cuda-mock 0.0.9

mock cuda runtime api

The plt hook technology used refers to plthook

mock pytorch cuda runtime interface

• update submodule
  git submodule update --init --recursive

• build wheel package
  python setup.py sdist bdist_wheel

• direct install
  pip install dist/*.whl

collect cuda operator call stack

• find nvcc installed path
  which nvcc
• replace nvcc with my nvcc
  mv /usr/local/bin/nvcc /usr/local/bin/nvcc_b
chmod 777 tools/nvcc
cp tools/nvcc /usr/local/bin/nvcc
• build and install pytorch
• build and install cuda_mock
• import cuda_mock after import torch
• run your torch train script
• we will dump the stack into console

收集cuda 算子调用堆栈

• 找到nvcc安装路径 which nvcc
• 用我们的nvcc替换系统的nvcc（我们只是在编译选项加了-g）
  mv /usr/local/bin/nvcc /usr/local/bin/nvcc_b
chmod 777 tools/nvcc
cp tools/nvcc /usr/local/bin/nvcc
• 构建并且安装pytorch
• 构建并且安装cuda_mock
• 注意要在import torch之后import cuda_mock
• 开始跑你的训练脚本
• 我们将会把堆栈打印到控制台

收集统计xpu runtime 内存分配信息/xpu_wait调用堆栈

• 打印xpu_malloc调用序列，统计实时内存使用情况以及历史使用的峰值内存，排查内存碎片问题

• 打印xpu_wait调用堆栈，排查流水中断处问题

• 注意要在import torch/import paddle之后import cuda_mock; cuda_mock.xpu_initialize()

• 使用方法:

  import paddle
  import cuda_mock; cuda_mock.xpu_initialize() # 加入这一行
  

• 关闭打印backtrace（获取backtrace性能下降比较严重）

  export HOOK_DISABLE_TRACE='xpuMemcpy=0,xpuSetDevice=0'
  

example

• python test/test_import_mock.py

debug

• export LOG_LEVEL=WARN,TRACE=INFO