Bump torch from 1.12.0+cpu to 1.13.0

[dependabot]

Bumps torch from 1.12.0+cpu to 1.13.0.

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)

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[pcaversaccio]

@dependabot recreate

[dependabot]

Superseded by #109.