[Model] Always use Transformers backend for PaliGemma and Gemma3-MM (#26715)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

docs/models/hardware_supported_models/tpu.md

@@ -16,8 +16,8 @@
 | meta-llama/Llama-4-*                                | Llama4ForConditionalGeneration | ❌ |
 | microsoft/Phi-3-mini-128k-instruct                  | Phi3ForCausalLM                | 🟨 |
 | microsoft/phi-4                                     | Phi3ForCausalLM                | ❌ |
-| google/gemma-3-27b-it                               | Gemma3ForConditionalGeneration | 🟨 |
-| google/gemma-3-4b-it                                | Gemma3ForConditionalGeneration | ❌ |
+| google/gemma-3-27b-it                               | TransformersForMultimodalLM    | 🟨 |
+| google/gemma-3-4b-it                                | TransformersForMultimodalLM    | ❌ |
 | deepseek-ai/DeepSeek-R1                             | DeepseekV3ForCausalLM          | ❌ |
 | deepseek-ai/DeepSeek-V3                             | DeepseekV3ForCausalLM          | ❌ |
 | RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8  | LlamaForCausalLM               | ✅ |

docs/models/supported_models.md

@@ -650,7 +650,6 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `DeepseekVLV2ForCausalLM`<sup>^</sup> | DeepSeek-VL2 | T + I<sup>+</sup> | `deepseek-ai/deepseek-vl2-tiny`, `deepseek-ai/deepseek-vl2-small`, `deepseek-ai/deepseek-vl2`, 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. | ✅︎ | ✅︎ |
@@ -679,7 +678,6 @@ 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. | ✅︎ | ✅︎ |
@@ -704,6 +702,8 @@ 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:
@@ -712,21 +712,7 @@ 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
-    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.
+    For `Gemma3ForConditionalGeneration`, `{"do_pan_and_scan": true}` is not supported in Transformers backend yet.
 
 !!! 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
@@ -778,9 +764,6 @@ 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.
 

examples/offline_inference/vision_language.py

@@ -248,7 +248,8 @@ def run_gemma3(questions: list[str], modality: str) -> ModelRequestData:
         model=model_name,
         max_model_len=2048,
         max_num_seqs=2,
-        mm_processor_kwargs={"do_pan_and_scan": True},
+        # TODO: Support this in transformers backend
+        # mm_processor_kwargs={"do_pan_and_scan": True},
         limit_mm_per_prompt={modality: 1},
     )
 

tests/models/language/generation/test_gemma.py

@@ -3,7 +3,7 @@
 import numpy as np
 import pytest
 
-MODELS = ["google/gemma-2b", "google/gemma-2-2b", "google/gemma-3-4b-it"]
+MODELS = ["google/gemma-2b", "google/gemma-2-2b"]
 
 
 @pytest.mark.parametrize("model", MODELS)
@@ -14,14 +14,8 @@ def test_dummy_loader(vllm_runner, monkeypatch, model: str) -> None:
             model,
             load_format="dummy",
         ) as llm:
-            if model == "google/gemma-3-4b-it":
-                normalizers = llm.llm.collective_rpc(
-                    lambda self: self.model_runner.model.language_model.model.normalizer.cpu().item()  # noqa: E501
-                )
-                config = llm.llm.llm_engine.model_config.hf_config.text_config
-            else:
-                normalizers = llm.llm.collective_rpc(
-                    lambda self: self.model_runner.model.model.normalizer.cpu().item()
+            normalizers = llm.apply_model(
+                lambda model: model.model.normalizer.cpu().item()
             )
             config = llm.llm.llm_engine.model_config.hf_config
             assert np.allclose(normalizers, config.hidden_size**0.5, rtol=2e-3)

tests/models/multimodal/generation/test_common.py

@@ -113,25 +113,6 @@ VLM_TEST_SETTINGS = {
         dtype="bfloat16" if current_platform.is_cpu() else "auto",
         marks=[pytest.mark.core_model, pytest.mark.cpu_model],
     ),
-    "paligemma": VLMTestInfo(
-        models=["google/paligemma-3b-mix-224"],
-        test_type=VLMTestType.IMAGE,
-        prompt_formatter=identity,
-        img_idx_to_prompt=lambda idx: "",
-        # Paligemma uses its own sample prompts because the default one fails
-        single_image_prompts=IMAGE_ASSETS.prompts(
-            {
-                "stop_sign": "caption es",
-                "cherry_blossom": "What is in the picture?",
-            }
-        ),
-        auto_cls=AutoModelForImageTextToText,
-        vllm_output_post_proc=model_utils.paligemma_vllm_to_hf_output,
-        dtype="bfloat16",
-        marks=[
-            pytest.mark.skip(reason="vLLM does not support PrefixLM attention mask")
-        ],
-    ),
     "qwen2_5_vl": VLMTestInfo(
         models=["Qwen/Qwen2.5-VL-3B-Instruct"],
         test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE, VLMTestType.VIDEO),
@@ -196,14 +177,24 @@ VLM_TEST_SETTINGS = {
     # Gemma3 has bidirectional mask on images
     "gemma3-transformers": VLMTestInfo(
         models=["google/gemma-3-4b-it"],
-        test_type=VLMTestType.IMAGE,
-        prompt_formatter=lambda vid_prompt: f"<'<bos><start_of_turn>user\n{vid_prompt}<start_of_image><end_of_turn>\n<start_of_turn>model\n",  # noqa: E501
-        max_model_len=4096,
+        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
+        prompt_formatter=lambda img_prompt: f"<bos><start_of_turn>user\n{img_prompt}<end_of_turn>\n<start_of_turn>model\n",  # noqa: E501
+        single_image_prompts=IMAGE_ASSETS.prompts(
+            {
+                "stop_sign": "<start_of_image>What's the content in the center of the image?",  # noqa: E501
+                "cherry_blossom": "<start_of_image>What is the season?",
+            }
+        ),
+        multi_image_prompt="<start_of_image><start_of_image>Describe the two images in detail.",  # noqa: E501
+        max_model_len=8192,
         auto_cls=AutoModelForImageTextToText,
+        # TODO: Support `do_pan_and_scan` in transformers backend
+        # patch_hf_runner=model_utils.gemma3_patch_hf_runner,
         vllm_output_post_proc=model_utils.gemma3_vllm_to_hf_output,
         image_size_factors=[(0.25, 0.5, 1.0)],
         vllm_runner_kwargs={
             "model_impl": "transformers",
+            # "mm_processor_kwargs": {"do_pan_and_scan": True},
         },
         marks=[pytest.mark.core_model],
     ),
@@ -222,6 +213,27 @@ VLM_TEST_SETTINGS = {
         },
         marks=[pytest.mark.core_model],
     ),
+    # PaliGemma has PrefixLM attention
+    "paligemma-transformers": VLMTestInfo(
+        models=["google/paligemma-3b-mix-224"],
+        test_type=VLMTestType.IMAGE,
+        prompt_formatter=identity,
+        img_idx_to_prompt=lambda idx: "",
+        # PaliGemma uses its own sample prompts because the default one fails
+        single_image_prompts=IMAGE_ASSETS.prompts(
+            {
+                "stop_sign": "caption es",
+                "cherry_blossom": "What is in the picture?",
+            }
+        ),
+        auto_cls=AutoModelForImageTextToText,
+        vllm_output_post_proc=model_utils.paligemma_vllm_to_hf_output,
+        image_size_factors=[(0.25, 0.5, 1.0)],
+        vllm_runner_kwargs={
+            "model_impl": "transformers",
+        },
+        marks=[pytest.mark.core_model],
+    ),
     # Pixel values from processor are not 4D or 5D arrays
     "qwen2_5_vl-transformers": VLMTestInfo(
         models=["Qwen/Qwen2.5-VL-3B-Instruct"],
@@ -348,24 +360,6 @@ VLM_TEST_SETTINGS = {
         image_size_factors=[(), (0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
         marks=[large_gpu_mark(min_gb=32)],
     ),
-    "gemma3": VLMTestInfo(
-        models=["google/gemma-3-4b-it"],
-        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
-        prompt_formatter=lambda img_prompt: f"<bos><start_of_turn>user\n{img_prompt}<end_of_turn>\n<start_of_turn>model\n",  # noqa: E501
-        single_image_prompts=IMAGE_ASSETS.prompts(
-            {
-                "stop_sign": "<start_of_image>What's the content in the center of the image?",  # noqa: E501
-                "cherry_blossom": "<start_of_image>What is the season?",
-            }
-        ),
-        multi_image_prompt="<start_of_image><start_of_image>Describe the two images in detail.",  # noqa: E501
-        max_model_len=4096,
-        max_num_seqs=2,
-        auto_cls=AutoModelForImageTextToText,
-        vllm_runner_kwargs={"mm_processor_kwargs": {"do_pan_and_scan": True}},
-        patch_hf_runner=model_utils.gemma3_patch_hf_runner,
-        num_logprobs=10,
-    ),
     "glm4v": VLMTestInfo(
         models=["zai-org/glm-4v-9b"],
         test_type=VLMTestType.IMAGE,

