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)
| 78 | 'Unexpected Quantization property.') |
| 79 | |
| 80 | def insert_dequantize_node( |
| 81 | self, graph: BaseGraph, |
| 82 | var: Variable, config: TensorQuantizationConfig, |
| 83 | op: Operation) -> Operation: |
| 84 | """ |
| 85 | Insert a DeQuantize Node on given variable, according to given TensorQuantizationConfig. |
| 86 | There is two basic type of DeQuantize Node: DeQuantizeLinear and DeQuantizeFloating. |
| 87 | """ |
| 88 | if config.policy.has_property(QuantizationProperty.LINEAR): |
| 89 | offset_dtype, value_type = self.infer_qtype(config) |
| 90 | scale = convert_any_to_torch_tensor(config.scale.clone(), dtype=torch.float32) |
| 91 | offset = ppq_tensor_round(config.offset.clone()).type(offset_dtype) |
| 92 | |
| 93 | created = graph.create_operation(op_type='DequantizeLinear', attributes={}) |
| 94 | if config.policy.has_property(QuantizationProperty.PER_CHANNEL): |
| 95 | created.attributes['axis'] = config.channel_axis |
| 96 | else: created.attributes['axis'] = 0 |
| 97 | |
| 98 | if var in op.inputs: graph.insert_op_before(A=created, B=op, input_idx=op.inputs.index(var)) |
| 99 | elif var in op.outputs: graph.insert_op_after(A=created, B=op, output_idx=op.outputs.index(var)) |
| 100 | else: raise ValueError(f'Unexpected Error in Exporting Op {op.name}({op.type}).') |
| 101 | |
| 102 | graph.create_variable(name=None, value=scale, is_parameter=True, dest_ops=[created]) |
| 103 | graph.create_variable(name=None, value=offset, is_parameter=True, dest_ops=[created]) |
| 104 | |
| 105 | created.inputs[0].dtype = value_type |
| 106 | created.inputs[0].shape = var.shape |
| 107 | created.outputs[0].shape = var.shape |
| 108 | created.outputs[0].dtype = torch.float32 |
| 109 | |
| 110 | return created |
| 111 | |
| 112 | elif config.policy.has_property(QuantizationProperty.FLOATING): |
| 113 | scale = convert_any_to_torch_tensor(config.scale.clone(), dtype=torch.float32) |
| 114 | offset = convert_any_to_torch_tensor(config.offset.clone(), dtype=torch.float32) |
| 115 | |
| 116 | created = graph.create_operation( |
| 117 | op_type='DequantizeFloating', |
| 118 | attributes={ |
| 119 | 'min': config.quant_min, |
| 120 | 'max': config.quant_max, |
| 121 | 'exponent': config.exponent_bits, |
| 122 | 'mantissa': config.mantissa_bits}) |
| 123 | |
| 124 | if config.policy.has_property(QuantizationProperty.PER_CHANNEL): |
| 125 | created.attributes['axis'] = config.channel_axis |
| 126 | else: created.attributes['axis'] = None |
| 127 | |
| 128 | if var in op.inputs: graph.insert_op_before(A=created, B=op, input_idx=op.inputs.index(var)) |
| 129 | elif var in op.outputs: graph.insert_op_after(A=created, B=op, output_idx=op.outputs.index(var)) |
| 130 | else: raise ValueError(f'Unexpected Error in Exporting Op {op.name}({op.type}).') |
| 131 | |
| 132 | graph.create_variable(name=None, value=scale, is_parameter=True, dest_ops=[created]) |
| 133 | graph.create_variable(name=None, value=offset, is_parameter=True, dest_ops=[created]) |
| 134 | |
| 135 | created.outputs[0].shape = var.shape |
| 136 | created.inputs[0].shape = var.shape |
| 137 |
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