vllm-ascend-model-adapter

---
name: vllm-ascend-model-adapter
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."
---

# 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=<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.**

## Execution playbook

### 1) Collect context

- Confirm model path (default `/models/<model-name>`; 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/<ModelName>.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/<ModelName>.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/<ModelName>.yaml` and follows the established schema (`model_name`, `hardware`, `tasks`, `num_fewshot`).
- Tutorial doc exists at `docs/source/tutorials/models/<ModelName>.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.