search

WZMIAOMIAO / deep-learning-for-image-processing

deep learning for image processing including classification and object-detection etc.
GNU General Public License v3.0
23.29k stars 8.01k forks source link

Bump torch from 1.11.0 to 1.13.1 in /deploying_service/deploying_pytorch/convert_openvino/convert_resnet34 #762

Open dependabot[bot] opened 1 year ago

dependabot[bot] commented 1 year ago

Bumps torch from 1.11.0 to 1.13.1.

Release notes

Sourced from torch's releases.

PyTorch 1.13.1 Release, small bug fix release

This release is meant to fix the following issues (regressions / silent correctness):

  • RuntimeError by torch.nn.modules.activation.MultiheadAttention with bias=False and batch_first=True #88669
  • Installation via pip on Amazon Linux 2, regression #88869
  • Installation using poetry on Mac M1, failure #88049
  • Missing masked tensor documentation #89734
  • 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.

Backwards Incompatible changes

... (truncated)

Changelog

Sourced from torch's changelog.

Releasing PyTorch

Release Compatibility Matrix

Following is the Release Compatibility Matrix for PyTorch releases:

PyTorch version Python Stable CUDA Experimental CUDA
2.1 >=3.8, <=3.11 CUDA 11.8, CUDNN 8.7.0.84 CUDA 12.1, CUDNN 8.9.2.26

... (truncated)

Commits


Dependabot compatibility score

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Dependabot commands and options
You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot show ignore conditions` will show all of the ignore conditions of the specified dependency - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself) You can disable automated security fix PRs for this repo from the [Security Alerts page](https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/network/alerts).