|
|
from ..quant_utils import TENSOR_NAME_QUANT_SUFFIX, QuantizedValue, QuantizedValueTypefrom .base_operator import QuantOperatorBasefrom .qdq_base_operator import QDQOperatorBase
# For operators that support 8bits operations directly, and output could# reuse input[0]'s type, zeropoint, scale; For example,Transpose, Reshape, etc.class Direct8BitOp(QuantOperatorBase): def __init__(self, onnx_quantizer, onnx_node): super().__init__(onnx_quantizer, onnx_node)
def quantize(self): node = self.node
if not self.quantizer.force_quantize_no_input_check: # Keep backward compatiblity # Quantize when input[0] is quantized already. Otherwise keep it. quantized_input_value = self.quantizer.find_quantized_value(node.input[0]) if quantized_input_value is None: self.quantizer.new_nodes += [node] return
quantized_output_value = QuantizedValue( node.output[0], node.output[0] + TENSOR_NAME_QUANT_SUFFIX, quantized_input_value.scale_name, quantized_input_value.zp_name, quantized_input_value.value_type, ) 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]
else: # Force quantize those ops if possible, use exclude node list if this is not you want if not self.quantizer.is_valid_quantize_weight(node.input[0]): super().quantize() return
( quantized_input_names, zero_point_names, scale_names, nodes, ) = self.quantizer.quantize_activation(node, [0]) if quantized_input_names is None: return super().quantize()
# Create an entry for output quantized value quantized_output_value = QuantizedValue( node.output[0], node.output[0] + TENSOR_NAME_QUANT_SUFFIX, scale_names[0], zero_point_names[0], QuantizedValueType.Input, ) self.quantizer.quantized_value_map[node.output[0]] = quantized_output_value
node.input[0] = quantized_input_names[0] node.output[0] = quantized_output_value.q_name nodes.append(node)
self.quantizer.new_nodes += nodes
class QDQDirect8BitOp(QDQOperatorBase): def __init__(self, onnx_quantizer, onnx_node): super().__init__(onnx_quantizer, onnx_node)
def quantize(self): if self.quantizer.force_quantize_no_input_check: self.quantizer.quantize_activation_tensor(self.node.input[0]) if not self.disable_qdq_for_node_output: self.quantizer.quantize_activation_tensor(self.node.output[0], self.node.input[0]) elif self.quantizer.is_tensor_quantized(self.node.input[0]) and not self.disable_qdq_for_node_output: self.quantizer.quantize_activation_tensor(self.node.output[0], self.node.input[0])
|
