[seyfal]

Overview

This PR fixes how vLLM picks KV cache pages when a model mixes layer types, which is what speculative decoding with a draft model on a hybrid (attention plus linear-attention) target does. FlashInfer now chooses kernel pages per layer, padded manager pages can split into kernel pages, and hybrid block alignment counts the drafter's KV. It is independent of #62961, #62962, #62963 and #62964, and of #63279, which carries the FA4 dense warmup coverage that started in this PR.

Claims

• FlashInfer kernel pages follow each attention group's own head counts, with query heads from its layers and KV heads from its spec. An MHA draft layer next to a GQA target otherwise gets the target's large pages, and its metadata builder rejects them at startup. AttentionBackend.get_supported_kernel_block_sizes_for_layers(kv_cache_spec, layer_names) carries this and defaults to get_supported_kernel_block_sizes. The model runner and the NIXL and Mooncake connectors pass each group's layer names, so P/D transfer uses the same kernel blocks as the runner.
• A padded manager page splits into uniformly strided kernel pages, and manager-page boundaries are kept. Interleaved layers and non-divisible padding still fail with a clear error.
• Hybrid block alignment includes the drafter's KV bytes per token. When a draft has more KV per token than the target, it sets the common page size, and every recurrent-state page is padded up to it. For a 64-layer Qwen3.5-style GDN model with a one-layer draft that is a 5.2x padding, and the KV pool holds about 61 concurrent requests of 2.5k tokens.

Validation

AI assistance (Claude) was used to prepare this change; I reviewed every changed line and ran the tests listed below.

The changed test files cover per-layer head counts and connector grouping, the attention utilities, the kernel block selection and the GPU model runner. I broke the head-count fix on purpose to check that a test catches it. On B300, making FlashInfer ignore the layer's heads fails the MHA-draft case.

On CPU (VLLM_TARGET_DEVICE=cpu, one pytest process per file), I ran the attention, KV-cache, spec-decode and worker files this PR touches or depends on.

Build	Files	Passed	Failed	Skipped
main (98468d0f66)	84	1335	143	957
this PR	84	1347	143	961

The 84 files include the NIXL and Mooncake connector tests. The 143 failures, and 35 errors at collection, are the same cases on both builds, and pre-commit passes on the range.

On B300 with FlashInfer installed, the FlashInfer and connector cases in test_group_head_counts.py pass (5 of 5), and test_attn_utils.py passes 62 of 62.

I served Qwen3.8-27B at TP4 on 4x B300 with this PR on main (98468d0f66), with the default attention backend (FlashInfer), VLLM_USE_V2_MODEL_RUNNER=1 and VLLM_GPU_SYNC_CHECK=error, with 33 SPEED-Bench prompts (greedy, 384 tokens each). The server logged no GPU-sync errors. At batch 1 the outputs are bitwise identical to main (33 of 33) and decode speed matches (226.8 tok/s against 226.0, one server each). At 16 concurrent, two servers of main itself agree on 22 of 33 outputs on this base, so equality there is not a usable check. This PR against main gives 23 of 33 in the same job, which is within that spread.

End to end, a chain speculator with an MHA draft on a GQA hybrid target starts with the default attention backend, where without this PR it starts only when forced to FLASH_ATTN, and it serves concurrent and mid-prefill (16k-token) requests. A TP8 hybrid target with a draft serves 20 of 20 requests within the near-tie criterion against no speculation. I ran these two on the previous base (56e67b46a3), before the head-count fix moved from the cache spec to the backend method.

pre-commit run --from-ref origin/main --to-ref HEAD
VLLM_TARGET_DEVICE=cpu python -m pytest -q tests/v1/attention/test_group_head_counts.py tests/v1/attention/test_kpool_indexer_block_sizes.py tests/v1/worker/test_attn_utils.py tests/v1/worker/test_gpu_model_runner.py
python -m pytest -q tests/v1/attention/test_group_head_counts.py tests/v1/worker/test_attn_utils.py   # on B300
VLLM_USE_V2_MODEL_RUNNER=1 VLLM_GPU_SYNC_CHECK=error vllm serve Qwen/Qwen3.8-27B --tensor-parallel-size 4 --max-model-len 8192 --max-num-seqs 16 --no-enable-prefix-caching

Not a duplicate

I ran the duplicate-work searches from AGENTS.md §1 (open PRs, with keywords on kernel block size, padded pages, hybrid drafter block size and Mamba page padding). They found related work, and none of it makes the same fix. This PR uses no code from them.

• [Bugfix][Attention] Resolve kernel block-size constraints per KV cache spec #58630 passes each layer's KV spec into kernel block-size selection. This PR adds the layer names that the FlashInfer check needs next to that spec, so the two are complementary.
• [Bugfix][Spec Decode] Keep SW draft block a divisor of the primary block #60252 and [Core] Pick hybrid KV cache group size by worst-case padding bytes; add --min-kv-cache-group-layers #58207 change how a hybrid model picks its group or block size. They leave the drafter's KV out of the alignment.
• [KV Cache] Map kernel blocks per attention group; GLM-5.3-Flash on the generic packed layout #59297 (draft) moves kernel-block mapping into each attention group, with a per-group kernel-block stride. It is a larger refactor of the mapping that the padded-page split here relies on, and if it lands first, the split here becomes a stride choice in its mapping.
• [Core][Spec Decode] Fix KV cache allocation for sliding-window drafters and local-attention pool sizing #50169 fixes KV allocation for sliding-window drafters. This PR fixes the page alignment that a full-attention drafter on a hybrid target forces.

