import onnx from onnx import onnx_pb as onnx_proto from ..quant_utils import TENSOR_NAME_QUANT_SUFFIX, QuantizedValue, QuantizedValueType, attribute_to_kwarg, ms_domain from .base_operator import QuantOperatorBase class QLinearBinaryOp(QuantOperatorBase): def __init__(self, onnx_quantizer, onnx_node): super().__init__(onnx_quantizer, onnx_node) def quantize(self): node = self.node ( data_found, output_scale_name, output_zp_name, _, _, ) = self.quantizer._get_quantization_params(node.output[0]) ( quantized_input_names, zero_point_names, scale_names, nodes, ) = self.quantizer.quantize_activation(node, [0, 1]) if not data_found or quantized_input_names is None: return super().quantize() qlinear_binary_math_output = node.output[0] + TENSOR_NAME_QUANT_SUFFIX qlinear_binary_math_name = node.name + "_quant" if node.name != "" else "" kwargs = {} for attribute in node.attribute: kwargs.update(attribute_to_kwarg(attribute)) kwargs["domain"] = ms_domain qlinear_binary_math_inputs = [] # Input 0 qlinear_binary_math_inputs.append(quantized_input_names[0]) qlinear_binary_math_inputs.append(scale_names[0]) qlinear_binary_math_inputs.append(zero_point_names[0]) # Input 1 qlinear_binary_math_inputs.append(quantized_input_names[1]) qlinear_binary_math_inputs.append(scale_names[1]) qlinear_binary_math_inputs.append(zero_point_names[1]) # Output qlinear_binary_math_inputs.append(output_scale_name) qlinear_binary_math_inputs.append(output_zp_name) qlinear_binary_math_node = onnx.helper.make_node( "QLinear" + node.op_type, qlinear_binary_math_inputs, [qlinear_binary_math_output], qlinear_binary_math_name, **kwargs, ) nodes.append(qlinear_binary_math_node) # Create an entry for this quantized value q_output = QuantizedValue( node.output[0], qlinear_binary_math_output, output_scale_name, output_zp_name, QuantizedValueType.Input, ) self.quantizer.quantized_value_map[node.output[0]] = q_output self.quantizer.new_nodes += nodes