Port LCM-padding fallback from #40128 into unify_kv_cache_spec_page_size

[jhsmith409]

What

Ports the LCM-padding logic from vllm-project/vllm#40128 (by @Sandermage) into this branch as a safety net for hybrid TurboQuant models whose attention and Mamba page sizes still mismatch after _align_hybrid_block_size.

Why not a duplicate

• fix: handle non-divisible page sizes in hybrid model KV cache unification vllm-project/vllm#40128 standalone does not subsume [Feature] TurboQuant: support hybrid models and uniform quantization vllm-project/vllm#39931 — [Feature] TurboQuant: support hybrid models and uniform quantization vllm-project/vllm#39931 is the umbrella hybrid-TQ PR that actually makes hybrid models viable.
• fix: handle non-divisible page sizes in hybrid model KV cache unification vllm-project/vllm#40128 author (@Sandermage) explicitly offered to close #40128 if the LCM fallback is folded into this PR.
• This PR is that fold. One file, one function, ~40 net lines.

The fix

Today unify_kv_cache_spec_page_size(...) in vllm/v1/core/kv_cache_utils.py raises NotImplementedError whenever the smaller page size doesn't evenly divide the largest. On hybrid models where _align_hybrid_block_size can't fully equalize (TurboQuant attention packed K|V with unusual head_dim vs. Mamba/DeltaNet state), this crashes model init.

Fast path is unchanged: if every smaller page size already divides the max, behavior is identical to today.

New slow path: compute LCM of the smaller sizes, round max_page_size up to the next multiple, and use the existing page_size_padded field (already present on AttentionSpec and MambaSpec on main) to pad the layer that held the original max. Overhead is logged at INFO and in practice sits well under 0.1 %.

Test commands + results

1. Direct probe of the LCM branch (inside the docker container, with the overlaid patch)

attn = FullAttentionSpec(block_size=1, num_kv_heads=1, head_size=194, dtype=torch.bfloat16)
mamba = MambaSpec(block_size=1, shapes=((3162112,),), dtypes=(torch.float32,))
# attn.page_size_bytes = 776   mamba.page_size_bytes = 12_648_448   not divisible
out = unify_kv_cache_spec_page_size({"attn0": attn, "mamba0": mamba})

Output:

INFO kv_cache_utils.py:955 Page size unification: padding max 12648448 -> 12648800
     (LCM of smaller = 776, overhead 0.003%)
attn0  -> 12648800  block_size=16300  pad=None
mamba0 -> 12648800  block_size=1      pad=12648800
UNIFIED OK  target=12648800  overhead=0.0028%

Confirms: slow path fires, both specs end at a single unified page_size_bytes, attention got block-scaled, Mamba got page_size_padded.

2. End-to-end serve: hybrid MoE + TurboQuant k8v4, 64 k context

Hardware: RTX 5090 (sm_120, 32 GiB), CUDA 13, vllm cu130-nightly image with this branch's four files overlaid. --enforce-eager only because the cudagraph-capture profiler spikes on a 32 GiB card; runtime KV fits fine.

docker compose up
  --model=RedHatAI/Qwen3.6-35B-A3B-NVFP4
  --kv-cache-dtype=turboquant_k8v4
  --max-model-len=65536
  --max-num-seqs=4
  --gpu-memory-utilization=0.90
  --enforce-eager

Key log lines:

[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 7.11 seconds
[gpu_model_runner.py:4837] Model loading took 21.88 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: 146,704 tokens
[api_server.py:602] Starting vLLM server on http://0.0.0.0:8000
INFO: Application startup complete.

Completion request (greedy, 24 tokens):

{"choices":[{"text":" a large context kv cache test.\n\n<think>\nHere's a thinking process:\n\n1.  **Analyze User","finish_reason":"length"}]}

On this particular model, _align_hybrid_block_size already equalizes pages (0.08 % pad), so unify_kv_cache_spec_page_size short-circuits at len(page_sizes) <= 1 and the new slow path is a silent safety net. Test #1 exercises the slow path directly to prove it works.

Accountability / AI-assist disclosure (per AGENTS.md)

• I (the human submitter, @jhsmith409) reviewed every changed line end-to-end and ran both tests above.
• Code was ported and docker test harness was drafted with assistance from Claude (Anthropic); attribution is in the commit trailer.
• Original LCM logic and rationale are from @Sandermage (fix: handle non-divisible page sizes in hybrid model KV cache unification vllm-project/vllm#40128); my ports: lint-conform split of one long line, raise ... from e on the TypeError guard, line-split logger.info args.

Happy to rebase / reword / drop the AI-assist trailer on request.

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[jhsmith409]

Additional verification: AWQ variant

Same harness, swapping weights to cyankiwi/Qwen3.6-35B-A3B-AWQ-4bit (so TQ KV cache + AWQ weights instead of NVFP4):

[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:

{"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), LCM branch is silent safety net. Both weight-quant backends behave identically w.r.t. the page-size code path.

[JartX]

Hi! Please move the import math to the top and we'll test the execution after the merge.

[JartX]

@jhsmith409 ?

[jhsmith409]

@jhsmith409 ?

I was busy running all those int2 models on the other thread. Just getting back to this.

[jhsmith409]

@JartX done — import math moved to the module-level stdlib block (after import hashlib, before import os). Pushed as 3dc96cc on top of 3cc3675; only the import location changed, no behavioral diff. Ready for your post-merge test whenever it suits you.

[jhsmith409]

Also mirrored your precommit # type: ignore[call-arg] on replace(layer_spec, page_size_padded=...) from vllm-project#39931 commit 7f4ce15 — pushed as dccd6d8 on top of 3dc96cc. Same line, identical annotation, no behavioral change. Should keep your post-merge precommit clean.