chore: import upstream snapshot with attribution

test/quantization/test_ptq.py

@@ -0,0 +1,297 @@
+# 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 numpy as np
+
+import paddle
+import paddle.nn.functional as F
+from paddle.nn import Conv2D, Linear, ReLU, Sequential
+from paddle.nn.quant.format import LinearDequanter, LinearQuanter
+from paddle.quantization import PTQ, QuantConfig
+from paddle.quantization.observers import AbsmaxObserver
+from paddle.quantization.observers.abs_max import AbsmaxObserverLayer
+
+
+class LeNetDygraph(paddle.nn.Layer):
+    def __init__(self, num_classes=10):
+        super().__init__()
+        self.num_classes = num_classes
+        self.features = Sequential(
+            Conv2D(1, 6, 3, stride=1, padding=1),
+            ReLU(),
+            paddle.nn.MaxPool2D(2, 2),
+            Conv2D(6, 16, 5, stride=1, padding=0),
+            ReLU(),
+            paddle.nn.MaxPool2D(2, 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 TestPTQ(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(self):
+        observer = AbsmaxObserver(quant_bits=8)
+        model = LeNetDygraph()
+        model.eval()
+        q_config = QuantConfig(activation=observer, weight=observer)
+        ptq = PTQ(q_config)
+        quant_model = ptq.quantize(model)
+        return quant_model, ptq
+
+    def _count_layers(self, model, layer_type):
+        count = 0
+        for _layer in model.sublayers(True):
+            if isinstance(_layer, layer_type):
+                count += 1
+        return count
+
+    def test_quantize(self):
+        ptq_model, _ = self._get_model_for_ptq()
+        image = paddle.rand([1, 1, 32, 32], dtype="float32")
+        out = ptq_model(image)
+        self.assertIsNotNone(out)
+
+        observer_count = self._count_layers(ptq_model, AbsmaxObserverLayer)
+        self.assertEqual(observer_count, 14)
+
+    def test_convert(self):
+        quant_model, ptq = self._get_model_for_ptq()
+
+        image = paddle.rand([1, 1, 32, 32], dtype="float32")
+        out = quant_model(image)
+        converted_model = ptq.convert(quant_model)
+        out = converted_model(image)
+        self.assertIsNotNone(out)
+
+        observer_count = self._count_layers(
+            converted_model, AbsmaxObserverLayer
+        )
+        quanter_count = self._count_layers(converted_model, LinearQuanter)
+        dequanter_count = self._count_layers(converted_model, LinearDequanter)
+        self.assertEqual(observer_count, 0)
+        self.assertEqual(dequanter_count, 14)
+        self.assertEqual(quanter_count, 9)
+
+        save_path = os.path.join(self.temp_dir.name, 'int8_infer')
+        paddle.jit.save(converted_model, save_path, [image])
+
+        paddle.enable_static()
+        exe = paddle.static.Executor(paddle.CPUPlace())
+        main_program = paddle.static.Program()
+        startup_program = paddle.static.Program()
+        with paddle.static.program_guard(main_program, startup_program):
+            [
+                inference_program,
+                feed_target_names,
+                fetch_targets,
+            ] = paddle.static.load_inference_model(save_path, exe)
+        tensor_img = np.array(
+            np.random.random((1, 1, 32, 32)), dtype=np.float32
+        )
+        results = exe.run(
+            inference_program,
+            feed={feed_target_names[0]: tensor_img},
+            fetch_list=fetch_targets,
+        )
+        self.assertIsNotNone(results)
+        paddle.disable_static()
+
+    def test_convert_2times(self):
+        quant_model, ptq = self._get_model_for_ptq()
+
+        image = paddle.rand([1, 1, 32, 32], dtype="float32")
+        out = quant_model(image)
+        converted_model = ptq.convert(quant_model)
+        converted_model = ptq.convert(converted_model)
+        out = converted_model(image)
+        self.assertIsNotNone(out)
+
+        observer_count = self._count_layers(
+            converted_model, AbsmaxObserverLayer
+        )
+        quanter_count = self._count_layers(converted_model, LinearQuanter)
+        dequanter_count = self._count_layers(converted_model, LinearDequanter)
+        self.assertEqual(observer_count, 0)
+        self.assertEqual(dequanter_count, 14)
+        self.assertEqual(quanter_count, 9)
+
+        save_path = os.path.join(self.temp_dir.name, 'int8_infer')
+        paddle.jit.save(converted_model, save_path, [image])
+
+        paddle.enable_static()
+        exe = paddle.static.Executor(paddle.CPUPlace())
+        main_program = paddle.static.Program()
+        startup_program = paddle.static.Program()
+        with paddle.static.program_guard(main_program, startup_program):
+            [
+                inference_program,
+                feed_target_names,
+                fetch_targets,
