chore: import upstream snapshot with attribution

python/paddle/nn/quant/qat/linear.py

@@ -0,0 +1,60 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from paddle.nn import functional as F
+
+from ...layer.layers import Layer
+from ..format import ConvertibleQuantedLayer
+
+
+class QuantedLinear(ConvertibleQuantedLayer):
+    """
+    The computational logic of QuantizedLinear is the same as Linear.
+    The only difference is that its inputs are all fake quantized.
+    """
+
+    def __init__(self, layer: Layer, q_config):
+        super().__init__()
+        # For Linear
+        self.weight = layer.weight
+        self.bias = layer.bias
+        self.name = layer.name
+        # For FakeQuant
+
+        self.weight_quanter = None
+        self.activation_quanter = None
+        if q_config.weight is not None:
+            self.weight_quanter = q_config.weight._instance(layer)
+        if q_config.activation is not None:
+            self.activation_quanter = q_config.activation._instance(layer)
+
+    def forward(self, input):
+        quant_input = input
+        quant_weight = self.weight
+        if self.activation_quanter is not None:
+            quant_input = self.activation_quanter(input)
+        if self.weight_quanter is not None:
+            quant_weight = self.weight_quanter(self.weight)
+        return self._linear_forward(quant_input, quant_weight)
+
+    def _linear_forward(self, input, weight):
+        out = F.linear(x=input, weight=weight, bias=self.bias, name=self.name)
+        return out
+
+    def weights_to_quanters(self):
+        return [('weight', 'weight_quanter')]
+
+    def activation_quanters(self):
+        return ['activation_quanter']