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120 lines
4.1 KiB
120 lines
4.1 KiB
/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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 <ThreadPool.h>
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#include <unistd.h>
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#include <string>
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#include <thread> // NOLINT
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#include "google/protobuf/text_format.h"
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#include "gtest/gtest.h"
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#include "paddle/fluid/distributed/ps.pb.h"
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#include "paddle/fluid/distributed/table/common_dense_table.h"
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#include "paddle/fluid/distributed/table/common_sparse_table.h"
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#include "paddle/fluid/distributed/table/sparse_geo_table.h"
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#include "paddle/fluid/distributed/table/table.h"
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namespace paddle {
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namespace distributed {
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// SparseGeoTable + SSUM
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TEST(SparseGeoTable, SSUM) {
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int emb_dim = 10;
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int trainers = 2;
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TableParameter table_config;
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table_config.set_table_class("SparseGeoTable");
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FsClientParameter fs_config;
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Table *table = new SparseGeoTable();
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TableAccessorParameter *accessor_config = table_config.mutable_accessor();
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accessor_config->set_accessor_class("CommMergeAccessor");
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CommonAccessorParameter *common_config = table_config.mutable_common();
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common_config->set_name("sum");
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common_config->set_table_name("ssum_test_table");
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common_config->set_trainer_num(trainers);
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common_config->add_params("Param");
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common_config->add_dims(emb_dim);
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common_config->add_initializers("fill_constant&1.0");
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auto ret = table->initialize(table_config, fs_config);
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ASSERT_EQ(ret, 0);
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// test push_sparse_param, and create params
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std::vector<uint64_t> init_keys = {0, 1, 2, 3, 4};
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std::vector<float> init_values;
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for (size_t i = 0; i < init_keys.size() * emb_dim; i++) {
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init_values.push_back(0.0);
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}
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table->push_sparse_param(init_keys.data(), init_values.data(),
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init_keys.size());
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std::vector<float> pull_values(init_values.size());
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table->pull_sparse(pull_values.data(), init_keys.data(), init_keys.size());
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for (size_t i = 0; i < init_keys.size() * emb_dim; i++) {
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ASSERT_TRUE(abs(pull_values[i] - init_values[i]) < 1e-5);
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}
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std::vector<std::vector<uint64_t>> trainer_keys;
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std::vector<std::vector<float>> trainer_values;
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trainer_keys.resize(trainers);
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trainer_values.resize(trainers);
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float start = 0.0;
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for (int i = 0; i < trainers; i++) {
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trainer_keys[i] = init_keys;
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for (size_t j = 0; j < trainer_keys[i].size(); j++) {
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auto id = trainer_keys[i][j];
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for (int k = 0; k < emb_dim; k++) {
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trainer_values[i].push_back(start);
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pull_values[id * emb_dim + k] += start;
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start += 0.1;
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}
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}
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}
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std::shared_ptr<::ThreadPool> pool_ =
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std::make_shared<::ThreadPool>(trainers);
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std::vector<std::future<void>> task_status;
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for (int i = 0; i < trainers; i++) {
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auto &push_keys = trainer_keys[i];
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auto &push_values = trainer_values[i];
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auto task = [table, &push_keys, &push_values] {
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table->push_sparse(push_keys.data(), push_values.data(),
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push_keys.size());
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};
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task_status.push_back(pool_->enqueue(std::move(task)));
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}
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for (auto &status : task_status) {
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status.wait();
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}
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std::vector<std::vector<uint64_t>> geo_pull_ids;
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std::vector<std::vector<float>> geo_pull_values;
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geo_pull_ids.resize(trainers);
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geo_pull_values.resize(trainers);
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for (int i = 0; i < trainers; i++) {
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table->pull_geo_param(i, &geo_pull_values[i], &geo_pull_ids[i]);
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ASSERT_EQ(geo_pull_values[i].size(), geo_pull_ids[i].size() * emb_dim);
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for (size_t j = 0; j < geo_pull_ids[i].size(); ++j) {
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auto id = geo_pull_ids[i][j];
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for (int k = 0; k < emb_dim; k++) {
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ASSERT_TRUE(abs(geo_pull_values[i][j * emb_dim + k] -
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pull_values[id * emb_dim + k]) < 1e-5);
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}
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}
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}
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}
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} // namespace distributed
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} // namespace paddle
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