[Bugfix] Do not FULL-capture spec-decode batches in TurboQuant attention backend - #53406
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TurboQuantMetadataBuilder declared cudagraph support UNIFORM_BATCH while also declaring supports_spec_as_decode=False. With speculative decoding enabled, the runner classifies uniform verify batches (query_len of 1 + num_speculative_tokens) as uniform decode by shape alone and FULL graph captures the attention forward. That forward routes spec-shaped batches through the per-request Python prefill loop, which iterates over CPU-resident metadata: capture bakes in the dummy metadata from build_for_cudagraph_capture (seq_lens filled with 1, so cached_len goes negative and the synthetic seq_lens slice is empty), and every replayed verify step returns zeros or reads out of bounds instead of attending. Symptoms: silent repetition collapse with num_speculative_tokens > 1, illegal memory access with num_speculative_tokens == 1. Eager mode and non-speculative graph mode are unaffected, which is why the bug only appears for the cudagraph + spec-decode combination. Declare UNIFORM_SINGLE_TOKEN_DECODE instead so spec-shaped batches fall back to piecewise cudagraphs and run the attention path eagerly with real metadata. Pure decode batches (query_len 1) keep FULL capture. Fixes vllm-project#52475 Signed-off-by: giannisanni <115853836+giannisanni@users.noreply.github.com>
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Purpose
Fixes #52475: MTP speculative decoding with any
turboquant_*KV cache dtype silently collapses into repetition, andnum_speculative_tokens: 1kills the engine with an illegal memory access.Root cause:
TurboQuantMetadataBuilderdeclares_cudagraph_support = UNIFORM_BATCHwhile also declaringsupports_spec_as_decode=False. Those two declarations are contradictory under speculative decoding:UNIFORM_BATCHtells the runner it may FULL-capture uniform batches, explicitly including spec-decode verify batches whose query_len is1 + num_speculative_tokens(see theAttentionCGSupport.UNIFORM_BATCHdocstring).GPUModelRunner._is_uniform_decodeclassifies these batches by query length alone and does not consultsupports_spec_as_decode.supports_spec_as_decode=Falsesetsreorder_batch_threshold=1, the TurboQuant forward routes every batch withmax_query_len > 1through its per-request Python prefill loop (_prefill_attention). That loop iterates over CPU-resident metadata (query_start_loc_cpu.tolist(),seq_lens_cpu.tolist()), so it is not graph-capturable: capture bakes in the dummy metadata frombuild_for_cudagraph_capture, which fillsseq_lenswith 1. With query_len 4 and seq_len 1,cached_len = seq_len - q_lenis negative and the synthetic seq_lens slice_arange_cache[cached_len + 1 : seq_len + 1]is empty, so the captured graph performs no attention at all. Every replayed verify step then returns zeros from all full-attention layers (the hybrid model's GDN layers keep the output superficially fluent, which is why the failure is silent and gradual). Withnum_speculative_tokens=1the same baked-in arithmetic indexes out of bounds instead, matching the illegal-memory-access crash in the issue.This also explains the exact on/off pattern reported in #52475: fp8 KV is clean (FlashAttention masks from GPU-side tensors and supports spec-as-decode), turboquant without MTP is clean (pure decode batches are capture-safe), and turboquant with MTP under
--enforce-eageris clean (same Python loop, real metadata).The fix declares
UNIFORM_SINGLE_TOKEN_DECODEso spec-shaped batches fall back to piecewise cudagraphs and the attention path runs eagerly with real metadata. Pure decode batches (query_len 1, no spec) keep FULL capture, so the non-speculative configuration is unchanged.Not a duplicate: no open PR references #52475, and the open TurboQuant PRs (#53060, #52745, #53231, #51082, #50248, #49798, #49465, #40858) touch the decode kernels, cache-shape resolution, or platform enablement, not the cudagraph support declaration. #53059 is adjacent but different: it fixes prefills that alias the uniform-decode shape, while this bug is genuine spec-decode batches being FULL-captured by a backend whose spec path cannot be captured.
Test Plan
Hardware: 2x RTX 5060 Ti (sm120), TP=2. Model: Qwen3.8-27B NVFP4 (
model_type: qwen3_5, hybrid GDN, head_dim 256, GQA 6), the same model family as the issue.Bisection matrix that isolated the bug, all with
--kv-cache-dtype turboquant_k8v4, greedy sampling, identical prompts:--no-async-schedulingwith cudagraphs+MTP still collapses, ruling out async scheduling as the trigger.The second failure mode from the issue is also resolved:
num_speculative_tokens: 1with cudagraphs, which previously died with an illegal memory access on the first request, now serves and generates coherently with this fix (same command as above with"num_speculative_tokens":1).After the fix, the full target configuration from the issue also works:
--max-model-len 262144 --kv-cache-dtype turboquant_4bit_ncwith MTP K3 and the vision tower loaded. 301,465-token KV pool (1.15x concurrency), needle retrieval passes at 12k and 60k token prompts, MTP draft acceptance 77.3% (per-position 0.864/0.727/0.727), prefill 10.6k tokens/s on this hardware.Test Result
Before (cudagraphs + MTP K3, turboquant_k8v4, greedy):
After (same command, this fix applied):
Output quality matches the
--enforce-eagercontrol. Non-speculative serving before/after is unchanged (still FULL-captures single-token decode batches).AI assistance was used to bisect this bug and draft this PR; I reviewed the change and ran the tests above.