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API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" +++ /dev/null @@ -1,3295 +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 | -<<<<<<< HEAD -<<<<<<< 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) -======= -| 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 | -| 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) - ``` - -> 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 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" deleted file mode 100644 index c280756f98..0000000000 --- "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.8.1.md" +++ /dev/null @@ -1,1232 +0,0 @@ -# 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) | 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) | 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) | 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) | 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) | 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) | 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 - -| 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) | 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) | 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) | 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 - -### 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) | 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) | 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) | 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) | 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) | 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) | 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) | 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 | - -# 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) | 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) | 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) | 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) | 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) | 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) | 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 | - - - -## [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) | 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) | 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) | 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 | - - - -## [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) | 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 | - - - -## [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) | 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 | - - - -## [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) | 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 | - - - -## [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) | 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) | 否 | -| 2 | [nn.EmbeddingBag](https://pytorch.org/docs/1.8.1/generated/torch.nn.EmbeddingBag.html#torch.nn.EmbeddingBag) | 否 | - - - -## [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) | 否 | -| 2 | [nn.PairwiseDistance](https://pytorch.org/docs/1.8.1/generated/torch.nn.PairwiseDistance.html#torch.nn.PairwiseDistance) | 否 | - - - -## [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) | 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) | 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 | - - - -## [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) | 否 | - - - -## [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) | 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) - - - -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_) | 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 | - - - -### 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) | 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) | 否 | - - - - - - - - - - - -# 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) | 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) | 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) | 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) | 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) | 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) | 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) | 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) | 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) | 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) | 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) | 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 | -| ---- | ------------------------------------- | ------------------------------------ | -------- | -| 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 - -| 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(var, m, v, lr, beta1, beta2, epsilon, grad, max_grad_norm, global_grad_norm, weight_decay, ) - -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') - ``` - - - diff --git "a/docs/zh/PyTorch\345\234\250\347\272\277\346\216\250\347\220\206\346\214\207\345\215\227/PyTorch\345\234\250\347\272\277\346\216\250\347\220\206\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\345\234\250\347\272\277\346\216\250\347\220\206\346\214\207\345\215\227/PyTorch\345\234\250\347\272\277\346\216\250\347\220\206\346\214\207\345\215\227.md" deleted file mode 100644 index 679aaa6685..0000000000 --- "a/docs/zh/PyTorch\345\234\250\347\272\277\346\216\250\347\220\206\346\214\207\345\215\227/PyTorch\345\234\250\347\272\277\346\216\250\347\220\206\346\214\207\345\215\227.md" +++ /dev/null @@ -1,599 +0,0 @@ -# PyTorch在线推理指南 -- [使用场景](#使用场景md) -- [基本使用流程](#基本使用流程md) - - [前提条件](#前提条件md) - - [在线推理流程](#在线推理流程md) - - [环境变量配置](#环境变量配置md) - - [样例参考](#样例参考md) -- [专题](#专题md) - - [混合精度](#混合精度md) -- [安装7.3.0版本gcc](#安装7-3-0版本gccmd) -

使用场景

- -在线推理是在AI框架内执行推理的场景，例如在PyTorch框架上，加载模型后，通过“model.eval\(\)“将模型切换为推理模式。 - -相比于离线推理场景，使用在线推理可以方便将原来基于PyTorch框架做推理的应用快速迁移到昇腾AI处理器，适用于数据中心推理场景。 - -### 支持的芯片型号 - -昇腾910 AI处理器 - -昇腾710 AI处理器 - -

基本使用流程

- -- **[前提条件](#前提条件md)** - -- **[在线推理流程](#在线推理流程md)** - -- **[环境变量配置](#环境变量配置md)** - -- **[样例参考](#样例参考md)** - - -

前提条件

- -已完成PyTorch框架及混合精度模块的安装，详情请参见《PyTorch安装指南》进行PyTorch相关运行环境搭建。 - -

在线推理流程

- -在线推理流程如[图1](#fig13802941161818)所示。 - -**图 1** 在线推理流程图 -![](figures/在线推理流程图.png "在线推理流程图") - -

环境变量配置

- -PyTorch在线推理进程启动所依赖的环境变量： - -``` -# 请依据实际在下列场景中选择其一，进行在线推理依赖包安装路径的环境变量设置。具体如下（以HwHiAiUser用户安装，安装路径为默认路径为例）： -# 场景一：昇腾设备安装部署开发套件包Ascend-cann-toolkit(此时开发环境可进行推理任务)。 -. /home/HwHiAiUser/Ascend/ascend-toolkit/set_env.sh -# 场景二：昇腾设备安装部署软件包Ascend-cann-nnae。 -. /home/HwHiAiUser/Ascend/nnae/set_env.sh - -# 若运行环境中存在多个python3版本时，需要在环境变量中配置python3.7.5的安装路径。 -export PATH=/usr/local/python3.7.5/bin:$PATH -export LD_LIBRARY_PATH=/usr/local/python3.7.5/lib:$LD_LIBRARY_PATH - -# 指定芯片的逻辑ID -export ASCEND_DEVICE_ID=0 - -# 输出日志信息,可根据实际修改 -export ASCEND_SLOG_PRINT_TO_STDOUT=1 -export ASCEND_GLOBAL_LOG_LEVEL=0 - -# TASK多线程下发 -export TASK_QUEUE_ENABLE=0 -``` - -**表 1** 环境变量说明 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

配置项

-

说明

-

必选/可选

-

LD_LIBRARY_PATH

-

动态库的查找路径，参考上述举例配置。

-
说明：

若系统环境安装了gcc7.3.0（例如CentOS7.6、Debian和BClinux系统），需要配置gcc相关环境变量，详情请参见5。

-
-

必选

-

PATH

-

可执行程序的查找路径，参考上述举例配置。

-

必选

-

ASCEND_DEVICE_ID

-

指定芯片的逻辑ID。

-

取值范围[0,N-1]，默认为0。其中N为当前物理机/虚拟机/容器内的设备总数。

-

可选

-

ASCEND_SLOG_PRINT_TO_STDOUT

-

是否开启日志打屏。取值：

-
  • 0或不配置：关闭日志打屏
  • 1：开启日志打屏
-

可选

-

ASCEND_GLOBAL_LOG_LEVEL

-

设置日志的全局日志级别。取值：

-
  • 0：对应DEBUG级别。
  • 1：对应INFO级别。
  • 2：对应WARNING级别。
  • 3：对应ERROR级别。
  • 4：对应NULL级别，不输出日志。
  • 其他值为非法值。
-

可选

-

TASK_QUEUE_ENABLE

-

是否开启TASK多线程下发，绝大多数情况下，打开该功能会进一步提升整网训练性能。取值：

-
  • 0或不配置：关闭TASK多线程下发。
  • 1：开启TASK多线程下发。
-

可选

-
- ->![](public_sys-resources/icon-note.gif) **说明：** ->更多日志信息，请参见《CANN 日志参考》。 - -

样例参考

- -#### 样例代码 - -在进行推理应用时，应尽量保证应用在生命周期内不频繁初始化。推理模式通过模型model.eval\(\)进行设置，并且推理过程要在“with torch.no\_grad\(\):”代码分支下运行。 - -本例中，我们将使用Resnet50网络的python代码进行说明。 - -样例代码 resnet50\_infer\_for\_pytorch.py： - -``` -import argparse -import os -import time -import torch -import torch.nn.parallel -import torch.optim -import torch.utils.data -import torch.utils.data.distributed -import torchvision.transforms as transforms -import torchvision.datasets as datasets -import torchvision.models as models - -import torch.npu -from apex import amp - -model_names = sorted(name for name in models.__dict__ - if name.islower() and not name.startswith("__") - and callable(models.__dict__[name])) - - -def parse_args(): - """ 用户自定义数据集路径、模型路径 """ - parser = argparse.ArgumentParser(description='PyTorch ImageNet Training') - parser.add_argument('--data', metavar='DIR', default="/data/imagenet", - help='path to dataset') - parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet50', - choices=model_names, - help='model architecture: ' + - ' | '.join(model_names) + - ' (default: resnet18)') - - parser.add_argument('--epochs', default=100, type=int, metavar='N', - help='number of total epochs to run') - - parser.add_argument('-b', '--batch_size', default=512, type=int, - metavar='N', - help='mini-batch size (default: 256), this is the total ' - 'batch size of all GPUs on the current node when ' - 'using Data Parallel or Distributed Data Parallel') - - parser.add_argument('--resume', default='', type=str, metavar='PATH', - help='path to latest checkpoint (default: none)') - - parser.add_argument('--pretrained', dest='pretrained', action='store_true', - help='use pre-trained model') - - parser.add_argument('--npu', default=None, type=int, - help='NPU id to use.') - - parser.add_argument('-j', '--workers', default=32, type=int, metavar='N', - help='number of data loading workers (default: 8)') - - parser.add_argument('--lr', '--learning_rate', default=0.1, type=float, - metavar='LR', help='initial learning rate', dest='lr') - parser.add_argument('--wd', '--weight_decay', default=1e-4, type=float, - metavar='W', help='weight decay (default: 1e-4)', - dest='weight_decay') - - args, unknown_args = parser.parse_known_args() - if len(unknown_args) > 0: - for bad_arg in unknown_args: - print("ERROR: Unknown command line arg: %s" % bad_arg) - raise ValueError("Invalid command line arg(s)") - - return args - - -# ========================================================================= -# 主函数入口 -# ========================================================================= -def main(): - args = parse_args() - if args.npu is None: - args.npu = 0 - global CALCULATE_DEVICE - CALCULATE_DEVICE = "npu:{}".format(args.npu) - torch.npu.set_device(CALCULATE_DEVICE) - print("use ", CALCULATE_DEVICE) - main_worker(args.npu, args) - - -def main_worker(npu, args): - global best_acc1 - args.npu = npu - - # ========================================================================= - # 创建模型 - # ========================================================================= - print("=> creating model '{}'".format(args.arch)) - model = models.__dict__[args.arch](zero_init_residual=True) - - # 将模型数据复制到昇腾AI处理器中 - model = model.to(CALCULATE_DEVICE) - - optimizer = torch.optim.SGD([ - {'params': [param for name, param in model.named_parameters() if name[-4:] == 'bias'], 'weight_decay': 0.0}, - {'params': [param for name, param in model.named_parameters() if name[-4:] != 'bias'], - 'weight_decay': args.weight_decay}], - args.lr) - - # ========================================================================= - # 初始化混合精度模型，使用后可加速运算，但结果的准确率可能会轻微降低。可根据实际场景选择使用 - # ========================================================================= - model, optimizer = amp.initialize(model, optimizer, opt_level="O2", loss_scale=1024, verbosity=1) - - # ========================================================================= - # 加载训练好的模型参数 - # ========================================================================= - # 从模型文件中恢复模型参数 - if os.path.isfile(args.resume): - print("=> loading checkpoint '{}'".format(args.resume)) - checkpoint = torch.load(args.resume) - - best_acc1 = checkpoint['best_acc1'] - best_acc1 = best_acc1.to("npu:{}".format(args.npu)) - - model.load_state_dict(checkpoint['state_dict']) - print("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch'])) - - else: - print("=> no checkpoint found at '{}'".format(args.resume)) - - # ========================================================================= - # 初始化数据集 - # ========================================================================= - # 图像数据加载与预处理 - valdir = os.path.join(args.data, 'val') - normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], - std=[0.229, 0.224, 0.225]) - - 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=True) - - # ========================================================================= - # 运行validate - # ========================================================================= - validate(val_loader, model, args) - - -# ========================================================================= -# 在线推理样例接口实现 -# ========================================================================= -def validate(val_loader, model, args): - batch_time = AverageMeter('Time', ':6.3f') - top1 = AverageMeter('Acc@1', ':6.2f') - top5 = AverageMeter('Acc@5', ':6.2f') - progress = ProgressMeter( - len(val_loader), - [batch_time, top1, top5], - prefix='Test: ') - - # ========================================================================= - # 切换到推理模式 - # ========================================================================= - model.eval() - - # ========================================================================= - # 在 torch.no_grad():分支下执行模型正向计算 - # ========================================================================= - with torch.no_grad(): - end = time.time() - for i, (images, target) in enumerate(val_loader): - - # 将图像数据置于NPU中 - images = images.to(CALCULATE_DEVICE, non_blocking=True) - target = target.to(torch.int32).to(CALCULATE_DEVICE, non_blocking=True) - - # 计算输出 - output = model(images) - - # 统计结果精度 - acc1, acc5 = accuracy(output, target, topk=(1, 5)) - top1.update(acc1[0], images.size(0)) - top5.update(acc5[0], images.size(0)) - - # 测量运行时间 - batch_time.update(time.time() - end) - end = time.time() - - # 打印推理运算过程日志 - progress.display(i) - - print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'.format(top1=top1, top5=top5)) - - return top1.avg - - -class AverageMeter(object): - """计算并存储平均值和当前值""" - def __init__(self, name, fmt=':f'): - self.name = name - self.fmt = fmt - self.reset() - self.start_count_index = 10 - - def reset(self): - self.val = 0 - self.avg = 0 - self.sum = 0 - self.count = 0 - - def update(self, val, n=1): - if self.count == 0: - self.batchsize = n - - self.val = val - self.count += n - if self.count > (self.start_count_index * self.batchsize): - self.sum += val * n - self.avg = self.sum / (self.count - self.start_count_index * self.batchsize) - - def __str__(self): - fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})' - return fmtstr.format(**self.__dict__) - - -class ProgressMeter(object): - """记录模型运算过程信息""" - def __init__(self, num_batches, meters, prefix=""): - self.batch_fmtstr = self._get_batch_fmtstr(num_batches) - self.meters = meters - self.prefix = prefix - - def display(self, batch): - entries = [self.prefix + self.batch_fmtstr.format(batch)] - entries += [str(meter) for meter in self.meters] - print('\t'.join(entries)) - - def _get_batch_fmtstr(self, num_batches): - num_digits = len(str(num_batches // 1)) - fmt = '{:' + str(num_digits) + 'd}' - return '[' + fmt + '/' + fmt.format(num_batches) + ']' - - -def accuracy(output, target, topk=(1,)): - """根据指定值k，计算k个顶部预测的精度""" - with torch.no_grad(): - maxk = max(topk) - batch_size = target.size(0) - - _, pred = output.topk(maxk, 1, True, True) - pred = pred.t() - correct = pred.eq(target.view(1, -1).expand_as(pred)) - - res = [] - for k in topk: - correct_k = correct[:k].view(-1).float().sum(0, keepdim=True) - res.append(correct_k.mul_(100.0 / batch_size)) - return res - - -if __name__ == '__main__': - main() -``` - -#### 样例执行 - -以ResNet50模型为例，执行在线推理样例。 - -1. 下载预训练模型。 - - 打开ModelZoo中[ResNet50详情页](https://ascend.huawei.com/zh/#/software/modelzoo/detail/C/cf20ab8b8bea4032a6b056ab503112e4)，单击该页面的“下载模型“下载已训练好的模型文件。 - -2. 编辑推理脚本。 - - 创建“resnet50\_infer\_for\_pytorch.py“模型脚本文件，并参考[样例代码]()写入相关代码。 - -3. 执行推理。 - - 参考[环境变量配置](#环境变量配置md)设置环境变量，并执行命令： - - ``` - python3 pytorch-resnet50-apex.py --data /data/imagenet \ - --npu 7 \ - --epochs 90 \ - --resume ./checkpoint.pth.tar # ./checkpoint.pth.tar为示例预训练模型文件路径 - ``` - - >![](public_sys-resources/icon-note.gif) **说明：** - >上述为样例输入，用户可根据实际修改传入参数。 - - -

专题

- -- **[混合精度](#混合精度md)** - - -

混合精度

- -#### 概述 - -基于NPU芯片的架构特性，模型运算会涉及到混合精度，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： - -- 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。 - -#### 特性支持 - -混合精度模块功能和优化描述如[表1](#zh-cn_topic_0278765773_table10717173813332)所示。 - -**表 1** 混合精度模块功能 - - - - - - - - - - - - - - - - - - - -

功能

-

描述

-

O1配置模式

-

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

-

O2配置模式

-

BN使用float32，其他绝大部分使用float16。

-

静态Loss Scale功能

-

静态设置参数确保混合精度训练收敛。

-

动态Loss Scale功能

-

动态计算Loss Scale值并判读是否溢出。

-
- ->![](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。 - -#### 混合精度模型初始化 - -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) - ``` - - 也可参考[样例代码](#样例参考md)中的“初始化混合精度模型”： - - ``` - model, optimizer = amp.initialize(model, optimizer, opt_level="O2", loss_scale=1024, verbosity=1) - ``` - - -#### 混合精度推理 - -按混合精度模型初始化后，正常执行模型正向计算即可。 - -参考代码：可参考[样例代码](#样例参考md)实现。 - -

安装7.3.0版本gcc

- -以下步骤请在root用户下执行。 - -1. 下载gcc-7.3.0.tar.gz，下载地址为[https://mirrors.tuna.tsinghua.edu.cn/gnu/gcc/gcc-7.3.0/gcc-7.3.0.tar.gz](https://mirrors.tuna.tsinghua.edu.cn/gnu/gcc/gcc-7.3.0/gcc-7.3.0.tar.gz)。 -2. 安装gcc时候会占用大量临时空间，请先执行下面的命令清空/tmp目录： - - ``` - sudo rm -rf /tmp/* - ``` - -3. 安装依赖（以CentOS和Ubuntu系统为例）。 - - CentOS执行如下命令安装。 - - ``` - yum install bzip2 - ``` - - - Ubuntu执行如下命令安装。 - - ``` - apt-get install bzip2 - ``` - -4. 编译安装gcc。 - 1. 进入gcc-7.3.0.tar.gz源码包所在目录，解压源码包，命令为： - - ``` - tar -zxvf gcc-7.3.0.tar.gz - ``` - - 2. 进入解压后的文件夹，执行如下命令下载gcc依赖包： - - ``` - cd gcc-7.3.0 - ./contrib/download_prerequisites - ``` - - 如果执行上述命令报错，需要执行如下命令在“gcc-7.3.0/“文件夹下下载依赖包： - - ``` - wget http://gcc.gnu.org/pub/gcc/infrastructure/gmp-6.1.0.tar.bz2 - wget http://gcc.gnu.org/pub/gcc/infrastructure/mpfr-3.1.4.tar.bz2 - wget http://gcc.gnu.org/pub/gcc/infrastructure/mpc-1.0.3.tar.gz - wget http://gcc.gnu.org/pub/gcc/infrastructure/isl-0.16.1.tar.bz2 - ``` - - 下载好上述依赖包后，重新执行以下命令： - - ``` - ./contrib/download_prerequisites - ``` - - 如果上述命令校验失败，需要确保依赖包为一次性下载成功，无重复下载现象。 - - 3. 执行配置、编译和安装命令： - - ``` - ./configure --enable-languages=c,c++ --disable-multilib --with-system-zlib --prefix=/usr/local/linux_gcc7.3.0 - make -j15 # 通过grep -w processor /proc/cpuinfo|wc -l查看cpu数，示例为15，用户可自行设置相应参数。 - make install - ``` - - >![](public_sys-resources/icon-notice.gif) **须知：** - >其中“--prefix“参数用于指定linux\_gcc7.3.0安装路径，用户可自行配置，但注意不要配置为“/usr/local“及“/usr“，因为会与系统使用软件源默认安装的gcc相冲突，导致系统原始gcc编译环境被破坏。示例指定为“/usr/local/linux\_gcc7.3.0“。 - - -5. 配置环境变量。 - - 当用户执行训练时，需要用到gcc升级后的编译环境，因此要在训练脚本中配置环境变量，通过如下命令配置。 - - ``` - export LD_LIBRARY_PATH=${install_path}/lib64:${LD_LIBRARY_PATH} - ``` - - 其中$\{install\_path\}为[3.](#zh-cn_topic_0000001146754749_zh-cn_topic_0000001072593337_l75d31a2874534a2092e80a5f865b46f0)中配置的gcc7.3.0安装路径，本示例为“/usr/local/linux\_gcc7.3.0/“。 - - >![](public_sys-resources/icon-note.gif) **说明：** - >本步骤为用户在需要用到gcc升级后的编译环境时才配置环境变量。 - - diff --git 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"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" deleted file mode 100644 index 449b418659..0000000000 --- "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" +++ /dev/null @@ -1,565 +0,0 @@ -# PyTorch安装指南 -- [简介](#简介md) -- [手动编译安装](#手动编译安装md) - - [前提条件](#前提条件md) - - [安装PyTorch框架](#安装PyTorch框架md) - - [配置环境变量](#配置环境变量md) - - [安装混合精度模块](#安装混合精度模块md) -- [参考信息](#参考信息md) - - [CMake安装方法](#CMake安装方法md) - - [安装7.3.0版本gcc](#安装7-3-0版本gccmd) - - [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。 - -**图 1** 环境准备流程图 - - -![](figures/210926103326800.png) - -

手动编译安装

- -- **[前提条件](#前提条件md)** - -- **[安装PyTorch框架](#安装PyTorch框架md)** - -- **[配置环境变量](#配置环境变量md)** - -- **[安装混合精度模块](#安装混合精度模块md)** - - -

前提条件

- -#### 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 - - ``` - apt-get install patch - apt-get install git - ``` - - - CentOS系统 - - ``` - yum install patch - yum install git - ``` - - - -

