veomni-uv-update

name	veomni-uv-update
description	Use this skill when updating dependencies managed by uv: bumping a package version, upgrading the uv tool itself, updating torch/CUDA stack, switching transformers version, or regenerating the lockfile. Trigger: 'update dependency', 'bump version', 'upgrade uv', 'update torch', 'update lockfile', 'uv sync fails'.

Before You Start

Read .agents/knowledge/uv.md for the full dependency architecture. The key things that make VeOmni's uv setup non-trivial:

• uv version is pinned in three places (must update together)
• torch uses direct wheel URLs (not just version bumps)
• only three extras: gpu / npu / npu_aarch64, mutually exclusive, each a complete superset

Scenario 1: Update uv Version

uv is pinned to a specific version. Update all three locations together:

1. pyproject.toml -> [tool.uv] -> required-version = "==X.Y.Z"
2. docker/cuda/Dockerfile.cu130 -> COPY --from=ghcr.io/astral-sh/uv:X.Y.Z
3. docker/ascend/Dockerfile.ascend_* -> same pattern (if present)

Then regenerate the lockfile:

uv lock
uv sync --extra gpu --dev

Verify the lockfile diff is reasonable (git diff uv.lock — should only show version changes, not wholesale rewrites).

Scenario 2: Update a Regular Dependency

1. Edit version constraint in pyproject.toml under [project.dependencies] or the relevant [project.optional-dependencies] extra.
2. Regenerate lockfile and sync:

uv lock
uv sync --extra gpu --dev

3. Run tests: pytest tests/
4. Commit both pyproject.toml and uv.lock together.

Scenario 3: Update torch / CUDA Stack

This is the most complex update. torch versions are pinned in multiple places:

For GPU (gpu extra):

• pyproject.toml -> [project.optional-dependencies] -> gpu list
• pyproject.toml -> [tool.uv] -> override-dependencies (the extra == 'gpu' entries)
• pyproject.toml -> [tool.uv.sources] -> torch (direct wheel URL — must update to matching wheel)
• Related packages: torchvision, torchaudio, torchcodec, nvidia-cusparselt-cu13, nvidia-nccl-cu13, nvidia-cutlass-dsl

For NPU (npu / npu_aarch64 extras):

• Same pattern but with +cpu suffix or no suffix

Steps:

1. Identify the target torch version and matching wheel URLs from https://download.pytorch.org/whl/
2. Update all pinned versions in pyproject.toml (extras, overrides, sources)
3. Check FA / FlashQLA wheel/source compatibility:
   • flash-attn (cp311 + cp312) and flash-attn-3 are pinned to Luosuu prebuilt wheel URLs in [tool.uv.sources] (cu130/torch2.11/cxx11abi=true). Bumping torch / Python / cuda requires a matching Luosuu release — see https://github.com/Luosuu/flash-attention3-wheels/releases.
   • flash-attn-4 and flash-qla source-build from git pins; torch ABI bumps may need bumping the git revs. flash-qla's [[tool.uv.dependency-metadata]] block mirrors its install_requires (tilelang==0.1.8, apache-tvm-ffi==0.1.9); refresh the pins if upstream bumps them.
4. Update torchcodec version if needed (compatibility note in pyproject.toml)
5. Regenerate lockfile:

uv lock
uv sync --extra gpu --dev

6. Run tests: pytest tests/
7. Update Docker images if torch version changed

Scenario 4: Update transformers Version

transformers is pinned by the transformers-stable dependency group (pyproject.toml -> [dependency-groups] transformers-stable), which is listed in [tool.uv] default-groups so uv sync installs it automatically.

Bump within v5 (e.g. 5.2.0 → 5.3.0):

1. Edit the pinned version in [dependency-groups] transformers-stable.
2. Regenerate lockfile and sync:

uv lock
uv sync --extra gpu --dev

3. Check for API breakage and adjust veomni/ accordingly. Forward-looking guards may be expressed with is_transformers_version_greater_or_equal_to() from veomni/utils/import_utils.py.
4. Run tests: pytest tests/models/ tests/e2e/
5. Regenerate model patches: make patchgen (with the target transformers installed)

Scenario 5: Regenerate Lockfile Only

When uv.lock is out of sync or corrupt:

uv lock
uv sync --extra gpu --dev

If uv lock fails due to version conflicts, check:

• [tool.uv] -> conflicts declarations
• override-dependencies markers
• Direct wheel URL availability

Common Pitfalls

• Forgetting to update Docker: uv version and torch version changes must be reflected in docker/ Dockerfiles, otherwise CI builds will fail.
• Partial torch updates: updating torch but not torchvision/torchaudio/torchcodec to matching versions causes import errors.
• flash-attn wheel mismatch: flash-attn wheels are built for specific torch+CUDA combinations. A torch version bump requires finding or building new wheels.
• Committing only pyproject.toml: always commit uv.lock together. Docker builds use --locked which requires the lockfile to match.
• override-dependencies markers: the extra == 'gpu' markers in overrides are critical. Removing them causes uv to download wrong torch variants from PyPI.
• no-build-isolation: flash-attn and flash-attn-3 are listed under no-build-isolation-package. They require torch to be installed first. If sync fails, try uv sync without these extras first, then add them.