Migrate Pixtral inputs to TensorSchema (#23472)

Signed-off-by: Benji Beck <benjibeck@meta.com>

vllm/model_executor/models/pixtral.py

@@ -5,7 +5,7 @@ import math
 from collections.abc import Iterable, Mapping, Sequence
 from dataclasses import dataclass, fields
 from functools import cached_property
-from typing import Literal, Optional, TypedDict, Union
+from typing import Annotated, Literal, Optional, Union
 
 import torch
 import torch.nn as nn
@@ -48,6 +48,7 @@ from vllm.platforms import current_platform
 from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.tokenizer import (MistralTokenizer,
                                                cached_tokenizer_from_config)
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
 from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
 from .utils import (flatten_bn, init_vllm_registered_model, maybe_prefix,
@@ -68,15 +69,20 @@ except ImportError:
 PATCH_MERGE = "patch_merge"
 
 
-class PixtralImagePixelInputs(TypedDict):
+class PixtralImagePixelInputs(TensorSchema):
-    type: Literal["pixel_values"]
-
-    images: Union[torch.Tensor, list[torch.Tensor]]
     """
-    Shape: `(batch_size * num_images, num_channels, image_width, image_height)`
+    Dimensions:
-
+        - bn: Batch size * number of images
+        - c: Number of channels (3)
+        - h: Height of each image
+        - w: Width of each image
+    
     The result of stacking `ImageEncoding.tokens` from each prompt.
     """
+    type: Literal["pixel_values"] = "pixel_values"
+
+    images: Annotated[Union[torch.Tensor, list[torch.Tensor]],
+                      TensorShape("bn", 3, "h", "w", dynamic_dims={"h", "w"})]
 
 
 class PixtralProcessorAdapter:
@@ -381,10 +387,6 @@ class PixtralForConditionalGeneration(nn.Module, SupportsMultiModal,
         if images is None:
             return None
 
-        if not isinstance(images, (torch.Tensor, list)):
-            raise ValueError("Incorrect type of images. "
-                             f"Got type: {type(images)}")
-
         return PixtralImagePixelInputs(
             type="pixel_values",
             images=flatten_bn(images),