[VLM] Check required fields before initializing field config in DictEmbeddingItems

docs/source/serving/multimodal_inputs.md

@@ -184,8 +184,8 @@ llm = LLM("openbmb/MiniCPM-V-2_6", trust_remote_code=True, limit_mm_per_prompt={
 mm_data = {
     "image": {
         "image_embeds": image_embeds,
-        # image_size_list is needed to calculate details of the sliced image.
-        "image_size_list": [image.size for image in images],  # list of image sizes
+        # image_sizes is needed to calculate details of the sliced image.
+        "image_sizes": [image.size for image in images],  # list of image sizes
     }
 }
 

vllm/model_executor/models/minicpmo.py

@@ -23,8 +23,8 @@
 # limitations under the License.
 """Inference-only MiniCPM-O model compatible with HuggingFace weights."""
 from functools import partial
-from typing import (Any, Dict, Iterable, List, Literal, Mapping, Optional, Set,
-                    Tuple, TypedDict, Union)
+from typing import (Any, Callable, Dict, Iterable, List, Literal, Mapping,
+                    Optional, Set, Tuple, TypedDict, Union)
 
 import torch
 from torch import nn
@@ -122,13 +122,16 @@ class MiniCPMOAudioEmbeddingItems(DictEmbeddingItems):
     def __init__(
         self,
         data: Mapping[str, torch.Tensor],
-        fields_config: Mapping[str, MultiModalFieldConfig],
+        fields_factory: Callable[
+            [Mapping[str, torch.Tensor]],
+            Mapping[str, MultiModalFieldConfig],
+        ],
     ) -> None:
         super().__init__(
             data,
             modality="image",
-            fields_config=fields_config,
             required_fields={"audio_embeds"},
+            fields_factory=fields_factory,
         )
 
 
@@ -141,7 +144,7 @@ def _parse_audio_data(
         if isinstance(data, dict):
             return MiniCPMOAudioEmbeddingItems(
                 data,
-                fields_config=_minicpmo_field_config(data),
+                fields_factory=_minicpmo_field_config,
             )
 
         return super()._parse_audio_data(data)

vllm/model_executor/models/minicpmv.py

@@ -255,13 +255,16 @@ class MiniCPMVImageEmbeddingItems(DictEmbeddingItems):
     def __init__(
         self,
         data: Mapping[str, torch.Tensor],
-        fields_config: Mapping[str, MultiModalFieldConfig],
+        fields_factory: Callable[
+            [Mapping[str, torch.Tensor]],
+            Mapping[str, MultiModalFieldConfig],
+        ],
     ) -> None:
         super().__init__(
             data,
             modality="image",
-            fields_config=fields_config,
             required_fields={"image_embeds", "image_sizes"},
+            fields_factory=fields_factory,
         )
 
     def get_image_size(self, index: int) -> ImageSize:
@@ -274,13 +277,16 @@ class MiniCPMVVideoEmbeddingItems(DictEmbeddingItems):
     def __init__(
         self,
         data: Mapping[str, torch.Tensor],
-        fields_config: Mapping[str, MultiModalFieldConfig],
+        fields_factory: Callable[
+            [Mapping[str, torch.Tensor]],
+            Mapping[str, MultiModalFieldConfig],
+        ],
     ) -> None:
         super().__init__(
             data,
             modality="video",
-            fields_config=fields_config,
             required_fields={"video_embeds", "video_image_sizes"},
+            fields_factory=fields_factory,
         )
 
     def get_frame_size(self, index: int) -> ImageSize:
@@ -300,7 +306,7 @@ def _parse_image_data(
         if isinstance(data, dict):
             return MiniCPMVImageEmbeddingItems(
                 data,
-                fields_config=_minicpmv_field_config(data),
+                fields_factory=_minicpmv_field_config,
             )
 
         return super()._parse_image_data(data)
@@ -312,7 +318,7 @@ def _parse_video_data(
         if isinstance(data, dict):
             return MiniCPMVVideoEmbeddingItems(
                 data,
-                fields_config=_minicpmv_field_config(data),
+                fields_factory=_minicpmv_field_config,
             )
 
         return super()._parse_video_data(data)

vllm/model_executor/models/qwen2_vl.py

@@ -691,8 +691,8 @@ def _parse_image_data(
             return DictEmbeddingItems(
                 data,
                 modality="image",
-                fields_config=_qwen2vl_field_config(data),
                 required_fields={"image_embeds", "image_grid_thw"},
+                fields_factory=_qwen2vl_field_config,
             )
 
         return super()._parse_image_data(data)
@@ -705,8 +705,8 @@ def _parse_video_data(
             return DictEmbeddingItems(
                 data,
                 modality="video",
-                fields_config=_qwen2vl_field_config(data),
                 required_fields={"video_embeds", "video_grid_thw"},
+                fields_factory=_qwen2vl_field_config,
             )
 
         return super()._parse_video_data(data)

vllm/multimodal/parse.py

@@ -125,24 +125,28 @@ def __init__(
         self,
         data: Mapping[str, torch.Tensor],
         modality: str,
-        fields_config: Mapping[str, MultiModalFieldConfig],
         required_fields: set[str],
+        fields_factory: Callable[
+            [Mapping[str, torch.Tensor]],
+            Mapping[str, MultiModalFieldConfig],
+        ],
     ) -> None:
         super().__init__(data, modality)
 
-        missing_required_fields = required_fields - fields_config.keys()
-        if missing_required_fields:
-            fields = set(fields_config.keys())
-            msg = f"{required_fields=} should be a subset of {fields=}"
-            raise ValueError(msg)
-
         missing_required_data_keys = required_fields - data.keys()
         if missing_required_data_keys:
             data_keys = set(data.keys())
             msg = (f"The data should contain the fields: {required_fields}, "
                    f"but only found the following keys: {data_keys}")
             raise ValueError(msg)
 
+        fields_config = fields_factory(data)
+        missing_required_fields = required_fields - fields_config.keys()
+        if missing_required_fields:
+            fields = set(fields_config.keys())
+            msg = f"{required_fields=} should be a subset of {fields=}"
+            raise ValueError(msg)
+
         self.fields_config = fields_config
         self.required_fields = required_fields