fix: handle non-divisible page sizes in hybrid model KV cache unification by Sandermage · Pull Request #40128 · vllm-project/vllm

Sandermage · 2026-04-17T09:34:49Z

Summary

unify_kv_cache_spec_page_size() raises NotImplementedError when page sizes across different layer types are not evenly divisible. This breaks hybrid models that mix attention (with TurboQuant KV cache) and recurrent layers (Mamba/DeltaNet).

Root cause: TurboQuant k8v4 with head_dim=256 yields page_size = block_size × num_kv_heads × 388 = 12416 bytes, while DeltaNet/Mamba state is ~12.6 MiB. 12648448 % 12416 ≠ 0, triggering the error.

Changes

Replace the hard NotImplementedError with LCM-based padding:

Fast path preserved: when all smaller page sizes divide max_page_size evenly, behavior is unchanged
Slow path (new): compute LCM of all smaller page sizes, pad max_page_size UP to the nearest multiple of that LCM
For the padded layer, use page_size_padded via dataclasses.replace(); for layers that divide evenly, scale block_size as before
Memory overhead is typically <0.1% (logged at INFO level)

Testing

Tested on Qwen3.6-35B-A3B-FP8 (hybrid: 30 MoE + 10 dense layers) with TurboQuant k8v4 KV cache on 2× RTX A5000:

Model loads successfully with unified page sizes
145+ tok/s, 160k context window, 10/10 stability runs

Without this fix: crash at model init with NotImplementedError: The page size of the layer is not divisible by the maximum page size.

…amba) unify_kv_cache_spec_page_size() raises NotImplementedError when page sizes are not evenly divisible, which happens in hybrid models. For example, TurboQuant k8v4 with head_dim=256 gives page_size=12416 bytes per token, while DeltaNet/Mamba state is ~12.6 MiB — these are not divisible. Fix: when max_page_size is not divisible by smaller page sizes, pad it UP to the nearest multiple of the LCM of all smaller page sizes. This uses page_size_padded on the layer with the largest page size. The memory overhead is typically <0.1%. The existing fast path (all sizes divide evenly) is preserved unchanged. Tested on Qwen3.6-35B-A3B-FP8 (hybrid: 30 MoE + 10 dense layers) with TurboQuant k8v4 KV cache on 2× RTX A5000. Refs: vllm-project#40124

claude

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github-actions · 2026-04-17T09:34:58Z

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Code Review

This pull request updates the unify_kv_cache_spec_page_size function to support hybrid models where page sizes are not naturally divisible by padding the target page size to the nearest multiple of the LCM of smaller page sizes. The review feedback suggests using the built-in math.lcm function and integer-based alignment arithmetic to improve code clarity and avoid potential floating-point precision issues.

gemini-code-assist · 2026-04-17T09:36:50Z

+        def _lcm(a, b):
+            return a * b // math.gcd(a, b)
+        smaller_lcm = reduce(_lcm, smaller_sizes)
+        target_page_size = math.ceil(max_page_size / smaller_lcm) * smaller_lcm


Since vLLM requires Python 3.9+, you can use math.lcm directly instead of a manual implementation with reduce. Additionally, it is safer to use integer arithmetic for alignment calculations to avoid potential floating-point precision issues, which can occur during division and ceiling operations even if they are unlikely at current memory scales.

Suggested change

def _lcm(a, b):

return a * b // math.gcd(a, b)

smaller_lcm = reduce(_lcm, smaller_sizes)

target_page_size = math.ceil(max_page_size / smaller_lcm) * smaller_lcm

smaller_lcm = math.lcm(*smaller_sizes)

target_page_size = ((max_page_size + smaller_lcm - 1) // smaller_lcm) * smaller_lcm

@gemini-code-assist

- Replace manual _lcm/reduce with math.lcm(*smaller_sizes) (Python 3.9+) - Use integer-only ceiling division instead of math.ceil(float division) to avoid potential floating-point precision issues Addresses review feedback from @gemini-code-assist.

vibhavagarwal5 · 2026-04-18T08:16:31Z

This is useful when using hybrid models only. Can you see and contribute to #39931 instead

Sandermage · 2026-04-19T03:05:02Z

Hi @vibhavagarwal5, thanks for the pointer.

(small disclaimer: my English isn't great so I use AI to help translate)

You're right, #39931 is the proper fix and I've been watching it. I opened this
PR only because our Qwen3.6-35B-A3B setup with turboquant_k8v4 wasn't starting
on current nightly — unify_kv_cache_spec_page_size raises NotImplementedError
when TurboQuant attention page size (12416 B) meets DeltaNet state (~12.6 MiB,
not divisible). I wrote the LCM-padding fix just to unblock my own deployment.

