From 539c42749e86b48fbd33f03f8c2e4ba2a8875f61 Mon Sep 17 00:00:00 2001
From: cai-weiwei1989 <734267852@qq.com>
Date: Tue, 2 Jul 2024 18:22:07 +0800
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profiler/README.md | 29 +-
profiler/advisor/README.md | 135 +++++-
...77\346\215\242\346\240\267\344\276\213.md" | 178 ++++++++
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...77\346\215\242\346\240\267\344\276\213.md" | 406 ++++++++++++++++++
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create mode 100644 profiler/advisor/doc/img/Equal.png
create mode 100644 profiler/advisor/doc/img/Mul.png
create mode 100644 profiler/advisor/doc/img/Pytorch_dtype.png
create mode 100644 profiler/advisor/doc/img/TensorEqual.png
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create mode 100644 profiler/advisor/doc/img/single_op_time_CANN63RC2.png
create mode 100644 profiler/advisor/doc/img/single_op_time_CANN70RC1.png
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create mode 100644 profiler/advisor/img/all.png
create mode 100644 profiler/advisor/img/cluster.png
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create mode 100644 profiler/advisor/img/computation.png
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diff --git a/profiler/README.md b/profiler/README.md
index 705fb84279..74b378e69c 100644
--- a/profiler/README.md
+++ b/profiler/README.md
@@ -172,7 +172,28 @@ ascend pytorch profiler数据目录结构如下：
## 工具使用
-| 工具名称 | 说明 |
-| ------------------------------------------------------------ | ------------------------------------------------------------ |
-| [compare_tools（性能比对工具）](https://gitee.com/ascend/att/tree/master/profiler/compare_tools) | 提供NPU与GPU性能拆解功能以及算子、通信、内存性能的比对功能。 |
-| [cluster_analyse（集群分析工具）](https://gitee.com/ascend/att/tree/master/profiler/cluster_analyse) | 提供多机多卡的集群分析能力（基于通信域的通信分析和迭代耗时分析）, 当前需要配合Ascend Insight的集群分析功能使用。 |
+```bash
+msprof-analyze advisor [-h] [-v]
+```
+
+```bash
+msprof-analyze compare [-h] [-v]
+```
+
+```bash
+msprof-analyze cluster [-h] [-v]
+```
+
+```bash
+msprof-analyze auto-completion [-h] [-v]
+```
+
+| 参数 | 说明 |
+| -------------------- | ------------------------------------------------------------ |
+| advisor | [advisor](./advisor/README.md)。将Ascend PyTorch Profiler或者msprof采集的PyThon场景性能数据进行分析，并输出性能调优建议。 |
+| compare | [compare_tools（性能比对工具）](./compare_tools/README.md)。提供NPU与GPU性能拆解功能以及算子、通信、内存性能的比对功能。 |
+| cluster | [cluster_analyse（集群分析工具）](./cluster_analyse/README.md)。提供多机多卡的集群分析能力（基于通信域的通信分析和迭代耗时分析）, 当前需要配合Ascend Insight的集群分析功能使用。 |
+| auto-completion | 自动补全。配置后在当前视图下配置msprof-analyze工具所有的子参数时，可以使用Tab将所有子参数自动补全。 |
+| -v，-V
--version | 查看版本号。 |
+| -h，-H
--help | 命令行参数帮助信息。 |
+
diff --git a/profiler/advisor/README.md b/profiler/advisor/README.md
index b81f79311a..303f3148b5 100644
--- a/profiler/advisor/README.md
