sneaxiy
6 years ago
parent
8a31b2eb75
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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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#include "paddle/fluid/framework/details/reference_count_pass_helper.h"
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namespace paddle {
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namespace framework {
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namespace details {} // namespace details
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} // namespace framework
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} // namespace paddle
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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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#include <algorithm>
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#ifdef PADDLE_WITH_CUDA
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#include "paddle/fluid/platform/cuda_device_guard.h"
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#endif
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#include "paddle/fluid/framework/garbage_collector.h"
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namespace paddle {
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namespace framework {
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GarbageCollector::GarbageCollector(const platform::Place &place,
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size_t max_memory_size)
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: max_memory_size_((std::max)(max_memory_size, static_cast<size_t>(1))) {
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garbages_.reset(new GarbageQueue());
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dev_ctx_ = platform::DeviceContextPool::Instance().Get(place);
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}
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CPUGarbageCollector::CPUGarbageCollector(const platform::CPUPlace &place,
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size_t max_memory_size)
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: GarbageCollector(place, max_memory_size) {}
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void CPUGarbageCollector::ClearCallback(const std::function<void()> &callback) {
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callback();
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}
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#ifdef PADDLE_WITH_CUDA
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UnsafeFastGPUGarbageCollector::UnsafeFastGPUGarbageCollector(
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const platform::CUDAPlace &place, size_t max_memory_size)
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: GarbageCollector(place, max_memory_size) {}
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void UnsafeFastGPUGarbageCollector::ClearCallback(
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const std::function<void()> &callback) {
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callback();
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}
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DefaultStreamGarbageCollector::DefaultStreamGarbageCollector(
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const platform::CUDAPlace &place, size_t max_memory_size)
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: GarbageCollector(place, max_memory_size) {}
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void DefaultStreamGarbageCollector::Wait() const {
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static_cast<platform::CUDADeviceContext *>(this->dev_ctx_)
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->WaitStreamCallback();
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}
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void DefaultStreamGarbageCollector::ClearCallback(
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const std::function<void()> &callback) {
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static_cast<platform::CUDADeviceContext *>(this->dev_ctx_)
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->AddStreamCallback(callback);
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}
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StreamGarbageCollector::StreamGarbageCollector(const platform::CUDAPlace &place,
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size_t max_memory_size)
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: GarbageCollector(place, max_memory_size) {
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platform::CUDADeviceGuard guard(place.device);
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PADDLE_ENFORCE(cudaStreamCreate(&stream_));
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callback_manager_.reset(new platform::StreamCallbackManager(stream_));
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}
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StreamGarbageCollector::~StreamGarbageCollector() {
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auto place = boost::get<platform::CUDAPlace>(this->dev_ctx_->GetPlace());
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platform::CUDADeviceGuard guard(place.device);
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PADDLE_ENFORCE(cudaStreamSynchronize(stream_));
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PADDLE_ENFORCE(cudaStreamDestroy(stream_));
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}
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cudaStream_t StreamGarbageCollector::stream() const { return stream_; }
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void StreamGarbageCollector::Wait() const { callback_manager_->Wait(); }
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void StreamGarbageCollector::ClearCallback(
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const std::function<void()> &callback) {
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callback_manager_->AddCallback(callback);
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}
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#endif
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} // namespace framework
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} // namespace paddle
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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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import unittest
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from test_eager_deletion_lstm_net import TestBase
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import paddle.fluid as fluid
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def gru_net(data,
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label,
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dict_dim,
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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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emb_lr=400.0):
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emb = fluid.layers.embedding(
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input=data,
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size=[dict_dim, emb_dim],
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param_attr=fluid.ParamAttr(learning_rate=emb_lr))
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fc0 = fluid.layers.fc(input=emb, size=hid_dim * 3)
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gru_h = fluid.layers.dynamic_gru(input=fc0, size=hid_dim, is_reverse=False)
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gru_max = fluid.layers.sequence_pool(input=gru_h, pool_type='max')
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gru_max_tanh = fluid.layers.tanh(gru_max)
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fc1 = fluid.layers.fc(input=gru_max_tanh, size=hid_dim2, act='tanh')
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prediction = fluid.layers.fc(input=fc1, 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 GRUTest(TestBase):
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def setUp(self):
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self.net = gru_net
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if __name__ == "__main__":
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unittest.main()
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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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import os
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os.environ['FLAGS_eager_delete_tensor_gb'] = '0.0'
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os.environ['CPU_NUM'] = '2'
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import six
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import unittest
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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 train(network, use_cuda, use_parallel_executor, batch_size=32, pass_num=2):
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if use_cuda and not core.is_compiled_with_cuda():
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print('Skip use_cuda=True because Paddle is not compiled with cuda')
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return
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word_dict = paddle.dataset.imdb.word_dict()
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train_reader = paddle.batch(
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paddle.dataset.imdb.train(word_dict), batch_size=batch_size)
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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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cost = network(data, label, len(word_dict))
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optimizer = fluid.optimizer.Adagrad(learning_rate=0.2)
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optimizer.minimize(cost)
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place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
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feeder = fluid.DataFeeder(feed_list=[data, label], place=place)
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reader = feeder.decorate_reader(
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train_reader, multi_devices=use_parallel_executor)
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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if use_parallel_executor:
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train_exe = fluid.ParallelExecutor(
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use_cuda=use_cuda, loss_name=cost.name)
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fetch_list = [cost.name]
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else:
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train_exe = exe
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fetch_list = [cost]
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for pass_id in six.moves.xrange(pass_num):
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batch_id = 0
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for data in reader():
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train_exe.run(feed=data,
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fetch_list=fetch_list if batch_id % 4 == 0 else [])
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batch_id += 1
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if batch_id > 16:
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break
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def lstm_net(data,
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label,
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dict_dim,
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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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emb_lr=30.0):
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emb = fluid.layers.embedding(
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input=data,
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size=[dict_dim, emb_dim],
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param_attr=fluid.ParamAttr(learning_rate=emb_lr))
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fc0 = fluid.layers.fc(input=emb, size=hid_dim * 4)
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lstm_h, c = fluid.layers.dynamic_lstm(
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input=fc0, size=hid_dim * 4, is_reverse=False)
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lstm_max = fluid.layers.sequence_pool(input=lstm_h, pool_type='max')
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lstm_max_tanh = fluid.layers.tanh(lstm_max)
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fc1 = fluid.layers.fc(input=lstm_max_tanh, size=hid_dim2, act='tanh')
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prediction = fluid.layers.fc(input=fc1, 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 TestBase(unittest.TestCase):
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def setUp(self):
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self.net = lstm_net
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def test_network(self):
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for use_cuda in [True, False]:
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for use_parallel_executor in [False, True]:
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print('network: {}, use_cuda: {}, use_parallel_executor: {}'.
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format(self.net.__name__, use_cuda,
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use_parallel_executor))
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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with fluid.scope_guard(core.Scope()):
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train(self.net, use_cuda, use_parallel_executor)
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if __name__ == "__main__":
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unittest.main()
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