Add TranslatedLayer.program method to get program (#26961)

* add TranslatedLayer.program method

* add unittests & update example code

* polish example code

* update program api example code

python/paddle/fluid/dygraph/io.py

@@ -556,89 +556,92 @@ class TranslatedLayer(layers.Layer):
         .. code-block:: python
 
             import numpy as np
-            import paddle.fluid as fluid
-            from paddle.fluid.dygraph import Linear
-            from paddle.fluid.dygraph import declarative
+            import paddle
+            import paddle.nn as nn
+            import paddle.optimizer as opt
 
-            BATCH_SIZE = 32
-            BATCH_NUM = 20
+            BATCH_SIZE = 16
+            BATCH_NUM = 4
+            EPOCH_NUM = 4
 
-            def random_batch_reader():
-                def _get_random_images_and_labels(image_shape, label_shape):
-                    image = np.random.random(size=image_shape).astype('float32')
-                    label = np.random.random(size=label_shape).astype('int64')
-                    return image, label
+            IMAGE_SIZE = 784
+            CLASS_NUM = 10
+
+            # define a random dataset
+            class RandomDataset(paddle.io.Dataset):
+                def __init__(self, num_samples):
+                    self.num_samples = num_samples
 
-                def __reader__():
-                    for _ in range(BATCH_NUM):
-                        batch_image, batch_label = _get_random_images_and_labels(
-                            [BATCH_SIZE, 784], [BATCH_SIZE, 1])
-                        yield batch_image, batch_label
+                def __getitem__(self, idx):
+                    image = np.random.random([IMAGE_SIZE]).astype('float32')
+                    label = np.random.randint(0, CLASS_NUM - 1, (1, )).astype('int64')
+                    return image, label
 
-                return __reader__
+                def __len__(self):
+                    return self.num_samples
 
-            class LinearNet(fluid.dygraph.Layer):
-                def __init__(self, in_size, out_size):
+            class LinearNet(nn.Layer):
+                def __init__(self):
                     super(LinearNet, self).__init__()
-                    self._linear = Linear(in_size, out_size)
+                    self._linear = nn.Linear(IMAGE_SIZE, CLASS_NUM)
 
-                @declarative
+                @paddle.jit.to_static
                 def forward(self, x):
                     return self._linear(x)
 
+            def train(layer, loader, loss_fn, opt):
+                for epoch_id in range(EPOCH_NUM):
+                    for batch_id, (image, label) in enumerate(loader()):
+                        out = layer(image)
+                        loss = loss_fn(out, label)
+                        loss.backward()
+                        opt.step()
+                        opt.clear_grad()
+                        print("Epoch {} batch {}: loss = {}".format(
+                            epoch_id, batch_id, np.mean(loss.numpy())))
+
             # enable dygraph mode
-            fluid.enable_dygraph() 
+            place = paddle.CPUPlace()
+            paddle.disable_static(place) 
 
             # 1. train & save model.
-            # create network
-            net = LinearNet(784, 1)
-            adam = fluid.optimizer.AdamOptimizer(learning_rate=0.1, parameter_list=net.parameters())
-            # create data loader
-            train_loader = fluid.io.DataLoader.from_generator(capacity=5)
-            train_loader.set_batch_generator(random_batch_reader())
-            # train
-            for data in train_loader():
-                img, label = data
-                label.stop_gradient = True
 
-                cost = net(img)
+            # create network
+            layer = LinearNet()
+            loss_fn = nn.CrossEntropyLoss()
+            adam = opt.Adam(learning_rate=0.001, parameters=layer.parameters())
 
-                loss = fluid.layers.cross_entropy(cost, label)
-                avg_loss = fluid.layers.mean(loss)
+            # create data loader
+            dataset = RandomDataset(BATCH_NUM * BATCH_SIZE)
+            loader = paddle.io.DataLoader(dataset,
+                places=place,
+                batch_size=BATCH_SIZE,
+                shuffle=True,
+                drop_last=True,
+                num_workers=2)
 
-                avg_loss.backward()
-                adam.minimize(avg_loss)
-                net.clear_gradients()
+            # train
+            train(layer, loader, loss_fn, adam)
 
+            # save
             model_path = "linear.example.model"
-            fluid.dygraph.jit.save(
-                layer=net,
-                model_path=model_path,
-                input_spec=[img])
+            paddle.jit.save(layer, model_path)
 
