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5c8a9803c8
Summary: This is a follow up to #78015. This PR * introduces namespace logic for generating `NativeFunctions.h`. * adds helper function to extract namespace from string * relaxes the constraint on the levels we support for custom kernel namespace to 2 Test Plan: Yaml entry: ``` - func: unsqueeze.out(Tensor(a) self, int dim, *, Tensor(a!) out) -> Tensor(a!) variants: function device_check: NoCheck dispatch: CPU: custom_1::custom_2::unsqueeze ``` Generated `NativeFunctions.h`: ``` namespace custom_1 { namespace custom_2 { namespace native { TORCH_API at::Tensor & unsqueeze(const at::Tensor & self, int64_t dim, at::Tensor & out); } // namespace native } // namespace custom_2 } // namespace custom_1 ``` Differential Revision: D37198111 Pull Request resolved: https://github.com/pytorch/pytorch/pull/79733 Approved by: https://github.com/bdhirsh |
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| .. | ||
| amd_build | ||
| autograd | ||
| bazel_tools | ||
| build_defs | ||
| code_analyzer | ||
| code_coverage | ||
| config | ||
| coverage_plugins_package | ||
| fast_nvcc | ||
| gdb | ||
| iwyu | ||
| jit | ||
| linter | ||
| lite_interpreter | ||
| lldb | ||
| onnx | ||
| pyi | ||
| rules | ||
| setup_helpers | ||
| shared | ||
| stats | ||
| test | ||
| testing | ||
| __init__.py | ||
| bazel.bzl | ||
| BUCK.bzl | ||
| BUCK.oss | ||
| build_libtorch.py | ||
| build_pytorch_libs.py | ||
| cpuinfo_target_definition.bzl | ||
| 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 | ||
| miniz_target_definition.bzl | ||
| nightly.py | ||
| nvcc_fix_deps.py | ||
| perf_kernel_defs.bzl | ||
| pytorch.version | ||
| README.md | ||
| render_junit.py | ||
| sgx_aten_target_definitions.bzl | ||
| sgx_caffe2_target_definitions.bzl | ||
| sgx_target_definitions.bzl | ||
| substitute.py | ||
| target_definitions.bzl | ||
| 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.)
- stats/test_history.py - Query S3 to display history of a single test across multiple jobs over time.
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.
- run-clang-tidy-in-ci.sh - Responsible for checking that C++ code is clang-tidy clean in CI on Travis