Insert a DeQuantize Node on given variable, according to given TensorQuantizationConfig. There is two basic type of DeQuantize Node: DeQuantizeLinear and DeQuantizeFloating.
(
self, graph: BaseGraph,
var: Variable, config: TensorQuantizationConfig,
op: Operation)
| 145 | 'Unexpected Quantization property.') |
| 146 | |
| 147 | def insert_dequantize_node( |
| 148 | self, graph: BaseGraph, |
| 149 | var: Variable, config: TensorQuantizationConfig, |
| 150 | op: Operation) -> Operation: |
| 151 | """ |
| 152 | Insert a DeQuantize Node on given variable, according to given TensorQuantizationConfig. |
| 153 | There is two basic type of DeQuantize Node: DeQuantizeLinear and DeQuantizeFloating. |
| 154 | """ |
| 155 | if config.policy.has_property(QuantizationProperty.LINEAR): |
| 156 | offset_dtype, value_type = self.infer_qtype(config) |
| 157 | scale = convert_any_to_torch_tensor(config.scale.clone(), dtype=torch.float32) |
| 158 | offset = ppq_tensor_round(config.offset.clone()).type(offset_dtype) |
| 159 | |
| 160 | created = graph.create_operation(op_type='DequantizeLinear', attributes={}) |
| 161 | if config.policy.has_property(QuantizationProperty.PER_CHANNEL): |
| 162 | created.attributes['axis'] = config.channel_axis |
| 163 | else: created.attributes['axis'] = None |
| 164 | |
| 165 | if var in op.inputs: graph.insert_op_before(A=created, B=op, input_idx=op.inputs.index(var)) |
| 166 | elif var in op.outputs: graph.insert_op_after(A=created, B=op, output_idx=op.outputs.index(var)) |
| 167 | else: raise ValueError(f'Unexpected Error in Exporting Op {op.name}({op.type}).') |
| 168 | |
| 169 | graph.create_variable(name=None, value=scale, is_parameter=True, dest_ops=[created]) |
| 170 | graph.create_variable(name=None, value=offset, is_parameter=True, dest_ops=[created]) |
| 171 | |
| 172 | created.inputs[0].dtype = value_type |
| 173 | created.inputs[0].shape = var.shape |
| 174 | created.outputs[0].shape = var.shape |
| 175 | created.outputs[0].dtype = torch.float32 |
| 176 | |
| 177 | return created |
| 178 | |
| 179 | elif config.policy.has_property(QuantizationProperty.FLOATING): |
| 180 | scale = convert_any_to_torch_tensor(config.scale.clone(), dtype=torch.float32) |
| 181 | offset = convert_any_to_torch_tensor(config.offset.clone(), dtype=torch.float32) |
| 182 | |
| 183 | created = graph.create_operation( |
| 184 | op_type='DequantizeFloating', |
| 185 | attributes={ |
| 186 | 'min': config.quant_min, |
| 187 | 'max': config.quant_max, |
| 188 | 'exponent': config.exponent_bits, |
| 189 | 'mantissa': config.mantissa_bits}) |
| 190 | |
| 191 | if config.policy.has_property(QuantizationProperty.PER_CHANNEL): |
| 192 | created.attributes['axis'] = config.channel_axis |
| 193 | else: created.attributes['axis'] = None |
| 194 | |
| 195 | if var in op.inputs: graph.insert_op_before(A=created, B=op, input_idx=op.inputs.index(var)) |
| 196 | elif var in op.outputs: graph.insert_op_after(A=created, B=op, output_idx=op.outputs.index(var)) |
| 197 | else: raise ValueError(f'Unexpected Error in Exporting Op {op.name}({op.type}).') |
| 198 | |
| 199 | graph.create_variable(name=None, value=scale, is_parameter=True, dest_ops=[created]) |
| 200 | graph.create_variable(name=None, value=offset, is_parameter=True, dest_ops=[created]) |
| 201 | |
| 202 | created.outputs[0].shape = var.shape |
| 203 | created.inputs[0].shape = var.shape |
| 204 |
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