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819 lines
27 KiB
819 lines
27 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import contextlib
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import unittest
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import numpy as np
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import six
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import itertools
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import paddle
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import paddle.fluid as fluid
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from paddle.fluid import core
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from paddle.fluid.optimizer import MomentumOptimizer, LarsMomentumOptimizer, AdagradOptimizer, AdamaxOptimizer, DpsgdOptimizer, DecayedAdagradOptimizer, AdadeltaOptimizer, RMSPropOptimizer, FtrlOptimizer
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from paddle.fluid.optimizer import ModelAverage, DGCMomentumOptimizer, ExponentialMovingAverage, PipelineOptimizer, LookaheadOptimizer, RecomputeOptimizer
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from paddle.fluid.dygraph import Linear
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from paddle.fluid.dygraph.base import to_variable
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from test_imperative_base import new_program_scope
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# Note(wangzhongpu)
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# In dygraph, don't support ModelAverage, DGCMomentumOptimizer, ExponentialMovingAverage, PipelineOptimizer, LookaheadOptimizer, RecomputeOptimizer.
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class MLP(fluid.Layer):
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def __init__(self, param_attr=None, bias_attr=None):
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super(MLP, self).__init__()
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self._fc1 = Linear(784, 10)
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self._fc2 = Linear(10, 10)
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def forward(self, inputs):
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y = self._fc1(inputs)
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y = self._fc2(y)
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return y
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class TestImperativeOptimizerBase(unittest.TestCase):
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def setUp(self):
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self.batch_num = 20
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def get_optimizer_dygraph(self, parameter_list):
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raise NotImplementedError()
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def get_optimizer(self):
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raise NotImplementedError()
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def reader_decorator(self, reader):
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def _reader_imple():
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for item in reader():
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image = np.array(item[0]).reshape(1, 784)
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label = np.array(item[1]).astype('int64').reshape(1)
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yield image, label
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return _reader_imple
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def _check_exception(self, exception_message, place=None):
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seed = 90
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batch_size = 128
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if place == None:
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place = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
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) else fluid.CPUPlace()
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try:
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paddle.disable_static()
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paddle.seed(seed)
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paddle.framework.random._manual_program_seed(seed)
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mlp = MLP()
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optimizer = self.get_optimizer_dygraph(
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parameter_list=mlp.parameters())
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except Exception as e:
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assert str(e) == exception_message
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finally:
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paddle.enable_static()
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def _check_mlp(self, place=None):
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seed = 90
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batch_size = 128
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if place == None:
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place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
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) else fluid.CUDAPlace(0)
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paddle.disable_static(place)
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paddle.seed(seed)
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paddle.framework.random._manual_program_seed(seed)
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mlp = MLP()
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optimizer = self.get_optimizer_dygraph(parameter_list=mlp.parameters())
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batch_py_reader = fluid.io.PyReader(capacity=1)
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batch_py_reader.decorate_sample_list_generator(
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paddle.batch(
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self.reader_decorator(paddle.dataset.mnist.train()),
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batch_size=batch_size,
