[Core][VLM] Support image embeddings as input

vllm/model_executor/models/blip2.py

@@ -1,4 +1,4 @@
-from typing import Iterable, List, Literal, Optional, Tuple, TypedDict
+from typing import Iterable, List, Literal, Optional, Tuple, TypedDict, Union
 
 import torch
 import torch.nn as nn
@@ -28,6 +28,25 @@
     "language_model.model": "language_model",
 }
 
+# We use this internally as placeholders since there is no image token
+# defined on the HuggingFace repo
+BLIP2_IMAGE_TOKEN = "<image>"
+BLIP2_IMAGE_TOKEN_ID = 50265
+
+
+class Blip2ImagePixelInputs(TypedDict):
+    type: Literal["pixel_values"]
+    data: torch.Tensor
+    """Shape: (batch_size, num_channels, height, width)"""
+
+
+class Blip2ImageEmbeddingInputs(TypedDict):
+    type: Literal["image_embeds"]
+    data: torch.Tensor
+
+
+Blip2ImageInputs = Union[Blip2ImagePixelInputs, Blip2ImageEmbeddingInputs]
+
 
 class Blip2QFormerMultiHeadAttention(nn.Module):
 
@@ -375,20 +394,6 @@ def forward(
         return sequence_output
 
 
-class Blip2ImagePixelInputs(TypedDict):
-    type: Literal["pixel_values"]
-    data: torch.Tensor
-    """Shape: (batch_size, num_channels, height, width)"""
-
-
-Blip2ImageInputs = Blip2ImagePixelInputs
-
-# We use this internally as placeholders since there is no image token
-# defined on the HuggingFace repo
-BLIP2_IMAGE_TOKEN = "<image>"
-BLIP2_IMAGE_TOKEN_ID = 50265
-
-
 def get_blip2_image_feature_size(hf_config: Blip2Config) -> int:
     return hf_config.num_query_tokens
 
@@ -506,18 +511,29 @@ def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
     def _parse_and_validate_image_input(
             self, **kwargs: object) -> Optional[Blip2ImageInputs]:
         pixel_values = kwargs.pop("pixel_values", None)
+        image_embeds = kwargs.pop("image_embeds", None)
 
-        if pixel_values is None:
+        if pixel_values is None and image_embeds is None:
             return None
 
-        if not isinstance(pixel_values, torch.Tensor):
-            raise ValueError("Incorrect type of pixel values. "
-                             f"Got type: {type(pixel_values)}")
+        if pixel_values is not None:
+            if not isinstance(pixel_values, torch.Tensor):
+                raise ValueError("Incorrect type of pixel values. "
+                                 f"Got type: {type(pixel_values)}")
 
-        return Blip2ImagePixelInputs(
-            type="pixel_values",
-            data=self._validate_pixel_values(pixel_values),
-        )
+            return Blip2ImagePixelInputs(
+                type="pixel_values",
+                data=self._validate_pixel_values(pixel_values),
+            )
+
+        if image_embeds is not None:
+            if not isinstance(image_embeds, torch.Tensor):
+                raise ValueError("Incorrect type of image embeddings. "
+                                 f"Got type: {type(image_embeds)}")
+            return Blip2ImageEmbeddingInputs(
+                type="image_embeds",
+                data=image_embeds,
+            )
 
     def _image_pixels_to_features(self, vision_model: BlipVisionModel,
                                   pixel_values: torch.Tensor) -> torch.Tensor:
@@ -538,6 +554,10 @@ def _process_image_pixels(self,
 
     def _process_image_input(self,
                              image_input: Blip2ImageInputs) -> torch.Tensor:
+
+        if image_input["type"] == "image_embeds":
+            return image_input["data"]
+
         assert self.vision_model is not None
         image_features = self._process_image_pixels(image_input)
 

vllm/model_executor/models/fuyu.py

@@ -16,7 +16,7 @@
 # limitations under the License.
 """ PyTorch Fuyu model."""
 import math
-from typing import Iterable, List, Literal, Optional, Tuple, TypedDict
+from typing import Iterable, List, Literal, Optional, Tuple, TypedDict, Union
 
 import torch
 import torch.nn as nn
@@ -62,6 +62,14 @@ class FuyuImagePixelInputs(TypedDict):
     """
 
 
+class FuyuImageEmbeddingInputs(TypedDict):
+    type: Literal["image_embeds"]
+    data: torch.Tensor
+
+
+FuyuImageInputs = Union[FuyuImagePixelInputs, FuyuImageEmbeddingInputs]
+
+
 def _calculate_num_image_tokens(
     height: int,
     width: int,
@@ -249,6 +257,16 @@ def _parse_and_validate_image_input(self, **kwargs: object):
                                         data=image_patches)
         return None
 