安装PyTorch框架

- -#### 安装流程 - -1. 以root或非root用户登录服务器。 -2. 依次执行如下命令安装PyTorch依赖环境。 - - 如果使用非root用户安装Python及其依赖，用户需要在本步骤中的每句命令结尾加上**--user**，命令示例为：**pip3.7 install pyyaml --user** - - ``` - pip3 install pyyaml - pip3 install wheel - ``` - -3. 获取PyTorch源代码。 - - 1. 运行如下命令，获取适配昇腾AI处理器的PyTorch源代码，并切换到所需的分支。 - - ``` - git clone https://gitee.com/ascend/pytorch.git - # 默认是masterf分支，若需要其他分支请使用git checkout 命令切换 - # git checkout -b 2.0.3.tr5 remotes/origin/2.0.3.tr5 - ``` - - 下载的源码主要目录结构如下所示： - - ``` - ├── patch # 昇腾AI处理器适配补丁目录 - │ ├── pytorch1.5.0_npu.patch # pytorch1.5.0版本补丁 - │ └── pytorch1.8.1_npu.patch # pytorch1.8.1版本补丁 - ├── pytorch1.5.0 # pytorch1.5.0源码及测试目录 - │ ├── access_control_test.py - │ ├── src # 源码目录 - │ └── test # 测试用例存放目录 - ├── pytorch1.8.1 # pytorch1.8.1源码及测试目录 - │ ├── access_control_test.py - │ ├── src # 源码目录 - │ └── test # 测试用例存放目录 - └── scripts # 编译构建目录 - ``` - - 2. 在当前仓根目录“/pytorch“下获取原生PyTorch源代码。 - - 若安装pytorch1.5.0版本，执行如下命令。 - - ``` - git clone -b v1.5.0 --depth=1 https://github.com/pytorch/pytorch.git - ``` - - - 若安装pytorch1.8.1版本，执行如下命令。 - - ``` - git clone -b v1.8.1 --depth=1 https://github.com/pytorch/pytorch.git - ``` - - 3. 运行如下命令，进入原生pytorch代码目录“pytorch“，并获取PyTorch被动依赖代码。 - - ``` - cd pytorch - git submodule sync - git submodule update --init --recursive - ``` - - >![](public_sys-resources/icon-note.gif) **说明：** - >受网络波动影响，源码获取时间可能较长，下载过程中请耐心等待。 下载完成之后若没有报错，即生成了PyTorch及其依赖的第三方代码。 - -4. 编译生成适配昇腾AI处理器的PyTorch安装包。 - 1. 进入“pytorch/scripts“文件夹，执行转换脚本，生成适配昇腾AI处理器的全量代码。 - - ``` - cd ../scripts - # 若安装1.5.0版本 - bash gen.sh - # 若安装1.8.1版本 - bash gen.sh -v 1.8.1 - ``` - - 将在"pytorch/pytorch"目录中生成适配昇腾AI处理器的全量代码。 - - 2. 进入到“pytorch/pytorch/“目录，依赖库安装。 - - ``` - cd ../pytorch - pip3 install -r requirements.txt - ``` - - 3. 编译生成pytorch的二进制安装包。 - - ``` - bash build.sh --python=3.7 - 或 - bash build.sh --python=3.8 - 或 - 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。 - -5. 安装PyTorch。 - - 进入“pytorch/pytorch/dist“文件夹目录，执行如下命令安装。 - - ``` - pip3 install --upgrade torch-1.5.0+ascend.post3-cp37-cp37m-linux_{arch}.whl - ``` - - **\{arch\}**表示架构信息，为aarch64或x86\_64。 - - >![](public_sys-resources/icon-note.gif) **说明：** - >若对环境中的PyTorch进行升级时，需要先卸载环境中已安装的PyTorch软件包再执行[5. 安装PyTorch。](#zh-cn_topic_0000001152776301_li49671667141)可以通过执行如下命令查询环境上是否已安装PyTorch。 - >**pip3 list | grep torch** - - -

配置环境变量

- -安装完软件包后，需要配置环境变量才能正常使用昇腾PyTorch。相关环境变量介绍参见[表1](#zh-cn_topic_0000001152616261_table42017516135)。 - -1. 配置运行环境变量，在适配昇腾AI处理器的PyTorch源代码根目录中运行如下命令。 - - ``` - source pytorch/env.sh - ``` - -2. 请依据实际场景，选择合适的HCCL初始化方式，并配置相应环境变量。 - - ``` - # 场景一：单机场景 - export HCCL_WHITELIST_DISABLE=1 # 关闭HCCL通信白名单 - # 场景二：多机场景。 - export HCCL_WHITELIST_DISABLE=1 # 关闭HCCL通信白名单 - export HCCL_IF_IP="1.1.1.1" # “1.1.1.1”为示例使用的host网卡IP，请根据实际修改。需要保证使用的网卡IP在集群内是互通的。 - ``` - -3. （可选）NPU场景下配置功能或性能环境变量。默认为不开启。 - - ``` - export DYNAMIC_COMPILE_ENABLE=1 # 动态shape特性功能，针对shape变化场景，可选，开启设置为1（PyTorch1.8.1不支持该环境变量） - export COMBINED_ENABLE=1 # 非连续两个算子组合类场景优化，可选，开启设置为1 - export TRI_COMBINED_ENABLE=1 # 非连续三个算子组合类场景优化，可选，开启设置为1 - export ACL_DUMP_DATA=1 # 算子数据dump功能，调试时使用，可选，开启设置为1 - export DYNAMIC_OP="ADD#MUL" # 算子实现，ADD和MUL算子在不同场景下有不同的性能表现。可选 - ``` - -4. （可选）当系统为openEuler及其继承操作系统时，如UOS，需设置此命令，取消CPU绑核。 - - ``` - # unset GOMP_CPU_AFFINITY - ``` - - -**表 1** 环境变量说明 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

配置项

-

说明

-

LD_LIBRARY_PATH

-

动态库的查找路径，参考上述举例配置。

-

若训练所在系统环境需要升级gcc（例如CentOS、Debian和BClinux系统），则“LD_LIBRARY_PATH”配置项处动态库查找路径需要添加“${install_path}/lib64”，其中“{install_path}”为gcc升级安装路径。请参见5。

-

PYTHONPATH

-

Python搜索路径，参考上述举例配置。

-

PATH

-

可执行程序的查找路径，参考上述举例配置。

-

ASCEND_OPP_PATH

-

算子根目录，参考上述举例配置。

-

OPTION_EXEC_EXTERN_PLUGIN_PATH

-

算子信息库路径。

-

ASCEND_AICPU_PATH

-

aicpu算子包路径。

-

TASK_QUEUE_ENABLE

-

使用异步任务下发，异步调用acl接口。建议开启，开启设置为1。

-

HCCL_WHITELIST_DISABLE

-

配置在使用HCCL时是否开启通信白名单。

-
  • 0：开启白名单，无需校验HCCL通信白名单。
  • 1：关闭白名单，需校验HCCL通信白名单。
-

缺省值为0，默认开启白名单。

-

HCCL_IF_IP

-

配置HCCL的初始化通信网卡IP。

-
  • ip格式为点分十进制。
  • 暂只支持host网卡。
-

缺省时，按照以下优先级选定host通信网卡名：docker/local以外网卡（网卡名字字典序升序排列）>docker 网卡 > local网卡

-

PTCOPY_ENABLE

-

使用PTCopy算子模式，加速转连续及copy等过程，建议开启，开启设置为1

-

ASCEND_SLOG_PRINT_TO_STDOUT

-

（可选）设置是否开启日志打屏。

-
  • 0：表示采用日志的默认输出方式。
  • 1：表示日志打屏显示。
  • 其他值为非法值。
-

ASCEND_GLOBAL_LOG_LEVEL

-

设置应用类日志的全局日志级别。

-
  • 0：对应DEBUG级别。
  • 1：对应INFO级别。
  • 2：对应WARNING级别。
  • 3：对应ERROR级别。
  • 4：对应NULL级别，不输出日志。
  • 其他值为非法值。
-

ASCEND_GLOBAL_EVENT_ENABLE

-

设置应用类日志是否开启Event日志。

-
  • 0：不开启Event日志。
  • 1：开启Event日志。
  • 其他值为非法值。
-

DYNAMIC_COMPILE_ENABLE

-

（可选）动态shape特性功能，针对shape变化场景，开启设置为1（PyTorch1.8.1不支持该环境变量）。

-

COMBINED_ENABLE

-

（可选）非连续两个算子组合类场景优化，开启设置为1。

-

RI_COMBINED_ENABLE

-

（可选）非连续三个算子组合类场景优化，开启设置为1。

-

ACL_DUMP_DATA

-

（可选）算子数据dump功能，调试时使用，开启设置为1。

-

DYNAMIC_OP

-

（可选）算子实现，ADD和MUL算子在不同场景下有不同的性能表现。默认不设置。

-

unset GOMP_CPU_AFFINITY

-

（可选）当系统为openEuler及其继承操作系统时，如UOS，需设置此命令，取消CPU绑核。

-
- -

安装混合精度模块

- -#### 前提条件 - -1. 请确保运行环境中适配昇腾AI处理器的PyTorch框架能正常使用。 -2. 编译安装Apex前，需参见[配置环境变量](#配置环境变量md)配置好编译过程依赖的环境变量。 - -#### 安装流程 - -1. 以root或非root用户登录服务器。 -2. 获取apex源代码。 - - 1. 运行如下命令，获取适配昇腾AI处理器的apex源代码。 - - ``` - git clone https://gitee.com/ascend/apex.git - ``` - - 下载的源码主要目录结构如下所示： - - ``` - apex - │ ├─patch # 昇腾AI处理器适配补丁目录 - │ ├─npu.patch - │ ├─scripts # 编译构建目录 - │ ├─gen.sh - │ ├─src # 源码目录 - │ ├─tests # 测试用例存放目录 - ``` - - 2. 运行如下命令，进入“apex“目录，并获取原生apex源代码。 - - ``` - cd apex - git clone https://github.com/NVIDIA/apex.git - ``` - - 下载原生apex源码后，代码主要目录结构如下所示： - - ``` - apex - │ ├─apex # 原生apex代码目录 - │ ├─patch # 昇腾AI处理器适配补丁目录 - │ ├─npu.patch - │ ├─scripts # 编译构建目录 - │ ├─gen.sh - │ ├─src # 源码目录 - │ ├─tests # 测试用例存放目录 - ``` - - 3. 进入原生apex代码目录，即“apex/apex“目录。切换至commitid为4ef930c1c884fdca5f472ab2ce7cb9b505d26c1a的代码分支。 - - ``` - cd apex - git checkout 4ef930c1c884fdca5f472ab2ce7cb9b505d26c1a - ``` - - >![](public_sys-resources/icon-note.gif) **说明：** - >受网络波动影响，源码获取时间可能较长，下载过程中请耐心等待。 - -3. 编译生成适配昇腾AI处理器的apex安装包。 - 1. 进入“apex/scripts“文件夹，执行转换脚本，生成适配昇腾AI处理器的全量代码。 - - ``` - cd ../scripts - bash gen.sh - ``` - - 将在"apex/apex"目录中生成适配昇腾AI处理器的全量代码。 - - 2. 进入适配后的全量代码目录，即“apex/apex“目录，编译生成apex的二进制安装包。 - - ``` - cd ../apex - python3 setup.py --cpp_ext --npu_float_status bdist_wheel - ``` - - Python版本需与PyTorch使用的Python一致，生成的二进制包在当前的dist目录下，即“apex/apex/dist”文件夹目录下。 - -4. 安装apex。 - - 进入“apex/apex/dist“文件夹目录，执行如下命令安装。 - - ``` - pip3 install --upgrade apex-0.1+ascend-cp37-cp37m-linux_{arch}.whl - ``` - - **\{arch\}**表示架构信息，为aarch64或x86\_64。 - - >![](public_sys-resources/icon-note.gif) **说明：** - >若对环境中的Apex进行升级时，需要先卸载环境中已安装的PyTorch软件包再执行[4. 安装apex。](#zh-cn_topic_0000001106176190_li425495374416)可以通过执行如下命令查询环境上是否已安装PyTorch。 - >**pip3 list | grep apex** - - -

参考信息

- -- **[CMake安装方法](#CMake安装方法md)** - -- **[安装7.3.0版本gcc](#安装7-3-0版本gccmd)** - -- **[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)** - - -

CMake安装方法

- -CMake版本升级为3.12.1的方法 - -1. 获取Cmake软件包。 - - ``` - wget https://cmake.org/files/v3.12/cmake-3.12.1.tar.gz --no-check-certificate - ``` - -2. 解压并进入软件包目录。 - - ``` - tar -xf cmake-3.12.1.tar.gz - cd cmake-3.12.1/ - ``` - -3. 执行配置、编译和安装命令。 - - ``` - ./configure --prefix=/usr/local/cmake - make && make install - ``` - -4. 设置软连接。 - - ``` - ln -s /usr/local/cmake/bin/cmake /usr/bin/cmake - ``` - -5. 执行如下命令验证是否安装成功。 - - ``` - cmake --version - ``` - - 如显示“cmake version 3.12.1”则表示安装成功。 - - -

安装7.3.0版本gcc

- -以下步骤请在root用户下执行。 - -1. 下载gcc-7.3.0.tar.gz，下载地址为[https://mirrors.tuna.tsinghua.edu.cn/gnu/gcc/gcc-7.3.0/gcc-7.3.0.tar.gz](https://mirrors.tuna.tsinghua.edu.cn/gnu/gcc/gcc-7.3.0/gcc-7.3.0.tar.gz)。 -2. 安装gcc时候会占用大量临时空间，所以先执行下面的命令清空/tmp目录： - - ``` - sudo rm -rf /tmp/* - ``` - -3. 安装依赖（以CentOS和Ubuntu系统为例）。 - - CentOS执行如下命令安装。 - - ``` - yum install bzip2 - ``` - - - Ubuntu执行如下命令安装。 - - ``` - apt-get install bzip2 - ``` - -4. 编译安装gcc。 - 1. 进入gcc-7.3.0.tar.gz源码包所在目录，解压源码包，命令为： - - ``` - tar -zxvf gcc-7.3.0.tar.gz - ``` - - 2. 进入解压后的文件夹，执行如下命令下载gcc依赖包： - - ``` - cd gcc-7.3.0 - ./contrib/download_prerequisites - ``` - - 如果执行上述命令报错，需要执行如下命令在“gcc-7.3.0/“文件夹下下载依赖包： - - ``` - wget http://gcc.gnu.org/pub/gcc/infrastructure/gmp-6.1.0.tar.bz2 - wget http://gcc.gnu.org/pub/gcc/infrastructure/mpfr-3.1.4.tar.bz2 - wget http://gcc.gnu.org/pub/gcc/infrastructure/mpc-1.0.3.tar.gz - wget http://gcc.gnu.org/pub/gcc/infrastructure/isl-0.16.1.tar.bz2 - ``` - - 下载好上述依赖包后，重新执行以下命令： - - ``` - ./contrib/download_prerequisites - ``` - - 如果上述命令校验失败，需要确保依赖包为一次性下载成功，无重复下载现象。 - - 3. 执行配置、编译和安装命令： - - ``` - ./configure --enable-languages=c,c++ --disable-multilib --with-system-zlib --prefix=/usr/local/linux_gcc7.3.0 - make -j15 # 通过grep -w processor /proc/cpuinfo|wc -l查看cpu数，示例为15，用户可自行设置相应参数。 - make install - ``` - - >![](public_sys-resources/icon-notice.gif) **须知：** - >其中“--prefix“参数用于指定linux\_gcc7.3.0安装路径，用户可自行配置，但注意不要配置为“/usr/local“及“/usr“，因为会与系统使用软件源默认安装的gcc相冲突，导致系统原始gcc编译环境被破坏。示例指定为“/usr/local/linux\_gcc7.3.0“。 - - -5. 配置环境变量。 - - 当用户执行训练时，需要用到gcc升级后的编译环境，因此要在训练脚本中配置环境变量，通过如下命令配置。 - - ``` - export LD_LIBRARY_PATH=${install_path}/lib64:${LD_LIBRARY_PATH} - ``` - - 其中$\{install\_path\}为[3.](#zh-cn_topic_0000001135347812_zh-cn_topic_0000001173199577_zh-cn_topic_0000001172534867_zh-cn_topic_0276688294_li1649343041310)中配置的gcc7.3.0安装路径，本示例为“/usr/local/gcc7.3.0/“。 - - >![](public_sys-resources/icon-note.gif) **说明：** - >本步骤为用户在需要用到gcc升级后的编译环境时才配置环境变量。 - - -

安装“torch-\*.whl ”提示“torch 1.5.0xxxx”与“torchvision”所依赖的版本不匹配

- -#### 现象描述 - -安装“torch-\*.whl”时，提示"ERROR：torchvision 0.6.0 has requirement torch==1.5.0, but you'll have torch 1.5.0a0+1977093 which is incompatible"。 - -![](figures/zh-cn_image_0000001190081735.png) - -#### 可能原因 - -安装torch时，会自动触发torchvision进行依赖版本检查，环境中安装的torchvision版本为0.6.0，检查时发现我们安装的torch-\*.whl的版本号与要求的1.5.0不一致，所以提示报错，但实际安装成功 。 - -#### 处理方法 - -对实际结果无影响，无需处理。 - diff --git "a/docs/zh/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227/figures/210926103326800.png" "b/docs/zh/PyTorch\345\256\211\350\243\205\346\214\207\345\215\227/figures/210926103326800.png" deleted file mode 100644 index c80fd7d78db31105935b2d63c07ad620e61b1803..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 14379 zcmbWe2{=}7*e|+JQ4vZ~5>cWgQz$ZIEb2grp69vm-*mtJ8t0Vgw(QwL5Colyvix~`^&tof zIhsxQZvBWoC%$a9QP#Ob5ZlVh|0t@hQ*ICh2caS_d%^MfM2v$r(`DAm`m&~-^12r? z7x)==Qc!JbOpy)FIuy5I=Xk&a8B0dN?Fw}7{Q|;|W$n<-d>}8+MY)B>FP?+rjBnx; 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"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" deleted file mode 100644 index 5dd8b21b75..0000000000 --- "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" +++ /dev/null @@ -1,922 +0,0 @@ -# 概述 - -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。 - -# 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. 准备模型。 - - 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。 - - 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。 - - 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 - -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。 - -## 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。 - - - 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. DDP模型。 - - 实现一个简单的样例main.py用于多机多卡训练。 - - ```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 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。 - - 1. 用户自行安装模型脚本需要的Python第三方库。 - - 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 - ``` - - `--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 - -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： - - ```shell - hostname -I - ``` - - 打印出所有IP，第一个为IP当前服务器的host IP。 - - 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,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 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 - ``` - - 在AI server1服务器上启动命令： - - ```shell - # 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 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 - ``` - - 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 - - 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。 - -# 多机多卡训练 - - - -## 常用概念及参数介绍 - -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 - -| 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。 | - -## 多机多卡训练流程 - -### 准备环境 - -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。 - -1. 准备开发和运行环境。 - - 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。 - - - 安装适配NPU的PyTorch，安装方法请参见《PyTorch安装指南》。 - -2. 准备组网。 - - 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 - - 使用hccn_tool工具配置device IP，hccn_tool工具CANN软件以提供。 - - ```shell - hccn_tool -i 0 -ip -s address 192.168.100.111 netmask 255.255.255.0 - ``` - - 配置device IP需遵守以下规则： - - 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 - - 使用hccn_tool工具验证device IP是否配置正确。 - - - 查询每个device的ip。 - - ```shell - hccn_tool -i 0 -ip –g - ``` - - 打印查询结果: - - > ipaddr:192.168.100.101 - > - > netmask:255.255.255.0 - - - 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。 - - ```shell - hccn_tool -i 0 -netdetect -s address xx.xx.xx.xx - - hccn_tool -i 0 -net_health –g - ``` - - -i：device序号 - - -s address：xx.xx.xx.xx是另外一台机器的device i的IP - - 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 - - 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 - - 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 - - trainloader=get_dataset() - ``` - -2. 实例化模型 - - ```python - # 实例化模型 - model = ToyModel().to(loc) - - # 加载模型权重 - if ckpt_path is not None: - model.load_state_dict(torch.load(ckpt_path)) - ``` - -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: - 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() - - torch.save(model.state_dict(), "%d.ckpt" % epoch) - ``` - - - -### 修改模型 - -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'] = xxx.xxx.xxx.xxx - os.environ['MASTER_PORT'] = 'xxx' - ``` - - 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 - - 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。 - - 上述变量需在调用torch.distributed.init_process_group()之前声明。 - -2. distributed初始化 - - 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。 - - `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 - - 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 - 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 - import torch.distributed as dist - - 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。 - 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) - - 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 - # 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) - ``` - -6. DDP模型训练 - - ```python - 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() - 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（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。 - - 若显示类似如下信息，说明已免密登录服务器10.90.140.231。 - - ``` - 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 - ``` - - 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. - ``` - - 3. 使用OpenMPI拉起模型训练。 - - 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。 - - ``` - # 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=${nnodes} --node_rank=i --nproc_per_node 8 --master_addr xxx.xxx.xx.xxx --master_port 29501 main.py --addr xxx.xxx.xx.xxx - ``` - - 脚本参数请参见手动启动。 - - 2. 编写启动脚本 - - ``` - # 配置mpirun环境变量 - export PATH=$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 - ``` - - 其中 - - -H：每个AI server的IP:启动进程数。 - - --bind-to：绑定进程的策略。 - - --mca：特定上下文的MCA参数，arg0为参数名，arg1为参数值。 - - --allow-run-as-root：允许使用root用户运行。 - - --prefix：mpirun4.0.2路径。 - - ./train.sh：各个AI server的启动脚本路径。 - - 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 - -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 -``` - - - - - - - - - - - - - - - diff --git "a/docs/zh/PyTorch\347\256\227\345\255\220\345\274\200\345\217\221\346\214\207\345\215\227/PyTorch\347\256\227\345\255\220\345\274\200\345\217\221\346\214\207\345\215\227.md" "b/docs/zh/PyTorch\347\256\227\345\255\220\345\274\200\345\217\221\346\214\207\345\215\227/PyTorch\347\256\227\345\255\220\345\274\200\345\217\221\346\214\207\345\215\227.md" deleted file mode 100644 index f48ac35865..0000000000 --- "a/docs/zh/PyTorch\347\256\227\345\255\220\345\274\200\345\217\221\346\214\207\345\215\227/PyTorch\347\256\227\345\255\220\345\274\200\345\217\221\346\214\207\345\215\227.md" +++ /dev/null @@ -1,1376 +0,0 @@ -# PyTorch算子开发指南 -- [简介](#简介md) -- [算子开发流程](#算子开发流程md) -- [算子开发准备](#算子开发准备md) - - [环境准备](#环境准备md) - - [算子速查](#算子速查md) -- [算子适配开发](#算子适配开发md) - - [前提条件](#前提条件md) - - [获取PyTorch源码](#获取PyTorch源码md) - - [注册算子开发](#注册算子开发md) - - [概述](#概述md) - - [PyTorch1.5.0 注册算子开发](#PyTorch1-5-0-注册算子开发md) - - [PyTorch1.8.1 注册算子开发](#PyTorch1-8-1-注册算子开发md) - - [算子适配插件开发](#算子适配插件开发md) - - [编译和安装PyTorch框架](#编译和安装PyTorch框架md) -- [算子功能验证](#算子功能验证md) - - [概述](#概述-0md) - - [实现过程](#实现过程md) -- [FAQ](#FAQmd) - - [Pillow==5.3.0安装失败](#Pillow-5-3-0安装失败md) - - [pip3.7 install torchvision安装失败](#pip3-7-install-torchvision安装失败md) - - [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) - - [如何查看测试的运行日志](#如何查看测试的运行日志md) - - [为什么我实现的“自定义TBE算子”无法调用到？](#为什么我实现的-自定义TBE算子-无法调用到md) - - [如何确定“TBE算子”是否被“PyTorch适配”正确调用](#如何确定-TBE算子-是否被-PyTorch适配-正确调用md) - - [PyTorch编译失败，提示“error: ld returned 1 exit status”](#PyTorch编译失败-提示-error-ld-returned-1-exit-statusmd) - - [PyTorch编译失败，提示“error: call of overload ....”](#PyTorch编译失败-提示-error-call-of-overloadmd) -- [附录](#附录md) - - [CMake安装方法](#CMake安装方法md) - - [自定义算子导出方法](#自定义算子导出方法md) -