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drop_last=True),
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places=fluid.CPUPlace())
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dy_param_init_value = {}
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for batch_id, data in enumerate(batch_py_reader()):
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if batch_id >= self.batch_num:
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break
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img = data[0]
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label = data[1]
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label.stop_gradient = True
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img = fluid.layers.reshape(img, shape=[batch_size, -1])
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cost = mlp(img)
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avg_loss = fluid.layers.reduce_mean(cost)
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dy_out = avg_loss.numpy()
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if batch_id == 0:
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for param in mlp.parameters():
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dy_param_init_value[param.name] = param.numpy()
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avg_loss.backward()
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optimizer.minimize(avg_loss)
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if isinstance(optimizer._learning_rate,
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paddle.optimizer.lr.LRScheduler):
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if isinstance(optimizer._learning_rate,
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paddle.optimizer.lr.ReduceOnPlateau):
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optimizer._learning_rate.step(avg_loss)
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else:
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optimizer._learning_rate.step()
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mlp.clear_gradients()
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dy_param_value = {}
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for param in mlp.parameters():
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dy_param_value[param.name] = param.numpy()
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paddle.enable_static()
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with new_program_scope():
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paddle.seed(seed)
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paddle.framework.random._manual_program_seed(seed)
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if place == None:
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place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
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) else fluid.CUDAPlace(0)
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exe = fluid.Executor(place)
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mlp = MLP()
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optimizer = self.get_optimizer()
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train_reader = paddle.batch(
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paddle.dataset.mnist.train(), batch_size=128, drop_last=True)
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img = fluid.layers.data(
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name='pixel', shape=[1, 28, 28], dtype='float32')
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label = fluid.layers.data(name='label', shape=[1], dtype='int64')
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img = fluid.layers.reshape(img, shape=[batch_size, 784])
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cost = mlp(img)
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avg_loss = fluid.layers.reduce_mean(cost)
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optimizer.minimize(avg_loss)
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# initialize params and fetch them
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static_param_init_value = {}
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static_param_name_list = []
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for param in mlp.parameters():
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static_param_name_list.append(param.name)
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out = exe.run(fluid.default_startup_program(),
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fetch_list=static_param_name_list)
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for i in range(len(static_param_name_list)):
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static_param_init_value[static_param_name_list[i]] = out[i]
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for batch_id, data in enumerate(train_reader()):
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if batch_id >= self.batch_num:
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break
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static_x_data = np.array(
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[x[0].reshape(1, 28, 28) for x in data]).astype('float32')
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y_data = np.array([x[1] for x in data]).astype('int64').reshape(
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[128, 1])
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fetch_list = [avg_loss.name]
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fetch_list.extend(static_param_name_list)
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out = exe.run(fluid.default_main_program(),
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feed={"pixel": static_x_data,
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"label": y_data},
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fetch_list=fetch_list)
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if isinstance(optimizer._learning_rate,
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paddle.optimizer.lr.LRScheduler):
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if isinstance(optimizer._learning_rate,
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paddle.optimizer.lr.ReduceOnPlateau):
