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unify_kv_cache_spec_page_size grows a layer's block size when its page divides the group maximum, and otherwise pads the physical page. The pad branch left block_size untouched, so a layer whose page does not divide the maximum kept its original small block while paying for a full max-size page. A speculative-decoding draft head next to a quantized primary hits this hard. With an nvfp4 target the unified page is ~3.2 MiB per layer, while a bf16 draft head keeps block_size 16 and a 64 KiB natural page. 98% of every page it claims is padding, and because per-request block counts are charged at the pool page size, one request reserves ~51x the blocks it needs. On a hybrid attention/Mamba model the effect is large enough to make the capacity check fail outright. Scale the block size by the whole part of the ratio first and pad only the remainder. The scaled block stays a multiple of the original, so kernel block alignment is preserved. Signed-off-by: lesj0610 <lesj0610@godoiksan.org> Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: lesj0610 <lesj0610@users.noreply.github.com>
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Included review availability: Your plan provides up to 10 included reviews per hour; 5 remain after this review. 📝 WalkthroughWalkthroughKV-cache page-size unification now scales non-MLA attention specs from natural page sizes, clears stale padding, and reapplies maximum-page padding. MLA specs continue to scale from aligned pages. Tests cover both paths. ChangesKV-cache page-size unification
Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: ⚪ Minimal · up to This localized fix corrects KV-cache block accounting for padded attention pages and includes regression coverage; no actionable merge-blocking risk remains at the current head after normal checks. Suggested reviewers: 🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
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In `@vllm/v1/core/kv_cache_utils.py`:
- Around line 1118-1122: Update the scaling logic around layer_spec and scaled
so ratio is computed from the natural unpadded page size, not page_size_bytes
that includes existing padding. Clear page_size_padded on the temporary scaled
spec before evaluating its size, then apply max_page_size only in the final
new_spec replacement.
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The pad branch computed its scale ratio from page_size_bytes, which includes any pre-existing page_size_padded, and the scaled candidate kept that stale padding. A pre-padded attention spec would under-scale, and once the grown natural page outran the old padding the page_size_padded >= unpadded assertion tripped while evaluating the candidate's page size. Take the ratio from unpadded_page_size_bytes and drop the stale padding from the scaled candidate before comparing it against the maximum. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Signed-off-by: lesj0610 <lesj0610@users.noreply.github.com>
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Inline comments:
In `@vllm/v1/core/kv_cache_utils.py`:
- Around line 1122-1127: Update the divisible-page branch in the page-size
scaling logic to account for pre-padded attention specs: base scaling on the
natural unpadded page size or apply the same natural-page ratio used by the
cleanup path, and clear page_size_padded after scaling so it cannot remain
smaller than the enlarged unpadded page. Preserve existing behavior for specs
without padding.
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The divisible branch picked both its branch condition and its ratio from page_size_bytes, which includes any pre-existing padding, and kept that padding on the scaled spec. A pre-padded attention spec whose stale padding divides the maximum would under-scale and then trip the page_size_padded >= unpadded assertion once the grown natural page outran the padding. Choose the branch and the ratio from the natural page for attention specs and drop the stale padding when scaling, matching the pad branch. Non- attention specs keep their page_size_bytes-based behavior. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Signed-off-by: lesj0610 <lesj0610@users.noreply.github.com>
MLAAttentionSpec is an AttentionSpec, but its padding is not stale caller state: _apply_alignment_padding reapplies it from the spec's own alignment in __post_init__ on every replace. Scaling MLA from the natural page rejected a long-supported configuration (aligned 512 B page into a 1024 B maximum) and turned the NotImplementedError fallback contract into an AssertionError for non-divisible maxima. Restrict the natural-page base and padding clear to non-MLA attention specs; MLA keeps the padded-page divisible behavior. Cover both MLA cases. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Signed-off-by: lesj0610 <lesj0610@users.noreply.github.com>
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Purpose
unify_kv_cache_spec_page_size()reconciles differing per-layer page sizes in two ways: when a layer's page divides the group maximum it scales that layer'sblock_sizeby the ratio, and otherwise it pads the physical page up to the maximum. Before this change, the pad branch leftblock_sizeuntouched, so the layer ended up holding its original (small) token count in a page sized for the maximum.That combination is expensive because per-request block demand is charged at the pool page size, not at the layer's own page.
