[Model] support input image embedding for minicpmv (#9237)

docs/source/models/supported_models.rst

@@ -378,7 +378,7 @@ Text Generation
     - ✅︎
   * - :code:`MiniCPMV`
     - MiniCPM-V
-    - Image\ :sup:`+`
+    - Image\ :sup:`E+`
     - :code:`openbmb/MiniCPM-V-2` (see note), :code:`openbmb/MiniCPM-Llama3-V-2_5`, :code:`openbmb/MiniCPM-V-2_6`, etc.
     - ✅︎
     - ✅︎

docs/source/models/vlm.rst

@@ -57,12 +57,19 @@ To pass an image to the model, note the following in :class:`vllm.inputs.PromptT
         print(generated_text)
 
     # Inference with image embeddings as input with additional parameters
-    # Specifically, we are conducting a trial run of Qwen2VL with the new input format, as the model utilizes additional parameters for calculating positional encoding.
+    # Specifically, we are conducting a trial run of Qwen2VL and MiniCPM-V with the new input format, which utilizes additional parameters.
-    image_embeds = torch.load(...) # torch.Tensor of shape (1, image_feature_size, hidden_size of LM)
+    mm_data = {}
-    image_grid_thw = torch.load(...) # torch.Tensor of shape (1, 3)
+
+    image_embeds = torch.load(...) # torch.Tensor of shape (num_images, image_feature_size, hidden_size of LM)
+    # For Qwen2VL, image_grid_thw is needed to calculate positional encoding.
     mm_data['image'] = {
         "image_embeds": image_embeds,
-        "image_grid_thw":  image_grid_thw,
+        "image_grid_thw": torch.load(...) # torch.Tensor of shape (1, 3),
+    }
+    # For MiniCPM-V, image_size_list is needed to calculate details of the sliced image.
+    mm_data['image'] = {
+        "image_embeds": image_embeds,
+        "image_size_list": [image.size] # list of image sizes
     }
     outputs = llm.generate({
         "prompt": prompt,

vllm/model_executor/models/minicpmv.py

@@ -24,8 +24,8 @@
 import math
 import re
 from functools import partial
-from typing import (Any, Callable, Iterable, List, Mapping, Optional, Tuple,
+from typing import (Any, Callable, Iterable, List, Literal, Mapping, Optional,
-                    TypedDict)
+                    Tuple, TypedDict, Union)
 
 import torch
 import torch.types
@@ -65,10 +65,12 @@ _KEYS_TO_MODIFY_MAPPING = {
     "llm.lm_head": "lm_head",
 }
 
+RawImageType = Union[Image.Image, torch.Tensor]
 
-class MiniCPMVImageInput(TypedDict):
+
+class MiniCPMVRawImageInput(TypedDict):
     """Input mapper input with auxiliary data for computing image bounds."""
-    image: Image.Image
+    image: RawImageType
 
     # Image bounds token ids in 0-dim scaler tensor.
     im_start_id: torch.Tensor
@@ -78,7 +80,8 @@ class MiniCPMVImageInput(TypedDict):
 
 
 class MiniCPMVImagePixelInputs(TypedDict):
-    pixel_values: List[torch.Tensor]
+    type: Literal["pixel_values"]
+    data: List[torch.Tensor]
     """
     Shape: `(batch_size * num_images, num_channels, height, width)`
 
@@ -101,6 +104,27 @@ class MiniCPMVImagePixelInputs(TypedDict):
     """
 
 
+class MiniCPMVImageEmbeddingInputs(TypedDict):
+    type: Literal["image_embeds"]
+    data: torch.Tensor
+    """
+    Shape: `(batch_size * num_images, image_feature_size, hidden_size)`
+
+    `hidden_size` must match the hidden size of language model backbone.
+    instead of a batched tensor.
+    """
+
+    image_bounds: torch.Tensor
+    """
+    Shape: `(batch_size * num_images, 2)`
+
+    This should be in `(start, stop)` format.
+    """
+
+
+MiniCPMVImageInputs = Union[MiniCPMVImagePixelInputs,
+                            MiniCPMVImageEmbeddingInputs]
+
 DEFAULT_LN = partial(nn.LayerNorm, eps=1e-6)
 