简介

- -### 概述 - -为了实现PyTorch深度学习框架在昇腾AI处理器上运行，需要将框架算子用TBE自定义开发。 - -

算子开发流程

- -PyTorch算子开发包含TBE算子开发和PyTorch框架下的算子适配。 - -1. TBE算子开发：昇腾AI软件栈中不包含相应的算子，需要先完成TBE算子的开发，再进行PyTorch框架下的算子适配。 - - TBE算子开发流程及方法请参见《CANN TBE自定义算子开发指南》。 - -2. PyTorch框架下的算子适配：昇腾AI软件栈中已实现了相应的TBE算子，可直接进行PyTorch框架适配。 - - PyTorch框架下的算子适配流程如下所示。 - - **图 1** PyTorch框架下的算子适配流程 - ![](figures/PyTorch框架下的算子适配流程.png "PyTorch框架下的算子适配流程") - - -**表 1** 算子开发步骤详解 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

序号

-

步骤

-

描述

-

参考

-

1

-

环境准备

-

准备算子开发及运行验证所依赖的开发环境与运行环境。

-

算子开发准备

-

2

-

算子速查

-

查看TBE算子支持列表和PyTorch算子适配列表。

-
  • 当前昇腾AI处理器支持的算子列表及支持的算子的详细规格约束。
  • 当前PyTorch适配的算子列表。
-

3

-

获取PyTorch源码

-

获取昇腾社区PyTorch源码。

-

算子适配开发

-

4

-

注册算子开发

-

将算子分发到昇腾AI处理器。

-

5

-

算子适配层开发

-

算子适配层开发，将基于第三方框架的算子属性映射成适配昇腾AI处理器的算子属性。

-

6

-

编译安装PyTorch框架

-

编译适配开发后的PyTorch源码，并安装编译的源码包。

-

7

-

算子功能验证

-

在真实的硬件环境中验证算子功能。

-

算子功能验证

-
- -

算子开发准备

- -- **[环境准备](#环境准备md)** - -- **[算子速查](#算子速查md)** - - -

环境准备

- -- 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python版本为3.7.5或3.8。 -- 需安装3.12.0及以上版本的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见《CANN 软件安装指南》中的“安装7.3.0版本gcc”章节。 -- 需确保环境中已安装git工具，以Ubuntu和CentOS系统为例，命令如下: - - Ubuntu系统 - - ``` - apt-get install patch - apt-get install git - ``` - - - CentOS系统 - - ``` - yum install patch - yum install git - ``` - - - -

算子速查

- -进行算子开发时，您可以查询当前昇腾AI处理器中支持的算子列表和当前PyTorch适配的算子列表。根据查询结果进行算子开发或PyTorch算子适配。 - -- 若昇腾AI处理器不支持算子，则需要进行TBE算子开发和PyTorch框架算子适配。 -- 若昇腾AI处理器支持算子但PyTorch框架没有算子适配，仅需要进行PyTorch框架下算子适配。 -- 若PyTorch框架已经适配算子，直接使用算子即可，不需要开发和适配。 - -昇腾AI处理器和PyTorch适配的算子查询方式如下。 - -- 当前昇腾AI处理器中支持的算子以及对应的算子约束可以通过以下两种方式查询。 - - 命令行开发方式下，您可以参见《CANN 算子清单 \(Ascend 910\)》进行离线查询。 - - MindStudio开发方式下，您可以通过MindStudio进行在线查询，详细查看方法可参见《MindStudio 用户指南》中的“算子&模型速查”章节。 - -- 当前PyTorch适配的算子列表可以参见《PyTorch适配算子清单》。 - -

算子适配开发

- -- **[前提条件](#前提条件md)** - -- **[获取PyTorch源码](#获取PyTorch源码md)** - -- **[注册算子开发](#注册算子开发md)** - -- **[算子适配插件开发](#算子适配插件开发md)** - -- **[编译和安装PyTorch框架](#编译和安装PyTorch框架md)** - - -

前提条件

- -- 完成开发及运行环境准备及相关依赖的安装，详情请参见[环境准备](#环境准备md)。 -- 完成相关的TBE算子开发及部署，详情请参见《CANN TBE自定义算子开发指南》。 - -

获取PyTorch源码

- -目前只支持PyTorch1.5.0和1.8.1版本，PyTorch源码获取请参见《PyTorch安装指南》中“安装PyTorch框架”章节，完成在"pytorch/pytorch"目录生成适配昇腾AI处理器的全量代码步骤。将在pytorch/pytorch目录中进行PyTorch 算子适配开发。 - -

注册算子开发

- -- **[概述](#概述md)** - -- **[PyTorch1.5.0 注册算子开发](#PyTorch1-5-0-注册算子开发md)** - -- **[PyTorch1.8.1 注册算子开发](#PyTorch1-8-1-注册算子开发md)** - - -

概述

- -当前制定的NPU适配派发原则是NPU算子的派发不经过框架公共函数，直接派发成NPU适配的函数，即算子执行调用栈中只包含NPU适配的函数调用，不包含框架公共函数。PyTorch框架在编译时，会根据 native\_functions.yaml 的定义，按框架中定义的类型和设备分发原则，生成相应的新算子的中间层的调用说明。对于NPU，会生成在 build/aten/src/ATen/NPUType.cpp。 - -

PyTorch1.5.0 注册算子开发

- -##### 注册算子开发方法 - -1. 打开native\_functions.yaml文件。 - - native\_functions.yaml 文件中，定义了所有算子函数原型，包括函数名称和参数等信息，每个算子函数支持不同硬件平台的派发信息。该文件所在路径为pytorch/aten/src/ATen/native/native\_functions.yaml。 - -2. 确定需要派发函数。 - - yaml 中已存在的算子 - - 将所有与待适配算子相关的函数进行派发。 - - - yaml中未存在的自定义算子 - - 由于yaml中没有相关算子的信息，需要手动添加相关函数，包括函数名，参数信息，返回类型信息。添加规则及方法请参见“pytorch/aten/src/ATen/native/README.md“。 - - ``` - - func：适配算子名称(输入参数信息) -> 返回类型 - ``` - -3. 修改native\_functions.yaml文件，添加实现该算子相关函数的分发描述。 - - yaml 文件编写规范： - - - yaml 中原有的算子函数适配使用 “npu\_dispatch”关键字； - - ``` - npu_dispatch: - NPU: NPU_Adapt_Fun_Name - ``` - - - yaml 中新增昇腾AI处理器自定义算子函数适配使用“npu\_dispatch\_only”关键字； - - ``` - npu_dispatch_only: - NPU: NPU_Adapt_Fun_Name - ``` - - >![](public_sys-resources/icon-note.gif) **说明：** - >NPU\_Adapt\_Fun\_Name的格式为 : - >- 如果原Fun\_Name无"\_"后缀，则格式：Fun\_Name + "\_" + "npu"，如：add --\> add\_npu。 - >- 如果原Fun\_Name有"\_"后缀，则格式：Fun\_Name + "npu\_"，如：add\_ --\> add\_npu\_。 - >该格式供参考，算子适配开发过程中的函数名需与NPU\_Adapt\_Fun\_Name保持一致。 - - -##### 示例 - -以torch.add\(\)算子为例介绍注册算子开发过程。 - -1. 打开native\_functions.yaml文件。 -2. 确定相关函数。 - - 在yaml中搜索add，找到与add算子相关的函数。 - -3. 添加分发描述。 - 1. add.Tensor 的函数分发描述。 - - ``` - - func: add.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor - use_c10_dispatcher: full - variants: function, method - dispatch: - CPU: add - CUDA: add - SparseCPU: add_sparse - SparseCUDA: add_sparse - MkldnnCPU: mkldnn_add - # 增加分发描述 - npu_dispatch: - NPU: add_npu - supports_named_tensor: True - ``` - - 2. add.Scalar 的函数分发描述。 - - ``` - - func: add.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor - use_c10_dispatcher: full - variants: function, method - supports_named_tensor: True - # 增加分发描述 - npu_dispatch: - NPU: add_npu - ``` - - 3. add\_.Tensor 的函数分发描述。 - - ``` - - func: add_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!) - variants: method - dispatch: - CPU: add_ - CUDA: add_ - SparseCPU: add_sparse_ - SparseCUDA: add_sparse_ - MkldnnCPU: mkldnn_add_ - # 增加分发描述 - npu_dispatch: - NPU: add_npu_ - supports_named_tensor: True - ``` - - 4. add\_.Scalar 的函数分发描述。 - - ``` - - func: add_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!) - variants: method - supports_named_tensor: True - # 增加分发描述 - npu_dispatch: - NPU: add_npu_ - ``` - - 5. add.out 的函数分发描述。 - - ``` - - func: add.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) - dispatch: - CPU: add_out - CUDA: add_out - SparseCPU: add_out_sparse_cpu - SparseCUDA: add_out_sparse_cuda - MkldnnCPU: mkldnn_add_out - # 增加分发描述 - npu_dispatch: - NPU: add_out_npu - supports_named_tensor: True - ``` - - - -

PyTorch1.8.1 注册算子开发

- -##### 注册算子开发方法 - -1. 打开native\_functions.yaml文件。 - - native\_functions.yaml 文件中，定义了所有算子函数原型，包括函数名称和参数等信息，每个算子函数支持不同硬件平台的派发信息。该文件所在路径为pytorch/aten/src/ATen/native/native\_functions.yaml。 - -2. 确定需要派发函数。 - - yaml 中已存在的算子 - - 将所有与待适配算子相关的函数进行派发。 - - - yaml中未存在的自定义算子 - - 由于yaml中没有相关算子的信息，需要手动添加相关函数，包括函数名，参数信息，返回类型信息。添加规则及方法请参见“pytorch/aten/src/ATen/native/README.md“。 - - ``` - - func：适配算子名称(输入参数信息) -> 返回类型 - ``` - - - -##### 示例 - -以torch.add\(\)算子为例介绍注册算子开发过程。 - -1. 打开native\_functions.yaml文件。 -2. 搜索相关函数。 - - 在yaml中搜索add，找到与add算子相关的函数描述func。由于add是PyTorch内置算子，不需要手动添加func。若是自定义算子，需要手动添加func。 - -3. 确定算子相关函数名称及其类型的func描述。 - - add.Tensor 的函数分发描述。 - - ``` - - func: add.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor - structured_delegate: add.out - variants: function, method - dispatch: - SparseCPU, SparseCUDA: add_sparse - MkldnnCPU: mkldnn_add - ``` - - - add.Scalar 的函数分发描述。 - - ``` - - func: add.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor - variants: function, method - dispatch: - DefaultBackend: add - ``` - - - add\_.Tensor 的函数分发描述。 - - ``` - - func: add_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!) - variants: method - structured_delegate: add.out - dispatch: - SparseCPU, SparseCUDA: add_sparse_ - MkldnnCPU: mkldnn_add_ - ``` - - - add\_.Scalar 的函数分发描述。 - - ``` - - func: add_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!) - variants: method - dispatch: - DefaultBackend: add_ - ``` - - - add.out 的函数分发描述。 - - ``` - - func: add.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) - structured: True - structured_inherits: TensorIteratorBase - dispatch: - CPU, CUDA: add_out - SparseCPU: add_out_sparse_cpu - SparseCUDA: add_out_sparse_cuda - MkldnnCPU: mkldnn_add_out - ``` - - - -

算子适配插件开发

- -#### 简介 - -用户通过开发算子适配插件，实现PyTorch原生算子的输入参数、输出参数和属性的格式转换，使转换后的格式与TBE算子的输入参数、输出参数和属性的格式相同。适配昇腾AI处理器的PyTorch源代码中提供了适配关联、类型转换和判别、处理动态shape等相关的方法供用户使用。 - -#### 适配插件实现 - -1. 创建适配插件文件。 - - NPU TBE算子适配文件保存在pytorch/aten/src/ATen/native/npu目录下，命名风格采用大驼峰，命名格式：<算子名\> + .cpp，如：AddKernelNpu.cpp。 - -2. 引入依赖头文件。 - - 适配昇腾AI处理器的PyTorch源代码在ATen/native/npu/utils中提供适配常用的工具供用户使用。 - - >![](public_sys-resources/icon-note.gif) **说明：** - >工具的功能和使用方法，可查看头文件和源码获得。 - -3. 定义算子适配主体函数。 - - 根据注册算子开发中的分发函数确定自定义算子适配主体函数。 - -4. 分别实现适配主体函数。 - - 实现算子适配主题函数，根据TBE算子原型构造得到对应的input、output、attr。 - -5. （仅1.8.1版本需要操作该步骤）使用TORCH\_LIBRARY\_IMPL宏关联注册算子开发中native\_functions.yaml文件的算子描述func。 - - TORCH\_LIBRARY\_IMPL是PyTorch提供的用于注册算子分发的宏，使用方法如下。 - - ``` - Torch_LIBRARY_IMPL(aten, PrivateUse1, m){ - m.impl("yaml中算子func方法名1", TORCH_FN("对应的适配主体函数名1")) - m.impl("yaml中算子func方法名2", TORCH_FN("对应的适配主体函数名2")) - } - ``` - - - aten为命名空间，可以根据实现文件命名空间自行定义。 - - PrivateUse1为dispatchKey, 固定设置NPU。 - - m为固定字段。 - - -#### 示例 - -以torch.add\(\)算子为例介绍算子适配开发过程。 - -1. 创建适配插件文件。 - - 在pytorch/aten/src/ATen/native/npu目录下创建AddKernelNpu.cpp适配文件。 - -2. 引入依赖头文件。 - - ``` - #include - #include "ATen/native/npu/utils/CalcuOpUtil.h" - #include "ATen/native/npu/utils/OpAdapter.h" - ``` - - >![](public_sys-resources/icon-note.gif) **说明：** - >" CalcuOpUtil.h "中主要包含类型转换和判别的函数。 - >" OpAdapter.h"文件中主要包含适配关联的头文件。 - -3. 定义算子适配主体函数。 - - ``` - Tensor add_npu(const Tensor& self, const Tensor& other, Scalar alpha) - Tensor add_npu(const Tensor& self, Scalar other, Scalar alpha) - Tensor& add_npu_(Tensor& self, const Tensor& other, Scalar alpha) - Tensor& add_npu_(Tensor& self, Scalar other, Scalar alpha) - Tensor& add_out_npu(Tensor& result, const Tensor& self, const Tensor& other, Scalar alpha) - ``` - -4. 分别实现适配主体函数。 - 1. add\_npu实现 - - ``` - // 输入参数为Tensor和Tensor时 - Tensor add_npu(const Tensor& self, const Tensor& other, Scalar alpha) { - alpha_check_npu(self.scalar_type(), alpha); - if ((!(self.is_contiguous() && other.is_contiguous())) && - (NpuUtils::check_5d_5d_match(self) || - NpuUtils::check_5d_5d_match(other)) && - check_size(self, other)) { - int64_t c0_len = 16; - Tensor self_use = stride_add_tensor_get(self); - Scalar self_c1_offset( - self.storage_offset() / (self.size(2) * self.size(3) * c0_len)); - Tensor other_use = stride_add_tensor_get(other); - Scalar other_c1_offset( - other.storage_offset() / (other.size(2) * other.size(3) * c0_len)); - Scalar stride_len(self.size(1) / c0_len); - Tensor result = at::npu_stride_add( - self_use, other_use, self_c1_offset, other_c1_offset, stride_len); - return result; - } - // calculate the output size - Tensor outputTensor = add_dest_output(self, other); - auto outputSize = broadcast_ops_npu_output_size(self, other); - - // construct the output tensor of the NPU - Tensor result = at::empty_with_format( - outputSize, - outputTensor.options(), - CalcuOpUtil::get_tensor_npu_format(outputTensor)); - - // calculate the output result of the NPU - add_out_npu_nocheck(result, self, other, alpha); - - return result; - } - - // 输入参数为Tensor和Scalar时 - Tensor add_npu(const Tensor& self, Scalar other, Scalar alpha) { - alpha_check_npu(self.scalar_type(), alpha); - // calculate the output size - auto outputSize = input_same_output_size(self); - // construct the output tensor of the NPU - Tensor result = at::empty_with_format( - outputSize, self.options(), CalcuOpUtil::get_tensor_npu_format(self)); - - // calculate the output result of the NPU - adds_out_npu_nocheck(result, self, other, alpha); - - return result; - } - - ``` - - 2. add\_npu\_实现（就地操作场景，返回值为self） - - ``` - // 输入参数为Tensor和Tensor时 - Tensor& add_npu_(Tensor& self, const Tensor& other, Scalar alpha) { - SmallVector inputs = {self, other}; - SmallVector outputs = {self}; - CalcuOpUtil::check_memory_over_laps(inputs, outputs); - - if (!NpuUtils::check_match(&self)) { - Tensor contiguousSelf = NpuUtils::format_contiguous(self); - Tensor result = add_out_npu_nocheck(contiguousSelf, contiguousSelf, other, alpha); - NpuUtils::format_fresh_view(self, result); - } else { - add_out_npu_nocheck(self, self, other, alpha); - } - - return self; - } - - // 输入参数为Tensor和Scalar时 - Tensor& add_npu_(Tensor& self, Scalar other, Scalar alpha) { - if (!NpuUtils::check_match(&self)) { - Tensor contiguousSelf = NpuUtils::format_contiguous(self); - Tensor result = adds_out_npu_nocheck(contiguousSelf, contiguousSelf, other, alpha); - NpuUtils::format_fresh_view(self, result); - } else { - adds_out_npu_nocheck(self, self, other, alpha); - } - - return self; - } - - ``` - - 3. add\_out\_npu实现（输入参数result为返回值场景） - - ``` - Tensor& add_out_npu( - Tensor& result, - const Tensor& self, - const Tensor& other, - Scalar alpha) { - bool isSelfWrapped = CalcuOpUtil::is_scalar_wrapped_to_tensor(self); - - Tensor outputTensor; - if (not isSelfWrapped) { - outputTensor = self; - } else { - outputTensor = other; - } - auto outputSize = broadcast_ops_npu_output_size(self, other); - OpPreparation::CheckOut( - {self}, - result, - CalcuOpUtil::get_tensor_npu_format(result), - outputTensor.scalar_type(), - outputSize); - - OpPipeWithDefinedOut pipe; - return pipe.CheckMemory({self, other}, {result}) - .Func([&self, &other, &alpha](Tensor& result){add_out_npu_nocheck(result, self, other, alpha);}) - .Call(result); - } - ``` - -5. （仅1.8.1版本需要操作该步骤）使用TORCH\_LIBRARY\_IMPL宏关联注册算子。 - - ``` - TORCH_LIBRARY_IMPL(aten, NPU, m) { - m.impl("add.Tensor", TORCH_FN(add_npu)); - m.impl("add_.Tensor", TORCH_FN(add_npu_)); - m.impl("add.out", TORCH_FN(add_out_npu));} - ``` - - ->![](public_sys-resources/icon-note.gif) **说明：** ->AddKernelNpu.cpp的详细实现代码请参见pytorch/aten/src/ATen/native/npu/AddKernelNpu.cpp文档。 - -

