[Bugfix] Fix MiniCPMV Image input inference failed (#22813)

Signed-off-by: HWH <67449739+jio-H@users.noreply.github.com>
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
Signed-off-by: Cyrus Leung <cyrus.tl.leung@gmail.com>
Co-authored-by: DarkLight1337 <tlleungac@connect.ust.hk>
Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>

vllm/model_executor/models/minicpmv.py

@@ -85,6 +85,23 @@ class MiniCPMVImagePixelInputs(TensorSchema):
         - w: Width
     """
 
+    def _validate_nested_tensors(
+        self,
+        value: Union[list[torch.Tensor], tuple[torch.Tensor, ...]],
+        field_name: str,
+        expected_shape: tuple[Union[int, str], ...],
+        dynamic_dims: set[str],
+    ) -> tuple[int, ...]:
+        # value[0] is the scaled image,
+        # and value[1:] is a collection of image slices.
+        # It is ensured that all slices in the collection
+        # have the same shape.
+        if field_name == "pixel_values":
+            value = value[1:] if len(value) > 1 else value
+
+        return super()._validate_nested_tensors(value, field_name,
+                                                expected_shape, dynamic_dims)
+
     type: Literal["pixel_values"] = "pixel_values"
 
     # Note that the image size may vary, so we pass it as a list instead of a

vllm/utils/tensor_schema.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from typing import Annotated, Any, Union, get_args, get_origin, get_type_hints
+from typing import (Annotated, Any, Optional, Union, get_args, get_origin,
+                    get_type_hints)
 
 import torch
 
@@ -11,9 +12,13 @@ logger = init_logger(__name__)
 
 class TensorShape:
 
-    def __init__(self,
-                 *dims: Union[int, str],
-                 dynamic_dims: set[str, ...] = None) -> None:
+    def __init__(
+        self,
+        *dims: Union[int, str],
+        dynamic_dims: Optional[set[str]] = None,
+    ) -> None:
+        super().__init__()
+
         self.dims = dims
         self.dynamic_dims = dynamic_dims if dynamic_dims else set()
 
@@ -44,11 +49,15 @@ class TensorShape:
 
 class TensorSchema:
 
-    def __init__(self,
-                 *,
-                 validate: bool = True,
-                 resolve_bindings: dict[str, int] = None,
-                 **kwargs: Any) -> None:
+    def __init__(
+        self,
+        *,
+        validate: bool = True,
+        resolve_bindings: Optional[dict[str, int]] = None,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__()
+
         self._resolve_bindings = resolve_bindings if resolve_bindings else {}
 
         for key, value in kwargs.items():
@@ -57,16 +66,19 @@ class TensorSchema:
         if validate:
             self.validate()
 
-    def __getitem__(self, item) -> Any:
-        return getattr(self, item)
+    def __getitem__(self, key: str) -> Any:
+        return getattr(self, key)
 
-    def get(self, item, default=None) -> Any:
-        return getattr(self, item, default)
+    def get(self, key: str, default: Any = None) -> Any:
+        return getattr(self, key, default)
 
-    def _match_shape_with_dynamic(self, actual: tuple[int, ...],
-                                  reference: tuple[int, ...],
-                                  expected_shape: tuple[Union[int, str], ...],
-                                  dynamic_dims: set[str, ...]) -> bool:
+    def _match_shape_with_dynamic(
+        self,
+        actual: tuple[int, ...],
+        reference: tuple[int, ...],
+        expected_shape: tuple[Union[int, str], ...],
+        dynamic_dims: set[str],
+    ) -> bool:
         if len(actual) != len(reference) or len(actual) > len(expected_shape):
             return False
 
@@ -84,10 +96,12 @@ class TensorSchema:
         return True
 
     def _validate_nested_tensors(
-            self, value: Union[list[torch.Tensor, ...],
-                               tuple[torch.Tensor, ...]], field_name: str,
-            expected_shape: tuple[Union[int, str], ...],
-            dynamic_dims: set[str, ...]) -> tuple[int, ...]:
+        self,
+        value: Union[list[torch.Tensor], tuple[torch.Tensor, ...]],
+        field_name: str,
+        expected_shape: tuple[Union[int, str], ...],
+        dynamic_dims: set[str],
+    ) -> tuple[int, ...]:
         """Validate a list/tuple of tensors and return the actual shape."""
         # Ensure all tensors in the list have the same
         # shape, besides dynamic dimensions
@@ -110,12 +124,14 @@ class TensorSchema:
         # shape = (len(list), *tensor.shape)
         return (len(value), ) + first.shape
 
-    def _validate_tensor_shape_expected(self, actual_shape: tuple[int, ...],
-                                        expected_shape: tuple[Union[int, str],
-                                                              ...],
-                                        field_name: str, shape_env: dict[str,
-                                                                         int],
-                                        dynamic_dims: set[str, ...]) -> None:
+    def _validate_tensor_shape_expected(
+        self,
+        actual_shape: tuple[int, ...],
+        expected_shape: tuple[Union[int, str], ...],
+        field_name: str,
+        shape_env: dict[str, int],
+        dynamic_dims: set[str],
+    ) -> None:
         """Validate that the actual tensor shape matches the expected shape."""
 
         if len(actual_shape) != len(expected_shape):