tests/models/multimodal/generation/vlm_utils/model_utils.py

@@ -328,16 +328,6 @@ def gemma3_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
 
     hf_model.processor = processor
 
-    orig_generate = hf_model.model.generate
-
-    def _generate(self, *args, **kwargs):
-        # FIXME: https://github.com/huggingface/transformers/issues/38333
-        kwargs["disable_compile"] = True
-
-        return orig_generate(*args, **kwargs)
-
-    hf_model.model.generate = types.MethodType(_generate, hf_model.model)
-
     return hf_model
 
 

tests/models/multimodal/processing/test_common.py

@@ -222,7 +222,6 @@ def _test_processing_correctness(
 _ADD_SPECIAL_TOKENS_OVERRIDES = {
     "ovis": False,
     "ovis2_5": False,
-    "paligemma": False,
     "ultravox": False,
     "whisper": False,
 }
@@ -333,7 +332,6 @@ def _test_processing_correctness_one(
         "deepseek-ai/deepseek-vl2-tiny",
         "baidu/ERNIE-4.5-VL-28B-A3B-PT",
         "adept/fuyu-8b",
-        "google/gemma-3-4b-it",
         "google/gemma-3n-E2B-it",
         "zai-org/glm-4v-9b",
         "zai-org/GLM-4.1V-9B-Thinking",
@@ -370,8 +368,6 @@ def _test_processing_correctness_one(
         "AIDC-AI/Ovis1.6-Llama3.2-3B",
         "AIDC-AI/Ovis2-1B",
         "AIDC-AI/Ovis2.5-2B",
-        "google/paligemma-3b-mix-224",
-        "google/paligemma2-3b-ft-docci-448",
         "microsoft/Phi-3.5-vision-instruct",
         "microsoft/Phi-4-multimodal-instruct",
         "mistralai/Pixtral-12B-2409",

tests/models/multimodal/processing/test_tensor_schema.py

@@ -48,7 +48,6 @@ ARCH_NEEDS_EXTRAS = [
     "Idefics3ForConditionalGeneration",
     "LlavaForConditionalGeneration",
     "MiniCPMV",
-    "PaliGemmaForConditionalGeneration",
 ]
 REPO_ID_TO_SKIP = {
     "nm-testing/pixtral-12b-FP8-dynamic": "duplicated test",

