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### What this PR does / why we need it This PR introduces the **first AI-assisted model-adaptation skill package** for `vllm-ascend`. The goal is to make model adaptation work (especially for recurring feature-request issues) **repeatable, auditable, and easier to hand off**. ### Scope in this PR This PR adds only skill/workflow assets under: - `.agents/skills/vllm-ascend-model-adapter/SKILL.md` - `.agents/skills/vllm-ascend-model-adapter/references/workflow-checklist.md` - `.agents/skills/vllm-ascend-model-adapter/references/troubleshooting.md` - `.agents/skills/vllm-ascend-model-adapter/references/multimodal-ep-aclgraph-lessons.md` - `.agents/skills/vllm-ascend-model-adapter/references/fp8-on-npu-lessons.md` - `.agents/skills/vllm-ascend-model-adapter/references/deliverables.md` ### Workflow improvements The skill standardizes: 1. **Environment assumptions** used in our Docker setup - implementation roots: `/vllm-workspace/vllm` and `/vllm-workspace/vllm-ascend` - serving root: `/workspace` - model path convention: `/models/<model-name>` 2. **Validation strategy** - Stage A: fast `--load-format dummy` gate - Stage B: mandatory real-weight gate before sign-off - avoid false-ready by requiring request-level checks (not startup log only) 3. **Feature-first verification checklist** - ACLGraph / EP / flashcomm1 / MTP / multimodal - explicit `supported / unsupported / not-applicable / checkpoint-missing` outcomes 4. **Delivery contract** - minimal scoped code changes - required artifacts (Chinese report + runbook, e2e config YAML, tutorial doc) - one signed commit in delivery repo ### What this PR does NOT do - No runtime/kernel/model patch is included in this PR. - No direct model support claim is made by this PR alone. - Model-specific adaptation/fix work should be submitted in follow-up PRs using this skill as the workflow baseline. ### Why this matters for maintainers This gives the repo a shared, explicit AI-assistance protocol, so future model-adaptation PRs are easier to review, compare, and reproduce. --------- Signed-off-by: QwertyJack <7554089+QwertyJack@users.noreply.github.com> Co-authored-by: QwertyJack <7554089+QwertyJack@users.noreply.github.com>
48 lines
2.8 KiB
Markdown
48 lines
2.8 KiB
Markdown
# Deliverables
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## Required outputs in current repo
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1. One final signed commit (`git commit -sm ...`) containing the adaptation changes.
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2. Chinese analysis report（精简但完整）:
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- model architecture summary
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- incompatibility root causes
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- code changes and rationale
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- startup and inference verification evidence
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- feature status matrix（supported / unsupported / checkpoint-missing / not-applicable）
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- max model len: config theoretical vs runtime practical
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- dummy-vs-real validation matrix（what dummy proved / what only real proved）
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- false-ready cases and final resolution path（if any）
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- fallback ladder evidence（which fallback was tried, what changed）
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3. Chinese compact runbook:
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- how to start server in `/workspace` (direct command, default `:8000`)
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- how to run OpenAI-compatible validation
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- optional eager fallback command
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- optional `TORCHDYNAMO_DISABLE=1` fallback command (if relevant)
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4. Test config YAML at `tests/e2e/models/configs/<ModelName>.yaml` — must include `model_name`, `hardware`, `tasks` with accuracy metrics (name + value), and `num_fewshot`. Use accuracy results from evaluation to populate metric values. Follow the schema of existing configs (e.g. `Qwen3-8B.yaml`).
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5. Tutorial doc at `docs/source/tutorials/models/<ModelName>.md` — must follow the standard template: Introduction, Supported Features, Environment Preparation (with docker tabs for A2/A3), 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, accuracy table).
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6. Post SKILL.md content or AI-assisted workflow summary as a comment on the originating GitHub issue.
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## Commit discipline
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- Keep one signed commit for code changes in the current working repo.
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- If implementation occurred in `/vllm-workspace/*`, backport minimal final diff to current repo before commit.
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- Keep diff scoped to target model adaptation.
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## Validation discipline
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- Always provide log file paths for key claims.
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- Keep docs synchronized with latest successful test mode (do not leave stale command variants as default).
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- Final report must include pass/fail reason for each key feature attempt: ACLGraph / EP / flashcomm1 / MTP / multimodal.
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- EP and flashcomm1 are MoE-only checks; for non-MoE models mark as not-applicable with evidence.
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- Final report should include baseline capacity result (`128k + bs16`) or explicit reason if not feasible.
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- Dummy-first can be used to speed up iterations, but real-weight gate is mandatory before final sign-off.
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- Startup-only evidence is insufficient; include first-request smoke results.
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## Suggested final response structure
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- What changed
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- What went well / what went wrong
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- Validation performed
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- Commit hash and changed files
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- Optional next step
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