[CI/Build] Improve Tensor Schema tests speed by avoid engine core initialization (#23357)

Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>

vllm/model_executor/models/granite_speech.py

@@ -549,7 +549,7 @@ class GraniteSpeechForConditionalGeneration(
 
         raise ValueError("Only audio modality is supported")
 
-    def __init__(self, *, vllm_config: VllmConfig, prefix: str):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config = vllm_config.model_config.hf_config
         quant_config = vllm_config.quant_config

vllm/model_executor/models/mllama.py

@@ -1371,7 +1371,8 @@ class MllamaForConditionalGeneration(nn.Module, SupportsMultiModal,
                 output_tensor[i, :t.size(0)] = t
             return output_tensor
 
-    def _parse_and_validate_image_input(self, **kwargs: object):
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[MllamaImagePixelInputs]:
         # tensor with the same shape will be batched together by
         # MultiModalKwargs.batch, so pixel_values here can be:
         #   - list[torch.Tensor]:

vllm/model_executor/models/ovis.py

@@ -209,7 +209,7 @@ class OvisImagePatchInputs(TypedDict):
     `(batch_size * num_patches, patch_size_x * patch_size_y * num_channels)`
     """
 
-    inducator_tokens: torch.Tensor
+    indicator_tokens: torch.Tensor
     """
     Shape: 
     `(batch_size * (num_patches + 1))`

vllm/model_executor/models/ovis2_5.py

@@ -3,7 +3,7 @@
 """ PyTorch Ovis model."""
 from collections.abc import Iterable, Mapping
 from functools import partial
-from typing import Optional, Union
+from typing import Literal, Optional, TypedDict, Union
 
 import torch
 import torch.nn as nn
@@ -50,6 +50,27 @@ IMAGE_PAD_TOKEN_ID_MAP = {
 }
 
 
+class OvisVideoPatchInputs(TypedDict):
+    type: Literal["video_patches"]
+    flat_data: torch.Tensor
+    """
+    Shape:
+    `(batch_size * num_patches, patch_size_x * patch_size_y * num_channels)`
+    """
+
+    indicator_tokens: torch.Tensor
+    """
+    Shape:
+    `(batch_size * (num_patches + 1))`
+    """
+
+    patches_per_image: list[int]
+    """
+    List of number of total patches for each frame in the video.
+    This is used to restore the first two dimensions of `flat_data`.
+    """
+
+
 def _ovis2_5_field_config():
     return dict(pixel_values=MultiModalFieldConfig.batched("image"),
                 grids=MultiModalFieldConfig.batched("image"),
@@ -429,17 +450,11 @@ class Ovis2_5(nn.Module, SupportsMultiModal, SupportsPP):
         self.make_empty_intermediate_tensors = (
             self.get_language_model().make_empty_intermediate_tensors)
 
-    def _parse_and_validate_visual_input(
-            self, is_video,
-            **kwargs: object) -> Optional[OvisImagePatchInputs]:
-        if is_video:
-            pixel_values = kwargs.pop("video_pixel_values", None)
-            indicator_tokens = kwargs.pop("video_indicator_tokens", None)
-            grids = kwargs.pop("video_grids", None)
-        else:
-            pixel_values = kwargs.pop("pixel_values", None)
-            indicator_tokens = kwargs.pop("indicator_tokens", None)
-            grids = kwargs.pop("grids", None)
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[OvisImagePatchInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        indicator_tokens = kwargs.pop("indicator_tokens", None)
+        grids = kwargs.pop("grids", None)
         if pixel_values is None and indicator_tokens is None:
             return None
 
@@ -466,8 +481,40 @@ class Ovis2_5(nn.Module, SupportsMultiModal, SupportsPP):
 
         raise AssertionError("This line should be unreachable.")
 
