[V1] Initial support of multimodal models for V1 re-arch (#10699)

Signed-off-by: Roger Wang <ywang@roblox.com>

vllm/model_executor/models/internvl.py

@@ -26,7 +26,7 @@ from vllm.model_executor.models.intern_vit import (InternVisionModel,
                                                    InternVisionPatchModel)
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargs
-from vllm.multimodal.inputs import NestedTensors
+from vllm.multimodal.inputs import NestedTensors, PlaceholderRange
 from vllm.multimodal.utils import cached_get_tokenizer
 from vllm.sequence import IntermediateTensors
 from vllm.utils import is_list_of
@@ -52,12 +52,18 @@ class InternVLImagePixelInputs(TypedDict):
     Shape:
     `(batch_size * num_images * (1 + num_patches), num_channels, height, width)`
     """
+    patches_per_image: List[int]
+    """
+    List of number of total patches for each image in the batch.
+    """
 
 
 class InternVLImageEmbeddingInputs(TypedDict):
     type: Literal["image_embeds"]
-    data: torch.Tensor
-    """Shape: `(batch_size * num_images, image_feature_size, hidden_size)`
+    data: NestedTensors
+    """ 
+    A tensor of shape `(num_images, total_image_feature_size, hidden_size)`
+    or a list of tensors of shape `(total_image_feature_size, hidden_size)`
 
     `hidden_size` must match the hidden size of language model backbone.
     """
@@ -349,10 +355,32 @@ class InternVLInputPipeline:
         new_prompt = self._expand_image_prompt(prompt, image_feature_sizes,
                                                num_patches)
         new_prompt_token_ids = tokenizer.encode(new_prompt)
+        img_context_token_id = tokenizer.encode(self.img_context_token,
+                                                add_special_tokens=False)
+        assert len(img_context_token_id) == 1, \
+            (f"Invalid image token '{self.img_context_token}': A valid image "
+            f"token encodes to a single token ID, got {img_context_token_id}.")
+        img_context_token_id = img_context_token_id[0]
 
-        return token_inputs(prompt=prompt,
-                            prompt_token_ids=new_prompt_token_ids,
-                            multi_modal_data=multi_modal_data)
+        # Get precise tracking of placeholder positions
+        token_idx = image_idx = 0
+        placeholder_ranges = []
+        while token_idx < len(new_prompt_token_ids):
+            if new_prompt_token_ids[token_idx] == img_context_token_id:
+                curr_image_featue_size = image_feature_sizes[image_idx]
+                placeholder_ranges.append(
+                    PlaceholderRange(offset=token_idx,
+                                     length=curr_image_featue_size))
+                image_idx += 1
+                token_idx += curr_image_featue_size
+            else:
+                token_idx += 1
+
+        return token_inputs(
+            prompt=prompt,
+            prompt_token_ids=new_prompt_token_ids,
+            multi_modal_data=multi_modal_data,
+            multi_modal_placeholders={"image": placeholder_ranges})
 
     def input_mapper(
         self,
@@ -614,26 +642,46 @@ class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP):
             if not isinstance(pixel_values, (torch.Tensor, list)):
                 raise ValueError("Incorrect type of pixel values. "
                                  f"Got type: {type(pixel_values)}")
+
+            patches_per_image = []
+            for request_pixel_values in pixel_values:
+                for image_pixel_values in request_pixel_values:
+                    patches_per_image.append(image_pixel_values.shape[0])
             # We need to flatten (B, N, P) to (B*N*P),
             # so we call flatten_bn twice.
             return InternVLImagePixelInputs(
                 type="pixel_values",
                 data=self._validate_pixel_values(
                     flatten_bn(flatten_bn(pixel_values), concat=True)),
-            )
+                patches_per_image=patches_per_image)
 
         raise AssertionError("This line should be unreachable.")
 
     def _process_image_input(
         self,
         image_input: InternVLImageInputs,
-    ) -> torch.Tensor:
+    ) -> Tuple[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"])
 
+        patches_per_image = image_input["patches_per_image"]
+        if len(patches_per_image) == 1:
+            image_embeds = image_embeds.unsqueeze(0)
+            return image_embeds
+
+        # NOTE: Image embeddings are split into separate tensors for each image
+        # by the size of each embedding.
+        feature_size = image_embeds.shape[1]
+        image_embeds = image_embeds.view(-1,
+                                         self.config.text_config.hidden_size)
+        image_feature_sizes = [
+            num_patches * feature_size for num_patches in patches_per_image
+        ]
+        image_embeds = image_embeds.split(image_feature_sizes)
         return image_embeds
 
     def _set_visual_token_mask(self, input_ids: torch.Tensor) -> torch.Tensor:
@@ -696,13 +744,11 @@ class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP):
             "inputs_embeds": inputs_embeds,
         }
 
+        # Only required if the model is mono-architecture
         if self.visual_token_mask is not None:
-            # overwrite visual_token_mask and img_context_token_id back to None,
-            # so that this doesn't need to depend on encoder output
             forward_kwargs.update(
                 {"visual_token_mask": self.visual_token_mask})
             self.visual_token_mask = None
-            self.img_context_token_id = None
 
         hidden_states = self.language_model.model(**forward_kwargs)
         return hidden_states