[Bugfix][Attention] Guard sparse MLA masked MHA workspace

[yimdev]

Purpose

Sparse MLA masked-MHA builds a bit-packed top-k mask whose size is approximately:

num_prefills × round_up(max_query_len, tile_m) × ceil(max_key_len / 32)
    × sizeof(int32)

The existing workspace was fixed at 64 MiB, while the routing matrix allows a
single 32K prefill to enter masked-MHA. Such a request requires exactly 128 MiB.
Heterogeneous batches can require even more despite staying within
max_num_batched_tokens.

Attempting to reshape the fixed workspace for an oversized mask can cause a CUDA
runtime error. Because CUDA execution is asynchronous, the API may have already
returned HTTP 200 before the worker reports the failure.

This PR:

• increases the top-k mask workspace from 64 MiB to 128 MiB, allowing the
  supported single-request 32K boundary to continue using masked-MHA;
• computes whether the actual workspace can hold every required suffix/context
  mask for the current prefill batch;
• includes that capacity check in the masked-MHA routing decision;
• falls back to the existing sparse MQA path when the mask does not fit;
• preserves the existing global-mask/per-context-chunk optimization behavior.

The workspace allocation increases by 64 MiB on workers where sparse masked-MHA
is available. Oversized batches do not trigger additional allocation.

Duplicate-work check

No open PR was found that addresses this masked-MHA top-k mask overflow.

Commands checked:

gh pr list --repo vllm-project/vllm --state open \
  --search "masked MHA workspace"

gh pr list --repo vllm-project/vllm --state open \
  --search "topk mask workspace"

gh pr list --repo vllm-project/vllm --state open \
  --search "sparse MLA workspace"

Related work is different in scope:

• [Bugfix][Attention] Size FlashInfer sparse MLA workspace for decode-context-parallel #50791 sizes the FlashInfer sparse-MLA decode workspace for DCP LSE.
• [Fix] Add guardrails and prefill workspace limits for FlashMLA (#50660) #50668 adds FlashMLA grid/workspace guardrails.
• [Attention][MLA] Per-request scheduling for MLA chunked context #50613 changes MLA chunked-context scheduling but does not guard this top-k
  mask allocation.

Test Plan

Run the workspace sizing and adjacent chunk-splitting tests:

.venv/bin/python -m pytest \
  tests/v1/attention/test_sparse_mla_backends.py \
  -k 'masked_mha_workspace_fits or split_prefill_chunks' -q

Run existing sparse prefill correctness coverage:

.venv/bin/python -m pytest \
  tests/v1/attention/test_sparse_mla_backends.py \
  -k sparse_backend_prefill_correctness -q

Run staged pre-commit hooks:

.venv/bin/pre-commit run

Hardware serving validation:

• 8× NVIDIA B300 SXM6
• nvidia/DeepSeek-V3.2-NVFP4
• tensor parallel size 8
• BF16 KV cache
• prefix caching disabled
• eager mode
• max_num_batched_tokens=32768

Cases:

1. A single 32,768-token prompt requiring exactly 128 MiB.
2. Eight heterogeneous prompts totaling 32,764 tokens, with a theoretical
   256 MiB batch mask.
3. A queued heterogeneous batch
   [16384, 4096, 4096, 4096, 4095] behind a 32K blocker, forcing an
   approximately 160 MiB masked-MHA candidate while the blocker remains active.
4. A normal chat-completion request to check generated output, not only HTTP
   status.
5. Service health and CUDA error log checks after every boundary case.

Test Result

8 passed, 533 deselected

for the workspace and chunk-splitting tests.

4 passed, 537 deselected

for sparse prefill correctness, covering:

• dense MHA;
• dense MHA with context;
• masked MHA;
• masked MHA with chunked context.

All staged pre-commit hooks passed.

Hardware serving results:

Case	Result
Single 32K / 128 MiB boundary	HTTP 200
Heterogeneous 256 MiB candidate	HTTP 200
Queued 160 MiB candidate	HTTP 200
Health after requests	HTTP 200
Semantic smoke test	Returned exactly REGRESSION_OK
CUDA/worker errors	None

The final server state reported Running: 0 and Waiting: 0.

No full model evaluation was run because this change does not alter attention
math for batches that fit. Oversized batches are redirected to the existing MQA
implementation instead of entering an invalid masked-MHA launch. Numerical
coverage is provided by the existing sparse prefill correctness tests, and
end-to-end output was validated on B300 hardware.

AI Assistance

OpenAI Codex was used.

[claude]

Claude Code Review

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[MatthewBonanni]

Made some tweaks, otherwise LGTM, thanks!

[yimdev]

Made some tweaks, otherwise LGTM, thanks!

Thanks