Migrate Mistral3ImagePixelInputs to TensorSchema

vllm/model_executor/models/mistral3.py

@@ -3,7 +3,7 @@
 
 from abc import abstractmethod
 from collections.abc import Iterable, Mapping, Sequence
-from typing import (Final, Literal, Optional, Protocol, TypedDict, TypeVar,
+from typing import (Annotated, Final, Literal, Optional, Protocol, TypeVar,
                     Union)
 
 import torch
@@ -32,6 +32,7 @@
                                         PromptUpdateDetails)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
 from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
                          SupportsMultiModal, SupportsPP)
@@ -42,16 +43,24 @@
 from .vision import get_vision_encoder_info
 
 
-class Mistral3ImagePixelInputs(TypedDict):
-    type: Literal["pixel_values_pixtral"]
-    pixel_values: Union[torch.Tensor, list[torch.Tensor]]
+class Mistral3ImagePixelInputs(TensorSchema):
     """
-    Shape: `(batch_size * num_images, num_channels, height, width)`
-
-    Note that `height` or `width` may be different per batch and image,
-    in which case the data is passed as a list instead of a batched tensor.
+    Dimensions:
+        - bn: Batch size * number of images
+        - c: Number of channels (3)
+        - h: Height of each image
+        - w: Width of each image
     """
 
+    type: Literal["pixel_values_pixtral"] = "pixel_values_pixtral"
+
+    # Note that `height` or `width` may be different per batch and image,
+    # in which case the data is passed as a list instead of a batched tensor.
+    pixel_values: Annotated[
+        Union[torch.Tensor, list[torch.Tensor]],
+        TensorShape("bn", 3, "h", "w", dynamic_dims={"h", "w"}),
+    ]
+
 
 class Mistral3PatchMerger(nn.Module):
     """
@@ -456,19 +465,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
         self.make_empty_intermediate_tensors = (
             self.language_model.make_empty_intermediate_tensors)
 
-    def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
-        h = w = self.config.vision_config.image_size
-        expected_dims = (3, h, w)
-        actual_dims = tuple(data.shape[1:])
-
-        if actual_dims != expected_dims:
-            expected_expr = ("batch_size", *map(str, expected_dims))
-            raise ValueError(
-                f"The expected shape of pixel values is {expected_expr}. "
-                f"You supplied {tuple(data.shape)}.")
-
-        return data
-
     def _parse_and_validate_image_input(
             self, **kwargs: object) -> Optional[Mistral3ImagePixelInputs]:
         pixel_values = kwargs.pop("pixel_values", None)