Bump torch from 1.5.0 to 1.13.1 - #11

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Bump torch from 1.5.0 to 1.13.1#11
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Bumps torch from 1.5.0 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.

StableBetaPrototype
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 devicesArm® 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 versionPythonStable CUDAExperimental CUDA
2.0>=3.8, <=3.11CUDA 11.7, CUDNN 8.5.0.96CUDA 11.8, CUDNN 8.7.0.84
1.13>=3.7, <=3.10CUDA 11.6, CUDNN 8.3.2.44CUDA 11.7, CUDNN 8.5.0.96
1.12>=3.7, <=3.10CUDA 11.3, CUDNN 8.3.2.44CUDA 11.6, CUDNN 8.3.2.44

General Overview

... (truncated)

Commits

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Bump torch from 1.5.0 to 1.13.1
6930d5d
Bumps [torch](https://github.com/pytorch/pytorch) from 1.5.0 to 1.13.1.
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@v1.5.0...v1.13.1)
---
updated-dependencies:
- dependency-name: torch
dependency-type: direct:production
...
Signed-off-by: dependabot[bot] <support@github.com>
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Skip to content
Bump torch from 1.5.0 to 1.13.1 - #11
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Bump torch from 1.5.0 to 1.13.1#11
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Bumps torch from 1.5.0 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.
StableBetaPrototype
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 devicesArm® 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 versionPythonStable CUDAExperimental CUDA
2.0>=3.8, <=3.11CUDA 11.7, CUDNN 8.5.0.96CUDA 11.8, CUDNN 8.7.0.84
1.13>=3.7, <=3.10CUDA 11.6, CUDNN 8.3.2.44CUDA 11.7, CUDNN 8.5.0.96
1.12>=3.7, <=3.10CUDA 11.3, CUDNN 8.3.2.44CUDA 11.6, CUDNN 8.3.2.44
General Overview
... (truncated)
Commits

Dependabot compatibility score
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Bump torch from 1.5.0 to 1.13.1
6930d5d
Bumps [torch](https://github.com/pytorch/pytorch) from 1.5.0 to 1.13.1.
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@v1.5.0...v1.13.1)
---
updated-dependencies:
- dependency-name: torch
dependency-type: direct:production
...
Signed-off-by: dependabot[bot] <support@github.com>
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, 'i');
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(function(){
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var __m = "*";
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Bump torch from 1.5.0 to 1.13.1 - #11
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Bump torch from 1.5.0 to 1.13.1#11
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Bumps torch from 1.5.0 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.
StableBetaPrototype
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 devicesArm® 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 versionPythonStable CUDAExperimental CUDA
2.0>=3.8, <=3.11CUDA 11.7, CUDNN 8.5.0.96CUDA 11.8, CUDNN 8.7.0.84
1.13>=3.7, <=3.10CUDA 11.6, CUDNN 8.3.2.44CUDA 11.7, CUDNN 8.5.0.96
1.12>=3.7, <=3.10CUDA 11.3, CUDNN 8.3.2.44CUDA 11.6, CUDNN 8.3.2.44
General Overview
... (truncated)
Commits

Dependabot compatibility score
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Bump torch from 1.5.0 to 1.13.1
6930d5d
Bumps [torch](https://github.com/pytorch/pytorch) from 1.5.0 to 1.13.1.
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@v1.5.0...v1.13.1)
---
updated-dependencies:
- dependency-name: torch
dependency-type: direct:production
...
Signed-off-by: dependabot[bot] <support@github.com>
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, 'i');
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Bump torch from 1.5.0 to 1.13.1 - #11
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Bump torch from 1.5.0 to 1.13.1#11
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Bumps torch from 1.5.0 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.
StableBetaPrototype
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 devicesArm® 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 versionPythonStable CUDAExperimental CUDA
2.0>=3.8, <=3.11CUDA 11.7, CUDNN 8.5.0.96CUDA 11.8, CUDNN 8.7.0.84
1.13>=3.7, <=3.10CUDA 11.6, CUDNN 8.3.2.44CUDA 11.7, CUDNN 8.5.0.96
1.12>=3.7, <=3.10CUDA 11.3, CUDNN 8.3.2.44CUDA 11.6, CUDNN 8.3.2.44
General Overview
... (truncated)
Commits

