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When we compute contiguity for a tensor with dynamic shapes we first: 1) Try to compute it without guarding. 2) If all shapes hinted, compute it with potentially adding guards. 3) if any input is not hinted, compute it symbolically. sym_is_contiguous return a SymBool that is then either evaluated or guard_or_false can be called on it to avoid data dependent errors. ex: bool is_contiguous = input.sym_is_contiguous().guard_or_false(__FILE__, __LINE__); is_contiguous_or_false is a helper function that does that. In this PR I only handle default contiguity, will follow up with changes for other formats like channel_last . We use this patter in this PR for several locations to avoid DDEs. Differential Revision: [D77183032](https://our.internmc.facebook.com/intern/diff/D77183032) Pull Request resolved: https://github.com/pytorch/pytorch/pull/155590 Approved by: https://github.com/ezyang
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.