mirror of
https://github.com/saymrwulf/pytorch.git
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2a6eaa2e6f
Resolves #141238 - #141238 Example output: ```console $ python3.12 tools/nightly.py checkout -b my-nightly-branch -p my-env --python python3.10 log file: /Users/PanXuehai/Projects/pytorch/nightly/log/2024-11-22_04h15m45s_63f8b29e-a845-11ef-bbf9-32c784498a7b/nightly.log Creating virtual environment Creating venv (Python 3.10.15): /Users/PanXuehai/Projects/pytorch/my-env Installing packages Upgrading package(s) (https://download.pytorch.org/whl/nightly/cpu): pip, setuptools, wheel Installing packages took 5.576 [s] Creating virtual environment took 9.505 [s] Downloading packages Downloading package(s) (https://download.pytorch.org/whl/nightly/cpu): torch Downloaded 9 file(s) to /var/folders/sq/7sf73d5s2qnb3w6jjsmhsw3h0000gn/T/pip-download-lty5dvz4: - mpmath-1.3.0-py3-none-any.whl - torch-2.6.0.dev20241121-cp310-none-macosx_11_0_arm64.whl - jinja2-3.1.4-py3-none-any.whl - sympy-1.13.1-py3-none-any.whl - MarkupSafe-3.0.2-cp310-cp310-macosx_11_0_arm64.whl - networkx-3.4.2-py3-none-any.whl - fsspec-2024.10.0-py3-none-any.whl - filelock-3.16.1-py3-none-any.whl - typing_extensions-4.12.2-py3-none-any.whl Downloading packages took 7.628 [s] Installing dependencies Installing packages Installing package(s) (https://download.pytorch.org/whl/nightly/cpu): numpy, cmake, ninja, packaging, ruff, mypy, pytest, hypothesis, ipython, rich, clang-format, clang-tidy, sphinx, mpmath-1.3.0-py3-none-any.whl, jinja2-3.1.4-py3-none-any.whl, sympy-1.13.1-py3-none-any.whl, MarkupSafe-3.0.2-cp310-cp310-macosx_11_0_arm64.whl, networkx-3.4.2-py3-none-any.whl, fsspec-2024.10.0-py3-none-any.whl, filelock-3.16.1-py3-none-any.whl, typing_extensions-4.12.2-py3-none-any.whl Installing packages took 42.514 [s] Installing dependencies took 42.515 [s] Unpacking wheel file Unpacking wheel file took 3.223 [s] Checking out nightly PyTorch Found released git version |
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| .. | ||
| alerts | ||
| amd_build | ||
| autograd | ||
| bazel_tools | ||
| build/bazel | ||
| build_defs | ||
| code_analyzer | ||
| code_coverage | ||
| config | ||
| coverage_plugins_package | ||
| dynamo | ||
| flight_recorder | ||
| gdb | ||
| github | ||
| 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 | ||
| build_with_debinfo.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 | ||
| nightly_hotpatch.py | ||
| nvcc_fix_deps.py | ||
| 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.