Allow Gemma3 to take image embeddings (#28483)

Signed-off-by: tingtinggithub <streamttt@gmail.com>

docs/models/supported_models.md

@@ -669,7 +669,7 @@ 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. | ✅︎ | ✅︎ |
+| `Gemma3ForConditionalGeneration` | Gemma 3 | T + I<sup>E+</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. | ✅︎ | ✅︎ |

vllm/model_executor/models/gemma3_mm.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import math
 from collections.abc import Iterable, Mapping, Sequence
-from typing import Annotated, Any, Literal
+from typing import Annotated, Any, Literal, TypeAlias
 
 import torch
 from torch import nn
@@ -20,7 +20,12 @@ from vllm.multimodal.inputs import (
     MultiModalFieldConfig,
     MultiModalKwargsItems,
 )
-from vllm.multimodal.parse import ImageProcessorItems, ImageSize, MultiModalDataItems
+from vllm.multimodal.parse import (
+    ImageEmbeddingItems,
+    ImageProcessorItems,
+    ImageSize,
+    MultiModalDataItems,
+)
 from vllm.multimodal.processing import (
     BaseMultiModalProcessor,
     BaseProcessingInfo,
@@ -71,7 +76,15 @@ class Gemma3ImagePixelInputs(TensorSchema):
     num_patches: Annotated[torch.Tensor, TensorShape("bn")]
 
 
-Gemma3ImageInputs = Gemma3ImagePixelInputs
+class Gemma3ImageEmbeddingInputs(TensorSchema):
+    type: Literal["image_embeds"] = "image_embeds"
+    image_embeds: Annotated[
+        torch.Tensor,
+        TensorShape("ni", "nf", "hs"),
+    ]
+
+
+Gemma3ImageInputs: TypeAlias = Gemma3ImagePixelInputs | Gemma3ImageEmbeddingInputs
 
 
 class Gemma3ProcessingInfo(BaseProcessingInfo):
@@ -178,8 +191,9 @@ class Gemma3ProcessingInfo(BaseProcessingInfo):
     def get_image_repl(
         self,
         *,
-        image_width: int,
-        image_height: int,
+        image_width: int | None,
+        image_height: int | None,
+        num_crops: int | None = None,
         processor: Gemma3Processor | None,
     ) -> PromptUpdateDetails[str]:
         if processor is None:
@@ -187,11 +201,13 @@ class Gemma3ProcessingInfo(BaseProcessingInfo):
 
         boi_token = processor.boi_token
 
-        num_crops = self.get_num_crops(
-            image_width=image_width,
-            image_height=image_height,
-            processor=processor,
-        )
+        if num_crops is None:
+            assert image_width is not None and image_height is not None
+            num_crops = self.get_num_crops(
+                image_width=image_width,
+                image_height=image_height,
+                processor=processor,
+            )
 
         if num_crops == 0:
             image_text = boi_token
@@ -321,6 +337,7 @@ class Gemma3MultiModalProcessor(BaseMultiModalProcessor[Gemma3ProcessingInfo]):
         return dict(
             pixel_values=MultiModalFieldConfig.flat_from_sizes("image", num_patches),
             num_patches=MultiModalFieldConfig.batched("image"),
+            image_embeds=MultiModalFieldConfig.batched("image"),
         )
 
     def _get_prompt_updates(
@@ -333,7 +350,19 @@ class Gemma3MultiModalProcessor(BaseMultiModalProcessor[Gemma3ProcessingInfo]):
         image_token = hf_processor.boi_token
 
         def get_replacement_gemma3(item_idx: int):
-            images = mm_items.get_items("image", ImageProcessorItems)
+            images = mm_items.get_items(
+                "image", (ImageEmbeddingItems, ImageProcessorItems)
+            )
+
+            if isinstance(images, ImageEmbeddingItems):
+                # For image embedding inputs, only support no crops cases
+                # since it's not supported in hf processor anyway
+                return self.info.get_image_repl(
+                    image_width=None,
+                    image_height=None,
+                    num_crops=0,
+                    processor=hf_processor,
+                )
 
             image_size = images.get_image_size(item_idx)
             return self.info.get_image_repl(
@@ -557,17 +586,19 @@ class Gemma3ForConditionalGeneration(
         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},
-        )
+        if pixel_values is not 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},
+            )
+        elif image_embeds is not None:
+            return Gemma3ImageEmbeddingInputs(
+                image_embeds=image_embeds,
+                type="image_embeds",
+            )
 
     def _image_pixels_to_features(
         self,
@@ -579,7 +610,9 @@ class Gemma3ForConditionalGeneration(
     def _process_image_input(
         self,
         image_input: Gemma3ImageInputs,
-    ) -> list[torch.Tensor]:
+    ) -> torch.Tensor | list[torch.Tensor]:
+        if image_input["type"] == "image_embeds":
+            return image_input["image_embeds"]
         assert self.vision_tower is not None
 
         pixel_values = image_input["pixel_values"]

vllm/multimodal/parse.py

@@ -359,8 +359,9 @@ class MultiModalDataParser:
         )
         self.video_needs_metadata = video_needs_metadata
 
-    def _is_embeddings(
-        self, data: object
+    @classmethod
+    def is_embeddings(
+        cls, data: object
     ) -> TypeGuard[torch.Tensor | list[torch.Tensor]]:
         if isinstance(data, torch.Tensor):
             return data.ndim == 3
@@ -420,7 +421,7 @@ class MultiModalDataParser:
         ):
             return None
 
-        if self._is_embeddings(data):
+        if self.is_embeddings(data):
             return AudioEmbeddingItems(data)
 
         data_items: list[AudioItem]
@@ -458,7 +459,7 @@ class MultiModalDataParser:
         if self._is_empty(data):
             return None
 
-        if self._is_embeddings(data):
+        if self.is_embeddings(data):
             return ImageEmbeddingItems(data)
 
         if (
@@ -484,7 +485,7 @@ class MultiModalDataParser:
         if self._is_empty(data):
             return None
 
-        if self._is_embeddings(data):
+        if self.is_embeddings(data):
             return VideoEmbeddingItems(data)
 
         data_items: list[VideoItem]

vllm/v1/engine/processor.py

@@ -14,6 +14,7 @@ from vllm.lora.request import LoRARequest
 from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
 from vllm.multimodal.cache import processor_cache_from_config
 from vllm.multimodal.inputs import MultiModalFeatureSpec, MultiModalUUIDDict
+from vllm.multimodal.parse import MultiModalDataParser
 from vllm.multimodal.processing import EncDecMultiModalProcessor
 from vllm.multimodal.utils import argsort_mm_positions
 from vllm.pooling_params import PoolingParams
@@ -340,7 +341,12 @@ class Processor:
 
         mm_uuids: dict[str, list[str | None] | str] = {}
         for modality, data in mm_data.items():
-            n = len(data) if isinstance(data, list) else 1
+            # Hash each item for embedding inputs.
+            n = (
+                len(data)
+                if isinstance(data, list) or MultiModalDataParser.is_embeddings(data)
+                else 1
+            )
             mm_uuids[modality] = [f"{request_id}-{modality}-{i}" for i in range(n)]
         return mm_uuids