[WIP] Refine MultiDevSSAGraph (#15040)
* refine parallel_exe test=develop * rename shared_var_device * code refine * add test_weight_decay * remove Sort test=develop * Add SortForReduce test=develop * code refine test=develop * follow comment test=developrevert-15207-remove_op_handle_lock_and_fix_var
chengduo
7 years ago
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# 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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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.core as core
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import paddle.fluid as fluid
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def get_places():
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places = []
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if core.is_compiled_with_cuda():
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places.append(core.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=4)()
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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_executor(self, place, feed_list, loss):
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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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loss_set = []
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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=[loss.name])
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print("loss %s" % (np.average(out)))
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loss_set.append(np.average(out))
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return loss_set
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def run_parallel_exe(self,
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place,
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feed_list,
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loss,
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use_cuda=True,
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use_reduce=False,
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use_fast_executor=False,
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use_ir_memory_optimize=False):
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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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exec_strategy = fluid.ExecutionStrategy()
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if use_fast_executor:
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exec_strategy.use_experimental_executor = True
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build_strategy = fluid.BuildStrategy()
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build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce \
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if use_reduce else fluid.BuildStrategy.ReduceStrategy.AllReduce
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build_strategy.memory_optimize = use_ir_memory_optimize
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parallel_exe = fluid.ParallelExecutor(
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use_cuda,
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loss_name=loss.name,
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exec_strategy=exec_strategy,
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build_strategy=build_strategy)
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loss_set = []
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for data in self.train_data:
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out = parallel_exe.run(feed=feeder.feed(data),
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fetch_list=[loss.name])
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print("loss %s" % (np.average(out)))
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loss_set.append(np.average(out))
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return loss_set
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def check_weight_decay(self,
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place,
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model,
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use_parallel_exe=False,
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use_reduce=False):
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main_prog = fluid.framework.Program()
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startup_prog = fluid.framework.Program()
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startup_prog.random_seed = 1
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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.Adagrad(
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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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if use_parallel_exe:
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loss = self.run_parallel_exe(
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place, [data, label],
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loss=avg_cost,
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use_cuda=True,
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use_reduce=use_reduce)
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else:
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loss = self.run_executor(place, [data, label], loss=avg_cost)
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return loss
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def test_weight_decay(self):
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model = partial(bow_net, is_sparse=False)
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for place in get_places():
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loss = self.check_weight_decay(place, model, use_parallel_exe=False)
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loss2 = self.check_weight_decay(
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place, model, use_parallel_exe=True, use_reduce=False)
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for i in range(len(loss)):
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assert np.isclose(a=loss[i], b=loss2[i], rtol=5e-5)
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loss3 = self.check_weight_decay(
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place, model, use_parallel_exe=True, use_reduce=True)
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for i in range(len(loss)):
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assert np.isclose(a=loss[i], b=loss3[i], rtol=5e-5)
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if __name__ == '__main__':
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unittest.main()
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