编译和安装PyTorch框架

- -#### 编译PyTorch框架 - -1. 进入PyTorch工作目录 ：“pytorch/pytorch“。 -2. 依赖库安装。 - - ``` - pip3 install -r requirements.txt - ``` - -3. 编译生成pytorch的二进制安装包。 - - ``` - bash build.sh --python=3.7 - 或 - bash build.sh --python=3.8 - ``` - - 请指定环境中python版本进行编译。编译成功后，会在pytorch/pytorch/dist”文件夹目录下生成二进制包 torch-\*.whl ，例如：torch-1.5.0+ascend.post3-cp37-cp37m-linux\_x86.whl或者torch-1.8.1+ascend-cp37-cp37m-linux\_x86.whl。 - - -#### 安装PyTorch框架 - -进入“pytorch/pytorch/dist“文件夹目录，执行如下命令安装。 - -``` -pip3 install --upgrade torch-1.5.0+ascend.post3-cp37-cp37m-linux_{arch}.whl -``` - -**\{arch\}**表示架构信息，为aarch64或x86\_64。 - ->![](public_sys-resources/icon-note.gif) **说明：** ->若环境中已安装PyTorch时，需要先卸载环境中已安装的PyTorch软件包再执行，可以通过执行如下命令查询环境上是否已安装PyTorch。 ->**pip3 list | grep torch** - -修改代码之后，需要重新执行“编译”和“安装”PyTorch过程。 - -

算子功能验证

- -- **[概述](#概述-0md)** - -- **[实现过程](#实现过程md)** - - -

概述

- -#### 简介 - -完成算子适配开发后，可通过运行适配昇腾处理器的PyTorch算子，验证算子运行结果是否正确。 - -算子功能验证会覆盖算子开发的所有交付件，包含实现文件，算子原型定义、算子信息库以及算子适配插件。本节仅对验证的方法做介绍。 - -#### 测试用例及测试工具 - -进行自定义算子功能验证，通过PyTorch前端构造自定义算子的函数并运行验证。 - -在https://gitee.com/ascend/pytorch中 "pytorch/test/test\_npu"目录下提供了测试用例及测试工具，供用户参考。 - -

实现过程

- -#### 简介 - -本章通过具体例子，完成PyTorch算子的功能测试步骤。 - -#### 操作步骤 - -1. 设置环境变量。 - - ``` - # 进行环境变量设置。具体如下（以HwHiAiUser用户安装，安装路径为默认路径为例）： - . /home/HwHiAiUser/Ascend/ascend-toolkit/set_env.sh - ``` - -2. 编写测试脚本。以add算子为例，在“pytorch/test/test\_npu/test\_network\_ops“路径下编写测试脚本文件： test\_add.py。 - - 以下示例仅为一个简单的用例实现供用户参考，具体测试用例的实现，需要根据算子定义进行完整的覆盖才能保证功能的基本正确。 - - ``` - # 引入依赖库 - import sys - sys.path.append('..') - 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 - - # 定义add测试用例类 - class TestAdd(TestCase): - - # 定义CPU和NPU add执行的函数 - def cpu_op_exec(self, input1, input2): - output = torch.add(input1, input2, alpha = 1) - output = output.numpy() - return output - def npu_op_exec_new(self, input1, input2): - output = torch.add(input1, input2, alpha = 1) - output = output.to("cpu") - output = output.numpy() - return output - - # 定义add对应场景通用函数，该函数负责输入数据并对比CPU和NPU的计算结果 - def add_result(self, shape_format): - for item in shape_format: - cpu_input1, npu_input1 = create_common_tensor(item, 0, 100) - cpu_input2, npu_input2 = create_common_tensor(item, 0, 100) - if cpu_input1.dtype == torch.float16: - cpu_input1 = cpu_input1.to(torch.float32) - cpu_input2 = cpu_input2.to(torch.float32) - cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2) - npu_output = self.npu_op_exec_new(npu_input1, npu_input2) - cpu_output = cpu_output.astype(npu_output.dtype) - self.assertRtolEqual(cpu_output, npu_output) - - # 定义具体add场景的测试用例，用例函数需要以test_开头 - def test_add_shape_format_fp32_2d(self, device): - format_list = [0, 3, 29] - shape_format = [ - [np.float32, i, [5, 256]] for i in format_list - ] - self.add_result(shape_format) - - instantiate_device_type_tests(TestAdd, globals(), except_for="cpu") - if __name__ == "__main__": - run_tests() - ``` - -3. 执行测试用例脚本 - - 进入test\_add.py所在的目录，执行： - - ``` - python3.7 test_add.py - ``` - - -

FAQ

- -- **[Pillow==5.3.0安装失败](#Pillow-5-3-0安装失败md)** - -- **[pip3.7 install torchvision安装失败](#pip3-7-install-torchvision安装失败md)** - -- **[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)** - -- **[如何查看测试的运行日志](#如何查看测试的运行日志md)** - -- **[为什么我实现的“自定义TBE算子”无法调用到？](#为什么我实现的-自定义TBE算子-无法调用到md)** - -- **[如何确定“TBE算子”是否被“PyTorch适配”正确调用](#如何确定-TBE算子-是否被-PyTorch适配-正确调用md)** - -- **[PyTorch编译失败，提示“error: ld returned 1 exit status”](#PyTorch编译失败-提示-error-ld-returned-1-exit-statusmd)** - -- **[PyTorch编译失败，提示“error: call of overload ....”](#PyTorch编译失败-提示-error-call-of-overloadmd)** - - -

Pillow==5.3.0安装失败

- -#### 现象描述 - -pillow==5.3.0安装失败。 - -#### 可能原因 - -缺少必要的依赖，如：libjpeg、python-devel、 zlib-devel 、libjpeg-turbo-devel等等。 - -#### 处理方法 - -执行以下命令安装所需依赖。 - -``` -apt-get install libjpeg python-devel zlib-devel libjpeg-turbo-devel -``` - -

pip3.7 install torchvision安装失败

- -#### 现象描述 - -pip3.7 install torchvision安装失败。 - -#### 可能原因 - -torch与torchvision版本不匹配。 - -#### 处理方法 - -执行以下命令。 - -``` -pip3.7 install torchvision --no-deps -``` - -

安装“torch-\*.whl ”提示“torch 1.5.0xxxx”与“torchvision”所依赖的版本不匹配

- -#### 现象描述 - -安装“torch-\*.whl”时，提示"ERROR：torchvision 0.6.0 has requirement torch==1.5.0, but you'll have torch 1.5.0a0+1977093 which is incompatible"。 - -![](figures/zh-cn_image_0000001144082048.png) - -但实际安装成功 - -#### 可能原因 - -安装torch时，会自动触发torchvision进行依赖版本检查，环境中安装的torchvision版本为0.6.0，检查时发现我们安装的torch-\*.whl的版本号与要求的1.5.0不一致，所以提示了一条错误 。 - -#### 处理方法 - -对实际结果无影响，无需处理。 - -

如何查看测试的运行日志

- -测试运行过程提示错误，但直接可参考的信息太少，如何才能看到更详细的运行日志？ - -设置日志输出到屏幕，重定向到指定文本文件 - -1. 设置环境变量，将当前用户的日志到屏幕。 - - ``` - export SLOG_PRINT_TO_STDOUT=1 - ``` - - 设置完成后，直接运行测试用例，就可以将相关的日志输出到屏幕，为了方便查看和回溯，建议根据需要使用[2](#zh-cn_topic_0000001125315889_li168732325719)运行。 - -2. 运行测试用例时，将日志重定向到指定文件。 - - ``` - python3.7 test_add.py > test_log.txt - ``` - - -

为什么我实现的“自定义TBE算子”无法调用到？

- -#### 现象描述 - -完成“自定义TBE算子”开发，和“PyTorch”适配开发，但执行测试用例，发现无法调用到新开发的算子。 - -#### 可能原因 - -- 环境变量没有正确设置。 -- yaml存在错误，导致没有正确分发。 -- 自定义TBE算子实现存在问题，导致无法调用到。 - -#### 处理方法 - -1. 参考“[算子功能验证](#算子功能验证md)”章节，完成运行环境设置，特别注意： - - ``` - . /home/HwHiAiUser/Ascend/ascend-toolkit/set_env.sh - ``` - -2. 检查yaml文件中对应算子的分发配置，是否正确的完整分发； -3. 分析排查代码实现，建议手段： - 1. 修改"pytorch"中的算子适配实现，让“test\_add.py”可以调用到“自定义算子包”中的TBE算子； - - "pytorch/aten/src/ATen/native/npu/AddKernelNpu.cpp" - - ![](figures/zh-cn_image_0000001144082088.png) - - 2. 完成上两步的编译、安装，调用“python3.7 test\_add.py”进行测试。 - - ``` - cd 至"test_add.py" 所在路径，调用 - test_add.py - 进行测试 - ``` - - 至此步骤，不应该有错误，屏幕应该输出 "add" 中增加的日志打印。若出错，请完成代码清理排查，保证无新开发的代码影响测试。 - - 3. 将新开发的“自定义TBE算子”合并到"cann"中，在对应的在算子入口增加日志打印，作为运行标识。 - 4. 完成上面的"cann"编译、安装，调用“python3.7.5 test\_add.py”进行测试。 - - >![](public_sys-resources/icon-note.gif) **说明：** - >根据Ascend的设计逻辑，用户开发安装的“custom”算子包优先级高于“built-in”的内置算子包，在运行加载时，会优先加载调度“custom”包中的算子。过程中，若解析“custom”中的算子信息文件失败，则会跳过“custom”算子包，不加载调度任何“custom”算子包中的任何算子。 - >- 若此步骤出错，或屏幕未输出 "add" 中增加的日志打印，则说明新开发的“自定义TBE算子”有错误，影响了“自定义算子包”的加载，建议**优先排查新开发的“自定义TBE算子”中的“算子信息定义”是否正确**。 - >- 若此步骤正确，至少说明 **新开发的“自定义TBE算子”中的“算子信息定义”不影响运行**。 - - 5. 调用“python3.7.5 xxx\_testcase.py”进行测试； - - >![](public_sys-resources/icon-note.gif) **说明：** - >- 若屏幕正常输出新开发的“自定义TBE算子”中增加的日志打印，则至少说明调度到了新开发的算子。 - >- 若屏幕未输出新开发的“自定义TBE算子”中增加的日志打印，则问题可能出现在“PyTorch适配”中，需要排查这一部分的实现代码，较多的可能会出现在“XxxxKernelNpu.cpp”中的输入、输出未能正确适配。 - - - -

如何确定“TBE算子”是否被“PyTorch适配”正确调用

- -不管是“custom”算子，还是“built-in”算子，在安装后，都是以\*.py源码的方式存放在安装目录下，所以我们可以通过编辑源码，在API入口增加日志的方式，打印输出入参，确定输入的参数是否正确。 - ->![](public_sys-resources/icon-caution.gif) **注意：** ->该操作存在风险，建议在操作前备份计划修改的文件。若未备份，损坏后无法恢复，请及时联系支持人员。 - -下面以"built-in"算子中的"zn\_2\_nchw"算子为例： - -1. 打开安装在用户目录下的算子包安装目录。 - - ``` - cd ~/.local/Ascend/opp/op_impl/built-in/ai_core/tbe/impl - ll - ``` - - 可以看到相应的算子\*.py源码文件均为“只读”，即此时不可编辑。 - - ![](figures/zh-cn_image_0000001190081791.png) - -2. 修改算子\*.py源码文件属性，增加“可写”权限。 - - ``` - sudo chmod +w zn_2_nchw.py - ll - ``` - - ![](figures/zh-cn_image_0000001190081803.png) - -3. 打开算子\*.py源码文件，增加日志，保存退出。 - - ``` - vi zn_2_nchw.py - ``` - - ![](figures/zh-cn_image_0000001190201951.png) - - 上面例子只加了个标识，实际调测时，可以增加打印输入参数信息。 - -4. 调用脚本执行测试用例，通过打印日志分析“输入参数信息”。 -5. 完成测试分析后，重新打开算子\*.py源码文件，删除增加的日志，保存退出。 -6. 修改算子\*.py源码文件属性，删除“可写”权限。 - - ``` - sudo chmod -w zn_2_nchw.py - ``` - - ![](figures/zh-cn_image_0000001144082072.png) - - -

PyTorch编译失败，提示“error: ld returned 1 exit status”

- -#### 现象描述 - -PyTorch编译失败，提示“ error: ld returned 1 exit status”。 - -![](figures/zh-cn_image_0000001190201973.png) - -#### 可能原因 - -通过日志分析，大概原因为XxxxKernelNpu.cpp中实现的适配函数，与PyTorch框架算子要求的分发实现接口参数不匹配。在上面的例子中，是“binary\_cross\_entropy\_npu”，打开对应的XxxxKernelNpu.cpp文件，找到相应的适配函数。 - -![](figures/zh-cn_image_0000001144241896.png) - -可以看出实现中，最后一个参数是"int" ，与要求的“long”不匹配。 - -#### 处理方法 - -修改XxxxKernelNpu.cpp中实现的适配函数，在上面的例子中，修改“binary\_cross\_entropy\_npu”方法最后一个参数类型为“int64\_t”\(cpp中“long”的类型使用"int64\_t"\)。 - -

PyTorch编译失败，提示“error: call of overload ....”

- -#### 现象描述 - -PyTorch编译失败，提示“error: call of overload ....”。 - -![](figures/zh-cn_image_0000001144082056.png) - -![](figures/zh-cn_image_0000001190201935.png) - -#### 可能原因 - -通过日志分析，错误出在XxxxKernelNpu.cpp中30行位置，NPUAttrDesc的参数不合法。在上面的例子中，是“binary\_cross\_entropy\_attr”，打开对应的XxxxKernelNpu.cpp文件，找到相应的适配函数。 - -![](figures/zh-cn_image_0000001144082064.png) - -可以看出实现中，NPUAttrDesc的入参，第二个参数是"int" ，与NPUAttrDesc定义不匹配。 - -#### 处理方法 - -1、将binary\_cross\_entropy\_attr\(\)中错误的行代码替换为上面的注释中的代码 - -2、binary\_cross\_entropy\_attr\(\)入参类型修改为 int64\_t。 - -

附录

- -- **[CMake安装方法](#CMake安装方法md)** - -- **[自定义算子导出方法](#自定义算子导出方法md)** - - -

CMake安装方法

- -以CMake版本升级为3.12.1为例说明。 - -1. 获取Cmake软件包。 - - ``` - wget https://cmake.org/files/v3.12/cmake-3.12.1.tar.gz --no-check-certificate - ``` - -2. 解压并进入软件包目录。 - - ``` - tar -xf cmake-3.12.1.tar.gz - cd cmake-3.12.1/ - ``` - -3. 执行配置、编译和安装命令。 - - ``` - ./configure --prefix=/usr/local/cmake - make && make install - ``` - -4. 设置软连接。 - - ``` - ln -s /usr/local/cmake/bin/cmake /usr/bin/cmake - ``` - -5. 执行如下命令验证是否安装成功。 - - ``` - cmake --version - ``` - - 显示“cmake version 3.12.1”则表示安装成功。 - - -

自定义算子导出方法

- -#### 简介 - -PyTorch训练模型中包含自定义算子，将自定义算子导出成onnx单算子模型，方便转移到其他AI框架中使用。自定义算子导出有三种形式，适配NPU的TBE算子导出、C++算子导出、纯Python算子导出。 - -#### 前提条件 - -安装PyTorch框架。 - -#### TBE算子导出 - -TBE算子导出有两种方式。 - -方式一： - -1. 定义和注册算子 - - ``` - # 定义算子 - @parse_args('v', 'v', 'f', 'i', 'i', 'i', 'i') - def symbolic_npu_roi_align(g, input, rois, spatial_scale, pooled_height, pooled_width, sample_num, roi_end_mode): - args = [input, rois] - kwargs = {"spatial_scale_f": spatial_scale, - "pooled_height_i": pooled_height, - "pooled_width_i": pooled_width, - "sample_num_i": sample_num, - "roi_end_mode_i": roi_end_mode} - - return g.op('torch::npu_roi_align',*args, **kwargs) - - # 注册算子 - import torch.onnx.symbolic_registry as sym_registry - def register_onnx_sym_npu_roi_align(): - sym_registry.register_op('npu_roi_align', symbolic_npu_roi_align, '', 11) - - register_onnx_sym_npu_roi_align() - ``` - -2. 自定义模型 - - ``` - # 定义模型 - class CustomModel_npu_op(torch.nn.Module): - def __init__(self,a,b): - super(CustomModel_npu_op, self).__init__() - - self.weight = Parameter(torch.Tensor(8,10,1024)) - init.kaiming_uniform_(self.weight, a=math.sqrt(5)) - - def forward(self, a, b, d): - spatial_scale=d[0].item() - pooled_height=d[1].item() - pooled_width=d[2].item() - sample_num=d[3].item() - roi_end_mode=d[4].item() - rtn = torch.npu_roi_align(a, self.weight, spatial_scale, pooled_height, pooled_width, sample_num,roi_end_mode) - - return rtn - ``` - -3. 导出onnx - - ``` - # 定义导出函数 - def do_export(model, inputs, f, *args, **kwargs): - out = torch.onnx._export(model, inputs, f, verbose=True, export_params=True, do_constant_folding=True,*args, **kwargs) - - # 初始化输入 - """ - 实现模型输入参数a,b,h1初始化，此处忽略，请参考详细代码。 - """ - - # 导出onnx - model = CustomModel_npu_op(a,b) - model = model.npu() - model.eval() - do_export(model, (a, b, h1), f, input_names=["intput"]+["","","","","","","npu_roi_align.weight"],opset_version=11) - ``` - - -方式二： - -1. 定义方法类 - - ``` - # 实现算子方法类及符号导出实现的方法 - class CustomClassOp_Func_npu_roi_align(Function): - @staticmethod - def forward(ctx, input, rois, spatial_scale, pooled_height, pooled_width , sample_num, roi_end_mode): - rtn = torch.npu_roi_align(input, rois, spatial_scale, pooled_height, pooled_width, sample_num, roi_end_mode) - return rtn - - @staticmethod - def symbolic(g, input, rois, spatial_scale, pooled_height, pooled_width , sample_num, roi_end_mode): - args = [input, rois] - kwargs = {"spatial_scale_f": spatial_scale, - "pooled_height_i": pooled_height, - "pooled_width_i": pooled_width, - "sample_num_i": sample_num, - "roi_end_mode_i": roi_end_mode} - return g.op('torch::npu_roi_align',*args, **kwargs) - ``` - -2. 自定义算子模型 - - ``` - # 实现算子模型 - class NpuOp_npu_roi_align_Module(torch.nn.Module): - def __init__(self): - super(NpuOp_npu_roi_align_Module, self).__init__() - - self.spatial_scale = torch.randn(10, dtype=torch.float32, requires_grad=False,device="cpu")[0].item() - self.pooled_height = 2 - self.pooled_width = 0 - self.sample_num = 1 - self.roi_end_mode = 1 - - self.weight = Parameter(torch.Tensor(8,10,1024)) - init.kaiming_uniform_(self.weight, a=math.sqrt(5)) - - self.func = CustomClassOp_Func_npu_roi_align.apply - self.test_npu_op=1 - - def forward(self, input): - rtn = self.func(input, self.weight, self.spatial_scale, self.pooled_height, self.pooled_width, self.sample_num, self.roi_end_mode) - return rtn - ``` - -3. 自定义模型 - - ``` - # 创建自定义模型 - class CustomModel_Module_op(torch.nn.Module): - def __init__(self,a,b): - super(CustomModel_Module_op, self).__init__() - self.npu_roi_align = NpuOp_npu_roi_align_Module() - #@staticmethod - def forward(self, a): - rtn = self.npu_roi_align(a) - return rtn - ``` - -4. 导出onnx - - ``` - # 构建数据 - a = torch.randn(5, 10, 1024, dtype=torch.float32, requires_grad=True,device=rnddata_device) - b = torch.randn(10, 10, 1024, dtype=torch.float32, requires_grad=True,device=rnddata_device) - - # 模型实例化 - model = CustomModel_Module_op(a,b) - model = model.npu() - model.eval() - a = a.to('npu:6') - b = b.to('npu:6') - - # 导出onnx - do_export(model, a, f=ONNX_NPU_OP_MODULE_FILENAME, input_names=["intput"]+["npu_roi_align.weight"],opset_version=11) - ``` - - ->![](public_sys-resources/icon-note.gif) **说明：** ->详细实现代码请参见[test\_custom\_ops\_npu\_demo.py](https://gitee.com/ascend/pytorch/blob/master/test/test_npu/test_onnx/torch.onnx/custom_ops_demo/test_custom_ops_npu_demo.py)，如无权限获取代码，请联系华为技术支持申请加入“Ascend”组织。 - -#### C++算子导出 - -1. 自定义算子 - - ``` - import torch - import torch.utils.cpp_extension - # 定义C++实现的算子 - def test_custom_add(): - op_source = """ - #include - - torch::Tensor custom_add(torch::Tensor self, torch::Tensor other) { - return self + other; - } - static auto registry = - torch::RegisterOperators("custom_namespace::custom_add",&custom_add); - """ - torch.utils.cpp_extension.load_inline( - name="custom_add", - cpp_sources=op_source, - is_python_module=False, - verbose=True, - ) - - test_custom_add() - ``` - -2. 注册自定义算子 - - ``` - # 定义算子注册方法并注册算子 - from torch.onnx import register_custom_op_symbolic - - def symbolic_custom_add(g, self, other): - return g.op('custom_namespace::custom_add', self, other) - - register_custom_op_symbolic('custom_namespace::custom_add', symbolic_custom_add, 9) - ``` - -3. 建立模型 - - ``` - # 建立算子模型 - class CustomAddModel(torch.nn.Module): - def forward(self, a, b): - return torch.ops.custom_namespace.custom_add(a, b) - ``` - -4. 导出算子onnx模型 - - ``` - # 将算子导出onnx模型 - def do_export(model, inputs, *args, **kwargs): - out = torch.onnx._export(model, inputs, "custom_demo.onnx", *args, **kwargs) - - x = torch.randn(2, 3, 4, requires_grad=False) - y = torch.randn(2, 3, 4, requires_grad=False) - model = CustomAddModel() - do_export(model, (x, y), opset_version=11) - ``` - - ->![](public_sys-resources/icon-note.gif) **说明：** ->详细实现代码请参见[test\_custom\_ops\_demo.py](https://gitee.com/ascend/pytorch/blob/master/test/test_npu/test_onnx/torch.onnx/custom_ops_demo/test_custom_ops_demo.py)，如无权限获取代码，请联系华为技术支持申请加入“Ascend”组织。 - -#### 纯Python算子导出 - -1. 自定义算子 - - ``` - import torch - import torch.onnx.symbolic_registry as sym_registry - - import torch.utils.cpp_extension - import torch.nn as nn - import torch.nn.modules as Module - from torch.autograd import Function - import numpy as np - - from torch.nn.parameter import Parameter - import math - from torch.nn import init - - # 定义算子类方法 - class CustomClassOp_Add_F(Function): - @staticmethod - def forward(ctx, input1,input2): - rtn = torch.add(input1,input2) - return torch.add(input1,rtn) - - @staticmethod - def symbolic(g,input1,input2): - rtn = g.op("Custom::CustomClassOp_Add", input1, input2,test_attr1_i=1,test_attr2_f=1.0) - rtn = g.op("ATen::CustomClassOp_Add", input1, rtn) - rtn = g.op("C10::CustomClassOp_Add", rtn, input2) - #erro doman: rtn = g.op("onnx::CustomClassOp_Add", input1, input2) - - return rtn - ``` - -2. 建立模型 - - ``` - # 注册算子并建立模型 - class CustomClassOp_Add(torch.nn.Module): - def __init__(self): - super(CustomClassOp_Add, self).__init__() - self.add = CustomClassOp_Add_F.apply - - #graph(%0 : Float(1, 8, 10, 1024), - # %1 : Float(8, 10, 1024)) - self.weight = Parameter(torch.Tensor(8,10,1024)) - - #%1 : Float(8, 10, 1024) = onnx::Constant[value=]() - self.weight1 = torch.Tensor(8,10,1024) - - init.kaiming_uniform_(self.weight, a=math.sqrt(5)) - init.kaiming_uniform_(self.weight1, a=math.sqrt(5)) - def forward(self, input): - rtn = torch.add(self.weight1, self.weight) - - rtn = self.add(self.weight, rtn) - rtn1 = self.add(self.weight, self.weight1) - rtn1 = self.add(self.weight1,rtn1) - rtn = self.add(rtn,rtn1) - - return rtn - ``` - -3. 导出算子onnx模型 - - ``` - ONNX_FILE_NAME = "./custom_python_module_demo.onnx" - def do_export(model, inputs, *args, **kwargs): - out = torch.onnx._export(model, inputs, ONNX_FILE_NAME, verbose=True,keep_initializers_as_inputs=True, *args, **kwargs) - - def test_class_export(): - model = CustomModel() - model.eval() - input_x_shape = [1, 8, 10, 1024] - input = torch.randn(input_x_shape) - output = model(input) - do_export(model, input, opset_version=11) - - # 将算子导出到onnx模型 - test_class_export() - ``` - - ->![](public_sys-resources/icon-note.gif) **说明：** ->详细实现代码请参见[test\_custom\_ops\_python\_module.py](https://gitee.com/ascend/pytorch/blob/master/test/test_npu/test_onnx/torch.onnx/custom_ops_demo/test_custom_ops_python_module.py)，如无权限获取代码，请联系华为技术支持申请加入“Ascend”组织。 - diff --git "a/docs/zh/PyTorch\347\256\227\345\255\220\345\274\200\345\217\221\346\214\207\345\215\227/figures/PyTorch\346\241\206\346\236\266\344\270\213\347\232\204\347\256\227\345\255\220\351\200\202\351\205\215\346\265\201\347\250\213.png" "b/docs/zh/PyTorch\347\256\227\345\255\220\345\274\200\345\217\221\346\214\207\345\215\227/figures/PyTorch\346\241\206\346\236\266\344\270\213\347\232\204\347\256\227\345\255\220\351\200\202\351\205\215\346\265\201\347\250\213.png" deleted file mode 100644 index edd26c07ece912850cc911226b4594ad2b30c322..0000000000000000000000000000000000000000 GIT binary patch literal 0 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概述

