[Model] Use merge_by_field_config for MM models (G) (vllm-project#26117)

Signed-off-by: DarkLight1337 <[email protected]>

Author: DarkLight1337

vllm/model_executor/models/gemma3_mm.py

@@ -36,7 +36,7 @@
 from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
                          SupportsMultiModal, SupportsPP)
 from .siglip import SiglipVisionModel
-from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
+from .utils import (AutoWeightsLoader, WeightsMapper,
                     init_vllm_registered_model, maybe_prefix)
 
 logger = init_logger(__name__)
@@ -289,7 +289,7 @@ def _call_hf_processor(
                                         processor=hf_processor)
                 for size in image_sizes
             ]
-            processed_outputs["num_crops"] = torch.tensor(num_crops)
+            processed_outputs["num_patches"] = torch.tensor(num_crops) + 1
 
         return processed_outputs
 
@@ -298,12 +298,12 @@ def _get_mm_fields_config(
         hf_inputs: BatchFeature,
         hf_processor_mm_kwargs: Mapping[str, object],
     ) -> Mapping[str, MultiModalFieldConfig]:
-        num_crops = hf_inputs.get("num_crops", torch.empty(0))
+        num_patches = hf_inputs.get("num_patches", torch.empty(0))
 
         return dict(
             pixel_values=MultiModalFieldConfig.flat_from_sizes(
-                "image", num_crops + 1),
-            num_crops=MultiModalFieldConfig.batched("image"),
+                "image", num_patches),
+            num_patches=MultiModalFieldConfig.batched("image"),
         )
 
     def _get_prompt_updates(
@@ -460,6 +460,8 @@ def forward(self, vision_outputs: torch.Tensor):
                                         dummy_inputs=Gemma3DummyInputsBuilder)
 class Gemma3ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP,
                                      SupportsLoRA):
+    merge_by_field_config = True
+
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",
@@ -526,29 +528,20 @@ def dtype(self):
     def _parse_and_validate_image_input(
             self, **kwargs: object) -> Optional[Gemma3ImageInputs]:
         pixel_values = kwargs.pop("pixel_values", None)
-        num_crops = kwargs.pop("num_crops", None)
+        num_patches = kwargs.pop("num_patches", None)
         image_embeds = kwargs.pop("image_embeds", None)
         assert image_embeds is None, "Gemma3 does not support image_embeds."
         if pixel_values 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 not isinstance(num_crops, (torch.Tensor, list)):
-            raise ValueError("Incorrect type of num_crops. "
-                             f"Got type: {type(num_crops)}")
-
         image_size = self.config.vision_config.image_size
 
-        return Gemma3ImagePixelInputs(
-            pixel_values=flatten_bn(pixel_values, concat=True),
-            num_patches=flatten_bn(num_crops, concat=True) + 1,
-            resolve_bindings={
-                "h": image_size,
-                "w": image_size
-            })
+        return Gemma3ImagePixelInputs(pixel_values=pixel_values,
+                                      num_patches=num_patches,
+                                      resolve_bindings={
+                                          "h": image_size,
+                                          "w": image_size
+                                      })
 
     def _image_pixels_to_features(
         self,

vllm/model_executor/models/gemma3n_mm.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from collections.abc import Iterable, Mapping, Sequence
-from typing import Any, Literal, Optional, TypedDict, Union, cast
+from typing import Annotated, Any, Literal, Optional, Union, cast
 
 import numpy as np
 import torch
@@ -41,6 +41,7 @@
 # yapf: enable
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
 from .interfaces import (MultiModalEmbeddings, SupportsMultiModal,
                          SupportsTranscription)
@@ -54,17 +55,28 @@
 TOKENS_PER_AUDIO = 188
 
 
-class Gemma3nImagePixelInputs(TypedDict):
-    pixel_values: torch.Tensor
-    """Shape: `(batch_size * num_images, num_channels, height, width)`"""
+class Gemma3nImagePixelInputs(TensorSchema):
+    """
+    Dimensions:
+        - bn: Batch size * number of images
+        - c: Number of channels (3)
+        - h: Height of each patch
+        - w: Width of each patch
+    """
+    type: Literal["pixel_values"] = "pixel_values"
+    pixel_values: Annotated[torch.Tensor, TensorShape("bn", 3, "h", "w")]
 
 
-class Gemma3nAudioInputs(TypedDict):
-    input_features: Union[torch.Tensor, list[torch.Tensor]]
-    input_features_padded: torch.Tensor
-    """Shape: `(batch_size * num_audio, seq_length, num_features)`"""
-    input_features_mask: torch.Tensor
-    """Shape: `(batch_size * num_audio, seq_length)`"""
+class Gemma3nAudioInputs(TensorSchema):
+    """
+    Dimensions:
+        - bn: Batch size * number of audios
+        - s: seq_length
+        - f: num_features
+    """
+    type: Literal["audio"] = "audio"
+    input_features_padded: Annotated[torch.Tensor, TensorShape("bn", "s", "f")]
+    input_features_mask: Annotated[torch.Tensor, TensorShape("bn", "s")]
 
