Bump torch from 2.13.0 to 2.14.1

[dependabot]

Bumps torch from 2.13.0 to 2.14.1.

Release notes

Sourced from torch's releases.

PyTorch 2.14.1 Release

This release is meant to fix the following regressions and silent correctness issues:

Silent correctness fixes

• Fix incorrect torch.linalg.lstsq solutions on MPS for complex batched underdetermined systems (#196113), fixed by #196128
• Fix non-orthogonal U and inaccurate small singular values from torch.linalg.svd on MPS for rank-deficient and ill-conditioned inputs (#196112), fixed by #196139 and #199063
• Update the CUDA 13.2 Linux binaries to CUDA 13.2.2 (#196351). This NVIDIA update resolves two critical issues that could produce incorrect results (CUDA 13.2.2 release notes):
  • cuBLAS: cublasLtMatmul() could ignore tensor-wide scaling for NVFP4 matrix multiplications (introduced in CUDA 13.2 Update 1)
  • Compiler: failed thread reconvergence could leave stale or corrupted register values in kernels with nested thread divergence (present since CUDA 12.8)

Regression fixes

• Fix torch.linalg.svd, torch.linalg.svdvals and torch.linalg.lstsq failing on MPS with a Metal pipeline-state error for inputs above 8192 elements (#195937), fixed by #195949 and #195950
• Fix internal assert in torch.svd(out=) on MPS for complex inputs (#195822), fixed by #195872

PyTorch 2.14.0 Release Notes

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

Highlights

For more details about these highlighted features, you can look at the release blogpost. Below are the full release notes for this release.

Backwards Incompatible Changes

torch.nn

• torch.nn.LinearCrossEntropyOptions no longer accepts acc_policy="balanced"; use "compact" instead (#188283)

  The "balanced" policy was removed because "compact" provides the same weight-gradient accumulation precision with lower memory use on CUDA, already uses the equivalent scratch layout for mixed-precision inputs on other devices, and was never selected by "auto". Constructing the options with acc_policy="balanced" now raises ValueError: invalid acc_policy: 'balanced'; expected one of 'auto', 'accurate', 'compact'.

... (truncated)

Commits

• 5c48869 [MPS] Fix Jacobi SVD convergence for small columns (#199086)
• 35223a2 [release/2.14] Run the release runner-group reconcile in pytorch/pytorch (#19...
• 41ffbc4 [release-only] Update version to 2.14.1 (#198661)
• e8f0c01 [release/2.14] [CD] Update to CUDA 13.2.2 for Linux binaries (#198644)
• 911665d [MPS] Cherry-pick native SVD fixes into release/2.14 (#198394)
• 2b3ec34 [release/2.14] Import SDPAParams in test_transformers to fix lint (#194970)
• 08187d9 [cuDNN] Add guards for cuDNN SDPA decode (#194963)
• 8ceea97 Pin cython < 3.3.0 for the Windows Triton wheel build (#194931)
• 99ecebc [Cherry-pick][release/2.14] [inductor] Fix loop-local load CSE lifetime (#194...
• ec283a7 Bump the Python 3.15 numpy pin to 2.5.2 (#194821)
• Additional commits viewable in compare view