_max_memory_usage_bytes_from_groups()computes_pool_bytes_per_block(groups) * total_blocks, wheretotal_blocksis the sum of each group'smax_memory_usage_bytes // page_size_bytes. A layer that keeps a 16-token block therefore contributes blocks at 16-token granularity while every one of those blocks is billed at the full pool page.The gap is widest when a quantized primary sets the unified page and a higher-precision spec-decode draft head has to pad up to it, because quantized per-token sizes rarely divide evenly. With an nvfp4 KV cache the primary spends 1152 bytes per token per layer, while a bf16 draft head spends 4096;
4096 / 1152is not an integer, so the draft always lands in the pad branch.Observed on
Qwen/Qwen3.8-27B(64 layers: 48 gated-delta-net + 16 full attention) served with--kv-cache-dtype nvfp4, plus thez-lab/Qwen3.8-27B-DFlash2draft head on a separate attention backend. The unified page is 3,354,624 bytes per layer. The draft group keepsblock_size=16, whose natural page is 65,536 bytes, so 98% of every page it claims is padding. Per-group block demand for one request at--max-model-len 262144:The draft group alone is 2177 of 2631 blocks, 83% of the total, and the capacity check fails:
The 2177 follows directly from the stale block size: the sliding-window admission bound is
min(sliding_window - 1 + extra_retained_tokens + max_in_flight_tokens, max_model_len), heremin(2047 + 0 + 32768, 262144) = 34815, andcdiv(34815, 16) + 1 = 2177. It scales withmax_in_flight_tokensrather than with the window, so lowering--max-num-batched-tokensfrom 16384 to 4096 only moves the requirement from 41.10 GiB to 17.11 GiB.Scaling the block size by the whole part of the ratio first leaves the same padded page but restores proportional block accounting: the draft block becomes
16 * 51 = 816, demand drops tocdiv(34815, 816) + 1 = 44blocks, and the model serves.Note that scaling and padding are not interchangeable here. Simply giving the draft the primary's
block_sizeof 2912 would make its own page 11.375 MiB per layer, which raises the pool page for every group and lands at 25.9 GiB — worse than leaving it alone would suggest but still unserveable. The fix has to grow the block only as far as the existing maximum page allows.AI assistance: Codex, Claude
Changes
unify_kv_cache_spec_page_size(): in the non-MLA attention pad branch, scaleblock_sizeby the whole part of the ratio before recordingpage_size_padded. The scaled block is a whole multiple of the original, so kernel block alignment is preserved, and the guard only applies the scale when the ratio is at least 2 and the scaled page still fits under the maximum. A ratio below 2 keeps the previous behaviour exactly.unpadded_page_size_bytes) and drop any pre-existingpage_size_paddedwhen scaling. A pre-padded spec previously under-scaled (ratio computed from the stale padded size) and could trip thepage_size_padded >= unpaddedassertion once the grown natural page outran the stale padding.MLAAttentionSpeckeeps the padded-page scaling base: its padding is reapplied from its ownalignmentby__post_init__on everyreplace, so it is never stale, and scaling from the natural page would reject a long-supported aligned-page configuration and break theNotImplementedErrorfallback contract for non-divisible maxima.test_unify_kv_cache_page_size_scales_block_before_padding(whole-ratio scale before padding, ratio-below-2 unchanged),test_unify_kv_cache_page_size_scales_pre_padded_spec_from_natural_page(pre-padded specs on both the pad and the divisible path), andtest_unify_kv_cache_page_size_mla_alignment_padding_is_the_scaling_base(MLA aligned-base scaling and the NotImplementedError fallback).Test Plan
.venv/bin/python -m ruff check \ vllm/v1/core/kv_cache_utils.py \ tests/v1/core/test_kv_cache_utils.py .venv/bin/python -m ruff format --check \ vllm/v1/core/kv_cache_utils.py \ tests/v1/core/test_kv_cache_utils.py .venv/bin/python -m pytest \ tests/v1/core/test_kv_cache_utils.py \ -k "scales_block_before_padding or pre_padded or mla_alignment" -q .venv/bin/python -m pytest tests/v1/core/test_kv_cache_utils.py -qEnd to end on one SM80 device with an nvfp4-quantized primary and a higher-precision speculative-decoding draft head, before and after this change, with and without the draft head.
Test Result
ruff check: passed.ruff format --check: 2 files already formatted.pytest -k "scales_block_before_padding or pre_padded or mla_alignment" -q: 3 passed. Reverting onlyvllm/v1/core/kv_cache_utils.pyto its pre-PR revision turns this into 2 failed, 1 passed: the two new-behaviour tests fail, while the MLA test pins behaviour this PR intentionally preserves and passes either way.pytest tests/v1/core/test_kv_cache_utils.py -q: 89 passed (includes the pre-padded and MLA regression tests above).test_unify_kv_cache_page_size_uses_padding_for_non_divisible_sizescase 4 assertsblock_size == 24for a 24 KiB page against a 32 KiB maximum; that ratio is 1, so it is unaffected.mypyfor Python 3.10.Serving, same host and same GPU memory budget:
Before the change the engine aborts during KV cache sizing with the 41.10 GiB
ValueErrorquoted above. After it the server reachesApplication startup complete. The remaining KV capacity difference against the no-draft baseline is the draft head's own weights and KV, not this change.The non-speculative baseline is byte-identical before and after: with a single KV dtype every page already matches,
unify_kv_cache_spec_page_size()returns early onlen(page_sizes) <= 1, and the touched branch is never reached.Essential Elements of an Effective PR Description Checklist
Summary by CodeRabbit
Bug Fixes
Tests