu") + >>> paddings = [1, 1, 1, 1] + >>> output = torch.npu_pad(input, paddings) + >>> output + tensor([[ 0., 0., 0., 0., 0., 0.], + [ 0., 20., 20., 10., 10., 0.], + [ 0., 0., 0., 0., 0., 0.]], device='npu:0', dtype=torch.float16) + ``` + +>npu_nms_with_mask(input, iou_threshold) -> (Tensor, Tensor, Tensor) + +The value 01 is generated for the nms operator to determine the valid bit + +- Parameters： + - **input** (Tensor) - the input tensor. + - **iou_threshold** (Number) - Threshold. If the value exceeds this threshold, the value is 1. Otherwise, the value is 0. + +- Returns: + + - **selected_boxes** - 2-D tensor with shape of [N,5], representing filtered boxes including proposal boxes and corresponding confidence scores. + - **selected_idx** - 1-D tensor with shape of [N], representing the index of input proposal boxes. + - **selected_mask** - 1-D tensor with shape of [N], the symbol judging whether the output proposal boxes is valid . + +- constraints： + + The 2nd-dim of input box_scores must be equal to 8. + +- Examples： + + ```python + >>> input = torch.tensor([[0.0, 1.0, 2.0, 3.0, 0.6], [6.0, 7.0, 8.0, 9.0, 0.4]], dtype=torch.float16).to("npu") + >>> iou_threshold = 0.5 + >>> output1, output2, output3, = torch.npu_nms_with_mask(input, iou_threshold) + >>> output1 + tensor([[0.0000, 1.0000, 2.0000, 3.0000, 0.6001], + [6.0000, 7.0000, 8.0000, 9.0000, 0.3999]], device='npu:0', + dtype=torch.float16) + >>> output2 + tensor([0, 1], device='npu:0', dtype=torch.int32) + >>> output3 + tensor([1, 1], device='npu:0', dtype=torch.uint8) + ``` + +>npu_bounding_box_encode(anchor_box, ground_truth_box, means0, means1, means2, means3, stds0, stds1, stds2, stds3) -> Tensor + +Computes the coordinate variations between bboxes and ground truth boxes. It is a customized FasterRcnn operator + +- Parameters： + - **anchor_box** (Tensor) - the input tensor.Anchor boxes. A 2D Tensor of float32 with shape (N, 4). "N" indicates the number of bounding boxes, and the value "4" refers to "x0", "x1", "y0", and "y1". + - **ground_truth_box** (Tensor) - the input tensor.Ground truth boxes. A 2D Tensor of float32 with shape (N, 4). "N" indicates the number of bounding boxes, and the value "4" refers to "x0", "x1", "y0", and "y1" + - **means0** (Number) - An index of type int + - **means1** (Number) - An index of type int + - **means2** (Number) - An index of type int + - **means3** (Number) - An index of type int. Defaults to [0,0,0,0]. "deltas" = "deltas" x "stds" + "means". + - **stds0** (Number) - An index of type int + - **stds1** (Number) - An index of type int + - **stds2** (Number) - An index of type int + - **stds3** (Number) - An index of type int Defaults to [1.0,1.0,1.0,1.0]. "deltas" = "deltas" x "stds" + "means" . + +- constraints： + + None + +- Examples： + + ```python + >>> anchor_box = torch.tensor([[1., 2., 3., 4.], [3.,4., 5., 6.]], dtype = torch.float32).to("npu") + >>> ground_truth_box = torch.tensor([[5., 6., 7., 8.], [7.,8., 9., 6.]], dtype = torch.float32).to("npu") + >>> output = torch.npu_bounding_box_encode(anchor_box, ground_truth_box, 0, 0, 0, 0, 0.1, 0.1, 0.2, 0.2) + >>> output + tensor([[13.3281, 13.3281, 0.0000, 0.0000], + [13.3281, 6.6641, 0.0000, -5.4922]], device='npu:0') + >>> + ``` + +>npu_bounding_box_decode(rois, deltas, means0, means1, means2, means3, stds0, stds1, stds2, stds3, max_shape, wh_ratio_clip) -> Tensor + +Generates bounding boxes based on "rois" and "deltas". It is a customized FasterRcnn operator . + +- Parameters： + - **rois** (Tensor) - Region of interests (ROIs) generated by the region proposal network (RPN). A 2D Tensor of type float32 or float16 with shape (N, 4). "N" indicates the number of ROIs, and the value "4" refers to "x0", "x1", "y0", and "y1". + - **deltas** (Tensor) - Absolute variation between the ROIs generated by the RPN and ground truth boxes. A 2D Tensor of type float32 or float16 with shape (N, 4). "N" indicates the number of errors, and 4 indicates "dx", "dy", "dw", and "dh" . + - **means0** (Number) - An index of type int + - **means1** (Number) - An index of type int + - **means2** (Number) - An index of type int + - **means3** (Number) - An index of type int. Defaults to [0,0,0,0]. "deltas" = "deltas" x "stds" + "means". + - **stds0** (Number) - An index of type int + - **stds1** (Number) - An index of type int + - **stds2** (Number) - An index of type int + - **stds3** (Number) - An index of type int Defaults to [1.0,1.0,1.0,1.0]. "deltas" = "deltas" x "stds" + "means" . + - **max_shape** (ListInt) - Shape [h, w], specifying the size of the image transferred to the network. Used to ensure that the bbox shape after conversion does not exceed "max_shape + - **wh_ratio_clip** (Number) - Defaults to "16/1000". The values of "dw" and "dh" fall within (-wh_ratio_clip, wh_ratio_clip) . + +- constraints： + + None + +- Examples： + + ```python + >>> rois = torch.tensor([[1., 2., 3., 4.], [3.,4., 5., 6.]], dtype = torch.float32).to("npu") + >>> deltas = torch.tensor([[5., 6., 7., 8.], [7.,8., 9., 6.]], dtype = torch.float32).to("npu") + >>> output = torch.npu_bounding_box_decode(rois, deltas, 0, 0, 0, 0, 1, 1, 1, 1, (10, 10), 0.1) + >>> output + tensor([[2.5000, 6.5000, 9.0000, 9.0000], + [9.0000, 9.0000, 9.0000, 9.0000]], device='npu:0') + ``` + +>npu_gru(input, hx, weight_input, weight_hidden, bias_input, bias_hidden, seq_length, has_biases, num_layers, dropout, train, bidirectional, batch_first) -> (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor) + +DynamicGRUV2 calculation. + +- Parameters： + - **input** (Tensor) - Must be one of the following types: float16. The format must be FRACTAL_NZ. + - **hx** (Tensor) - Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. + - **weight_input** (Tensor) - Must be one of the following types: float16. The format must be FRACTAL_Z. + - **weight_hidden** (Tensor) - Must be one of the following types: float16. The format must be FRACTAL_Z. + - **bias_input** (Tensor) - Must be one of the following types: float16, float32. The format must be ND. + - **bias_hidden** (Tensor) - Must be one of the following types: float16, float32. The format must be ND. + - **seq_length** (Tensor) - Must be one of the following types: int32. The format must be ND. + - **has_biases** (bool) - Default to true. + - **num_layers** (Number) + - **dropout** (Number) + - **train** (bool) - An bool identifying is training in the op. Default to true. + - **bidirectional** (bool) - Default to true. + - **batch_first** (bool) - Default to true. + +- Returns: + + - **y** (Tensor) - Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. + - **output_h** (Tensor) - output_h:Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. + - **update** (Tensor) - update:Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. + - **reset** (Tensor) - reset:Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. + - **new** (Tensor) - Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. + - **hidden_new** (Tensor) - Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. + +- constraints： + + None + +- Examples： + + None + +>npu_random_choice_with_mask(x, count=256, seed=0, seed2=0) -> (Tensor, Tensor) + +Shuffle index of no-zero element + +- Parameters： + - **x** (Tensor) - the input tensor. + - **count** (Number) - the count of output, if 0, out all no-zero elements. + - **seed** (Number) - type int32 or int64. + - **seed2** (Number) - type int32 or int64. + +- Returns: + + - **y** - 2-D tensor, no-zero element index. + - **mask** - 1-D, whether the corresponding index is valid. + +- constraints： + + None + +- Examples： + + ```python + >>> x = torch.tensor([1, 0, 1, 0], dtype=torch.bool).to("npu") + >>> result, mask = torch.npu_random_choice_with_mask(x, 2, 1, 0) + >>> result + tensor([[0], + [2]], device='npu:0', dtype=torch.int32) + >>> mask + tensor([True, True], device='npu:0') + ``` + +>npu_batch_nms(self, scores, score_threshold, iou_threshold, max_size_per_class, max_total_size, change_coordinate_frame=False, transpose_box=False) -> (Tensor, Tensor, Tensor, Tensor) + +Computes nms for input boxes and score, support multiple batch and classes. will do clip to window, score filter, top_k, and nms + +- Parameters： + - **self** (Tensor) - the input tensor. + - **scores** (Tensor) - the input tensor. + - **score_threshold** (Number) - A required attribute of type float32, specifying the score filter iou iou_threshold. + - **iou_threshold** (Number) - A required attribute of type float32, specifying the nms iou iou_threshold. + - **max_size_per_class** (Number) - A required attribute of type int, specifying the nms output num per class. + - **max_total_size** (Number) - A required attribute of type int, specifying the the nms output num per batch. + - **change_coordinate_frame** (bool) - A optional attribute of type bool, whether to normalize coordinates after clipping. + - **transpose_box** (bool) - A optional attribute of type bool, whether inserted transpose before this op. must be "false" + +- Returns: + + - **nmsed_boxes** (Tensor) - A 3D Tensor of type float16 with shape (batch, max_total_size, 4),specifying the output nms boxes per batch. + - **nmsed_scores** (Tensor) - A 2D Tensor of type float16 with shape (batch, max_total_size),specifying the output nms score per batch. + - **nmsed_classes** (Tensor) - A 2D Tensor of type float16 with shape (batch, max_total_size),specifying the output nms class per batch. + - **nmsed_num** (Tensor) - A 1D Tensor of type int32 with shape (batch), specifying the valid num of