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fed8d8975a
Summary: - Speed up hipify_python.py by blacklisting useless (and quite large) directory trees that it would otherwise recurse into - Pass around relative paths instead of absolute paths. This makes it easier to do filename matches based on the root of the tree. - Redo the streaming output to contain more useful information - Make it handle c10/cuda correctly, rewrite c10::cuda to c10::hip, and the header name from CUDAMathCompat.h to CUDAHIPCompat.h Signed-off-by: Edward Z. Yang <ezyang@fb.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/13973 Differential Revision: D13062374 Pulled By: ezyang fbshipit-source-id: f0858dd18c94d449ff5dbadc22534c695dc0f8fb
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):
- nnwrap - Generates the THNN/THCUNN wrappers which make legacy functionality available. (TODO: What exactly does this implement?)
- 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.sh - Script that builds all of the constituent libraries of PyTorch, but not the PyTorch Python extension itself. We are working on eliminating this script in favor of a unified cmake build.
- build_pytorch_libs.bat - Same as above, but for Windows.
- build_libtorch.py - Script for building libtorch, a standalone C++ library without Python support. This build script is tested in CI.
Developer tools which you might find useful:
- clang_tidy.py - Script for running clang-tidy on lines of your script which you changed.
- 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.)
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_caffe2_amd.py - Script for HIPifying the Caffe2 codebase.
- build_pytorch_amd.py - Script for HIPifying the PyTorch codebase.
Tools which are only situationally useful:
- aten_mirror.sh - Mirroring script responsible for keeping https://github.com/zdevito/ATen up-to-date.
- 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