vllm/model_executor/models/gemma3_mm.py

-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import math
-from collections.abc import Iterable, Mapping, Sequence
-from typing import Annotated, Any, Literal
-
-import torch
-from torch import nn
-from transformers import BatchFeature, Gemma3Config, Gemma3Processor
-from transformers.models.gemma3.processing_gemma3 import Gemma3ProcessorKwargs
-
-import vllm.envs as envs
-from vllm.config import VllmConfig
-from vllm.config.multimodal import BaseDummyOptions
-from vllm.logger import init_logger
-from vllm.model_executor.layers.layernorm import GemmaRMSNorm
-from vllm.model_executor.models.module_mapping import MultiModelKeys
-from vllm.multimodal import MULTIMODAL_REGISTRY
-from vllm.multimodal.inputs import (
-    MultiModalDataDict,
-    MultiModalFieldConfig,
-    MultiModalKwargsItems,
-)
-from vllm.multimodal.parse import ImageProcessorItems, ImageSize, MultiModalDataItems
-from vllm.multimodal.processing import (
-    BaseMultiModalProcessor,
-    BaseProcessingInfo,
-    MultiModalPromptUpdates,
-    MultiModalPromptUpdatesApplyResult,
-    PlaceholderFeaturesInfo,
-    PromptReplacement,
-    PromptUpdate,
-    PromptUpdateDetails,
-    replace_token_matches,
-)
-from vllm.multimodal.profiling import BaseDummyInputsBuilder
-from vllm.sequence import IntermediateTensors
-from vllm.utils.tensor_schema import TensorSchema, TensorShape
-
-from .interfaces import (
-    MultiModalEmbeddings,
-    SupportsLoRA,
-    SupportsMultiModal,
-    SupportsPP,
-)
-from .siglip import SiglipVisionModel
-from .utils import (
-    AutoWeightsLoader,
-    WeightsMapper,
-    init_vllm_registered_model,
-    maybe_prefix,
-)
-
-logger = init_logger(__name__)
-
-
-class Gemma3ImagePixelInputs(TensorSchema):
-    """
-    Dimensions:
-        - p: Number of patches total (over each image over each prompt in the
-          batch)
-        - c: Number of channels (3)
-        - h: Height of each patch
-        - w: Width of each patch
-        - bn: Batch size * number of images
-    """
-
-    type: Literal["pixel_values"] = "pixel_values"
-
-    pixel_values: Annotated[torch.Tensor, TensorShape("p", 3, "h", "w")]
-
-    num_patches: Annotated[torch.Tensor, TensorShape("bn")]
-
-
-Gemma3ImageInputs = Gemma3ImagePixelInputs
-
-
-class Gemma3ProcessingInfo(BaseProcessingInfo):
-    def get_hf_config(self):
-        return self.ctx.get_hf_config(Gemma3Config)
-
-    def get_hf_processor(self, **kwargs: object):
-        return self.ctx.get_hf_processor(Gemma3Processor, **kwargs)
-
-    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
-        return {"image": None}
-
-    def _resolve_image_kwargs(
-        self,
-        processor: Gemma3Processor,
-        keys: set[str],
-    ) -> dict[str, Any]:
-        image_processor = processor.image_processor
-        kwargs = processor._merge_kwargs(
-            Gemma3ProcessorKwargs,
-            tokenizer_init_kwargs=processor.tokenizer.init_kwargs,
-        )
-
-        images_kwargs = kwargs["images_kwargs"]
-
-        def _resolve_kw(key: str):
-            val = getattr(image_processor, key)
-            if val is None:
-                val = images_kwargs[key]
-
-            return val
-
-        return {k: _resolve_kw(k) for k in keys}
-
-    def get_num_crops(
-        self,
-        *,
-        image_width: int,
-        image_height: int,
-        processor: Gemma3Processor | None,
-    ) -> int:
-        if processor is None:
-            processor = self.get_hf_processor()
-
-        images_kwargs = self._resolve_image_kwargs(
-            processor,
-            {
-                "do_pan_and_scan",
-                "pan_and_scan_min_crop_size",
-                "pan_and_scan_max_num_crops",
-                "pan_and_scan_min_ratio_to_activate",
-            },
-        )
-
-        do_pan_and_scan = images_kwargs["do_pan_and_scan"]
-        pan_and_scan_min_crop_size = images_kwargs["pan_and_scan_min_crop_size"]
-        pan_and_scan_max_num_crops = images_kwargs["pan_and_scan_max_num_crops"]
-        pan_and_scan_min_ratio_to_activate = images_kwargs[
-            "pan_and_scan_min_ratio_to_activate"
-        ]
-
-        if not do_pan_and_scan:
-            return 0
-
-        if envs.VLLM_USE_V1:
-            logger.warning_once(
-                "`do_pan_and_scan=True` has suboptimal results on V1 "
-                "because of the simplified attention pattern being used."
-            )
-
-        # Based on Gemma3ImageProcessor.pan_and_scan
-        if image_width >= image_height:
-            if image_width / image_height < pan_and_scan_min_ratio_to_activate:
-                return 0
-
-            num_crops_w = min(
-                int(math.floor(image_width / pan_and_scan_min_crop_size)),
-                int(math.floor(image_width / image_height + 0.5)),
-            )
-
-            num_crops_w = max(2, num_crops_w)
-            num_crops_w = min(pan_and_scan_max_num_crops, num_crops_w)
-            num_crops_h = 1
-        else:
-            if image_height / image_width < pan_and_scan_min_ratio_to_activate:
-                return 0
-
-            num_crops_h = min(
-                int(math.floor(image_height / pan_and_scan_min_crop_size)),
-                int(math.floor(image_height / image_width + 0.5)),
-            )
-
-            num_crops_h = max(2, num_crops_h)
-            num_crops_h = min(pan_and_scan_max_num_crops, num_crops_h)
-            num_crops_w = 1
-
-        crop_size_w = int(math.ceil(image_width / num_crops_w))
-        crop_size_h = int(math.ceil(image_height / num_crops_h))
-
-        if min(crop_size_w, crop_size_h) < pan_and_scan_min_crop_size:
-            return 0
-
-        return num_crops_w * num_crops_h
-
-    def get_image_repl(
-        self,
-        *,
-        image_width: int,
-        image_height: int,
-        processor: Gemma3Processor | None,
-    ) -> PromptUpdateDetails[str]:
-        if processor is None:
-            processor = self.get_hf_processor()
-
-        boi_token = processor.boi_token
-
-        num_crops = self.get_num_crops(
-            image_width=image_width,
-            image_height=image_height,
-            processor=processor,
-        )
-
-        if num_crops == 0:
-            image_text = boi_token
-        else:
-            crops_image_tokens = " ".join(boi_token for _ in range(num_crops))
-            image_text = (
-                f"Here is the original image {boi_token} and here are some "
-                f"crops to help you see better {crops_image_tokens}"
-            )
-
-        repl_full = image_text.replace(boi_token, processor.full_image_sequence)
-
-        tokenizer = processor.tokenizer
-        vocab = tokenizer.get_vocab()
-        image_token_id = vocab[tokenizer.image_token]
-
-        return PromptUpdateDetails.select_token_id(repl_full, image_token_id)
-
-    def get_num_image_tokens(
-        self,
-        *,
-        image_width: int,
-        image_height: int,
-        processor: Gemma3Processor | None,
-    ) -> int:
-        if processor is None:
-            processor = self.get_hf_processor()
-
-        num_crops = self.get_num_crops(
-            image_width=image_width,
-            image_height=image_height,
-            processor=processor,
-        )
-        image_seq_len = processor.image_seq_length
-
-        return (num_crops + 1) * image_seq_len
-
-    def get_image_size_with_most_features(self) -> ImageSize:
-        processor = self.get_hf_processor()
-
-        images_kwargs = self._resolve_image_kwargs(
-            processor, {"pan_and_scan_max_num_crops"}
-        )
-        max_num_crops = images_kwargs["pan_and_scan_max_num_crops"]
-
-        # Result in the max possible feature size (h:w = max_num_crops:1)
-        return ImageSize(height=50 * max_num_crops, width=50)
-
-
-class Gemma3DummyInputsBuilder(BaseDummyInputsBuilder[Gemma3ProcessingInfo]):
-    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
-        num_images = mm_counts.get("image", 0)
-
-        processor = self.info.get_hf_processor()
