NXP backend: Improve quantization annotation process

- fixes multiple subsequent nodes with SharedSpecPattern problem

Author: roman-janik-nxp

backends/nxp/quantizer/neutron_quantizer.py

@@ -10,7 +10,6 @@
 from executorch.backends.nxp.aten_passes.neutron_aten_pass_manager import (
     NeutronAtenPassManager,
 )
-
 from executorch.backends.nxp.quantizer.patterns import (
     AddmmPattern,
     AvgPoolPattern,
@@ -24,6 +23,7 @@
     ReluInPlacePattern,
     ReluPattern,
     ReshapePattern,
+    SharedSpecPattern,
     SoftMaxPattern,
     ViewPattern,
 )
@@ -204,9 +204,36 @@ def __init__(self):
                 NeutronAtenQuantizer(ViewPattern(), static_qconfig),
             ]
         )
+        # Mapping ops defined in quantizer partition types to its quantizer
+        self.op_to_quantizer = {
+            pt: q for q in self.quantizers for pt in q.pattern.partition_types()
+        }
+        # Mapping ops to the quantizer application state
+        self.op_to_applied_quantizer = {
+            pt: False for q in self.quantizers for pt in q.pattern.partition_types()
+        }
 
     def transform_for_annotation(
         self, model: torch.fx.GraphModule
     ) -> torch.fx.GraphModule:
         pass_runner = NeutronAtenPassManager()
         return pass_runner(model).graph_module
+
+    def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule:
+        nodes = list(model.graph.nodes)
+        for node in nodes:
+            if (
+                node.target not in self.op_to_quantizer
+                or self.op_to_applied_quantizer[node.target]
+            ):
+                continue
+            else:
+                quantizer = self.op_to_quantizer[node.target]
+                quantizer.annotate(model)
+                if not isinstance(quantizer.pattern, SharedSpecPattern):
+                    self.op_to_applied_quantizer[node.target] = True
+
+        return model
+
+    def validate(self, model: torch.fx.GraphModule) -> None:
+        return super().validate(model)