- -当前阶段针对PyTorch框架实现的对接适配昇腾AI处理器的方案为在线对接方案。 - -**方案特性及优点** - -昇腾AI处理器的加速实现方式是以各种算子为粒度进行调用（OP-based），即通过AscendCL调用一个或几个D亲和算子组合的形式，代替原有GPU的实现方式。其逻辑模型如[图1](#fig2267112413239)所示。 - -**图 1** 逻辑模型 - - -![](figures/pytorch适配逻辑结构图-优化.png) - -当前选择在线对接适配方案的主要原因有以下几点： - -1. 最大限度的继承PyTorch框架动态图的特性。 -2. 最大限度的继承GPU在PyTorch上的使用方式，可以使用户在将模型移植到昇腾AI处理器设备进行训练时，在开发方式和代码重用方面做到最小的改动。 -3. 最大限度的继承PyTorch原生的体系结构，保留框架本身出色的特性，比如自动微分、动态分发、Debug、Profiling、Storage共享机制以及设备侧的动态内存管理等。 -4. 扩展性好。在打通流程的通路之上，对于新增的网络类型或结构，只需涉及相关计算类算子的开发和实现。框架类算子，反向图建立和实现机制等结构可保持复用。 -5. 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，用户无需进一步关注昇腾AI处理器具体的底层细节。这样可以确保用户的最小化修改，迁移成本较低。 - -

约束与限制

- -- infershape阶段算子不支持unknowshape的推导。 -- cube计算的算子只支持float16。 -- 不支持float16类型的inf/nan数据输入输出 。 -- 出现4D以上的format时不能降维。 -- Apex当前版本的实现方式为python实现，不支持APEX中的自定义优化CUDA Kernel。 -- Apex当前版本只支持适配昇腾AI处理器的混合精度计算和多种融合优化器功能，其他功能暂未支持。 -- 集合通信约束： - - 数据并行模式中不同device上执行的图相同。 - - 只支持1/2/4/8P粒度的分配。 - - 只支持int8，int32，float16和float32数据类型。 - - -

迁移流程

- -模型迁移主要指将开源社区中实现过的模型迁移到昇腾AI处理器上，主要流程如[图1](#fig759451810422)所示。 - -**图 1** 迁移流程 -![](figures/迁移流程.png "迁移流程") - -**表 1** 迁移流程说明 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

场景

-

说明

-

模型选取

-

选取需要迁移的模型。

-

模型移植评估

-

详情请参见模型移植评估。

-

算子开发

-

详情请参见《PyTorch算子开发指南》。

-

环境准备

-

详情请参见环境准备。

-

模型迁移

-

详情请参见模型迁移。

-

模型训练

-

详情请参见模型训练。

-

错误分析

-

详情请参见《CANN 日志参考》和《CANN 开发辅助工具指南》中“AI Core Error分析工具使用指南”章节。

-

性能调优和分析

-

详情请参见性能调优和分析。

-

精度调测

-

详情请参见精度调测。

-

模型保存与转换

-

详情请参见模型保存与转换和《CANN 开发辅助工具指南》中“ATC工具使用指南”章节。

-

应用软件开发

-

详情请参见《CANN 应用软件开发指南(C&C++, 推理)》。

-

FAQ

-

主要涉及环境准备、模型迁移、模型调测和其他常见问题的解决方法。详情请参见FAQ。

-
- -## 快速上手 - -### 简介 - -对ResNet50模型进行迁移，帮助用户快速了解迁移过程。 - -### 模型选取 - -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。 - -### 模型移植评估 - -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。 - -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。 - -ResNet50模型用到的算子已经在昇腾AI处理器上支持。 - -### 环境准备 - -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。 - -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。 - -### 模型迁移 - -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。 - -#### 单卡训练迁移 - -1. 在main.py脚本中导入torch.npu模块。 - - ```python - import torch.npu - ``` - -2. 在main.py中定义训练设备。 - - ```python - CALCULATE_DEVICE = "npu:0" - ``` - -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： - - ```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: - ...... - ``` - -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 ############# - ``` - -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： - - ```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 ############# - ``` - - - 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 ############# - ``` - -6. 设置当前正在使用的device。 - - 代码位置：main.py文件中的主函数入口： - - ```python - if __name__ == '__main__': - ############## npu modify begin ############# - if 'npu' in CALCULATE_DEVICE: - torch.npu.set_device(CALCULATE_DEVICE) - ############## npu modify begin ############# - main() - ``` - -#### 单机多卡训练修改 - -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。 - - ```python - import torch.npu - from apex import amp - ``` - -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。 - - ```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。 - - ```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 - ``` - -4. 指定训练服务器的ip和端口。 - - 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）。 - - ```python - def main(): - args = parser.parse_args() - ############## npu modify begin ############# - os.environ['MASTER_ADDR'] = args.addr - os.environ['MASTER_PORT'] = '29688' - ############## npu modify end ############# - ``` - -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。 - - 代码位置：main.py文件中的主函数main\(\)。 - - ```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() - ``` - -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。 - - 代码位置：main.py文件中的main\_worker\(\)。 - - ```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 - ``` - -7. 初始化进程组，屏蔽掉初始化方式。 - - 代码位置：main.py文件中的main\_worker\(\)。 - - ```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) - ``` - -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。 - - 代码位置：main.py文件中的main\_worker\(\)。 - - ```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() - ``` - -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\(\)。 - - ```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 - ``` - -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**。 - - 代码位置：main.py文件中的main\_worker\(\)。 - - ```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 ############# - ``` - -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。 - - 代码位置：main.py文件中的main\_worker\(\)中验证数据加载后。 - - ```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 ############# - ``` - -12. 断点checkpoint保存需要与混合精度训练结合，修改如下。 - - 代码位置：main.py文件中的main\_worker\(\)（修改部分为字体加粗部分）。 - - ```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 ############# - ``` - -13. 训练时，需要将数据集迁移到昇腾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

-

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。 - -**环境准备** - -详情请参考《CANN 软件安装指南》安装开发环境。 - -

操作指南

- -**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** - -自定义转换规则样例如下： - -``` -{ - "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" - } - ] - } -} -``` - -**表 2** 参数说明 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

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** - -1. 进入脚本转换工具所在路径。 - - ``` - cd Ascend-cann-toolkit安装目录/ascend-toolkit/{version}/{arch}-linux/toolkit/tools/ms_fmk_transplt - ``` - -2. 执行脚本转换工具。 - - ``` - 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] - ``` - - >![](public_sys-resources/icon-note.gif) **说明：** - >distributed及其参数-m、-t在语句最后指定。 - -3. 完成脚本转换。 - -

结果解析

- -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。 - -``` -├── 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。 -``` - -

手工迁移

- - -

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： - -``` - 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) -``` - -迁移到昇腾AI处理器上代码为： - -``` - 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) -``` - -更多迁移细节请参见[单P训练修改](#单P训练修改md)。 - -

多P训练模型迁移

- -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。 - -PyTorch分布式训练代码示例（部分代码省略）： - -``` -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) -``` - -更多迁移细节请参见[分布式训练修改](#分布式训练修改md)。 - -

PyTorch接口替换

- -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** 设备接口替换 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

PyTorch原始接口

-

适配昇腾AI处理器后的接口

-

说明

-

torch.cuda.is_available()

-

torch.npu.is_available()

-

判断当前环境上设备是否可用（不代表最后结果）。

-

torch.cuda.current_device()

-

torch.npu.current_device()

-

获取当前正在使用的device。

-

torch.cuda.device_count()

-

torch.npu.device_count()

-

获取当前环境上的设备数量。

-

torch.cuda.set_device()

-

torch.npu.set_device()

-

设置当前正在使用的device。

-

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

-

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

-

判断某个tensor是否是cuda/npu设备上的格式。

-

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

-

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

-

将某个tensor转换成cuda/npu格式。

-

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。

-
- -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。 - - **表 2** tensor创建接口替换 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

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])

-
- - -更多接口请参见《PyTorch API支持清单》。 - -

混合精度

- -**概述** - -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： - -- 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。 - -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）。 - -**特性支持** - -混合精度模块功能和优化描述如[表1](#table10717173813332)所示。 - -**表 1** 混合精度模块功能 - - - - - - - - - - - - - - - - - - - -

功能

-

描述

-

O1配置模式

-

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

-

O2配置模式

-

除了BN使用float32外，其他绝大部分使用float16。

-

静态Loss Scale功能

-

静态设置参数确保混合精度训练收敛。

-

动态Loss Scale功能

-

动态计算loss Scale值并判断是否溢出。

-
- ->![](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。 - - ``` - 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() - ``` - - >![](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。 - - 2. 解析性能数据文件。 - - 请参见《CANN 开发辅助工具指南》中“Profiling工具使用指南（训练）”章节。 - - - -**获取算子信息OP\_INFO** - -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。 - -1. 编写get\_ascend\_op\_info.py脚本获取算子信息，脚本内容如下。 - - ``` - # -*- 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) - ``` - -2. 设置环境变量，将host日志打屏。 - - ``` - export ASCEND_SLOG_PRINT_TO_STDOUT=1 - ``` - -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。 - - ``` - python3 get_ascend_op_info.py --host_log_folder $HOME/ascend/log/plog - ``` - -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)** - - -
概述
- -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。 - -- 修改CPU性能模式。 -- 安装高性能pillow库。 -- （可选）安装指定版本OpenCV库。 - -
修改CPU性能模式（X86服务器）
- -**设置电源策略为高性能模式** - -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)。 - - **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") - -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") - -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。 - - **图 3** Socket Configuration - ![](figures/Socket-Configuration.png "Socket-Configuration") - -5. 进入Advanced Power Mgmt. Configuration，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.png "设置电源策略") - -6. 按下“F10”保存配置并重启服务器。 - -**将CPU设置为performance模式** - -请使用root用户执行如下操作。 - -1. 使用如下命令查看当前CPU模式。 - - ``` - cat /sys/devices/system/cpu/cpu0/cpufreq/scaling_governor - ``` - - 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。 - - **表 1** CPU模式 - - - - - - - - - - - - - - - - - - - - - - - - - -

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。

-
- -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。 - -**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。 - -**框架瓶颈优化** - -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) - - 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。 - -**编译瓶颈优化** - -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)。 - -### 端到端性能工具（E2E prof）使用说明 - -#### 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，并且一般只需要profiling1个或者2个setp即可。） - -#### 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。 - -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。 - - ``` - /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 - - -- 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：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： - -- 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 - -- 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 - -- ACL_AICORE_NONE = 0xFF：表示不采集 - -​ - -### 亲和库 - - -

来源介绍

- -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。 - -

功能介绍

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

函数名

-

位置

-

功能说明

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pairwise_iou

-

torch.contrib.npu.optimized_lib

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计算两个目标框的IOU。

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fast_rcnn_inference_single_image

-

torch.contrib.npu.optimized_lib

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Maskrcnn和Fasterrcnn模型的推理接口。

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ChannelShuffle

-

torch.contrib.npu.optimized_lib

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

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PreLoader

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torch.contrib.npu.optimized_lib

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提供针对昇腾AI处理器加速的数据加载方法。

-
- ->![](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。 - -

精度调测

- - -

前提条件

- -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。 - -

调测过程

- -- **[总体思路](#总体思路-4md)** - -- **[精度调优方法](#精度调优方法md)** - - -

总体思路

- -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。 - - - 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。 - - - 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）。 - -3. 参数更新错误。 - - - 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。 - - - 规避方案：使用同等语义其他算子替代。 - - - 解决方案：改进计算grad的算子精度或功能问题。 - -4. 多卡计算错误。 - - - 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。 - - - 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。 - -- **[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 - -- 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)。 - - 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。 - -- 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》。 - - - 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"${PY_VERSION}" setup.py build bdist_wheel - ``` - - - （可选）在GPU环境PyTorch安装，若对模型算子精度对比，请执行此操作，否则请忽略。 - - 编译前修改build.sh，在脚本中增加`USE_DUMP=1`、`USE_NCCL=0`字段，将 `USE_HCCL`、`USE_NPU`字段的值修改为0，将`USE_CUDA`字段的值修改为1。 - - ```bash - DEBUG=0 USE_DISTRIBUTED=1 USE_HCCL=0 USE_NCCL=0 USE_MKLDNN=0 USE_CUDA=1 USE_NPU=0 BUILD_TEST=0 USE_NNPACK=0 USE_DUMP=1 python"${PY_VERSION}" setup.py build bdist_wheel - ``` - -##### 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。 - -- 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
- -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： - -``` -# 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 -``` - -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。 - -查看Dump数据： - -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。 - -解决方法： - -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)。 - -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 - -前提条件： - -- 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。 - -操作步骤： - -1. 准备好需要映射的算子.h5文件。 - - - 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。 - - - 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。 - - ``` - h5copy -pv -i "./input.h5" -o "./output.h5" -s "/op1/seqid/" -d "/op1/seqid/" - ``` - - -i 为输入精度对比文件 - - -o 为输出需要映射的算子.h5文件路径 - - -s 为需要提取的源算子名称及seqid - - -d 为需要提取的目的算子名称及seqid - - 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。 - - 该命令需-s和-d参数相同。 - - 示例： - - ``` - h5copy -pv -i "./dump_npu.h5" -o "./output.h5" -s "/numpy_T/1/" -d "/numpy_T/1/" - ``` - - 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。 - -2. 配置acl.json文件。 - - 在模型目录下创建acl dump功能所需的的配置文件acl.json - - ``` - { - "dump": - { - "dump_list":[] - "dump_path":"./output_IR2TBE"# 映射结果输出路径 - "dump_mode":"all" - "dump_op_switch":"on" - } - - } - ``` - - 需将`dump_path`修改为结果输出路径，其他字段不需要修改。 - -3. 修改训练脚本。 - - 在训练脚本中添加`with`语句开启IR映射TBE功能。 - - ```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) - ``` - -4. 模型运行。 - - 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。 - -5. 获得映射文件。 - - 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。 - -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。 - - 代码样例（本样例只体现基本方法，禁止直接复制）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) - ``` - - 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。 - - 代码样例（本样例只体现基本方法，禁止直接复制）如下： - - ``` - # 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) - ``` - - 通过分析打印正向反向传播中的inputs, outputs来确定。 - -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： - - ``` - # 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) - ``` - - -

模型保存与转换

- -- **[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() - ``` - - - ->![](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。 - -

导出ONNX模型

- -**简介** - -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。 - -**.pth或.pt文件导出ONNX模型** - -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。 - -``` -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() -``` - ->![](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。 - -``` -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() -``` - -

模型调优样例

- -

ShuffleNet模型调优示例

- -- **[样例获取](#样例获取-5md)** - -- **[模型评估](#模型评估md)** - -- **[网络迁移](#网络迁移md)** - -- **[网络调测](#网络调测md)** - - -

样例获取

- -**样例获取** - -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. ShuffleNet模型参考PyTorch官网模型[ShuffleNet V2](https://pytorch.org/hub/pytorch_vision_shufflenet_v2/)，实际使用在脚本执行中直接指定参数arch为shufflenet\_v2\_x1\_0。 - - ``` - --arch shufflenet_v2_x1_0 - ``` - - >![](public_sys-resources/icon-note.gif) **说明：** - >ShuffleNet为PyTorch内置模型，了解更多内置模型请前往[Pytorch官网](https://pytorch.org/)。 - -**目录结构** - -主要文件目录结构如下所示： - -``` -├──main.py -``` - -

模型评估

- -模型评估主要关注算子适配情况，使用dump op方法获取ShuffleNet网络算子信息，与《PyTorch适配算子清单》算子进行对比，若是发现某个算子当前暂不支持，对于简单场景我们可以考虑先暂时替换成类似的算子或者把该算子单独放到cpu上执行两种方式规避，复杂场景不支持算子需要参见《PyTorch算子开发指南》进行算子开发。 - -

网络迁移

- -训练脚本迁移请参见[单P训练修改](#单P训练修改md)和[分布式训练修改](#分布式训练修改md)。脚本执行时注意选择参数--arch shufflenet\_v2\_x1\_0。 - -

