[Spec Decode] Support mixed KV page sizes for DFlash

[pst2154]

Summary

This PR addresses the KV-cache infrastructure gap needed by DFlash-style speculative decoding when the target and draft models have different KV page sizes.

It adds:

• A padding fallback in unify_kv_cache_spec_page_size for non-divisible page sizes.
• Padded KV-cache reshape handling for FlashAttention-style layouts where the block dimension is not the first physical dimension.
• A shared attention KV-cache reshape helper so padded-page stride handling stays consistent across both KV-cache reshape paths.
• Focused tests for mixed target/draft page sizes, padded FlashAttention KV strides, HND stride-order layouts, DiffKV-style layouts, and per-token-head scale storage.

Related: #45056

Existing Work Check

This is not intended to duplicate existing DFlash or KV-cache PRs:

• [WIP][Model] Enable MiMo 2.5 DFlash #45056 is a broader MiMo model-enablement PR that touches model code and MoE MXFP4 support. This PR deliberately keeps those pieces out and only addresses the generic KV-cache padding/layout gap.
• [Spec Decode] Support DFlash FlashInfer backend with SWA and FP8 KV cache #39995 focuses on DFlash with the FlashInfer backend, SWA metadata, and FP8 KV cache behavior. This PR is backend-layout infrastructure for mixed physical page sizes and does not add DFlash backend selection or SWA speculator behavior.
• [Bugfix] Fix hybrid KV manager for quantized per-token-head KV cache #40308 addresses hybrid quantized per-token-head KV correctness. This PR includes a per-token-head stride regression test because padded pages must preserve inline scale storage, but its functional change is DFlash-style mixed target/draft page padding and reshape correctness.
• fix: handle non-divisible page sizes in hybrid model KV cache unification #40128 proposed an LCM-style page unification approach and is closed. This PR keeps logical block sizes unchanged for non-divisible attention page sizes and pads the smaller physical page instead.

Scope

This is intentionally limited to the KV-cache behavior. It does not include the MiMo base model changes or MoE MXFP4 work from #45056.

The intended split is:

• This PR: mixed target/draft KV page sizes and padded KV-cache layout.
• Follow-up/model PRs: TP != 8 serving and MoE MXFP4 support.

Why

DFlash drafters can have a smaller KV head size than the target model. For example, MiMo uses 192-dim target KV heads while the DFlash draft uses 128-dim KV heads. That creates a 3:2 page-size relationship, so the existing block-size scaling path cannot safely unify page sizes by multiplying block size.

Instead, this PR keeps the logical block size unchanged and pads the smaller physical page to the target page size. The KV-cache view then needs correct strides so logical K/V rows skip the padding between physical pages.

Validation

• uv venv --python 3.12 .venv
• uv pip install ruff==0.14.0
• .venv/bin/python -m py_compile vllm/v1/core/kv_cache_utils.py vllm/v1/worker/gpu/attn_utils.py vllm/v1/worker/gpu_model_runner.py tests/v1/worker/test_attn_utils.py tests/v1/core/test_kv_cache_utils.py
• .venv/bin/python -m ruff check vllm/v1/core/kv_cache_utils.py vllm/v1/worker/gpu/attn_utils.py vllm/v1/worker/gpu_model_runner.py tests/v1/core/test_kv_cache_utils.py tests/v1/worker/test_attn_utils.py
• git diff --check
• pre-commit run ruff-check --files ...
• pre-commit run ruff-format --files ...
• pre-commit run check-spdx-header --files ...
• pre-commit run check-root-lazy-imports --files ...
• pre-commit run check-filenames --files ...
• pre-commit run mypy-3.10 --files ...
• On an 8xB200 node using vllm/vllm-openai:v0.22.1-ubuntu2404, with this branch source mounted into a test sandbox and the packaged compiled extensions copied in:

PYTHONPATH=/tmp/vllm_pr_test pytest -q \
  tests/v1/core/test_kv_cache_utils.py \
  tests/v1/worker/test_attn_utils.py

Result: 63 passed.

