101 lines
4.7 KiB
Python
101 lines
4.7 KiB
Python
import onnx
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from ..quant_utils import (
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TENSOR_NAME_QUANT_SUFFIX,
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QuantizedValue,
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QuantizedValueType,
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attribute_to_kwarg,
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quantize_nparray,
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)
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from .base_operator import QuantOperatorBase
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class QPad(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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assert node.op_type == "Pad"
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# Only after version 11, it has the optional constant_value
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# If input[0] is not quantized, do not quanitize this node
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if (self.quantizer.opset_version < 11) or (node.input[0] not in self.quantizer.quantized_value_map):
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super().quantize()
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return
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quantized_input_value = self.quantizer.quantized_value_map[node.input[0]]
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kwargs = {}
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for attribute in node.attribute:
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kv = attribute_to_kwarg(attribute)
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kwargs.update(kv)
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if "mode" not in kwargs or kwargs["mode"] == b"constant":
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if len(node.input) > 2 and node.input[2] != "": # There is 3rd input 'constant_value'
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zp_tensor = self.quantizer.model.get_initializer(quantized_input_value.zp_name)
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scale_tensor = self.quantizer.model.get_initializer(quantized_input_value.scale_name)
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if zp_tensor is None or scale_tensor is None:
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super().quantize()
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return
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padding_constant_initializer = self.quantizer.model.get_initializer(node.input[2])
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if padding_constant_initializer is not None:
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zp_array = onnx.numpy_helper.to_array(zp_tensor)
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zp_value = zp_array.item() if zp_array.ndim == 0 else zp_array[0]
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scale_array = onnx.numpy_helper.to_array(scale_tensor)
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scale_value = scale_array.item() if scale_array.ndim == 0 else scale_array[0]
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padding_constant_array = onnx.numpy_helper.to_array(padding_constant_initializer)
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quantized_padding_constant_array = quantize_nparray(
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self.quantizer.activation_qType,
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padding_constant_array,
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scale_value,
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zp_value,
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)
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quantized_padding_constant_name = node.input[2] + TENSOR_NAME_QUANT_SUFFIX
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quantized_padding_constant_initializer = onnx.numpy_helper.from_array(
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quantized_padding_constant_array,
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quantized_padding_constant_name,
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)
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# Suppose this padding constant initializer only used by the node
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self.quantizer.model.remove_initializer(padding_constant_initializer)
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self.quantizer.model.add_initializer(quantized_padding_constant_initializer)
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node.input[2] = quantized_padding_constant_name
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else:
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# TODO: check quantize_inputs after sub graph is supported
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pad_value_qnodes = self.quantizer._get_quantize_input_nodes(
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node,
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2,
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self.quantizer.activation_qType,
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quantized_input_value.scale_name,
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quantized_input_value.zp_name,
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initial_type=scale_tensor.data_type,
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)
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self.quantizer.new_nodes.extend(pad_value_qnodes)
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node.input[2] = pad_value_qnodes[0].output[0]
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else:
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# In quantized format, the `zero` before quantization is mapped
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# to quantized_input_value.zp_name. Thus, padding 0 to
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# original tensor should become padding zero point to quantized
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# tensor.
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if len(node.input) == 2:
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# Feed quantization's zero point to padding node.
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node.input.append(quantized_input_value.zp_name)
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else:
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# Assign quantization's zero point to padding node.
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assert node.input[2] == ""
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node.input[2] = quantized_input_value.zp_name
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# Create an entry for output quantized value
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quantized_output_value = QuantizedValue(
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node.output[0],
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node.output[0] + TENSOR_NAME_QUANT_SUFFIX,
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quantized_input_value.scale_name,
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quantized_input_value.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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node.input[0] = quantized_input_value.q_name
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node.output[0] = quantized_output_value.q_name
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self.quantizer.new_nodes += [node]
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