网络调测

- -网络调测具体方法请参见[调测过程](#调测过程md)。经排查ShuffleNet运行时相关算子耗时过大，以下给出耗时数据及解决方法。 - -**前向排查** - -前向排查记录表如下： - -**表 1** 前向排查 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

序号

-

time(ms)

-

batch_size

-

detail

-

1

-

1100

-

512

-

channel_shuffle操作使用channel_shuffle_index_select替代。

-

2

-

600

-

512

-

使用两个channel_shuffle_index_select操作消减chunk带来的不连续。

-

3

-

300

-

512

-

通过框架层，指定concat输出格式为NCHW以消除过多的transdata。

-

4

-

285

-

512

-

修复了未初始化weight格式。

-

5

-

275

-

512

-

修复了DWCONV没有指定输出格式为5HD的问题。

-
- -详细说明如下： - -- 由于原生实现的torch.transpose\(x, 1, 2\).contiguous\(\)是使用了View类框架算子transpose，造成了非连续场景，如[copy瓶颈优化](#训练过程性能优化md)所描述Copy瓶颈，使用channel\_shuffle\_index\_select，在语义相同的情况下使用计算类算子替换框架类算子，从而减少耗时。 -- 由于shufflenetv2中含有大量的chunk操作，而chunk操作在Pytorch中为框架类算子，其结果会将一个tensor分割为几个等长的非连续的tensor，而非连续转连续这个操作目前耗时较长，故使用计算类算子消除非连续，如[copy瓶颈优化](#训练过程性能优化md)所描述Copy瓶颈。 -- 适配层在适配算子时默认指定输出格式为输入格式，但是concat不支持C轴非16整数倍的5HD的格式，会转为4D进行处理，又由于concat后面接的是gatherv2算子，也是仅支持4D格式的算子，所以导致数据格式转换过程为5HD-\>4D-\>concat-\>5HD-\>4D-\>gatherv2-\>5HD，解决方法是修改concat输出格式，当非16整数倍时指定输出格式为4D，优化后数据格式转换过程为5HD-\>4D-\>concat-\>gatherv2-\>5HD，当前针对ShuffleNet的做法具体可参考pytorch/aten/src/ATen/native/npu/CatKernelNpu.cpp 第121行。 -- 设置weight初始化格式避免计算过程中反复的transdata，如[copy瓶颈优化](#训练过程性能优化md)所描述框架瓶颈。 -- 修复了DWCONV weight输出格式指定，避免一些不必要5HD-\>4D。 - -**整网排查** - -整网排查记录表如下： - -**表 2** 整网排查 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

序号

-

time(ms)

-

batch_size

-

detail

-

1

-

5500

-

512

-

通过框架层，index tocpu成index_add操作

-

2

-

4000

-

512

-

通过自定义算子，预生成index，不再tocpu

-

3

-

1800

-

512

-

通过自定义算子，融合index_add和chunk

-

4

-

885

-

512

-

添加contiguous_with_gatherv2

-

5

-

3480

-

1024

-

修改batchsize

-

6

-

1650

-

1024

-

修改batchsize + contiguous_with_gatherv2

-

7

-

1424

-

1024

-

通过自定义算子，融合cat+shuffle+chunk，消除不连续

-

8

-

1360

-

1024

-

通过框架层，修改relugrad传入的grad格式

-

9

-

1300

-

1024

-

修改IndexSelectFullImplementation的bp传入格式

-

10

-

920

-

1024

-

修改amp O1

-

11

-

860

-

1024

-

修改amp O2

-

12

-

830

-

1024

-

消除BN参数更新时AXPY引入的过多的transdata

-

13

-

800

-

1024

-

消除所有fp bp parm_updata间的流同步

-

14

-

461

-

1024

-

针对非32对齐场景，改进GatherV2算子

-

15

-

429

-

1024

-

针对ShufflenetV2场景再次优化GatherV2算子-->GatherV3

-
- -详细说明如下： - -1. 使用计算类算子替换框架类算子。 - -2. 使用buffer记录index信息到npu，消除index.to\('npu'\) 的操作。 - -3. 使用计算类算子消除非连续。 - -4. contiguous\_with\_gatherv2是使用aicore算子GatherV2来完成非连续转连续操作。 - -5. 修改batchsize。 - -6. 修改batchsize + contiguous\_with\_gatherv2。 - -7. 由于concat算子的反向是chunk，会引起非连续问题，故自定义concat算子反向，使用Gatherv2替代chunk，将其融合成cat+shuffle+chunk，消除不连续。 - -8. ReluGrad算子有两个输入：grad\_output（反向的输入），self（正向的输出），在shufflenet中有时会出现4D + 5HD的场景，而FE的格式对齐往往对齐第一个tensor的format，结果就会导致\(4D, 5HD\)-\>\(4D, 4D\)-\>ReluGrad-\>4D-\>5HD。由于正向的输出格式基本就是输入格式，而relu往往是配合在Conv+BN+Relu这样使用，所以可以认为，在这个场景下，输出5HD是更合适的选择。于是手动插入npu\_format\_cast，\(4D, 5HD\)-\>\(5HD, 5HD\)-\>ReluGrad-\>5HD。 - -9. IndexSelectFullImplementation中涉及到了对一个5HD的tensor做两次gatherv2操作，这个时候会导致两次的5HD-\>4D，可以手动先做一次5HD-\>4D，这样就可以在gatherv2时不做transdata，从而消减一次transdata操作。 - -10. 加入混合精度O1。 - -11. 加入混合精度O2。 -12. 由于Axpy算子的参数校验，所有网络在参数更新时，如C不整除16则会transdata为4D进行Axpy运算，引入了大量的transdata算子，通过增加一个函数，当Axpy的input的shape一致时结束校验，从而避免了格式转换，增加了运行效率。 - -13. 删除所有的流同步操作，原因是容易导致不收敛，没有采纳。 - -14. 使用针对非对齐优化后的Gatherv2算子后，整体性能提速至交付水平。 - -15. 使用针对ShufflenetV2场景再次优化后的Gatherv3算子后，整体性能还能继续提升。 - -**Python侧优化细节** - -Python侧优化主要是通过一些同等语义的修改，使网络在NPU上边的更加亲和。当前非连续转连续容易成为性能瓶颈，而ShufflenetV2中的channel\_shuffle操作就涉及了permute后转连续的操作，导致整网性能在NPU上较差。通过对channel\_shuffle操作进行同等语义的修改，加上和concat操作的融合，使得整网性能得到飞升。采用的是torchvision版本参见[开源链接](https://github.com/pytorch/vision/blob/master/torchvision/models/shufflenetv2.py)。 - -- 框架原始channel\_shuffle操作。 - - ``` - def channel_shuffle(x, groups): - # type: (torch.Tensor, int) -> torch.Tensor - batchsize, num_channels, height, width = x.data.size() - channels_per_group = num_channels // groups - # reshape - x = x.view(batchsize, groups, - channels_per_group, height, width) - x = torch.transpose(x, 1, 2).contiguous() - # flatten - x = x.view(batchsize, -1, height, width) - return x - - class InvertedResidual(nn.Module): - def __init__(self, inp, oup, stride): - super(InvertedResidual, self).__init__() - if not (1 <= stride <= 3): - raise ValueError('illegal stride value') - self.stride = stride - branch_features = oup // 2 - assert (self.stride != 1) or (inp == branch_features << 1) - if self.stride > 1: - self.branch1 = nn.Sequential( - self.depthwise_conv(inp, inp, kernel_size=3, stride=self.stride, padding=1), - nn.BatchNorm2d(inp), - nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False), - nn.BatchNorm2d(branch_features), - nn.ReLU(inplace=True), - ) - else: - self.branch1 = nn.Sequential() - - self.branch2 = nn.Sequential( - nn.Conv2d(inp if (self.stride > 1) else branch_features, - branch_features, kernel_size=1, stride=1, padding=0, bias=False), - nn.BatchNorm2d(branch_features), - nn.ReLU(inplace=True), - self.depthwise_conv(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1), - nn.BatchNorm2d(branch_features), - nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False), - nn.BatchNorm2d(branch_features), - nn.ReLU(inplace=True), - ) - - @staticmethod - def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False): - return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i) - - def forward(self, x): - if self.stride == 1: - x1, x2 = x.chunk(2, dim=1) - out = torch.cat((x1, self.branch2(x2)), dim=1) - else: - out = torch.cat((self.branch1(x), self.branch2(x)), dim=1) - - out = channel_shuffle(out, 2) - - return out - ``` - -- 同等语义改写。 - - ``` - def channel_shuffle_index_select(x, groups=2): - N, C, H, W = x.shape - inp = C - # channel_shuffle操作是对C维按一定规则的重排的工作，可以被表达为一次简单的重排 - group_len = inp // groups - index = torch.from_numpy(np.array(list(range(inp))).reshape(groups, group_len).transpose(1, 0).flatten()).long() - - x = x.index_select(1, index) - return x - - # 对两个操作进行结果对比，可以看到语义是相等的 - x = torch.randn(2, 232, 14, 14) - for group in [2, 4, 8]: - out1 = channel_shuffle(x, group) - out2 = channel_shuffle_index_select(x, group) - print((out1 - out2).sum()) - ``` - -- 昇腾AI处理器亲和写法。 - - ``` - # 对应 out = channel_shuffle(torch.cat((self.branch1(x), self.branch2(x)), dim=1)) 的情形 - # 使用channel_shuffle_index_select替代channel_shuffle - # 自定义OP，融合channel_shuffle_index_select和cat，使用计算类算子来消减非连续 - class IndexSelectFullImplementation(torch.autograd.Function): - @staticmethod - def forward(ctx, x1, x2, fp_index, bp_index1, bp_index2): - # 强制流同步，仅稳定训练作用 - stream = torch.npu.current_stream() - stream.synchronize() - - # 对ctx注册bp_index1, bp_index2使反向时可以使用 - ctx.bp_index1 = bp_index1 - ctx.bp_index2 = bp_index2 - - x = torch.cat([x1, x2], dim=1) - - # 使用index_select替代channel_shuffle操作，这里是后面不接chunk算子的场景 - result = x.index_select(1, fp_index) - - return result - - @staticmethod - def backward(ctx, grad_output): - # 强制流同步，仅稳定训练作用 - stream = torch.npu.current_stream() - stream.synchronize() - - # 由于index_select不支持5HD格式，将格式转换为NCHW来减少额外的transdata - grad_output.data = grad_output.data.npu_format_cast(0) - - # 依据正向推导得到的反向的表达式，使用index_select同时完成对index_select和cat的反向 - out1 = grad_output.index_select(1, ctx.bp_index1) - out2 = grad_output.index_select(1, ctx.bp_index2) - return out1, out2, None, None, None, None - - - class IndexSelectHalfImplementation(torch.autograd.Function): - @staticmethod - def forward(ctx, x1, x2, fp_index1, fp_index2, bp_index1, bp_index2): - ctx.bp_index1 = bp_index1 - ctx.bp_index2 = bp_index2 - x = torch.cat([x1, x2], dim=1) - - # 使用index_select替代channel_shuffle操作，这里是后面接chunk算子的场景 - return x.index_select(1, fp_index1), x.index_select(1, fp_index2) - - @staticmethod - def backward(ctx, grad_output1, grad_output2): - grad_output = torch.cat([grad_output1, grad_output2], 1) - - out1 = grad_output.index_select(1, ctx.bp_index1) - out2 = grad_output.index_select(1, ctx.bp_index2) - return out1, out2, None, None, None, None - - - class Channel_Shuffle(nn.Module): - def __init__(self, inp, groups=2, split_shuffle=True): - super(Channel_Shuffle, self).__init__() - - self.split_shuffle = split_shuffle - self.group_len = inp // groups - - # 初始化channel_shuffle_index_select中需要使用的fp_index - self.out = np.array(list(range(inp))).reshape(groups, self.group_len).transpose(1, 0).flatten().tolist() - - # 将初始化的fp_index按需注册为module的buffer，在to.device的时候顺路带到设备，减少h2dcopy的耗时 - # 此处仅展示常用的group=2的场景下的使用方式，其他情形请自行拓展 - if self.split_shuffle: - self.register_buffer('fp_index1', torch.tensor(self.out[:self.group_len], dtype=torch.int32)) - self.register_buffer('fp_index2', torch.tensor(self.out[self.group_len:], dtype=torch.int32)) - else: - self.register_buffer('fp_index', torch.tensor(self.out, dtype=torch.int32)) - - # 将对应的bp_index按需注册为module的buffer，在to.device的时候顺路带到设备，减少h2dcopy的耗时 - self.register_buffer('bp_index1', torch.tensor(list(range(0, inp, 2)), dtype=torch.int32)) - self.register_buffer('bp_index2', torch.tensor(list(range(1, inp, 2)), dtype=torch.int32)) - - def forward(self, x1, x2): - if self.split_shuffle: - return IndexSelectHalfImplementation.apply(x1, x2, self.fp_index1, self.fp_index2, self.bp_index1, - self.bp_index2) - else: - return IndexSelectFullImplementation.apply(x1, x2, self.fp_index, self.bp_index1, self.bp_index2) - - - class InvertedResidual(nn.Module): - def __init__(self, inp, oup, stride, split_shuffle=True): - super(InvertedResidual, self).__init__() - - if not (1 <= stride <= 3): - raise ValueError('illegal stride value') - self.stride = stride - - branch_features = oup // 2 - assert (self.stride != 1) or (inp == branch_features << 1) - - if self.stride > 1: - self.branch1 = nn.Sequential( - self.depthwise_conv(inp, inp, kernel_size=3, stride=self.stride, padding=1), - nn.BatchNorm2d(inp), - nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False), - nn.BatchNorm2d(branch_features), - nn.ReLU(inplace=True), - ) - else: - self.branch1 = nn.Sequential() - - self.branch2 = nn.Sequential( - nn.Conv2d(inp if (self.stride > 1) else branch_features, - branch_features, kernel_size=1, stride=1, padding=0, bias=False), - nn.BatchNorm2d(branch_features), - nn.ReLU(inplace=True), - self.depthwise_conv(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1), - nn.BatchNorm2d(branch_features), - nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False), - nn.BatchNorm2d(branch_features), - nn.ReLU(inplace=True), - ) - - if self.stride > 1: - self.channel_shuffle = Channel_Shuffle(inp=branch_features + branch_features, groups=2, - split_shuffle=split_shuffle) - else: - self.channel_shuffle = Channel_Shuffle(inp=inp, groups=2, split_shuffle=split_shuffle) - - @staticmethod - def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False): - return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i) - - def forward(self, x): - - # 删除concat和chunk操作，融合进self.channel_shuffle内处理 - if self.stride == 1: - x1, x2 = x - x2 = self.branch2(x2) - else: - x1 = self.branch1(x) - x2 = self.branch2(x) - - out = self.channel_shuffle(x1, x2) - - return out - ``` - - -

参考信息

- -- **[单算子样例编写说明](#单算子样例编写说明md)** - -- **[单算子dump方法](#单算子dump方法md)** - -- **[常用环境变量说明](#常用环境变量说明md)** - -- **[dump op方法](#dump-op方法md)** - -- **[编译选项设置](#编译选项设置md)** - -- **[安装7.3.0版本gcc](#安装7-3-0版本gccmd)** - -- **[编译安装hdf5](#编译安装hdf5md)** - - -

单算子样例编写说明

- -在模型中遇到问题时，使用整网复现问题成本较大，可以构建测试用例来复现精度或性能问题，便于定位解决。构建测试用例一般有如下两种方式。单算子dump方法请参见[单算子dump方法](#单算子dump方法md)。 - -1. 单算子测试用例构建，直接调用该算子即可复现错误场景。 - - 例如构建max算子的单算子样例如下： - - ``` - import torch - import copy - from torch.testing._internal.common_utils import TestCase, run_tests - class TestMax(TestCase): - def cpu_op_exec(self, input1): - # 调用算子 - output = torch.max(input1) - output = output.to('cpu') - output = output.numpy() - return output - - def npu_op_exec(self, input1): - # 调用对应npu算子 - output = torch.max(input1) - return output - - def test_max(self): - input = torch.randn(10,20)) - input = input.to(torch.int64) # 数据dtype转换 - input_cpu = copy.deepcopy(input) - input_npu = copy.deepcopy(input).npu() - - output_cpu = self.cpu_op_exec(input_cpu) - output_npu = self.npu_op_exec(input_npu) - - # 比较cpu和npu的计算结果，prec为允许误差 - self.assertEqual(output_cpu, output_npu, prec = 1e-4) - - if __name__ == '__main__': - run_tests() - ``` - - >![](public_sys-resources/icon-note.gif) **说明：** - >- 运行上述代码，如果发现报错信息与模型中的max算子报错信息相同，则说明单算子测试用例构建成功。 - >- 假设注释掉输入数据dtype转换代码，发现测试用例无报错，则可以说明在输入参数为torch.int64时，max算子在npu上报错。 - -2. 基于上下文的单算子测试用例构建。 - - 这里虽然是单算子样例，但有时候不仅仅为一个操作，而是带有上下文的场景，还有时候是一个带参数Module，Module的方式是更通用的方法。此处构建一个包含两个OP的Module，构建样例如下： - - ``` - import torch - import copy - from torch.testing._internal.common_utils import TestCase, run_tests - - class Model(nn.Module): - def __init__(self, in_channels=1, hooks=False): - super(Model, self).__init__() - self.conv = nn.Conv2d(in_channels, in_channels*2, kernel_size=64) - if hooks: - self.conv.weight.register_hook(lambda grad: print(grad)) - def forward(self, x): - out = self.conv(x) - return out - - class TestConv2d(TestCase): - def test_conv2d(self): - - model = Model(in_channels=16) - - # 若需要获取反向计算结果，则加入hooks获取反向即可 - # model = Model(in_channels=16, hooks=True) - # 创建输入tensor - input_tensor = torch.randn(4,16,64,64) - - input_tensor_cpu= copy.deepcopy(input_tensor) - out = model(input_tensor_cpu) - loss = out.sum() - loss.backward() - cpuout = out - - # 3 to NPU 运行，将model和input_tensor放到NPU运行 - torch.npu.set_device("npu:0") # 一般先set_device设定运行卡 - model_npu = Model(in_channels=16).npu() - input_tensor_npu= copy.deepcopy(input_tensor).npu() - out = model_npu(input_tensor_npu) - loss = out.sum() - loss.backward() - npuout = out - #根据结果，确定是否为报错场景 - self.assertEqual(cpuout, npuout, prec = 1e-4) - - if __name__ == '__main__': - run_tests() - ``` - - -

单算子dump方法

- -#### 采集Dump数据 - -当前适配昇腾AI处理器的PyTorch通过torch.npu中的init\_dump\(\)、set\_dump\(\)和finalize\_dump接口来进行算子dump数据的采集。首先init\_dump\(\)会进行初始化dump配置，然后通过set\_dump\(\)接口通过传入配置文件来配置dump参数，最后通过finalize\_dump来结束dump。以下以add\_算子为例，介绍算子dump数据采集方法。 - -``` -import torch -torch.npu.set_device("npu:0") -torch.npu.init_dump() -torch.npu.set_dump("/home/HwHiAiUser/dump.json") # "/home/HwHiAiUser/dump.json"为配置文件路径，用户自行配置 -a = torch.tensor([2, 2]).to("npu:0") -a.add_(1) -torch.npu.finalize_dump() -``` - -其中**dump.json**配置方法如下。 - -``` -{ - "dump": - { - "dump_list":[], - "dump_path":"/home/HwHiAiUser/dump/output", - "dump_mode":"all", - "dump_op_switch":"on" - } -``` - -**dump.json**字段解释如下。 - - - - - - - - - - - - - - - - - - - -

字段名

-

说明

-

dump_list

-

待dump数据的算子模型。为空，无需配置。

-

dump_path

-

dump数据文件存储到运行环境的目录，支持配置绝对路径或相对路径：

-
  • 绝对路径配置以“/”开头，例如：/home/HwHiAiUser/output。
  • 相对路径配置直接以目录名开始，例如：output。
-

例如：dump_path配置为/home/HwHiAiUser/output，则dump数据文件存储到运行环境的/home/HwHiAiUser/output目录下。

-

dump_mode

-

dump数据模式，配置如下。

-
  • output：dump算子的输出数据，默认取值output。
  • input：dump算子的输入数据。
  • all：dump算子的输入、输出数据。
-

dump_op_switch

-

单算子模型dump数据开关，配置如下。

-
  • off：关闭单算子模型dump，默认取值off。
-
  • on：开启单算子模型dump。
-
- -#### 查看溢出数据 - -采集的dump数据会在\{dump\_path\}/\{time\}/\{deviceid\}/\{model\_id\}/\{data\_index\}目录下生成例如：“/home/HwHiAiUser/output/20200808163566/0/0”目录。 - -存放路径及文件命名规则： - -- dump\_path：用户配置的溢出数据存放路径，例如/home/HwHiAiUser/output。 - -- time：时间戳，例如20200808163566。 -- deviceid：Device设备ID号。 -- model\_id：子图ID。 -- dump文件：命名规则如\{op\_type\}.\{op\_name\}.\{taskid\}.\{stream\_id\}.\{timestamp\}，如果op\_type、op\_name出现了“.”、“/”、“\\”、空格时，会转换为下划线表示。 - -#### 解析溢出算子的dump文件 - -1. 请根据实际情况，将\{op\_type\}.\{op\_name\}.\{taskid\}.\{stream\_id\}.\{timestamp\}上传到安装有CANN软件包的环境。 -2. 进入解析脚本所在路径，假设CANN软件包安装目录为：/home/HwHiAiUser/Ascend。 - - **cd /home/HwHiAiUser/Ascend/ascend-toolkit/latest/toolkit/tools/operator\_cmp/compare** - -3. 执行msaccucmp.pyc脚本，转换dump文件为numpy文件。举例： - - **python3 msaccucmp.pyc convert -d /home/HwHiAiUser/dump -out /home/HwHiAiUser/dumptonumpy -v 2** - - >![](public_sys-resources/icon-note.gif) **说明：** - >-d参数支持传入单个文件，对单个dump文件进行转换，也支持传入目录，对整个path下所有的dump文件进行转换。 - -4. 调用Python，转换numpy文件为txt文件。举例： - - **$ python3** - - **\>\>\> import numpy as np** - - **\>\>\> a = np.load\("/home/HwHiAiUser/dumptonumpy/Pooling.pool1.1147.1589195081588018.output.0.npy"\)** - - **\>\>\> b = a.flatten\(\)** - - **\>\>\> np.savetxt\("/home/HwHiAiUser/dumptonumpy/Pooling.pool1.1147.1589195081588018.output.0.txt", b\)** - - 转换为.txt格式文件后，维度信息、Dtype均不存在。详细的使用方法请参考numpy官网介绍。 - - -

