Update torch requirement from ~=1.9.1 to ~=1.13.0

[dependabot]

Updates the requirements on torch to permit the latest version.

Release notes

Sourced from torch's releases.

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

Python API

uint8 and all integer dtype masks are no longer allowed in Transformer (#87106)

Prior to 1.13, key_padding_mask could be set to uint8 or other integer dtypes in TransformerEncoder and MultiheadAttention, which might generate unexpected results. In this release, these dtypes are not allowed for the mask anymore. Please convert them to torch.bool before using.

1.12.1

>>> layer = nn.TransformerEncoderLayer(2, 4, 2)
>>> encoder = nn.TransformerEncoder(layer, 2)
>>> pad_mask = torch.tensor([[1, 1, 0, 0]], dtype=torch.uint8)
>>> inputs = torch.cat([torch.randn(1, 2, 2), torch.zeros(1, 2, 2)], dim=1)
# works before 1.13
>>> outputs = encoder(inputs, src_key_padding_mask=pad_mask)

... (truncated)

Changelog

Sourced from torch's changelog.

Releasing PyTorch

• General Overview
• Cutting a release branch preparations
• Cutting release branches
  • pytorch/pytorch
  • pytorch/builder / PyTorch domain libraries
  • Making release branch specific changes for PyTorch
  • Making release branch specific changes for domain libraries
• Drafting RCs (https://github.com/pytorch/pytorch/blob/master/Release Candidates) for PyTorch and domain libraries
  • Release Candidate Storage
  • Release Candidate health validation
  • Cherry Picking Fixes
• Promoting RCs to Stable
• Additional Steps to prepare for release day
  • Modify release matrix
  • Open Google Colab issue
• Patch Releases
  • Patch Release Criteria
  • Patch Release Process
    • Triage
    • Building a release schedule / cherry picking
    • Building Binaries / Promotion to Stable
• Hardware / Software Support in Binary Build Matrix
  • Python
    • TL;DR
  • Accelerator Software
    • Special support cases
• Special Topics
  • Updating submodules for a release

General Overview

Releasing a new version of PyTorch generally entails 3 major steps:

0. Cutting a release branch preparations
1. Cutting a release branch and making release branch specific changes
2. Drafting RCs (Release Candidates), and merging cherry picks
3. 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 : python github_analyze.py --repo-path ~/local/pytorch --remote upstream --branch release/1.11 --milestone-id 26 --missing-in-branch

... (truncated)

Commits

• 7c98e70 attempted fix for nvrtc with lovelace (#87611) (#87618)
• 4e1a4b1 fix docs push (#87498) (#87628)
• 341c377 Add General Project Policies (#87385) (#87613)
• fdb18da Fix distributed issue by including distributed files (#87612)
• 8569a44 [MPS] Revamp copy_to_mps_ implementation (#87475)
• 6a8be2c [ONNX] Reland: Update training state logic to support ScriptedModule (#86745)...
• f6c42ae Reenable isinstance with torch.distributed.ReduceOp (#87303) (#87463)
• 51fa4fa Move PadNd from ATen/native to ATen (#87456)
• d3aecbd Delete torch::deploy from pytorch core (#85953) (#85953) (#87454)
• d253eb2 Avoid calling logging.basicConfig (#86959) (#87455)
• Additional commits viewable in compare view

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[dependabot]

Superseded by #228.