[Gemma4] Allow per-layer attention backend selection for heterogeneou…

[CunXin1]

Purpose

Fix #38887: Gemma 4 models are extremely slow on vLLM v0.19.0 (~9 tok/s on RTX 4090 for E4B) because Gemma4Config forces all layers to use TRITON_ATTN, even though ~83% of layers (sliding-window, head_dim=256) are fully compatible with FlashAttention.
Root cause: Gemma 4 has heterogeneous head dimensions — sliding-window layers use head_dim=256 and
full-attention layers use global_head_dim=512. Since FlashAttention's kernel limit is head_size <= 256,
the previous code forced TRITON_ATTN globally to avoid mixed-backend usage. However, vLLM's
get_attn_backend() already supports per-layer backend selection via its @cache-decorated selector
(distinct head_size arguments produce distinct backend choices). The global forcing was unnecessary and
penalized the majority of layers.

What this PR does:

• Removes the forced attention_config.backend = TRITON_ATTN override
• Adds informational logging showing the sliding/full layer count split
• Adds an early warning if the user explicitly sets --attention-backend to a backend that cannot handle
  global_head_dim

Layer breakdown across all Gemma 4 variants:

Variant	Sliding (FlashAttn)	Full (Triton)	% on FlashAttn
E2B (35 layers)	28	7	80%
E4B (42 layers)	35	7	83%
26B-A4B (30 layers)	25	5	83%
31B (60 layers)	50	10	83%

Not a duplicate: Searched open PRs for 38887 in:body and Gemma4 FlashAttention heterogeneous — no
results. PR #38879 optimizes Gemma4 prefill (YOCO fast prefill) and is complementary; it does not address
the decode bottleneck caused by the forced TRITON_ATTN backend.

Test Plan

# Lint (pre-commit)
pre-commit run ruff-check --files vllm/model_executor/models/config.py
pre-commit run ruff-format --files vllm/model_executor/models/config.py
pre-commit run typos --files vllm/model_executor/models/config.py

# Unit tests (requires GPU + model access)
.venv/bin/python -m pytest tests/models/multimodal/processing/test_gemma4.py -v

# Serving benchmark (Gemma4 E4B on RTX 4090)
# Before (forced TRITON_ATTN):
vllm serve google/gemma-4-e4b-it --max-model-len 8192 --dtype bfloat16

# After (per-layer backend selection):
vllm serve google/gemma-4-e4b-it --max-model-len 8192 --dtype bfloat16
# Expected log output:
#   Gemma4 model has heterogeneous head dimensions (head_dim=256, global_head_dim=512).
#   35 sliding-window layers will use FlashAttention;
#   7 full-attention layers will fall back to a compatible backend.

Test Result

Lint: All passed (ruff-check, ruff-format, typos).

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[gemini-code-assist]

Code Review

This pull request updates the Gemma4Config to allow per-layer attention backend selection for models with heterogeneous head dimensions, moving away from the previous approach of forcing a global Triton backend. This change enables layers with smaller head dimensions to utilize FlashAttention for improved performance. Feedback was provided to refine the logging logic, as the current informational message can be misleading when a backend is explicitly selected or when all head dimensions are small enough to support FlashAttention without a fallback.

[CunXin1]

Ready for review. This PR removes the forced TRITON_ATTN override for Gemma 4, allowing ~83% of layers to
use FlashAttention. Would appreciate a look from someone familiar with the attention backend selection.
Thanks!

[HelloWorldU]

Looks good to me

[janreges]

@CunXin1 - It works great. On the RTX 6000 Pro 96GB Blackwell, it increased the speed from 60 to 70 tok/s with the Gemma 4 31B AWQ model. Thank you! :)

[LucasWilkinson]

@lucianommartins mentioned using mixed backends were causing numerical issues; could you please provide accuracy evaluations ? gsm8k would be a good place to start 👍

[jeffye-dev]

This would cause serious garbled output problem.

[lucianommartins]

that would be great to have accuracy evals like @LucasWilkinson mentioned (gsm8k. mmlu, etc - anyone would be great), @CunXin1

[snivertynv]

I tested the changes in this PR on Gemma-4-31B-it (TP=2 H100, bf16). The mixed-backend dispatch seems to be numerically indistinguishable from all-Triton.

  |                    Eval                      | Baseline (all-Triton) | Mixed (this PR)      |    Δ    |
  | mmlu_pro (12,032 Qs, 14 subjects, 5-shot)    | 0.8472 ± 0.0032       | 0.8473 ± 0.0032      | +0.0001 |
  | gsm8k_cot_zeroshot flex-extract (1,319 Qs)   | 0.8795 ± 0.009        | 0.8825 ± 0.0089      | +0.0030 |

The mmlu_pro score isn't far from Google's published 85.2%.

Setup details:

• lm-evaluation-harness >= 0.4.5 via local-chat-completions
• --apply_chat_template --gen_kwargs max_gen_toks=1024 for gsm8k_cot_zeroshot.
• strict-match is 0.0 across all samples for both variants