No problem closing this — #39931 is the right place. If the approach here is
useful as a starting point for them, I can port it as a commit over there.
Otherwise I'll keep it in my personal runtime patcher only

@Sandermage

Ports the LCM-padding logic from vllm-project#40128 so hybrid TurboQuant models (Qwen3.5-A3B, Qwen3-Next, ...) stop crashing at model init when the attention page size (e.g. 12416 B for turboquant_k8v4, head_dim=256) does not evenly divide the Mamba/DeltaNet state page size (~12.6 MiB, `12648448 % 12416 != 0`). Fast path unchanged: when every smaller page size divides the max, we still scale block_size. New slow path: compute LCM of the smaller sizes, round max_page_size up to the next multiple, and use page_size_padded on the layer that held the original max. Overhead is logged at INFO and typically <0.1%. Credit to @Sandermage (vllm-project#40128), who offered to close that PR in favor of this port landing on top of vllm-project#39931. Co-authored-by: Sandermage <sandermage@users.noreply.github.com> Co-authored-by: Claude <noreply@anthropic.com> Signed-off-by: Jim Smith <jhsmith0@me.com>

jhsmith409 · 2026-04-20T14:51:23Z

Ported the LCM-padding fallback onto JartX:feature/hybrid_turboquant as a cross-fork PR: JartX#10 — JartX#10

@Sandermage — credit and Co-authored-by: trailer preserved. Feel free to close this once that lands (or keep it open as a reference; your call).

End-to-end verified on RTX 5090 with RedHatAI/Qwen3.6-35B-A3B-NVFP4 + turboquant_k8v4 @ 64 k context. On that model, _align_hybrid_block_size already equalizes pages (0.08 % mamba pad), so the new slow path acts as a safety net; a direct unit-level call into unify_kv_cache_spec_page_size with hand-crafted non-divisible specs confirms the LCM branch fires and unifies correctly.

jhsmith409 · 2026-04-20T15:02:30Z

Follow-up verification on JartX#10 — swapped weights from RedHatAI/Qwen3.6-35B-A3B-NVFP4 to cyankiwi/Qwen3.6-35B-A3B-AWQ-4bit (TurboQuant k8v4 KV cache + AWQ weights this time), everything else identical (RTX 5090, cu130 nightly with the overlay, --max-model-len=65536, --enforce-eager).

[config.py:195]  TQ hybrid: full-attention layers [3, 7, 11, 15, 19, 23, 27, 31, 35, 39]
[cuda.py:368]    Using TURBOQUANT attention backend out of potential backends: ['TURBOQUANT']
[default_loader.py:384] Loading weights took 6.97 seconds
[gpu_model_runner.py:4837] Model loading took 22.41 GiB memory
[interface.py:639] Setting attention block size to 2768 tokens to ensure that attention page size is >= mamba page size.
[interface.py:663] Padding mamba page size by 0.08% to ensure that mamba page size and attention page size are exactly equal.
[kv_cache_utils.py:1363] GPU KV cache size: 127,328 tokens
[api_server.py:602]     Starting vLLM server on http://0.0.0.0:8000
INFO: Application startup complete.

Completion (greedy, 24 tokens):

{"choices":[{"text":" jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over","finish_reason":"length"}]}

Same fast-path outcome as NVFP4: _align_hybrid_block_size equalizes pages (0.08 % mamba pad), so the LCM branch from this PR stays a silent safety net. Direct unit-level call into unify_kv_cache_spec_page_size with hand-crafted non-divisible specs (attn page 776, mamba page 12_648_448) was still used to prove the slow path fires — that's in the JartX#10 body.

Port LCM-padding fallback from vllm-project#40128 into unify_kv_cache_spec_page_size

Sandermage · 2026-04-21T10:36:12Z

Thanks @vibhavagarwal5 for the pointer, and @jhsmith409 for porting the LCM-padding logic onto the JartX#10 cross-fork with credit preserved. Closing this upstream PR as the fix has a better home on #39931's hybrid TurboQuant track — anyone hitting the same unify_kv_cache_spec_page_size NotImplementedError should follow that PR and JartX's fork.

Sandermage requested review from ApostaC, WoosukKwon, alexm-redhat, heheda12345, njhill, orozery, robertgshaw2-redhat and ywang96 as code owners April 17, 2026 09:34

claude Bot reviewed Apr 17, 2026

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mergify Bot added the v1 label Apr 17, 2026

gemini-code-assist Bot reviewed Apr 17, 2026

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gaby mentioned this pull request Apr 17, 2026

[Tracking issue]: TurboQuant/HIGGS Attention follow-ups #40069

Open

13 tasks

Sandermage mentioned this pull request Apr 18, 2026

[Bug/Feature] TurboQuant + Hybrid MoE (Qwen3.6-35B-A3B) broken on Ampere (SM 80-86) — 13 patches with fixes #40124

Open

jhsmith409 mentioned this pull request Apr 20, 2026

Port LCM-padding fallback from #40128 into unify_kv_cache_spec_page_size JartX/vllm#10

Merged

jhsmith409 mentioned this pull request Apr 20, 2026

[Feature] TurboQuant: support hybrid models and uniform quantization #39931

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JartX added a commit to JartX/vllm that referenced this pull request Apr 21, 2026

Merge pull request #10 from jhsmith409/port/tq-hybrid-lcm-padding

20c6d3e

Port LCM-padding fallback from vllm-project#40128 into unify_kv_cache_spec_page_size

jhsmith409 mentioned this pull request Apr 21, 2026

[Bug]: TurboQuant _continuation_prefill OOMs and kills engine at long-context prefill (~185K actual tokens) #40420

Open

Sandermage closed this Apr 21, 2026

m199369309 mentioned this pull request May 7, 2026

feat: add modular vLLM post-install patch framework with hybrid KV cache fix xorbitsai/inference#4879

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