-        image_token = processor.boi_token
-
-        return image_token * num_images
-
-    def get_dummy_mm_data(
-        self,
-        seq_len: int,
-        mm_counts: Mapping[str, int],
-        mm_options: Mapping[str, BaseDummyOptions] | None = None,
-    ) -> MultiModalDataDict:
-        num_images = mm_counts.get("image", 0)
-
-        target_width, target_height = self.info.get_image_size_with_most_features()
-
-        image_overrides = mm_options.get("image") if mm_options else None
-
-        return {
-            "image": self._get_dummy_images(
-                width=target_width,
-                height=target_height,
-                num_images=num_images,
-                overrides=image_overrides,
-            )
-        }
-
-
-class Gemma3MultiModalProcessor(BaseMultiModalProcessor[Gemma3ProcessingInfo]):
-    def _call_hf_processor(
-        self,
-        prompt: str,
-        mm_data: Mapping[str, object],
-        mm_kwargs: Mapping[str, object],
-        tok_kwargs: Mapping[str, object],
-    ) -> BatchFeature:
-        processed_outputs = super()._call_hf_processor(
-            prompt,
-            mm_data,
-            mm_kwargs,
-            tok_kwargs,
-        )
-
-        # HF processor pops the `num_crops` kwarg, which is needed by vLLM
-        if (images := mm_data.get("images")) is not None:
-            parsed_images = (
-                self._get_data_parser()
-                .parse_mm_data({"image": images})
-                .get_items("image", ImageProcessorItems)
-            )
-            image_sizes = [
-                parsed_images.get_image_size(i) for i in range(len(parsed_images))
-            ]
-            hf_processor = self.info.get_hf_processor(**mm_kwargs)
-
-            num_crops = [
-                self.info.get_num_crops(
-                    image_width=size.width,
-                    image_height=size.height,
-                    processor=hf_processor,
-                )
-                for size in image_sizes
-            ]
-            processed_outputs["num_patches"] = torch.tensor(num_crops) + 1
-
-        return processed_outputs
-
-    def _get_mm_fields_config(
-        self,
-        hf_inputs: BatchFeature,
-        hf_processor_mm_kwargs: Mapping[str, object],
-    ) -> Mapping[str, MultiModalFieldConfig]:
-        num_patches = hf_inputs.get("num_patches", torch.empty(0))
-
-        return dict(
-            pixel_values=MultiModalFieldConfig.flat_from_sizes("image", num_patches),
-            num_patches=MultiModalFieldConfig.batched("image"),
-        )
-
-    def _get_prompt_updates(
-        self,
-        mm_items: MultiModalDataItems,
-        hf_processor_mm_kwargs: Mapping[str, Any],
-        out_mm_kwargs: MultiModalKwargsItems,
-    ) -> Sequence[PromptUpdate]:
-        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
-        image_token = hf_processor.boi_token
-
-        def get_replacement_gemma3(item_idx: int):
-            images = mm_items.get_items("image", ImageProcessorItems)
-
-            image_size = images.get_image_size(item_idx)
-            return self.info.get_image_repl(
-                image_width=image_size.width,
-                image_height=image_size.height,
-                processor=hf_processor,
-            )
-
-        return [
-            PromptReplacement(
-                modality="image",
-                target=image_token,
-                replacement=get_replacement_gemma3,
-            )
-        ]
-
-    def _apply_token_matches(
-        self,
-        prompt: list[int],
-        mm_prompt_updates: MultiModalPromptUpdates,
-    ) -> tuple[list[int], MultiModalPromptUpdatesApplyResult]:
-        token_ids, res = super()._apply_token_matches(prompt, mm_prompt_updates)
-
-        # "\n\n\n" and "\n\n\n\n" are single tokens
-        # Since our replacement can insert "\n\n" next to "\n"
-        # tokens, we have to combine them to be consistent with
-        # the output of the tokenizer
-        tokenizer = self.info.get_tokenizer()
-        vocab = tokenizer.get_vocab()
-        newline_1 = vocab["\n"]
-        newline_2 = vocab["\n\n"]
-        newline_3 = vocab["\n\n\n"]
-        newline_4 = vocab["\n\n\n\n"]
-
-        token_ids = replace_token_matches(
-            token_ids,
-            [newline_1, newline_2],
-            [newline_3],
-        )
-        token_ids = replace_token_matches(
-            token_ids,
-            [newline_2, newline_1],
-            [newline_3],
-        )
-        token_ids = replace_token_matches(
-            token_ids,
-            [newline_2, newline_2],
-            [newline_4],
-        )
-
-        return token_ids, res
-
-    def _find_mm_placeholders(
-        self,
-        new_token_ids: list[int],
-        mm_prompt_updates: MultiModalPromptUpdates,
-    ) -> Mapping[str, list[PlaceholderFeaturesInfo]]:
-        # We need to detect "\n\n" inside "\n\n\n" and "\n\n\n\n"
-        tokenizer = self.info.get_tokenizer()
-        vocab = tokenizer.get_vocab()
-        newline_1 = vocab["\n"]
-        newline_2 = vocab["\n\n"]
-        newline_3 = vocab["\n\n\n"]
-        newline_4 = vocab["\n\n\n\n"]
-
-        def get_repl_toks(tok: int) -> list[int]:
-            if tok == newline_3:
-                return [newline_1, newline_2]
-            if tok == newline_4:
-                return [newline_2, newline_2]
-
-            return [tok]
-
-        repl_token_ids = list[int]()
-        repl_orig_idxs = list[int]()
-        for orig_idx, orig_tok in enumerate(new_token_ids):
-            repl_toks = get_repl_toks(orig_tok)
-            repl_token_ids.extend(repl_toks)
-            repl_orig_idxs.extend(orig_idx for _ in range(len(repl_toks)))
-
-        repls = super()._find_mm_placeholders(repl_token_ids, mm_prompt_updates)
-
-        return {
-            modality: [
-                PlaceholderFeaturesInfo(
-                    modality=p.modality,
-                    item_idx=p.item_idx,
-                    start_idx=repl_orig_idxs[p.start_idx],
-                    tokens=p.tokens,
-                    is_embed=p.is_embed,
-                )
-                for p in placeholders
-            ]
-            for modality, placeholders in repls.items()
-        }
-
-
-class Gemma3MultiModalProjector(nn.Module):
-    def __init__(self, config: Gemma3Config):
-        super().__init__()
-
-        self.mm_input_projection_weight = nn.Parameter(
-            torch.zeros(
-                config.vision_config.hidden_size, config.text_config.hidden_size
-            )
-        )
-
-        self.mm_soft_emb_norm = GemmaRMSNorm(
-            config.vision_config.hidden_size, eps=config.vision_config.layer_norm_eps
-        )
-
-        self.patches_per_image = int(
-            config.vision_config.image_size // config.vision_config.patch_size
-        )
-        self.tokens_per_side = int(config.mm_tokens_per_image**0.5)
-        self.kernel_size = self.patches_per_image // self.tokens_per_side
-        self.avg_pool = nn.AvgPool2d(
-            kernel_size=self.kernel_size, stride=self.kernel_size
-        )
-
-    def forward(self, vision_outputs: torch.Tensor):
-        batch_size, _, seq_length = vision_outputs.shape
-
-        reshaped_vision_outputs = vision_outputs.transpose(1, 2)
-        reshaped_vision_outputs = reshaped_vision_outputs.reshape(
-            batch_size, seq_length, self.patches_per_image, self.patches_per_image
-        )
-        reshaped_vision_outputs = reshaped_vision_outputs.contiguous()
-
-        pooled_vision_outputs = self.avg_pool(reshaped_vision_outputs)
-        pooled_vision_outputs = pooled_vision_outputs.flatten(2)
-        pooled_vision_outputs = pooled_vision_outputs.transpose(1, 2)
-
-        normed_vision_outputs = self.mm_soft_emb_norm(pooled_vision_outputs)
-
-        projected_vision_outputs = torch.matmul(
-            normed_vision_outputs, self.mm_input_projection_weight
-        )
-        return projected_vision_outputs.type_as(vision_outputs)
-
-
-@MULTIMODAL_REGISTRY.register_processor(
-    Gemma3MultiModalProcessor,
-    info=Gemma3ProcessingInfo,
