From 7cf5a48191daf69e77d547510656bf47fc67596d Mon Sep 17 00:00:00 2001
From: Ekaterina Ignasheva <eigen@meta.com>
Date: Thu, 5 Feb 2026 15:12:02 -0800
Subject: [PATCH] Unify fusion passes. (#17251)

Summary:

Group fusion passes together.

Differential Revision: D92339098
---
 backends/cadence/aot/fuse_ops.py | 39 ++++++++++++++++++++------------
 1 file changed, 25 insertions(+), 14 deletions(-)

diff --git a/backends/cadence/aot/fuse_ops.py b/backends/cadence/aot/fuse_ops.py
index d8265e6ab86..78d00637225 100644
--- a/backends/cadence/aot/fuse_ops.py
+++ b/backends/cadence/aot/fuse_ops.py
@@ -203,8 +203,9 @@ class FuseBatchNormWithConv(ExportPass):
     in the graph.
     """
 
-    def fuse_batch_norm_with_conv(self, graph_module: torch.fx.GraphModule) -> None:
+    def fuse_batch_norm_with_conv(self, graph_module: torch.fx.GraphModule) -> bool:
         graph = graph_module.graph
+        modified = False
         for conv in graph.nodes:
             # We want to discover a chain of conv1d -> batch_norm.
             # Only proceed if the current node is a conv1d node, and has a single
@@ -252,6 +253,8 @@ def fuse_batch_norm_with_conv(self, graph_module: torch.fx.GraphModule) -> None:
             assert isinstance(running_var, torch.Tensor)
             eps = bn.args[-1]
 
+            modified = True
+
             # Compute the updated weight and bias after fusing conv op
             # with batchnorm op.
             fused_weight, fused_bias = fuse_conv_bn_weights(
@@ -287,16 +290,18 @@ def fuse_batch_norm_with_conv(self, graph_module: torch.fx.GraphModule) -> None:
                 user.replace_all_uses_with(conv)
             self.counter += 1
 
-        graph_module.recompile()
+        if modified:
+            graph_module.recompile()
+        return modified
 
     def __init__(self):
         super().__init__()
         self.counter = 0
 
     def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
-        self.fuse_batch_norm_with_conv(graph_module)
+        modified = self.fuse_batch_norm_with_conv(graph_module)
         result = super().call(graph_module)
-        return result
+        return PassResult(result.graph_module, result.modified & modified)
 
 
 @register_cadence_pass(CadencePassAttribute(opt_level=1))
@@ -310,8 +315,9 @@ class FuseQuantizedBatchNormWithConv(ExportPass):
 
     def fuse_quantized_batch_norm_with_conv(
         self, graph_module: torch.fx.GraphModule
-    ) -> None:
+    ) -> bool:
         graph = graph_module.graph
+        modified = False
         for conv in graph.nodes:
             # We want to discover a chain of quantized::conv1d ->
             # quantized::batch_norm. Only proceed if the current node is a
@@ -386,6 +392,8 @@ def fuse_quantized_batch_norm_with_conv(
             assert isinstance(running_mean_tensor, torch.Tensor)
             assert isinstance(running_var_tensor, torch.Tensor)
 
+            modified = True
+
             # Get the fused weights and bias
             fused_weight, fused_bias = fuse_conv_bn_weights(
                 conv_weight_tensor,
@@ -439,22 +447,25 @@ def fuse_quantized_batch_norm_with_conv(
             graph.erase_node(bn)
             self.counter += 1
 
-        # Note: there is a quantized.conv2d.new operator in the resulting graph
-        # that takes a torch.classes.quantized.Conv2dPackedParamsBase as one of the input
-        # this prevents us to directly call graph_module.recompile().
-        # pyre-fixme[16]: `GraphModule` has no attribute `_code`.
-        # pyre-fixme[16]: Item `Tensor` of `Tensor | Module` has no attribute
-        #  `python_code`.
-        graph_module._code = graph_module._graph.python_code(root_module="self").src
+        if modified:
+            # Note: there is a quantized.conv2d.new operator in the resulting graph
+            # that takes a torch.classes.quantized.Conv2dPackedParamsBase as one of the input
+            # this prevents us to directly call graph_module.recompile().
+            # pyre-fixme[16]: `GraphModule` has no attribute `_code`.
+            # pyre-fixme[16]: Item `Tensor` of `Tensor | Module` has no attribute
+            #  `python_code`.
+            graph_module._code = graph_module._graph.python_code(root_module="self").src
+
+        return modified
 
     def __init__(self):
         super().__init__()
         self.counter = 0
 
     def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
-        self.fuse_quantized_batch_norm_with_conv(graph_module)
+        modified = self.fuse_quantized_batch_norm_with_conv(graph_module)
         result = super().call(graph_module)
-        return result
+        return PassResult(result.graph_module, result.modified & modified)
 
 
 @register_cadence_pass(CadencePassAttribute(opt_level=1))