[Spec Decode] Allow DFlash drafter to autoselect non-causal-capable backend on Gemma 4#42069
[Spec Decode] Allow DFlash drafter to autoselect non-causal-capable backend on Gemma 4#42069mikeumus wants to merge 2 commits into
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…mma4 target
When the target is Gemma4 (heterogeneous head dimensions:
head_dim=256, global_head_dim=512), Gemma4Config.verify_and_update_config
force-locks attention_config.backend to TRITON_ATTN. The base proposer
at llm_base_proposer.py:1320-1326 should reset that to None for the
drafter (spec_cfg.attention_backend default) but in our reproducer the
drafter still ends up on Triton, which DFlash's non-causal drafter
attention then rejects at engine init.
This patch:
1. Defensively forces backend=None in DFlashProposer._create_draft_vllm_config
so the drafter goes through autoselect (likely FlashInfer for non-causal)
2. Adds a [DIVINCI-FORK] diagnostic log showing the backend values
across the base->fix transition, so we can confirm where the
unexpected Triton override originates
If (1) is sufficient, this is the proper fix and we'll clean up the
diagnostic before the upstream PR. If (1) is NOT sufficient (drafter
still ends up on Triton), the diagnostic will tell us where the
override comes from after our reset, narrowing the next iteration.
Signed-off-by: Mike Mooring <mike@divinci.ai>
…ackend on Gemma 4
When the target is Gemma 4 (heterogeneous head dimensions:
head_dim=256, global_head_dim=512), Gemma4Config.verify_and_update_config
in vllm/model_executor/models/config.py force-locks
attention_config.backend to TRITON_ATTN to prevent mixed-backend
numerical divergence within the target's forward (sliding vs global
attention layers).
This is correct for the target's own forward pass. But in spec-decode,
target and drafter are separate models with separate KV caches and
separate forwards — they're algorithmically independent and rejection
sampling tolerates numerical drift by design. The base
SpecDecodeBaseProposer._create_draft_vllm_config explicitly says
"Never inherit the attention backend from base" and resets to
spec_cfg.attention_backend (default None) for exactly this reason.
For DFlash specifically, the drafter requires non-causal (bidirectional)
attention — TRITON_ATTN doesn't support this and rejects the drafter
at engine init with:
ValueError: Selected backend AttentionBackendEnum.TRITON_ATTN is not
valid for this configuration. Reason: ['non-causal attention not supported']
This patch defensively forces backend=None on the drafter's
attention_config, letting the standard autoselect pick a backend
that supports non-causal (e.g. FLEX_ATTENTION). use_non_causal=True
is preserved.
Verified end-to-end on Modal H100 with target=google/gemma-4-31B-it,
drafter=z-lab/gemma-4-31B-it-DFlash:
- Engine init succeeds (drafter picks FLEX_ATTENTION via autoselect)
- Both models load, torch.compile completes for backbone + eagle_head
- 10-prompt A/B (with vs without DFlash) shows 1.28x average,
4.4x peak speedup on math reasoning prompts
Related issue: filed as Divinci-AI/vllm fork demonstration; full vLLM
issue + PR will reference this commit's e2e validation.
Signed-off-by: Mike Mooring <mike@divinci.ai>
mikeumus
requested review from
MatthewBonanni,
benchislett and
luccafong
as code owners
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Code Review
This pull request modifies the _create_draft_vllm_config method in vllm/v1/spec_decode/dflash.py to explicitly set the attention backend to None. This change ensures that the drafter can utilize the standard autoselect path to choose a backend supporting non-causal attention (such as FLEX_ATTENTION), preventing it from being locked into a backend like TRITON_ATTN which is often forced by models like Gemma 4. I have no feedback to provide as there were no review comments.
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Summary
Fixes #42068.
When the target is Gemma 4 (heterogeneous head dimensions:
head_dim=256,global_head_dim=512),Gemma4Config.verify_and_update_configforce-locksattention_config.backendtoTRITON_ATTNto prevent mixed-backend numerical divergence within the target's own forward (sliding vs global attention).That lock is correct for the target's intra-forward consistency, but it propagates to the drafter via
DFlashProposer._create_draft_vllm_config. DFlash drafters use non-causal (bidirectional) attention to draft a whole block in one pass —TRITON_ATTNrejects this:Result: Gemma 4 + DFlash speculative decoding is structurally impossible upstream today.
Why the lock doesn't apply to spec-decode
The "mixed-backend numerical divergence" risk is legitimate inside one forward pass (Gemma 4's own sliding-vs-global layers). It is not legitimate across spec-decode where target and drafter are separate
nn.Modules with separate KV caches and separate forwards — rejection sampling tolerates numerical drift by design. The MTP case (#41745) is the exception (KV-shared with target — must inherit backend); DFlash and any other independent-KV drafter is the general case where the drafter should be free to autoselect.The change
One method override in
vllm/v1/spec_decode/dflash.py:backend=Nonelets the standard autoselect pick a backend that supports non-causal attention (FLEX_ATTENTION/FLASHINFER) on the drafter, while leaving the target'sTRITON_ATTNlock untouched.use_non_causal=Trueis preserved.Precedent
vllm-ascend(sibling project for Ascend NPUs) already merged the equivalent decoupling: vllm-project/vllm-ascend#7342 — "Separate attention backend for target and draft model" by @SidaoY.Measured impact
Modal H100, 10 mixed prompts (5 math + 5 conversational),
temperature=0.0,max_new_tokens=256, vLLM nightly + this patch overlay:google/gemma-4-31B-it(stock)google/gemma-4-31B-it+ Divinci QLoRA-DFO (merged bf16)Phase 2's QLoRA-fine-tuned target retains 92% of the stock-target speedup despite the drafter being conditioned on stock Gemma 4 hidden states — confirming the fix is broadly useful for the fine-tune ecosystem, not just stock targets. Output text was bit-identical between with-DFlash and without-DFlash runs (verifier's lossless guarantee held).
Test plan
target=google/gemma-4-31B-it,drafter=z-lab/gemma-4-31B-it-DFlashFLEX_ATTENTIONvia autoselect (vs the failingTRITON_ATTNon main)torch.compilecompletes for backbone + eagle_headRelated
DCO sign-off: yes (
Signed-off-by: Mike Mooring <mike@divinci.ai>)🤖 Generated with Claude Code