add layout opt rule for group norm

PiperOrigin-RevId: 712372815

Author: chunnienc

ai_edge_torch/_convert/fx_passes/optimize_layout_transposes_pass/layout_check.py

@@ -155,6 +155,7 @@ def _qdq_layout_sensitive_inputs_getter(node: Node):
 @layout_sensitive_inputs_getters.register(
     aten._native_batch_norm_legit_no_training
 )
+@layout_sensitive_inputs_getters.register(aten.group_norm)
 @layout_sensitive_inputs_getters.register(aten.native_group_norm)
 def _first_arg_getter(node):
   return [node.args[0]]
@@ -188,6 +189,14 @@ def _aten_norm_checker(node):
   return NHWCable(can_be=len(val[0].shape) == 4, must_be=False)
 
 
+@nhwcable_node_checkers.register(aten.group_norm)
+def _aten_group_norm_checker(node):
+  val = node.meta.get("val")
+  if not hasattr(val, "shape"):
+    return NHWCable(can_be=False, must_be=False)
+  return NHWCable(can_be=len(val.shape) == 4, must_be=False)
+
+
 @nhwcable_node_checkers.register(aten.native_group_norm)
 def _aten_native_group_norm_checker(node):
   val = node.meta.get("val")

ai_edge_torch/_convert/fx_passes/optimize_layout_transposes_pass/layout_rewrite.py

@@ -342,6 +342,18 @@ def batch_norm(input, weight, bias, running_mean, running_var, momentum, eps):
   node.target = batch_norm
 
 
+@rewriters.register(aten.group_norm.default)
+def _aten_group_norm(node):
+  def group_norm(input, num_groups: int, weight=None, bias=None, eps=1e-5):
+    # Disable NHWC rewriter with native decomposied ops due to precision issue.
+    # TODO(b/354780253): Re-enable NHWC rewriter with proper lowering.
+    input = utils.tensor_to_nchw(input)
+    res = aten.group_norm.default(input, num_groups, weight, bias, eps=eps)
+    return utils.tensor_to_nhwc(res)
+
+  node.target = group_norm
+
+
 @rewriters.register(aten.native_group_norm.default)
 def _aten_native_group_norm(node):
 
@@ -354,6 +366,7 @@ def native_group_norm(
       flattened_inner_size: int,
       num_groups: int,
       eps: float,
+      **kwargs,
   ):
     input_reshaped = torch.reshape(
         input,