chore: import upstream snapshot with attribution

test/quantization/test_observer_convert.py

@@ -0,0 +1,90 @@
+# 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 os
+import tempfile
+import unittest
+
+import paddle
+from paddle.nn import Linear, Sequential
+from paddle.quantization import PTQ, QuantConfig
+from paddle.quantization.observers import (
+    AbsmaxObserver,
+    GroupWiseWeightObserver,
+)
+
+
+class LinearDygraph(paddle.nn.Layer):
+    def __init__(self):
+        super().__init__()
+        self.fc = Sequential(
+            Linear(128, 128), Linear(128, 128), Linear(128, 128)
+        )
+
+    def forward(self, inputs):
+        out = self.fc(inputs)
+        return out
+
+
+class TestPTQGroupWise(unittest.TestCase):
+    def setUp(self):
+        self.temp_dir = tempfile.TemporaryDirectory()
+        self.path = os.path.join(self.temp_dir.name, 'ptq')
+
+    def tearDown(self):
+        self.temp_dir.cleanup()
+
+    def _get_model_for_ptq_groupwise(self):
+        observer = GroupWiseWeightObserver(quant_bits=4, group_size=128)
+        model = LinearDygraph()
+        model.eval()
+        q_config = QuantConfig(activation=None, weight=observer)
+        ptq = PTQ(q_config)
+        quant_model = ptq.quantize(model)
+        inputs = paddle.rand([128, 128], dtype="float32")
+        out = model(inputs)
+        return quant_model, ptq
+
+    def _get_model_for_ptq_absmax(self):
+        observer = AbsmaxObserver(quant_bits=8)
+        model = LinearDygraph()
+        model.eval()
+        q_config = QuantConfig(activation=observer, weight=observer)
+        ptq = PTQ(q_config)
+        quant_model = ptq.quantize(model)
+        inputs = paddle.rand([128, 128], dtype="float32")
+        out = model(inputs)
+        return quant_model, ptq
+
+    def test_quantize(self):
+        ptq_model, ptq = self._get_model_for_ptq_groupwise()
+        inputs = paddle.rand([128, 128], dtype="float32")
+        out = ptq_model(inputs)
+        self.assertIsNotNone(out)
+        converted_model = ptq.convert(ptq_model)
+        out = converted_model(inputs)
+        self.assertIsNotNone(out)
+
+    def test_quantize_absmax(self):
+        ptq_model, ptq = self._get_model_for_ptq_absmax()
+        inputs = paddle.rand([128, 128], dtype="float32")
+        out = ptq_model(inputs)
+        self.assertIsNotNone(out)
+        converted_model = ptq.convert(ptq_model)
+        out = converted_model(inputs)
+        self.assertIsNotNone(out)
+
+
+if __name__ == '__main__':
+    unittest.main()