torch.jit.annotations.parse_type_line is not safe (command injection) #88868
Use the Python frame safely in _pythonCallstack #88993
Double-backward with full_backward_hook causes RuntimeError #88312
Fix logical error in get_default_qat_qconfig #88876
Fix cuda/cpu check on NoneType and unit test #88854 and #88970
Onnx ATen Fallback for BUILD_CAFFE2=0 for ONNX-only ops #88504
Onnx operator_export_type on the new registry #87735
torchrun AttributeError caused by file_based_local_timer on Windows #85427
The release tracker should contain all relevant pull requests related to this release as well as links to related issues
PyTorch 1.13: beta versions of functorch and improved support for Apple’s new M1 chips are now available
Pytorch 1.13 Release Notes
Highlights
Backwards Incompatible Changes
New Features
Improvements
Performance
Documentation
Developers
Highlights
We are excited to announce the release of PyTorch 1.13! This includes stable versions of BetterTransformer. We deprecated CUDA 10.2 and 11.3 and completed migration of CUDA 11.6 and 11.7. Beta includes improved support for Apple M1 chips and functorch, a library that offers composable vmap (vectorization) and autodiff transforms, being included in-tree with the PyTorch release. This release is composed of over 3,749 commits and 467 contributors since 1.12.1. We want to sincerely thank our dedicated community for your contributions.
Summary:
The BetterTransformer feature set supports fastpath execution for common Transformer models during Inference out-of-the-box, without the need to modify the model. Additional improvements include accelerated add+matmul linear algebra kernels for sizes commonly used in Transformer models and Nested Tensors is now enabled by default.
Timely deprecating older CUDA versions allows us to proceed with introducing the latest CUDA version as they are introduced by Nvidia®, and hence allows support for C++17 in PyTorch and new NVIDIA Open GPU Kernel Modules.
Previously, functorch was released out-of-tree in a separate package. After installing PyTorch, a user will be able to import functorch and use functorch without needing to install another package.
PyTorch is offering native builds for Apple® silicon machines that use Apple's new M1 chip as a beta feature, providing improved support across PyTorch's APIs.
Stable
Beta
Prototype
Better TransformerCUDA 10.2 and 11.3 CI/CD Deprecation
Enable Intel® VTune™ Profiler's Instrumentation and Tracing Technology APIsExtend NNC to support channels last and bf16Functorch now in PyTorch Core LibraryBeta Support for M1 devices
Arm® Compute Library backend support for AWS Graviton CUDA Sanitizer
You can check the blogpost that shows the new features here.
Releasing a new version of PyTorch generally entails 3 major steps:
Cutting a release branch preparations
Cutting a release branch and making release branch specific changes
Drafting RCs (Release Candidates), and merging cherry picks
Promoting RCs to stable and performing release day tasks
Cutting a release branch preparations
Following Requirements needs to be met prior to final RC Cut:
Resolve all outstanding issues in the milestones(for example 1.11.0)before first RC cut is completed. After RC cut is completed following script should be executed from builder repo in order to validate the presence of the fixes in the release branch :
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Bumps torch from 1.11.0+cu113 to 1.13.1.
Release notes
Sourced from torch's releases.
... (truncated)
Changelog
Sourced from torch's changelog.
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Commits
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