README.md

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.

Legacy infrastructure (we should kill this):

• cwrap - Implementation of legacy code generation for THNN/THCUNN. This is used by nnwrap.

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:

• clang_tidy.py - Script for running clang-tidy on lines of your script which you changed.
• extract_scripts.py - Extract shell scripts from .github/workflows/*.yml into a specified dir, on which run_shellcheck.sh can be run. Exits with nonzero status if any of the extracted scripts contain GitHub Actions expressions: ${{ <expression> }}
• 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.)
• mypy_wrapper.py - Run mypy on a single file using the appropriate subset of our mypy*.ini configs.
• run_shellcheck.sh - Find *.sh files (recursively) in the directories specified as arguments, and run ShellCheck on all of them.
• test_history.py - Query S3 to display history of a single test across multiple jobs over time.
• trailing_newlines.py - Take names of UTF-8 files from stdin, print names of nonempty files whose contents don't end in exactly one trailing newline, exit with status 1 if no output printed or 0 if some filenames were printed.
• translate_annotations.py - Read Flake8 or clang-tidy warnings (according to a --regex) from a --file, convert to the JSON format accepted by pytorch/add-annotations-github-action, and translate line numbers from HEAD back in time to the given --commit by running git diff-index --unified=0 appropriately.

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: pytorch#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