[Bugfix][Gemma4] Fix vision fp16 overflow causing <pad> output

[wenqiangire-commits]

Purpose

Fixes #40290.

Gemma4 SigLIP's final (h - std_bias) * std_scale overflows fp16: |std_bias| reaches ~5.4e4 in the 31B / 26B-A4B checkpoints, fp16 max is ±6.55e4. The intermediate h - std_bias saturates to -inf, NaNs through the downstream RMSNorm, and the LM samples <pad> for every image token — silently, with no warning in vLLM logs.

Affects variants with vision_config.standardize=True (gemma-4-31B-it, gemma-4-26B-A4B-it) on fp16 engines (AWQ or --dtype float16). E4B (standardize=False) is unaffected.

Fix: keep vision_tower in bf16 when standardize=True, cast pixel_values to match. The projector's existing target_dtype = embedding_projection.weight.dtype cast handles the bf16 → fp16 conversion at the projector boundary. Full root-cause analysis, per-stage tensor evidence, and ruled-out hypotheses in #40290.

Test Plan

Run gemma-4-31B-it AWQ under fp16 engine, send an image chat completion, verify LM returns a normal description rather than <pad>.

vllm serve /path/to/gemma-4-31B-awq --quantization awq_marlin \
  --limit-mm-per-prompt '{"image":1}'

import base64, json, urllib.request
b64 = base64.b64encode(open("test.jpg","rb").read()).decode()
r = urllib.request.urlopen(urllib.request.Request(
    "http://localhost:8000/v1/chat/completions",
    data=json.dumps({
        "model": "/path/to/gemma-4-31B-awq",
        "messages": [{"role":"user","content":[
            {"type":"image_url","image_url":{"url":f"data:image/jpeg;base64,{b64}"}},
            {"type":"text","text":"Describe this image."},
        ]}],
        "max_tokens": 50, "temperature": 0.1,
        "skip_special_tokens": False,
    }).encode(),
    headers={"Content-Type":"application/json"}))
print(json.loads(r.read())["choices"][0]["message"]["content"])

Test Result

Before: <pad> × 50, finish_reason="length".
After: normal image description, finish_reason="stop". Verified end-to-end through a downstream chat agent on gemma-4-31B-it AWQ (RTX 5090, vLLM 0.19). E4B init path unchanged (guard evaluates False).

[claude]

Claude Code Review

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[github-actions]

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[gemini-code-assist]

Code Review

This pull request modifies the Gemma4 multimodal model to prevent fp16 overflow during standardization by casting the vision tower to bfloat16 and ensuring image inputs are cast to the matching dtype. Feedback indicates that hardcoding bfloat16 may lead to failures on hardware without native support, suggesting a more robust dtype selection strategy using platform checks. Furthermore, the reviewer noted that the video processing path requires similar input casting to avoid dtype mismatches and potential crashes.

[gemini-code-assist]

Forcing torch.bfloat16 for the vision tower can lead to runtime errors on hardware that does not support it (e.g., NVIDIA Turing GPUs like the T4). Since the goal is to avoid fp16 overflow during standardization, it would be safer to use torch.float32 as a fallback when bf16 is unavailable. Consider using vllm.platforms.current_platform.is_bf16_supported() to determine the appropriate dtype.

[gemini-code-assist]

This input casting is also required in the video processing path (_process_video_input) to prevent dtype mismatches when the vision tower is in bf16. Currently, line 1172 in _process_video_input is missing the .to(vt_dtype) call, which will cause a crash or incorrect results when processing videos on fp16 engines.