+    def _process_image_input(self,
+                             image_input: FuyuImageInputs) -> torch.Tensor:
+
+        if image_input["type"] == "image_embeds":
+            return image_input["data"]
+
+        assert self.vision_embed_tokens is not None
+        vision_embeddings, _ = self.vision_embed_tokens(image_input["data"])
+        return vision_embeddings
+
     def forward(
         self,
         input_ids: torch.Tensor,
@@ -261,8 +279,7 @@ def forward(
         image_input = self._parse_and_validate_image_input(**kwargs)
 
         if image_input is not None:
-            vision_embeddings, _ = self.vision_embed_tokens(
-                image_input["data"])
+            vision_embeddings = self._process_image_input(image_input)
             inputs_embeds = self.language_model.model.embed_tokens(input_ids)
             inputs_embeds = merge_vision_embeddings(input_ids, inputs_embeds,
                                                     vision_embeddings,

vllm/model_executor/models/internvl.py

@@ -50,6 +50,15 @@ class InternVLImagePixelInputs(TypedDict):
     """
 
 
+class InternVLImageEmbeddingInputs(TypedDict):
+    type: Literal["image_embeds"]
+    data: Union[torch.Tensor, List[torch.Tensor]]
+
+
+InternVLImageInputs = Union[InternVLImagePixelInputs,
+                            InternVLImageEmbeddingInputs]
+
+
 # copied from https://huggingface.co/OpenGVLab/InternVL2-1B
 def build_transform(input_size):
     MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
@@ -378,13 +387,23 @@ def _validate_shape(d: torch.Tensor):
         return data
 
     def _parse_and_validate_image_input(
-            self, **kwargs: object) -> Optional[InternVLImagePixelInputs]:
+            self, **kwargs: object) -> Optional[InternVLImageInputs]:
         pixel_values = kwargs.pop("pixel_values", None)
         image_token_id = kwargs.pop("image_token_id", None)
+        image_embeds = kwargs.pop("image_embeds", None)
 
-        if pixel_values is None:
+        if pixel_values is None and image_embeds is None:
             return None
 
+        if image_embeds is not None:
+            if not isinstance(image_embeds, torch.Tensor):
+                raise ValueError("Incorrect type of image embeddings. "
+                                 f"Got type: {type(image_embeds)}")
+            return InternVLImageEmbeddingInputs(
+                type="image_embeds",
+                data=image_embeds,
+            )
+
         self.img_context_token_id = image_token_id[0]
 
         if not isinstance(pixel_values, (torch.Tensor, list)):
@@ -396,6 +415,19 @@ def _parse_and_validate_image_input(
             data=self._validate_pixel_values(pixel_values),
         )
 
+    def _process_image_input(
+        self,
+        image_input: InternVLImageInputs,
+    ) -> torch.Tensor:
+
+        if image_input["type"] == "image_embeds":
+            return image_input["data"]
+
+        assert self.vision_model is not None
+        image_embeds = self.extract_feature(image_input["data"])
+
+        return image_embeds
+
     def forward(
         self,
         input_ids: torch.Tensor,
@@ -409,7 +441,7 @@ def forward(
         if image_input is not None:
             inputs_embeds = self.language_model.model.get_input_embeddings(
                 input_ids)
-            vit_embeds = self.extract_feature(image_input["data"])
+            vit_embeds = self._process_image_input(image_input)
             inputs_embeds = merge_vision_embeddings(input_ids, inputs_embeds,
                                                     vit_embeds,
                                                     self.img_context_token_id)

vllm/model_executor/models/llava.py

@@ -27,6 +27,20 @@
                     merge_vision_embeddings)
 
 
+class LlavaImagePixelInputs(TypedDict):
+    type: Literal["pixel_values"]
+    data: torch.Tensor
+    """Shape: `(batch_size, num_channels, height, width)`"""
+
+
+class LlavaImageEmbeddingInputs(TypedDict):
+    type: Literal["image_embeds"]
+    data: torch.Tensor
+
+
+LlavaImageInputs = Union[LlavaImagePixelInputs, LlavaImageEmbeddingInputs]
+
+
 # TODO(xwjiang): Run benchmark and decide if TP.
 class LlavaMultiModalProjector(nn.Module):
 
@@ -49,15 +63,6 @@ def forward(self, image_features: torch.Tensor) -> torch.Tensor:
         return hidden_states
 
 
-class LlavaImagePixelInputs(TypedDict):
-    type: Literal["pixel_values"]
-    data: torch.Tensor
-    """Shape: `(batch_size, num_channels, height, width)`"""
-
-
-LlavaImageInputs = LlavaImagePixelInputs
-
-
 def get_max_llava_image_tokens(ctx: InputContext):
     hf_config = ctx.get_hf_config(LlavaConfig)
     vision_config = hf_config.vision_config
@@ -210,18 +215,28 @@ def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
     def _parse_and_validate_image_input(
             self, **kwargs: object) -> Optional[LlavaImageInputs]:
         pixel_values = kwargs.pop("pixel_values", None)
+        image_embeds = kwargs.pop("image_embeds", None)
 
-        if pixel_values is None:
+        if pixel_values is None and image_embeds is None:
             return None
 