Includes and credits

This PR contains no code from other open PRs.

Details

• Query heads stay out of the cache spec, which describes only the KV cache layout (as in [Bugfix][Spec Decode] Use group geometry for FlashAttention metadata #53002). FlashInfer reads them from the group's layers when kernel blocks are chosen at KV cache initialization. Callers without layer names, and calls with no current config, keep the model-wide answer, so other models pick the same pages as before.
• The alignment change builds a fresh spec for the draft, so the draft does not inherit the target's value-head dimension. Speculative-state reservations are unchanged.
• All GPU measurements are on B300.

---

Pull Request Checklist

• I used vLLM's /pr-checklist skill. (Mandatory for agents, optional for humans).

• AI assistance was used during the creation of this PR.

• Design Fit: Minimizes impact on core components, reuses existing functionality, and justifies added complexity.

• Testing and Validation: Validates the change and ensures any added tests are meaningful and reliable, with CI coverage or documented CI resource constraints and validation performed outside CI.

• Code Quality and Style: Keeps code and comments clear and concise, and updates relevant documentation and examples.

• Pull Request Contents: Includes a brief summary and relevant links, supports claims with evidence, explains root causes and implementation trade-offs, and follows the contributing guide.

[claude]

Claude Code Review

This pull request is from a fork — automated review is disabled. A repository maintainer can comment @claude review to run a one-time review.

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🚀

[LopezCastroRoberto]

This PR mixes two independent changes: the KV-cache bugfixes and the FA4 warmup coverage. I’d suggest splitting it into two PRs.

[LopezCastroRoberto]

I think we can drop this test

[seyfal]

Dropped the CPU key-listing tests and kept only the native compilation-cache test, which is the one that catches the large-page table bug. It's in #63279

[LopezCastroRoberto]

Are the causal=False warmups ever used? get_warmup_keys only accepts FullAttentionSpec and SlidingWindowSpec

[seyfal]

You're right, these specs always run causal attentio. Removed causal from the warmup key. Fixed in #63279

[LopezCastroRoberto]

On the generic branch, batch_size (and max_seqlen_q) are raw values in the CompileKey, but FA4 doesn’t key on either. It keys on derived flags, e.g., q_stage/is_static_persistent, and the batch-lookup flags (cu_total_m_blocks/blocks_to_batch_idx, set when batch_size > BIN_BATCH_SEARCH_THRESH).

As written, WarmupChoices(2, max_num_seqs) duplicates a kernel whenever max_num_seqs <= 256 and quietly depends on a private FA threshold.

The key should use these flags directly, as the previous q_stage/is_single_batch key did, with concrete shapes picked only in compile(). For example, use_batch_lookup: bool, warmed only when max_num_seqs > BIN_BATCH_SEARCH_THRESH (imported from FA, not copied).

[seyfal]

Done. The key now holds FA4's own flags (q_stage, is_static_persistent, use_batch_lookup) and compile() picks the shapes, with the batch lookup warmed only when max_num_seqs exceeds BIN_BATCH_SEARCH_THRESH imported from FA4. Each key compiles a distinct kernel (5 from 5 keys at max_num_seqs=256, 8 from 8 at 1024), in #63279

[seyfal]

Thanks, I split it. #62800 keeps the KV cache page fixes (per-layer FlashInfer kernel pages, padded page splits and the drafter's KV in hybrid block alignment), and the FA4 dense warmup coverage moved unchanged to #63279. Neither depends on the other, so they can land in any order.

[mergify]

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

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

[TheEpicDolphin]

Although tracking num_query_heads in AttentionSpec is a straightforward way to address the issue for FlashInfer, I don't think it's the right long-term solution. KVCacheSpec properties should describe the memory layout of the KV cache, and q heads have no effect on that. It also gets dropped when specs are merged, since FullAttentionSpec.merge doesn't carry it over. #53002 avoided adding q heads to the spec for the same reason.

Metadata builders already get the number of q heads for an attention group from get_num_attention_heads_from_layers, which takes a list of layer names. I think the best direction is a get_supported_kernel_block_sizes_for_layers(kv_cache_spec, layer_names) method on AttentionBackend that defaults to get_supported_kernel_block_sizes, with FlashInfer overriding it to derive the num q heads. What do you think about this direction?

[mergify]

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

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

[seyfal]

@TheEpicDolphin Makes sense, I'll switch to that. I'll also take the KV heads from the spec while I'm in there, because the current check reads them from the model config too, so the draft gets the target's counts. I'll push it together with the rebase.

[seyfal]

@TheEpicDolphin Pushed. It now uses get_supported_kernel_block_sizes_for_layers, and the NIXL and Mooncake connectors pass the group layer names too, so P/D picks the same kernel blocks as the runner. PTAL when you get a chance.