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https://github.com/NVIDIA/TensorRT-LLM.git
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6755a3f077
* Update TensorRT-LLM --------- Co-authored-by: Tltin <TltinDeng01@gmail.com> Co-authored-by: zhaohb <zhaohbcloud@126.com> Co-authored-by: Bradley Heilbrun <brad@repl.it> Co-authored-by: nqbao11 <nqbao11.01@gmail.com> Co-authored-by: Nikhil Varghese <nikhil@bot-it.ai>
108 lines
3.4 KiB
C++
108 lines
3.4 KiB
C++
/*
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* Copyright (c) 2022-2023, 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 "tensorrt_llm/runtime/worldConfig.h"
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#include "tensorrt_llm/common/assert.h"
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#include "tensorrt_llm/common/stringUtils.h"
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#include "tensorrt_llm/runtime/tllmLogger.h"
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#include "tensorrt_llm/runtime/utils/multiDeviceUtils.h"
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#include <csignal>
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#include <cstdlib>
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#include <mpi.h>
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using namespace tensorrt_llm::runtime;
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namespace tc = tensorrt_llm::common;
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namespace
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{
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bool mpiInitialized = false;
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void initMpi(nvinfer1::ILogger& logger, int threadMode = MPI_THREAD_FUNNELED)
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{
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if (mpiInitialized)
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{
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return;
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}
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int initialized = 0;
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TLLM_MPI_CHECK(MPI_Initialized(&initialized));
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if (!initialized)
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{
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logger.log(
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nvinfer1::ILogger::Severity::kINFO, tc::fmtstr("Initializing MPI with thread mode %d", threadMode).c_str());
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int providedMode;
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TLLM_MPI_CHECK(MPI_Init_thread(nullptr, nullptr, threadMode, &providedMode));
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TLLM_CHECK_WITH_INFO(providedMode >= threadMode, "MPI_Init_thread failed");
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std::atexit([]() { MPI_Finalize(); });
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auto previousHandler = std::signal(SIGABRT, [](int signal) { MPI_Abort(MPI_COMM_WORLD, EXIT_FAILURE); });
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TLLM_CHECK_WITH_INFO(previousHandler != SIG_ERR, "Signal handler setup failed");
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}
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mpiInitialized = true;
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}
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} // namespace
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bool WorldConfig::validConfig(nvinfer1::ILogger& logger, SizeType tensorParallelism, SizeType pipelineParallelism)
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{
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initMpi(logger);
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int mpiSize;
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TLLM_MPI_CHECK(MPI_Comm_size(MPI_COMM_WORLD, &mpiSize));
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return mpiSize == tensorParallelism * pipelineParallelism;
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}
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WorldConfig WorldConfig::mpi(nvinfer1::ILogger& logger, SizeType gpusPerNode, std::optional<SizeType> tensorParallelism,
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std::optional<SizeType> pipelineParallelism)
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{
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initMpi(logger);
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int mpiSize, mpiRank;
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TLLM_MPI_CHECK(MPI_Comm_size(MPI_COMM_WORLD, &mpiSize));
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TLLM_MPI_CHECK(MPI_Comm_rank(MPI_COMM_WORLD, &mpiRank));
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logger.log(nvinfer1::ILogger::Severity::kINFO, tc::fmtstr("MPI size: %d, rank: %d", mpiSize, mpiRank).c_str());
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auto pp = pipelineParallelism.value_or(1);
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auto tp = tensorParallelism.value_or(mpiSize / pp);
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TLLM_CHECK(mpiSize == tp * pp);
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return WorldConfig{tp, pp, mpiRank, gpusPerNode};
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}
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WorldConfig WorldConfig::mpi(
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SizeType gpusPerNode, std::optional<SizeType> tensorParallelism, std::optional<SizeType> pipelineParallelism)
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{
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TllmLogger logger{};
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return mpi(logger, gpusPerNode, tensorParallelism, pipelineParallelism);
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}
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std::vector<SizeType> WorldConfig::getPipelineParallelGroup() const
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{
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auto const pp = getPipelineParallelism();
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auto const tp = getTensorParallelism();
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auto const worldSize = getSize();
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std::vector<SizeType> group;
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group.reserve(pp);
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for (SizeType idx = getTensorParallelRank(); idx < worldSize; idx += tp)
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{
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group.push_back(idx);
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}
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return group;
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}
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