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Bump torch from 1.5.0 to 1.13.1
6930d5d
Bumps [torch](https://github.com/pytorch/pytorch) from 1.5.0 to 1.13.1.
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@v1.5.0...v1.13.1)
---
updated-dependencies:
- dependency-name: torch
dependency-type: direct:production
...
Signed-off-by: dependabot[bot] <support@github.com>
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
Bump torch from 1.5.0 to 1.13.1 - #11
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Bump torch from 1.5.0 to 1.13.1#11
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Bumps torch from 1.5.0 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.
StableBetaPrototype
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 devicesArm® 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 versionPythonStable CUDAExperimental CUDA
2.0>=3.8, <=3.11CUDA 11.7, CUDNN 8.5.0.96CUDA 11.8, CUDNN 8.7.0.84
1.13>=3.7, <=3.10CUDA 11.6, CUDNN 8.3.2.44CUDA 11.7, CUDNN 8.5.0.96
1.12>=3.7, <=3.10CUDA 11.3, CUDNN 8.3.2.44CUDA 11.6, CUDNN 8.3.2.44
General Overview
... (truncated)
Commits

Dependabot compatibility score
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Bump torch from 1.5.0 to 1.13.1
6930d5d
Bumps [torch](https://github.com/pytorch/pytorch) from 1.5.0 to 1.13.1.
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@v1.5.0...v1.13.1)
---
updated-dependencies:
- dependency-name: torch
dependency-type: direct:production
...
Signed-off-by: dependabot[bot] <support@github.com>
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Bump torch from 1.5.0 to 1.13.1 - #11
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Bump torch from 1.5.0 to 1.13.1#11
dependabot[bot] wants to merge 1 commit into
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Bumps torch from 1.5.0 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.
StableBetaPrototype
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 devicesArm® 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 versionPythonStable CUDAExperimental CUDA
2.0>=3.8, <=3.11CUDA 11.7, CUDNN 8.5.0.96CUDA 11.8, CUDNN 8.7.0.84
1.13>=3.7, <=3.10CUDA 11.6, CUDNN 8.3.2.44CUDA 11.7, CUDNN 8.5.0.96
1.12>=3.7, <=3.10CUDA 11.3, CUDNN 8.3.2.44CUDA 11.6, CUDNN 8.3.2.44
General Overview
... (truncated)
Commits

Dependabot compatibility score
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You can disable automated security fix PRs for this repo from the Security Alerts page.
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Bump torch from 1.5.0 to 1.13.1
6930d5d
Bumps [torch](https://github.com/pytorch/pytorch) from 1.5.0 to 1.13.1.
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@v1.5.0...v1.13.1)
---
updated-dependencies:
- dependency-name: torch
dependency-type: direct:production
...
Signed-off-by: dependabot[bot] <support@github.com>
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Bump torch from 1.5.0 to 1.13.1 - #11
Open
dependabot[bot] wants to merge 1 commit into
masterfrom 
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Bump torch from 1.5.0 to 1.13.1#11
dependabot[bot] wants to merge 1 commit into
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Bumps torch from 1.5.0 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.
StableBetaPrototype
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 devicesArm® 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 versionPythonStable CUDAExperimental CUDA
2.0>=3.8, <=3.11CUDA 11.7, CUDNN 8.5.0.96CUDA 11.8, CUDNN 8.7.0.84
1.13>=3.7, <=3.10CUDA 11.6, CUDNN 8.3.2.44CUDA 11.7, CUDNN 8.5.0.96
1.12>=3.7, <=3.10CUDA 11.3, CUDNN 8.3.2.44CUDA 11.6, CUDNN 8.3.2.44
General Overview
... (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
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You can disable automated security fix PRs for this repo from the Security Alerts page.
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Bump torch from 1.5.0 to 1.13.1
6930d5d
Bumps [torch](https://github.com/pytorch/pytorch) from 1.5.0 to 1.13.1.
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@v1.5.0...v1.13.1)
---
updated-dependencies:
- dependency-name: torch
dependency-type: direct:production
...
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Bump torch from 1.5.0 to 1.13.1 - #11
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Bump torch from 1.5.0 to 1.13.1#11
dependabot[bot] wants to merge 1 commit into
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Bumps torch from 1.5.0 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.
StableBetaPrototype
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 devicesArm® 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 versionPythonStable CUDAExperimental CUDA
2.0>=3.8, <=3.11CUDA 11.7, CUDNN 8.5.0.96CUDA 11.8, CUDNN 8.7.0.84
1.13>=3.7, <=3.10CUDA 11.6, CUDNN 8.3.2.44CUDA 11.7, CUDNN 8.5.0.96
1.12>=3.7, <=3.10CUDA 11.3, CUDNN 8.3.2.44CUDA 11.6, CUDNN 8.3.2.44
General Overview
... (truncated)
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Bump torch from 1.5.0 to 1.13.1
6930d5d
Bumps [torch](https://github.com/pytorch/pytorch) from 1.5.0 to 1.13.1.
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@v1.5.0...v1.13.1)
---
updated-dependencies:
- dependency-name: torch
dependency-type: direct:production
...
Signed-off-by: dependabot[bot] <support@github.com>
@dependabotdependabotBot
added
the 
dependencies
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label
Jul 23, 2023
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