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### This change - Implements the ruff linter in pytorch lintrunner. It is adapted from https://github.com/justinchuby/lintrunner-adapters/blob/main/lintrunner_adapters/adapters/ruff_linter.py. It does **both linting and fixing**. 🔧 - Migrated all flake8 configs to the ruff config and enabled it for the repo. ✅ - **`ruff` lints the whole repo in under 2s** 🤯 Fixes https://github.com/pytorch/pytorch/issues/94737 Replaces #99280 @huydhn @Skylion007 <!-- copilot:all --> ### <samp>🤖 Generated by Copilot at 6b982dd</samp> ### Summary 🧹🛠️🎨 <!-- 1. 🧹 This emoji represents cleaning or tidying up, which is what `ruff` does by formatting and linting the code. It also suggests improving the code quality and removing unnecessary or redundant code. 2. 🛠️ This emoji represents tools or fixing, which is what `ruff` is as a code formatter and linter. It also suggests enhancing the code functionality and performance, and resolving potential issues or bugs. 3. 🎨 This emoji represents art or creativity, which is what `ruff` allows by providing a consistent and configurable style for the code. It also suggests adding some flair or personality to the code, and making it more readable and enjoyable. --> Add `[tool.ruff]` section to `pyproject.toml` to configure `ruff` code formatter and linter. This change aims to improve code quality and consistency with a single tool. > _`ruff` cleans the code_ > _like a spring breeze in the fields_ > _`pyproject.toml`_ ### Walkthrough * Configure `ruff` code formatter and linter for the whole project ([link](https://github.com/pytorch/pytorch/pull/99785/files?diff=unified&w=0#diff-50c86b7ed8ac2cf95bd48334961bf0530cdc77b5a56f852c5c61b89d735fd711R22-R79)) Pull Request resolved: https://github.com/pytorch/pytorch/pull/99785 Approved by: https://github.com/malfet, https://github.com/Skylion007 |
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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.
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.