             # 2. load model as TranslatedLayer
-            translated_layer = fluid.dygraph.jit.load(model_path)
+
+            # load
+            translated_layer = paddle.jit.load(model_path)
+
             # inference
             translated_layer.eval()
-            x = fluid.dygraph.to_variable(np.random.random((1, 784)).astype('float32'))
+            x = paddle.randn([1, IMAGE_SIZE], 'float32')
             pred = translated_layer(x)
+
             # fine-tune
             translated_layer.train()
-            adam = fluid.optimizer.AdamOptimizer(learning_rate=0.1, parameter_list=translated_layer.parameters())
-            train_loader = fluid.io.DataLoader.from_generator(capacity=5)
-            train_loader.set_batch_generator(random_batch_reader())
-            for data in train_loader():
-                img, label = data
-                label.stop_gradient = True
+            adam = opt.Adam(learning_rate=0.001, parameters=translated_layer.parameters())
+            train(translated_layer, loader, loss_fn, adam)
 
-                cost = translated_layer(img)
-
-                loss = fluid.layers.cross_entropy(cost, label)
-                avg_loss = fluid.layers.mean(loss)
-
-                avg_loss.backward()
-                adam.minimize(avg_loss)
-                translated_layer.clear_gradients()
     """
 
     def __init__(self, programs, persistable_vars):
@@ -814,3 +817,107 @@ class TranslatedLayer(layers.Layer):
 
     def eval(self):
         self._is_test = True
+
+    def program(self, method_name='forward'):
+        """
+        Gets translated program of specified method.
+
+        Args:
+            - method_name (string): mehtod name corresponding to the program
+                to be obtained. Default: 'forward'.
+        
+        Returns:
+            Program
+
+        Examples:
+            .. code-block:: python
+            
+                import numpy as np
+                import paddle
+                import paddle.nn as nn
+                import paddle.optimizer as opt
+
+                BATCH_SIZE = 16
+                BATCH_NUM = 4
+                EPOCH_NUM = 4
+
+                IMAGE_SIZE = 784
+                CLASS_NUM = 10
+
+                # define a random dataset
+                class RandomDataset(paddle.io.Dataset):
+                    def __init__(self, num_samples):
+                        self.num_samples = num_samples
+
+                    def __getitem__(self, idx):
+                        image = np.random.random([IMAGE_SIZE]).astype('float32')
+                        label = np.random.randint(0, CLASS_NUM - 1, (1, )).astype('int64')
+                        return image, label
+
+                    def __len__(self):
+                        return self.num_samples
+
+                class LinearNet(nn.Layer):
+                    def __init__(self):
+                        super(LinearNet, self).__init__()
+                        self._linear = nn.Linear(IMAGE_SIZE, CLASS_NUM)
+
+                    @paddle.jit.to_static
+                    def forward(self, x):
+                        return self._linear(x)
+
+                def train(layer, loader, loss_fn, opt):
+                    for epoch_id in range(EPOCH_NUM):
+                        for batch_id, (image, label) in enumerate(loader()):
+                            out = layer(image)
+                            loss = loss_fn(out, label)
+                            loss.backward()
+                            opt.step()
+                            opt.clear_grad()
+                            print("Epoch {} batch {}: loss = {}".format(
+                                epoch_id, batch_id, np.mean(loss.numpy())))
+
+                # enable dygraph mode
+                place = paddle.CPUPlace()
+                paddle.disable_static(place) 
+
+                # create network
+                layer = LinearNet()
+                loss_fn = nn.CrossEntropyLoss()
+                adam = opt.Adam(learning_rate=0.001, parameters=layer.parameters())
+
+                # create data loader
+                dataset = RandomDataset(BATCH_NUM * BATCH_SIZE)
+                loader = paddle.io.DataLoader(dataset,
+                    places=place,
+                    batch_size=BATCH_SIZE,
+                    shuffle=True,
+                    drop_last=True,
+                    num_workers=2)
+
+                # train
+                train(layer, loader, loss_fn, adam)
+
+                # save
+                model_path = "linear.example.model"
+                paddle.jit.save(layer, model_path)
+
+                # load
+                translated_layer = paddle.jit.load(model_path)
+
+                # get program
+                program = translated_layer.program()
+        """
+        # 1. get program holder
+        program_holder = self._program_holder_dict.get(method_name, None)
+        if program_holder is None:
+            raise ValueError(
+                "The method `%s` is not exists in loaded TranslatedLayer." %
+                method_name)
+
+        # 2. get inference program desc
+        program_desc = program_holder.infer_program
+
+        # 3. construct program
+        program = _build_program_by_desc(program_desc)
+        return program