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optimizer._learning_rate.step(out[0])
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else:
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optimizer._learning_rate.step()
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static_param_value = {}
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static_out = out[0]
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for i in range(1, len(out)):
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static_param_value[static_param_name_list[i - 1]] = out[i]
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for key, value in six.iteritems(static_param_init_value):
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self.assertTrue(np.allclose(value, dy_param_init_value[key]))
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self.assertTrue(np.allclose(static_out, dy_out))
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for key, value in six.iteritems(static_param_value):
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self.assertTrue(np.allclose(value, dy_param_value[key]))
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class TestImperativeOptimizerPiecewiseDecay(TestImperativeOptimizerBase):
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def get_optimizer_dygraph(self, parameter_list):
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bd = [3, 6, 9]
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.PiecewiseDecay(
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boundaries=bd,
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values=[0.1 * (0.1**i) for i in range(len(bd) + 1)]),
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parameters=parameter_list)
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return optimizer
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def get_optimizer(self):
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bd = [3, 6, 9]
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.PiecewiseDecay(
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boundaries=bd,
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values=[0.1 * (0.1**i) for i in range(len(bd) + 1)]))
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return optimizer
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def test_sgd(self):
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self._check_mlp()
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class TestImperativeOptimizerNaturalExpDecay(TestImperativeOptimizerBase):
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def get_optimizer_dygraph(self, parameter_list):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.NaturalExpDecay(
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learning_rate=0.5, gamma=0.9),
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parameters=parameter_list)
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return optimizer
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def get_optimizer(self):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.NaturalExpDecay(
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learning_rate=0.5, gamma=0.9))
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return optimizer
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def test_sgd(self):
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self._check_mlp()
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class TestImperativeOptimizerExponentialDecay(TestImperativeOptimizerBase):
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def get_optimizer_dygraph(self, parameter_list):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.ExponentialDecay(
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learning_rate=0.5, gamma=0.9),
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parameters=parameter_list)
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return optimizer
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def get_optimizer(self):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.ExponentialDecay(
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learning_rate=0.5, gamma=0.9))
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return optimizer
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def test_sgd(self):
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self._check_mlp()
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class TestImperativeOptimizerInverseTimeDecay(TestImperativeOptimizerBase):
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def get_optimizer_dygraph(self, parameter_list):
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optimizer = paddle.optimizer.Adam(
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learning_rate=paddle.optimizer.lr.InverseTimeDecay(
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learning_rate=0.5, gamma=0.9),
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parameters=parameter_list)
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return optimizer
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def get_optimizer(self):
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optimizer = paddle.optimizer.Adam(
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learning_rate=paddle.optimizer.lr.InverseTimeDecay(
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learning_rate=0.5, gamma=0.9))
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return optimizer
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def test_adam(self):
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self._check_mlp()
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class TestImperativeOptimizerPolynomialDecay(TestImperativeOptimizerBase):
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def get_optimizer_dygraph(self, parameter_list):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.PolynomialDecay(
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learning_rate=0.5, decay_steps=5, cycle=self.cycle),
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parameters=parameter_list)
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return optimizer
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def get_optimizer(self):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.PolynomialDecay(