常用环境变量说明

- -1. 开启TASK多线程下发，绝大多数情况下，打开该功能会进一步提升整网训练性能。 - - **export TASK\_QUEUE\_ENABLE=1** - -2. 开启重定向日志到stdout，用于导出host日志到屏幕。 - - **export ASCEND\_SLOG\_PRINT\_TO\_STDOUT=0** - -3. 设置日志级别，日志级别设置，信息从多到少分别是 debug --\> info --\> warning --\> error --\> null，一般设置为error，调试时使用info。请参考《CANN 日志参考》设置日志级别。 - - **export ASCEND\_GLOBAL\_LOG\_LEVEL=3** - -4. dump图，主要用于查看图结构。 - - **export DUMP\_GE\_GRAPH=2** - - **export DUMP\_GRAPH\_LEVEL=3** - -5. 设置Event日志开启标志。 - - **export ASCEND\_GLOBAL\_EVENT\_ENABLE=0** - -6. 设置是否开启PTCopy。 - - **export PTCOPY\_ENABLE=1** - -7. 设置是否开启combined标志。 - - **export COMBINED\_ENABLE=1** - -8. 设置特殊场景是否需要重新编译,不需要修改。 - - **export DYNAMIC\_OP="ADD\#MUL"** - -9. HCCL白名单开关。 - - **export HCCL\_WHITELIST\_DISABLE=1** - - -

dump op方法

- -1. 使用profile接口对原始代码训练脚本的loss计算和优化过程进行改造，打印算子信息。代码样例如下： - - ``` - with torch.autograd.profiler.profile() as prof: - out = model(input_tensor) - loss = out.sum() - loss.backward() - # 也可导出文件 - print(prof.key_averages().table(sort_by="self_cpu_time_total")) - ``` - -2. 将改造后的训练脚本在CPU上进行训练，屏幕会打印相关算子信息。 - -

编译选项设置

- -用于配置算子编译过程中的属性，可用于提升性能，通过ACL接口实现。用法及解释如下： - -``` -import torch -option = {key: val} -torch.npu.set_option(option) # 以dict方式进行设置 - -其中key可选值和对应的含义如下： -ACL_OP_SELECT_IMPL_MODE, //选择算子是高精度实现还是高性能实现 -ACL_OPTYPELIST_FOR_IMPLMODE, //列举算子类型的列表，该列表中的算子使用ACL_OP_SELECT_IMPL_MODE指定的模式 -ACL_OP_DEBUG_LEVEL, //TBE算子编译debug功能开关 -ACL_DEBUG_DIR, //保存模型转换、网络迁移过程中算子编译生成的调试相关过程文件的路径，包括算子.o/.json/.cce等文件。路径必须已经存在。 -ACL_OP_COMPILER_CACHE_MODE, //算子编译磁盘缓存模式 -ACL_OP_COMPILER_CACHE_DIR, //算子编译磁盘缓存的路径，路径必须已经存在。 - -key对应的val值解释和可设置值如下： -ACL_OP_SELECT_IMPL_MODE： 用于选择算子是高精度实现还是高性能实现。如果不配置该编译选项，默认采用high_precision。 - high_precision：表示网络模型中所有算子选择高精度实现。 - high_performance：表示网络模型中所有算子选择高性能实现。 - -ACL_OPTYPELIST_FOR_IMPLMODE：设置optype列表中算子的实现方式，该参数当前仅支持设置某个具体算子的实现方式，不支持设置多个算子。当前仅支持配置的算子为Pooling、SoftmaxV2、LRN、ROIAlign。算子类型的列表中的算子使用ACL_OP_SELECT_IMPL_MODE指定的模式。 - -ACL_OP_DEBUG_LEVEL：用于配置TBE算子编译debug功能开关。 - 0：不开启算子debug功能。在执行atc命令当前路径算子编译生成的kernel_meta文件夹中不保留.o（算子二进制文件）和.json文件（算子描述文件）。 - 1：开启算子debug功能，在执行atc命令当前路径算子编译生成的kernel_meta文件夹中生成TBE指令映射文件（算子cce文件*.cce和python-cce映射文件*_loc.json），用于后续工具进行AICore Error问题定位。 - 2：开启算子debug功能，在执行atc命令当前路径算子编译生成的kernel_meta文件夹中生成TBE指令映射文件（算子cce文件*.cce和python-cce映射文件*_loc.json），并关闭编译优化开关并且开启ccec调试功能（ccec编译器选项设置为-O0-g），用于后续工具进行AICore Error问题定位。 - 3：不开启算子debug功能，在执行atc命令当前路径算子编译生成的kernel_meta文件夹中保留.o（算子二进制文件）和.json文件（算子描述文件）。 - 4：不开启算子debug功能，在执行atc命令当前路径算子编译生成的kernel_meta文件夹中保留.o（算子二进制文件）和.json文件（算子描述文件），生成TBE指令映射文件（算子cce文件*.cce）和UB融合计算描述文件（{$kernel_name}_compute.json）。 - -ACL_DEBUG_DIR：用于配置保存模型转换、网络迁移过程中算子编译生成的调试相关过程文件的路径，包括算子.o/.json/.cce等文件。 - -ACL_OP_COMPILER_CACHE_MODE：用于配置算子编译磁盘缓存模式。该编译选项需要与ACL_OP_COMPILER_CACHE_DIR配合使用。 - enable：表示启用算子编译缓存。 - disable：表示禁用算子编译缓存。 - force：表示强制刷新缓存，即先删除已有缓存，再重新编译并加入缓存。当用户的python或者依赖库等发生变化时，需要指定为force用于清理已有的缓存。 - -ACL_OP_COMPILER_CACHE_DIR：用于配置算子编译磁盘缓存的目录。该编译选项需要与ACL_OP_COMPILER_CACHE_MODE配合使用。 -``` - -

安装7.3.0版本gcc

- -以下步骤请在root用户下执行。 - -1. 下载gcc-7.3.0.tar.gz，下载地址为[https://mirrors.tuna.tsinghua.edu.cn/gnu/gcc/gcc-7.3.0/gcc-7.3.0.tar.gz](https://mirrors.tuna.tsinghua.edu.cn/gnu/gcc/gcc-7.3.0/gcc-7.3.0.tar.gz)。 -2. 安装gcc时候会占用大量临时空间，所以先执行下面的命令清空/tmp目录： - - ``` - sudo rm -rf /tmp/* - ``` - -3. 安装依赖。 - - centos/bclinux执行如下命令安装。 - - ``` - yum install bzip2 - ``` - - ubuntu/debian执行如下命令安装。 - - ``` - apt-get install bzip2 - ``` - -4. 编译安装gcc。 - 1. 进入gcc-7.3.0.tar.gz源码包所在目录，解压源码包，命令为： - - ``` - tar -zxvf gcc-7.3.0.tar.gz - ``` - - 2. 进入解压后的文件夹，执行如下命令下载gcc依赖包： - - ``` - cd gcc-7.3.0 - ./contrib/download_prerequisites - ``` - - 如果执行上述命令报错，需要执行如下命令在“gcc-7.3.0/“文件夹下下载依赖包： - - ``` - wget http://gcc.gnu.org/pub/gcc/infrastructure/gmp-6.1.0.tar.bz2 - wget http://gcc.gnu.org/pub/gcc/infrastructure/mpfr-3.1.4.tar.bz2 - wget http://gcc.gnu.org/pub/gcc/infrastructure/mpc-1.0.3.tar.gz - wget http://gcc.gnu.org/pub/gcc/infrastructure/isl-0.16.1.tar.bz2 - ``` - - 下载好上述依赖包后，重新执行以下命令： - - ``` - ./contrib/download_prerequisites - ``` - - 如果上述命令校验失败，需要确保依赖包为一次性下载成功，无重复下载现象。 - - 3. 执行配置、编译和安装命令： - - ``` - ./configure --enable-languages=c,c++ --disable-multilib --with-system-zlib --prefix=/usr/local/linux_gcc7.3.0 - make -j15 # 通过grep -w processor /proc/cpuinfo|wc -l查看cpu数，示例为15，用户可自行设置相应参数。 - make install - ``` - - >![](public_sys-resources/icon-caution.gif) **注意：** - >其中“--prefix“参数用于指定linux\_gcc7.3.0安装路径，用户可自行配置，但注意不要配置为“/usr/local“及“/usr“，因为会与系统使用软件源默认安装的gcc相冲突，导致系统原始gcc编译环境被破坏。示例指定为“/usr/local/linux\_gcc7.3.0“。 - - -5. 配置环境变量。 - - 当用户执行训练时，需要用到gcc升级后的编译环境，因此要在训练脚本中配置环境变量，通过如下命令配置。 - - ``` - export LD_LIBRARY_PATH=${install_path}/lib64:${LD_LIBRARY_PATH} - ``` - - 其中$\{install\_path\}为[3.](#zh-cn_topic_0000001173199577_zh-cn_topic_0000001172534867_zh-cn_topic_0276688294_li1649343041310)中配置的gcc7.3.0安装路径，本示例为“/usr/local/gcc7.3.0/“。 - - >![](public_sys-resources/icon-note.gif) **说明：** - >本步骤为用户在需要用到gcc升级后的编译环境时才配置环境变量。 - - -

编译安装hdf5

- -以下步骤请在root用户下执行。 - -1. 获取代码。 - - ``` - git clone https://github.com/HDFGroup/hdf5.git - ``` - -2. 切换到hdf5-1\_10\_7分支。 - - ``` - cd hdf5 - git checkout remotes/origin/hdf5_1_10_7 - ``` - -3. 编译hdf5。 - - ``` - ./configure --prefix=/usr/local/hdf5 --enable-cxx - make -j72 #-j 后的数值可以根据CPU的核数设置 - make check # run test suite. - make install - make check-install # verify installation. - ``` - -4. 添加环境变量。 - - ``` - export PATH=/usr/local/hdf5/bin:$PATH - export LD_LIBRARY_PATH=/usr/local/hdf5/lib:$LD_LIBRARY_PATH - export LIBRARY_PATH=/usr/local/hdf5/lib:$LIBRARY_PATH - export CPATH=/usr/local/hdf5/include:$CPATH - ``` - - -

FAQ

- -- **[软件安装常见问题](#软件安装常见问题md)** - -- **[模型和算子运行常见问题](#模型和算子运行常见问题md)** - -- **[模型调测常见问题](#模型调测常见问题md)** - -- **[其他操作相关问题](#其他操作相关问题md)** - -- **[模型分布式训练常见问题](#模型分布式训练常见问题md)** - - -

软件安装常见问题

- -- **[pip3.7 install Pillow==5.3.0安装失败](#pip3-7-install-Pillow-5-3-0安装失败md)** - - -

pip3.7 install Pillow==5.3.0安装失败

- -**现象描述** - -pip3.7 install pillow==5.3.0安装失败。 - -**可能原因** - -缺少必要的依赖，如：libjpeg、python-devel、 zlib-devel 、libjpeg-turbo-devel等等。 - -**处理方法** - -安装相关依赖，通过如下命令安装： - -- CentOS/EulerOS/Tlinux/BClinux/Suse - - **yum install libjpeg python-devel zlib-devel libjpeg-turbo-devel** - -- Ubuntu/Debian/UOS - - **apt-get install libjpeg python-devel zlib-devel libjpeg-turbo-devel** - - -

模型和算子运行常见问题

- -- **[在模型运行或者算子运行时遇到报错“RuntimeError: ExchangeDevice:”](#在模型运行或者算子运行时遇到报错-RuntimeError-ExchangeDevicemd)** - -- **[在模型运行或者算子运行时遇到报错“Error in atexit.\_run\_exitfuncs:”](#在模型运行或者算子运行时遇到报错-Error-in-atexit-_run_exitfuncsmd)** - -- **[在模型运行时遇到报错“terminate called after throwing an instance of 'c10::Error' what\(\): HelpACLExecute:”](#在模型运行时遇到报错-terminate-called-after-throwing-an-instance-of-c10-Error-what-HelpACLExecutemd)** - -- **[在模型运行时遇到报错“terminate called after throwing an instance of 'c10::Error' what\(\): 0 INTERNAL ASSERT”](#在模型运行时遇到报错-terminate-called-after-throwing-an-instance-of-c10-Error-what-0-INTERNAL-ASSERTmd)** - -- **[在模型运行时遇到报错“ImportError: libhccl.so.”](#在模型运行时遇到报错-ImportError-libhccl-somd)** - -- **[在模型运行时遇到报错“RuntimeError: Initialize.”](#在模型运行时遇到报错-RuntimeError-Initializemd)** - -- **[在模型运行时遇到报错“TVM/te/cce error.”](#在模型运行时遇到报错-TVM-te-cce-errormd)** - -- **[在模型运行时遇到报错“MemCopySync:drvMemcpy failed.”](#在模型运行时遇到报错-MemCopySync-drvMemcpy-failedmd)** - -- **[在模型运行时遇到报错“MemCopySync:drvMemcpy failed.”](#在模型运行时遇到报错-MemCopySync-drvMemcpy-failed-6md)** - -- **[在模型运行时将多任务下发关闭\(export TASK\_QUEUE\_ENABLE=0\)后仍然遇到报错“HelpACLExecute.”](#在模型运行时将多任务下发关闭export-TASK_QUEUE_ENABLE-0后仍然遇到报错-HelpACLExecutemd)** - -- **[在模型运行时遇到报错“55056 GetInputConstDataOut: ErrorNo: -1\(failed\)”](#在模型运行时遇到报错-55056-GetInputConstDataOut-ErrorNo--1failedmd)** - - -

在模型运行或者算子运行时遇到报错“RuntimeError: ExchangeDevice:”

- -**现象描述** - -![](figures/FAQ1.png) - -**可能原因** - -目前在一个线程内，只能调用一个NPU设备，当切换不同的npu device时，出现上述错误。 - -**处理方法** - -检查代码中在调用torch.npu.set\_device\(device\)、tensor.to\(device\)或者model.to\(device\)时，同一个线程内前后调用时device名称不一致。对于多个线程情况（如多卡训练），每个线程同样只能调用固定的npu device。 - -

在模型运行或者算子运行时遇到报错“Error in atexit.\_run\_exitfuncs:”

- -**现象描述** - -![](figures/FAQ2.png) - -**可能原因** - -在torch初始化时，若未通过torch.npu.device\(id\)指定npu设备，则默认使用device 0设备。若直接使用其他NPU设备，如指定在device 1上创建tensor，那么在运行时会出现上述错误。 - -**处理方法** - -在调用NPU设备之前，通过torch.npu.set\_device\(device\)指定需要使用的NPU设备即可。 - -

在模型运行时遇到报错“terminate called after throwing an instance of 'c10::Error' what\(\): HelpACLExecute:”

- -**现象描述** - -![](figures/FAQ3.png) - -**可能原因** - -目前HelpACLExecute的报错信息无法直接找到报错位置，此处在task任务下发时报错，是由于开启了TASK多线程下发（export TASK\_QUEUE\_ENABLE=1），上层封装了报错信息，导致无法获取更加详细的报错日志。 - -**处理方法** - -可通过如下两种方式处理： - -- 查看具体的host报错日志信息。日志默认路径为/var/log/npu/slog/host-0/，根据时间标识查找以host-0为前缀的日志文件，打开日志文件，搜索“ERROR”，查询具体的报错信息。 -- 关闭多线程下发\(export TASK\_QUEUE\_ENABLE=0\)，再次运行代码，一般可根据终端报错信息定位错误原因。 - -

在模型运行时遇到报错“terminate called after throwing an instance of 'c10::Error' what\(\): 0 INTERNAL ASSERT”

- -**现象描述** - -``` -import torch - -npu = "npu" - -def test_cpu(): - input = torch.randn(2000, 1000).detach().requires_grad_() - output = torch.sum(input) - output.backward(torch.ones_like(output)) - -def test_npu(): - input = torch.randn(2000, 1000).detach().requires_grad_().npu() - output = torch.sum(input) - output.backward(torch.ones_like(output)) - -if __name__ == "__main__": - test_cpu() - torch.npu.set_device(f"{npu}:1") - test_npu() -``` - -执行代码后出现如下报错。 - -![](figures/zh-cn_image_0000001208897433.png) - -**可能原因** - -在运行backward运算后，通过set\_decice\(\)方法手动设置device设备，导致报错。在运行backward运算时，若没有设置device，程序会自动默认初始化device为0，相当于执行了set\_device\("npu:0"\)。由于目前不支持切换device进行计算，若再通过set\_decice\(\)方法手动设置device设备，则可能出现该错误。 - -**处理方法** - -在运行backward运算前，通过set\_decice\(\)方法手动设置device。修改如下。 - -``` -if __name__ == "__main__": - torch.npu.set_device(f"{npu}:1") - test_cpu() - test_npu() -``` - -

在模型运行时遇到报错“ImportError: libhccl.so.”

- -**现象描述** - -![](figures/FAQ7.png) - -**可能原因** - -目前对外发布的pytorch安装包，默认使用NPU和HCCL功能，因此在调用时需要将HCCL模块路径添加到环境变量中。根据报错信息“can not find libhccl.so”，出现上述错误原因为缺少hccl库文件。 - -**处理方法** - -将hccl模块的路径添加到环境变量中，一般情况下hccl库文件路径为安装包下的.../fwkacllib/python/site-packages/hccl。 - -

在模型运行时遇到报错“RuntimeError: Initialize.”

- -**现象描述** - -![](figures/FAQ9.png) - -**可能原因** - -根据报错信息，初步判断为npu设备初始化错误。进一步查找host日志报错信息如下： - -![](figures/FAQ9-1.png) - -根据日志信息定位报错原因为系统在拉起npu设备时报错。 - -**处理方法** - -可通过以下步骤解决该问题。 - -1. 重启服务器和所有npu device。 - - 如果问题解决，处理完毕。 - - 如果问题未解决，请执行[2](#li77121667913)。 - -2. 检查安装的driver和firmware版本是否匹配。 - - 如果不匹配，请执行[3](#li967615545918)。 - - 如果匹配，请执行[4](#li475615212912)。 - -3. 更换正确版本的driver和firmware。 - - 如果问题解决，处理完毕。 - - 如果问题未解决，执行步骤四 - -4. 联系华为工程师。 - -

在模型运行时遇到报错“TVM/te/cce error.”

- -**现象描述** - -![](figures/FAQ10.png) - -**可能原因** - -pytorch内调用npu类型算子时，强依赖于te、cce、tvm组件，pytorch、CANN/nnae和te版本需要一致。在更新CANN/nnae后，te等组件不会自动更新，当版本不匹配时，则会出现该报错。 - -**处理方法** - -更新te等组件版本，具体需要更新te-_\*.whl和topi-\*_.whl安装包。在安装的CANN或者nnae的lib64子目录下（以root安装用户为例：默认安装路径在/usr/local/Ascend/ascend-toolkit/latest/lib64目录下，更新安装包即可。在该目录下有安装包topi-0.4.0-py3-none-any.whl和te-0.4.0-py3-none-any.whl，分别执行pip3 install --upgrade topi-0.4.0-py3-none-any.whl，pip install --upgrade te-0.4.0-py3-none-any.whl。 - -![](figures/FAQ10-1.png) - -

在模型运行时遇到报错“MemCopySync:drvMemcpy failed.”