-    dummy_inputs=Gemma3DummyInputsBuilder,
-)
-class Gemma3ForConditionalGeneration(
-    nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA
-):
-    merge_by_field_config = True
-
-    packed_modules_mapping = {
-        "qkv_proj": [
-            "q_proj",
-            "k_proj",
-            "v_proj",
-        ],
-        "gate_up_proj": [
-            "gate_proj",
-            "up_proj",
-        ],
-    }
-
-    hf_to_vllm_mapper = WeightsMapper(
-        orig_to_new_prefix={
-            # mapping for new names in checkpoint saved after transformers v4.52
-            "model.language_model.": "language_model.model.",
-            "model.vision_tower.": "vision_tower.",
-            "model.multi_modal_projector.": "multi_modal_projector.",
-            "lm_head.": "language_model.lm_head.",
-        }
-    )
-
-    @classmethod
-    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
-        if modality.startswith("image"):
-            return "<start_of_image>"
-
-        raise ValueError("Only image modality is supported")
-
-    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
-        super().__init__()
-        config = vllm_config.model_config.hf_config
-        quant_config = vllm_config.quant_config
-        multimodal_config = vllm_config.model_config.multimodal_config
-        self.config = config
-        self.quant_config = quant_config
-        self.multimodal_config = multimodal_config
-
-        self.vision_tower = SiglipVisionModel(
-            config.vision_config,
-            quant_config,
-            prefix=maybe_prefix(prefix, "vision_tower"),
-        )
-        self.multi_modal_projector = Gemma3MultiModalProjector(config)
-
-        self.language_model = init_vllm_registered_model(
-            vllm_config=vllm_config,
-            hf_config=config.text_config,
-            prefix=maybe_prefix(prefix, "language_model"),
-            architectures=["Gemma3ForCausalLM"],
-        )
-        logit_scale = getattr(config, "logit_scale", 1.0)
-
-        if hasattr(self.language_model, "logits_processor"):
-            # The logits processor can be unset if we're using
-            # automatic conversion to pooling model.
-            self.language_model.logits_processor.scale *= logit_scale
-
-        self.make_empty_intermediate_tensors = (
-            self.language_model.make_empty_intermediate_tensors
-        )
-
-    @property
-    def dtype(self):
-        return next(self.parameters()).dtype
-
-    def _parse_and_validate_image_input(
-        self, **kwargs: object
-    ) -> Gemma3ImageInputs | None:
-        pixel_values = kwargs.pop("pixel_values", None)
-        num_patches = kwargs.pop("num_patches", None)
-        image_embeds = kwargs.pop("image_embeds", None)
-        assert image_embeds is None, "Gemma3 does not support image_embeds."
-        if pixel_values is None:
-            return None
-
-        image_size = self.config.vision_config.image_size
-
-        return Gemma3ImagePixelInputs(
-            pixel_values=pixel_values,
-            num_patches=num_patches,
-            resolve_bindings={"h": image_size, "w": image_size},
-        )
-
-    def _image_pixels_to_features(
-        self,
-        vision_tower: SiglipVisionModel,
-        pixel_values: torch.Tensor,
-    ) -> torch.Tensor:
-        return vision_tower(pixel_values)
-
-    def _process_image_input(
-        self,
-        image_input: Gemma3ImageInputs,
-    ) -> list[torch.Tensor]:
-        assert self.vision_tower is not None
-
-        pixel_values = image_input["pixel_values"]
-        num_patches = image_input["num_patches"]
-
-        image_features = self._image_pixels_to_features(
-            self.vision_tower,
-            pixel_values,
-        )
-        image_embeds = self.multi_modal_projector(image_features)
-
-        return [e.flatten(0, 1) for e in image_embeds.split(num_patches.tolist())]
-
-    def get_language_model(self) -> torch.nn.Module:
-        return self.language_model
-
-    def get_multimodal_embeddings(self, **kwargs: object) -> MultiModalEmbeddings:
-        image_input = self._parse_and_validate_image_input(**kwargs)
-        if image_input is None:
-            return []
-
-        return self._process_image_input(image_input)
-
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        positions: torch.Tensor,
-        intermediate_tensors: IntermediateTensors | None = None,
-        inputs_embeds: torch.Tensor | None = None,
-        **kwargs: object,
-    ) -> IntermediateTensors:
-        if intermediate_tensors is not None:
-            inputs_embeds = None
-
-        hidden_states = self.language_model.model(
-            input_ids,
-            positions,
-            intermediate_tensors,
-            inputs_embeds=inputs_embeds,
-            **kwargs,
-        )
-
-        return hidden_states
-
-    def prepare_attn_masks(
-        self,
-        input_ids: torch.Tensor,
-        positions: torch.Tensor,
-        mask_dtype: torch.dtype,
-        **kwargs,
-    ):
-        kwargs["has_images"] = True
-        # NOTE(woosuk): Here, we distinguish the sequences by the position id 0.
-        # This is a HACK. Fix this.
-        start_indices = (positions == 0).cpu().nonzero()
-        num_seqs = len(start_indices)
-        seq_lens = []
-        for i in range(num_seqs):
-            start_idx = start_indices[i].item()
-            if i < num_seqs - 1:
-                end_idx = start_indices[i + 1].item()
-            else:
-                end_idx = len(input_ids)
-            seq_lens.append(end_idx - start_idx)
-        kwargs["seq_lens"] = seq_lens
-
-        global_attn_masks = []
-        local_attn_masks = []
-        start_idx = 0
-        for seq_len in seq_lens:
-            end_idx = start_idx + seq_len
-            input_token_ids = input_ids[start_idx:end_idx]
-            start_idx = end_idx
-            # Create a global causal mask.
-            global_attn_mask = torch.empty(
-                1,
-                1,
-                seq_len,
-                seq_len,
-                dtype=mask_dtype,
-                device=input_ids.device,
-            )
-            global_attn_mask.fill_(float("-inf"))
-            # Fill the lower triangle with 0.
-            global_attn_mask = global_attn_mask.triu(diagonal=1)
-
-            # Consider the bidirectional attention between image tokens.
-            img_mask = torch.zeros_like(global_attn_mask)
-            img_pos = input_token_ids == self.config.image_token_index
-            img_mask[:, :, :, img_pos] += 1
-            img_mask[:, :, img_pos, :] += 1
-            global_attn_mask = torch.where(img_mask == 2, 0, global_attn_mask)
-            global_attn_masks.append(global_attn_mask)
-
-            sliding_window = self.config.text_config.sliding_window
-            if sliding_window is not None:
-                # Create a local causal mask with sliding window (1024).
-                local_attn_mask = torch.ones_like(global_attn_mask)
-                local_attn_mask = torch.tril(local_attn_mask, diagonal=-sliding_window)
-                local_attn_mask = torch.where(
-                    local_attn_mask == 0, global_attn_mask, float("-inf")
-                )
-                local_attn_masks.append(local_attn_mask)
-        kwargs["global_attn_masks"] = global_attn_masks
-        kwargs["local_attn_masks"] = local_attn_masks
-        return kwargs
-
-    def compute_logits(
-        self,
-        hidden_states: torch.Tensor,
-    ) -> torch.Tensor | None:
-        return self.language_model.compute_logits(hidden_states)
-
-    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
-        loader = AutoWeightsLoader(self)
-        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
-
-    def get_mm_mapping(self) -> MultiModelKeys:
-        """
-        Get the module prefix in multimodal models
-        """
-        return MultiModelKeys.from_string_field(
-            language_model="language_model",
-            connector="multi_modal_projector",
-            tower_model="vision_tower",
-        )

