[VLM] Cleanup validation and update docs (#6149)

vllm/model_executor/models/phi3v.py

@@ -263,7 +263,8 @@ class Phi3VImagePixelInputs(TypedDict):
     """
     Shape: `(batch_size, 1 + num_patches, num_channels, height, width)`
 
-    Note that `num_patches` may be different for each batch.
+    Note that `num_patches` may be different for each batch, in which case
+    the data is passed as a list instead of a batched tensor.
     """
 
     image_sizes: torch.Tensor
@@ -466,8 +467,8 @@ class Phi3VForCausalLM(nn.Module, SupportsVision):
     def _validate_image_sizes(self, data: torch.Tensor) -> torch.Tensor:
         if list(data.shape[1:]) != [2]:
             raise ValueError(
-                f"The expected image sizes shape is batch dimension plus "
-                f"{[2]}. You supplied {data.shape}.")
+                f"The expected shape of image sizes is batch dimension plus "
+                f"{[2]}. You supplied {tuple(data.shape)}.")
 
         return data
 
@@ -475,19 +476,20 @@ class Phi3VForCausalLM(nn.Module, SupportsVision):
         self, data: Union[torch.Tensor, List[torch.Tensor]]
     ) -> Union[torch.Tensor, List[torch.Tensor]]:
 
-        def _validate_shape(data: torch.Tensor):
-            if list(data.shape)[2:] != [
-                    3, CLIP_VIT_LARGE_PATCH14_336_CONFIG.image_size,
-                    CLIP_VIT_LARGE_PATCH14_336_CONFIG.image_size
-            ]:
-                raise ValueError(
-                    "The expected pixel value tensor shape is batch dimension "
-                    "plus patch number, channel, height and width.")
+        h = w = CLIP_VIT_LARGE_PATCH14_336_CONFIG.image_size
+        expected_dims = (3, h, w)
 
-        if isinstance(data, torch.Tensor):
-            _validate_shape(data)
-        else:
-            [_validate_shape(d) for d in data]
+        def _validate_shape(d: torch.Tensor):
+            actual_dims = tuple(d.shape[1:])
+
+            if actual_dims != expected_dims:
+                expected_expr = ("num_patches", *map(str, expected_dims))
+                raise ValueError(
+                    "The expected shape of pixel values in each batch element "
+                    f"is {expected_expr}. You supplied {tuple(d.shape)}.")
+
+        for d in data:
+            _validate_shape(d)
 
         return data