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https://github.com/pytorch/pytorch.git
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14004cbef6
Summary: - Move batch norm from TH(CU)NN to native - Speedups in many cases (e.g. #12006) for CUDA due to new block/grid layout and Welford-type mean/variance calculations (the latter for training mode) - It splits the forward kernel in two pieces and reuses the evaluation kernel for the transformation. - We change the meaning of save_mean and save_invstd (aka save_var) to accscalar to maintain reasonable precision. Compared to the ill-fated #12368 - I changed the CPU kernel to not call `.sum()` from within parallel for. This seemed to have caused the breakage (NaN-results) in TestModels.test_dcgan_netG (thank you houseroad for the repro, errors in assessment of the fix are my own) - I updated the Half->Float upcasting in tensors to go through `t.type().scalarType()` instead of `t.dtype()`. - I have merged master Pull Request resolved: https://github.com/pytorch/pytorch/pull/13263 Differential Revision: D12946254 Pulled By: SsnL fbshipit-source-id: 3bb717ee250fbccaf10afe73722996aa4713d10d
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.
Legacy infrastructure (we should kill this):
- nnwrap - Generates the THNN/THCUNN wrappers which make legacy functionality available. (TODO: What exactly does this implement?)
- cwrap - Implementation of legacy code generation for THNN/THCUNN. This is used by nnwrap.
Build system pieces:
- setup_helpers - Helper code for searching for third-party dependencies on the user system.
- build_pytorch_libs.sh - Script that builds all of the constituent libraries of PyTorch, but not the PyTorch Python extension itself. We are working on eliminating this script in favor of a unified cmake build.
- build_pytorch_libs.bat - Same as above, but for Windows.
- 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:
- clang_tidy.py - Script for running clang-tidy on lines of your script which you changed.
- 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_caffe2_amd.py - Script for HIPifying the Caffe2 codebase.
- build_pytorch_amd.py - Script for HIPifying the PyTorch codebase.
Tools which only situationally useful:
- aten_mirror.sh - Mirroring script responsible for keeping https://github.com/zdevito/ATen up-to-date.
- 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