python/paddle/fluid/tests/unittests/test_translated_layer.py

+# Copyright (c) 2020 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 __future__ import print_function
+
+import unittest
+import numpy as np
+import paddle
+import paddle.nn as nn
+import paddle.optimizer as opt
+
+BATCH_SIZE = 16
+BATCH_NUM = 4
+EPOCH_NUM = 4
+SEED = 10
+
+IMAGE_SIZE = 784
+CLASS_NUM = 10
+
+
+# define a random dataset
+class RandomDataset(paddle.io.Dataset):
+    def __init__(self, num_samples):
+        self.num_samples = num_samples
+
+    def __getitem__(self, idx):
+        np.random.seed(SEED)
+        image = np.random.random([IMAGE_SIZE]).astype('float32')
+        label = np.random.randint(0, CLASS_NUM - 1, (1, )).astype('int64')
+        return image, label
+
+    def __len__(self):
+        return self.num_samples
+
+
+class LinearNet(nn.Layer):
+    def __init__(self):
+        super(LinearNet, self).__init__()
+        self._linear = nn.Linear(IMAGE_SIZE, CLASS_NUM)
+
+    @paddle.jit.to_static
+    def forward(self, x):
+        return self._linear(x)
+
+
+def train(layer, loader, loss_fn, opt):
+    for epoch_id in range(EPOCH_NUM):
+        for batch_id, (image, label) in enumerate(loader()):
+            out = layer(image)
+            loss = loss_fn(out, label)
+            loss.backward()
+            opt.step()
+            opt.clear_grad()
+            print("Epoch {} batch {}: loss = {}".format(epoch_id, batch_id,
+                                                        np.mean(loss.numpy())))
+    return loss
+
+
+class TestTranslatedLayer(unittest.TestCase):
+    def setUp(self):
+        # enable dygraph mode
+        place = paddle.CPUPlace()
+        paddle.disable_static(place)
+
+        # config seed
+        paddle.manual_seed(SEED)
+        paddle.framework.random._manual_program_seed(SEED)
+
+        # create network
+        self.layer = LinearNet()
+        self.loss_fn = nn.CrossEntropyLoss()
+        self.sgd = opt.SGD(learning_rate=0.001,
+                           parameters=self.layer.parameters())
+
+        # create data loader
+        dataset = RandomDataset(BATCH_NUM * BATCH_SIZE)
+        self.loader = paddle.io.DataLoader(
+            dataset,
+            places=place,
+            batch_size=BATCH_SIZE,
+            shuffle=True,
+            drop_last=True,
+            num_workers=2)
+
+        # train
+        train(self.layer, self.loader, self.loss_fn, self.sgd)
+
+        # save
+        self.model_path = "linear.example.model"
+        paddle.jit.save(self.layer, self.model_path)
+
+    def test_inference_and_fine_tuning(self):
+        self.load_and_inference()
+        self.load_and_fine_tuning()
+
+    def load_and_inference(self):
+        # load
+        translated_layer = paddle.jit.load(self.model_path)
+
+        # inference
+        x = paddle.randn([1, IMAGE_SIZE], 'float32')
+
+        self.layer.eval()
+        orig_pred = self.layer(x)
+
+        translated_layer.eval()
+        pred = translated_layer(x)
+
+        self.assertTrue(np.array_equal(orig_pred.numpy(), pred.numpy()))
+
+    def load_and_fine_tuning(self):
+        # load
+        translated_layer = paddle.jit.load(self.model_path)
+
+        # train original layer continue
+        self.layer.train()
+        orig_loss = train(self.layer, self.loader, self.loss_fn, self.sgd)
+
+        # fine-tuning
+        translated_layer.train()
+        sgd = opt.SGD(learning_rate=0.001,
+                      parameters=translated_layer.parameters())
+        loss = train(translated_layer, self.loader, self.loss_fn, sgd)
+
+        self.assertTrue(
+            np.array_equal(orig_loss.numpy(), loss.numpy()),
+            msg="original loss:\n{}\nnew loss:\n{}\n".format(orig_loss.numpy(),
+                                                             loss.numpy()))
+
+    def test_get_program(self):
+        # load
+        translated_layer = paddle.jit.load(self.model_path)
+
+        program = translated_layer.program()
+        self.assertTrue(isinstance(program, paddle.static.Program))
+
+    def test_get_program_method_not_exists(self):
+        # load
+        translated_layer = paddle.jit.load(self.model_path)
+
+        with self.assertRaises(ValueError):
+            program = translated_layer.program('not_exists')
+
+
+if __name__ == '__main__':
+    unittest.main()