chore: import upstream snapshot with attribution

test/legacy_test/test_imperative_optimizer.py

@@ -0,0 +1,364 @@
+# Copyright (c) 2018 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
+from op_test import get_device_place
+from test_imperative_base import new_program_scope
+
+import paddle
+from paddle import base
+from paddle.base import core
+from paddle.distributed.fleet.meta_optimizers import DGCMomentumOptimizer
+
+# Note(wangzhongpu)
+# In dygraph, don't support ModelAverage, DGCMomentumOptimizer, ExponentialMovingAverage, PipelineOptimizer, LookaheadOptimizer, RecomputeOptimizer.
+
+
+class MLP(paddle.nn.Layer):
+    def __init__(self, param_attr=None, bias_attr=None):
+        super().__init__()
+
+        self._fc1 = paddle.nn.Linear(784, 10)
+        self._fc2 = paddle.nn.Linear(10, 10)
+
+    def forward(self, inputs):
+        y = self._fc1(inputs)
+        y = self._fc2(y)
+        return y
+
+
+class TestImperativeOptimizerBase(unittest.TestCase):
+    def setUp(self):
+        self.batch_num = 20
+
+    def get_optimizer_dygraph(self, parameter_list):
+        raise NotImplementedError
+
+    def get_optimizer(self):
+        raise NotImplementedError
+
+    def reader_decorator(self, reader):
+        def _reader_simple():
+            for item in reader():
+                image = np.array(item[0]).reshape(1, 784)
+                label = np.array(item[1]).astype('int64').reshape(1)
+                yield image, label
+
+        return _reader_simple
+
+    def _check_exception(self, exception_message, place=None):
+        seed = 90
+        batch_size = 128
+        if place is None:
+            place = get_device_place()
+
+        with base.dygraph.guard(place):
+            try:
+                paddle.seed(seed)
+                paddle.framework.random._manual_program_seed(seed)
+                mlp = MLP()
+                optimizer = self.get_optimizer_dygraph(
+                    parameter_list=mlp.parameters()
+                )
+            except Exception as e:
+                assert str(e) == exception_message
+
+    def _check_mlp(self, place=None):
+        seed = 90
+        batch_size = 128
+
+        if place is None:
+            place = get_device_place()
+
+        with base.dygraph.guard(place):
+            paddle.seed(seed)
+            paddle.framework.random._manual_program_seed(seed)
+
+            mlp = MLP()
+            optimizer = self.get_optimizer_dygraph(
+                parameter_list=mlp.parameters()
+            )
+
+            batch_py_reader = base.io.PyReader(capacity=1)
+            batch_py_reader.decorate_sample_list_generator(
+                paddle.batch(
+                    self.reader_decorator(paddle.dataset.mnist.train()),
+                    batch_size=batch_size,
+                    drop_last=True,
+                ),
+                places=base.CPUPlace(),
+            )
+
+            dy_param_init_value = {}
+            for batch_id, data in enumerate(batch_py_reader()):
+                if batch_id >= self.batch_num:
+                    break
+
+                img = data[0]
+                label = data[1]
+                label.stop_gradient = True
+
+                img = paddle.reshape(img, shape=[batch_size, -1])
+                cost = mlp(img)
+                avg_loss = paddle.mean(cost)
+                dy_out = avg_loss.numpy()
+
+                if batch_id == 0:
+                    for param in mlp.parameters():
+                        dy_param_init_value[param.name] = param.numpy()
+
+                avg_loss.backward()
+                optimizer.minimize(avg_loss)
+                mlp.clear_gradients()
+                dy_param_value = {}
+                for param in mlp.parameters():
+                    dy_param_value[param.name] = param.numpy()
+
+        with new_program_scope():
+            paddle.seed(seed)
+            paddle.framework.random._manual_program_seed(seed)
+
+            if place is None:
+                place = get_device_place()
+
+            exe = base.Executor(place)
+
+            mlp = MLP()
+            optimizer = self.get_optimizer()
+            train_reader = paddle.batch(
+                paddle.dataset.mnist.train(), batch_size=128, drop_last=True
+            )
+
+            img = paddle.static.data(
+                name='pixel', shape=[-1, 1, 28, 28], dtype='float32'
+            )
+            label = paddle.static.data(
+                name='label', shape=[-1, 1], dtype='int64'
+            )
+            img = paddle.reshape(img, shape=[batch_size, 784])
+            cost = mlp(img)
+            avg_loss = paddle.mean(cost)
+            optimizer.minimize(avg_loss)
+
+            # initialize params and fetch them
+            static_param_init_value = {}
+            static_param_name_list = []
+            for param in mlp.parameters():
+                static_param_name_list.append(param.name)
+
+            out = exe.run(
+                base.default_startup_program(),
+                fetch_list=static_param_name_list,
+            )
+
+            for i in range(len(static_param_name_list)):
+                static_param_init_value[static_param_name_list[i]] = out[i]
+
+            for batch_id, data in enumerate(train_reader()):
+                if batch_id >= self.batch_num:
+                    break
+
+                static_x_data = np.array(
+                    [x[0].reshape(1, 28, 28) for x in data]
+                ).astype('float32')
+                y_data = (
+                    np.array([x[1] for x in data])
+                    .astype('int64')
+                    .reshape([128, 1])
+                )
+
+                fetch_list = [avg_loss.name]
+                fetch_list.extend(static_param_name_list)
