Bump the uv-updates group with 5 updates

Merged
DE
15d ago
+340-260

Bumps the uv-updates group with 5 updates:

PackageFromTo
mkl-fft2.2.12.3.2
torch2.13.02.14.1
cupy-cuda12x14.1.114.2.0
matplotlib3.11.13.11.2
mypy2.3.02.4.0

Updates mkl-fft from 2.2.1 to 2.3.2

Release notes

Sourced from mkl-fft's releases.

2.3.2

This is a bug-fix release, identical to 2.3.0 and 2.3.1 in terms of features.

The macOS RPATH fix shipped in 2.3.1 was incomplete. This release emits one -rpath argument per path instead of colon-joining them, which is an ELF-only convention that produced an unusable LC_RPATH entry on macOS.

Fixed

  • Emit one -rpath linker argument per path instead of colon-joining them, so the runtime library search path is baked correctly into macOS (Mach-O) extensions gh-358

Full Changelog: https://github.com/IntelPython/mkl_fft/compare/2.3.1...2.3.2

2.3.1

This release is identical to 2.3.0 in terms of features

This release only fixes RPATH logic for OSX, which was mistakenly broken in migration to meson-python build system.

2.3.0

Overview

This release migrates mkl_fft to a modern Meson-based build system, replacing the legacy setuptools/setup.py pipeline. It also introduces a command-line interface for patching NumPy's FFT with MKL-accelerated implementations.

Highlights

🏗️ New Build System

  • Migrated from setuptools to meson-python, removing setup.py in favor of meson.build, aligning mkl_fft with the build system used by NumPy and SciPy
  • MKL is now discovered via CMake, and ninja/cmake are formal build requirements

🆕 NumPy FFT patching CLI

  • Added python -m mkl_fft patch install/uninstall/status for persistent patching across all Python sessions
  • Added python -m mkl_fft with_patch <command> for one-shot execution with MKL acceleration

Python Version Support

  • âś… Python 3.10, 3.11, 3.12, 3.13, 3.14

Detailed Changes

Added

  • Added CLI for persistent and ephemeral NumPy FFT patching: python -m mkl_fft patch install/uninstall/status for persistent patching across all Python sessions, and python -m mkl_fft with_patch <command> for one-shot execution with MKL acceleration gh-319

Changed

  • Removed numpy-base dependency and USE_NUMPY_BASE environment variable from conda recipe gh-318
  • Migrated the build system from setuptools to meson-python, removing setup.py in favor of meson.build gh-303

Contributors

... (truncated)

Changelog

Sourced from mkl-fft's changelog.

[2.3.2] - 2026-08-04

Fixed

  • Emit one -rpath linker argument per path instead of colon-joining them, so the runtime library search path is baked correctly into macOS (Mach-O) extensions gh-358

[2.3.1] - 2026-07-31

Fixed

  • Fixed RPATH logic for OSX builds gh-351

[2.3.0] - 2026-07-30

Added

  • Added CLI for persistent and ephemeral NumPy FFT patching: python -m mkl_fft patch install/uninstall/status for persistent patching across all Python sessions, and python -m mkl_fft with_patch <command> for one-shot execution with MKL acceleration gh-319

Changed

  • Removed numpy-base dependency and USE_NUMPY_BASE environment variable from conda recipe gh-318
  • Migrated the build system from setuptools to meson-python, removing setup.py in favor of meson.build gh-303
Commits

Updates 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

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

Updates cupy-cuda12x from 14.1.1 to 14.2.0

Release notes

Sourced from cupy-cuda12x's releases.

v14.2.0

CuPy v14.2.0 Release Note

This release for the CuPy v14 series introduces new features, enhancements, and bug fixes.

đź’¬ Join the Matrix chat to talk with developers and users and ask quick questions!

🙌 Help us sustain the project by sponsoring CuPy!

✨ Highlights

SciPy-compatible Sparse Array Classes

We are happy to present to you the new sparse array classes, following SciPy counterparts for CPU. We encourage users to migrate from sparse matrices to sparse arrays following SciPy’s migration guide. New user code should stick to sparse arrays.

In this release, the supported formats include COO, CSR, CSC, and DIA. All formats support 2D and COO/CSR additionally support 1D. Both 32-bit and 64-bit indexing are supported. See our sparse array documentation for more information.

Windows on Arm Support (experimental)

CuPy now offers experimental support for CUDA 13.4 Developer Preview on RTX Spark. Building CuPy from source on WoA (Windows on Arm) is now possible. Experimental WoA wheels will be uploaded to PyPI in a few days after the release are available on PyPI (uploaded on 2026-09-01).

Initial integration with nvmath-python

As part of tighter coupling with NVIDIA CUDA Python platform, CuPy has begun the integration with nvmath-python, NVIDIA Math Libraries for the Python Ecosystem. CuPy can now be built from source against nvmath-python v1.0+ with the environment variable CUPY_USE_CUDA_PYTHON=1 to access CUDA math libraries. nvmath-python will become a default dependency in a future CuPy release.

Python 3.14 free-threaded Wheels for Windows

In addition to the Linux wheels provided in v14.1, CuPy v14.2 now offers Python 3.14t wheels for Windows.

đź“ť Changes

See here for the complete list of merged PRs.

