[Model] LLaVA model refactor

vllm/model_executor/models/llava.py

@@ -1,4 +1,4 @@
-from typing import Iterable, List, Optional, Tuple
+from typing import Iterable, List, Literal, Optional, Tuple, TypedDict, Union
 
 import torch
 from torch import nn
@@ -67,6 +67,21 @@ def _merge_vision_embeddings(input_ids: torch.Tensor,
     return inputs_embeds
 
 
+class LlavaImagePixelInputs(TypedDict):
+    type: Literal["pixel_values"]
+    data: torch.Tensor
+    """Shape: (batch_size, num_channels, height, width)"""
+
+
+class LlavaImageFeatureInputs(TypedDict):
+    type: Literal["image_features"]
+    data: torch.Tensor
+    """Shape: (batch_size, image_feature_size, hidden_size)"""
+
+
+LlavaImageInputs = Union[LlavaImagePixelInputs, LlavaImageFeatureInputs]
+
+
 class LlavaForConditionalGeneration(VisionLanguageModelBase):
 
     def __init__(self,
@@ -102,6 +117,92 @@ def __init__(self,
                                                 config.vocab_size, logit_scale)
         self.sampler = Sampler()
 
+    def _validate_image_data(self, data: torch.Tensor) -> torch.Tensor:
+        if list(data.shape[1:]) != list(
+                self.vision_language_config.image_input_shape[1:]):
+            raise ValueError(
+                f"The expected image tensor shape is batch dimension plus "
+                f"{self.vision_language_config.image_input_shape[1:]}. "
+                f"You supplied {data.shape}. "
+                f"If you are using vLLM's entrypoint, make sure your "
+                f"supplied image input is consistent with "
+                f"image_input_shape in engine args.")
+
+        return data
+
+    def _parse_and_validate_image_input(
+            self, data: object) -> Optional[LlavaImageInputs]:
+        expected_input_type = self.vision_language_config.image_input_type
+        ImageInputType = VisionLanguageConfig.ImageInputType
+
+        if expected_input_type == ImageInputType.PIXEL_VALUES:
+            if data is None:
+                return None
+
+            if not isinstance(data, torch.Tensor):
+                raise ValueError("Incorrect type of pixel values")
+
+            return LlavaImagePixelInputs(
+                type="pixel_values",
+                data=self._validate_image_data(data),
+            )
+
+        if expected_input_type == ImageInputType.IMAGE_FEATURES:
+            if data is None:
+                return None
+
+            if not isinstance(data, torch.Tensor):
+                raise ValueError("Incorrect type of image features")
+
+            return LlavaImageFeatureInputs(
+                type="image_features",
+                data=self._validate_image_data(data),
+            )
+
+        return None
+
+    def _select_image_features(self, image_features: torch.Tensor, *,
+                               strategy: str) -> torch.Tensor:
+        # Copied from https://github.com/huggingface/transformers/blob/39c3c0a72af6fbda5614dde02ff236069bb79827/src/transformers/models/llava/modeling_llava.py#L421  # noqa
+        if strategy == "default":
+            return image_features[:, 1:]
+        elif strategy == "full":
+            return image_features
+
+        raise ValueError(f"Unexpected select feature strategy: {strategy}")
+
+    def _image_pixels_to_features(self, vision_tower: CLIPVisionModel,
+                                  pixel_values: torch.Tensor) -> torch.Tensor:
+        # TODO(xwjiang): Maybe port minimal CLIPVisionModel over.
+        image_outputs = vision_tower(pixel_values.to(vision_tower.device),
+                                     output_hidden_states=True)
+
+        image_features = image_outputs.hidden_states[
+            self.config.vision_feature_layer]
+
+        return self._select_image_features(
+            image_features,
+            strategy=self.config.vision_feature_select_strategy,
+        )
+
+    def _process_image_pixels(self,
+                              inputs: LlavaImagePixelInputs) -> torch.Tensor:
+        assert self.vision_tower is not None
+
+        pixel_values = inputs["data"]
+
+        return self._image_pixels_to_features(self.vision_tower, pixel_values)
+
+    def _process_image_input(self,
+                             image_input: LlavaImageInputs) -> torch.Tensor:
+        if image_input["type"] == "pixel_values":
+            assert self.vision_tower is not None
+            image_features = self._process_image_pixels(image_input)
+        else:
+            image_features = image_input["data"]
+
+        return self.multi_modal_projector(image_features)
+
     def forward(self,
                 input_ids: torch.Tensor,
                 positions: torch.Tensor,
@@ -144,42 +245,20 @@ def forward(self,
                 For PIXEL_VALUES, expecting [1, 3, 336, 336].
                 For IMAGE_FEATURES, expecting [1, 576, 1024].
         """
-        if image_input is not None:
-            if list(image_input.shape[1:]) != list(
-                    self.vision_language_config.image_input_shape[1:]):
-                raise ValueError(
-                    f"The expected image tensor shape is batch dimension "
-                    f"plus "
-                    f"{self.vision_language_config.image_input_shape[1:]}."
-                    f" You supplied {image_input.shape}. "
-                    f"If you are using vLLM's entrypoint, make sure your "
-                    f"supplied image input is consistent with "
-                    f"image_input_shape in engine args.")
-            if self.vision_tower is not None:
-                # TODO(xwjiang): Maybe port minimal CLIPVisionModel over.
-                image_outputs = self.vision_tower(image_input,
-                                                  output_hidden_states=True)
-                image_features = image_outputs.hidden_states[
-                    self.config.vision_feature_layer]
-                # Copied from https://github.com/huggingface/transformers/blob/39c3c0a72af6fbda5614dde02ff236069bb79827/src/transformers/models/llava/modeling_llava.py#L421  # noqa
-                if self.config.vision_feature_select_strategy == "default":
-                    image_features = image_features[:, 1:]
-                elif self.config.vision_feature_select_strategy == "full":
-                    image_features = image_features
-                else:
-                    raise ValueError(
-                        f"Unexpected select feature strategy: "
-                        f"{self.config.vision_feature_select_strategy}")
-            else:
-                image_features = image_input
-            vision_embeddings = self.multi_modal_projector(image_features)
+        parsed_image_input = self._parse_and_validate_image_input(image_input)
+
+        if parsed_image_input is not None:
+            vision_embeddings = self._process_image_input(parsed_image_input)
             inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+
             inputs_embeds = _merge_vision_embeddings(
                 input_ids, inputs_embeds, vision_embeddings,
                 self.vision_language_config.image_token_id)
+
             input_ids = None
         else:
             inputs_embeds = None
+
         hidden_states = self.language_model(input_ids,
                                             positions,
                                             kv_caches,