Build(deps): Bump the torch group in /requirements with 2 updates

[dependabot]

Bumps the torch group in /requirements with 2 updates: torch and torchvision.

Updates torch from 2.12.1 to 2.13.0

Release notes

Sourced from torch's releases.

PyTorch 2.13.0 Release Notes

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

Tracked Regressions

ROCm wheels break torch.compile on CPU in environments without a GPU

Running a torch==2.13.0+rocm7.2 wheel in an environment where no GPU is available (torch.cuda.is_available() is False) breaks torch.compile on the CPU path: the first compile raises RuntimeError: Can't detect vectorized ISA for CPU (#189194). This is a regression from torch==2.12.1+rocm7.2, which compiles CPU code fine (detecting e.g. VecAVX2) in the same setup. The 2.13 ROCm wheel appears to rely on something present in the ROCm builder image to detect the CPU vectorized ISA, so it works when run on a ROCm image but fails on a plain CPU-only image.

Workaround: run the +rocm wheel on a ROCm image, or install a standard CPU/CUDA build for GPU-less environments.

Backwards Incompatible Changes

• Stop building CPython 3.13t (free-threaded) binaries (#182951)

  Upstream pypa/manylinux removed CPython 3.13t (free-threaded) on 2026-05-07, because 3.13t was experimental and has been superseded by the now-non-experimental CPython 3.14t. As a result, PyTorch 2.13 no longer ships cp313t wheels (Linux, Triton, and related artifacts). Users on the free-threaded interpreter should move to Python 3.14t.

  PyTorch 2.12:

  # cp313t (free-threaded 3.13) wheels were available
  python3.13t -m pip install torch

  PyTorch 2.13:

... (truncated)

Commits

• cf30153 [release/2.13] Strip +PTX from CUDA arch list on release/RC builds (#188914) ...
• 3e3e24b [release/2.13] Restrict cuda-bindings to Python < 3.15 for CUDA 12.9 builds (...
• 7986b06 [release/2.13] Bump binary build timeout 280 -> 400 minutes (#188551)
• 0bdbc26 [release/2.13] Add CUDA 12.9 to TORCH_CUDA_ARCH_LIST tables (#188443)
• 9cabb45 [release/2.13] Update manywheel docker image pin to 78e737ad (#188409)
• 78e737a [release/2.13] Revert "Tighten generalized scatter graph target (#184075)" (#...
• 0bb9b5b [release/2.13] Revert "dynamo: round-trip torch.cuda.stream ctx mgr across gr...
• aaac2bf [release/2.13] Revert "[Reland] Port D104346887/PR 182675 for index_add fast ...
• 9330813 Fix build_with_debinfo.py broken by CONFIGURE_DEPENDS globbing (#188192)
• 4e077a7 Remove setuptools upper bound (#188190)
• Additional commits viewable in compare view

Updates torchvision from 0.27.1 to 0.28.0

Release notes

Sourced from torchvision's releases.

TorchVision 0.28.0 Release

TorchVision 0.28 is out! This is a small release whose main change is the removal of the deprecated video decoding and encoding APIs (read_video, write_video, read_video_timestamps, VideoReader) from torchvision.io. Video decoding and encoding has fully migrated to TorchCodec as a part of our efforts to re-focus TorchVision. On the transforms side, v2.Resize now accepts string interpolation modes (e.g. "bilinear") and correctly honors NEAREST_EXACT on masks.

Backwards Incompatible Changes

• Video decoding/encoding removed from torchvision.io; migrated to TorchCodec (#9451)

  read_video, write_video, read_video_timestamps, and VideoReader are no longer available in OSS torchvision, and the video-backed datasets now require TorchCodec. These APIs were deprecated (with warnings) in prior releases and are now gone.

  Symptoms you may hit:

  • ImportError: cannot import name 'read_video' from 'torchvision.io'
  • import torchvision.io.video raising ModuleNotFoundError/ImportError
  • From video datasets: ImportError: Video decoding capabilities were removed from torchvision and migrated to TorchCodec. Please install TorchCodec following instructions at https://github.com/pytorch/torchcodec#installing-torchcodec

  As an example, if you performed the following on TorchVision 0.27:

  from torchvision.io import read_video
  frames, audio, info = read_video("video.mp4")

  You would now achieve that with:

  # pip install torchcodec
  from torchcodec.decoders import VideoDecoder
  decoder = VideoDecoder("video.mp4")
  frames = decoder[:]  # or decoder.get_frames_in_range(...), etc.

  See the TorchCodec documentation for tutorials and API references.

Improvements

• Allow passing interpolation as a string (e.g. "bilinear", "nearest") in resize transforms, in addition to the InterpolationMode enum (#9461)
• tv_tensors.wrap() now preserves metadata for custom TVTensor subclasses — a subclass can define its own .wrap() method to control how it is re-wrapped (#9490)

Bug fixes

• Fix F.resize on tv_tensors.Mask to honor NEAREST_EXACT interpolation. Previously the interpolation argument was ignored for mask inputs (resize_mask hardcoded NEAREST), so NEAREST_EXACT silently produced plain NEAREST output (#9497)
• Fix a GIF decoder bug on malformed GIFs that could write outside the allocated tensor's memory (#9520)

Commits

• 8fb8771 [Cherry-pick for 0.28] Fix gif decoder bug (#9520) (#9521)
• 5a5dd12 Release 0.28 (#9511)
• 85a1dcc Remove prototype folder (#9505)
• 4252acb Move lint CI job to 3.10 (#9507)
• 8b6ba92 [torchvision] Move video utilities to internal fb/io location (#9433) (#9451)
• bd94a05 Remove use of deprecated quantized APIs in C++ code (#9500)
• 45eb6dd Fix resize_mask to honor NEAREST_EXACT interpolation (#9497)
• 87fb856 Remove obsolete FIXME in autoaugment.py (#9496)
• 7bba561 Allow strings for interpolation param in resize transforms (#9461)
• 4d4e406 Let wrap() preserve metadata for custom TVTensor subclasses (#9490)
• Additional commits viewable in compare view

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