 
@@ -194,21 +218,21 @@ class Resampler2_5(BaseResampler):
 
 
 def _build_image_input(ctx: InputContext,
-                       image: Image.Image) -> MiniCPMVImageInput:
+                       image: RawImageType) -> MiniCPMVRawImageInput:
     tokenizer = cached_get_tokenizer(
         ctx.model_config.tokenizer,
         trust_remote_code=ctx.model_config.trust_remote_code)
     if hasattr(tokenizer, "slice_start_id"):
-        return MiniCPMVImageInput(
+        return MiniCPMVRawImageInput(
             image=image,
             im_start_id=torch.tensor(tokenizer.im_start_id),
             im_end_id=torch.tensor(tokenizer.im_end_id),
             slice_start_id=torch.tensor(tokenizer.slice_start_id),
             slice_end_id=torch.tensor(tokenizer.slice_end_id))
     else:
-        return MiniCPMVImageInput(image=image,
+        return MiniCPMVRawImageInput(
-                                  im_start_id=torch.tensor(
+            image=image,
-                                      tokenizer.im_start_id),
+            im_start_id=torch.tensor(tokenizer.im_start_id),
             im_end_id=torch.tensor(tokenizer.im_end_id))
 
 
@@ -280,20 +304,25 @@ def input_processor_for_minicpmv(ctx: InputContext, llm_inputs: LLMInputs):
 
     pattern = "(<image>./</image>)"
     images = multi_modal_data["image"]
-    if isinstance(images, Image.Image):
-        images = [images]
     image_tags = re.findall(pattern, prompt)
-
     if len(image_tags) == 0:
         new_token_ids = token_ids
         new_prompt = prompt
     else:
+        if isinstance(images, dict):
+            image_size_list = images.get("image_size_list")
+            images = [images.get("image_embeds")]
+        else:
+            if isinstance(images, Image.Image):
+                images = [images]
+            image_size_list = [image.size for image in images]
+
         text_chunks = prompt.split(pattern)
         new_prompt_chunks: List[str] = []
-        for i in range(len(images)):
+        for i in range(len(image_size_list)):
             new_prompt_chunks += [
                 text_chunks[i],
-                get_placeholder(images[i].size, i)
+                get_placeholder(image_size_list[i], i)
             ]
         new_prompt_chunks.append(text_chunks[-1])
         new_prompt = "".join(new_prompt_chunks)
@@ -323,6 +352,12 @@ def input_mapper_for_minicpmv(ctx: InputContext, data: object):
     if not isinstance(data, list):
         raise ValueError(
             "Image input must be list of MiniCPMVImageInput, got (%s)", data)
+
+    if len(data) > 0 and isinstance(data[0]['image'], torch.Tensor):
+        batch_data = {
+            "image_embeds": data[0]['image'],
+        }
+    else:
         batch_data = image_processor \
             .preprocess([img["image"] for img in data], return_tensors="pt") \
             .data
@@ -380,7 +415,7 @@ class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
     def get_embedding(
         self,
         input_ids: torch.Tensor,
-        image_inputs: Optional[MiniCPMVImagePixelInputs],
+        image_inputs: Optional[MiniCPMVImageInputs],
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         vlm_embedding: torch.Tensor = self.llm.embed_tokens(input_ids)
         if hasattr(self.config, "scale_emb"):
@@ -389,7 +424,12 @@ class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
         if image_inputs is None:  # No image
             vision_hidden_states = torch.tensor([], device=input_ids.device)
         else:
-            vision_hidden_states = self.get_vision_hidden_states(image_inputs)
+            if image_inputs["type"] == "image_embeds":
+                vision_hidden_states = (image_inputs["data"].type(
+                    vlm_embedding.dtype).to(vlm_embedding.device))
+            else:
+                vision_hidden_states = self.get_vision_hidden_states(
+                    image_inputs)
 