+    def _parse_and_validate_video_input(
+            self, **kwargs: object) -> Optional[OvisImagePatchInputs]:
+        pixel_values = kwargs.pop("video_pixel_values", None)
+        indicator_tokens = kwargs.pop("video_indicator_tokens", None)
+        grids = kwargs.pop("video_grids", None)
+        if pixel_values is None and indicator_tokens is None:
+            return None
+
+        if pixel_values is not None and indicator_tokens is not 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(indicator_tokens, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of indicator_tokens. "
+                                 f"Got type: {type(indicator_tokens)}")
+
+            return OvisVideoPatchInputs(
+                type="video_patches",
+                flat_data=flatten_bn(flatten_bn(pixel_values), concat=True),
+                patches_per_image=[
+                    x.shape[0] // (self.config.vit_config.hidden_stride**2)
+                    for x in flatten_bn(pixel_values)
+                ],
+                indicator_tokens=flatten_bn(flatten_bn(indicator_tokens),
+                                            concat=True),
+                grids=flatten_bn(flatten_bn(grids), concat=True),
+            )
+
+        raise AssertionError("This line should be unreachable.")
+
     def _process_image_input(
-            self, image_input: OvisImagePatchInputs) -> MultiModalEmbeddings:
+        self, image_input: Union[OvisImagePatchInputs, OvisVideoPatchInputs]
+    ) -> MultiModalEmbeddings:
         image_patches_flat = image_input["flat_data"]
         patches_per_image = image_input["patches_per_image"]
         indicator_tokens = image_input["indicator_tokens"]
@@ -500,21 +547,44 @@ class Ovis2_5(nn.Module, SupportsMultiModal, SupportsPP):
                 torch.cat(vision_embeddings_per_image, dim=0))
         return tuple(vision_embeddings)
 
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
-        embeddings = []
+    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
+        modalities = {}
 
-        # NOTE: _parse_and_validate_visual_input has side-effects and pops
-        # keys from kwargs. We process images first, then videos.
-        image_input = self._parse_and_validate_visual_input(False, **kwargs)
-        if image_input:
-            embeddings.extend(self._process_image_input(image_input))
+        # Preserve the order of modalities if there are multiple of them
+        # from the order of kwargs.
+        for input_key in kwargs:
+            if input_key in ("pixel_values", "indicator_tokens",
+                             "grids") and "images" not in modalities:
+                modalities["images"] = self._parse_and_validate_image_input(
+                    **kwargs)
+            if input_key in ("video_pixel_values", "video_indicator_tokens",
+                             "video_grids") and "videos" not in modalities:
+                modalities["videos"] = self._parse_and_validate_video_input(
+                    **kwargs)
 
-        video_input = self._parse_and_validate_visual_input(True, **kwargs)
-        if video_input:
-            embeddings.extend(self._process_image_input(video_input))
+        return modalities
 
-        return tuple(embeddings) if embeddings else None
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
+
+        modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
+        if not modalities:
+            return []
+
+        multimodal_embeddings: tuple[torch.Tensor, ...] = ()
+        # NOTE: It is important to iterate over the keys in this dictionary
+        # to preserve the order of the modalities.
+        for modality in modalities:
+            if modality == "images":
+                image_input = modalities["images"]
+                vision_embeddings = self._process_image_input(image_input)
+                multimodal_embeddings += vision_embeddings
+            if modality == "videos":
+                video_input = modalities["videos"]
+                video_embeddings = self._process_image_input(video_input)
+                multimodal_embeddings += video_embeddings
+
+        return multimodal_embeddings
 
     def get_input_embeddings(
         self,

vllm/model_executor/models/phi4mm.py

@@ -1031,8 +1031,8 @@ class Phi4MMForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):
         ]
         return audio_embeds
 
-    def _parse_and_validate_image_input(self,
-                                        **kwargs: object) -> Optional[dict]:
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[Phi4MMImagePixelInputs]:
         input_image_embeds: NestedTensors = kwargs.get("input_image_embeds")
         if input_image_embeds is None:
             return None