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This PR is in preparation for implementing `logdet` and `slogdet` as structured kernels + implementing them with more efficient derivatives We implement forward AD for det. We also simplify the implementation of the backward, and leave a note on how to implement it properly for singular matrices. We leave thad for future work. Note (by looking at the OpInfo) that the current implementation passes the same tests as the one before. We skip the forward-over-backward in the singular case, as that one was not working in the gradgrad case either. Pull Request resolved: https://github.com/pytorch/pytorch/pull/79487 Approved by: https://github.com/nikitaved, https://github.com/albanD |
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| .. | ||
| amd_build | ||
| autograd | ||
| 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 | ||
| build_libtorch.py | ||
| build_pytorch_libs.py | ||
| cpuinfo_target_definition.bzl | ||
| download_mnist.py | ||
| extract_scripts.py | ||
| gen_flatbuffers.sh | ||
| 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