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https://github.com/pytorch/pytorch.git
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0176fb4cd6
After https://github.com/pytorch/pytorch/pull/96665 landed, fast_nvcc tool is no longer available. This commit removes the documentation for it so as not to confuse users. Pull Request resolved: https://github.com/pytorch/pytorch/pull/97624 Approved by: https://github.com/drisspg
60 lines
2.6 KiB
Markdown
60 lines
2.6 KiB
Markdown
This folder contains a number of scripts which are used as
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part of the PyTorch build process. This directory also doubles
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as a Python module hierarchy (thus the `__init__.py`).
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## Overview
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Modern infrastructure:
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* [autograd](autograd) - Code generation for autograd. This
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includes definitions of all our derivatives.
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* [jit](jit) - Code generation for JIT
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* [shared](shared) - Generic infrastructure that scripts in
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tools may find useful.
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* [module_loader.py](shared/module_loader.py) - Makes it easier
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to import arbitrary Python files in a script, without having to add
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them to the PYTHONPATH first.
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Build system pieces:
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* [setup_helpers](setup_helpers) - Helper code for searching for
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third-party dependencies on the user system.
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* [build_pytorch_libs.py](build_pytorch_libs.py) - cross-platform script that
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builds all of the constituent libraries of PyTorch,
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but not the PyTorch Python extension itself.
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* [build_libtorch.py](build_libtorch.py) - Script for building
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libtorch, a standalone C++ library without Python support. This
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build script is tested in CI.
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Developer tools which you might find useful:
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* [git_add_generated_dirs.sh](git_add_generated_dirs.sh) and
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[git_reset_generated_dirs.sh](git_reset_generated_dirs.sh) -
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Use this to force add generated files to your Git index, so that you
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can conveniently run diffs on them when working on code-generation.
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(See also [generated_dirs.txt](generated_dirs.txt) which
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specifies the list of directories with generated files.)
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Important if you want to run on AMD GPU:
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* [amd_build](amd_build) - HIPify scripts, for transpiling CUDA
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into AMD HIP. Right now, PyTorch and Caffe2 share logic for how to
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do this transpilation, but have separate entry-points for transpiling
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either PyTorch or Caffe2 code.
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* [build_amd.py](amd_build/build_amd.py) - Top-level entry
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point for HIPifying our codebase.
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Tools which are only situationally useful:
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* [docker](docker) - Dockerfile for running (but not developing)
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PyTorch, using the official conda binary distribution. Context:
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https://github.com/pytorch/pytorch/issues/1619
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* [download_mnist.py](download_mnist.py) - Download the MNIST
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dataset; this is necessary if you want to run the C++ API tests.
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[actions/github-script]: https://github.com/actions/github-script
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[flake8]: https://flake8.pycqa.org/en/latest/
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[github actions expressions]: https://docs.github.com/en/actions/reference/context-and-expression-syntax-for-github-actions#about-contexts-and-expressions
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[pytorch/add-annotations-github-action]: https://github.com/pytorch/add-annotations-github-action
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[shellcheck]: https://github.com/koalaman/shellcheck
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