             # See NOTE in _parse_and_validate_inputs
             image_bounds = image_inputs["image_bounds"]
@@ -440,9 +480,23 @@ class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
         self,
         input_ids: torch.Tensor,
         **kwargs: object,
-    ) -> Optional[MiniCPMVImagePixelInputs]:
+    ) -> Optional[MiniCPMVImageInputs]:
         pixel_values = kwargs.pop("pixel_values", [])
         tgt_sizes = kwargs.pop("tgt_sizes", [])
+        im_start_id = kwargs.pop("im_start_id", None)
+        im_end_id = kwargs.pop("im_end_id", None)
+        slice_start_id = kwargs.pop("slice_start_id", None)
+        slice_end_id = kwargs.pop("slice_end_id", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+
+        if image_embeds is not None:
+            return MiniCPMVImageEmbeddingInputs(
+                image_bounds=self._get_image_bounds(input_ids, im_start_id,
+                                                    im_end_id, slice_start_id,
+                                                    slice_end_id),
+                data=image_embeds,
+                type="image_embeds",
+            )
 
         if not isinstance(pixel_values, (torch.Tensor, list)):
             raise ValueError("Incorrect type of pixel values. "
@@ -477,10 +531,6 @@ class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
         if len(pixel_values_flat) == 0:
             return None
 
-        im_start_id = kwargs.pop("im_start_id", None)
-        im_end_id = kwargs.pop("im_end_id", None)
-        slice_start_id = kwargs.pop("slice_start_id", None)
-        slice_end_id = kwargs.pop("slice_end_id", None)
         if im_start_id is None:
             return None
 
@@ -488,8 +538,9 @@ class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
             image_bounds=self._get_image_bounds(input_ids, im_start_id,
                                                 im_end_id, slice_start_id,
                                                 slice_end_id),
-            pixel_values=pixel_values_flat,
+            data=pixel_values_flat,
             tgt_sizes=torch.stack(tgt_sizes_flat),
+            type="pixel_values",
         )
 
     def forward(
@@ -610,8 +661,8 @@ class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
     ) -> torch.Tensor:
         raise NotImplementedError
 
-    def get_vision_hidden_states(
+    def get_vision_hidden_states(self,
-            self, data: MiniCPMVImagePixelInputs) -> torch.Tensor:
+                                 data: MiniCPMVImageInputs) -> torch.Tensor:
         raise NotImplementedError
 
     def is_default_weight_loading(self, name: str) -> bool:
@@ -705,9 +756,9 @@ class MiniCPMV2_0(MiniCPMVBaseModel):
             res.append(self.resampler(vision_embedding, tgt_size))
         return torch.vstack(res)
 
-    def get_vision_hidden_states(
+    def get_vision_hidden_states(self,
-            self, data: MiniCPMVImagePixelInputs) -> torch.Tensor:
+                                 data: MiniCPMVImageInputs) -> torch.Tensor:
-        pixel_values = data["pixel_values"]
+        pixel_values = data["data"]
 
         return self.get_vision_embedding(pixel_values)
 
@@ -793,9 +844,9 @@ class MiniCPMV2_5(MiniCPMVBaseModel, SupportsLoRA):
         vision_embedding = self.resampler(vision_embedding, tgt_sizes)
         return vision_embedding
 
-    def get_vision_hidden_states(
+    def get_vision_hidden_states(self,
-            self, data: MiniCPMVImagePixelInputs) -> torch.Tensor:
+                                 data: MiniCPMVImageInputs) -> torch.Tensor:
-        pixel_values = data["pixel_values"]
+        pixel_values = data["data"]
         tgt_sizes = data["tgt_sizes"]
 
         device = self.vpm.embeddings.position_embedding.weight.device
@@ -909,9 +960,9 @@ class MiniCPMV2_6(MiniCPMVBaseModel, SupportsLoRA):
         )
         return vision_embedding
 
-    def get_vision_hidden_states(
+    def get_vision_hidden_states(self,
-            self, data: MiniCPMVImagePixelInputs) -> torch.Tensor:
+                                 data: MiniCPMVImageInputs) -> torch.Tensor:
-        pixel_values = data["pixel_values"]
+        pixel_values = data["data"]
         tgt_sizes = data["tgt_sizes"]
 
         device = self.vpm.embeddings.position_embedding.weight.device