New Features

  • Use nvmath bindings for CUDA library bindings (#10112)
  • ENH: add explicit copy arg to reshape (#10147)
  • Add cumulative_sum and cumulative_prod (Array API) (#10200)

Enhancements

  • TST: Assert cupy.linalg.solve throws LinAlgError (#9987)
  • Define NULL in cupyx.scipy.spatial Delaunay kernel string (#9991)
  • Replace deprecated HIP API calls with their non-deprecated equivalents (#9993)
  • FeistelBijection RawKernel: select <stdint.h> on HIP, keep <cuda/std/cstdint> on CUDA (#9994)
  • Resolve symlinks when deriving ROCm install path from hipcc (#9995)
  • Test with and document support for cusparseLt v0.9.1 (#10004)
  • Provide HIPRTC fallback for cupy.ndarray.byteswap (#10006)
  • Update xsf submodule (#10027)
  • Add SciPy-compatible sparse array classes (sparray) [split 1/3] (#10032)
  • ENH: It seems cooperative groups do not need rdc (anymore) (#10038)

... (truncated)

Commits
  • dc1552a Merge pull request #10217 from kmaehashi/bump-docker-v14.2.0
  • dcb70f2 bump version in Dockerfile
  • 78bf2c7 Merge pull request #10209 from kmaehashi/bump-v14.2.0
  • 232d0de bump version to v14.2.0
  • 1aee546 Merge pull request #10208 from chainer-ci/bp-10003-v14-weak-old-fusion
  • 2181d94 Merge pull request #10003 from seberg/weak-old-fusion
  • 6de78f4 Merge pull request #10207 from chainer-ci/bp-10190-v14-ci/pfnci-followups
  • da29eea Merge pull request #10190 from leofang/ci/pfnci-followups
  • 82dea1c Merge pull request #10206 from chainer-ci/bp-10205-v14-copilot/generate-gitat...
  • c1975b0 Merge pull request #10203 from chainer-ci/bp-10177-v14-name-expr-cache-mapping
  • Additional commits viewable in compare view

Updates matplotlib from 3.11.1 to 3.11.2

Release notes

Sourced from matplotlib's releases.

REL: v3.11.2

This is the second bugfix release of the 3.11.x series.

This release contains several bug-fixes and adjustments:

  • Speed up RGBA-stage image resampling
  • Fix hexbin clipping in PDF output
  • Fix \text with internal braces
  • Fix some crashes and make some checks more robust to unlikely cases
  • Fix frame skew when saving GIF animations with PillowWriter
  • Fix HiDPI handling in Qt toolbar and WebAgg embeddings
  • Fix ResizeEvent handling for TextBox
  • Fix bug with PGF hatch linewidth and color
  • Fix a bug with drawing an empty Collection
  • Fix incorrect glyphs in Cairo backends
  • Improve build system to prevent conflicts between wheels and system libraries
Commits
  • d3ca917 REL: v3.11.2
  • b76fea9 DOC: Set current version to 3.11.2
  • 39c7f06 DOC: Prepare GitHub stats for 3.11.2
  • 5dd13e3 Merge branch 'v3.11.1-doc' into release-3.11.2
  • aa484be Merge branch 'v3.11.0-doc' into v3.11.1-doc
  • 0e10f83 Merge pull request #32330 from meeseeksmachine/auto-backport-of-pr-32324-on-v...
  • ebe947b Backport PR #32324: Don't force any diagnostics to be errors in bundled Harfbuzz
  • 8f4b7c5 Merge pull request #32322 from meeseeksmachine/auto-backport-of-pr-32321-on-v...
  • 93278ad Backport PR #32321: DOC: correct note about PyOS_InputHook
  • b0aee78 Merge pull request #32319 from meeseeksmachine/auto-backport-of-pr-32305-on-v...
  • Additional commits viewable in compare view

Updates mypy from 2.3.0 to 2.4.0

Changelog

Sourced from mypy's changelog.

Mypy Release Notes

Next Release

Mypy 2.4

We've just uploaded mypy 2.4.0 to the Python Package Index (PyPI). Mypy is a static type checker for Python. This release includes new features, performance improvements and bug fixes. You can install it as follows:

python3 -m pip install -U mypy

You can read the full documentation for this release on Read the Docs.

Python 3.15 Support

Mypy 2.4 supports running on Python 3.15 and type checking most Python 3.15 features. This includes the new builtin sentinel type for sentinel values (PEP 661), which mypy now supports (see below for details). Lazy imports (PEP 810) and unpacking in comprehensions (PEP 798) are also supported. Closed TypedDicts (PEP 728) have been supported since mypy 2.2, but the extra_items TypedDict argument, also introduced in PEP 728, is still unsupported. Support for extra_items will be added in a future mypy release.

Native Parser Enabled by Default

Mypy now uses the new native parser by default. It's based on the Ruff parser, and it's significantly faster than the legacy parser, which uses the stdlib ast module. The native parser also has other benefits:

  • You can target newer Python versions and use recent Python syntax even when running mypy on an older Python version. For example, you can use --python-version 3.15 when running mypy on Python 3.14.
  • Stub files can use syntax that is newer than the target Python version. For example, stubs can use the PEP 695 generic class syntax (class Box[T]: ...) when running on or targeting Python 3.10.
  • Parallel type checking requires the native parser.

The legacy parser is still available through --no-native-parser, or native_parser = False in the config file (native_parser = false under [tool.mypy] in pyproject.toml). We are planning to remove the legacy parser in early 2027. If you run into a problem with the native parser, please report it on the issue tracker.

Unlike the legacy parser, the native parser doesn't support type comments for variables defined by for and with statements. These type comments are silently ignored, and the types of the variables are

... (truncated)

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