nmsed_boxes. + +- constraints： + + None + +- Examples： + + ```python + >>> boxes = torch.randn(8, 2, 4, 4, dtype = torch.float32).to("npu") + >>> scores = torch.randn(3, 2, 4, dtype = torch.float32).to("npu") + >>> nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_num = torch.npu_batch_nms(boxes, scores, 0.3, 0.5, 3, 4) + >>> nmsed_boxes + >>> nmsed_scores + >>> nmsed_classes + >>> nmsed_num + ``` + +>npu_slice(self, offsets, size) -> Tensor + +Extracts a slice from a tensor + +- Parameters： + - **self** (Tensor) - the input tensor. + - **offsets** (ListInt) - type int32 or int64. + - **size** (ListInt) - type int32 or int64. + +- constraints： + + None + +- Examples： + + ```python + >>> input = torch.tensor([[1,2,3,4,5], [6,7,8,9,10]], dtype=torch.float16).to("npu") + >>> offsets = [0, 0] + >>> size = [2, 2] + >>> output = torch.npu_slice(input, offsets, size) + >>> output + tensor([[1., 2.], + [6., 7.]], device='npu:0', dtype=torch.float16) + ``` + +>npu_dropoutV2(self, seed, p) -> (Tensor, Tensor, Tensor(a!)) + +count dropout result with seed + +- Parameters： + - **self** (Tensor) - The input Tensor. + - **seed** (Tensor) - The input Tensor. + - **p** (Float) - Dropout probability. + +- Returns： + + - **y** - A tensor with the same shape and type as "x". + - **mask** - A tensor with the same shape and type as "x". + - **new_seed** - A tensor with the same shape and type as "seed". + +- constraints： + + None + +- Examples： + + ```python + >>> input = torch.tensor([1.,2.,3.,4.]).npu() + >>> input + tensor([1., 2., 3., 4.], device='npu:0') + >>> seed = torch.rand((32,),dtype=torch.float32).npu() + >>> seed + tensor([0.4368, 0.7351, 0.8459, 0.4657, 0.6783, 0.8914, 0.8995, 0.4401, 0.4408, + 0.4453, 0.2404, 0.9680, 0.0999, 0.8665, 0.2993, 0.5787, 0.0251, 0.6783, + 0.7411, 0.0670, 0.9430, 0.9165, 0.3983, 0.5849, 0.7722, 0.4659, 0.0486, + 0.2693, 0.6451, 0.2734, 0.3176, 0.0176], device='npu:0') + >>> prob = 0.3 + >>> output, mask, out_seed = torch.npu_dropoutV2(input, seed, prob) + >>> output + tensor([0.4408, 0.4453, 0.2404, 0.9680], device='npu:0') + >>> mask + tensor([0., 0., 0., 0.], device='npu:0') + >>> out_seed + tensor([0.4408, 0.4453, 0.2404, 0.9680, 0.0999, 0.8665, 0.2993, 0.5787, 0.0251, + 0.6783, 0.7411, 0.0670, 0.9430, 0.9165, 0.3983, 0.5849, 0.7722, 0.4659, + 0.0486, 0.2693, 0.6451, 0.2734, 0.3176, 0.0176, 0.0000, 0.0000, 0.0000, + 0.0000, 0.0000, 0.0000, 0.0000, 0.0000], device='npu:0') + ``` + +>_npu_dropout(self, p) -> (Tensor, Tensor) + +count dropout result without seed + +- Parameters： + Similar to `torch.dropout`, optimize implemention to npu device. + - **self** (Tensor) - The input Tensor. + - **p** (Float) - Dropout probability. + +- constraints： + + None + +- Examples： + + ```python + >>> input = torch.tensor([1.,2.,3.,4.]).npu() + >>> input + tensor([1., 2., 3., 4.], device='npu:0') + >>> prob = 0.3 + >>> output, mask = torch._npu_dropout(input, prob) + >>> output + tensor([0.0000, 2.8571, 0.0000, 0.0000], device='npu:0') + >>> mask + tensor([ 98, 255, 188, 186, 120, 157, 175, 159, 77, 223, 127, 79, 247, 151, + 253, 255], device='npu:0', dtype=torch.uint8) + ``` + +>_npu_dropout_inplace(result, p) -> (Tensor(a!), Tensor) + +count dropout result inplace. + +- Parameters： + Similar to `torch.dropout_`, optimize implemention to npu device. + - **result** (Tensor) - The Tensor dropout inplace. + - **p** (Float) - Dropout probability. + +- constraints： + + None + +- Examples： + + ```python + >>> input = torch.tensor([1.,2.,3.,4.]).npu() + >>> input + tensor([1., 2., 3., 4.], device='npu:0') + >>> prob = 0.3 + >>> output, mask = torch._npu_dropout_inplace(input, prob) + >>> output + tensor([0.0000, 2.8571, 0.0000, 0.0000], device='npu:0') + >>> input + tensor([0.0000, 2.8571, 4.2857, 5.7143], device='npu:0') + >>> mask + tensor([ 98, 255, 188, 186, 120, 157, 175, 159, 77, 223, 127, 79, 247, 151, + 253, 255], device='npu:0', dtype=torch.uint8) + ``` + +>npu_indexing(self, begin, end, strides, begin_mask=0, end_mask=0, ellipsis_mask=0, new_axis_mask=0, shrink_axis_mask=0) -> Tensor + +count indexing result by begin,end,strides array. + +- Parameters： + - **self** (Tensor) - A Input Tensor. + - **begin** (ListInt) - The index of the first value to select. + - **end** (ListInt) - The index of the last value to select. + - **strides** (ListInt) - The index increment. + - **begin_mask** (Number) - A bitmask where a bit "i" being "1" means to ignore the begin + value and instead use the largest interval possible. + - **end_mask** (Number) - Analogous to "begin_mask". + - **ellipsis_mask** (Number) - A bitmask where bit "i" being "1" means the "i"th position + is actually an ellipsis. + - **new_axis_mask** (Number) - A bitmask where bit "i" being "1" means the "i"th + specification creates a new shape 1 dimension. + - **shrink_axis_mask** (Number) - A bitmask where bit "i" implies that the "i"th + specification should shrink the dimensionality. + +- constraints： + + None + +- Examples： + + ```python + >>> input = torch.tensor([[1, 2, 3, 4],[5, 6, 7, 8]], dtype=torch.int32).to("npu") + >>> input + tensor([[1, 2, 3, 4], + [5, 6, 7, 8]], device='npu:0', dtype=torch.int32) + >>> output = torch.npu_indexing(input1, [0, 0], [2, 2], [1, 1]) + >>> output + tensor([[1, 2], + [5, 6]], device='npu:0', dtype=torch.int32) + ``` + +>npu_ifmr(Tensor data, Tensor data_min, Tensor data_max, Tensor cumsum, float min_percentile, float max_percentile, float search_start, float search_end, float search_step, bool with_offset) -> (Tensor, Tensor) + +count ifmr result by begin,end,strides array, Input Feature Map Reconstruction + +- Parameters： + - **data** (Tensor) - A Tensor of feature map. + - **data_min** (Tensor) - A Tensor of min value of feature map. + - **data_max** (Tensor) - A Tensor of max value of feature map. + - **cumsum** (Tensor) - A Tensor of cumsum bin of data. + - **min_percentile** (Float) - min init percentile. + - **max_percentile** (Float) - max init percentile. + - **search_start** (Float) - search start. + - **search_end** (Float) - search end. + - **search_step** (Float) - step size of searching. + - **with_offset** (bool) - whether using offset. + +- Returns: + + - **scale** - optimal scale. + - **offset** - optimal offset . + +- constraints： + + None + +- Examples： + + ```python + >>> input = torch.rand((2,2,3,4),dtype=torch.float32).npu() + >>> input + tensor([[[[0.4508, 0.6513, 0.4734, 0.1924], + [0.0402, 0.5502, 0.0694, 0.9032], + [0.4844, 0.5361, 0.9369, 0.7874]], + + [[0.5157, 0.1863, 0.4574, 0.8033], + [0.5986, 0.8090, 0.7605, 0.8252], + [0.4264, 0.8952, 0.2279, 0.9746]]], + + [[[0.0803, 0.7114, 0.8773, 0.2341], + [0.6497, 0.0423, 0.8407, 0.9515], + [0.1821, 0.5931, 0.7160, 0.4968]], + + [[0.7977, 0.0899, 0.9572, 0.0146], + [0.2804, 0.8569, 0.2292, 0.1118], + [0.5747, 0.4064, 0.8370, 0.1611]]]], device='npu:0') + >>> min_value = torch.min(input) + >>> min_value + tensor(0.0146, device='npu:0') + >>> max_value = torch.max(input) + >>> max_value + tensor(0.9746, device='npu:0') + >>> hist = torch.histc(input.to('cpu'), + bins=128, + min=min_value.to('cpu'), + max=max_value.to('cpu')) + >>> hist + tensor([1., 0., 0., 2., 0., 0., 0., 1., 1., 0., 1., 0., 1., 0., 0., 0., 0., 0., + 0., 1., 0., 0., 2., 1., 0., 0., 0., 0., 2., 1., 0., 0., 0., 0., 0., 1., + 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., + 1., 0., 0., 0., 1., 1., 0., 1., 1., 0., 1., 0., 1., 0., 0., 1., 0., 1., + 0., 0., 1., 0., 0., 2., 0., 0., 0., 0., 0., 0., 2., 0., 0., 0., 0., 0., + 0., 0., 1., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 1., 1., 2., 0., 0., + 1., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 0., 0., 0., 1., 0., 1., 1., + 0., 1.]) + >>> cdf = torch.cumsum(hist,dim=0).int().npu() + >>> cdf + tensor([ 1, 1, 1, 3, 3, 3, 3, 4, 5, 5, 6, 6, 7, 7, 7, 7, 7, 7, + 7, 8, 8, 8, 10, 11, 11, 11, 11, 11, 13, 14, 14, 14, 14, 14, 14, 15, + 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, + 17, 17, 17, 17, 18, 19, 19, 20, 21, 21, 22, 22, 23, 23, 23, 24, 24, 25, + 25, 25, 26, 26, 26, 28, 28, 28, 28, 28, 28, 28, 30, 30, 30, 30, 30, 30, + 30, 30, 31, 32, 32, 32, 32, 32, 32, 33, 33, 33, 33, 34, 35, 37, 37, 37, + 38, 39, 40, 40, 41, 41, 41, 42, 42, 43, 44, 44, 44, 44, 45, 45, 46, 47, + 47, 48], device='npu:0', dtype=torch.int32) + >>> scale, offset = torch.npu_ifmr(input, + min_value, + max_value, + cdf, + min_percentile=0.999999, + max_percentile=0.999999, + search_start=0.7, + search_end=1.3, + search_step=0.01, + with_offset=False) + >>> scale + tensor(0.0080, device='npu:0') + >>> offset + tensor(0., device='npu:0') + ``` + +>npu_max.dim(self, dim, keepdim=False) -> (Tensor, Tensor) + +count max result with dim. + +- Parameters： + Similar to `torch.max`, optimize implemention to npu device. + + - **self** (Tensor) – the input tensor. + - **dim** (Number) – the dimension to reduce. + - **keepdim** (bool) – whether the output tensor has dim retained or not. + +- Returns: + + - **values** - max values in the input tensor. + - **indices** - index of max values in the input tensor. + +- constraints： + + None + +- Examples： + + ```python + >>> input = torch.randn(2, 2, 2, 2, dtype = torch.float32).npu() + >>> input + tensor([[[[-1.8135, 0.2078], + [-0.6678, 0.7846]], + + [[ 0.6458, -0.0923], + [-0.2124, -1.9112]]], + + [[[-0.5800, -0.4979], + [ 0.2580, 1.1335]], + + [[ 0.6669, 0.1876], + [ 0.1160, -0.1061]]]], device='npu:0') + >>> outputs, indices = torch.npu_max(input, 2) + >>> outputs + tensor([[[-0.6678, 0.7846], + [ 0.6458, -0.0923]], + + [[ 0.2580, 1.1335], + [ 0.6669, 