chore: import upstream snapshot with attribution

test/quantization/test_qat.py

@@ -0,0 +1,100 @@
+# 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.
+
+import unittest
+
+import numpy as np
+
+import paddle
+import paddle.nn.functional as F
+from paddle.io import Dataset
+from paddle.nn import Conv2D, Linear, ReLU, Sequential
+from paddle.quantization import QAT, QuantConfig
+from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
+from paddle.quantization.quanters.abs_max import (
+    FakeQuanterWithAbsMaxObserverLayer,
+)
+
+
+class RandomDataset(Dataset):
+    def __init__(self, num_samples):
+        self.num_samples = num_samples
+
+    def __getitem__(self, idx):
+        data = np.random.random([3, 32, 32]).astype('float32')
+        return data
+
+    def __len__(self):
+        return self.num_samples
+
+
+class Model(paddle.nn.Layer):
+    def __init__(self, num_classes=10):
+        super().__init__()
+        self.num_classes = num_classes
+        self.features = Sequential(
+            Conv2D(3, 6, 3, stride=1, padding=1),
+            ReLU(),
+            paddle.nn.MaxPool2D(2, stride=2),
+            Conv2D(6, 16, 5, stride=1, padding=0),
+            ReLU(),
+            paddle.nn.MaxPool2D(2, stride=2),
+        )
+
+        if num_classes > 0:
+            self.fc = Sequential(
+                Linear(576, 120), Linear(120, 84), Linear(84, 10)
+            )
+
+    def forward(self, inputs):
+        x = self.features(inputs)
+        if self.num_classes > 0:
+            x = paddle.flatten(x, 1)
+            x = self.fc(x)
+        out = F.relu(x)
+        return out
+
+
+class TestQAT(unittest.TestCase):
+    def test_qat(self):
+        nums_batch = 100
+        batch_size = 32
+        dataset = RandomDataset(nums_batch * batch_size)
+        loader = paddle.io.DataLoader(
+            dataset,
+            batch_size=batch_size,
+            shuffle=False,
+            drop_last=True,
+            num_workers=0,
+        )
+        model = Model()
+        quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
+        q_config = QuantConfig(activation=quanter, weight=quanter)
+        qat = QAT(q_config)
+        print(model)
+        quant_model = qat.quantize(model)
+        print(quant_model)
+        quanter_count = 0
+        for _layer in quant_model.sublayers(True):
+            if isinstance(_layer, FakeQuanterWithAbsMaxObserverLayer):
+                quanter_count += 1
+        self.assertEqual(quanter_count, 14)
+
+        for _, data in enumerate(loader):
+            out = quant_model(data)
+            out.backward()
+
+
+if __name__ == '__main__':
+    unittest.main()