 
 Gemma3nImageInputs = Gemma3nImagePixelInputs
@@ -212,9 +224,9 @@ def _get_mm_fields_config(
 
         return dict(
             pixel_values=MultiModalFieldConfig.batched("image"),
-            input_features=MultiModalFieldConfig.batched("audio"),
             input_features_padded=MultiModalFieldConfig.batched("audio"),
-            input_features_mask=MultiModalFieldConfig.batched("audio"))
+            input_features_mask=MultiModalFieldConfig.batched("audio"),
+        )
 
     def _get_prompt_updates(
         self,
@@ -422,6 +434,7 @@ def forward(
                                         dummy_inputs=Gemma3nDummyInputsBuilder)
 class Gemma3nForConditionalGeneration(nn.Module, SupportsMultiModal,
                                       SupportsTranscription):
+    merge_by_field_config = True
     supported_languages = ISO639_1_SUPPORTED_LANGS
 
     packed_modules_mapping = {
@@ -482,14 +495,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
             device=self.language_model.model.embed_tokens.weight.device,
             dtype=self.language_model.model.embed_tokens.weight.dtype)
 
-    @property
-    def dtype(self):
-        return next(self.parameters()).dtype
-
-    def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
-        # TODO check if there are any
-        return data
-
     def _parse_and_validate_image_input(
             self, **kwargs: object) -> Optional[Gemma3nImageInputs]:
         pixel_values = kwargs.pop("pixel_values", None)
@@ -499,34 +504,22 @@ def _parse_and_validate_image_input(
         if pixel_values 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)}")
-
-        pixel_values = flatten_bn(pixel_values, concat=True)
-        pixel_values = pixel_values.contiguous()
-
-        return Gemma3nImagePixelInputs(
-            pixel_values=self._validate_pixel_values(pixel_values), )
+        return Gemma3nImagePixelInputs(pixel_values=pixel_values)
 
     def _parse_and_validate_audio_input(
             self, **kwargs: object) -> Optional[Gemma3nAudioInputs]:
-        input_features = kwargs.pop("input_features", None)
-        if input_features is None:
+
+        input_features_padded = kwargs.pop("input_features_padded", None)
+        if input_features_padded is None:
             return None
 
         input_features_mask = kwargs.pop("input_features_mask", None)
         if input_features_mask is None:
             return None
 
-        input_features_padded = kwargs.pop("input_features_padded", None)
-        if input_features_padded is None:
-            return None
-
         return Gemma3nAudioInputs(
-            input_features=input_features,
-            input_features_mask=input_features_mask,
             input_features_padded=input_features_padded,
+            input_features_mask=input_features_mask,
         )
 
     def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
@@ -539,7 +532,7 @@ def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
                              ) and "image" not in mm_input_by_modality:
                 mm_input_by_modality[
                     "image"] = self._parse_and_validate_image_input(**kwargs)
-            if input_key == "input_features" \
+            if input_key == "input_features_padded" \
                 and "audio" not in mm_input_by_modality:
                 mm_input_by_modality[
                     "audio"] = self._parse_and_validate_audio_input(**kwargs)

vllm/model_executor/models/glm4_1v.py

@@ -1319,6 +1319,8 @@ def get_video_replacement_glm4v(item_idx: int):
 )
 class Glm4vForConditionalGeneration(nn.Module, SupportsMultiModal,
                                     SupportsLoRA, SupportsPP):
+    merge_by_field_config = True
+
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",
@@ -1381,22 +1383,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.make_empty_intermediate_tensors = (
             self.language_model.make_empty_intermediate_tensors)
 
-    def _validate_and_reshape_mm_tensor(self, mm_input: object,
-                                        name: str) -> torch.Tensor:
-        if not isinstance(mm_input, (torch.Tensor, list)):
-            raise ValueError(
-                f"Incorrect type of {name}. Got type: {type(mm_input)}")
-        if isinstance(mm_input, torch.Tensor):
-            if mm_input.ndim == 2:
-                return mm_input
-            if mm_input.ndim != 3:
-                raise ValueError(f"{name} should be 2D or batched 3D tensor. "
-                                 f"Got ndim: {mm_input.ndim} "
-                                 f"(shape={mm_input.shape})")
-            return mm_input.reshape(-1, mm_input.shape[-1])
-        else:
-            return torch.concat(mm_input)
-
     def _parse_and_validate_image_input(
             self, **kwargs: object) -> Optional[Glm4vImageInputs]:
         pixel_values = kwargs.pop("pixel_values", None)
@@ -1407,23 +1393,13 @@ def _parse_and_validate_image_input(
             return None
 
