skip op in partitioning if there are bool input tensors

backends/vulkan/partitioner/vulkan_partitioner.py

@@ -61,7 +61,7 @@ def __init__(
         self.buffer_limit = buffer_limit
         self.require_dynamic_shapes = require_dynamic_shape
 
-    def op_node_is_compatible(
+    def op_node_is_compatible(  # noqa: C901: Function is too complex
         self, node: torch.fx.Node, features: Optional[OpFeatures] = None
     ) -> Tuple[bool, str]:
         """
@@ -98,8 +98,12 @@ def op_node_is_compatible(
                 and utils.is_tensor_node(arg)
                 and i not in features.skip_limits_check
             ):
+                # Check for bool inputs
+                if utils.tensor_node_is_bool(arg):
+                    return False, "contains bool tensor"
+
                 # Check for high dimensional tensors
-                if utils.is_tensor_node(arg) and utils.tensor_node_is_high_dim(arg):
+                if utils.tensor_node_is_high_dim(arg):
                     return False, "contains high dim tensor"
 
                 arg_texture_layouts = utils.possible_node_memory_layouts(

backends/vulkan/utils.py

@@ -80,6 +80,20 @@ def is_tensor_node(node: torch.fx.Node) -> bool:
     return False
 
 
+def tensor_node_is_bool(node: torch.fx.Node) -> bool:
+    """
+    Returns true if a given node contains a tensor with bool dtype
+    """
+    if isinstance(node.meta["val"], FakeTensor):
+        return node.meta["val"].dtype == torch.bool
+    if isinstance(node.meta["val"], list) or isinstance(node.meta["val"], tuple):
+        for fake_tensor in node.meta["val"]:
+            if isinstance(fake_tensor, FakeTensor):
+                if fake_tensor.dtype == torch.bool:
+                    return True
+    return False
+
+
 ##
 ## Memory Layout, Storage Type Determination
 ##