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learning_rate=0.5, decay_steps=5, cycle=self.cycle))
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return optimizer
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def test_sgd_cycle(self):
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self.cycle = True
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self._check_mlp()
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def test_sgd(self):
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self.cycle = False
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self._check_mlp()
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class TestImperativeOptimizerCosineAnnealingDecay(TestImperativeOptimizerBase):
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def get_optimizer_dygraph(self, parameter_list):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.CosineAnnealingDecay(
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learning_rate=0.5, T_max=5),
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parameters=parameter_list)
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return optimizer
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def get_optimizer(self):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.CosineAnnealingDecay(
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learning_rate=0.5, T_max=5))
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return optimizer
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def test_sgd(self):
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self._check_mlp()
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class TestImperativeOptimizerNoamDecay(TestImperativeOptimizerBase):
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def get_optimizer_dygraph(self, parameter_list):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.NoamDecay(
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d_model=0.01, warmup_steps=100, verbose=True),
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parameters=parameter_list)
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return optimizer
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def get_optimizer(self):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.NoamDecay(
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d_model=0.01, warmup_steps=100))
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return optimizer
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def test_sgd(self):
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self._check_mlp()
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class TestImperativeOptimizerLambdaDecay(TestImperativeOptimizerBase):
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def get_optimizer_dygraph(self, parameter_list):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.LambdaDecay(
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learning_rate=0.5, lr_lambda=lambda epoch: 0.9**epoch),
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parameters=parameter_list)
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return optimizer
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def get_optimizer(self):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.LambdaDecay(
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learning_rate=0.5, lr_lambda=lambda epoch: 0.9**epoch))
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return optimizer
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def test_sgd(self):
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self._check_mlp()
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class TestImperativeOptimizerLinearWarmup(TestImperativeOptimizerBase):
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def get_optimizer_dygraph(self, parameter_list):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.LinearWarmup(
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learning_rate=0.5, warmup_steps=20, start_lr=0, end_lr=0.5),
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parameters=parameter_list)
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return optimizer
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def get_optimizer(self):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.LinearWarmup(
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learning_rate=0.5,
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warmup_steps=20,
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start_lr=0,
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end_lr=0.5,
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verbose=True))
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return optimizer
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def test_sgd(self):
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self._check_mlp()
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class TestImperativeOptimizerMultiStepDecay(TestImperativeOptimizerBase):
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def get_optimizer_dygraph(self, parameter_list):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.MultiStepDecay(
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learning_rate=0.5, milestones=[2, 4, 6], gamma=0.8),
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parameters=parameter_list)
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return optimizer
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def get_optimizer(self):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.MultiStepDecay(
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learning_rate=0.5, milestones=[2, 4, 6], gamma=0.8))
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return optimizer
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def test_sgd(self):
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self._check_mlp()
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class TestImperativeOptimizerStepLR(TestImperativeOptimizerBase):
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def get_optimizer_dygraph(self, parameter_list):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.StepDecay(
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learning_rate=0.5, step_size=5, gamma=0.8),
