Arm backend: Prioritise FuseBatchnorm2D over DecomposeBatchNormPass (pytorch#8140)

As it is preferable to fuse batch_norm over decomposing it, prioritize this pass higher

Signed-off-by: Tom Allsop <[email protected]>

Author: tom-arm

backends/arm/_passes/arm_pass_manager.py

@@ -123,6 +123,7 @@ def _tosa_080_MI_pipeline(self, exported_program: ExportedProgram) -> GraphModul
         self.add_pass(FuseQuantizedActivationPass())
         self.add_pass(RemoveGetItemPass())
         self.add_pass(ConvertSplitToSlicePass())
+        self.add_pass(FuseBatchnorm2DPass(exported_program))
         self.add_pass(ConvertMmToBmmPass())
         self.add_pass(DecomposeLinearPass())
         self.add_pass(DecomposeBatchNormPass())
@@ -132,7 +133,6 @@ def _tosa_080_MI_pipeline(self, exported_program: ExportedProgram) -> GraphModul
         self.add_pass(ConvertMeanDimToAveragePoolPass())
         self.add_pass(DecomposeDivPass())
         self.add_pass(DecomposeSoftmaxesPass())
-        self.add_pass(FuseBatchnorm2DPass(exported_program))
 
         self.add_pass(AnnotateDecomposedMatmulPass())
         self.add_pass(QuantizeOperatorArguments())