wehub-resource-sync
61 lines
2.1 KiB
Python
61 lines
2.1 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from paddle.nn import functional as F
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from ...layer.layers import Layer
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from ..format import ConvertibleQuantedLayer
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class QuantedLinear(ConvertibleQuantedLayer):
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"""
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The computational logic of QuantizedLinear is the same as Linear.
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The only difference is that its inputs are all fake quantized.
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"""
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def __init__(self, layer: Layer, q_config):
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super().__init__()
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# For Linear
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self.weight = layer.weight
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self.bias = layer.bias
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self.name = layer.name
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# For FakeQuant
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self.weight_quanter = None
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self.activation_quanter = None
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if q_config.weight is not None:
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self.weight_quanter = q_config.weight._instance(layer)
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if q_config.activation is not None:
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self.activation_quanter = q_config.activation._instance(layer)
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def forward(self, input):
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quant_input = input
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quant_weight = self.weight
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if self.activation_quanter is not None:
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quant_input = self.activation_quanter(input)
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if self.weight_quanter is not None:
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quant_weight = self.weight_quanter(self.weight)
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return self._linear_forward(quant_input, quant_weight)
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def _linear_forward(self, input, weight):
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out = F.linear(x=input, weight=weight, bias=self.bias, name=self.name)
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return out
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def weights_to_quanters(self):
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return [('weight', 'weight_quanter')]
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def activation_quanters(self):
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return ['activation_quanter']
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