Add LeakyRelu and Sigmoid QLinear Quantization support (#5116)

* Add LeakyRelu and Sigmoid QLinear Quantization support

* Change due to reflect master changes.

onnxruntime/python/tools/quantization/operators/activation.py

@@ -1,5 +1,6 @@
 import onnx
 from .base_operator import QuantOperatorBase
+from ..quant_utils import QuantizedValue, QuantizedValueType, attribute_to_kwarg, ms_domain
 from onnx import onnx_pb as onnx_proto
 
 
@@ -7,7 +8,7 @@ class QLinearActivation(QuantOperatorBase):
     def __init__(self, onnx_quantizer, onnx_node):
         super().__init__(onnx_quantizer, onnx_node)
 
-    def quantize(self):
+    def QuantizeClipRelu(self):
         node = self.node
         assert (node.op_type == "Relu" or node.op_type == 'Clip')
 
@@ -20,3 +21,42 @@ class QLinearActivation(QuantOperatorBase):
 
         quantized_value = self.quantizer.quantized_value_map[node.input[0]]
         self.quantizer.quantized_value_map[node.output[0]] = quantized_value
+
+    def quantize(self):
+        node = self.node
+        if node.op_type == "Relu" or node.op_type == 'Clip':
+            self.QuantizeClipRelu()
+            return
+
+        # No assert on op_type as it is controlled by registry
+        # only try to quantize when given quantization parameters for it
+        data_found, output_scale_name, output_zp_name, _, _ = self.quantizer._get_quantization_params(node.output[0])
+        if not data_found:
+            super().quantize()
+            return
+
+        quantized_input_names, zero_point_names, scale_names, nodes = self.quantizer.quantize_inputs(node, [0])
+
+        qlinear_activation_output = node.output[0] + "_quantized"
+        qlinear_activation_name = ""
+        if node.name != "":
+            qlinear_activation_name = node.name + "_quant"
+        kwargs = {}
+        for attribute in node.attribute:
+            kwargs.update(attribute_to_kwarg(attribute))
+        kwargs["domain"] = ms_domain
+
+        qlinear_activation_inputs = [quantized_input_names[0], scale_names[0], zero_point_names[0], output_scale_name, output_zp_name]
+
+        qlinear_activation_node = onnx.helper.make_node(
+            "QLinear" + node.op_type, qlinear_activation_inputs,
+            [qlinear_activation_output], qlinear_activation_name, **kwargs)
+
+        # Create an entry for this quantized value
+        q_output = QuantizedValue(node.output[0], qlinear_activation_output, output_scale_name,
+                                  output_zp_name, QuantizedValueType.Input)
+        self.quantizer.quantized_value_map[node.output[0]] = q_output
+
+        nodes.append(qlinear_activation_node)
+        self.quantizer.new_nodes += nodes
+

onnxruntime/python/tools/quantization/registry.py

@@ -25,6 +25,8 @@ QLinearOpsRegistry = {
     "Mul": QLinearBinaryOp,
     "Relu": QLinearActivation,
     "Clip": QLinearActivation,
+    "LeakyRelu" : QLinearActivation,
+    "Sigmoid" : QLinearActivation,
     "MaxPool": QMaxPool,
 }
 QLinearOpsRegistry.update(CommonOpsRegistry)