Make Relu quantization non-shared

Author: roman-janik-nxp

backends/nxp/quantizer/patterns.py

@@ -121,6 +121,24 @@ def get_anchors(
         )
 
 
+class SingleInputBasicPattern(QuantizationPattern):
+    @abstractmethod
+    def partition_types(self) -> list[OpOverload]:
+        pass
+
+    def get_anchors(
+            self, gm: fx.GraphModule, fused_partition: list[fx.GraphModule]
+    ) -> PartitionAnchors | None:
+        node = fused_partition[0].nodes[-1]
+
+        return PartitionAnchors(
+            inputs=[(node, NodeArgsIdx(0))],
+            weights=[],
+            biases=[],
+            output=[(node,)],
+        )
+
+
 def get_anchors_for_fixed_quant_specs(
     fused_partition: list[fx.GraphModule],
     scale: float,
@@ -376,7 +394,7 @@ def partition_types(self):
         return [torch.ops.aten.flatten.using_ints]
 
 
-class HardTanhPattern(QuantizationPattern):
+class HardTanhPattern(SingleInputBasicPattern):
     """
     Quantizer for HardTanh operator. Shared quantization spec is selected, as activation functions usually follows
     computation layer.
@@ -385,23 +403,12 @@ class HardTanhPattern(QuantizationPattern):
     def partition_types(self):
         return [torch.ops.aten.hardtanh.default]
 
-    def get_anchors(
-        self, gm: fx.GraphModule, fused_partition: list[fx.GraphModule]
-    ) -> PartitionAnchors | None:
-        node = fused_partition[0].nodes[-1]
-
-        return PartitionAnchors(
-            inputs=[(node, NodeArgsIdx(0))],
-            weights=[],
-            biases=[],
-            output=[(node,)],
-        )
 
     def replacement_op(self):
         raise AssertionError()
 
 
-class HardTanhInPlacePattern(QuantizationPattern):
+class HardTanhInPlacePattern(SingleInputBasicPattern):
     """
     Quantizer for HardTanh operator with param inplace=True. Shared quantization spec is selected, as activation
     functions usually follows computation layer.
@@ -410,18 +417,6 @@ class HardTanhInPlacePattern(QuantizationPattern):
     def partition_types(self):
         return [torch.ops.aten.hardtanh_.default]
 
-    def get_anchors(
-        self, gm: fx.GraphModule, fused_partition: list[fx.GraphModule]
-    ) -> PartitionAnchors | None:
-        node = fused_partition[0].nodes[-1]
-
-        return PartitionAnchors(
-            inputs=[(node, NodeArgsIdx(0))],
-            weights=[],
-            biases=[],
-            output=[(node,)],
-        )
-
     def replacement_op(self):
         raise AssertionError()
 
@@ -513,19 +508,18 @@ def partition_types(self):
         return [torch.ops.aten.permute.default]
 
 
-class ReluPattern(SharedSpecPattern):
+class ReluPattern(SingleInputBasicPattern):
     """
-    Quantizer for Relu operator. Shared quantization spec is selected, as ReLU usually follows computation layer.
+    Quantizer for Relu operator.
     """
 
     def partition_types(self):
         return [torch.ops.aten.relu.default]
 
 
-class ReluInPlacePattern(SharedSpecPattern):
+class ReluInPlacePattern(SingleInputBasicPattern):
     """
-    Quantizer for Relu operator with param inplace=True. Shared quantization spec is selected, as ReLU usually
-    follows computation layer.
+    Quantizer for Relu operator with param inplace=True.
     """
 
     def partition_types(self):