+++ b/profiler/advisor/README.md
@@ -2,8 +2,12 @@
msprof-analyze的advisor功能是将Ascend PyTorch Profiler或者msprof采集的PyThon场景性能数据进行分析，并输出性能调优建议（当前暂不支持对db格式文件分析）。
+性能数据采集方法请参见《[性能分析工具](https://www.hiascend.com/document/detail/zh/mindstudio/70RC1/mscommandtoolug/mscommandug/atlasprofiling_16_0001.html)》。
+
## 工具使用（命令行方式方式）
+### 操作步骤
+
1. 参见《[性能工具](../README.md)》完成工具安装。建议安装最新版本。
2. 执行分析。
@@ -11,31 +15,146 @@ msprof-analyze的advisor功能是将Ascend PyTorch Profiler或者msprof采集的
- 总体性能瓶颈
```bash
- msprof-analyze advisor all -d [待分析性能数据文件所在路径] -bp [基准性能数据文件所在路径]
+ msprof-analyze advisor all -d $HOME/profiling_data/
```
- 计算瓶颈
```bash
- msprof-analyze advisor computation -d [待分析性能数据文件所在路径]
+ msprof-analyze advisor computation -d $HOME/profiling_data/
```
- 调度瓶颈
```bash
- msprof-analyze advisor schedule -d [待分析性能数据文件所在路径]
+ msprof-analyze advisor schedule -d $HOME/profiling_data/
```
-
- -d（必选）：待分析性能数据文件所在路径。
-
- -bp（可选）：基准性能数据文件所在路径。
+ 以上命令更多参数介绍请参见“**命令详解**”。
单卡场景需要指定到性能数据文件`*_ascend_pt`目录；多卡或集群场景需要指定到`*_ascend_pt`目录的父目录层级。
3. 查看结果。
- 分析结果打屏展示并生成html和csv文件。
+ 分析结果输出相关简略建议到执行终端中，并生成`att_advisor_{timestamp}.html`和`att_advisor_{timestamp}.xlsx`文件供用户预览。
+
+ `att_advisor_{timestamp}.xlsx`文件内容与执行终端输出一致。
+
+ `att_advisor_{timestamp}.html`文件分析详见“**报告解析**”。
+
+ 执行终端输出示例如下：
+
+ 总体性能瓶颈
+
+ 
+
+ 计算瓶颈
+
+ 
+
+ 调度瓶颈
+
+ 
+
+
+
+### 命令详解
+
+#### 命令功能介绍
+
+| dimension | mode | 参数释义 |
+| ---------- | -------------------------- | ---------------------------------------- |
+| overall | overall_summary | 计算、通信、空闲等维度对性能数据进行拆解 |
+| cluster | slow_rank | 慢卡识别 |
+| | slow_link | 慢链路识别 |
+| computing | aicpu | AI CPU调优 |
+| | dynamic_shape_analysis | 识别动态Shape算子 |
+| | block_dim_analysis | block dim算子调优 |
+| | operator_no_bound_analysis | operator no bound |
+| | graph | 融合算子图调优 |
+| scheduling | timeline_fusion_ops | 亲和API替换调优 |
+| | timeline_op_dispatch | 识别算子下发问题(路径3/路径5) |
+
+- all
+
+ 总体性能瓶颈：包含上表中所有功能。
+
+- computation
+
+ 计算瓶颈：包含上表中computing功能。
+
+- schedule
+
+ 调度瓶颈：包含上表中scheduling功能。
+
+#### 命令格式
+
+- 总体性能瓶颈
+
+ ```bash
+ msprof-analyze advisor all -d {profiling_path} [-bp benchmark_profiling_path] [-cv cann_version] [-tv torch_version] [-pt profiling_type] [-d] [-h]
+ ```
+
+- 计算瓶颈
+
+ ```bash
+ msprof-analyze advisor computation -d {profiling_path} [-bp benchmark_profiling_path] [-cv cann_version] [-tv torch_version] [-pt profiling_type] [-d] [-h]
+ ```
+
+- 调度瓶颈
+
+ ```bash
+ msprof-analyze advisor schedule -d {profiling_path} [-bp benchmark_profiling_path] [-cv cann_version] [-tv torch_version] [-d] [-h]
+ ```
+
+#### 参数介绍
+
+| 参数 | 说明 | 是否必选 |
+| ---------------------------------- | ------------------------------------------------------------ | -------- |
+| -d
--profiling_path | 性能数据所在目录。性能数据通过Profiling工具采集获取。请确保性能数据采集时配置“aic-metrics”参数为“PipeUtilization”，“aicpu”参数为“on”。advisor依赖Profiling工具解析后的timeline数据、summary数据以及info.json*文件，请确保指定的“profiling_dir”目录下存在以上文件。 | 是 |
