mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
92 lines
2.9 KiB
C++
92 lines
2.9 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/worldConfig.h"
|
|
|
|
#include "tensorrt_llm/common/assert.h"
|
|
#include "tensorrt_llm/common/stringUtils.h"
|
|
#include "tensorrt_llm/runtime/tllmLogger.h"
|
|
#include "tensorrt_llm/runtime/utils/multiDeviceUtils.h"
|
|
|
|
#include <cstdlib>
|
|
#include <mpi.h>
|
|
|
|
using namespace tensorrt_llm::runtime;
|
|
namespace tc = tensorrt_llm::common;
|
|
|
|
namespace
|
|
{
|
|
|
|
bool mpiInitialized = false;
|
|
|
|
void initMpi(nvinfer1::ILogger& logger, int threadMode = MPI_THREAD_FUNNELED)
|
|
{
|
|
if (mpiInitialized)
|
|
{
|
|
return;
|
|
}
|
|
|
|
int initialized = 0;
|
|
TLLM_MPI_CHECK(MPI_Initialized(&initialized), logger);
|
|
if (!initialized)
|
|
{
|
|
logger.log(
|
|
nvinfer1::ILogger::Severity::kINFO, tc::fmtstr("Initializing MPI with thread mode %d", threadMode).c_str());
|
|
int providedMode;
|
|
TLLM_MPI_CHECK(MPI_Init_thread(nullptr, nullptr, threadMode, &providedMode), logger);
|
|
TLLM_CHECK_WITH_INFO(providedMode >= threadMode, "MPI_Init_thread failed");
|
|
std::atexit([]() { MPI_Finalize(); });
|
|
}
|
|
|
|
mpiInitialized = true;
|
|
}
|
|
|
|
} // namespace
|
|
|
|
WorldConfig WorldConfig::mpi(nvinfer1::ILogger& logger, SizeType gpusPerNode, std::optional<SizeType> tensorParallelism,
|
|
std::optional<SizeType> pipelineParallelism)
|
|
{
|
|
initMpi(logger);
|
|
|
|
int mpiSize, mpiRank;
|
|
TLLM_MPI_CHECK(MPI_Comm_size(MPI_COMM_WORLD, &mpiSize), logger);
|
|
TLLM_MPI_CHECK(MPI_Comm_rank(MPI_COMM_WORLD, &mpiRank), logger);
|
|
logger.log(nvinfer1::ILogger::Severity::kINFO, tc::fmtstr("MPI size: %d, rank: %d", mpiSize, mpiRank).c_str());
|
|
|
|
auto pp = pipelineParallelism.value_or(1);
|
|
auto tp = tensorParallelism.value_or(mpiSize / pp);
|
|
TLLM_CHECK(mpiSize == tp * pp);
|
|
return WorldConfig{tp, pp, mpiRank, gpusPerNode};
|
|
}
|
|
|
|
WorldConfig WorldConfig::mpi(
|
|
SizeType gpusPerNode, std::optional<SizeType> tensorParallelism, std::optional<SizeType> pipelineParallelism)
|
|
{
|
|
TllmLogger logger{};
|
|
return mpi(logger, gpusPerNode, tensorParallelism, pipelineParallelism);
|
|
}
|
|
|
|
std::vector<SizeType> WorldConfig::getPipelineParallelGroup() const
|
|
{
|
|
std::vector<SizeType> group;
|
|
auto const groupIdx = getTensorParallelRank();
|
|
for (SizeType i = 0; i < getPipelineParallelism(); ++i)
|
|
{
|
|
group.push_back(groupIdx + i * getTensorParallelism());
|
|
}
|
|
return group;
|
|
}
|