         if pixel_values is not None:
-            pixel_values = self._validate_and_reshape_mm_tensor(
-                pixel_values, "image pixel values")
-            image_grid_thw = self._validate_and_reshape_mm_tensor(
-                image_grid_thw, "image grid_thw")
-
             return Glm4vImagePixelInputs(
                 type="pixel_values",
                 pixel_values=pixel_values,
                 image_grid_thw=image_grid_thw,
             )
 
         if image_embeds is not None:
-            image_embeds = self._validate_and_reshape_mm_tensor(
-                image_embeds, "image embeds")
-            image_grid_thw = self._validate_and_reshape_mm_tensor(
-                image_grid_thw, "image grid_thw")
-
             return Glm4vImageEmbeddingInputs(
                 type="image_embeds",
                 image_embeds=image_embeds,
@@ -1440,23 +1416,13 @@ def _parse_and_validate_video_input(
             return None
 
         if pixel_values_videos is not None:
-            pixel_values_videos = self._validate_and_reshape_mm_tensor(
-                pixel_values_videos, "video pixel values")
-            video_grid_thw = self._validate_and_reshape_mm_tensor(
-                video_grid_thw, "video grid_thw")
-
             return Glm4vVideoPixelInputs(
                 type="pixel_values_videos",
                 pixel_values_videos=pixel_values_videos,
                 video_grid_thw=video_grid_thw,
             )
 
         if video_embeds is not None:
-            video_embeds = self._validate_and_reshape_mm_tensor(
-                video_embeds, "video embeds")
-            video_grid_thw = self._validate_and_reshape_mm_tensor(
-                video_grid_thw, "video grid_thw")
-
             return Glm4vVideoEmbeddingInputs(
                 type="video_embeds",
                 video_embeds=video_embeds,

vllm/model_executor/models/glm4v.py

@@ -43,7 +43,6 @@
 from .chatglm import ChatGLMBaseModel, ChatGLMModel
 from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
                          SupportsMultiModal, SupportsPP)
-from .utils import flatten_bn
 
 
 class GLMVImagePixelInputs(TensorSchema):
@@ -529,8 +528,9 @@ def get_replacement(item_idx: int):
 @MULTIMODAL_REGISTRY.register_processor(GLM4VMultiModalProcessor,
                                         info=GLM4VProcessingInfo,
                                         dummy_inputs=GLM4VDummyInputsBuilder)
-class GLM4VForCausalLM(ChatGLMBaseModel, SupportsLoRA, SupportsPP,
-                       SupportsMultiModal):
+class GLM4VForCausalLM(ChatGLMBaseModel, SupportsMultiModal, SupportsLoRA,
+                       SupportsPP):
+    merge_by_field_config = True
 
     packed_modules_mapping = {
         "query_key_value": ["query_key_value"],
@@ -574,14 +574,9 @@ def _parse_and_validate_image_input(
         pixel_values = kwargs.pop("pixel_values", None)
 
         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)}")
-
             expected_h = expected_w = self.config.vision_config["image_size"]
             return GLMVImagePixelInputs(type="pixel_values",
-                                        data=flatten_bn(pixel_values,
-                                                        concat=True),
+                                        data=pixel_values,
                                         resolve_bindings={
                                             "h": expected_h,
                                             "w": expected_w
@@ -598,6 +593,8 @@ def _process_image_input(
     def get_language_model(self) -> torch.nn.Module:
         return self.transformer
 
+    get_input_embeddings = SupportsMultiModal.get_input_embeddings
+
     def get_multimodal_embeddings(self,
                                   **kwargs: object) -> MultiModalEmbeddings:
         image_input = self._parse_and_validate_image_input(**kwargs)

vllm/model_executor/models/granite_speech.py

@@ -168,10 +168,8 @@ def _call_hf_processor(
             # Calculate the number of audio tokens per entry in the batch;
             # This is used to split the batch back out after padding.
             audio_token_index = self.info.get_hf_config().audio_token_index
-            processed_outputs["audio_embed_sizes"] = [
-                torch.sum(indices == audio_token_index).item()
-                for indices in processed_outputs["input_ids"]
-            ]
+            processed_outputs["audio_embed_sizes"] = (
+                processed_outputs["input_ids"] == audio_token_index).sum(-1)
 
         return processed_outputs
 
@@ -527,6 +525,7 @@ class GraniteSpeechForConditionalGeneration(
         SupportsPP,
         SupportsLoRA,
 ):
+    merge_by_field_config = True
 
     packed_modules_mapping = {
         "qkv_proj": [