upgrade-vllm

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Actualizado17 de julio de 2026 a las 20:24

Bump the pinned vLLM version in `pyproject.toml`, re-sync the upstream test plugin, and triage the breakages that the bump exposes. Use whenever a user asks to upgrade/bump/update vLLM, pull up the lower bound, or chase a specific upstream commit. Most failures after a bump are not in our code — they are upstream API churn (worker `load_model` wrappers, platform `is_*()` predicates, KV-cache layout flips, new constructor kwargs) that our `_enum = PlatformEnum.OOT` platform doesn't get the CPU/GPU short-circuits for.

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Fuente

torch-spyre

torch-spyre/spyre-inference

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SKILL.md

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name

upgrade-vllm

description

Upgrade spyre-inference to the vLLM version specified by the user.

IMPORTANT: If the user hasn't specified a target version, ask them which version before proceeding.

Follow the complete procedure in docs/contributing/vllm-upgrade-procedure.md.

When you see {VERSION} in that document, replace it with the actual version number provided by the user (e.g., 0.23.0).

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Debug numerical, compilation, or correctness failures anywhere the Spyre backend is involved — custom ops, attention, model runner, end-to-end vLLM runs. Use whenever a user reports failing Spyre tests, accuracy/tolerance mismatches against a CPU reference, torch.compile errors on `spyre` device, silent CPU fallbacks, or generally "something on Spyre is broken." Most bugs here are not in our code — they are torch-spyre op gaps or dtype/layout limitations, so debugging requires tracing into the torch-spyre site-packages, not just reading `spyre_inference/`. For attention-specific hints (cache-seeding, KV alignment, MHA/MQA, broadcast diagnosis), see `attention-notes.md` next to this file.

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