chore: import upstream snapshot with attribution
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wehub-resource-sync
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/* Copyright (c) 2016 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 "paddle/fluid/framework/selected_rows_utils.h"
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#include <ctime>
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#include <thread> // NOLINT
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#include "gtest/gtest.h"
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namespace paddle {
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namespace framework {
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class SelectedRowsTester : public ::testing::Test {
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public:
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void SetUp() override {
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std::vector<int64_t> rows{0, 4, 7};
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int64_t height = 10;
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int64_t row_numel = 100;
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selected_rows_ = std::make_unique<phi::SelectedRows>(rows, height);
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phi::DenseTensor* value = selected_rows_->mutable_value();
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auto* data = value->mutable_data<float>(
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common::make_ddim({static_cast<int64_t>(rows.size()), row_numel}),
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place_);
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for (int64_t i = 0; i < value->numel(); ++i) {
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data[i] = static_cast<float>(i);
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}
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}
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protected:
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phi::CPUPlace place_;
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std::unique_ptr<phi::SelectedRows> selected_rows_{nullptr};
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};
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TEST_F(SelectedRowsTester, height) { ASSERT_EQ(selected_rows_->height(), 10); }
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TEST_F(SelectedRowsTester, dims) {
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ASSERT_EQ(selected_rows_->value().dims(), common::make_ddim({3, 100}));
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}
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TEST_F(SelectedRowsTester, complete_dims) {
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ASSERT_EQ(selected_rows_->GetCompleteDims(), common::make_ddim({10, 100}));
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}
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TEST_F(SelectedRowsTester, SerializeAndDeserialize) {
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phi::SelectedRows dst_tensor;
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phi::CPUContext cpu_ctx(place_);
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std::ostringstream oss;
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phi::SerializeToStream(oss, *selected_rows_, cpu_ctx);
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std::istringstream iss(oss.str());
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phi::DeserializeFromStream(iss, &dst_tensor, cpu_ctx);
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ASSERT_EQ(selected_rows_->rows(), dst_tensor.rows());
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ASSERT_EQ(selected_rows_->height(), dst_tensor.height());
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ASSERT_EQ(selected_rows_->value().dims(), dst_tensor.value().dims());
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ASSERT_EQ(selected_rows_->GetCompleteDims(), dst_tensor.GetCompleteDims());
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auto* dst_data = dst_tensor.value().data<float>();
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for (int64_t i = 0; i < dst_tensor.value().numel(); ++i) {
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ASSERT_EQ(dst_data[i], static_cast<float>(i));
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}
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}
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TEST(SelectedRows, SparseTable) {
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phi::CPUPlace cpu;
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phi::SelectedRows table;
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int64_t table_size = 100;
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int64_t embedding_width = 8;
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// initialize a sparse table
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table.mutable_value()->Resize(
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common::make_ddim({table_size, embedding_width}));
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auto* data = table.mutable_value()->mutable_data<float>(cpu);
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for (int64_t i = 0; i < table_size; ++i) {
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for (int64_t j = 0; j < embedding_width; ++j) {
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data[i * embedding_width + j] = static_cast<float>(i);
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}
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}
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ASSERT_EQ(table.AutoGrownIndex(10, true, false), 0);
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ASSERT_EQ(table.AutoGrownIndex(8, true, false), 1);
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ASSERT_EQ(table.AutoGrownIndex(8, true, false), 1);
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ASSERT_EQ(table.AutoGrownIndex(6, true, false), 2);
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for (int64_t i = 11; i < 20; i++) {
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ASSERT_EQ(table.AutoGrownIndex(i, true, true), -1);
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ASSERT_TRUE(!table.HasKey(i));
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}
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ASSERT_TRUE(table.HasKey(10));
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ASSERT_TRUE(table.HasKey(8));
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ASSERT_TRUE(table.HasKey(6));
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ASSERT_EQ(table.rows().size(), 3UL);
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phi::DenseTensor ids;
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ids.Resize(common::make_ddim({4}));
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auto* ids_data = ids.mutable_data<int64_t>(cpu);
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ids_data[0] = static_cast<int64_t>(6);
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ids_data[1] = static_cast<int64_t>(6);
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ids_data[2] = static_cast<int64_t>(8);
