chore: import upstream snapshot with attribution

python/paddle/quantization/wrapper.py

@@ -0,0 +1,48 @@
+# 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 Layer
+
+from .base_quanter import BaseQuanter
+
+
+class ObserveWrapper(Layer):
+    r"""
+    Put an observer layer and an observed layer into a wrapping layer.
+    It is used to insert layers into the model for QAT or PTQ.
+    Args:
+        observer(BaseQuanter): Observer layer
+        observed(Layer): Observed layer
+        observe_input(bool): If it is true the observer layer will be called before observed layer.
+            If it is false the observed layer will be called before observer layer. Default: True.
+    """
+
+    def __init__(
+        self,
+        observer: BaseQuanter,
+        observed: Layer,
+        observe_input=True,
+    ):
+        super().__init__()
+        self._observer = observer
+        self._observed = observed
+        self._observe_input = observe_input
+
+    def forward(self, *inputs, **kwargs):
+        if self._observe_input:
+            out = self._observer(*inputs, **kwargs)
+            return self._observed(out, **kwargs)
+        else:
+            out = self._observed(*inputs, **kwargs)
+            return self._observer(out, **kwargs)