[V1][Mamba] Opt-in granular prefill to fix align-mode prefix-cache misses on incremental requests (#43587)

[hoobnn]

Purpose

Fixes #43587. In mamba_cache_mode="align" (the only prefix-caching mode
Qwen3.5 supports), incremental multimodal / agentic multi-turn requests
get num_cached_tokens == 0 even when they share many full prefix blocks
with a previous request and those block hashes are identical.

Root cause

In align mode, MambaManager.allocate_new_blocks keeps only one real
running-state block per prefill chunk and fills the intermediate aligned
boundaries with null blocks; BlockPool.cache_full_blocks skips null
blocks. So a request only caches the Mamba state at its chunk's final
aligned boundary. With the default large max_num_batched_tokens the
whole prefix is a single chunk, so only one boundary (the request's last
full block) is cached.

When a later request shares an early prefix but diverges before that
boundary (e.g. a new image inserted mid-sequence followed by a shared
instruction suffix), the shared blocks were never cached for the Mamba
group. Because BlockPool.get_cached_block requires a hit in every
KV-cache group, the attention-group hits are discarded and the request
reports 0 cached tokens.

Note: the minimal repro attached to the issue (image followed only by a
short "What next?") does not trigger this -- there the cached boundary
falls inside the shared region, so it hits. Reproducing requires
substantial shared content after the inserted image, which real
agentic prompts (history + fixed instruction template) have.

Fix

Add opt-in env var VLLM_MAMBA_ALIGN_GRANULAR_PREFILL (default off). When
set, _mamba_block_aligned_split caps each align-mode prefill step to one
aligned block, so every boundary's Mamba state is materialized and cached,
enabling partial prefix-cache hits. This reuses the existing align caching
/ state-writeback path unchanged (no kernel changes), so correctness is
preserved by construction. Trade-off: prefill throughput (one block per
step) -- hence opt-in.

Test plan & results

Repro: 4-image prompt, then a 5-image prompt sharing the first 4 images +
a long shared instruction tail, on Qwen3.5 (align mode), measuring
RequestOutput.num_cached_tokens. Run inside the official
vllm/vllm-openai:v0.19.0 image on a Tesla T4 with qwen3.5-0.8b
(block_size=544, mamba_cache_mode=align), using a backport of the same
one-line cap to that image's scheduler.py.

request	baseline / flag=0	flag=1 (fix)
4-img, repeated	96.5%	96.5%
5-img incremental	0.0%	35.6%
5-img, repeated	94.9%	94.9%

• flag=1: incremental request hits the 3 shared prefix blocks before the
  divergence (1632/4584 tokens); same-prompt repeats unchanged.
• flag=0: identical to baseline -> default behaviour and throughput are
  unchanged.
• Also confirmed the bug still reproduces on vllm/vllm-openai:v0.21.0
  (incremental still 0%).

Not yet covered (flagging for review): spec-decode (MTP/Eagle) combined
with this flag, and a dedicated regression test. Maintainer test suite
(tests/v1/core/test_prefix_caching.py,
tests/v1/e2e/general/test_mamba_prefix_cache.py) should be run.

Not a duplicate

Searched open PRs (mamba prefix cache align). Related but distinct:
#42406 / #42792 (Model Runner V2 + spec decode), #42547 / #42554 (PD/Nixl),
#33937 (null-block padding), and #36734 which goes the opposite
direction (auto-increases max_num_batched_tokens under align mode for
throughput). None address prefix-cache granularity for incremental
requests in align mode.

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[gemini-code-assist]

Code Review

This pull request introduces a new environment variable VLLM_MAMBA_ALIGN_GRANULAR_PREFILL to allow capping each prefill step to one aligned block in Mamba cache 'align' mode. This enables partial prefix-cache hits for requests that share an early prefix but diverge later, at the cost of prefill throughput. A review comment pointed out that parsing the environment variable using int() can raise a ValueError if users configure it with standard boolean strings like 'True' or 'true', and suggested a more robust parsing logic.

[ZJY0516]

I think cache every block for mamba will increase memory usage

[hoobnn]

Thanks @ZJY0516 — you're right it increases Mamba cache memory. To scope it precisely:

• Live / admission memory is unchanged: allocate_new_blocks still nulls intermediate boundaries and remove_skipped_blocks still frees running blocks, so per-request live footprint stays at (2 + num_spec) blocks.
• What grows is the committed prefix-cache: every aligned boundary is committed instead of just the chunk's last one, scaling with prefix length. But these blocks are LRU-evictable (shared with attention KV), so reclaimed under pressure rather than hard-reserved.

So it's a soft, opt-in (default-off) memory ↔ hit-rate trade-off. Unlike all mode, live memory stays at align levels — only the evictable cached-prefix span grows.

[QilaiZhang]

@hoobnn Hi! This change seems like it would significantly increase the number of scheduling rounds. PR #37898 appears to be a more appropriate solution and is worth a try.

[hoobnn]

Thanks @QilaiZhang, you're right. Capping each prefill step to one aligned block forces many more scheduling rounds for every request that hits the flag, regardless of whether a shared prefix actually exists — a static, unconditional cost.