0.1876]]], device='npu:0') + >>> indices + tensor([[[1, 1], + [0, 0]], + + [[1, 1], + [0, 0]]], device='npu:0', dtype=torch.int32) + ``` + +>npu_min.dim(self, dim, keepdim=False) -> (Tensor, Tensor) + +count min result with dim. + +- Parameters： + Similar to `torch.min`, optimize implemention to npu device. + - **self** (Tensor) – the input tensor. + - **dim** (Number) – the dimension to reduce. + - **keepdim** (bool) – whether the output tensor has dim retained or not. + +- Returns: + + - **values** - min values in the input tensor. + - **indices** - index of min values in the input tensor. + +- constraints： + + None + +- Examples： + + ```python + >>> input = torch.randn(2, 2, 2, 2, dtype = torch.float32).npu() + >>> input + tensor([[[[-0.9909, -0.2369], + [-0.9569, -0.6223]], + + [[ 0.1157, -0.3147], + [-0.7761, 0.1344]]], + + [[[ 1.6292, 0.5953], + [ 0.6940, -0.6367]], + + [[-1.2335, 0.2131], + [ 1.0748, -0.7046]]]], device='npu:0') + >>> outputs, indices = torch.npu_min(input, 2) + >>> outputs + tensor([[[-0.9909, -0.6223], + [-0.7761, -0.3147]], + + [[ 0.6940, -0.6367], + [-1.2335, -0.7046]]], device='npu:0') + >>> indices + tensor([[[0, 1], + [1, 0]], + + [[1, 1], + [0, 1]]], device='npu:0', dtype=torch.int32) + ``` + +>npu_scatter(self, indices, updates, dim) -> Tensor + +count scatter result with dim. + +- Parameters： + Similar to `torch.scatter`, optimize implemention to npu device. + + - **self** (Tensor) - the input tensor. + - **indices** (Tensor) – the indices of elements to scatter, can be either empty or of the same dimensionality as src. When empty, the operation returns self unchanged. + - **updates** (Tensor) – the source element(s) to scatter. +- **dim** (Number) – the axis along which to index + +- constraints： + + None + +- Examples： + + ```python + >>> input = torch.tensor([[1.6279, 0.1226], [0.9041, 1.0980]]).npu() + >>> input + tensor([[1.6279, 0.1226], + [0.9041, 1.0980]], device='npu:0') + >>> indices = torch.tensor([0, 1],dtype=torch.int32).npu() + >>> indices + tensor([0, 1], device='npu:0', dtype=torch.int32) + >>> updates = torch.tensor([-1.1993, -1.5247]).npu() + >>> updates + tensor([-1.1993, -1.5247], device='npu:0') + >>> dim = 0 + >>> output = torch.npu_scatter(input, indices, updates, dim) + >>> output + tensor([[-1.1993, 0.1226], + [ 0.9041, -1.5247]], device='npu:0') + ``` + +>npu_layer_norm_eval(input, normalized_shape, weight=None, bias=None, eps=1e-05) -> Tensor + +count layer norm result. + +- Parameters： + The same as `torch.nn.functional.layer_norm`, optimize implemention to npu device. + - **input** (Tensor) - The input Tensor. + - **normalized_shape** (ListInt) – input shape from an expected input of size. + - **weight** (Tensor) - The gamma Tensor. + - **bias** (Tensor) - The beta Tensor. + - **eps** (Float) – The epsilon value added to the denominator for numerical stability. Default: 1e-5. + +- constraints： + + None + +- Examples： + + ```python + >>> input = torch.rand((6, 4), dtype=torch.float32).npu() + >>> input + tensor([[0.1863, 0.3755, 0.1115, 0.7308], + [0.6004, 0.6832, 0.8951, 0.2087], + [0.8548, 0.0176, 0.8498, 0.3703], + [0.5609, 0.0114, 0.5021, 0.1242], + [0.3966, 0.3022, 0.2323, 0.3914], + [0.1554, 0.0149, 0.1718, 0.4972]], device='npu:0') + >>> normalized_shape = input.size()[1:] + >>> normalized_shape + torch.Size([4]) + >>> weight = torch.Tensor(*normalized_shape).npu() + >>> weight + tensor([ nan, 6.1223e-41, -8.3159e-20, 9.1834e-41], device='npu:0') + >>> bias = torch.Tensor(*normalized_shape).npu() + >>> bias + tensor([5.6033e-39, 6.1224e-41, 6.1757e-39, 6.1224e-41], device='npu:0') + >>> output = torch.npu_layer_norm_eval(input, normalized_shape, weight, bias, 1e-5) + >>> output + tensor([[ nan, 6.7474e-41, 8.3182e-20, 2.0687e-40], + [ nan, 8.2494e-41, -9.9784e-20, -8.2186e-41], + [ nan, -2.6695e-41, -7.7173e-20, 2.1353e-41], + [ nan, -1.3497e-41, -7.1281e-20, -6.9827e-42], + [ nan, 3.5663e-41, 1.2002e-19, 1.4314e-40], + [ nan, -6.2792e-42, 1.7902e-20, 2.1050e-40]], device='npu:0') + ``` + +>npu_alloc_float_status(self) -> Tensor + +Produces eight numbers with a value of zero + +- Parameters： + + - **self** (Tensor) - Any Tensor + +- constraints： + + None + +- Examples： + + ```python + >>> input = torch.randn([1,2,3]).npu() + >>> output = torch.npu_alloc_float_status(input) + >>> input + tensor([[[ 2.2324, 0.2478, -0.1056], + [ 1.1273, -0.2573, 1.0558]]], device='npu:0') + >>> output + tensor([0., 0., 0., 0., 0., 0., 0., 0.], device='npu:0') + ``` + +> npu_get_float_status(self) -> Tensor + +Computes NPU get float status operator function. + +- Parameters： + + - **self** (Tensor) - A Tensor of data memory address. Must be float32 . + +- Constraints： + + None + +- Examples： + + ```python + >>> x = torch.rand(2).npu() + >>> torch.npu_get_float_status(x) + tensor([0., 0., 0., 0., 0., 0., 0., 0.], device='npu:0') + ``` + +> npu_clear_float_status(self) -> Tensor + +Set the value of address 0x40000 to 0 in each core. + +- Parameters： + + - **self** (Tensor) - A tensor of type float32. + +- Constraints： + + None + +- Examples： + + ```python + >>> x = torch.rand(2).npu() + >>> torch.npu_clear_float_status(x) + tensor([0., 0., 0., 0., 0., 0., 0., 0.], device='npu:0') + ``` + +> npu_confusion_transpose(self, perm, shape, transpose_first) -> Tensor + +Confuse reshape and transpose. + +- Parameters： + + - **self** (Tensor) - A Tensor. Must be one of the following types: float16, float32, int8, int16, int32, int64, uint8, uint16, uint32, uint64. + - **perm** (ListInt) - A permutation of the dimensions of "x". + - **shape** (ListInt) - The shape of the input. + - **transpose_first** (bool) - If True, the transpose is first, otherwise the reshape is first. + +- Constraints： + + None + +- Examples： + + ```python + >>> x = torch.rand(2, 3, 4, 6).npu() + >>> x.shape + torch.Size([2, 3, 4, 6]) + >>> y = torch.npu_confusion_transpose(x, (0, 2, 1, 3), (2, 4, 18), True) + >>> y.shape + torch.Size([2, 4, 18]) + >>> y2 = torch.npu_confusion_transpose(x, (0, 2, 1), (2, 12, 6), False) + >>> y2.shape + torch.Size([2, 6, 12]) + ``` + +> npu_bmmV2(self, mat2, output_sizes) -> Tensor + +Multiplies matrix "a" by matrix "b", producing "a * b" . + +- Parameters： + - **self** (Tensor) - A matrix Tensor. Must be one of the following types: float16, float32, int32. 2D or higher. Has format [ND, NHWC, FRACTAL_NZ]. + - **mat2** (Tensor) - A matrix Tensor. Must be one of the following types: float16, float32, int32. 2D or higher. Has format [ND, NHWC, FRACTAL_NZ]. + - **output_sizes** (ListInt) - Output's shape, used in matmul's backpropagation, default []. + +- Constraints： + + None + +- Examples： + + ```python + >>> mat1 = torch.randn(10, 3, 4).npu() + >>> mat2 = torch.randn(10, 4, 5).npu() + >>> res = torch.npu_bmmV2(mat1, mat2, []) + >>> res.shape + torch.Size([10, 3, 5]) + ``` + +> fast_gelu(self) -> Tensor + +Computes the gradient for the fast_gelu of "x" . + +- Parameters： + + - **self** (Tensor) - A Tensor. Must be one of the following types: float16, float32 + +- Constraints： + + None + +- Examples： + + ```python + >>> x = torch.rand(2).npu() + >>> x + tensor([0.5991, 0.4094], device='npu:0') + >>> torch.fast_gelu(x) + tensor([0.4403, 0.2733], device='npu:0') + ``` + +> npu_sub_sample(self, per_images, positive_fraction) -> Tensor + +Randomly sample a subset of positive and negative examples,and overwrite the label vector to the ignore value (-1) for all elements that are not included in the sample. + +- Parameters： + + - **self** (Tensor) - shape of labels,(N, ) label vector with values. + - **per_images** (Number) - A require attribute of type int. + - **positive_fraction** (Float) - A require attribute of type float. + +- Constraints： + + None + +- Examples： + + ```python + >>> x = torch.tensor([-2, 3, 6, -7, -2, 8, 1, -5, 7, 4]).int().npu() + >>> x + tensor([-2, 3, 6, -7, -2, 8, 1, -5, 7, 4], device='npu:0', + dtype=torch.int32) + >>> torch.npu_sub_sample(x, 5, 0.6) + tensor([-1, -1, -1, -1, -1, -1, 1, -1, -1, -1], device='npu:0', + dtype=torch.int32) + ``` + +> npu_deformable_conv2d(input, weight, offset, bias, kernel_size, stride, padding, dilation=[1,1,1,1], groups=1, deformable_groups=1, modulated=True) -> (Tensor, Tensor) + +Computes the deformed convolution output with the expected input. + +- Parameters： + + - **self** (Tensor) - A 4D tensor of input image. With the format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. + - **weight** (Tensor) - A 4D tensor of learnable filters. Must have the same type as "x". With the format "HWCN" , the data is stored in the order of: [filter_height, filter_width, in_channels / groups, out_channels]. + - **offset** (Tensor) - A 4D tensor of x-y coordinates offset and mask. With the format "NHWC", the data is stored in the order of: [batch, out_height, out_width, deformable_groups * filter_height * filter_width * 3]. + - **bias** (Tensor) - An optional 1D tensor of additive biases to the filter outputs. The data is stored in the order of: [out_channels]. + - **kernel_size** (ListInt) - A tuple/list of 2 integers.kernel size. + - **stride** (ListInt) - Required. A list of 4 integers. The stride of the sliding window for each dimension of input. The dimension order is interpreted according to the data format of "x". The N and C dimensions must be set to 1. + - **padding** (ListInt) - Required. A list of 4 integers. The number of pixels to add to each (top, bottom, left, right) side of the input. + - **dilations** (ListInt) - Optional. A list of 4 integers. The dilation factor for each dimension of