pytorch_learn/tools

• Overview

This folder contains a number of scripts which are used as
part of the PyTorch build process. This directory also doubles
as a Python module hierarchy (thus the __init__.py).

Overview

Modern infrastructure:

• autograd - Code generation for autograd. This
  includes definitions of all our derivatives.
• jit - Code generation for JIT
• shared - Generic infrastructure that scripts in
  tools may find useful.
  • module_loader.py - Makes it easier
    to import arbitrary Python files in a script, without having to add
    them to the PYTHONPATH first.

Build system pieces:

• setup_helpers - Helper code for searching for
  third-party dependencies on the user system.
• build_pytorch_libs.py - cross-platform script that
  builds all of the constituent libraries of PyTorch,
  but not the PyTorch Python extension itself.
• build_libtorch.py - Script for building
  libtorch, a standalone C++ library without Python support. This
  build script is tested in CI.
• fast_nvcc - Mostly-transparent wrapper over nvcc that
  parallelizes compilation when used to build CUDA files for multiple
  architectures at once.
  • fast_nvcc.py - Python script, entrypoint to the
    fast nvcc wrapper.

Developer tools which you might find useful:

• git_add_generated_dirs.sh and
  git_reset_generated_dirs.sh -
  Use this to force add generated files to your Git index, so that you
  can conveniently run diffs on them when working on code-generation.
  (See also generated_dirs.txt which
  specifies the list of directories with generated files.)
• stats/test_history.py - Query S3 to display history of a single
  test across multiple jobs over time.

Important if you want to run on AMD GPU:

• amd_build - HIPify scripts, for transpiling CUDA
  into AMD HIP. Right now, PyTorch and Caffe2 share logic for how to
  do this transpilation, but have separate entry-points for transpiling
  either PyTorch or Caffe2 code.
  • build_amd.py - Top-level entry
    point for HIPifying our codebase.

Tools which are only situationally useful:

• docker - Dockerfile for running (but not developing)
  PyTorch, using the official conda binary distribution. Context:
  https://github.com/pytorch/pytorch/issues/1619
• download_mnist.py - Download the MNIST
  dataset; this is necessary if you want to run the C++ API tests.
• run-clang-tidy-in-ci.sh - Responsible
  for checking that C++ code is clang-tidy clean in CI on Travis