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parameters=parameter_list)
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return optimizer
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def get_optimizer(self):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.StepDecay(
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learning_rate=0.5, step_size=5, gamma=0.8))
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return optimizer
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def test_sgd(self):
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self._check_mlp()
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class TestImperativeOptimizerReduceOnPlateau(TestImperativeOptimizerBase):
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def get_optimizer_dygraph(self, parameter_list):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.ReduceOnPlateau(
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learning_rate=0.5),
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parameters=parameter_list)
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return optimizer
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def get_optimizer(self):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.ReduceOnPlateau(
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learning_rate=0.5))
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return optimizer
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def test_sgd(self):
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self._check_mlp()
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class TestOptimizerLearningRate(unittest.TestCase):
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def test_constant_lr(self):
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with fluid.dygraph.guard():
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a = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
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linear = fluid.dygraph.nn.Linear(10, 10)
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a = fluid.dygraph.to_variable(a)
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b = linear(a)
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loss = fluid.layers.reduce_mean(b)
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adam = paddle.optimizer.Adam(0.001, parameters=linear.parameters())
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self.assertTrue(
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np.allclose(
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adam.get_lr(), 0.001, rtol=1e-06, atol=0.0))
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for i in range(10):
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adam.minimize(loss)
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lr = adam.get_lr()
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self.assertTrue(np.allclose(lr, 0.001, rtol=1e-06, atol=0.0))
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def test_lr_decay(self):
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with fluid.dygraph.guard():
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a = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
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linear = fluid.dygraph.nn.Linear(10, 10)
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a = fluid.dygraph.to_variable(a)
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b = linear(a)
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loss = fluid.layers.reduce_mean(b)
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bd = [2, 4, 6, 8]
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value = [0.2, 0.4, 0.6, 0.8, 1.0]
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scheduler = paddle.optimizer.lr.PiecewiseDecay(bd, value)
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adam = paddle.optimizer.Adam(
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scheduler, parameters=linear.parameters())
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self.assertTrue(
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np.allclose(
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adam.get_lr(), 0.2, rtol=1e-06, atol=0.0))
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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]
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|
for i in range(12):
|
|
adam.minimize(loss)
|
|
lr = adam.get_lr()
|
|
self.assertTrue(np.allclose(lr, ret[i], rtol=1e-06, atol=0.0))
|
|
scheduler.step()
|
|
|
|
def test_lr_scheduler_natural_exp(self):
|
|
with fluid.dygraph.guard():
|
|
a = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
|
|
|
|
linear = fluid.dygraph.nn.Linear(10, 10)
|
|
a = fluid.dygraph.to_variable(a)
|
|
b = linear(a)
|
|
|
|
loss = fluid.layers.reduce_mean(b)
|
|
base_lr = 1.0
|
|
|
|
scheduler = paddle.optimizer.lr.NaturalExpDecay(1.0, gamma=0.5)
|
|
adam = paddle.optimizer.Adam(
|
|
scheduler, parameters=linear.parameters())
|
|
|
|
self.assertTrue(
|
|
np.allclose(
|
|
adam.get_lr(), 1.0, rtol=1e-06, atol=0.0))
|
|
|
|
ret = [1.0, np.exp(-0.5), np.exp(-1)]
|
|
for i in range(3):
|
|
adam.minimize(loss)
|
|
lr = adam.get_lr()
|
|
self.assertTrue(np.allclose(lr, ret[i], rtol=1e-06, atol=0.0))
|
|
scheduler.step()
|
|
|
|
def test_set_lr(self):
|
|
with fluid.dygraph.guard():
|
|
a = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
|
|
|
|
linear = fluid.dygraph.nn.Linear(10, 10)
|
|
|
|
a = fluid.dygraph.to_variable(a)
|
|
|
|
b = linear(a)
|
|
|
|
loss = fluid.layers.reduce_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()
|
|
self.assertTrue(
|
|
np.allclose(
|
|
lr, lr_list[i], rtol=1e-06, atol=0.0))
|
|
|
|
with self.assertRaises(TypeError):
|
|
lr_var = fluid.layers.create_global_var(
|
|
shape=[1], value=0.7, dtype='float32')
|
|
adam.set_lr(lr_var)
|
|
|
|
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)
|
|
|
|
|
|
class TestImperativeMomentumOptimizer(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = MomentumOptimizer(
|
|
learning_rate=0.001, momentum=0.9, parameter_list=parameter_list)
|
|
return optimizer
|
|
|
|
def get_optimizer(self):
|
|
optimizer = MomentumOptimizer(learning_rate=0.001, momentum=0.9)
|
|
return optimizer
|
|
|
|
def test_momentum(self):
|
|
self._check_mlp()
|
|
|
|
|
|