-        if not isinstance(pixel_values, torch.Tensor):
-            raise ValueError("Incorrect type of pixel values. "
-                             f"Got type: {type(pixel_values)}")
-
-        return LlavaImagePixelInputs(
-            type="pixel_values",
-            data=self._validate_pixel_values(pixel_values),
-        )
+        if pixel_values is not None:
+            if not isinstance(pixel_values, torch.Tensor):
+                raise ValueError("Incorrect type of pixel values. "
+                                 f"Got type: {type(pixel_values)}")
+            return LlavaImagePixelInputs(
+                type="pixel_values",
+                data=self._validate_pixel_values(pixel_values),
+            )
+
+        if image_embeds is not None:
+            if not isinstance(image_embeds, torch.Tensor):
+                raise ValueError("Incorrect type of image embeddings. "
+                                 f"Got type: {type(image_embeds)}")
+            return LlavaImageEmbeddingInputs(
+                type="image_embeds",
+                data=image_embeds,
+            )
 
     def _select_image_features(self, image_features: torch.Tensor, *,
                                strategy: str) -> torch.Tensor:
@@ -258,6 +273,10 @@ def _process_image_pixels(self,
 
     def _process_image_input(self,
                              image_input: LlavaImageInputs) -> torch.Tensor:
+
+        if image_input["type"] == "image_embeds":
+            return image_input["data"]
+
         assert self.vision_tower is not None
         image_features = self._process_image_pixels(image_input)
         return self.multi_modal_projector(image_features)

vllm/model_executor/models/llava_next.py

@@ -60,7 +60,13 @@ class LlavaNextImagePixelInputs(TypedDict):
     """
 
 
-LlavaNextImageInputs = LlavaNextImagePixelInputs
+class LlavaNextImageEmbeddingInputs(TypedDict):
+    type: Literal["image_embeds"]
+    data: torch.Tensor
+
+
+LlavaNextImageInputs = Union[LlavaNextImagePixelInputs,
+                             LlavaNextImageEmbeddingInputs]
 
 
 # Based on: https://github.com/huggingface/text-generation-inference/blob/v2.2.0/server/text_generation_server/models/vlm_causal_lm.py#L79
@@ -208,7 +214,7 @@ def input_processor_for_llava_next(ctx: InputContext, llm_inputs: LLMInputs):
             input_width=width,
         )
     elif isinstance(image_data, torch.Tensor):
-        raise NotImplementedError("Embeddings input is not supported yet")
+        image_feature_size = image_data.shape[0]
     else:
         raise TypeError(f"Invalid image type: {type(image_data)}")
 
@@ -320,26 +326,38 @@ def _validate_shape(d: torch.Tensor):
         return data
 
     def _parse_and_validate_image_input(
-            self, **kwargs: object) -> Optional[LlavaNextImagePixelInputs]:
+            self, **kwargs: object) -> Optional[LlavaNextImageInputs]:
         pixel_values = kwargs.pop("pixel_values", None)
         image_sizes = kwargs.pop("image_sizes", None)
+        image_embeds = kwargs.pop("image_embeds", None)
 
-        if pixel_values is None:
+        if pixel_values is None and image_embeds is None:
             return None
 
-        if not isinstance(pixel_values, (torch.Tensor, list)):
-            raise ValueError("Incorrect type of pixel values. "
-                             f"Got type: {type(pixel_values)}")
+        if pixel_values is not None:
+            if not isinstance(pixel_values, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of pixel values. "
+                                 f"Got type: {type(pixel_values)}")
 
-        if not isinstance(image_sizes, torch.Tensor):
-            raise ValueError("Incorrect type of image sizes. "
-                             f"Got type: {type(image_sizes)}")
+            if not isinstance(image_sizes, torch.Tensor):
+                raise ValueError("Incorrect type of image sizes. "
+                                 f"Got type: {type(image_sizes)}")
 
-        return LlavaNextImagePixelInputs(
-            type="pixel_values",
-            data=self._validate_pixel_values(pixel_values),
-            image_sizes=self._validate_image_sizes(image_sizes),
-        )
+            return LlavaNextImagePixelInputs(
+                type="pixel_values",
+                data=self._validate_pixel_values(pixel_values),
+                image_sizes=self._validate_image_sizes(image_sizes),
+            )
+
+        if image_embeds is not None:
+            if not isinstance(image_embeds, torch.Tensor):
+                raise ValueError("Incorrect type of image embeds. "
+                                 f"Got type: {type(image_embeds)}")
+
+            return LlavaNextImageEmbeddingInputs(
+                type="image_embeds",
+                data=image_embeds,
+            )
 
     def _select_image_features(self, image_features: torch.Tensor, *,
                                strategy: str) -> torch.Tensor:
@@ -466,6 +484,10 @@ def _process_image_input(
         self,
         image_input: LlavaNextImageInputs,
     ) -> Union[torch.Tensor, List[torch.Tensor]]:
+
+        if image_input["type"] == "image_embeds":
+            return [image_input["data"]]
+
         patch_embeddings = self._process_image_pixels(image_input)
 
         image_sizes = image_input.get("image_sizes")