+            ] = paddle.static.load_inference_model(save_path, exe)
+        tensor_img = np.array(
+            np.random.random((1, 1, 32, 32)), dtype=np.float32
+        )
+        results = exe.run(
+            inference_program,
+            feed={feed_target_names[0]: tensor_img},
+            fetch_list=fetch_targets,
+        )
+        self.assertIsNotNone(results)
+        paddle.disable_static()
+
+
+class TestPTQFP8(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(self):
+        weight_observer = AbsmaxObserver(quant_bits=(4, 3))
+        act_observer = AbsmaxObserver(quant_bits=(5, 2))
+        model = LeNetDygraph()
+        model.eval()
+        q_config = QuantConfig(activation=act_observer, weight=weight_observer)
+        ptq = PTQ(q_config)
+        quant_model = ptq.quantize(model)
+        return quant_model, ptq
+
+    def _count_layers(self, model, layer_type):
+        count = 0
+        for _layer in model.sublayers(True):
+            if isinstance(_layer, layer_type):
+                count += 1
+        return count
+
+    def test_quantize(self):
+        ptq_model, _ = self._get_model_for_ptq()
+        image = paddle.rand([1, 1, 32, 32], dtype="float32")
+        out = ptq_model(image)
+        self.assertIsNotNone(out)
+
+        observer_count = self._count_layers(ptq_model, AbsmaxObserverLayer)
+        self.assertEqual(observer_count, 14)
+
+    def test_convert(self):
+        quant_model, ptq = self._get_model_for_ptq()
+
+        image = paddle.rand([1, 1, 32, 32], dtype="float32")
+        out = quant_model(image)
+        converted_model = ptq.convert(quant_model)
+        out = converted_model(image)
+        self.assertIsNotNone(out)
+
+        observer_count = self._count_layers(
+            converted_model, AbsmaxObserverLayer
+        )
+        quanter_count = self._count_layers(converted_model, LinearQuanter)
+        dequanter_count = self._count_layers(converted_model, LinearDequanter)
+        self.assertEqual(observer_count, 0)
+        self.assertEqual(dequanter_count, 14)
+        self.assertEqual(quanter_count, 9)
+
+        save_path = os.path.join(self.temp_dir.name, 'int8_infer')
+        paddle.jit.save(converted_model, save_path, [image])
+
+        paddle.enable_static()
+        exe = paddle.static.Executor(paddle.CPUPlace())
+        main_program = paddle.static.Program()
+        startup_program = paddle.static.Program()
+        with paddle.static.program_guard(main_program, startup_program):
+            [
+                inference_program,
+                feed_target_names,
+                fetch_targets,
+            ] = paddle.static.load_inference_model(save_path, exe)
+        tensor_img = np.array(
+            np.random.random((1, 1, 32, 32)), dtype=np.float32
+        )
+        results = exe.run(
+            inference_program,
+            feed={feed_target_names[0]: tensor_img},
+            fetch_list=fetch_targets,
+        )
+        self.assertIsNotNone(results)
+        paddle.disable_static()
+
+    def test_convert_2times(self):
+        quant_model, ptq = self._get_model_for_ptq()
+
+        image = paddle.rand([1, 1, 32, 32], dtype="float32")
+        out = quant_model(image)
+        converted_model = ptq.convert(quant_model)
+        converted_model = ptq.convert(converted_model)
+        out = converted_model(image)
+        self.assertIsNotNone(out)
+
+        observer_count = self._count_layers(
+            converted_model, AbsmaxObserverLayer
+        )
+        quanter_count = self._count_layers(converted_model, LinearQuanter)
+        dequanter_count = self._count_layers(converted_model, LinearDequanter)
+        self.assertEqual(observer_count, 0)
+        self.assertEqual(dequanter_count, 14)
+        self.assertEqual(quanter_count, 9)
+
+        save_path = os.path.join(self.temp_dir.name, 'int8_infer')
+        paddle.jit.save(converted_model, save_path, [image])
+
+        paddle.enable_static()
+        exe = paddle.static.Executor(paddle.CPUPlace())
+        main_program = paddle.static.Program()
+        startup_program = paddle.static.Program()
+        with paddle.static.program_guard(main_program, startup_program):
+            [
+                inference_program,
+                feed_target_names,
+                fetch_targets,
+            ] = paddle.static.load_inference_model(save_path, exe)
+        tensor_img = np.array(
+            np.random.random((1, 1, 32, 32)), dtype=np.float32
+        )
+        results = exe.run(
+            inference_program,
+            feed={feed_target_names[0]: tensor_img},
+            fetch_list=fetch_targets,
+        )
+        self.assertIsNotNone(results)
+        paddle.disable_static()
+
+
+if __name__ == '__main__':
+    unittest.main()