input. The dimension order is interpreted according to the data format of "x". The N and C dimensions must be set to 1. Defaults to [1, 1, 1, 1]. + - **groups** (Number) - Optional. An integer of type int32. The number of blocked connections from input channels to output channels. In_channels and out_channels must both be divisible by "groups". Defaults to 1. + - **deformable_groups** (Number) - Optional. An integer of type int32. The number of deformable group partitions. In_channels must be divisible by "deformable_groups". Defaults to 1. + - **modulated** (bool) - Optional. Specify version of DeformableConv2D, true means v2, false means v1, currently only support v2. + +- Constraints： + + None + +- Examples： + + ```python + >>> x = torch.rand(16, 32, 32, 32).npu() + >>> weight = torch.rand(32, 32, 5, 5).npu() + >>> offset = torch.rand(16, 75, 32, 32).npu() + >>> output, _ = torch.npu_deformable_conv2d(x, weight, offset, None, kernel_size=[5, 5], stride = [1, 1, 1, 1], padding = [2, 2, 2, 2]) + >>> output.shape + torch.Size([16, 32, 32, 32]) + ``` + +> npu_mish(self) -> Tensor + +Computes hyperbolic tangent of "x" element-wise. + +- Parameters： + + - **self** (Tensor) - A Tensor. Must be one of the following types: float16, float32. + +- Constraints： + + None + +- Examples： + + ```python + >>> x = torch.rand(10, 30, 10).npu() + >>> y = torch.npu_mish(x) + >>> y.shape + torch.Size([10, 30, 10]) + ``` + +> npu_anchor_response_flags(self, featmap_size, stride, num_base_anchors) -> Tensor + +Generate the responsible flags of anchor in a single feature map. + +- Parameters： + - **self** (Tensor) - Ground truth box, 2-D Tensor with shape [batch, 4]. + - **featmap_size** (ListInt) - The size of feature maps, listint. + - **strides** (ListInt) - Stride of current level, listint. + - **num_base_anchors** (Number) - The number of base anchors. + +- Constraints： + + None + +- Examples： + + ```python + >>> x = torch.rand(100, 4).npu() + >>> y = torch.npu_anchor_response_flags(x, [60, 60], [2, 2], 9) + >>> y.shape + torch.Size([32400]) + ``` + +> npu_yolo_boxes_encode(self, gt_bboxes, stride, performance_mode=False) -> Tensor + +Generates bounding boxes based on yolo's "anchor" and "ground-truth" boxes. It is a customized mmdetection operator. + +- Parameters： + - **self** (Tensor) - anchor boxes generated by the yolo training set. A 2D Tensor of type float32 or float16 with shape (N, 4). "N" indicates the number of ROIs, "N" indicates the number of ROIs, and the value "4" refers to (tx, ty, tw, th). + - **gt_bboxes** (Tensor) - target of the transformation, e.g, ground-truth boxes. A 2D Tensor of type float32 or float16 with shape (N, 4). "N" indicates the number of ROIs, and 4 indicates "dx", "dy", "dw", and "dh". + - **strides** (Tensor) - Scale for each box. A 1D Tensor of type int32 shape (N,). "N" indicates the number of ROIs. +- **performance_mode** (bool) - Select performance mode, "high_precision" or "high_performance". select "high_precision" when input type is float32, the output tensor precision will be smaller than 0.0001, select "high_performance" when input type is float32, the ops will be best performance, but precision will be only smaller than 0.005. + +- Constraints： + + input anchor boxes only support maximum N=20480. + +- Examples： + + ```python + >>> anchor_boxes = torch.rand(2, 4).npu() + >>> gt_bboxes = torch.rand(2, 4).npu() + >>> stride = torch.tensor([2, 2], dtype=torch.int32).npu() + >>> output = torch.npu_yolo_boxes_encode(anchor_boxes, gt_bboxes, stride, False) + >>> output.shape + torch.Size([2, 4]) + ``` + +> npu_grid_assign_positive(self, overlaps, box_responsible_flags, max_overlaps, argmax_overlaps, gt_max_overlaps, gt_argmax_overlaps, num_gts, pos_iou_thr, min_pos_iou, gt_max_assign_all) -> Tensor + +Performs Position Sensitive PS ROI Pooling Grad. + +- Parameters： + - **self** (Tensor) - Tensor of type float16 or float32, shape (n, ) + - **overlaps** (Tensor) - A Tensor. Datatype is same as assigned_gt_inds. IOU between gt_bboxes and bboxes. shape(k, n) + - **box_responsible_flags** (Tensor) - A Tensor. Support uint8. Flag to indicate whether box is responsible. + - **max_overlaps** (Tensor) - A Tensor. Datatype is same as assigned_gt_inds. overlaps.max(axis=0). + - **argmax_overlaps** (Tensor) - A Tensor. Support int32. overlaps.argmax(axis=0). + - **gt_max_overlaps** (Tensor) - A Tensor. Datatype is same as assigned_gt_inds. overlaps.max(axis=1). + - **gt_argmax_overlaps** (Tensor) - A Tensor. Support int32. overlaps.argmax(axis=1). + - **num_gts** (Number) - A Tensor. Support int32. real k. shape (1, ) + - **pos_iou_thr** (Float) - loat. IOU threshold for positive bboxes. + - **min_pos_iou** (Float) - float. minimum iou for a bbox to be considered as a positive bbox + - **gt_max_assign_all** (bool) - bool. whether to assign all bboxes with the same highest overlap with some gt to that gt. + +- Constraints： + + None + +- Examples： + + ```python + >>> assigned_gt_inds = torch.rand(4).npu() + >>> overlaps = torch.rand(2,4).npu() + >>> box_responsible_flags = torch.tensor([1, 1, 1, 0], dtype=torch.uint8).npu() + >>> max_overlap = torch.rand(4).npu() + >>> argmax_overlap = torch.tensor([1, 0, 1, 0], dtype=torch.int32).npu() + >>> gt_max_overlaps = torch.rand(2).npu() + >>> gt_argmax_overlaps = torch.tensor([1, 0],dtype=torch.int32).npu() + >>> output = torch.npu_grid_assign_positive(assigned_gt_inds, overlaps, box_responsible_flags, max_overlap, argmax_overlap, gt_max_overlaps, gt_argmax_overlaps, 128, 0.5, 0., True) + >>> output.shape + torch.Size([4]) + ``` + +> npu_normalize_batch(self, seq_len, normalize_type=0) -> Tensor + + Performs batch normalization . + +- Parameters： + + - **self** (Tensor) - A Tensor. Support float32. shape (n, c, d). + - **seq_len** (Tensor) - A Tensor. Each batch normalize data num. Support Int32. Shape (n, ). + - **normalize_type** (Number) - Str. Support "per_feature" or "all_features". + +- constraints： + + None + +- Examples： + ```python + >>> a=np.random.uniform(1,10,(2,3,6)).astype(np.float32) + >>> b=np.random.uniform(3,6,(2)).astype(np.int32) + >>> x=torch.from_numpy(a).to("npu") + >>> seqlen=torch.from_numpy(b).to("npu") + >>> out = torch.npu_normalize_batch(x, seqlen, 0) + >>> out + tensor([[[ 1.1496, -0.6685, -0.4812, 1.7611, -0.5187, 0.7571], + [ 1.1445, -0.4393, -0.7051, 1.0474, -0.2646, -0.1582], + [ 0.1477, 0.9179, -1.0656, -6.8692, -6.7437, 2.8621]], + + [[-0.6880, 0.1337, 1.3623, -0.8081, -1.2291, -0.9410], + [ 0.3070, 0.5489, -1.4858, 0.6300, 0.6428, 0.0433], + [-0.5387, 0.8204, -1.1401, 0.8584, -0.3686, 0.8444]]], + device='npu:0') + ``` + +> npu_masked_fill_range(self, start, end, value, axis=-1) -> Tensor + +masked fill tensor along with one axis by range.boxes. It is a customized masked fill range operator . + +- Parameters： + + - **self** (Tensor) - input tensor. A ND Tensor of float32/float16/int32/int8 with shapes 1-D (D,), 2-D(N, D), 3-D(N, C, D). + - **start** (Tensor) - masked fill start pos. A 3D Tensor of int32 with shape (num, N). + - **end** (Tensor) - masked fill end pos. A 3D Tensor of int32 with shape (num, N). + - **value** (Tensor) - masked fill value. A 2D Tensor of float32/float16/int32/int8 with shape (num,). + - **axis** (Number) - axis with masked fill of int32. Defaults to -1. + +- constraints： + + None + +- Examples： + ```python + >>> a=torch.rand(4,4).npu() + >>> a + tensor([[0.9419, 0.4919, 0.2874, 0.6560], + [0.6691, 0.6668, 0.0330, 0.1006], + [0.3888, 0.7011, 0.7141, 0.7878], + [0.0366, 0.9738, 0.4689, 0.0979]], device='npu:0') + >>> start = torch.tensor([[0,1,2]], dtype=torch.int32).npu() + >>> end = torch.tensor([[1,2,3]], dtype=torch.int32).npu() + >>> value = torch.tensor([1], dtype=torch.float).npu() + >>> out = torch.npu_masked_fill_range(a, start, end, value, 1) + >>> out + tensor([[1.0000, 0.4919, 0.2874, 0.6560], + [0.6691, 1.0000, 0.0330, 0.1006], + [0.3888, 0.7011, 1.0000, 0.7878], + [0.0366, 0.9738, 0.4689, 0.0979]], device='npu:0') + ``` + +> npu_linear(input, weight, bias=None) -> Tensor + + Multiplies matrix "a" by matrix "b", producing "a * b" . + +- Parameters： + + - **input** (Tensor) - A matrix Tensor. 2D. Must be one of the following types: float32, float16, int32, int8. Has format [ND, NHWC, FRACTAL_NZ]. + - **weight** (Tensor) - A matrix Tensor. 