[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

[github-actions]

Original Dependabot description

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

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