--- name: vllm-ascend-model-adapter-5daa57b7435d description: "Adapt and debug existing or new models for vLLM on Ascend NPU. Implement in /vllm-workspace/vllm and /vllm-workspace/vllm-ascend, validate via direct vllm serve from /workspace, and deliver one signed commit in the current repo." disable-model-invocation: true --- # vLLM Ascend Model Adapter ## Overview 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. ## Read order 1. Start with `references/workflow-checklist.md`. 2. Read `references/multimodal-ep-aclgraph-lessons.md` (feature-first checklist). 3. If startup/inference fails, read `references/troubleshooting.md`. 4. If checkpoint is fp8-on-NPU, read `references/fp8-on-npu-lessons.md`. 5. Before handoff, read `references/deliverables.md`. ## Hard constraints - 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=:$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.** ## Execution playbook ### 1) Collect context - Confirm model path (default `/models/`; if environment differs, confirm with user explicitly). - Confirm implementation roots (`/vllm-workspace/vllm`, `/vllm-workspace/vllm-ascend`). - Confirm delivery root (the current git repo where the final commit is expected). - Confirm runtime import path points to `/vllm-workspace/*` install. - Use default expected feature set: ACLGraph + EP + flashcomm1 + MTP + multimodal (if model has VL capability). - User requirements extend this baseline, not replace it. ### 2) Analyze model first - Inspect `config.json`, processor files, modeling files, tokenizer files. - Identify architecture class, attention variant, quantization type, and multimodal requirements. - Check state-dict key prefixes (and safetensors index) to infer mapping needs. - Decide whether support already exists in `vllm/model_executor/models/registry.py`. ### 3) Choose adaptation strategy (new-model capable) - Reuse existing vLLM architecture if compatible. - If architecture is missing or incompatible, implement native support: - add model adapter under `vllm/model_executor/models/`; - add processor under `vllm/transformers_utils/processors/` when needed; - register architecture in `vllm/model_executor/models/registry.py`; - implement explicit weight loading/remap rules (including fp8 scale pairing, KV/QK norm sharding, rope variants). - If remote code needs newer transformers symbols, do not upgrade dependency. - If unavoidable, copy required modeling files from sibling transformers source and keep scope explicit. - If failure is backend-specific (kernel/op/platform), patch minimal required code in `/vllm-workspace/vllm-ascend`. ### 4) Implement minimal code changes (in implementation roots) - Touch only files required for this model adaptation. - Keep weight mapping explicit and auditable. - Avoid unrelated refactors. ### 5) Two-stage validation on Ascend (direct run) #### Stage A: dummy fast gate (recommended first) - Run from `/workspace` with `--load-format dummy`. - Goal: fast validate architecture path / operator path / API path. - Do not treat `Application startup complete` as pass by itself; request smoke is mandatory. - Require at least: - startup readiness (`/v1/models` 200), - one text request 200, - if VL model, one text+image request 200, - ACLGraph evidence where expected. #### Stage B: real-weight mandatory gate (must pass before sign-off) - Remove `--load-format dummy` and validate with real checkpoint. - Goal: validate real-only risks: - weight key mapping, - fp8/fp4 dequantization path, - KV/QK norm sharding with real tensor shapes, - load-time/runtime stability. - Require HTTP 200 and non-empty output before declaring success. - Do not pass Stage B on startup-only evidence. ### 6) Validate inference and features - 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. ### 7) Backport, generate artifacts, and commit in delivery repo - If implementation happened in `/vllm-workspace/*`, backport minimal final diff to current working repo. - Generate test config YAML at `tests/e2e/models/configs/.yaml` following the schema of existing configs (must include `model_name`, `hardware`, `tasks` with accuracy metrics, and `num_fewshot`). Use accuracy results from evaluation to populate metric values. - Generate tutorial markdown at `docs/source/tutorials/models/.md` following the standard template (Introduction, Supported Features, Environment Preparation with docker tabs, Deployment with serve script, Functional Verification with curl example, Accuracy Evaluation, Performance). Fill in model-specific details: HF path, hardware requirements, TP size, max-model-len, served-model-name, sample curl, and accuracy table. - Update `docs/source/tutorials/models/index.md` to include the new tutorial. - Confirm test config YAML and tutorial doc are included in the staged files. - Commit code changes once (single signed commit). ### 8) Prepare handoff artifacts - Write comprehensive Chinese analysis report. - Write compact Chinese runbook for server startup and validation commands. - Include feature status matrix (supported / unsupported / checkpoint-missing / not-applicable). - Include dummy-vs-real validation matrix and explicit non-equivalence notes. - Include changed-file list, key logs, and final commit hash. - Post the SKILL.md content (or a link to it) as a comment on the originating GitHub issue to document the AI-assisted workflow. ## Quality gate before final answer - Service starts successfully from `/workspace` with direct command. - OpenAI-compatible inference request succeeds (not startup-only). - Key feature set is attempted and reported: ACLGraph / EP / flashcomm1 / MTP / multimodal. - Capacity baseline (`128k + bs16`) result is reported, or explicit reason why not feasible. - **Dummy stage evidence is present (if used), and real-weight stage evidence is present (mandatory).** - Test config YAML exists at `tests/e2e/models/configs/.yaml` and follows the established schema (`model_name`, `hardware`, `tasks`, `num_fewshot`). - Tutorial doc exists at `docs/source/tutorials/models/.md` and follows the standard template (Introduction, Supported Features, Environment Preparation, Deployment, Functional Verification, Accuracy Evaluation, Performance). - Tutorial index at `docs/source/tutorials/models/index.md` includes the new model entry. - Exactly one signed commit contains all code changes in current working repo. - Final response includes commit hash, file paths, key commands, known limits, and failure reasons where applicable. --- Source attribution: vllm-project Original project: https://github.com/vllm-project/vllm-ascend/blob/e131256399c84309c72ad2592b8c211d366f6d25/.agents/skills/vllm-ascend-model-adapter/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. # Mega AI Hub resource metadata Creator / publisher: vllm-project Original project: https://github.com/vllm-project/vllm-ascend/blob/e131256399c84309c72ad2592b8c211d366f6d25/.agents/skills/vllm-ascend-model-adapter/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering Roles: Backend Developer, Data Scientist, ML / AI Engineer Specialties: Backend Tools: Docker, Vllm Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: fcf44dab173f6e4ef0ef09cbb93a87d52fea77bb96fe938255971edbf4150335