2D. Must be one of the following types: float32, float16, int32, int8. Has format [ND, NHWC, FRACTAL_NZ]. + - **bias** (Tensor) - A 1D Tensor. Must be one of the following types: float32, float16, int32. Has format [ND, NHWC]. + +- constraints： + + None + +- Examples： + ```python + >>> x=torch.rand(2,16).npu() + >>> w=torch.rand(4,16).npu() + >>> b=torch.rand(4).npu() + >>> output = torch.npu_linear(x, w, b) + >>> output + tensor([[3.6335, 4.3713, 2.4440, 2.0081], + [5.3273, 6.3089, 3.9601, 3.2410]], device='npu:0') + ``` + +> npu_bert_apply_adam.old(Tensor(a!) var, Tensor(b!) m, Tensor(c!) v, lr, beta1, beta2, epsilon, grad, max_grad_norm, global_grad_norm, weight_decay, step_size=None, adam_mode=0) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + + count adam result. + +- Parameters： + + - **var** (Tensor) - A Tensor. Support float16/float32. + - **m**(Tensor) - A Tensor. Datatype and shape are same as exp_avg. + - **v**(Tensor) - A Tensor. Datatype and shape are same as exp_avg. + - **lr** (Number) - A Tensor. Datatype is same as exp_avg. + - **beta1** (Number) - A Tensor. Datatype is same as exp_avg. + - **beta2** (Number) - A Tensor. Datatype is same as exp_avg. + - **epsilon** (Number) - A Tensor. Datatype is same as exp_avg. + - **grad**(Tensor) - A Tensor. Datatype and shape are same as exp_avg. + - **max_grad_norm** (Number) - A Tensor. Datatype is same as exp_avg. + - **global_grad_norm** (Number) - A Tensor. Datatype is same as exp_avg. + - **weight_decay** (Number) - A Tensor. Datatype is same as exp_avg. + +- constraints： + + None + +- Examples： + ```python + >>> var_in = torch.rand(321538).uniform_(-32., 21.).npu() + >>> m_in = torch.zeros(321538).npu() + >>> v_in = torch.zeros(321538).npu() + >>> grad = torch.rand(321538).uniform_(-0.05, 0.03).npu() + >>> max_grad_norm = -1. + >>> beta1 = 0.9 + >>> beta2 = 0.99 + >>> weight_decay = 0. + >>> lr = 0. + >>> epsilon = 1e-06 + >>> global_grad_norm = 0. + >>> var_out, m_out, v_out = torch.npu_bert_apply_adam(var_in, m_in, v_in, lr, beta1, beta2, epsilon, grad, max_grad_norm, global_grad_norm, weight_decay) + >>> var_out + tensor([ 14.7733, -30.1218, -1.3647, ..., -16.6840, 7.1518, 8.4872], + device='npu:0') + ``` + +> npu_bert_apply_adam(lr, beta1, beta2, epsilon, grad, max_grad_norm, global_grad_norm, weight_decay, step_size=None, adam_mode=0, *, out=（var,m,v）) + + count adam result. + +- Parameters: + + - **var** (Tensor) - A Tensor. Support float16/float32. + - **m** (Tensor) - A Tensor. Datatype and shape are same as exp_avg. + - **v** (Tensor) - A Tensor. Datatype and shape are same as exp_avg. + - **lr** (Number) - Datatype is same as exp_avg. + - **beta1** (Number) - Datatype is same as exp_avg. + - **beta2** (Number) - Datatype is same as exp_avg. + - **epsilon** (Number) - Datatype is same as exp_avg. + - **grad** (Tensor) - A Tensor. Datatype and shape are same as exp_avg. + - **max_grad_norm** (Number) - Datatype is same as exp_avg. + - **global_grad_norm** (Number) - Datatype is same as exp_avg. + - **weight_decay** (Number) - Datatype is same as exp_avg. + +- Keyword Arguments : + + - **out** :A Tensor, optional. The output tensor. + +- constraints: + + None + +- Examples： + ```python + >>> var_in = torch.rand(321538).uniform_(-32., 21.).npu() + >>> m_in = torch.zeros(321538).npu() + >>> v_in = torch.zeros(321538).npu() + >>> grad = torch.rand(321538).uniform_(-0.05, 0.03).npu() + >>> max_grad_norm = -1. + >>> beta1 = 0.9 + >>> beta2 = 0.99 + >>> weight_decay = 0. + >>> lr = 0. + >>> epsilon = 1e-06 + >>> global_grad_norm = 0. + >>> var_out, m_out, v_out = torch.npu_bert_apply_adam(lr, beta1, beta2, epsilon, grad, max_grad_norm, global_grad_norm, weight_decay, out=(var_in, m_in, v_in)) + >>> var_out + tensor([ 14.7733, -30.1218, -1.3647, ..., -16.6840, 7.1518, 8.4872], + device='npu:0') + ``` + +> npu_giou(self, gtboxes, trans=False, is_cross=False, mode=0) -> Tensor + +First calculate the minimum closure area of the two boxes, IoU, the proportion of the closed area that does not belong to the two boxes in the closure area, and finally subtract this proportion from IoU to get GIoU . + +- Parameters： + + - **self** (Tensor) - Bounding boxes, a 2D Tensor of type float16 or float32 with shape (N, 4). "N" indicates the number of bounding boxes, and the value "4" refers to [x1, y1, x2, y2] or [x, y, w, h]. + - **gtboxes** (Tensor) - Ground-truth boxes, a 2D Tensor of type float16 or float32 with shape (M, 4). "M" indicates the number of ground truth boxes, and the value "4" refers to [x1, y1, x2, y2] or [x, y, w, h]. + - **trans** (bool) - An optional bool, true for 'xywh', false for 'xyxy'. + - **is_cross** (bool) - An optional bool, control whether the output shape is [M, N] or [1, N]. + - **mode:** (Number) - Computation mode, a character string with the value range of [iou, iof] . + +- constraints： + + None + +- Examples： + ```python + >>> a=np.random.uniform(0,1,(4,10)).astype(np.float16) + >>> b=np.random.uniform(0,1,(4,10)).astype(np.float16) + >>> box1=torch.from_numpy(a).to("npu") + >>> box2=torch.from_numpy(a).to("npu") + >>> output = torch.npu_giou(box1, box2, trans=True, is_cross=False, mode=0) + >>> output + tensor([[1.], + [1.], + [1.], + [1.], + [1.], + [1.], + [1.], + [1.], + [1.], + [1.]], device='npu:0', dtype=torch.float16) + ``` + +> npu_silu(self) -> Tensor + +Computes the for the Swish of "x" . + +- Parameters： + + - **self** (Tensor) - A Tensor. Must be one of the following types: float16, float32 + +- constraints： + + None + +- Examples： +```python +>>> a=torch.rand(2,8).npu() +>>> output = torch.npu_silu(a) +>>> output +tensor([[0.4397, 0.7178, 0.5190, 0.2654, 0.2230, 0.2674, 0.6051, 0.3522], + [0.4679, 0.1764, 0.6650, 0.3175, 0.0530, 0.4787, 0.5621, 0.4026]], + device='npu:0') +``` + +> npu_reshape(self, shape, bool can_refresh=False) -> Tensor + +Reshapes a tensor. Only the tensor shape is changed, without changing the data. + +- Parameters： + + - **self** (Tensor) - A Tensor. + - **shape** (ListInt) - Defines the shape of the output tensor. + - **can_refresh** (bool) - Used to specify whether reshape can be refreshed in place. + +- constraints： + + This operator cannot be directly called by the acllopExecute API. + +- Examples： + ```python + >>> a=torch.rand(2,8).npu() + >>> out=torch.npu_reshape(a,(4,4)) + >>> out + tensor([[0.6657, 0.9857, 0.7614, 0.4368], + [0.3761, 0.4397, 0.8609, 0.5544], + [0.7002, 0.3063, 0.9279, 0.5085], + [0.1009, 0.7133, 0.8118, 0.6193]], device='npu:0') + ``` + +> npu_rotated_overlaps(self, query_boxes, trans=False) -> Tensor + +Calculate the overlapping area of the rotated box. + +- Parameters： + + - **self** (Tensor) - data of grad increment, a 3D Tensor of type float32 with shape (B, 5, N). + - **query_boxes** (Tensor) - Bounding boxes, a 3D Tensor of type float32 with shape (B, 5, K). + - **trans** (bool) - An optional attr, true for 'xyxyt', false for 'xywht'. + +- constraints： + + None + +- Examples： + ```python + >>> a=np.random.uniform(0,1,(1,3,5)).astype(np.float16) + >>> b=np.random.uniform(0,1,(1,2,5)).astype(np.float16) + >>> box1=torch.from_numpy(a).to("npu") + >>> box2=torch.from_numpy(a).to("npu") + >>> output = torch.npu_rotated_overlaps(box1, box2, trans=False) + >>> output + tensor([[[0.0000, 0.1562, 0.0000], + [0.1562, 0.3713, 0.0611], + [0.0000, 0.0611, 0.0000]]], device='npu:0', dtype=torch.float16) + ``` + +> npu_rotated_iou(self, query_boxes, trans=False, mode=0, is_cross=True) -> Tensor + +Calculate the IOU of the rotated box. + +- Parameters： + + - **self** (Tensor) - data of grad increment, a 3D Tensor of type float32 with shape (B, 5, N). + - **query_boxes** (Tensor) - Bounding boxes, a 3D Tensor of type float32 with shape (B, 5, K). + - **trans** (bool) - An optional attr, true for 'xyxyt', false for 'xywht'. + - **is_cross** (bool) -Cross calculation when it is True, and one-to-one calculation when it is False. + - **mode** (Number) - Computation mode, a character string with the value range of [iou, iof, giou] . + +- constraints： + + None + +- Examples： + ```python + >>> a=np.random.uniform(0,1,(2,2,5)).astype(np.float16) + >>> b=np.random.uniform(0,1,(2,3,5)).astype(np.float16) + >>> box1=torch.from_numpy(a).to("npu") + >>> box2=torch.from_numpy(a).to("npu") + >>> output = torch.npu_rotated_iou(box1, box2, trans=False, mode=0, is_cross=True) + >>> output + tensor([[[3.3325e-01, 1.0162e-01], + [1.0162e-01, 1.0000e+00]], + + [[0.0000e+00, 0.0000e+00], + [0.0000e+00, 5.9605e-08]]], device='npu:0', dtype=torch.float16) + ``` -- Gitee From aacb1fc7ae5ff9733993b8c97c4ce35f21f5e86e Mon Sep 17 00:00:00 2001 From: pengzirong Date: Fri, 31 Dec 2021 15:28:18 +0800 Subject: [PATCH 42/55] delete --- "docs/\344\270\264\346\227\266/.keep" | 0 "docs/\344\270\264\346\227\266/1.png" | Bin 8207 -> 0 bytes "docs/\344\270\264\346\227\266/2.png" | Bin 10834 -> 0 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11:12:44 +0800 Subject: [PATCH 43/55] 12.29.pytorch --- ...55\347\273\203\346\214\207\345\215\227.md" | 67 +++++++++++++++++++ 1 file changed, 67 insertions(+) diff --git "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" index 176c1f806ab..75f86030e73 100644 --- "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" +++ "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" @@ -2134,12 +2134,79 @@ with torch.npu.profile(profiler_result_path="./results", use_e2e_profiler=True - ACL_AICORE_MEMORY_BANDWIDTH = 2：表示外部内存读写类指令占比，包括采集项ub_read_bw、ub_write_bw、l1_read_bw、l1_write_bw、l2_read_bw、l2_write_bw、main_mem_read_bw、main_mem_write_bw +<<<<<<< HEAD - ACL_AICORE_L0B_AND_WIDTH ：表示内部内存读写类指令占比，包括采集项scalar_ld_ratio、scalar_st_ratio、l0a_read_bw、l0a_write_bw、l0b_read_bw、l0b_write_bw、l0c_read_bw、l0c_write_bw - ACL_AICORE_RESOURCE_CONFLICT_RATIO ：表示流水线队列类指令占比，包括采集项vec_bankgroup_cflt_ratio、vec_bank_cflt_ratio、vec_resc_cflt_ratio、mte1_iq_full_ratio、mte2_iq_full_ratio、mte3_iq_full_ratio、cube_iq_full_ratio、vec_iq_full_ratio、iq_full_ratio +======= +![](D:\projects\pzr\pytorch\docs\zh\PyTorch网络模型移植&训练指南\figures\1.png) + +切换至如上图路径后，执行脚本。 + +``` +/usr/local/Ascend/ascend-toolkit/latest/toolkit/tools/profiler/bin/msprof --export=on --output=./ +``` + +- output：原始数据路径。 + +运行完成后，在原始数据路径下输出timeline目录。如下图： + +![](D:\projects\pzr\pytorch\docs\zh\PyTorch网络模型移植&训练指南\figures\2.png) + +timeline路径下为解析得到的性能数据，可以在chrome://tracing/中打开。 + +内容示例如下图： + + + +该示例分为4个层次，由上到下，第一层（MsprofTx）为Pytorch框架数据，第二层（AscendCL）为ACL层面数据，第三层（Task Scheduler）为device数据，第四层（AI CPU）为AICPU数据。 + +#### E2E prof高级设置 + +E2E prof工具默认配置获取上述所有层面数据。获取数据过程亦会影响性能，若获取数据过多，会导致性能数据不具备参考价值。因此，E2E prof工具提供了可配置选项，用于精细化控制获取部分层面数据。 + +``` +with torch.npu.profile(profiler_result_path="./results", use_e2e_profiler=True，config=torch.npu. profileConfig(ACL_PROF_ACL_API=True, ACL_PROF_TASK_TIME=True, ACL_PROF_AICORE_METRICS=True,ACL_PROF_AICPU=True, ACL_PROF_L2CACHE=True, ACL_PROF_HCCL_TRACE=True, ACL_PROF_TRAINING_TRACE=True, aiCoreMetricsType=0)): +``` + +- ACL_PROF_ACL_API：表示采集AscendCL接口的性能数据，默认True +>>>>>>> 20bbcb6 (12.29.pytorch) - ACL_AICORE_NONE = 0xFF：表示不采集 +<<<<<<< HEAD +======= +- ACL_PROF_TASK_TIME：采集AI Core算子的执行时间，默认True + + +- ·ACL_PROF_AICORE_METRICS：表示采集AI Core性能指标数据，aicore_metrics入参处配置的性能指标采集项才有效，默认为True + + +- ACL_PROF_AICPU：0x0008，集AI CPU任务的开始、结束轨迹数据，默认为True + +- · ACL_PROF_L2CACHE：表示采集L2 Cache数据，默认True + +- ACL_PROF_HCCL_TRACE：表示采集HCCL数据，默认为True + +- ACL_PROF_TRAINING_TRACE：未知，待解释，默认为True + +其中，aiCoreMetricsType的取值和定义如下，默认为0： + +``` +ACL_AICORE_ARITHMETIC_UTILIZATION = 0, //各种计算类指标占比统计，包括采集项mac_fp16_ratio、mac_int8_ratio、vec_fp32_ratio、vec_fp16_ratio、vec_int32_ratio、vec_misc_ratio + + ACL_AICORE_PIPE_UTILIZATION = 1, //计算单元和搬运单元耗时占比，包括采集项vec_ratio、mac_ratio、scalar_ratio、mte1_ratio、mte2_ratio、mte3_ratio、icache_miss_rate + + ACL_AICORE_MEMORY_BANDWIDTH = 2, //外部内存读写类指令占比，包括采集项ub_read_bw、ub_write_bw、l1_read_bw、l1_write_bw、l2_read_bw、l2_write_bw、main_mem_read_bw、main_mem_write_bw + + ACL_AICORE_L0B_AND_WIDTH = 3, //内部内存读写类指令占比，包括采集项scalar_ld_ratio、scalar_st_ratio、l0a_read_bw、l0a_write_bw、l0b_read_bw、l0b_write_bw、l0c_read_bw、l0c_write_bw + + ACL_AICORE_RESOURCE_CONFLICT_RATIO = 4, //流水线队列类指令占比，包括采集项vec_bankgroup_cflt_ratio、vec_bank_cflt_ratio、vec_resc_cflt_ratio、mte1_iq_full_ratio、mte2_iq_full_ratio、mte3_iq_full_ratio、cube_iq_full_ratio、vec_iq_full_ratio、iq_full_ratio + + ACL_AICORE_NONE = 0xFF 不采集 +``` + +>>>>>>> 20bbcb6 (12.29.pytorch) ​ ### 亲和库 -- Gitee From d285f9fec1e4c71a2c6cf01a0d23682df8e34fc9 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Wed, 5 Jan 2022 11:13:54 +0800 Subject: [PATCH 44/55] new pic --- ...55\347\273\203\346\214\207\345\215\227.md" | 46 +++++++++++-------- 1 file changed, 27 insertions(+), 19 deletions(-) diff --git "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" index 75f86030e73..18d2dafb9d4 100644 --- "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" +++ "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" @@ -2073,6 +2073,7 @@ with torch.npu.profile(profiler_result_path="./result",use_e2e_profiler=Ture): 通过E2E prof工具获得的结果为原始数据，需要通过解析后查看。 +<<<<<<< HEAD 1. 以使用教程中路径为例，工具会在profiler_result_path路径下创建文件夹以保存原始数据。![](figures/1.png) 2. 切换至如上图./result路径后，执行脚本。 @@ -2140,26 +2141,35 @@ with torch.npu.profile(profiler_result_path="./results", use_e2e_profiler=True - ACL_AICORE_RESOURCE_CONFLICT_RATIO ：表示流水线队列类指令占比，包括采集项vec_bankgroup_cflt_ratio、vec_bank_cflt_ratio、vec_resc_cflt_ratio、mte1_iq_full_ratio、mte2_iq_full_ratio、mte3_iq_full_ratio、cube_iq_full_ratio、vec_iq_full_ratio、iq_full_ratio ======= ![](D:\projects\pzr\pytorch\docs\zh\PyTorch网络模型移植&训练指南\figures\1.png) +======= +1. 以使用教程中路径为例，工具会在profiler_result_path路径下创建文件夹以保存原始数据。![](/figures/1.png) +>>>>>>> 63c0136 (new pic) -切换至如上图路径后，执行脚本。 +2. 切换至如上图./result路径后，执行脚本。 -``` -/usr/local/Ascend/ascend-toolkit/latest/toolkit/tools/profiler/bin/msprof --export=on --output=./ -``` + ``` + /usr/local/Ascend/ascend-toolkit/latest/toolkit/tools/profiler/bin/msprof --export=on --output=./ + ``` + + - output：原始数据路径。 + +3. 运行完成后，在原始数据路径下输出timeline目录。如下图： + + ![](/figures/2.png) -- output：原始数据路径。 +4. timeline路径下为解析得到的性能数据，可以通过chrome://tracing/中打开。 -运行完成后，在原始数据路径下输出timeline目录。如下图： + 1. 浏览器进入chrome://tracing/。 -![](D:\projects\pzr\pytorch\docs\zh\PyTorch网络模型移植&训练指南\figures\2.png) + 2. 点击load，上传文件查看。 -timeline路径下为解析得到的性能数据，可以在chrome://tracing/中打开。 + -内容示例如下图： + 内容示例如下图： - + -该示例分为4个层次，由上到下，第一层（MsprofTx）为Pytorch框架数据，第二层（AscendCL）为ACL层面数据，第三层（Task Scheduler）为device数据，第四层（AI CPU）为AICPU数据。 + 该示例分为4个层次，由上到下，第一层（MsprofTx）为Pytorch框架数据，第二层（AscendCL）为ACL层面数据，第三层（Task Scheduler）为device数据，第四层（AI CPU）为AICPU数据。 #### E2E prof高级设置 @@ -2192,19 +2202,17 @@ with torch.npu.profile(profiler_result_path="./results", use_e2e_profiler=True 其中，aiCoreMetricsType的取值和定义如下，默认为0： -``` -ACL_AICORE_ARITHMETIC_UTILIZATION = 0, //各种计算类指标占比统计，包括采集项mac_fp16_ratio、mac_int8_ratio、vec_fp32_ratio、vec_fp16_ratio、vec_int32_ratio、vec_misc_ratio +- ACL_AICORE_ARITHMETIC_UTILIZATION = 0：表示各种计算类指标占比统计，包括采集项mac_fp16_ratio、mac_int8_ratio、vec_fp32_ratio、vec_fp16_ratio、vec_int32_ratio、vec_misc_ratio - ACL_AICORE_PIPE_UTILIZATION = 1, //计算单元和搬运单元耗时占比，包括采集项vec_ratio、mac_ratio、scalar_ratio、mte1_ratio、mte2_ratio、mte3_ratio、icache_miss_rate +- ACL_AICORE_PIPE_UTILIZATION = 1：表示计算单元和搬运单元耗时占比，包括采集项vec_ratio、mac_ratio、scalar_ratio、mte1_ratio、mte2_ratio、mte3_ratio、icache_miss_rate - ACL_AICORE_MEMORY_BANDWIDTH = 2, //外部内存读写类指令占比，包括采集项ub_read_bw、ub_write_bw、l1_read_bw、l1_write_bw、l2_read_bw、l2_write_bw、main_mem_read_bw、main_mem_write_bw +- ACL_AICORE_MEMORY_BANDWIDTH = 2：表示外部内存读写类指令占比，包括采集项ub_read_bw、ub_write_bw、l1_read_bw、l1_write_bw、l2_read_bw、l2_write_bw、main_mem_read_bw、main_mem_write_bw - ACL_AICORE_L0B_AND_WIDTH = 3, //内部内存读写类指令占比，包括采集项scalar_ld_ratio、scalar_st_ratio、l0a_read_bw、l0a_write_bw、l0b_read_bw、l0b_write_bw、l0c_read_bw、l0c_write_bw +- ACL_AICORE_L0B_AND_WIDTH ：表示内部内存读写类指令占比，包括采集项scalar_ld_ratio、scalar_st_ratio、l0a_read_bw、l0a_write_bw、l0b_read_bw、l0b_write_bw、l0c_read_bw、l0c_write_bw - ACL_AICORE_RESOURCE_CONFLICT_RATIO = 4, //流水线队列类指令占比，包括采集项vec_bankgroup_cflt_ratio、vec_bank_cflt_ratio、vec_resc_cflt_ratio、mte1_iq_full_ratio、mte2_iq_full_ratio、mte3_iq_full_ratio、cube_iq_full_ratio、vec_iq_full_ratio、iq_full_ratio +- ACL_AICORE_RESOURCE_CONFLICT_RATIO ：表示流水线队列类指令占比，包括采集项vec_bankgroup_cflt_ratio、vec_bank_cflt_ratio、vec_resc_cflt_ratio、mte1_iq_full_ratio、mte2_iq_full_ratio、mte3_iq_full_ratio、cube_iq_full_ratio、vec_iq_full_ratio、iq_full_ratio - ACL_AICORE_NONE = 0xFF 不采集 -``` +- ACL_AICORE_NONE = 0xFF：表示不采集 >>>>>>> 20bbcb6 (12.29.pytorch) ​ -- Gitee From 94359e37a135913dadfc59e973907374b04e3531 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Wed, 5 Jan 2022 11:14:17 +0800 Subject: [PATCH 45/55] show pic --- ...350\256\255\347\273\203\346\214\207\345\215\227.md" | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" index 18d2dafb9d4..95e99af61fa 100644 --- "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" +++ "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" @@ -2073,6 +2073,7 @@ with torch.npu.profile(profiler_result_path="./result",use_e2e_profiler=Ture): 通过E2E prof工具获得的结果为原始数据，需要通过解析后查看。 +<<<<<<< HEAD <<<<<<< HEAD 1. 以使用教程中路径为例，工具会在profiler_result_path路径下创建文件夹以保存原始数据。![](figures/1.png) @@ -2144,6 +2145,9 @@ with torch.npu.profile(profiler_result_path="./results", use_e2e_profiler=True ======= 1. 以使用教程中路径为例，工具会在profiler_result_path路径下创建文件夹以保存原始数据。![](/figures/1.png) >>>>>>> 63c0136 (new pic) +======= +1. 以使用教程中路径为例，工具会在profiler_result_path路径下创建文件夹以保存原始数据。![](figures/1.png) +>>>>>>> fd8cd8e (show pic) 2. 切换至如上图./result路径后，执行脚本。 @@ -2155,7 +2159,7 @@ with torch.npu.profile(profiler_result_path="./results", use_e2e_profiler=True 3. 运行完成后，在原始数据路径下输出timeline目录。如下图： - ![](/figures/2.png) + ![](figures/2.png) 4. timeline路径下为解析得到的性能数据，可以通过chrome://tracing/中打开。 @@ -2163,11 +2167,11 @@ with torch.npu.profile(profiler_result_path="./results", use_e2e_profiler=True 2. 点击load，上传文件查看。 - + 内容示例如下图： - + 该示例分为4个层次，由上到下，第一层（MsprofTx）为Pytorch框架数据，第二层（AscendCL）为ACL层面数据，第三层（Task Scheduler）为device数据，第四层（AI CPU）为AICPU数据。 -- Gitee From 235cb8eefd6854a6c79aac89efd28ef5482c236c Mon Sep 17 00:00:00 2001 From: pengzirong Date: Wed, 5 Jan 2022 11:14:37 +0800 Subject: [PATCH 46/55] text update --- ...44\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" | 3 +++ 1 file changed, 3 insertions(+) diff --git "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" index 95e99af61fa..80e3dbde5ab 100644 --- "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" +++ "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" @@ -2127,6 +2127,7 @@ with torch.npu.profile(profiler_result_path="./results", use_e2e_profiler=True - ACL_PROF_HCCL_TRACE：表示采集HCCL数据，默认为True - ACL_PROF_TRAINING_TRACE：表示迭代轨迹数据，记录模型正向和反向等步骤，默认为True +<<<<<<< HEAD 其中，aiCoreMetricsType的取值和定义如下，默认为0： @@ -2203,6 +2204,8 @@ with torch.npu.profile(profiler_result_path="./results", use_e2e_profiler=True - ACL_PROF_HCCL_TRACE：表示采集HCCL数据，默认为True - ACL_PROF_TRAINING_TRACE：未知，待解释，默认为True +======= +>>>>>>> c4de28b (text update) 其中，aiCoreMetricsType的取值和定义如下，默认为0： -- Gitee From 0cef003b3904e7949b5b114fd17808da6eb6e78a Mon Sep 17 00:00:00 2001 From: pengzirong Date: Wed, 5 Jan 2022 11:14:53 +0800 Subject: [PATCH 47/55] new api support --- ...\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" | 4 ++++ 1 file changed, 4 insertions(+) diff --git "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" index 28ebfbe84af..b4f1e81826e 100644 --- "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" +++ "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" @@ -788,7 +788,11 @@ | 121 | torch.nn.BatchNorm3d | 是 | | 122 | torch.nn.GroupNorm | 是 | | 123 | torch.nn.SyncBatchNorm | 是 | +<<<<<<< HEAD | 124 | torch.nn.SyncBatchNorm.convert_sync_batchnorm | 是 | +======= +| 124 | torch.nn.SyncBatchNorm.convert_sync_batchnorm | 否 | +>>>>>>> 34d323c (new api support) | 125 | torch.nn.InstanceNorm1d | 是 | | 126 | torch.nn.InstanceNorm2d | 是 | | 127 | torch.nn.InstanceNorm3d | 是 | -- Gitee From 210b0efb96c1db02349749fa743c18eccb1754bc Mon Sep 17 00:00:00 2001 From: pengzirong Date: Wed, 5 Jan 2022 11:15:17 +0800 Subject: [PATCH 48/55] new api support --- ...