Bump torch from 1.11.0 to 2.3.1

[dependabot[bot]]

trafficstars

Bumps torch from 1.11.0 to 2.3.1.

Release notes

Sourced from torch's releases.

PyTorch 2.3.1 Release, bug fix release

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

Torch.compile:

• Remove runtime dependency on JAX/XLA, when importing torch.__dynamo (pytorch/pytorch#124634)
• Hide Plan failed with a cudnnException warning (pytorch/pytorch#125790)
• Fix CUDA memory leak (pytorch/pytorch#124238) (pytorch/pytorch#120756)

Distributed:

• Fix format_utils executable, which was causing it to run as a no-op (pytorch/pytorch#123407)
• Fix regression with device_mesh in 2.3.0 during initialization causing memory spikes (pytorch/pytorch#124780)
• Fix crash of FSDP + DTensor with ShardingStrategy.SHARD_GRAD_OP (pytorch/pytorch#123617)
• Fix failure with distributed checkpointing + FSDP if at least 1 forward/backward pass has not been run. (pytorch/pytorch#121544) (pytorch/pytorch#127069)
• Fix error with distributed checkpointing + FSDP, and with use_orig_params = False and activation checkpointing (pytorch/pytorch#124698) (pytorch/pytorch#126935)
• Fix set_model_state_dict errors on compiled module with non-persistent buffer with distributed checkpointing (pytorch/pytorch#125336) (pytorch/pytorch#125337)

MPS:

• Fix data corruption when coping large (>4GiB) tensors (pytorch/pytorch#124635)
• Fix Tensor.abs() for complex (pytorch/pytorch#125662)

Packaging:

• Fix UTF-8 encoding on Windows .pyi files (pytorch/pytorch#124932)
• Fix import torch failure when wheel is installed for a single user on Windows(pytorch/pytorch#125684)
• Fix compatibility with torchdata 0.7.1 (pytorch/pytorch#122616)
• Fix aarch64 docker publishing to https://ghcr.io (pytorch/pytorch#125617)
• Fix performance regression an aarch64 linux (pytorch/builder#1803)

Other:

• Fix DeepSpeed transformer extension build on ROCm (pytorch/pytorch#121030)
• Fix kernel crash on tensor.dtype.to_complex() after ~100 calls in ipython kernel (pytorch/pytorch#125154)

Release tracker pytorch/pytorch#125425 contains all relevant pull requests related to this release as well as links to related issues.

PyTorch 2.3: User-Defined Triton Kernels in torch.compile, Tensor Parallelism in Distributed

PyTorch 2.3 Release notes

• Highlights
• Backwards Incompatible Changes
• Deprecations
• New Features
• Improvements
• Bug fixes
• Performance
• Documentation

Highlights

We are excited to announce the release of PyTorch® 2.3! PyTorch 2.3 offers support for user-defined Triton kernels in torch.compile, allowing for users to migrate their own Triton kernels from eager without experiencing performance complications or graph breaks. As well, Tensor Parallelism improves the experience for training Large Language Models using native PyTorch functions, which has been validated on training runs for 100B parameter models.

This release is composed of 3393 commits and 426 contributors since PyTorch 2.2. 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 2.3. More information about how to get started with the PyTorch 2-series can be found at our Getting Started page.

... (truncated)

Changelog

Sourced from torch's changelog.

Releasing PyTorch

• Release Compatibility Matrix
• Release Cadence
• General Overview
  • Frequently Asked Questions
• 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
• Running Launch Execution team Core XFN sync
• Drafting RCs (https://github.com/pytorch/pytorch/blob/main/Release Candidates) for PyTorch and domain libraries
  • Release Candidate Storage
  • Release Candidate health validation
  • Cherry Picking Fixes
    • How to do Cherry Picking
  • Cherry Picking Reverts
• Preparing and Creating Final Release candidate
• 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
    • Patch Release Process Description
    • Triage
    • Issue Tracker for Patch releases
    • 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
  • Operating Systems
• Submitting Tutorials
• Special Topics
  • Updating submodules for a release
  • Triton dependency for the release

Release Compatibility Matrix

Following is the Release Compatibility Matrix for PyTorch releases:

... (truncated)

Commits

• 63d5e92 [EZ] Pin scipy to 1.12 for Py-3.12 (#127322)
• 91bdec3 Update hf_BirdBird periodic-dynamo-benchmarks results (#127312)
• d44533f Put back "[Release only] Release 2.3 start using triton package from pypi"" (...
• bd1040c [DSD] Fix to remove non_persistent buffer in distributed state dict (#125337)...
• 81b8854 [DSD] Add a test to verify FSDP lazy initialization case (#127069) (#127130)
• e63004b [DCP][state_dict] Remove the check of FSDP has root (#121544) (#126557)
• 00804a7 [DSD] Correctly handle _extra_state (#125336) (#126567)
• cd033a1 [Cherry-pick][DCP][AC] Add test for apply AC with FSDP1 (#126935) (#126992)
• 19058a6 Remove activation checkpointing tag to get correct FQNs (#124698) (#126559)
• 30650e0 [FSDP1] fix _same_storage check for DTensor (#123617) (#126957)
• Additional commits viewable in compare view

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 <dependency name> 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)