mirror of
https://github.com/saymrwulf/pytorch.git
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6db196b744
Before this, my assumption was that the workflow was only run on the main branch. This is not correct anymore as it could also now be run as part of the PR, i.e. https://hud.pytorch.org/pr/91316. So this change does two things: * Always upload inductor-A100-perf-nightly artifacts to S3 once completed by removing the main branch gating. * Add `head_branch` to Rockset records, so that the [dashboard](https://torchci-git-fork-huydhn-add-compilers-bench-74abf8-fbopensource.vercel.app/benchmark/compilers) knows if the records come from the daily schedule on the main branch or from experimental PR. The `head_branch` would be set to `master` in the former. Pull Request resolved: https://github.com/pytorch/pytorch/pull/97643 Approved by: https://github.com/desertfire |
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| .. | ||
| amd_build | ||
| autograd | ||
| bazel_tools | ||
| build_defs | ||
| code_analyzer | ||
| code_coverage | ||
| config | ||
| coverage_plugins_package | ||
| dynamo | ||
| gdb | ||
| iwyu | ||
| jit | ||
| linter | ||
| lite_interpreter | ||
| lldb | ||
| onnx | ||
| pyi | ||
| rules | ||
| rules_cc | ||
| setup_helpers | ||
| shared | ||
| stats | ||
| test | ||
| testing | ||
| __init__.py | ||
| bazel.bzl | ||
| BUCK.bzl | ||
| BUCK.oss | ||
| build_libtorch.py | ||
| build_pytorch_libs.py | ||
| download_mnist.py | ||
| extract_scripts.py | ||
| gen_flatbuffers.sh | ||
| gen_vulkan_spv.py | ||
| generate_torch_version.py | ||
| generated_dirs.txt | ||
| git_add_generated_dirs.sh | ||
| git_reset_generated_dirs.sh | ||
| nightly.py | ||
| nvcc_fix_deps.py | ||
| pytorch.version | ||
| README.md | ||
| render_junit.py | ||
| substitute.py | ||
| update_masked_docs.py | ||
| vscode_settings.py | ||
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.)
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.