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[Model] Revert PR #26715: Restore custom PaliGemma and Gemma3-MM impl… (#27309)

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Signed-off-by: Luciano Martins <lucianommartins@users.noreply.github.com>
Co-authored-by: Luciano Martins <lucianommartins@users.noreply.github.com>
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Luciano Martins
2025-10-22 14:05:34 -03:00
committed by GitHub
parent 084a9dae80
commit e05a6754a8
12 changed files with 1219 additions and 54 deletions
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docs/models/supported_models.md
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@@ -642,6 +642,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
| `DeepseekOCRForCausalLM` | DeepSeek-OCR | T + I<sup>+</sup> | `deepseek-ai/DeepSeek-OCR`, etc. | | ✅︎ |
| `Ernie4_5_VLMoeForConditionalGeneration` | Ernie4.5-VL | T + I<sup>+</sup>/ V<sup>+</sup> | `baidu/ERNIE-4.5-VL-28B-A3B-PT`, `baidu/ERNIE-4.5-VL-424B-A47B-PT` | | ✅︎ |
| `FuyuForCausalLM` | Fuyu | T + I | `adept/fuyu-8b`, etc. | | ✅︎ |
| `Gemma3ForConditionalGeneration` | Gemma 3 | T + I<sup>+</sup> | `google/gemma-3-4b-it`, `google/gemma-3-27b-it`, etc. | ✅︎ | ✅︎ |
| `Gemma3nForConditionalGeneration` | Gemma 3n | T + I + A | `google/gemma-3n-E2B-it`, `google/gemma-3n-E4B-it`, etc. | | |
| `GLM4VForCausalLM`<sup>^</sup> | GLM-4V | T + I | `zai-org/glm-4v-9b`, `zai-org/cogagent-9b-20241220`, etc. | ✅︎ | ✅︎ |
| `Glm4vForConditionalGeneration` | GLM-4.1V-Thinking | T + I<sup>E+</sup> + V<sup>E+</sup> | `zai-org/GLM-4.1V-9B-Thinking`, etc. | ✅︎ | ✅︎ |
@@ -671,6 +672,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
| `NVLM_D_Model` | NVLM-D 1.0 | T + I<sup>+</sup> | `nvidia/NVLM-D-72B`, etc. | | ✅︎ |
| `Ovis` | Ovis2, Ovis1.6 | T + I<sup>+</sup> | `AIDC-AI/Ovis2-1B`, `AIDC-AI/Ovis1.6-Llama3.2-3B`, etc. | | ✅︎ |
| `Ovis2_5` | Ovis2.5 | T + I<sup>+</sup> + V | `AIDC-AI/Ovis2.5-9B`, etc. | | |
| `PaliGemmaForConditionalGeneration` | PaliGemma, PaliGemma 2 | T + I<sup>E</sup> | `google/paligemma-3b-pt-224`, `google/paligemma-3b-mix-224`, `google/paligemma2-3b-ft-docci-448`, etc. | | ✅︎ |
| `Phi3VForCausalLM` | Phi-3-Vision, Phi-3.5-Vision | T + I<sup>E+</sup> | `microsoft/Phi-3-vision-128k-instruct`, `microsoft/Phi-3.5-vision-instruct`, etc. | | ✅︎ |
| `Phi4MMForCausalLM` | Phi-4-multimodal | T + I<sup>+</sup> / T + A<sup>+</sup> / I<sup>+</sup> + A<sup>+</sup> | `microsoft/Phi-4-multimodal-instruct`, etc. | ✅︎ | ✅︎ |
| `Phi4MultimodalForCausalLM` | Phi-4-multimodal (HF Transformers) | T + I<sup>+</sup> / T + A<sup>+</sup> / I<sup>+</sup> + A<sup>+</sup> | `microsoft/Phi-4-multimodal-instruct` (with revision `refs/pr/70`), etc. | ✅︎ | ✅︎ |
@@ -695,8 +697,6 @@ Some models are supported only via the [Transformers backend](#transformers). Th
| Architecture | Models | Inputs | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/parallelism_scaling.md) |
|--------------|--------|--------|-------------------|-----------------------------|-----------------------------------------|
| `Emu3ForConditionalGeneration` | Emu3 | T + I | `BAAI/Emu3-Chat-hf` | ✅︎ | ✅︎ |
| `Gemma3ForConditionalGeneration` | Gemma 3 | T + I<sup>+</sup> | `google/gemma-3-4b-it`, `google/gemma-3-27b-it`, etc. | ✅︎ | ✅︎ |
| `PaliGemmaForConditionalGeneration` | PaliGemma, PaliGemma 2 | T + I<sup>E</sup> | `google/paligemma-3b-pt-224`, `google/paligemma-3b-mix-224`, `google/paligemma2-3b-ft-docci-448`, etc. | ✅︎ | ✅︎ |
<sup>^</sup> You need to set the architecture name via `--hf-overrides` to match the one in vLLM.
&nbsp;&nbsp;&nbsp;&nbsp;• For example, to use DeepSeek-VL2 series models:
@@ -705,7 +705,21 @@ Some models are supported only via the [Transformers backend](#transformers). Th
<sup>+</sup> Multiple items can be inputted per text prompt for this modality.
!!! warning
For `Gemma3ForConditionalGeneration`, `{"do_pan_and_scan": true}` is not supported in Transformers backend yet.
Both V0 and V1 support `Gemma3ForConditionalGeneration` for text-only inputs.
However, there are differences in how they handle text + image inputs:
V0 correctly implements the model's attention pattern:
- Uses bidirectional attention between the image tokens corresponding to the same image
- Uses causal attention for other tokens
- Implemented via (naive) PyTorch SDPA with masking tensors
- Note: May use significant memory for long prompts with image
V1 currently uses a simplified attention pattern:
- Uses causal attention for all tokens, including image tokens
- Generates reasonable outputs but does not match the original model's attention for text + image inputs, especially when `{"do_pan_and_scan": true}`
- Will be updated in the future to support the correct behavior
This limitation exists because the model's mixed attention pattern (bidirectional for images, causal otherwise) is not yet supported by vLLM's attention backends.
!!! note
`Gemma3nForConditionalGeneration` is only supported on V1 due to shared KV caching and it depends on `timm>=1.0.17` to make use of its
@@ -757,6 +771,9 @@ Some models are supported only via the [Transformers backend](#transformers). Th
The official `openbmb/MiniCPM-V-2` doesn't work yet, so we need to use a fork (`HwwwH/MiniCPM-V-2`) for now.
For more details, please see: <https://github.com/vllm-project/vllm/pull/4087#issuecomment-2250397630>
!!! warning
Our PaliGemma implementations have the same problem as Gemma 3 (see above) for both V0 and V1.
!!! note
For Qwen2.5-Omni and Qwen3-Omni, reading audio from video pre-processing (`--mm-processor-kwargs '{"use_audio_in_video": true}'`) is currently work in progress and not yet supported.