mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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72 lines
2.1 KiB
C++
72 lines
2.1 KiB
C++
/*
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* Copyright (c) 2022-2024, NVIDIA CORPORATION. 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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*/
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#include "workerPool.h"
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#include "tensorrt_llm/common/cudaUtils.h"
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namespace tensorrt_llm::runtime
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{
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WorkerPool::WorkerPool(std::size_t numWorkers, std::int32_t deviceId)
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{
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for (std::size_t i = 0; i < numWorkers; ++i)
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{
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mWorkers.emplace_back(
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[this, deviceId]
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{
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if (deviceId >= 0)
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{
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TLLM_CUDA_CHECK(cudaSetDevice(deviceId));
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}
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else
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{
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TLLM_LOG_WARNING("WorkerPool did not set cuda device");
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}
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while (true)
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{
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std::function<void()> task;
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{
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std::unique_lock<std::mutex> lock(this->mQueueMutex);
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this->condition.wait(lock, [this] { return this->stop || !this->mTasks.empty(); });
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if (this->stop && this->mTasks.empty())
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{
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return;
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}
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task = std::move(this->mTasks.front());
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this->mTasks.pop();
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}
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task();
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}
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});
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}
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}
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WorkerPool::~WorkerPool()
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{
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{
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std::unique_lock<std::mutex> lock(mQueueMutex);
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stop = true;
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}
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condition.notify_all();
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for (std::thread& worker : mWorkers)
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{
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worker.join();
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}
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}
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} // namespace tensorrt_llm::runtime
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