class TestImperativeLarsMomentumOptimizer(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = LarsMomentumOptimizer(
|
|
learning_rate=0.001, momentum=0.9, parameter_list=parameter_list)
|
|
return optimizer
|
|
|
|
def get_optimizer(self):
|
|
optimizer = LarsMomentumOptimizer(learning_rate=0.001, momentum=0.9)
|
|
return optimizer
|
|
|
|
def test_larsmomentum(self):
|
|
self._check_mlp()
|
|
|
|
|
|
class TestImperativeAdagradOptimizer(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = AdagradOptimizer(
|
|
learning_rate=0.2, parameter_list=parameter_list)
|
|
return optimizer
|
|
|
|
def get_optimizer(self):
|
|
optimizer = AdagradOptimizer(learning_rate=0.2)
|
|
return optimizer
|
|
|
|
def test_adagrad(self):
|
|
self._check_mlp()
|
|
|
|
|
|
class TestImperativeAdamaxOptimizer(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = AdamaxOptimizer(
|
|
learning_rate=0.2, parameter_list=parameter_list)
|
|
return optimizer
|
|
|
|
def get_optimizer(self):
|
|
optimizer = AdamaxOptimizer(learning_rate=0.2)
|
|
return optimizer
|
|
|
|
def test_adamax(self):
|
|
self._check_mlp()
|
|
|
|
|
|
class TestImperativeDpsgdOptimizer(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = DpsgdOptimizer(
|
|
learning_rate=0.01,
|
|
clip=10.0,
|
|
batch_size=16.0,
|
|
sigma=1.0,
|
|
parameter_list=parameter_list)
|
|
optimizer._seed = 100
|
|
return optimizer
|
|
|
|
def get_optimizer(self):
|
|
optimizer = DpsgdOptimizer(
|
|
learning_rate=0.01, clip=10.0, batch_size=16.0, sigma=1.0)
|
|
optimizer._seed = 100
|
|
return optimizer
|
|
|
|
def test_dpsgd(self):
|
|
self._check_mlp(place=fluid.CPUPlace())
|
|
|
|
|
|
class TestImperativeDecayedAdagradOptimizer(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = DecayedAdagradOptimizer(
|
|
learning_rate=0.2, parameter_list=parameter_list)
|
|
return optimizer
|
|
|
|
def get_optimizer(self):
|
|
optimizer = DecayedAdagradOptimizer(learning_rate=0.2)
|
|
return optimizer
|
|
|
|
def test_decayadagrad(self):
|
|
self._check_mlp()
|
|
|
|
|
|
class TestImperativeAdadeltaOptimizer(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = AdadeltaOptimizer(
|
|
learning_rate=0.0003,
|
|
epsilon=1.0e-6,
|
|
rho=0.95,
|
|
parameter_list=parameter_list)
|
|
return optimizer
|
|
|
|
def get_optimizer(self):
|
|
optimizer = AdadeltaOptimizer(
|
|
learning_rate=0.0003, epsilon=1.0e-6, rho=0.95)
|
|
return optimizer
|
|
|
|
def test_adadelta(self):
|
|
self._check_mlp()
|
|
|
|
|
|
class TestImperativeRMSPropOptimizer(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = RMSPropOptimizer(
|
|
learning_rate=0.1, parameter_list=parameter_list)
|
|
return optimizer
|
|
|
|
def get_optimizer(self):
|
|
optimizer = RMSPropOptimizer(learning_rate=0.1)
|
|
return optimizer
|
|
|
|
def test_rmsprop(self):
|
|
self._check_mlp()
|
|
|
|
|
|
class TestImperativeFtrlOptimizer(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = FtrlOptimizer(
|
|
learning_rate=0.1, parameter_list=parameter_list)
|
|
return optimizer
|
|
|
|
def get_optimizer(self):
|
|
optimizer = FtrlOptimizer(learning_rate=0.1)
|
|
return optimizer
|
|
|
|
def test_ftrl(self):
|
|
self._check_mlp()
|
|
|
|
|
|
def exclude_fn(param):
|
|
return param.name.endswith('.b_0')
|
|
|
|
|
|
class TestImperativeLambOptimizer(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = paddle.optimizer.Lamb(
|
|
learning_rate=0.002,
|
|
exclude_from_weight_decay_fn=exclude_fn,
|
|
parameters=parameter_list)
|
|
return optimizer
|
|
|
|
def get_optimizer(self):
|
|
optimizer = paddle.optimizer.Lamb(
|
|
learning_rate=0.002, exclude_from_weight_decay_fn=exclude_fn)
|
|
return optimizer
|
|
|
|
def test_lamb(self):
|
|
self._check_mlp()
|
|
|
|
|
|
class TestImperativeModelAverage(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = ModelAverage(
|
|
0.15, min_average_window=10000, max_average_window=12500)
|
|
return optimizer
|
|
|
|
def test_modelaverage(self):
|
|
exception_message = "In dygraph, don't support ModelAverage."
|
|
self._check_exception(exception_message)
|
|
|
|
|
|
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)
|
|
|
|
|
|
class TestImperativeExponentialMovingAverage(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = ExponentialMovingAverage(0.999)
|
|
return optimizer
|
|
|
|
def test_exponentialmoving(self):
|
|
exception_message = "In dygraph, don't support ExponentialMovingAverage."
|
|
self._check_exception(exception_message)
|
|
|
|
|
|
class TestImperativePipelineOptimizer(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = paddle.optimizer.SGD(learning_rate=0.5,
|
|
parameters=parameter_list)
|
|
optimizer = PipelineOptimizer(optimizer)
|
|
return optimizer
|
|
|
|
def test_pipline(self):
|
|
exception_message = "In dygraph, don't support PipelineOptimizer."
|
|
self._check_exception(exception_message)
|
|
|
|
|
|
class TestImperativeLookaheadOptimizer(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = paddle.optimizer.SGD(learning_rate=0.5,
|
|
parameters=parameter_list)
|
|
optimizer = LookaheadOptimizer(optimizer, alpha=0.5, k=5)
|
|
return optimizer
|
|
|
|
def test_lookahead(self):
|
|
exception_message = "In dygraph, don't support LookaheadOptimizer."
|
|
self._check_exception(exception_message)
|
|
|
|
|
|
class TestImperativeRecomputeOptimizer(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = paddle.optimizer.SGD(learning_rate=0.5,
|
|
parameters=parameter_list)
|
|
optimizer = RecomputeOptimizer(optimizer)
|
|
return optimizer
|
|
|
|
def test_recompute(self):
|
|
exception_message = "In dygraph, don't support RecomputeOptimizer."
|
|
self._check_exception(exception_message)
|
|
|
|
|
|
class TestImperativeOptimizerList(unittest.TestCase):
|
|
def test_parameter_list(self):
|
|
with fluid.dygraph.guard():
|
|
linear_1 = Linear(10, 10)
|
|
linear_2 = Linear(10, 10)
|
|
|
|
sgd = paddle.optimizer.SGD(1.0,
|
|
parameters=itertools.chain(
|
|
linear_1.parameters(),
|
|
linear_2.parameters()))
|
|
|
|
in_np = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
|
|
in_data = fluid.dygraph.to_variable(in_np)
|
|
|
|
y = linear_1(in_data)
|
|
y = linear_2(y)
|
|
loss = fluid.layers.reduce_mean(y)
|
|
loss.backward()
|
|
sgd.minimize(loss)
|
|
|
|
self.assertTrue(
|
|
len(sgd._parameter_list) ==
|
|
len(linear_1.parameters() + linear_2.parameters()))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|