chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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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 "gtest/gtest.h"
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#include "paddle/common/flags.h"
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#include "paddle/fluid/framework/trainer.h"
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#ifdef PADDLE_WITH_GLOO
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#include "paddle/fluid/framework/fleet/gloo_wrapper.h"
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#endif
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#if defined _WIN32 || defined __APPLE__
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#else
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#define _LINUX
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#endif
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COMMON_DECLARE_bool(enable_exit_when_partial_worker);
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namespace paddle {
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namespace framework {
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TEST(DisMultiTrainerTest, test1) {
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#ifdef _LINUX
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std::shared_ptr<DistMultiTrainer> tmp1 = std::make_shared<DistMultiTrainer>();
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TrainerDesc t;
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t.set_class_name("DistMultiTrainer");
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t.set_device_worker_name("DownpourWorker");
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t.set_thread_num(1);
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auto* m = t.mutable_downpour_param()->add_program_config();
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m->set_program_id("123");
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std::string str;
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str += "name: \"MultiSlotDataFeed\"\nbatch_size: 2\nmulti_slot_desc {\n";
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str += "slots {\nname: \"words\"\ntype: \"uint64\"\nis_dense: false\n";
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str += "is_used: true\n}\nslots {\nname: \"label\"\ntype: \"uint64\"\n";
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str += "is_dense: false\nis_used: true\n}\n}\n";
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std::shared_ptr<MultiSlotDataset> dataset =
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std::make_shared<MultiSlotDataset>();
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dataset->SetFileList(std::vector<std::string>());
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dataset->SetThreadNum(1);
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dataset->SetTrainerNum(1);
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dataset->SetDataFeedDesc(str);
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dataset->CreateReaders();
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Scope root_scope;
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tmp1->SetScope(&root_scope);
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tmp1->Initialize(t, dataset.get());
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ProgramDesc p;
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tmp1->InitOtherEnv(p);
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tmp1->Finalize();
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#endif
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}
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TEST(DisMultiTrainerTest, testforgpugraph) {
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#ifdef _LINUX
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TrainerDesc t;
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t.set_class_name("MultiTrainer");
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t.set_device_worker_name("HogwildWorker");
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t.set_thread_num(1);
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auto* m = t.mutable_downpour_param()->add_program_config();
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m->set_program_id("123");
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std::string str;
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str += "name: \"MultiSlotDataFeed\"\nbatch_size: 2\nmulti_slot_desc {\n";
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str += "slots {\nname: \"words\"\ntype: \"uint64\"\nis_dense: false\n";
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str += "is_used: true\n}\nslots {\nname: \"label\"\ntype: \"uint64\"\n";
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str += "is_dense: false\nis_used: true\n}\n}\n";
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std::shared_ptr<MultiSlotDataset> dataset =
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std::make_shared<MultiSlotDataset>();
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dataset->SetFileList(std::vector<std::string>());
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dataset->SetThreadNum(1);
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dataset->SetTrainerNum(1);
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dataset->SetDataFeedDesc(str);
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dataset->CreateReaders();
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dataset->SetGpuGraphMode(true);
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dataset->GetMemoryDataSize();
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dataset->SetPassId(2);
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dataset->GetPassID();
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dataset->GetEpochFinish();
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#endif
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}
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TEST(DisMultiTrainerTest, test2) {
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#ifdef _LINUX
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FLAGS_enable_exit_when_partial_worker = true;
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std::shared_ptr<MultiTrainer> tmp1 = std::make_shared<MultiTrainer>();
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TrainerDesc t;
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t.set_class_name("MultiTrainer");
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t.set_device_worker_name("HogwildWorker");
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t.set_thread_num(1);
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auto* m = t.mutable_downpour_param()->add_program_config();
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m->set_program_id("123");
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std::string str;
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// str += "name: \"MultiSlotDataFeed\"\nbatch_size: 2\nmulti_slot_desc {\n";
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str +=
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"name: \"SlotRecordInMemoryDataFeed\"\nbatch_size: 2\nmulti_slot_desc "
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"{\n";
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str += "slots {\nname: \"words\"\ntype: \"uint64\"\nis_dense: false\n";
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str += "is_used: true\n}\nslots {\nname: \"label\"\ntype: \"uint64\"\n";
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str += "is_dense: false\nis_used: true\n}\n}\n";
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str += "graph_config {\n";
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str += "gpu_graph_training: true\n}";
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// std::shared_ptr<MultiSlotDataset> dataset =
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// std::make_shared<MultiSlotDataset>();
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std::shared_ptr<SlotRecordDataset> dataset =
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std::make_shared<SlotRecordDataset>();
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dataset->SetFileList(std::vector<std::string>());
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dataset->SetThreadNum(1);
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dataset->SetTrainerNum(1);
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dataset->SetDataFeedDesc(str);
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dataset->CreateChannel();
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dataset->CreateReaders();
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Scope root_scope;
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tmp1->SetScope(&root_scope);
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tmp1->Initialize(t, dataset.get());
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tmp1->SetDebug(false);
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ProgramDesc p;
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tmp1->InitOtherEnv(p);
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tmp1->Run();
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tmp1->Finalize();
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#endif
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}
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TEST(DisMultiTrainerTest, test3) {
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#ifdef _LINUX
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FLAGS_enable_exit_when_partial_worker = true;
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std::shared_ptr<MultiTrainer> tmp1 = std::make_shared<MultiTrainer>();
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TrainerDesc t;
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t.set_class_name("MultiTrainer");
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t.set_device_worker_name("HogwildWorker");
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t.set_thread_num(1);
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auto* m = t.mutable_downpour_param()->add_program_config();
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m->set_program_id("123");
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std::string str;
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// str += "name: \"MultiSlotDataFeed\"\nbatch_size: 2\nmulti_slot_desc {\n";
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str +=
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"name: \"SlotRecordInMemoryDataFeed\"\nbatch_size: 2\nmulti_slot_desc "
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"{\n";
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str += "slots {\nname: \"words\"\ntype: \"uint64\"\nis_dense: false\n";
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str += "is_used: true\n}\nslots {\nname: \"label\"\ntype: \"uint64\"\n";
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str += "is_dense: false\nis_used: true\n}\n}\n";
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str += "graph_config {\n";
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str += "gpu_graph_training: true\n}";
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// std::shared_ptr<MultiSlotDataset> dataset =
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// std::make_shared<MultiSlotDataset>();
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std::shared_ptr<SlotRecordDataset> dataset =
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std::make_shared<SlotRecordDataset>();
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dataset->SetFileList(std::vector<std::string>());
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dataset->SetThreadNum(1);
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dataset->SetTrainerNum(1);
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dataset->SetDataFeedDesc(str);
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dataset->CreateChannel();
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dataset->SetGpuGraphMode(true);
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dataset->CreateReaders();
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auto readers = dataset->GetReaders();
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readers[0]->SetGpuGraphMode(true);
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Scope root_scope;
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tmp1->SetScope(&root_scope);
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tmp1->Initialize(t, dataset.get());
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tmp1->SetDebug(true);
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ProgramDesc p;
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tmp1->InitOtherEnv(p);
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// tmp1->Run();
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tmp1->Finalize();
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#endif
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}
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} // namespace framework
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} // namespace paddle
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