[ET-VK][ez] Always partition batch norm as it will be fused

Differential Revision: D93511630

Pull Request resolved: #17508

Author: SS-JIA

backends/vulkan/op_registry.py

@@ -1233,25 +1233,11 @@ def register_embedding():
 
 @update_features(exir_ops.edge.aten._native_batch_norm_legit_no_training.default)
 def register_native_batch_norm_legit_no_training():
-    def check_batch_norm_node(node: torch.fx.Node) -> bool:
-        x = node.args[0]
-        if not isinstance(x, torch.fx.Node):
-            return False
-        x_val = x.meta.get("val", None)
-        if x_val is None:
-            return False
-        x_shape = x_val.size()
-        # Only support 4-D input tensors since this is a restriction enforced by the
-        # operator implementation.
-        # TODO(ssjia): Add shape agnostic support for batch norm
-        return len(x_shape) == 4
-
     return OpFeatures(
         inputs_storage=utils.CHANNELS_PACKED_TEXTURE,
         inputs_dtypes=utils.FP_T,
         supports_prepacking=True,
         supports_resize=True,
-        are_node_inputs_supported_fn=check_batch_norm_node,
     )
 
 

backends/vulkan/vulkan_preprocess.py

@@ -162,10 +162,10 @@ def preprocess(  # noqa: C901
         program = apply_passes(
             program,
             [
+                AddmmToLinearTransform(),
                 FuseBatchNormPass(program),
                 FusePatternsPass(),
                 FuseClampPass(),
-                AddmmToLinearTransform(),
                 RemoveRedundantOpsTransform(),
                 FuseQuantizedOpsTransform(),
                 FoldQDQPass(),