vllm/model_executor/models/paligemma.py

-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from collections.abc import Iterable, Mapping, Sequence
-from typing import Annotated, Literal, TypeAlias
-
-import torch
-from torch import nn
-from transformers import BatchFeature, PaliGemmaConfig
-
-from vllm.config import VllmConfig
-from vllm.config.multimodal import BaseDummyOptions
-from vllm.logger import init_logger
-from vllm.multimodal import MULTIMODAL_REGISTRY
-from vllm.multimodal.inputs import (
-    MultiModalDataDict,
-    MultiModalFieldConfig,
-    MultiModalInputs,
-    MultiModalKwargsItems,
-    MultiModalUUIDDict,
-)
-from vllm.multimodal.parse import (
-    ImageEmbeddingItems,
-    ImageProcessorItems,
-    MultiModalDataItems,
-)
-from vllm.multimodal.processing import (
-    BaseMultiModalProcessor,
-    BaseProcessingInfo,
-    PromptIndexTargets,
-    PromptInsertion,
-    PromptUpdate,
-    PromptUpdateDetails,
-)
-from vllm.multimodal.profiling import BaseDummyInputsBuilder
-from vllm.sequence import IntermediateTensors
-from vllm.utils.tensor_schema import TensorSchema, TensorShape
-
-from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
-from .siglip import SiglipVisionModel
-from .utils import (
-    AutoWeightsLoader,
-    WeightsMapper,
-    flatten_bn,
-    init_vllm_registered_model,
-    maybe_prefix,
-)
-from .vision import get_vision_encoder_info
-
-logger = init_logger(__name__)
-
-
-class PaliGemmaImagePixelInputs(TensorSchema):
-    """
-    Dimensions:
-        - bn: Batch size * number of images
-        - c: Number of channels (3)
-        - h: Height
-        - w: Width
-    """
-
-    type: Literal["pixel_values"] = "pixel_values"
-    data: Annotated[torch.Tensor, TensorShape("bn", 3, "h", "w")]
-
-
-class PaliGemmaImageEmbeddingInputs(TensorSchema):
-    """
-    Dimensions:
-        - bn: Batch size * number of images
-        - ifs: Image feature size
-        - hs: Hidden size (must match language model backbone)
-    """
-
-    type: Literal["image_embeds"] = "image_embeds"
-    data: Annotated[torch.Tensor, TensorShape("bn", "ifs", "hs")]
-
-
-PaliGemmaImageInputs: TypeAlias = (
-    PaliGemmaImagePixelInputs | PaliGemmaImageEmbeddingInputs
-)
-
-
-class PaliGemmaMultiModalProjector(nn.Module):
-    def __init__(self, vision_hidden_size: int, projection_dim: int):
-        super().__init__()
-
-        self.linear = nn.Linear(vision_hidden_size, projection_dim, bias=True)
-
-    def forward(self, image_features: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.linear(image_features)
-        return hidden_states
-
-
-class PaliGemmaProcessingInfo(BaseProcessingInfo):
-    def get_hf_config(self):
-        return self.ctx.get_hf_config(PaliGemmaConfig)
-
-    def get_vision_encoder_info(self):
-        return get_vision_encoder_info(self.get_hf_config())
-
-    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
-        return {"image": 1}
-
-    def get_num_image_tokens(
-        self,
-        *,
-        image_width: int,
-        image_height: int,
-    ) -> int:
-        vision_encoder_info = self.get_vision_encoder_info()
-
-        return vision_encoder_info.get_num_image_tokens(
-            image_width=image_width,
-            image_height=image_height,
-        )
-
-
-class PaliGemmaDummyInputsBuilder(BaseDummyInputsBuilder[PaliGemmaProcessingInfo]):
-    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
-        return ""
-
-    def get_dummy_mm_data(
-        self,
-        seq_len: int,
-        mm_counts: Mapping[str, int],
-        mm_options: Mapping[str, BaseDummyOptions] | None = None,
-    ) -> MultiModalDataDict:
-        hf_config = self.info.get_hf_config()
-        vision_config = hf_config.vision_config
-        max_image_size = vision_config.image_size
-
-        num_images = mm_counts.get("image", 0)
-
-        image_overrides = mm_options.get("image") if mm_options else None
-
-        return {
-            "image": self._get_dummy_images(
-                width=max_image_size,
-                height=max_image_size,
-                num_images=num_images,
-                overrides=image_overrides,
-            )
-        }
-
-
-class PaliGemmaMultiModalProcessor(BaseMultiModalProcessor[PaliGemmaProcessingInfo]):
-    def _call_hf_processor(
-        self,
-        prompt: str,
-        mm_data: Mapping[str, object],
-        mm_kwargs: Mapping[str, object],
-        tok_kwargs: Mapping[str, object],
-    ) -> BatchFeature:
-        tokenizer = self.info.get_tokenizer()
-        if not mm_data:
-            prompt_ids = tokenizer.encode(prompt, add_special_tokens=False)
-            return BatchFeature(dict(input_ids=[prompt_ids]), tensor_type="pt")
-
-        return super()._call_hf_processor(
-            prompt=prompt,
-            mm_data=mm_data,
-            mm_kwargs=mm_kwargs,
-            tok_kwargs=tok_kwargs,
-        )
-
-    def _get_mm_fields_config(
-        self,
-        hf_inputs: BatchFeature,
-        hf_processor_mm_kwargs: Mapping[str, object],
-    ) -> Mapping[str, MultiModalFieldConfig]:
-        return dict(pixel_values=MultiModalFieldConfig.batched("image"))
-
-    def _get_prompt_updates(
-        self,
-        mm_items: MultiModalDataItems,
-        hf_processor_mm_kwargs: Mapping[str, object],
-        out_mm_kwargs: MultiModalKwargsItems,
-    ) -> Sequence[PromptUpdate]:
-        hf_config = self.info.get_hf_config()
-        image_token_id = hf_config.image_token_index
-
-        tokenizer = self.info.get_tokenizer()
-
-        bos_token_id = tokenizer.bos_token_id
-        assert isinstance(bos_token_id, int)
-
-        def get_insertion(item_idx: int):
-            images = mm_items.get_items(
-                "image", (ImageEmbeddingItems, ImageProcessorItems)
-            )
-
-            if isinstance(images, ImageEmbeddingItems):
-                num_image_tokens = images.get_feature_size(item_idx)
-            else:
-                image_size = images.get_image_size(item_idx)
-                num_image_tokens = self.info.get_num_image_tokens(
-                    image_width=image_size.width,
-                    image_height=image_size.height,
-                )
-
-            image_tokens = [image_token_id] * num_image_tokens
-
-            return PromptUpdateDetails.select_token_id(
-                image_tokens + [bos_token_id],
-                embed_token_id=image_token_id,
-            )
-
-        # Paligemma 1 and 2 have different tokenizer.add_bos_token
-        # Insert <image>*n + <bos> after <bos> for Paligemma 1
-        # Insert <image>*n + <bos> for Paligemma 2
-        return [
-            PromptInsertion(
-                modality="image",
-                target=PromptIndexTargets.prefix(
-                    [bos_token_id] if tokenizer.add_bos_token else []
-                ),
-                insertion=get_insertion,
-            )
-        ]
-
