68 lines
2.2 KiB
Python
68 lines
2.2 KiB
Python
import onnx
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from ..quant_utils import TENSOR_NAME_QUANT_SUFFIX, QuantizedValue, QuantizedValueType, attribute_to_kwarg, ms_domain
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from .base_operator import QuantOperatorBase
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class QLinearPool(QuantOperatorBase):
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def __init__(self, onnx_quantizer, onnx_node):
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super().__init__(onnx_quantizer, onnx_node)
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def quantize(self):
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node = self.node
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# only try to quantize when given quantization parameters for it
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(
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data_found,
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output_scale_name,
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output_zp_name,
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_,
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_,
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) = self.quantizer._get_quantization_params(node.output[0])
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# get quantized input tensor names, quantize input if needed
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(
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quantized_input_names,
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input_zero_point_names,
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input_scale_names,
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nodes,
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) = self.quantizer.quantize_activation(node, [0])
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if not data_found or quantized_input_names is None:
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return super().quantize()
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# Create an entry for output quantized value.
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qlinear_output_name = node.output[0] + TENSOR_NAME_QUANT_SUFFIX
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quantized_output_value = QuantizedValue(
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node.output[0],
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qlinear_output_name,
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output_scale_name,
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output_zp_name,
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QuantizedValueType.Input,
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)
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self.quantizer.quantized_value_map[node.output[0]] = quantized_output_value
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# Create qlinear pool node for given type (AveragePool, etc)
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kwargs = {}
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for attribute in node.attribute:
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kwargs.update(attribute_to_kwarg(attribute))
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kwargs["domain"] = ms_domain
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qlinear_node_name = node.name + "_quant" if node.name else ""
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qnode = onnx.helper.make_node(
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"QLinear" + node.op_type,
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[
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quantized_input_names[0],
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input_scale_names[0],
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input_zero_point_names[0],
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output_scale_name,
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output_zp_name,
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],
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[qlinear_output_name],
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qlinear_node_name,
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**kwargs,
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)
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# add all newly created nodes
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nodes.append(qnode)
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self.quantizer.new_nodes += nodes
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