vllm-ascend-model-adapter

Adapt Hugging Face or local models to run on vllm-ascend with minimal changes, deterministic validation, and single-commit delivery. This skill is for both already-supported models and new architectures not yet registered in vLLM.

• Send GET /v1/models first. • Send at least one OpenAI-compatible text request. • For multimodal models, require at least one text+image request. • Validate architecture registration and loader path with logs (no unresolved architecture, no fatal missing-key errors). • Try feature-first validation: EP + ACLGraph path first; eager path as fallback/isolation. • If startup succeeds but first request crashes (false-ready), treat as runtime failure and continue root-cause isolation. • For torch._dynamo + interpolate + NPU contiguous failures on VL paths, try TORCHDYNAMO_DISABLE=1 as diagnostic/stability fallback. • For multimodal processor API mismatch (for example skip_tensor_conversion signature mismatch), use text-only isolation (--limit-mm-per-prompt set image/video/audio to 0) to separate processor issues from core weight loading issues. • Capacity baseline by default (single machine): max-model-len=128k + max-num-seqs=16. • Then expand concurrency (e.g., 32/64) if requested or feasible.

• Start with references/workflow-checklist.md. • Read references/multimodal-ep-aclgraph-lessons.md (feature-first checklist). • If startup/inference fails, read references/troubleshooting.md. • If checkpoint is fp8-on-NPU, read references/fp8-on-npu-lessons.md. • Before handoff, read references/deliverables.md.

• Never upgrade transformers. • Primary implementation roots are fixed by Dockerfile: • /vllm-workspace/vllm • /vllm-workspace/vllm-ascend • Start vllm serve from /workspace with direct command by default. • Default API port is 8000 unless user explicitly asks otherwise. • Feature-first default: try best to validate ACLGraph / EP / flashcomm1 / MTP / multimodal out-of-box. • --enable-expert-parallel and flashcomm1 checks are MoE-only; for non-MoE models mark as not-applicable with evidence. • If any feature cannot be enabled, keep evidence and explain reason in final report. • Do not rely on PYTHONPATH=<modified-src>:$PYTHONPATH unless debugging fallback is strictly needed. • Keep code changes minimal and focused on the target model. • Final deliverable commit must be one single signed commit in the current working repo (git commit -sm ...). • Keep final docs in Chinese and compact. • Dummy-first is encouraged for speed, but dummy is NOT fully equivalent to real weights. • Never sign off adaptation using dummy-only evidence; real-weight gate is mandatory.