+| -bp
--benchmark_profiling_path | 基准性能数据所在目录，用于性能比对。性能数据通过Profiling工具采集获取。
**computation和schedule不支持该参数。** | 否 |
+| -cv
--cann_version | 使用Profiling工具采集时对应的CANN软件版本，可通过在环境中执行如下命令获取其version字段，目前配套的兼容版本为“6.3.RC2”，“7.0.RC1”、“7.0.0”、“8.0.RC1”，此字段不填默认按“8.0.RC1”版本数据进行处理，其余版本采集的Profiling数据在分析时可能会导致不可知问题：`cat /usr/local/Ascend/ascend-toolkit/latest/aarch64-linux/ascend_toolkit_install.info` | 否 |
+| -tv
--torch_version | 运行环境的torch版本，默认为1.11.0，支持torch1.11.0和torch2.1.0，当运行环境torch版本为其他版本如torch1.11.3时，可以忽略小版本号差异选择相近的torch版本如1.11.0。 | 否 |
+| -pt
--profiling_type | 配置性能数据采集使用的Profiling工具类型。可取值：
ascend_pytorch_profiler：使用Ascend PyThon Profiler接口方式采集的性能数据时配置，默认值。
msprof：使用msprof命令行方式采集的性能数据时配置。
mslite：使用[Benchmark](https://gitee.com/ascend/tools/tree/master/ais-bench_workload/tool/ais_bench)工具采集的性能数据时配置。
**schedule不支持该参数。** | 否 |
+| -D
--debug | 工具执行报错时可打开此开关，将会展示详细保存堆栈信息。 | 否 |
+| -h，-H
--help | 在需要查询当前命令附属子命令或相关参数时，给出帮助建议。 | 否 |
+
+### 报告解析
+
+如下图所示，工具会从集群、单卡性能拆解、调度和计算等维度进行问题诊断并给出相应的调优建议。
+
+
+
+cluster模块的分析包含快慢卡和快慢链路分析，仅识别问题，不提供调优建议。
+如下图示例，识别到当前训练任务的通信和下发（free较多说明存在任务下发存在问题）存在问题。
+
+
+
+overall模块的分析包含当前训练任务慢卡的性能拆解，按照计算、通信和下发三个维度进行耗时的统计，可以基于该分析识别到训练性能瓶颈是计算、通信还是下发问题，同样不提供调优建议。
+
+
+
+schedule模块包含亲和API、aclOpCompile、syncBatchNorm、SynchronizeStream等多项检测。
+如下图示例，Operator Dispatch Issues提示需要在运行脚本的最开头添加如下代码用于消除aclOpCompile：
+
+```python
+torch_npu.npu.set_compile_mode(jit_compile=False);
+torch_npu.npu.config.allow_internal_format = False
+```
+
+
+
+如下图示例，Synchronize Stream Issues提示存在耗时较多的同步流，并给出触发同步流的堆栈，需要根据堆栈来修改对应代码消除同步流。
+
+
+
+如下图示例，Affinity API Issues提示存在可以替换的亲和API并给出对应的堆栈，用户可以根据堆栈找到需要修改的代码，并给出修改案例（API instruction超链接）。
+
+
+
+computation模块从device计算性能维度进行分析，能够识别AI CPU、计算bound、动态Shape等问题并给出相应建议。此处不再详细展开，按照报告进行调优即可。
+
+
## 工具使用（Jupyter Notebook方式）
diff --git "a/profiler/advisor/doc/AI CPU \347\256\227\345\255\220\346\233\277\346\215\242\346\240\267\344\276\213.md" "b/profiler/advisor/doc/AI CPU \347\256\227\345\255\220\346\233\277\346\215\242\346\240\267\344\276\213.md"
new file mode 100644
index 0000000000..6a72ecee2e
--- /dev/null
+++ "b/profiler/advisor/doc/AI CPU \347\256\227\345\255\220\346\233\277\346\215\242\346\240\267\344\276\213.md"
@@ -0,0 +1,178 @@
+# AI CPU 算子替换样例
+
+部分算子因为数据输入类型问题或者算子实现问题，导致会在昇腾芯片的AI CPU上执行，没有充分利用AI CORE的资源，从而导致计算性能较差，影响训练速度。部分场景下，可以通过修改Python代码来减少这类AI CPU算子，从而提升训练性能。
+
+当前对 AICPU 算子识别到的调优方式主要包含两种：
+
+- PyTorch数据类型转换，将执行在AICPU上的类型算子转换为执行在AICORE单元的算子。
+- 等价的算子替换。
+
+## 类型转换方式
+
+当前PyTorch支持的dtype类型如下，详见[Link](https://pytorch.org/docs/stable/tensor_attributes.html)。
+
+图1 PyTorch支持的dtype
+
+
+
+基于此对常见的算子如MUL、Equal、TensorEqual等做单算子测试，看有哪些类型的算子是执行在AICPU上的，然后尝试转换到支持AICORE单元的类型dtype上计算，实现效率提升的目的。
+
+### MUL
+
+图2 Mul
+
+
+
+AICORE支持的dtype。
+
+```python
+float, float32, float16, dt_bf16, int32, int64, int8, uint8, complex64
+```
+