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ids_data[3] = static_cast<int64_t>(10);
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phi::DenseTensor get_value;
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auto* value_data = get_value.mutable_data<float>(
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common::make_ddim({4, embedding_width}), cpu);
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table.Get(ids, &get_value);
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for (int j = 0; j < embedding_width; ++j) {
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ASSERT_EQ(value_data[0 * embedding_width + j], 2);
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}
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for (int j = 0; j < embedding_width; ++j) {
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ASSERT_EQ(value_data[1 * embedding_width + j], 2);
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}
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for (int j = 0; j < embedding_width; ++j) {
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ASSERT_EQ(value_data[2 * embedding_width + j], 1);
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}
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for (int j = 0; j < embedding_width; ++j) {
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ASSERT_EQ(value_data[3 * embedding_width + j], 0);
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}
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}
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void f1(phi::SelectedRows* table, int table_size) {
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for (int i = 1000000; i > 0; --i) {
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auto id = i % table_size;
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int64_t index1 = table->AutoGrownIndex(id, true);
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int64_t index2 = table->AutoGrownIndex(id, false);
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int64_t index3 = table->AutoGrownIndex(id, true);
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ASSERT_EQ(index1, index2);
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ASSERT_EQ(index2, index3);
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}
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}
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void f2(phi::SelectedRows* table, int table_size) {
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for (int i = 0; i < 1000000; ++i) {
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auto id = i % table_size;
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int64_t index1 = table->AutoGrownIndex(id, true);
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int64_t index2 = table->AutoGrownIndex(id, false);
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int64_t index3 = table->AutoGrownIndex(id, true);
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ASSERT_EQ(index1, index2);
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ASSERT_EQ(index2, index3);
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}
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}
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void f3(phi::SelectedRows* table, int table_size) {
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clock_t t1 = clock();
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for (int i = 100000; i > 0; --i) {
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auto id1 = table->AutoGrownIndex(i % table_size, true);
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auto id2 = table->Index(i % table_size);
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ASSERT_EQ(id1, id2);
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}
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clock_t t2 = clock();
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std::cout << "f3 run time:" << t2 - t1 << std::endl;
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}
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void f4(phi::SelectedRows* table, int table_size) {
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clock_t t1 = clock();
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for (int i = 0; i < 100000; ++i) {
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auto id1 = table->AutoGrownIndex(i % table_size, true);
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auto id2 = table->Index(i % table_size);
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ASSERT_EQ(id1, id2);
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}
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clock_t t2 = clock();
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std::cout << "f4 run time:" << t2 - t1 << std::endl;
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}
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TEST(SelectedRows, MultiThreadAutoIndex) {
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phi::CPUPlace cpu;
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phi::SelectedRows table;
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int64_t table_size = 100000;
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int64_t embedding_width = 8;
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// initialize a sparse table
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table.mutable_value()->Resize(
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common::make_ddim({table_size, embedding_width}));
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auto* data = table.mutable_value()->mutable_data<float>(cpu);
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for (int64_t i = 0; i < table_size; ++i) {
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for (int64_t j = 0; j < embedding_width; ++j) {
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data[i * embedding_width + j] = static_cast<float>(i);
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}
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}
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std::thread t1(f1, &table, table_size);
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std::thread t11(f1, &table, table_size);
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std::thread t2(f2, &table, table_size);
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std::thread t22(f2, &table, table_size);
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t1.join();
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t11.join();
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t2.join();
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t22.join();
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std::thread t3(f3, &table, table_size);
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std::thread t4(f4, &table, table_size);
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t3.join();
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t4.join();
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}
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} // namespace framework
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} // namespace paddle
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