+                out = exe.run(
+                    base.default_main_program(),
+                    feed={"pixel": static_x_data, "label": y_data},
+                    fetch_list=fetch_list,
+                )
+
+                static_param_value = {}
+                static_out = out[0]
+                for i in range(1, len(out)):
+                    static_param_value[static_param_name_list[i - 1]] = out[i]
+
+        for key, value in static_param_init_value.items():
+            np.testing.assert_allclose(
+                value, dy_param_init_value[key], rtol=1e-05
+            )
+
+        if core.is_compiled_with_rocm():
+            np.testing.assert_allclose(
+                static_out, dy_out, rtol=1e-05, atol=0.001
+            )
+        else:
+            np.testing.assert_allclose(static_out, dy_out, rtol=1e-05)
+
+        for key, value in static_param_value.items():
+            if core.is_compiled_with_rocm():
+                np.testing.assert_allclose(
+                    value, dy_param_value[key], rtol=1e-05, atol=0.001
+                )
+            else:
+                np.testing.assert_allclose(
+                    value, dy_param_value[key], rtol=1e-05
+                )
+
+
+class TestOptimizerLearningRate(unittest.TestCase):
+    def test_constant_lr(self):
+        with base.dygraph.guard():
+            a = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
+
+            linear = paddle.nn.Linear(10, 10)
+
+            a = paddle.to_tensor(a)
+
+            b = linear(a)
+
+            loss = paddle.mean(b)
+
+            adam = paddle.optimizer.Adam(0.001, parameters=linear.parameters())
+
+            np.testing.assert_allclose(
+                adam.get_lr(), 0.001, rtol=1e-06, atol=0.0
+            )
+
+            for i in range(10):
+                adam.minimize(loss)
+                lr = adam.get_lr()
+
+                np.testing.assert_allclose(lr, 0.001, rtol=1e-06, atol=0.0)
+
+    def test_lr_decay(self):
+        with base.dygraph.guard():
+            a = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
+
+            linear = paddle.nn.Linear(10, 10)
+
+            a = paddle.to_tensor(a)
+
+            b = linear(a)
+
+            loss = paddle.mean(b)
+
+            bd = [2, 4, 6, 8]
+            value = [0.2, 0.4, 0.6, 0.8, 1.0]
+
+            scheduler = paddle.optimizer.lr.PiecewiseDecay(bd, value)
+            adam = paddle.optimizer.Adam(
+                scheduler,
+                parameters=linear.parameters(),
+            )
+
+            np.testing.assert_allclose(adam.get_lr(), 0.2, rtol=1e-06, atol=0.0)
+
+            ret = [0.2, 0.2, 0.4, 0.4, 0.6, 0.6, 0.8, 0.8, 1.0, 1.0, 1.0, 1.0]
+            for i in range(12):
+                adam.minimize(loss)
+                lr = adam.get_lr()
+                adam.step()
+                scheduler.step()
+                np.testing.assert_allclose(lr, ret[i], rtol=1e-06, atol=0.0)
+
+    def test_lr_decay_natural_exp(self):
+        with base.dygraph.guard():
+            a = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
+
+            linear = paddle.nn.Linear(10, 10)
+
+            a = paddle.to_tensor(a)
+
+            b = linear(a)
+
+            loss = paddle.mean(b)
+            base_lr = 1.0
+
+            scheduler = paddle.optimizer.lr.NaturalExpDecay(
+                learning_rate=base_lr,
+                gamma=0.5,
+            )
+            adam = paddle.optimizer.Adam(
+                learning_rate=scheduler,
+                parameters=linear.parameters(),
+            )
+
+            np.testing.assert_allclose(adam.get_lr(), 1.0, rtol=1e-06, atol=0.0)
+
+            ret = [1.0, 1.0, 1.0, np.exp(-0.5), np.exp(-0.5)]
+            counter = 0
+            for i in range(5):
+                adam.minimize(loss)
+                lr = adam.get_lr()
+                counter += 1
+                if counter % 3 == 0:
+                    adam.step()
+                    scheduler.step()
+                np.testing.assert_allclose(lr, ret[i], rtol=1e-06, atol=0.0)
+
+    def test_set_lr(self):
+        with base.dygraph.guard():
+            a = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
+
+            linear = paddle.nn.Linear(10, 10)
+
+            a = paddle.to_tensor(a)
+
+            b = linear(a)
+
+            loss = paddle.mean(b)
+
+            adam = paddle.optimizer.Adam(0.1, parameters=linear.parameters())
+
+            lr_list = [0.2, 0.3, 0.4, 0.5, 0.6]
+            for i in range(5):
+                adam.set_lr(lr_list[i])
+                adam.minimize(loss)
+                lr = adam.get_lr()
+                np.testing.assert_allclose(lr, lr_list[i], rtol=1e-06, atol=0.0)
+
+            with self.assertRaises(RuntimeError):
+                adam = paddle.optimizer.Adam(
+                    paddle.optimizer.lr.NaturalExpDecay(
+                        learning_rate=0.1,
+                        gamma=0.5,
+                    ),
+                    parameters=linear.parameters(),
+                )
+                adam.set_lr(0.01)
+
+
+def exclude_fn(param):
+    return param.name.endswith('.b_0')
+
+
+class TestImperativeDGCMomentumOptimizer(TestImperativeOptimizerBase):
+    def get_optimizer_dygraph(self, parameter_list):
+        optimizer = DGCMomentumOptimizer(
+            learning_rate=0.0001,
+            momentum=0.9,
+            rampup_step=1000,
+            rampup_begin_step=1252,
+            sparsity=[0.999, 0.999],
+        )
+        return optimizer
+
+    def test_dgcmomentum(self):
+        exception_message = "In dygraph, don't support DGCMomentumOptimizer."
+        self._check_exception(exception_message)
+
+
+if __name__ == '__main__':
+    paddle.enable_static()
+    unittest.main()