+AICPU 类型的 dtype。
+
+```python
+int16, complex128
+```
+
+### Equal
+
+图3 Equal
+
+
+
+AICORE支持的dtype。
+
+```python
+float, float32, float16, dt_bf16, bool, int32, int64, int8, uint81
+```
+
+AICPU 类型的 dtype。
+
+```python
+int16, complex64, complex128
+```
+
+### TensorEqual
+
+图4 TensorEqual
+
+
+
+AICORE支持的dtype。
+
+```python
+float, float32, float16, dt_bf16, float64, bool, int32, int8, uint81
+```
+
+AICPU 类型的 dtype。
+
+```python
+int16, int64
+```
+
+## 算子等价替换
+
+### Index算子替换
+
+- 情形一 ：index by index
+
+ 这种操作会造成输出的shape和输入的shape不一致，我们可以直接用index\_select(gatherV2)操作替换该算子运行在aicore性能高上很多。
+
+ 图5 index by index
+
+ 
+
+- 情形二：index\_put by index
+
+ ```python
+ tensor[index] = 3
+ ```
+
+ 这类操作尽量避免，没有特别好的替代方式，可以将index转化成mask，或者一开始就生成mask作为索引而不是index。
+
+ 如果要替换可以用scatter算子替换，目前发现用到这种场景时index一般比较少，所以用index方式可能性能更高。
+
+- 情形三：index\_put by mask
+
+ ```python
+ tensor\_a[mask] = 3
+ ```
+
+ index\_put by mask可以通过where (selectV2)算子来替代。这种方式与原先语义不同的是，会返回一个新的tensor。
+
+ 图6 index\_put by mask
+
+ 
+
+ index by mask或者index_put by mask相对来说对NPU和框架比较友好。关键在保持shape这样不需要contiguous，然后将必要的index抽取操作放在最后。在index比较少的情况下，index操作就比较快了，可能优于替换。
+
+### IndexPut算子替换
+
+在tensor类型的赋值和切片操作时，会使用IndexPut算子执行，一般都在AICPU上执行，可以转换为等价的tensor操作转换到CUBE单元上执行。例如：
+
+```python
+masked_input[input_mask] = 0
+```
+
+建议替换为：
+
+```python
+masked_input *= ~input_mask
+```
+
+此处是将IndexPut的masked_input是float类型的tensor数据，input_mask是和masked_input shape 一致的bool类型tensor或者01矩阵。由于是赋0操作，所以先对input_mask 取反后再进行乘法操作。
+
+以赋0操作为例，在shape = (512, 32, 64) 类型float32 数据上测试，替换前耗时: 9.639978408813477 ms，替换之后耗时为 0.1747608184814453 ms，如下图，替换前，总体耗时在9.902ms，Host下发到device侧执行5个算子，其中aclnnIndexPutImpl_IndexPut_IndexPut是执行在 AICPU上。
+
+图7 替换前耗时
+
+
+
+替换后，总体耗时226.131us。下发三个执行算子，均执行在AI CORE上。
+
+图8 替换后耗时
+
+
+
+### ArgMin算子优化
+
+ArgMin在CANN 6.3 RC2版本上算子下发到 AICPU执行，在CANN 7.0RC1上下发到AI_CORE 上边执行。出现此类情形建议升级CANN包版本。
+
+在shape大小是 (1024, 1024) 的tensor上测试，结果如下：CANN 6.3.RC2上，单算子执行时间 2.603 ms。
+
+图9 单算子执行时间（CANN 6.3.RC2）
+
+
+
+CANN7.0 RC1上，单算子执行时间 223.516 us。
+
+图10 单算子执行时间（CANN7.0 RC1）
+
+
+
+### nonzero算子优化
+
+将mask转化为index，对于所有值大于0的tensor在某些计算中可以利用乘法替代。比如要对mask的tensor求和。tensor_a[mask].sum()就相当于(tensor_a * mask).sum()。
+
+例如：
+
+```python
+shape = (1024, )
+mask= torch.randint(-1, 2, shape).npu()
+tensor_a = torch.ones(shape).float().npu()
+mask_inds = torch.nonzero(
+ gt_inds > 0, as_tuple=False).squeeze(1)
+
+tensor_sum = tensor_a[mask_inds].sum()
+```
+
+就相当于：
+
+```python
+shape = (1024, )
+mask= torch.randint(-1, 2, shape).npu()
+tensor_a = torch.ones(shape).float().npu()
+mask_inds = torch.nonzero( gt_inds > 0, as_tuple=False).squeeze(1)
+tensor_sum2 = (tensor_a * mask_inds2).sum()
+```
\ No newline at end of file
diff --git a/profiler/advisor/doc/img/Equal.png b/profiler/advisor/doc/img/Equal.png
new file mode 100644
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