\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" | 4 ++++ 1 file changed, 4 insertions(+) diff --git "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" index b4f1e81826e..2b38f36dcaf 100644 --- "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" +++ "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" @@ -789,10 +789,14 @@ | 122 | torch.nn.GroupNorm | 是 | | 123 | torch.nn.SyncBatchNorm | 是 | <<<<<<< HEAD +<<<<<<< HEAD | 124 | torch.nn.SyncBatchNorm.convert_sync_batchnorm | 是 | ======= | 124 | torch.nn.SyncBatchNorm.convert_sync_batchnorm | 否 | >>>>>>> 34d323c (new api support) +======= +| 124 | torch.nn.SyncBatchNorm.convert_sync_batchnorm | 是 | +>>>>>>> 0585771 (new api support) | 125 | torch.nn.InstanceNorm1d | 是 | | 126 | torch.nn.InstanceNorm2d | 是 | | 127 | torch.nn.InstanceNorm3d | 是 | -- Gitee From 55af19025ad5ea16ad2764c15b3babc50da4a4a8 Mon Sep 17 00:00:00 2001 From: pengzirong Date: Wed, 5 Jan 2022 11:17:19 +0800 Subject: [PATCH 49/55] new file --- ...\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" | 4 ++++ 1 file changed, 4 insertions(+) diff --git "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" index 2b38f36dcaf..eca3ea19e43 100644 --- "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" +++ "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" @@ -790,6 +790,7 @@ | 123 | torch.nn.SyncBatchNorm | 是 | <<<<<<< HEAD <<<<<<< HEAD +<<<<<<< HEAD | 124 | torch.nn.SyncBatchNorm.convert_sync_batchnorm | 是 | ======= | 124 | torch.nn.SyncBatchNorm.convert_sync_batchnorm | 否 | @@ -797,6 +798,9 @@ ======= | 124 | torch.nn.SyncBatchNorm.convert_sync_batchnorm | 是 | >>>>>>> 0585771 (new api support) +======= +| 124 | torch.nn.SyncBatchNorm.convert_sync_batchnorm | 是 | +>>>>>>> ba861db (new file) | 125 | torch.nn.InstanceNorm1d | 是 | | 126 | torch.nn.InstanceNorm2d | 是 | | 127 | torch.nn.InstanceNorm3d | 是 | -- Gitee From afb66e9e1265566a2666ada92ead44e88b00e42c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BD=AD=E5=AD=90=E8=8D=A3?= Date: Wed, 5 Jan 2022 07:58:24 +0000 Subject: [PATCH 50/55] =?UTF-8?q?add=20docs/=E4=B8=B4=E6=97=B6.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- "docs/\344\270\264\346\227\266" | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 "docs/\344\270\264\346\227\266" diff --git "a/docs/\344\270\264\346\227\266" "b/docs/\344\270\264\346\227\266" new file mode 100644 index 00000000000..e69de29bb2d -- Gitee From 27475b29345966a0040196124e8a2262615a5507 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BD=AD=E5=AD=90=E8=8D=A3?= Date: Wed, 5 Jan 2022 07:59:43 +0000 Subject: [PATCH 51/55] =?UTF-8?q?update=20docs/=E4=B8=B4=E6=97=B6.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- "docs/\344\270\264\346\227\266" | 48 +++++++++++++++++++++++++++++++++ 1 file changed, 48 insertions(+) diff --git "a/docs/\344\270\264\346\227\266" "b/docs/\344\270\264\346\227\266" index e69de29bb2d..f26bd47e395 100644 --- "a/docs/\344\270\264\346\227\266" +++ "b/docs/\344\270\264\346\227\266" @@ -0,0 +1,48 @@ + + + npu_rotated_box_encode(anchor_box, gt_bboxes, weight) -> Tensor + +Rotate Bounding Box Encoding + +- Parameters： + - anchor_box (Tensor) - A 3D Tensor with shape (B, 5, N). the input tensor.Anchor boxes. "B" indicates the number of batch size, "N" indicates the number of bounding boxes, and the value "5" refers to "x0", "x1", "y0", "y1" and "angle" . + - gt_bboxes (Tensor) - A 3D Tensor of float32 (float16) with shape (B, 5, N). + - weight (Tensor) - A float list for "x0", "x1", "y0", "y1" and "angle", defaults to [1.0, 1.0, 1.0, 1.0, 1.0]. +- constraints： + None +- Examples： + >>> anchor_boxes = torch.tensor([[[30.69], [32.6], [45.94], [59.88], [-44.53]]], dtype=torch.float16).to("npu") + >>> gt_bboxes = torch.tensor([[[30.44], [18.72], [33.22], [45.56], [8.5]]], dtype=torch.float16).to("npu") + >>> weight = torch.tensor([1., 1., 1., 1., 1.], dtype=torch.float16).npu() + >>> out = torch.npu_rotated_box_encode(anchor_boxes, gt_bboxes, weight) + >>> out + tensor([[[-0.4253], + [-0.5166], + [-1.7021], + [-0.0162], + [ 1.1328]]], device='npu:0', dtype=torch.float16) + + npu_rotated_box_decode(anchor_boxes, deltas, weight) -> Tensor + +Rotate Bounding Box Encoding + +- Parameters： + - anchor_box (Tensor) - A 3D Tensor with shape (B, 5, N). the input tensor.Anchor boxes. "B" indicates the number of batch size, "N" indicates the number of bounding boxes, and the value "5" refers to "x0", "x1", "y0", "y1" and "angle" . + - deltas (Tensor) - A 3D Tensor of float32 (float16) with shape (B, 5, N). + - weight (Tensor) - A float list for "x0", "x1", "y0", "y1" and "angle", defaults to [1.0, 1.0, 1.0, 1.0, 1.0]. +- constraints： + None +- Examples： + >>> anchor_boxes = torch.tensor([[[4.137],[33.72],[29.4], [54.06], [41.28]]], dtype=torch.float16).to("npu") + >>> deltas = torch.tensor([[[0.0244], [-1.992], [0.2109], [0.315], [-37.25]]], dtype=torch.float16).to("npu") + >>> weight = torch.tensor([1., 1., 1., 1., 1.], dtype=torch.float16).npu() + >>> out = torch.npu_rotated_box_decode(anchor_boxes, deltas, weight) + >>> out + tensor([[[ 1.7861], + [-10.5781], + [ 33.0000], + [ 17.2969], + [-88.4375]]], device='npu:0', dtype=torch.float16) + + + -- Gitee From 5cd75cd19cd87de7f65849d1897f9084c08a3515 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BD=AD=E5=AD=90=E8=8D=A3?= Date: Wed, 5 Jan 2022 08:15:42 +0000 Subject: [PATCH 52/55] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20do?= =?UTF-8?q?cs/=E4=B8=B4=E6=97=B6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- "docs/\344\270\264\346\227\266" | 48 --------------------------------- 1 file changed, 48 deletions(-) delete mode 100644 "docs/\344\270\264\346\227\266" diff --git "a/docs/\344\270\264\346\227\266" "b/docs/\344\270\264\346\227\266" deleted file mode 100644 index f26bd47e395..00000000000 --- "a/docs/\344\270\264\346\227\266" +++ /dev/null @@ -1,48 +0,0 @@ - - - npu_rotated_box_encode(anchor_box, gt_bboxes, weight) -> Tensor - -Rotate Bounding Box Encoding - -- Parameters： - - anchor_box (Tensor) - A 3D Tensor with shape (B, 5, N). the input tensor.Anchor boxes. "B" indicates the number of batch size, "N" indicates the number of bounding boxes, and the value "5" refers to "x0", "x1", "y0", "y1" and "angle" . - - gt_bboxes (Tensor) - A 3D Tensor of float32 (float16) with shape (B, 5, N). - - weight (Tensor) - A float list for "x0", "x1", "y0", "y1" and "angle", defaults to [1.0, 1.0, 1.0, 1.0, 1.0]. -- constraints： - None -- Examples： - >>> anchor_boxes = torch.tensor([[[30.69], [32.6], [45.94], [59.88], [-44.53]]], dtype=torch.float16).to("npu") - >>> gt_bboxes = torch.tensor([[[30.44], [18.72], [33.22], [45.56], [8.5]]], dtype=torch.float16).to("npu") - >>> weight = torch.tensor([1., 1., 1., 1., 1.], dtype=torch.float16).npu() - >>> out = torch.npu_rotated_box_encode(anchor_boxes, gt_bboxes, weight) - >>> out - tensor([[[-0.4253], - [-0.5166], - [-1.7021], - [-0.0162], - [ 1.1328]]], device='npu:0', dtype=torch.float16) - - npu_rotated_box_decode(anchor_boxes, deltas, weight) -> Tensor - -Rotate Bounding Box Encoding - -- Parameters： - - anchor_box (Tensor) - A 3D Tensor with shape (B, 5, N). the input tensor.Anchor boxes. "B" indicates the number of batch size, "N" indicates the number of bounding boxes, and the value "5" refers to "x0", "x1", "y0", "y1" and "angle" . - - deltas (Tensor) - A 3D Tensor of float32 (float16) with shape (B, 5, N). - - weight (Tensor) - A float list for "x0", "x1", "y0", "y1" and "angle", defaults to [1.0, 1.0, 1.0, 1.0, 1.0]. -- constraints： - None -- Examples： - >>> anchor_boxes = torch.tensor([[[4.137],[33.72],[29.4], [54.06], [41.28]]], dtype=torch.float16).to("npu") - >>> deltas = torch.tensor([[[0.0244], [-1.992], [0.2109], [0.315], [-37.25]]], dtype=torch.float16).to("npu") - >>> weight = torch.tensor([1., 1., 1., 1., 1.], dtype=torch.float16).npu() - >>> out = torch.npu_rotated_box_decode(anchor_boxes, deltas, weight) - >>> out - tensor([[[ 1.7861], - [-10.5781], - [ 33.0000], - [ 17.2969], - [-88.4375]]], device='npu:0', dtype=torch.float16) - - - -- Gitee From 5c4ee3ba38c16ed3f4d2eedf1cfdd06ee1ea0b8a Mon Sep 17 00:00:00 2001 From: pengzirong Date: Wed, 5 Jan 2022 16:16:26 +0800 Subject: [PATCH 53/55] API LIST update from cq 22.1.5 --- ...\214\201\346\270\205\345\215\225_1.5.0.md" | 59 +++++++++++++ ...55\347\273\203\346\214\207\345\215\227.md" | 82 ------------------- "docs/\344\270\264\346\227\266" | 48 ----------- 3 files changed, 59 insertions(+), 130 deletions(-) delete mode 100644 "docs/\344\270\264\346\227\266" diff --git "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" index eca3ea19e43..31c03490c47 100644 --- "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" +++ "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" @@ -3234,3 +3234,62 @@ Calculate the IOU of the rotated box. [[0.0000e+00, 0.0000e+00], [0.0000e+00, 5.9605e-08]]], device='npu:0', dtype=torch.float16) ``` + +> npu_rotated_box_encode(anchor_box, gt_bboxes, weight) -> Tensor + +Rotate Bounding Box Encoding. + +- Parameters： + + - anchor_box (Tensor) - A 3D Tensor with shape (B, 5, N). the input tensor.Anchor boxes. "B" indicates the number of batch size, "N" indicates the number of bounding boxes, and the value "5" refers to "x0", "x1", "y0", "y1" and "angle" . + - gt_bboxes (Tensor) - A 3D Tensor of float32 (float16) with shape (B, 5, N). + - weight (Tensor) - A float list for "x0", "x1", "y0", "y1" and "angle", defaults to [1.0, 1.0, 1.0, 1.0, 1.0]. + +- constraints： + + None + +- Examples： + + ```python + >>> anchor_boxes = torch.tensor([[[30.69], [32.6], [45.94], [59.88], [-44.53]]], dtype=torch.float16).to("npu") + >>> gt_bboxes = torch.tensor([[[30.44], [18.72], [33.22], [45.56], [8.5]]], dtype=torch.float16).to("npu") + >>> weight = torch.tensor([1., 1., 1., 1., 1.], dtype=torch.float16).npu() + >>> out = torch.npu_rotated_box_encode(anchor_boxes, gt_bboxes, weight) + >>> out + tensor([[[-0.4253], + [-0.5166], + [-1.7021], + [-0.0162], + [ 1.1328]]], device='npu:0', dtype=torch.float16) + ``` + +> npu_rotated_box_decode(anchor_boxes, deltas, weight) -> Tensor + +Rotate Bounding Box Encoding + +- Parameters： + + - anchor_box (Tensor) - A 3D Tensor with shape (B, 5, N). the input tensor.Anchor boxes. "B" indicates the number of batch size, "N" indicates the number of bounding boxes, and the value "5" refers to "x0", "x1", "y0", "y1" and "angle" . + - deltas (Tensor) - A 3D Tensor of float32 (float16) with shape (B, 5, N). + - weight (Tensor) - A float list for "x0", "x1", "y0", "y1" and "angle", defaults to [1.0, 1.0, 1.0, 1.0, 1.0]. + +- constraints： + + None + +- Examples： + + ```python + >>> anchor_boxes = torch.tensor([[[4.137],[33.72],[29.4], [54.06], [41.28]]], dtype=torch.float16).to("npu") + >>> deltas = torch.tensor([[[0.0244], [-1.992], [0.2109], [0.315], [-37.25]]], dtype=torch.float16).to("npu") + >>> weight = torch.tensor([1., 1., 1., 1., 1.], dtype=torch.float16).npu() + >>> out = torch.npu_rotated_box_decode(anchor_boxes, deltas, weight) + >>> out + tensor([[[ 1.7861], + [-10.5781], + [ 33.0000], + [ 17.2969], + [-88.4375]]], device='npu:0', dtype=torch.float16) + ``` + diff --git "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" index 80e3dbde5ab..176c1f806ab 100644 --- "a/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" +++ "b/docs/zh/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227/PyTorch\347\275\221\347\273\234\346\250\241\345\236\213\347\247\273\346\244\215&\350\256\255\347\273\203\346\214\207\345\215\227.md" @@ -2073,8 +2073,6 @@ with torch.npu.profile(profiler_result_path="./result",use_e2e_profiler=Ture): 通过E2E prof工具获得的结果为原始数据，需要通过解析后查看。 -<<<<<<< HEAD -<<<<<<< HEAD 1. 以使用教程中路径为例，工具会在profiler_result_path路径下创建文件夹以保存原始数据。![](figures/1.png) 2. 切换至如上图./result路径后，执行脚本。 @@ -2127,85 +2125,6 @@ with torch.npu.profile(profiler_result_path="./results", use_e2e_profiler=True - ACL_PROF_HCCL_TRACE：表示采集HCCL数据，默认为True - ACL_PROF_TRAINING_TRACE：表示迭代轨迹数据，记录模型正向和反向等步骤，默认为True -<<<<<<< HEAD - -其中，aiCoreMetricsType的取值和定义如下，默认为0： - -- ACL_AICORE_ARITHMETIC_UTILIZATION = 0：表示各种计算类指标占比统计，包括采集项mac_fp16_ratio、mac_int8_ratio、vec_fp32_ratio、vec_fp16_ratio、vec_int32_ratio、vec_misc_ratio - -- ACL_AICORE_PIPE_UTILIZATION = 1：表示计算单元和搬运单元耗时占比，包括采集项vec_ratio、mac_ratio、scalar_ratio、mte1_ratio、mte2_ratio、mte3_ratio、icache_miss_rate - -- ACL_AICORE_MEMORY_BANDWIDTH = 2：表示外部内存读写类指令占比，包括采集项ub_read_bw、ub_write_bw、l1_read_bw、l1_write_bw、l2_read_bw、l2_write_bw、main_mem_read_bw、main_mem_write_bw - -<<<<<<< HEAD -- ACL_AICORE_L0B_AND_WIDTH ：表示内部内存读写类指令占比，包括采集项scalar_ld_ratio、scalar_st_ratio、l0a_read_bw、l0a_write_bw、l0b_read_bw、l0b_write_bw、l0c_read_bw、l0c_write_bw - -- ACL_AICORE_RESOURCE_CONFLICT_RATIO ：表示流水线队列类指令占比，包括采集项vec_bankgroup_cflt_ratio、vec_bank_cflt_ratio、vec_resc_cflt_ratio、mte1_iq_full_ratio、mte2_iq_full_ratio、mte3_iq_full_ratio、cube_iq_full_ratio、vec_iq_full_ratio、iq_full_ratio -======= -![](D:\projects\pzr\pytorch\docs\zh\PyTorch网络模型移植&训练指南\figures\1.png) -======= -1. 以使用教程中路径为例，工具会在profiler_result_path路径下创建文件夹以保存原始数据。![](/figures/1.png) ->>>>>>> 63c0136 (new pic) -======= -1. 以使用教程中路径为例，工具会在profiler_result_path路径下创建文件夹以保存原始数据。![](figures/1.png) ->>>>>>> fd8cd8e (show pic) - -2. 切换至如上图./result路径后，执行脚本。 - - ``` - /usr/local/Ascend/ascend-toolkit/latest/toolkit/tools/profiler/bin/msprof --export=on --output=./ - ``` - - - output：原始数据路径。 - -3. 运行完成后，在原始数据路径下输出timeline目录。如下图： - - ![](figures/2.png) - -4. timeline路径下为解析得到的性能数据，可以通过chrome://tracing/中打开。 - - 1. 浏览器进入chrome://tracing/。 - - 2. 点击load，上传文件查看。 - - - - 内容示例如下图： - - - - 该示例分为4个层次，由上到下，第一层（MsprofTx）为Pytorch框架数据，第二层（AscendCL）为ACL层面数据，第三层（Task Scheduler）为device数据，第四层（AI CPU）为AICPU数据。 - -#### E2E prof高级设置 - -E2E prof工具默认配置获取上述所有层面数据。获取数据过程亦会影响性能，若获取数据过多，会导致性能数据不具备参考价值。因此，E2E prof工具提供了可配置选项，用于精细化控制获取部分层面数据。 - -``` -with torch.npu.profile(profiler_result_path="./results", use_e2e_profiler=True，config=torch.npu. profileConfig(ACL_PROF_ACL_API=True, ACL_PROF_TASK_TIME=True, ACL_PROF_AICORE_METRICS=True,ACL_PROF_AICPU=True, ACL_PROF_L2CACHE=True, ACL_PROF_HCCL_TRACE=True, ACL_PROF_TRAINING_TRACE=True, aiCoreMetricsType=0)): -``` - -- ACL_PROF_ACL_API：表示采集AscendCL接口的性能数据，默认True ->>>>>>> 20bbcb6 (12.29.pytorch) - -- ACL_AICORE_NONE = 0xFF：表示不采集 - -<<<<<<< HEAD -======= -- ACL_PROF_TASK_TIME：采集AI Core算子的执行时间，默认True - - -- ·ACL_PROF_AICORE_METRICS：表示采集AI Core性能指标数据，aicore_metrics入参处配置的性能指标采集项才有效，默认为True - - -- ACL_PROF_AICPU：0x0008，集AI CPU任务的开始、结束轨迹数据，默认为True - -- · ACL_PROF_L2CACHE：表示采集L2 Cache数据，默认True - -- ACL_PROF_HCCL_TRACE：表示采集HCCL数据，默认为True - -- ACL_PROF_TRAINING_TRACE：未知，待解释，默认为True -======= ->>>>>>> c4de28b (text update) 其中，aiCoreMetricsType的取值和定义如下，默认为0： @@ -2221,7 +2140,6 @@ with torch.npu.profile(profiler_result_path="./results", use_e2e_profiler=True - ACL_AICORE_NONE = 0xFF：表示不采集 ->>>>>>> 20bbcb6 (12.29.pytorch) ​ ### 亲和库 diff --git "a/docs/\344\270\264\346\227\266" "b/docs/\344\270\264\346\227\266" deleted file mode 100644 index f26bd47e395..00000000000 --- "a/docs/\344\270\264\346\227\266" +++ /dev/null @@ -1,48 +0,0 @@ - - - npu_rotated_box_encode(anchor_box, gt_bboxes, weight) -> Tensor - -Rotate Bounding Box Encoding - -- Parameters： - - anchor_box (Tensor) - A 3D Tensor with shape (B, 5, N). the input tensor.Anchor boxes. "B" indicates the number of batch size, "N" indicates the number of bounding boxes, and the value "5" refers to "x0", "x1", "y0", "y1" and "angle" . - - gt_bboxes (Tensor) - A 3D Tensor of float32 (float16) with shape (B, 5, N). - - weight (Tensor) - A float list for "x0", "x1", "y0", "y1" and "angle", defaults to [1.0, 1.0, 1.0, 1.0, 1.0]. -- constraints： - None -- Examples： - >>> anchor_boxes = torch.tensor([[[30.69], [32.6], [45.94], [59.88], [-44.53]]], dtype=torch.float16).to("npu") - >>> gt_bboxes = torch.tensor([[[30.44], [18.72], [33.22], [45.56], [8.5]]], dtype=torch.float16).to("npu") - >>> weight = torch.tensor([1., 1., 1., 1., 1.], dtype=torch.float16).npu() - >>> out = torch.npu_rotated_box_encode(anchor_boxes, gt_bboxes, weight) - >>> out - tensor([[[-0.4253], - [-0.5166], - [-1.7021], - [-0.0162], - [ 1.1328]]], device='npu:0', dtype=torch.float16) - - npu_rotated_box_decode(anchor_boxes, deltas, weight) -> Tensor - -Rotate Bounding Box Encoding - -- Parameters： - - anchor_box (Tensor) - A 3D Tensor with shape (B, 5, N). the input tensor.Anchor boxes. "B" indicates the number of batch size, "N" indicates the number of bounding boxes, and the value "5" refers to "x0", "x1", "y0", "y1" and "angle" . - - deltas (Tensor) - A 3D Tensor of float32 (float16) with shape (B, 5, N). - - weight (Tensor) - A float list for "x0", "x1", "y0", "y1" and "angle", defaults to [1.0, 1.0, 1.0, 1.0, 1.0]. -- constraints： - None -- Examples： - >>> anchor_boxes = torch.tensor([[[4.137],[33.72],[29.4], [54.06], [41.28]]], dtype=torch.float16).to("npu") - >>> deltas = torch.tensor([[[0.0244], [-1.992], [0.2109], [0.315], [-37.25]]], dtype=torch.float16).to("npu") - >>> weight = torch.tensor([1., 1., 1., 1., 1.], dtype=torch.float16).npu() - >>> out = torch.npu_rotated_box_decode(anchor_boxes, deltas, weight) - >>> out - tensor([[[ 1.7861], - [-10.5781], - [ 33.0000], - [ 17.2969], - [-88.4375]]], device='npu:0', dtype=torch.float16) - - - -- Gitee From f665523647e0b293fa2cf0853b278ba27377741a Mon Sep 17 00:00:00 2001 From: pengzirong Date: Thu, 6 Jan 2022 10:12:22 +0800 Subject: [PATCH 54/55] rm pytorch1.5 support for python3.9 --- ...6\211\350\243\205\346\214\207\345\215\227.md" | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git "a/docs/zh/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227.md" index 47ded36e5d8..6f5bb27da1b 100644 --- "a/docs/zh/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227.md" +++ "b/docs/zh/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227.md" @@ -33,10 +33,16 @@ #### 前提条件 -- 需完成CANN开发或运行环境的安装，具体操作请参考《CANN 软件安装指南》。 -- 需安装3.12.0以上版本的CMake，安装方法请参考[CMake安装方法](#CMake安装方法md)。 -- 需确保已安装7.3.0以上版本的gcc，7.3.0版本gcc具体安装及使用方式请参考[安装7.3.0版本gcc](#安装7-3-0版本gccmd)。 -- 需安装python版本为3.7.5、3.8、3.9。 +- 需完成CANN开发或运行环境的安装，具体操作请参考《CANN 软件安装指南》。 + +- 需安装3.12.0以上版本的CMake，安装方法请参考[CMake安装方法](#CMake安装方法md)。 + +- 需确保已安装7.3.0以上版本的gcc，7.3.0版本gcc具体安装及使用方式请参考[安装7.3.0版本gcc](#安装7-3-0版本gccmd)。 + +- 需安装python版本为3.7.5、3.8、3.9。 + +- 需注意torch1.5版本不再支持python3.9编译安装，仅torch1.8.1版本支持python版本3.9进行编译安装。 + - 需确保环境中已安装patch、git工具，以Ubuntu和CentOS系统为例，命令如下： - Ubuntu系统 @@ -146,7 +152,7 @@ 或 bash build.sh --python=3.8 或 - bash build.sh --python=3.9 + bash build.sh --python=3.9 #torch1.5不支持使用python3.9编译安装 ``` 请指定环境中python版本进行编译。生成的二进制包在当前的dist目录下，即“pytorch/pytorch/dist”文件夹目录下。 -- Gitee From 4bf14bb8343dc1c380e640bfe8d7cc6259c762ce Mon Sep 17 00:00:00 2001 From: pengzirong Date: Thu, 6 Jan 2022 10:37:33 +0800 Subject: [PATCH 55/55] update support of torch1.5 --- .../PyTorch\345\256\211\350\243\205\346\214\207\345\215\227.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/docs/zh/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227.md" index 6f5bb27da1b..449b4186596 100644 --- "a/docs/zh/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227.md" +++ "b/docs/zh/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227.md" @@ -41,7 +41,7 @@ - 需安装python版本为3.7.5、3.8、3.9。 -- 需注意torch1.5版本不再支持python3.9编译安装，仅torch1.8.1版本支持python版本3.9进行编译安装。 +- 需注意torch1.5版本不支持python3.9编译安装（与官方保持一致），仅torch1.8.1版本支持python版本3.9进行编译安装。 - 需确保环境中已安装patch、git工具，以Ubuntu和CentOS系统为例，命令如下： - Ubuntu系统 -- Gitee