-    def apply(
-        self,
-        prompt: str | list[int],
-        mm_data: MultiModalDataDict,
-        hf_processor_mm_kwargs: Mapping[str, object],
-        tokenization_kwargs: Mapping[str, object] | None = None,
-        mm_uuids: MultiModalUUIDDict | None = None,
-    ) -> MultiModalInputs:
-        mm_inputs = super().apply(
-            prompt,
-            mm_data,
-            hf_processor_mm_kwargs,
-            tokenization_kwargs,
-            mm_uuids=mm_uuids,
-        )
-        prompt_token_ids = mm_inputs["prompt_token_ids"]
-
-        tokenizer = self.info.get_tokenizer()
-        newline_prompt = "\n"
-        newline_token_id = tokenizer.encode(newline_prompt)[-1]  # 108
-        # Force to add newline at the end of prompt for paligemma's format
-        # This step can NOT be replacemented by current PromptUpdate methods
-        if len(prompt_token_ids) and prompt_token_ids[-1] != newline_token_id:
-            prompt_token_ids.append(newline_token_id)
-            mm_inputs["prompt_token_ids"] = prompt_token_ids
-
-        return mm_inputs
-
-
-@MULTIMODAL_REGISTRY.register_processor(
-    PaliGemmaMultiModalProcessor,
-    info=PaliGemmaProcessingInfo,
-    dummy_inputs=PaliGemmaDummyInputsBuilder,
-)
-class PaliGemmaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
-    packed_modules_mapping = {
-        "qkv_proj": [
-            "q_proj",
-            "k_proj",
-            "v_proj",
-        ],
-        "gate_up_proj": [
-            "gate_proj",
-            "up_proj",
-        ],
-    }
-
-    hf_to_vllm_mapper = WeightsMapper(
-        orig_to_new_prefix={
-            # mapping for new names in checkpoint saved after transformers v4.52
-            "model.language_model.": "language_model.model.",
-            "model.vision_tower.": "vision_tower.",
-            "model.multi_modal_projector.": "multi_modal_projector.",
-            "lm_head.": "language_model.lm_head.",
-        }
-    )
-
-    @classmethod
-    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
-        if modality.startswith("image"):
-            return None
-
-        raise ValueError("Only image modality is supported")
-
-    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
-        super().__init__()
-        config = vllm_config.model_config.hf_config
-        quant_config = vllm_config.quant_config
-        multimodal_config = vllm_config.model_config.multimodal_config
-        self.config = config
-        self.multimodal_config = multimodal_config
-
-        self.vision_tower = SiglipVisionModel(
-            config.vision_config,
-            quant_config,
-            prefix=maybe_prefix(prefix, "vision_tower"),
-        )
-        self.multi_modal_projector = PaliGemmaMultiModalProjector(
-            vision_hidden_size=config.vision_config.hidden_size,
-            projection_dim=config.vision_config.projection_dim,
-        )
-
-        self.quant_config = quant_config
-
-        if config.text_config.model_type == "gemma":
-            config.text_config.architectures = ["GemmaForCausalLM"]
-        else:
-            config.text_config.architectures = ["Gemma2ForCausalLM"]
-        self.language_model = init_vllm_registered_model(
-            vllm_config=vllm_config,
-            hf_config=config.text_config,
-            prefix=maybe_prefix(prefix, "language_model"),
-        )
-        logit_scale = getattr(config, "logit_scale", 1.0)
-        self.language_model.logits_processor.scale *= logit_scale
-
-        self.make_empty_intermediate_tensors = (
-            self.language_model.make_empty_intermediate_tensors
-        )
-
-    def _parse_and_validate_image_input(
-        self, **kwargs: object
-    ) -> PaliGemmaImageInputs | None:
-        pixel_values = kwargs.pop("pixel_values", None)
-        image_embeds = kwargs.pop("image_embeds", None)
-
-        if pixel_values is None and image_embeds is None:
-            return None
-
-        if pixel_values is not None:
-            pixel_values = flatten_bn(pixel_values, concat=True)
-
-            h = w = self.config.vision_config.image_size
-            return PaliGemmaImagePixelInputs(
-                type="pixel_values",
-                data=pixel_values,
-                resolve_bindings={"h": h, "w": w},
-            )
-
-        if image_embeds is not None:
-            image_embeds = flatten_bn(image_embeds, concat=True)
-
-            return PaliGemmaImageEmbeddingInputs(
-                type="image_embeds",
-                data=image_embeds,
-            )
-
-        raise AssertionError("This line should be unreachable.")
-
-    def _image_pixels_to_features(
-        self,
-        vision_tower: SiglipVisionModel,
-        pixel_values: torch.Tensor,
-    ) -> torch.Tensor:
-        target_dtype = vision_tower.get_input_embeddings().weight.dtype
-        image_features = vision_tower(pixel_values.to(dtype=target_dtype))
-
-        return image_features
-
-    def _process_image_input(
-        self,
-        image_input: PaliGemmaImageInputs,
-    ) -> torch.Tensor:
-        if image_input["type"] == "image_embeds":
-            return image_input["data"]
-
-        assert self.vision_tower is not None
-        pixel_values = image_input["data"]
-        image_features = self._image_pixels_to_features(
-            self.vision_tower,
-            pixel_values,
-        )
-
-        return self.multi_modal_projector(image_features)
-
-    def get_language_model(self) -> torch.nn.Module:
-        return self.language_model
-
-    def get_multimodal_embeddings(self, **kwargs: object) -> MultiModalEmbeddings:
-        image_input = self._parse_and_validate_image_input(**kwargs)
-        if image_input is None:
-            return []
-        vision_embeddings = self._process_image_input(image_input)
-        # https://github.com/huggingface/transformers/blob/main/src/transformers/models/paligemma/modeling_paligemma.py#L294 # noqa
-        vision_embeddings = vision_embeddings * (self.config.hidden_size**-0.5)
-        return vision_embeddings
-
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        positions: torch.Tensor,
-        intermediate_tensors: IntermediateTensors | None = None,
-        inputs_embeds: torch.Tensor | None = None,
-        **kwargs: object,
-    ) -> IntermediateTensors:
-        if intermediate_tensors is not None:
-            inputs_embeds = None
-
-        hidden_states = self.language_model.model(
-            input_ids, positions, intermediate_tensors, inputs_embeds=inputs_embeds
-        )
-
-        return hidden_states
-
-    def compute_logits(
-        self,
-        hidden_states: torch.Tensor,
-    ) -> torch.Tensor | None:
-        return self.language_model.compute_logits(hidden_states)
-
-    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
-        loader = AutoWeightsLoader(self)
-        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)