• B200 container import smoke: PYTHONPATH=/tmp/vllm_pr_test python3 -c "import vllm.v1.worker.gpu.attn_utils; import vllm.v1.worker.gpu_model_runner; print(1)"

Runtime smoke on umb-b200-236: served XiaomiMiMo/MiMo-V2.5-Pro-FP4-DFlash with vLLM on 8xB200 and successfully answered /v1/models and /v1/completions. DFlash acceptance was low in the quick smoke, so this PR focuses only on the KV-cache serving/runtime blocker rather than claiming a performance fix.

AI assistance was used for this change. The changed lines were reviewed by the human submitter.

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🚀

[benchislett]

Is it safe to just bump the block size like this? Will this affect any attention backends?

[pst2154]

Good question. The block-size bump here is the pre-existing behavior in unify_kv_cache_spec_page_size: when the smaller page evenly divides the max page, we keep using a larger logical block size so the page sizes become uniform.

The new path in this PR is only the non-divisible case, and only for AttentionSpec. In that case we do not bump the logical block size; we set page_size_padded=max_page_size and keep the backend-visible block size unchanged. The reshape path then uses a strided view over the padded physical page, so attention backends still see their normal logical KV shape. I added coverage for the standard attention layout where the K/V dim is adjacent to the block dim, which is the case that made the older padding assumption unsafe.

So: divisible case keeps existing behavior; non-divisible attention pages use padding instead of changing backend semantics. If you prefer, I can make the comment/docstring more explicit so this is easier to see from the code.

[heheda12345]

does all attention backend support stride view correctly?

[TheEpicDolphin]

A more conservative approach would be to add a supports_padded_kv_pages: ClassVar[bool] = False property to AttentionBackend, and only set it to True for FlashAttentionBackend for now. We can check this flag during unify_kv_cache_spec_page_size and only pad if True. Thoughts?

[pst2154]

Done in d8c9fe4 — thanks, went with this conservative approach.

Added supports_padded_kv_pages: ClassVar[bool] = False to AttentionBackend and set it True only on FlashAttentionBackend (the DiffKV subclass inherits it). unify_kv_cache_spec_page_size now pads an attention layer only when its backend opts in; otherwise it raises NotImplementedError instead of silently padding.

Re @heheda12345's question — no, not every backend. The strided padded-page view is only correct where the kernel indexes blocks through the cache tensor's actual stride (FlashAttention, via block_table). Backends with their own fixed-layout paged KV (e.g. FlashInfer) would mis-read a padded page, so they stay opt-out by default.

One note vs the exact suggestion: checking the flag purely inside unify_kv_cache_spec_page_size via a layer→backend map misses the draft model's attention layers — they aren't in the main vllm_config layer registry, and the draft layer is the one that needs padding. So instead I carry the flag onto the AttentionSpec at creation (Attention.get_kv_cache_spec reads it from the layer's backend) and propagate it through FullAttentionSpec/SlidingWindowSpec.merge; unify then just checks layer_spec.supports_padded_kv_pages.

Verified on 8×B200: MiMo-V2.5-Pro-FP4-DFlash loads and generates with the gate (DiffKV target + FlashAttention draft both opt in); a non-opting backend now raises. Added a negative test covering that.

[TheEpicDolphin]

Ah, so if I understand correctly: The num_blocks dimension being physically before the layer dimension is pretty much evidence that the attention backend supports padded pages, because it must support striding over layers. So we can use that final check in use_uniform_kv_cache in place of supports_padded_kv_pages. Thx for the insight!

