Fix ordering

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

vllm/multimodal/inputs.py

@@ -225,8 +225,8 @@ def _build_elem(
     @abstractmethod
     def build_elems(
         self,
-        key: str,
         modality: str,
+        key: str,
         data: NestedTensors,
     ) -> Sequence[MultiModalFieldElem]:
         """
@@ -277,11 +277,11 @@ class MultiModalBatchedField(BaseMultiModalField):
 
     def build_elems(
         self,
-        key: str,
         modality: str,
+        key: str,
         data: NestedTensors,
     ) -> Sequence[MultiModalFieldElem]:
-        return [self._build_elem(key, modality, item) for item in data]
+        return [self._build_elem(modality, key, item) for item in data]
 
     def _reduce_data(self, batch: list[NestedTensors]) -> NestedTensors:
         if len(batch) > 0 and is_list_of(batch, torch.Tensor, check="all"):
@@ -320,11 +320,11 @@ class MultiModalFlatField(BaseMultiModalField):
 
     def build_elems(
         self,
-        key: str,
         modality: str,
+        key: str,
         data: NestedTensors,
     ) -> Sequence[MultiModalFieldElem]:
-        return [self._build_elem(key, modality, data[s]) for s in self.slices]
+        return [self._build_elem(modality, key, data[s]) for s in self.slices]
 
     def _reduce_data(self, batch: list[NestedTensors]) -> NestedTensors:
         if len(batch) > 0 and is_list_of(batch, torch.Tensor, check="all"):
@@ -365,11 +365,11 @@ class MultiModalSharedField(BaseMultiModalField):
 
     def build_elems(
         self,
-        key: str,
         modality: str,
+        key: str,
         data: NestedTensors,
     ) -> Sequence[MultiModalFieldElem]:
-        return [self._build_elem(key, modality, data)] * self.batch_size
+        return [self._build_elem(modality, key, data)] * self.batch_size
 
     def _reduce_data(self, batch: list[NestedTensors]) -> NestedTensors:
         return batch[0]