Add mixed dtype check for XNNPACK partitioner (pytorch#9612)

### Summary
Fixes pytorch#9023
Prevents the partitioner from handling ops with mixed dtypes.

### Test plan
Unable to directly test due to auto-casting of dtypes and existing dtype
checks in verifier.py.

Author: keyprocedure

backends/xnnpack/partition/config/xnnpack_config.py

@@ -144,9 +144,10 @@ def check_common_constraints(
         return True
 
     def _check_inputs_are_valid_dtypes(self, node, valid_dtypes):
-        # Check inputs are valid dtypes
+        # Check inputs are valid and have the same dtypes
         # Gather all args which are nodes
         args_to_check = []
+        reference_dtype = None
         for arg in node.args:
             if isinstance(arg, list) or isinstance(arg, tuple):
                 for item in arg:
@@ -174,11 +175,32 @@ def _check_inputs_are_valid_dtypes(self, node, valid_dtypes):
             if arg_val.dtype not in valid_dtypes:
                 return False
 
+            # Use the first dtype as reference
+            reference_dtype = reference_dtype or arg_val.dtype
+
+            # Check for mixed dtypes
+            if arg_val.dtype != reference_dtype:
+                # Get op name if the attribute exists, otherwise use the full node target for logging
+                op_name = (
+                    node.target.__name__
+                    if hasattr(node.target, "__name__")
+                    else str(node.target)
+                )
+                why(
+                    node,
+                    reason=(
+                        f"{op_name} does not support mixed input dtypes, "
+                        f"got: [{reference_dtype}, {arg_val.dtype}]"
+                    ),
+                )
+                return False
+
         return True
 
     def _check_outputs_are_valid_dtypes(self, node, valid_dtypes):
-        # Check outputs are valid dtype
+        # Check outputs are valid
         node_val = node.meta.get("val", None)
+
         if node_val is None:
             return True