Bump torch from 1.13.1 to 2.1.1

[dependabot]

Bumps torch from 1.13.1 to 2.1.1.

Release notes

Sourced from torch's releases.

PyTorch 2.1.1 Release, bug fix release

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

• Remove spurious warning in comparison ops (#112170)
• Fix segfault in foreach_* operations when input list length does not match (#112349)
• Fix cuda driver API to load the appropriate .so file (#112996)
• Fix missing CUDA initialization when calling FFT operations (#110326)
• Ignore beartype==0.16.0 within the onnx package as it is incompatible (#111861)
• Fix the behavior of torch.new_zeros in onnx due to TorchScript behavior change (#111694)
• Remove unnecessary slow code in torch.distributed.checkpoint.optimizer.load_sharded_optimizer_state_dict (#111687)
• Add planner argument to torch.distributed.checkpoint.optimizer.load_sharded_optimizer_state_dict (#111393)
• Continue if param not exist in sharded load in torch.distributed.FSDP (#109116)
• Fix handling of non-contiguous bias_mask in torch.nn.functional.scaled_dot_product_attention (#112673)
• Fix the meta device implementation for nn.functional.scaled_dot_product_attention (#110893)
• Fix copy from mps to cpu device when storage_offset is non-zero (#109557)
• Fix segfault in torch.sparse.mm for non-contiguous inputs (#111742)
• Fix circular import between Dynamo and einops (#110575)
• Verify flatbuffer module fields are initialized for mobile deserialization (#109794)

The pytorch/pytorch#110961 contains all relevant pull requests related to this release as well as links to related issues.

PyTorch 2.1: automatic dynamic shape compilation, distributed checkpointing

PyTorch 2.1 Release Notes

• Highlights
• Backwards Incompatible Change
• Deprecations
• New Features
• Improvements
• Bug fixes
• Performance
• Documentation
• Developers
• Security

Highlights

We are excited to announce the release of PyTorch® 2.1! PyTorch 2.1 offers automatic dynamic shape support in torch.compile, torch.distributed.checkpoint for saving/loading distributed training jobs on multiple ranks in parallel, and torch.compile support for the NumPy API.

In addition, this release offers numerous performance improvements (e.g. CPU inductor improvements, AVX512 support, scaled-dot-product-attention support) as well as a prototype release of torch.export, a sound full-graph capture mechanism, and torch.export-based quantization.

Along with 2.1, we are also releasing a series of updates to the PyTorch domain libraries. More details can be found in the library updates blog.

This release is composed of 6,682 commits and 784 contributors since 2.0. We want to sincerely thank our dedicated community for your contributions. As always, we encourage you to try these out and report any issues as we improve 2.1. More information about how to get started with the PyTorch 2-series can be found at our Getting Started page.

Summary:

• torch.compile now includes automatic support for detecting and minimizing recompilations due to tensor shape changes using automatic dynamic shapes.
• torch.distributed.checkpoint enables saving and loading models from multiple ranks in parallel, as well as resharding due to changes in cluster topology.
• torch.compile can now compile NumPy operations via translating them into PyTorch-equivalent operations.
• torch.compile now includes improved support for Python 3.11.
• New CPU performance features include inductor improvements (e.g. bfloat16 support and dynamic shapes), AVX512 kernel support, and scaled-dot-product-attention kernels.
• torch.export, a sound full-graph capture mechanism is introduced as a prototype feature, as well as torch.export-based quantization.

... (truncated)

Commits

• 4c55dc5 remove _shard_tensor() call (#111687)
• f58669b c10::DriverAPI Try opening libcuda.so.1 (#113096)
• 33106b7 [DCP] Add test for planner option for load_sharded_optimizer_state_dict (#11...
• 4b4c012 Enable planner to be used for loading sharded optimizer state dict (#112520)
• 47ac502 [DCP][test] Make dim_0 size of params scale with world_size in torch/distribu...
• dc96ecb Fix mem eff bias bug (#112673) (#112796)
• 18a2ed1 Mirror of Xformers Fix (#112267) (#112795)
• b2e1277 Fix the meta func for mem_eff_backward (#110893) (#112792)
• b249946 [Release-only] Pin Docker images to 2.1 for release (#112665)
• ee79fc8 Revert "Fix bug: not creating empty tensor with correct sizes and device. (#1...
• Additional commits viewable in compare view

Most Recent Ignore Conditions Applied to This Pull Request

Dependency Name	Ignore Conditions
torch	[>= 1.9.a, < 1.10]

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[codecov-commenter]

Codecov Report

Merging #2329 (a5a8fcc) into main (ab389e7) will decrease coverage by 0.12%.
The diff coverage is 66.66%.

❗ Your organization needs to install the Codecov GitHub app to enable full functionality.

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #2329      +/-   ##
==========================================
- Coverage   85.60%   85.49%   -0.12%     
==========================================
  Files         324      324              
  Lines       29326    29331       +5     
  Branches     5407     5409       +2     
==========================================
- Hits        25104    25076      -28     
- Misses       2840     2862      +22     
- Partials     1382     1393      +11     

Files	Coverage Δ
...asion/adversarial_patch/adversarial_patch_numpy.py	74.25% <ø> (ø)
art/attacks/evasion/dpatch.py	91.25% <ø> (ø)
...cks/evasion/imperceptible_asr/imperceptible_asr.py	90.33% <100.00%> (ø)
...ks/inference/membership_inference/shadow_models.py	93.96% <ø> (ø)
...cks/poisoning/perturbations/audio_perturbations.py	88.09% <100.00%> (+0.29%)	⬆️
...efences/trainer/adversarial_trainer_awp_pytorch.py	88.07% <ø> (ø)
...nces/trainer/adversarial_trainer_trades_pytorch.py	88.39% <ø> (ø)
art/defences/transformer/poisoning/strip.py	100.00% <100.00%> (ø)
...timators/classification/deep_partition_ensemble.py	67.27% <ø> (ø)
art/estimators/regression/blackbox.py	100.00% <ø> (ø)
... and 6 more

... and 2 files with indirect coverage changes

[beat-buesser]

@dependabot rebase