deps(quantum-prod): bump the quantum-prod-deps group across 1 directory with 2 updates

[dependabot]

Bumps the quantum-prod-deps group with 2 updates in the /ai-projects/quantum-ml/production directory: mypy and torch.

Updates mypy from 2.1.0 to 2.2.0

Changelog

Sourced from mypy's changelog.

Mypy Release Notes

Next Release

Mypy 2.2

We've just uploaded mypy 2.2.0 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.

Support for Closed TypedDicts (PEP 728)

Mypy now supports closed TypedDicts as specified in PEP 728. A closed TypedDict cannot have extra keys beyond those explicitly defined. This allows the type checker to determine that certain operations are safe when they otherwise wouldn't be due to the potential presence of unknown keys.

You can use the closed keyword argument with TypedDict:

HasName = TypedDict("HasName", {"name": str})
HasOnlyName = TypedDict("HasOnlyName", {"name": str}, closed=True)
Movie = TypedDict("Movie", {"name": str, "year": int})
movie: Movie = {"name": "Nimona", "year": 2023}
has_name: HasName = movie  # OK: HasName is open (default)
has_only_name: HasOnlyName = movie  # Error: HasOnlyName is closed and Movie has extra "year" key

Closed TypedDicts enable more precise type checking because the type checker knows exactly which keys are present. This is particularly useful when working with TypedDict unions or when you want to ensure that a TypedDict conforms to an exact shape.

The closed keyword also enables safe type narrowing with in checks:

Book = TypedDict('Book', {'book': str}, closed=True)
DVD = TypedDict('DVD', {'dvd': str}, closed=True)
type Inventory = Book | DVD
def print_type(inventory: Inventory) -> None:
if "book" in inventory:
# Type is narrowed to Book here - safe because DVD is closed
print(inventory["book"])
else:
# Type is narrowed to DVD here
print(inventory["dvd"])
</tr></table>

... (truncated)

Commits

• b3c4a5b Mark release
• 396f508 [Chore] Update changelog for 2.2 (#21691)
• 9592a9e Upload wasm wheels to PyPI (#21671)
• 1dc6962 Bump ast-serialize to 0.6.0 (#21664)
• 96a7913 Bump librt to 0.12.0 (#21663)
• 5ef0902 Fix the exportjson tool (.ff cache to .json conversion) (#21628)
• 8d621ad Support --shadow-file with --native-parser (#21623)
• 74ecdd8 Sync typeshed (#21612)
• 0cd1541 [mypyc] Make function wrappers thread-safe on free-threaded builds (#21620)
• 22a9cfd [mypyc] Make list remove and index thread-safe on free-threaded builds (#21614)
• Additional commits viewable in compare view

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

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