- -**现象描述** - -脚本： - -``` - import torch - - def test_sum(): - xs_shape = [22400, 8] - ys_shape = [22400, 8] - gt_bboxes_shape = [22400, 8,4] - xs = torch.rand(xs_shape).npu() - ys = torch.rand(ys_shape).npu() - gt_bboxes = torch.rand(gt_bboxes_shape).npu().half() - left = xs - gt_bboxes[..., 0] - right = gt_bboxes[..., 2] - xs - top = ys - gt_bboxes[..., 1] - bottom = gt_bboxes[..., 3] - ys - # stream = torch.npu.current_stream() - # stream.synchronize() - # left, top 结果是fp32， right, bottom 结果是fp16， - # print(left.dtype, top.dtype, right.dtype, bottom.dtype) - bbox_targets = torch.stack((left, top, right, bottom), -1) #报错位置在这里 - # stream.synchronize() - - bbox_targets = torch.sum(bbox_targets) -``` - -shell报错信息： - -``` - RuntimeError: Run:/usr1/workspace/PyTorch_Apex_Daily_c20tr5/CODE/aten/src/ATen/native/npu/utils/OpParamMaker.h:280 NPU error,NPU error code is:500002 - [ERROR] RUNTIME(160809)kernel task happen error, retCode=0x28, [aicpu timeout]. - [ERROR] RUNTIME(160809)aicpu kernel execute failed, device_id=0, stream_id=512, task_id=24, fault so_name=, fault kernel_name=, extend_info=. - Error in atexit._run_exitfuncs: - Traceback (most recent call last): - File "/usr/local/python3.7.5/lib/python3.7/site-packages/torch/__init__.py", line 429, in _npu_shutdown - torch._C._npu_shutdown() - RuntimeError: npuSynchronizeDevice:/usr1/workspace/PyTorch_Apex_Daily_c20tr5/CODE/c10/npu/NPUStream.cpp:806 NPU error, error code is 0 -``` - -日志信息： - -``` - [ERROR] RUNTIME(12731,python3.7):2021-02-02-22:23:56.475.679 [../../../../../../runtime/feature/src/npu_driver.cc:1408]12828 MemCopySync:drvMemcpy failed: dst=0x108040288000, destMax=1240, src=0x7fe7649556d0, size=1240, kind=1, drvRetCode=17! - [ERROR] RUNTIME(12731,python3.7):2021-02-02-22:23:56.475.698 [../../../../../../runtime/feature/src/logger.cc:113]12828 KernelLaunch:launch kernel failed, kernel=140631803535760/ArgMinWithValue_tvmbin, dim=32, stream=0x55b22b3def50 - [ERROR] RUNTIME(12731,python3.7):2021-02-02-22:23:56.475.717 [../../../../../../runtime/feature/src/api_c.cc:224]12828 rtKernelLaunch:ErrCode=207001, desc=[module new memory error], InnerCode=0x70a0002 -``` - -**可能原因** - -根据shell和日志报错信息，两者报错信息不匹配。 - -shell报错是在同步操作中和AI CPU错误，而日志报错信息却是在min算子（内部调用ArgMinWithValue\_tvmbin），二者报错信息不对应。一般这类问题出现的原因是由于日志生成的报错信息滞后。 - -报错信息滞后可能是由于AI CPU算子的异步执行，导致报错信息滞后。 - -**处理方法** - -对于该报错需要根据实际的错误来定位，可参考如下步骤进行处理： - -1. 通过关闭多任务算子下发后发现结果不变，推断在shell脚本报错位置和日志报错算子之前就已出现错误。 -2. 根据报错加上stream同步操作，缩小错误范围，定位错误算子。stream同步操作的作用在于其要求代码所运行到的位置之前的所有计算必须为完成状态，从而定位错误位置。 -3. 通过在代码中加上stream同步操作，确定报错算子为stack。 -4. 打印stack所有参数的shape、dtype、npu\_format，通过构造单算子用例复现问题。定位到问题原因为减法计算输入参数数据类型不同，导致a-b和b-a结果的数据类型不一致，最终在stack算子中报错。 -5. 将stack入参数据类型转换为一致即可临时规避问题。 - -

在模型运行时遇到报错“MemCopySync:drvMemcpy failed.”

- -**现象描述** - -脚本： - -``` - import torch - - def test_sum(): - xs_shape = [22400, 8] - ys_shape = [22400, 8] - gt_bboxes_shape = [22400, 8,4] - xs = torch.rand(xs_shape).npu() - ys = torch.rand(ys_shape).npu() - gt_bboxes = torch.rand(gt_bboxes_shape).npu().half() - left = xs - gt_bboxes[..., 0] - right = gt_bboxes[..., 2] - xs - top = ys - gt_bboxes[..., 1] - bottom = gt_bboxes[..., 3] - ys - # stream = torch.npu.current_stream() - # stream.synchronize() - # left, top 结果是fp32， right, bottom 结果是fp16， - # print(left.dtype, top.dtype, right.dtype, bottom.dtype) - bbox_targets = torch.stack((left, top, right, bottom), -1) #报错位置在这里 - # stream.synchronize() - - bbox_targets = torch.sum(bbox_targets) -``` - -shell报错信息： - -``` - RuntimeError: Run:/usr1/workspace/PyTorch_Apex_Daily_c20tr5/CODE/aten/src/ATen/native/npu/utils/OpParamMaker.h:280 NPU error,NPU error code is:500002 - [ERROR] RUNTIME(160809)kernel task happen error, retCode=0x28, [aicpu timeout]. - [ERROR] RUNTIME(160809)aicpu kernel execute failed, device_id=0, stream_id=512, task_id=24, fault so_name=, fault kernel_name=, extend_info=. - Error in atexit._run_exitfuncs: - Traceback (most recent call last): - File "/usr/local/python3.7.5/lib/python3.7/site-packages/torch/__init__.py", line 429, in _npu_shutdown - torch._C._npu_shutdown() - RuntimeError: npuSynchronizeDevice:/usr1/workspace/PyTorch_Apex_Daily_c20tr5/CODE/c10/npu/NPUStream.cpp:806 NPU error, error code is 0 -``` - -日志信息： - -``` - [ERROR] RUNTIME(12731,python3.7):2021-02-02-22:23:56.475.679 [../../../../../../runtime/feature/src/npu_driver.cc:1408]12828 MemCopySync:drvMemcpy failed: dst=0x108040288000, destMax=1240, src=0x7fe7649556d0, size=1240, kind=1, drvRetCode=17! - [ERROR] RUNTIME(12731,python3.7):2021-02-02-22:23:56.475.698 [../../../../../../runtime/feature/src/logger.cc:113]12828 KernelLaunch:launch kernel failed, kernel=140631803535760/ArgMinWithValue_tvmbin, dim=32, stream=0x55b22b3def50 - [ERROR] RUNTIME(12731,python3.7):2021-02-02-22:23:56.475.717 [../../../../../../runtime/feature/src/api_c.cc:224]12828 rtKernelLaunch:ErrCode=207001, desc=[module new memory error], InnerCode=0x70a0002 -``` - -**可能原因** - -根据shell和日志报错信息，两者报错信息不匹配。 - -shell报错是在同步操作中和ai cpu错误，而日志报错信息却是在min算子（内部调用ArgMinWithValue\_tvmbin），二者报错信息不对应。一般这类问题出现的原因是由于日志生成的报错信息滞后。 - -报错信息滞后可能是由于AI cpu算子的异步执行，导致报错信息滞后。 - -**处理方法** - -对于该报错需要根据实际的错误来定位，可参考如下步骤进行处理： - -1. 通过关闭多任务算子下发后发现结果不变，推断在shell脚本报错位置和日志报错算子之前就已出现错误。 -2. 根据报错加上stream同步操作，缩小错误范围，定位错误算子。stream同步操作的作用在于其要求代码所运行到的位置之前的所有计算必须为完成状态，从而定位错误位置。 -3. 通过在代码中加上stream同步操作，确定报错算子为stack。 -4. 打印stack所有参数的shape、dtype、npu\_format，通过构造单算子用例复现问题。定位到问题原因为减法计算输入参数数据类型不同，导致a-b和b-a结果的数据类型不一致，最终在stack算子中报错。 -5. 将stack入参数据类型转换为一致即可临时规避问题。 - -

在模型运行时将多任务下发关闭\(export TASK\_QUEUE\_ENABLE=0\)后仍然遇到报错“HelpACLExecute.”

- -**现象描述** - -![](figures/FAQ8.png) - -**可能原因** - -pytorch算子在npu上运行，通过ACL接口调用底层经过优化的算子。由于在上层报错信息显示为HelpACLExecute. 时，内部也正在对报错信息与日志进行完善，导致部分算子发生错误时，报错信息获取异常。 - -**处理方法** - -查看host日志，确定报错算子和位置，日志默认路径为/var/log/npu/slog/host-0。查找对应时间的log文件，搜索ERROR字段，查找错误信息。如对上述的错误，查询日志中的ERROR字段为： - -![](figures/FAQ8-1.png) - -从日志信息EEROR部分可以发现，报错算子为topKD，报错原因为“The number of attrs in op desc and op store does not match. ”，定位到错误原因为topk算子参数不匹配。 - -在模型代码中查找topk算子调用位置，确定该算子是否可由其他算子代替，若可由其他算子报错，暂时使用代替方案，并将算子报错信息报告华为工程师。若无替代算子，请将算子报错信息通知华为工程师解决。 - -

在模型运行时遇到报错“55056 GetInputConstDataOut: ErrorNo: -1\(failed\)”

- -**现象描述** - -模型训练过程中，查看host训练日志（路径：“/root/ascend/log/plog/“），可能出现如下报错信息。 - -![](figures/20210720-102720(WeLinkPC).png) - -**可能原因** - -该报错信息是由于调用某一公共API接口导致。 - -**处理方法** - -该报错信息不影响训练功能与性能，可忽略该报错信息。 - -

模型调测常见问题

- -- **[在模型调测时遇到报错“RuntimeError: malloc:/..../pytorch/c10/npu/NPUCachingAllocator.cpp:293 NPU error, error code is 500000.”](#在模型调测时遇到报错-RuntimeError-malloc-pytorch-c10-npu-NPUCachingAllocator-cpp-293-NPU-error-error-code-is-5md)** - -- **[在模型调测时遇到报错“RuntimeError: Could not run 'aten::trunc.out' with arguments from the 'NPUTensorId' backend.”](#在模型调测时遇到报错-RuntimeError-Could-not-run-aten-trunc-out-with-arguments-from-the-NPUTensorId-backendmd)** - -- **[在模型调测时遇到如MaxPoolGradWithArgmaxV1算子和max算子报错](#在模型调测时遇到如MaxPoolGradWithArgmaxV1算子和max算子报错md)** - -- **[在调用torch时遇到报错“ModuleNotFoundError: No module named 'torch.\_C'”](#在调用torch时遇到报错-ModuleNotFoundError-No-module-named-torch-_Cmd)** - - -

在模型调测时遇到报错“RuntimeError: malloc:/..../pytorch/c10/npu/NPUCachingAllocator.cpp:293 NPU error, error code is 500000.”

- -**现象描述** - -![](figures/FAQ4.png) - -**可能原因** - -对于NPUCachingAllocator中malloc类型的错误原因一般为NPU显存不足，所需显存大于npu上可用显存。 - -**处理方法** - -在模型调测中，可用通过减小batch size参数来减少NPU显存的分配，解决该问题。 - -

在模型调测时遇到报错“RuntimeError: Could not run 'aten::trunc.out' with arguments from the 'NPUTensorId' backend.”

- -**现象描述** - -![](figures/FAQ5.png) - -**可能原因** - -目前npu设备仅支持pytorch部分算子，对于不支持的算子在使用时均会报上述错误，算子正在不断开发中。算子支持情况可参考[PyTorch原生算子](https://support.huaweicloud.com/opl-pytorch/atlasptol_09_0001.html)，持续更新。 - -**处理方法** - -在模型调测中，可通过减小batch size参数，来减少NPU显存的占用，解决该问题。 - -

在模型调测时遇到如MaxPoolGradWithArgmaxV1算子和max算子报错

- -**现象描述** - -![](figures/FAQ6.png) - -![](figures/FAQ6-1.png) - -**可能原因** - -在模型搭建中，算子输入参数是多样的。某些算子（如MaxPoolGradWithArgmaxV1算子和max算子）在特定参数下，计算报错或者不支持，根据报错信息可以定位到具体算子。 - -**处理方法** - -根据报错信息定位到具体算子，解决步骤如下： - -1. 排查模型中对该算子的调用方式和参数是否正确； -2. 根据报错算子构建单算子用例，构建报错场景； -3. 一般算子错误无法在python侧解决，构建出报错场景。在论坛中发帖附上报错场景，求助华为工程师即可。 - - >![](public_sys-resources/icon-note.gif) **说明：** - >输入参数shape和dtype需要重点关注，一般是导致算子报错的主要原因。 - - -前述图中，根据报错信息，定位到是MaxPoolGradWithArgmaxV1算子和max算子报错。MaxPoolGradWithArgmaxV1是在反向计算过程中报错，那么构建测试用例时需要构建对应的反向场景；而对于max算子，是正向计算时报错，构建正向场景即可。 - -在模型中遇到算子报错，首选是仅构建单算子测试用例，确定报错场景和原因即可；若无法在单算子中构建单算子用例，则需要构建基于上下文的单算子场景, 可以参考[单算子样例编写说明](#单算子样例编写说明md)编写用例。 - -

在调用torch时遇到报错“ModuleNotFoundError: No module named 'torch.\_C'”

- -**现象描述** - -![](figures/FAQ11.png) - -**可能原因** - -首先确定报错位置，上述报错路径为.../code/pytorch/torch/\_\_init\_\_.py，而当前运行路径在.../code/pytorch下，在执行import torch时，默认首先在当前目录下查找torch文件夹，因此报错。此处应是调用在系统目录下安装的torch包，而不是当前目录下的torch。 - -**处理方法** - -切换到其他目录执行脚本。 - -

其他操作相关问题

- -- **[cuda流同步操作报错](#cuda流同步操作报错md)** - -- **[aicpu\_kernels/libpt\_kernels.so不存在](#aicpu_kernels-libpt_kernels-so不存在md)** - -- **[使用npu-smi info查看显存时发现python进程残留](#使用npu-smi-info查看显存时发现python进程残留md)** - -- **[动态shape报错“match op inputs failed”](#动态shape报错-match-op-inputs-failedmd)** - -- **[Op type SigmoidCrossEntropyWithLogitsV2 of ops kernel AIcoreEngine is unsupported](#Op-type-SigmoidCrossEntropyWithLogitsV2-of-ops-kernel-AIcoreEngine-is-unsupportedmd)** - -- **[Hook失败](#Hook失败md)** - -- **[加载权重时遇到报错“load state\_dict error.”](#加载权重时遇到报错-load-state_dict-errormd)** - - -

cuda流同步操作报错

- -**现象描述** - -![](figures/model_faq11_20210728.jpg) - -**可能原因** - -npu未使用npu的流同步方法。 - -**处理方法** - -使用NPU的流同步方法： - -``` -stream = torch.npu.current_stream() -stream.synchronize() -``` - -

aicpu\_kernels/libpt\_kernels.so不存在

- -**现象描述** - -![](figures/FAQ13.png) - -**可能原因** - -未导入AICPU。 - -**处理方法** - -导入AICPU（以root用户安装CANN软件包，安装路径为默认路径为例）： - -``` -export ASCEND_AICPU_PATH=/usr/local/Ascend/ascend-toolkit/latest -``` - -

使用npu-smi info查看显存时发现python进程残留

- -**现象描述** - -![](figures/FAQ14.png) - -**可能原因** - -python进程残留，需要kill。 - -**处理方法** - -终止python进程： - -``` -pkill -9 python -``` - -

动态shape报错“match op inputs failed”

- -**现象描述** - -![](figures/FAQ15.png) - -**可能原因** - -PTIndexPut编译的算子和输入的shape不一致， 并有acl\_dynamic\_shape\_op打头的日志字样，确定为动态shape报错。 - -**处理方法** - -PTIndexPut对应tensor\[indices\] = value，需要在代码中找到对应的地方将动态shape修改为固定shape。 - -

Op type SigmoidCrossEntropyWithLogitsV2 of ops kernel AIcoreEngine is unsupported

- -**现象描述** - -``` -[ERROR] GE(24836,python3.7):2021-01-27-18:27:51.562.111 [../../../../../../graphengine/ge/engine_manager/dnnengine_manager.cc:266]25155 GetDNNEngineName: ErrorNo: 1343242282(assign engine failed) GetDNNEngineName:Op type SigmoidCrossEntropyWithLogitsV2 of ops kernel AIcoreEngine is unsupported, reason:Op SigmoidCrossEntropyWithLogitsV2 not supported reason: The type of this op is not found in op store, check whether the op store has this type of op. Op store name is tbe-custom. -The dtype, format or shape of input in op desc is not supported in op store, check the dtype, format or shape of input between the op store and the graph. Op store name is tbe-builtin. -``` - -**可能原因** - -SigmoidCrossEntropyWithLogitsV2算子输入了不支持的数据类型，可能是输入int64类型导致的错误。 - -**处理方法** - -检查对应python代码中输入的数据类型，并修改。 - -

Hook失败

- -**现象描述** - -``` -Traceback (most recent call last): - File "tools/train.py", line 227, in - main() - File "tools/train.py", line 221, in main - meta=meta) - File "/root/YoloV3/mmdetection/mmdet/apis/train.py", line 192, in train_detector - runner.run(data_loaders, cfg.workflow, cfg.total_epochs) - File "/usr/local/python3.7.5/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 166, in run - epoch_runner(data_loaders[i], **kwargs) - File "/usr/local/python3.7.5/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 50, in train - self.run_iter(data_batch, train_mode=True) - File "/usr/local/python3.7.5/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 30, in run_iter - outputs = self.model.train_step(data_batch, self.optimizer, **kwargs) - File "/usr/local/python3.7.5/lib/python3.7/site-packages/mmcv/parallel/data_parallel.py", line 100, in train_step - return self.module.train_step(*inputs[0], **kwargs[0]) - File "/root/YoloV3/mmdetection/mmdet/models/detectors/base.py", line 251, in train_step - losses = self(**data) - File "/usr/local/python3.7.5/lib/python3.7/site-packages/torch/nn/modules/module.py", line 660, in __call__ - var = next((v for v in var.values() if isinstance(v, torch.Tensor))) -StopIteration -``` - -**可能原因** - -mmdet的loss部分结构触发了pytorch原生hook的bug，导致死循环。 - -**处理方法** - -解决方案是在/usr/local/python3.7.5/lib/python3.7/site-packages/torch/nn/modules/module.py这个文件的658行加上try跳过： - -``` -if len(self._backward_hooks) > 0: - var = result - try: - while not isinstance(var, torch.Tensor): - if isinstance(var, dict): - var = next((v for v in var.values() if isinstance(v, torch.Tensor))) - else: - var = var[0] - grad_fn = var.grad_fn - if grad_fn is not None: - for hook in self._backward_hooks.values(): - wrapper = functools.partial(hook, self) - functools.update_wrapper(wrapper, hook) - grad_fn.register_hook(wrapper) - except Exception as e: - print('hook failed..') - print(str(e)) -return result -``` - -

加载权重时遇到报错“load state\_dict error.”

- -**现象描述** - -![](figures/FAQ18.png) - -![](figures/FAQ18-1.png) - -**可能原因** - -模型训练后保存的state\_dict的key值与加载时state\_dict的key值不一致，保存时会在每个key的最前面多一个module前缀。 - -**处理方法** - -加载权重时先遍历state\_dict字典，修改key值，并使用新建的字典，具体用例参考demo.py。 - -脚本如下： - -``` - ckpt = torch.load("checkpoint.pth", map_location=loc) - # model.load_state_dict(ckpt['state_dict']) - state_dict_old = ckpt['state_dict'] - state_dict = {} - for key, value in state_dict_old.items(): - key = key[7:] - state_dict[key] = value - model.load_state_dict(state_dict) -``` - -

模型分布式训练常见问题

- -- **[在进行模型分布式训练时遇到报错“host not found.”](#在进行模型分布式训练时遇到报错-host-not-foundmd)** - -- **[在进行模型分布式训练时遇到报错“RuntimeError：connect\(\) timed out.”](#在进行模型分布式训练时遇到报错-RuntimeError-connect()-timed-outmd)** - - -

在进行模型分布式训练时遇到报错“host not found.”

- -**现象描述** - -![](figures/FAQ19.png) - -**可能原因** - -对模型进行分布式训练时，会调用集合通信模块HCCL，需要根据实际情况设置IP和端口信息。根据报错信息，确定是IP地址设置错误。 - -**处理方法** - -在运行脚本中设置正确的IP地址，对于单机情况，设置为本机的IP地址即可；对于多机情况，每个服务器上脚本中的IP需要设置为master节点的IP。 - -

在进行模型分布式训练时遇到报错“RuntimeError：connect\(\) timed out.”

- -**现象描述** - -![](figures/1234.png) - -**可能原因** - -模型进行分布式训练时，系统防火墙可能会阻截HCCL的集合通信端口的通信。需要根据报错信息，排查通信端口的开放情况，并进行相应设置。 - -**处理方法** - -查询出被系统防火墙阻截的集合通信端口，并开放相应端口。 - 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/1.png" "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/figures/1.png" deleted file mode 100644 index 1c7c3c517beb810563232e71e93698a74106fc09..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 8207 zcmZvAcRbr&_;yrPX;G_cSE*4WHDi0It+c4BnJQ|}*d$bIui7O@&9r7~tB75-X{Zsz zh%I*P@#^#Z-rwi_2mvG z0|0OqXsIf{@UtXjcY8mQyAz&$Q~&6jKkc3C5ryd;EujF{d#bbdHCqLw$(vaU3>+Ls z`hH&e1~sg$8Kf4ZX|KIoA}lX269&J5)1v6@*MowClNHaQoKo<1kd~KWoO2qNa^Pz6 z@2+z(P4xTSLyro$ayeZ8vd-X;z1Uxq#EIRJ!6m(Y?hE`>SNdpQ3!W9dQsi?-#kc8o 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[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) -- [模型训练](#模型训练md) -- [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) -- [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) - - [模型和算子运行常见问题](#模型和算子运行常见问题md) - - [模型调测常见问题](#模型调测常见问题md) - - [其他操作相关问题](#其他操作相关问题md) - - [模型分布式训练常见问题](#模型分布式训练常见问题md) -