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b5cd4ac950
Fixes https://github.com/pytorch/executorch/issues/8711 In ExecuTorch when we try to parse the following schema: ``` aten::__lshift__.Scalar(Tensor self, Scalar other) -> Tensor ``` Repro: ```python from torchgen.model import FunctionSchema native_schema = FunctionSchema.parse("aten::__lshift__.Scalar(Tensor self, Scalar other) -> Tensor") ``` It's failing because `BaseOperatorName` categorizes it to be a inplace operator. I understand we are not supposed to pass in namespace "aten::" into `FunctionSchema.parse()` but unfortunately ExecuTorch requires this feature to work. This PR adds a new `namespace` attribute to `BaseOperatorName` and makes sure the rest of the stack works as before, if a schema without namespace is passed in Pull Request resolved: https://github.com/pytorch/pytorch/pull/148038 Approved by: https://github.com/bdhirsh
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.