[ivanium]

👍 Yes exactly. And both FlashInfer and FlashAttn support use_uniform_kv_cache so the supporting matrix is also accurate

[LucasWilkinson]

Apologies for the delayed review; do we know of any backends that don't support a page_stride larger then the page size? I don't know if any, I think we can remove this complexity. Personally I think we should just force all backends to support this; this is a very reasonable requirement for attention backends

[ivanium]

AFAIK it's mostly MLA attn backends. I have a PR to fix them; please feel free to comment/review: #45111

[LucasWilkinson]

will do thanks! (out at a wedding so may not be able to finalize a review till tmrw afternoon/evening)

[TheEpicDolphin]

I updated the PR with @ivanium's new suggested approach: leveraging the existing check from use_uniform_kv_cache(). This check was separated into its own helper method, which is is now the indexes_kv_by_block_stride class method in AttentionBackend. This method's return value is cached as a property of AttentionSpec, and used in both use_uniform_kv_cache() and unify_kv_cache_spec_page_size.

I ran some evals to check that there are no accuracy regressions from these changes:

GSM8K

Model	V1 / Prefix Enabled (main)	V1 / Prefix Enabled (#45181)	V1 / Prefix Disabled (main)	V1 / Prefix Disabled (#45181)	V2 / Prefix Enabled (main)	V2 / Prefix Enabled (#45181)	V2 / Prefix Disabled (main)	V2 / Prefix Disabled (#45181)
llama3 8b	0.7672	0.7589	0.7627	0.7718	0.7771	0.7771	0.7551	0.7597
bamba 9b	0.3624	0.3518	0.3525	0.4056	x	x	x	x
gemma4 e2b	0.8211	0.834	0.7483	0.8158	0.7976	0.7506	0.7862	0.7968
deepseek v4 flash	0.9545	0.9553	0.9575	0.9515	0.9477	0.956	0.9462	0.9522

AIME 2025

Model	V1 / Prefix Enabled (main)	V1 / Prefix Enabled (#45181)	V1 / Prefix Disabled (main)	V1 / Prefix Disabled (#45181)	V2 / Prefix Enabled (main)	V2 / Prefix Enabled (#45181)	V2 / Prefix Disabled (main)	V2 / Prefix Disabled (#45181)
gemma4 e2b	0.2833	0.3417	0.3583	0.3417	0.3583	0.3333	0.325	0.325
deepseek v4 flash	0.975	0.9583	0.9667	0.9667	0.9417	0.9667	0.95	0.95

Additionally, I ran mimo + dflash with MT-Bench and verified that the acceptance rate is in the expected range, validating that this PR works as intended:

---------------Speculative Decoding---------------
Acceptance rate (%):                     29.88     
Acceptance length:                       3.39      
Drafts:                                  6078      
Draft tokens:                            48624     
Accepted tokens:                         14531     
Per-position acceptance (%):
  Position 0:                            72.95     
  Position 1:                            51.60     
  Position 2:                            36.23     
  Position 3:                            26.34     
  Position 4:                            19.55     
  Position 5:                            14.56     
  Position 6:                            10.79     
  Position 7:                            7.06      
============================================================

[ivanium]

Thanks for the PR and the neat changes! Overall good; I left a few comments for details.

[ivanium]

I feel the name indexes_kv_by_block_stride is not very intuitive. How about sth like block_stride_agnostic to hint the property that attn kernels can handle block strides correctly?

[ivanium]

Also I feel the comment is a bit verbose. Maybe we can drop it?

[TheEpicDolphin]

Yeah, I agree it's a bit verbose. I moved this comment to the docstring of AttentionBackend.indexes_kv_by_block_stride. However, I'm still in favor of the current property name indexes_kv_by_block_stride because it's aligned with the method it caches, and is clear about what it indicates. block_stride_agnostic could be misinterpreted to mean that block striding is ignored. I'm open to other ideas though

[ivanium]

On second thought indexes_kv_by_block_stride sounds okay too. Let's keep it then

[ivanium]

I feel there we need to add this to all merge() in different KV specs.

[ivanium]

I tried to add them here: dca79da

Feel free to double check and cherry pick it.

[ivanium]

Thanks for the effort!

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @pst2154.

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