mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
16d2467ea8
* Update TensorRT-LLM --------- Co-authored-by: Denis Kayshev <topenkoff@gmail.com> Co-authored-by: akhoroshev <arthoroshev@gmail.com> Co-authored-by: Patrick Reiter Horn <patrick.horn@gmail.com> Update
115 lines
3.0 KiB
C++
115 lines
3.0 KiB
C++
/*
|
|
* Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved.
|
|
*
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
*/
|
|
|
|
#include "tensorrt_llm/runtime/workerPool.h"
|
|
|
|
#include <gtest/gtest.h>
|
|
#include <random>
|
|
|
|
namespace tensorrt_llm::runtime
|
|
{
|
|
|
|
TEST(WorkerPool, basic)
|
|
{
|
|
WorkerPool pool(2);
|
|
|
|
auto fn = []() { return 12345; };
|
|
auto resultFuture = pool.enqueue(fn);
|
|
|
|
auto fn2 = []() { return 12.345f; };
|
|
auto f2 = pool.enqueue(fn2);
|
|
|
|
auto fn3 = []() { return 40.78f; };
|
|
auto f3 = pool.enqueue(fn3);
|
|
|
|
auto r1 = resultFuture.get();
|
|
auto r2 = f2.get();
|
|
auto r3 = f3.get();
|
|
|
|
EXPECT_EQ(12345, r1);
|
|
EXPECT_FLOAT_EQ(12.345f, r2);
|
|
EXPECT_FLOAT_EQ(40.78f, r3);
|
|
}
|
|
|
|
TEST(WorkerPool, voidReturn)
|
|
{
|
|
WorkerPool pool(2);
|
|
|
|
int32_t returnVal1 = 0;
|
|
int32_t returnVal2 = 0;
|
|
int32_t returnVal3 = 0;
|
|
|
|
auto fn1 = [&returnVal1]() { returnVal1 = 10001; };
|
|
auto f1 = pool.enqueue(fn1);
|
|
|
|
auto fn2 = [&returnVal2]() { returnVal2 = 10002; };
|
|
auto f2 = pool.enqueue(fn2);
|
|
|
|
auto fn3 = [&returnVal3]() { returnVal3 = 10003; };
|
|
auto f3 = pool.enqueue(fn3);
|
|
|
|
f1.get();
|
|
f2.get();
|
|
f3.get();
|
|
|
|
EXPECT_EQ(returnVal1, 10001);
|
|
EXPECT_EQ(returnVal2, 10002);
|
|
EXPECT_EQ(returnVal3, 10003);
|
|
}
|
|
|
|
class WorkerPoolTest : public ::testing::TestWithParam<std::tuple<int, int>>
|
|
{
|
|
protected:
|
|
void SetUp() override
|
|
{
|
|
mNumTasks = std::get<0>(GetParam());
|
|
mNumWorkers = std::get<1>(GetParam());
|
|
pool = std::make_unique<WorkerPool>(mNumWorkers);
|
|
}
|
|
|
|
int mNumTasks;
|
|
int mNumWorkers;
|
|
std::unique_ptr<WorkerPool> pool;
|
|
};
|
|
|
|
TEST_P(WorkerPoolTest, ScheduleTasks)
|
|
{
|
|
std::vector<std::future<void>> futures;
|
|
std::random_device randomDevice;
|
|
std::mt19937 generator(randomDevice());
|
|
std::uniform_int_distribution<> distribution(1, 5);
|
|
|
|
for (int i = 0; i < mNumTasks; ++i)
|
|
{
|
|
futures.push_back(
|
|
pool->enqueue([&]() { std::this_thread::sleep_for(std::chrono::milliseconds(distribution(generator))); }));
|
|
}
|
|
|
|
for (auto& f : futures)
|
|
{
|
|
f.get();
|
|
}
|
|
|
|
// This is a smoke test to try and catch threading and synchronization issues by stress testing. No assertion.
|
|
}
|
|
|
|
INSTANTIATE_TEST_SUITE_P(WorkerPoolTests, WorkerPoolTest,
|
|
::testing::Combine(::testing::Values(1, 2, 4, 8, 16, 32, 64, 128), // Range for number of tasks
|
|
::testing::Values(1, 2, 4, 8, 16, 32, 64, 128) // Range for number of workers
|
|
));
|
|
|
|
} // namespace tensorrt_llm::runtime
|