chore: import upstream snapshot with attribution

test/quantization/test_stub.py

@@ -0,0 +1,64 @@
+# copyright (c) 2023 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.
+
+import unittest
+
+import paddle
+from paddle.nn import Conv2D
+from paddle.nn.quant import Stub
+from paddle.quantization import QAT, QuantConfig
+from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
+from paddle.quantization.quanters.abs_max import (
+    FakeQuanterWithAbsMaxObserverLayer,
+)
+
+quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
+
+
+class Model(paddle.nn.Layer):
+    def __init__(self, num_classes=10):
+        super().__init__()
+        self.quant_in = Stub()
+        self.conv = Conv2D(3, 6, 3, stride=1, padding=1)
+        self.quant = Stub(quanter)
+        self.quant_out = Stub()
+
+    def forward(self, inputs):
+        out = self.conv(inputs)
+        out = self.quant(out)
+        out = paddle.nn.functional.relu(out)
+        return self.quant_out(out)
+
+
+class TestStub(unittest.TestCase):
+    def test_stub(self):
+        model = Model()
+        q_config = QuantConfig(activation=quanter, weight=quanter)
+        qat = QAT(q_config)
+        q_config.add_layer_config(model.quant_in, activation=None, weight=None)
+        quant_model = qat.quantize(model)
+        image = paddle.rand([1, 3, 32, 32], dtype="float32")
+        out = model(image)
+        out = quant_model(image)
+        out.backward()
+
+        quanter_count = 0
+        for _layer in quant_model.sublayers(True):
+            if isinstance(_layer, FakeQuanterWithAbsMaxObserverLayer):
+                quanter_count += 1
+        self.assertEqual(quanter_count, 5)
+
+
+if __name__ == '__main__':
+    unittest.main()