Fix DeepSeek V4 FlashMLA auto KV cache dtype

[Yuzu23]

Purpose

DeepSeek V4 FlashMLA uses the fp8_ds_mla KV cache layout. When --kv-cache-dtype is left at the CLI default auto, the current DeepSeek V4 FlashMLA dtype resolver rejects it because auto does not start with fp8.

This changes the FlashMLA layout path to treat auto as fp8, allowing the existing normalization to fp8_ds_mla to run. Explicit non-fp8 values still fail, but now with an actionable ValueError instead of an assertion.

The FlashInfer path is unchanged, so its existing auto / bf16 behavior is preserved.

Test Plan

Static check:

python3.9 -m py_compile vllm/models/deepseek_v4/attention.py

E2E validation on a 2x NVIDIA H20 machine with DeepSeek-V4-Flash.

Baseline command, without --kv-cache-dtype, failed before the fix:

vllm serve /root/bentoml/models/DeepSeek-V4-Flash \
  --tensor-parallel-size 2 \
  --trust-remote-code \
  --served-model-name DeepSeek-V4-Flash \
  --port 31001 \
  --tokenizer /root/bentoml/models/DeepSeek-V4-Flash \
  --gpu-memory-utilization 0.9 \
  --max-num-seqs 4 \
  --dtype auto \
  --tool-call-parser deepseek_v4 \
  --enable-auto-tool-choice \
  --reasoning-parser deepseek_v4 \
  --max-model-len 4096

Patched command, still without --kv-cache-dtype, was then started and queried:

curl http://127.0.0.1:31001/v1/models

curl -X POST http://127.0.0.1:31001/v1/chat/completions \
  -H "Content-Type: application/json" \
  --data-binary @request.json

Test Result

Static check passed.

Baseline failed with:

AssertionError: DeepseekV4 only supports fp8 kv-cache format for now, got auto

After the fix, the same no---kv-cache-dtype startup proceeded with kv_cache_dtype=auto in engine config and then selected the DeepSeek fp8 layout automatically:

kv_cache_dtype=auto
Using DeepSeek's fp8_ds_mla KV cache format.

The model loaded all checkpoint shards:

Loading safetensors checkpoint shards: 100% Completed | 46/46
Application startup complete.

GET /v1/models returned HTTP 200 with DeepSeek-V4-Flash.

A real chat completion request also returned HTTP 200:

{
  "model": "DeepSeek-V4-Flash",
  "choices": [
    {
      "message": {
        "role": "assistant",
        "content": "ok"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 9,
    "completion_tokens": 2,
    "total_tokens": 11
  }
}

[github-actions]

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[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @Yuzu23.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[khluu]

I refreshed this branch onto current main using the enabled maintainer-edit permission and resolved the conflict against the current use_fp8_ds_mla_layout implementation. The PR is mergeable again and the stale needs-rebase label is removed.

This is now the exact fix for the deterministic current-main failure in H200 Basic Models Extra Initialization shard 7:

• #86957: https://buildkite.com/vllm/ci/builds/86957#01a06435-5c40-4538-8105-4e2a87ed92bd
• failure: DeepseekV4 fp8_ds_mla layout only supports fp8 kv-cache, got auto
• the other 27 cases in the shard passed

Focused validation on the refreshed head passed: default auto canonicalizes both the returned dtype and CacheConfig.cache_dtype to fp8_ds_mla with torch.uint8, while explicit bfloat16 still raises the intended ValueError. I will attach exact H200 shard evidence once the targeted run is available.

The merge/conflict-resolution work was prepared with AI assistance and reviewed line by line by the maintainer making this update.

[khluu]

Targeted exact validation is running in Buildkite #86983 at refreshed head ebdac8148bb7ebaf98fe250ea091f55ccb431031, using the contributor's real branch and PR metadata with only basic-models-tests-extra-initialization.

Bootstrap passed and all 14 H200 shards rendered successfully; shard 7 is the decisive DeepSeek-V4 gate. The branch is 0 commits behind main and GitHub reports it mergeable. I did not request or add a ready label because the pre-run check explicitly instructs AI agents not to request that label.

[khluu]

Exact H200 validation passed: Buildkite #86983 is terminal green at head ebdac8148bb7ebaf98fe250ea091f55ccb431031.

The decisive Basic Models Extra Initialization shard 7 job passed 28/28 in 12m25s. Its DeepSeek-V4 case initialized successfully, resolved DeepseekV4ForConditionalGeneration, and logged Using DeepSeek's fp8_ds_mla KV cache format. All 14 rendered shards and the complete selective build passed.

This converts the exact main failure in #86957 (1 DeepSeek-V4 failure / 27 passes) to green on the fix head. Remaining lifecycle gate: human review/merge, then an exact post-merge main pass.

[hmellor]

/ci run

[github-actions]

✅ Triggered Buildkite CI #87072 for commit ebdac8148bb7.

[hmellor]

@claude review

[claude]

Looks good, a small and correct error-handling fix.

What was reviewed: the updated _resolve_dsv4_kv_cache_dtype in vllm/models/deepseek_v4/attention.py — confirmed auto is normalized to fp8 only inside the use_fp8_ds_mla_layout branch, leaving the FlashInfer bf16/auto path (use_fp8_ds_mla_layout=False, e.g. DeepseekV4FlashInferMLAAttention) untouched; traced backend selection in vllm/models/deepseek_v4/nvidia/model.py to check the new error's "select a backend that supports bfloat16 KV cache" advice — it holds for SM100 (FlashInfer MLA has use_fp8_ds_mla_layout=False) but not SM120, where every selectable class uses the fp8_ds_mla layout; this is a minor wording gap, not a functional bug.

Extended reasoning...

Overview

The diff is confined to one helper, _resolve_dsv4_kv_cache_dtype, in vllm/models/deepseek_v4/attention.py. It replaces a bare assert with an explicit normalization (auto -> fp8) plus a ValueError carrying an actionable message, when the DeepSeek V4 fp8_ds_mla KV-cache layout is requested with an incompatible --kv-cache-dtype. No other logic in the function changes.

Security risks

None. This is pure control-flow/validation code in model configuration resolution; it does not touch auth, crypto, deserialization, or externally-supplied untrusted input beyond a CLI flag that was already validated elsewhere as an enum-like string.

Level of scrutiny

Low-to-moderate is appropriate: the change is small, self-contained, and mechanical (assert -> explicit exception with better UX), but it does affect a runtime code path used at model-load time for a production model family, so it merited a quick trace through the caller graph rather than a rubber-stamp.

Other factors

Traced use_fp8_ds_mla_layout across vllm/models/deepseek_v4/nvidia/flashinfer_sparse.py and vllm/models/deepseek_v4/nvidia/model.py to confirm the new normalization only affects the fp8_ds_mla layout branch and that the FlashInfer bf16 path is unaffected, matching the PR description. Found one minor, non-functional nit (the new error message's suggestion to "select a backend that supports bfloat16 KV cache" doesn't hold on SM120, where every selectable DSv4 attention class is fp8_ds_mla-only) — this was already surfaced as a ruled-out candidate issue and is a wording precision issue, not a bug, so it doesn't block approval. A maintainer (hmellor) has already reviewed and approved this PR.

[tlrmchlsmth]

/ci run

[github-actions]

✅ Triggered Buildkite CI #87086 for commit 900fee7ed4f0.