vllm/model_executor/models/registry.py

@@ -263,7 +263,6 @@ _MULTIMODAL_MODELS = {
         "Ernie4_5_VLMoeForConditionalGeneration",
     ),
     "FuyuForCausalLM": ("fuyu", "FuyuForCausalLM"),
-    "Gemma3ForConditionalGeneration": ("gemma3_mm", "Gemma3ForConditionalGeneration"),  # noqa: E501
     "Gemma3nForConditionalGeneration": (
         "gemma3n_mm",
         "Gemma3nForConditionalGeneration",
@@ -329,10 +328,6 @@ _MULTIMODAL_MODELS = {
     "NVLM_D": ("nvlm_d", "NVLM_D_Model"),
     "Ovis": ("ovis", "Ovis"),
     "Ovis2_5": ("ovis2_5", "Ovis2_5"),
-    "PaliGemmaForConditionalGeneration": (
-        "paligemma",
-        "PaliGemmaForConditionalGeneration",
-    ),
     "Phi3VForCausalLM": ("phi3v", "Phi3VForCausalLM"),
     "Phi4MMForCausalLM": ("phi4mm", "Phi4MMForCausalLM"),
     "Phi4MultimodalForCausalLM": ("phi4_multimodal", "Phi4MultimodalForCausalLM"),  # noqa: E501
@@ -405,6 +400,14 @@ _TRANSFORMERS_SUPPORTED_MODELS = {
         "transformers",
         "TransformersMultiModalForCausalLM",
     ),
+    "Gemma3ForConditionalGeneration": (
+        "transformers",
+        "TransformersMultiModalForCausalLM",
+    ),
+    "PaliGemmaForConditionalGeneration": (
+        "transformers",
+        "TransformersMultiModalForCausalLM",
+    ),
 }
 
 _TRANSFORMERS_BACKEND_MODELS = {

vllm/platforms/rocm.py

@@ -59,9 +59,6 @@ _ROCM_PARTIALLY_SUPPORTED_MODELS: dict[str, str] = {
     "Qwen2ForCausalLM": _ROCM_SWA_REASON,
     "MistralForCausalLM": _ROCM_SWA_REASON,
     "MixtralForCausalLM": _ROCM_SWA_REASON,
-    "PaliGemmaForConditionalGeneration": (
-        "ROCm flash attention does not yet fully support 32-bit precision on PaliGemma"
-    ),
     "Phi3VForCausalLM": (
         "ROCm Triton flash attention may run into compilation errors due to "
         "excessive use of shared memory. If this happens, disable Triton FA "