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158 lines
5.4 KiB
158 lines
5.4 KiB
# Copyright (c) 2019 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 unittest
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from functools import partial
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import numpy as np
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import paddle
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import paddle.fluid as fluid
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import contextlib
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paddle.enable_static()
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def get_places():
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places = [fluid.CPUPlace()]
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if fluid.core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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return places
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@contextlib.contextmanager
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def prog_scope_guard(main_prog, startup_prog):
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scope = fluid.core.Scope()
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with fluid.unique_name.guard():
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with fluid.scope_guard(scope):
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with fluid.program_guard(main_prog, startup_prog):
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yield
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def bow_net(data,
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label,
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dict_dim,
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is_sparse=False,
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emb_dim=128,
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hid_dim=128,
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hid_dim2=96,
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class_dim=2):
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"""
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BOW net
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This model is from https://github.com/PaddlePaddle/models:
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fluid/PaddleNLP/text_classification/nets.py
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"""
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emb = fluid.layers.embedding(
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input=data, is_sparse=is_sparse, size=[dict_dim, emb_dim])
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bow = fluid.layers.sequence_pool(input=emb, pool_type='sum')
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bow_tanh = fluid.layers.tanh(bow)
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fc_1 = fluid.layers.fc(input=bow_tanh, size=hid_dim, act="tanh")
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fc_2 = fluid.layers.fc(input=fc_1, size=hid_dim2, act="tanh")
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prediction = fluid.layers.fc(input=[fc_2], size=class_dim, act="softmax")
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cost = fluid.layers.cross_entropy(input=prediction, label=label)
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avg_cost = fluid.layers.mean(x=cost)
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return avg_cost
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class TestWeightDecay(unittest.TestCase):
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def setUp(self):
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self.word_dict = paddle.dataset.imdb.word_dict()
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reader = paddle.batch(
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paddle.dataset.imdb.train(self.word_dict), batch_size=2)()
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self.train_data = [next(reader) for _ in range(5)]
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self.learning_rate = .5
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def run_program(self, place, feed_list):
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exe = fluid.Executor(place)
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feeder = fluid.DataFeeder(feed_list=feed_list, place=place)
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exe.run(fluid.default_startup_program())
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main_prog = fluid.default_main_program()
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param_list = [var.name for var in main_prog.block(0).all_parameters()]
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param_sum = []
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for data in self.train_data:
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out = exe.run(main_prog,
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feed=feeder.feed(data),
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fetch_list=param_list)
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p_sum = 0
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for v in out:
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p_sum += np.sum(np.abs(v))
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param_sum.append(p_sum)
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return param_sum
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def check_weight_decay(self, place, model):
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paddle.manual_seed(1)
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paddle.framework.random._manual_program_seed(1)
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main_prog = fluid.framework.Program()
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startup_prog = fluid.framework.Program()
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with prog_scope_guard(main_prog=main_prog, startup_prog=startup_prog):
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data = fluid.layers.data(
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name="words", shape=[1], dtype="int64", lod_level=1)
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label = fluid.layers.data(name="label", shape=[1], dtype="int64")
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avg_cost = model(data, label, len(self.word_dict))
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AdamW = fluid.contrib.extend_with_decoupled_weight_decay(
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fluid.optimizer.Adam)
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optimizer = AdamW(
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learning_rate=self.learning_rate,
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weight_decay=self.learning_rate)
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optimizer.minimize(avg_cost)
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param_sum = self.run_program(place, [data, label])
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return param_sum
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def check_weight_decay2(self, place, model):
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paddle.manual_seed(1)
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paddle.framework.random._manual_program_seed(1)
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main_prog = fluid.framework.Program()
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startup_prog = fluid.framework.Program()
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with prog_scope_guard(main_prog=main_prog, startup_prog=startup_prog):
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data = fluid.layers.data(
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name="words", shape=[1], dtype="int64", lod_level=1)
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label = fluid.layers.data(name="label", shape=[1], dtype="int64")
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avg_cost = model(data, label, len(self.word_dict))
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param_list = [(var, var * self.learning_rate)
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for var in main_prog.block(0).all_parameters()]
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optimizer = fluid.optimizer.Adam(learning_rate=self.learning_rate)
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optimizer.minimize(avg_cost)
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for params in param_list:
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updated_p = fluid.layers.elementwise_sub(
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x=params[0], y=params[1])
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fluid.layers.assign(input=updated_p, output=params[0])
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param_sum = self.run_program(place, [data, label])
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return param_sum
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def test_weight_decay(self):
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for place in get_places():
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model = partial(bow_net, is_sparse=False)
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param_sum1 = self.check_weight_decay(place, model)
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param_sum2 = self.check_weight_decay2(place, model)
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for i in range(len(param_sum1)):
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assert np.isclose(a=param_sum1[i], b=param_sum2[i], rtol=5e-5)
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if __name__ == '__main__':
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unittest.main()
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