CnOCRService/Lib/site-packages/onnxruntime/quantization/operators/pad.py

import numpy as np
import onnx

from ..quant_utils import (
    TENSOR_NAME_QUANT_SUFFIX,
    QuantizedValue,
    QuantizedValueType,
    attribute_to_kwarg,
    quantize_nparray,
)
from .base_operator import QuantOperatorBase


class QPad(QuantOperatorBase):
    def __init__(self, onnx_quantizer, onnx_node):
        super().__init__(onnx_quantizer, onnx_node)

    def quantize(self):
        node = self.node
        assert node.op_type == "Pad"

        # Only after version 11, it has the optional constant_value
        # If input[0] is not quantized, do not quanitize this node
        if (self.quantizer.opset_version < 11) or (node.input[0] not in self.quantizer.quantized_value_map):
            super().quantize()
            return
        quantized_input_value = self.quantizer.quantized_value_map[node.input[0]]

        kwargs = {}
        for attribute in node.attribute:
            kv = attribute_to_kwarg(attribute)
            kwargs.update(kv)

        if "mode" not in kwargs or kwargs["mode"] == b"constant":
            if len(node.input) > 2:  # There is 3rd input 'constant_value'
                zp_tensor = self.quantizer.model.get_initializer(quantized_input_value.zp_name)
                scale_tensor = self.quantizer.model.get_initializer(quantized_input_value.scale_name)
                if zp_tensor is None or scale_tensor is None:
                    super().quantize()
                    return

                padding_constant_initializer = self.quantizer.model.get_initializer(node.input[2])
                if padding_constant_initializer is not None:
                    zp_array = onnx.numpy_helper.to_array(zp_tensor)
                    zp_value = zp_array.item() if zp_array.ndim == 0 else zp_array[0]
                    scale_array = onnx.numpy_helper.to_array(scale_tensor)
                    scale_value = scale_array.item() if scale_array.ndim == 0 else scale_array[0]
                    padding_constant_array = onnx.numpy_helper.to_array(padding_constant_initializer)
                    quantized_padding_constant_array = quantize_nparray(
                        self.quantizer.activation_qType,
                        padding_constant_array,
                        scale_value,
                        zp_value,
                    )
                    quantized_padding_constant_name = node.input[2] + TENSOR_NAME_QUANT_SUFFIX
                    quantized_padding_constant_initializer = onnx.numpy_helper.from_array(
                        quantized_padding_constant_array,
                        quantized_padding_constant_name,
                    )
                    # Suppose this padding constant initializer only used by the node
                    self.quantizer.model.remove_initializer(padding_constant_initializer)
                    self.quantizer.model.add_initializer(quantized_padding_constant_initializer)
                    node.input[2] = quantized_padding_constant_name
                else:
                    # TODO: check quantize_inputs after sub graph is supported
                    pad_value_qnodes = self.quantizer._get_quantize_input_nodes(
                        node,
                        2,
                        self.quantizer.activation_qType,
                        quantized_input_value.scale_name,
                        quantized_input_value.zp_name,
                    )
                    self.quantizer.new_nodes.extend(pad_value_qnodes)
                    node.input[2] = pad_value_qnodes[0].output[0]
            else:
                node.input.extend([quantized_input_value.zp_name])  # pad zero_point for original zero

        # Create an entry for output quantized value
        quantized_output_value = QuantizedValue(
            node.output[0],
            node.output[0] + TENSOR_NAME_QUANT_SUFFIX,
            quantized_input_value.scale_name,
            quantized_input_value.zp_name,
            QuantizedValueType.Input,
        )
        self.quantizer.quantized_value_map[node.output[0]] = quantized_output_value

        node.input[0] = quantized_input_value.q_name
        node.output[0] = quantized_output_value.q_name
        self.quantizer.new_nodes += [node]