Fix equality checking

Signed-off-by: DarkLight1337 <[email protected]>

vllm/multimodal/inputs.py

@@ -108,6 +108,18 @@ class PlaceholderRange(TypedDict):
 Uses a list instead of a tensor if the dimensions of each element do not match.
 """
 
+
+def nested_tensors_equal(a: NestedTensors, b: NestedTensors) -> bool:
+    """Equality check between :data:`NestedTensors` objects."""
+    if isinstance(a, torch.Tensor):
+        return isinstance(b, torch.Tensor) and bool((a == b).all().item())
+    if isinstance(b, torch.Tensor):
+        return isinstance(a, torch.Tensor) and bool((b == a).all().item())
+
+    return (len(a) == len(b)
+            and all(nested_tensors_equal(a_, b_) for a_, b_ in zip(a, b)))
+
+
 BatchedTensorInputs: TypeAlias = dict[str, NestedTensors]
 """
 A dictionary containing nested tensors which have been batched via
@@ -122,6 +134,14 @@ class MultiModalFieldItem:
     modality: str
     data: NestedTensors
 
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, self.__class__):
+            return False
+
+        a_tupl = (type(self.field), self.modality)
+        b_tupl = (type(other.field), other.modality)
+        return a_tupl == b_tupl and nested_tensors_equal(self.data, other.data)
+
 
 class MultiModalField(ABC):
     """Represents a field in :class:`MultiModalKwargs`."""
@@ -356,6 +376,16 @@ def as_kwargs(
 
         return cast(BatchedTensorInputs, json_mapped)
 
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, self.__class__):
+            return False
+        if self._items_by_key != other._items_by_key:
+            return False
+
+        ks = self.keys()
+        return (ks == other.keys()
+                and all(nested_tensors_equal(self[k], other[k]) for k in ks))
+
     def get_item(self, key: str, item_index: int) -> MultiModalFieldItem:
         return self._items_by_key[key][item_index]