mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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108 lines
3.9 KiB
C++
108 lines
3.9 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 "tensorrt_llm/runtime/worldConfig.h"
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#include "tensorrt_llm/common/assert.h"
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#include "tensorrt_llm/common/logger.h"
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#include "tensorrt_llm/common/mpiUtils.h"
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#include "tensorrt_llm/common/stringUtils.h"
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#include <algorithm>
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#include <numeric>
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#include <set>
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using namespace tensorrt_llm::runtime;
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namespace tc = tensorrt_llm::common;
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WorldConfig::WorldConfig(SizeType tensorParallelism, SizeType pipelineParallelism, SizeType rank, SizeType gpusPerNode,
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std::optional<std::vector<SizeType>> const& deviceIds)
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: mTensorParallelism{tensorParallelism}
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, mPipelineParallelism{pipelineParallelism}
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, mRank{rank}
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, mGpusPerNode{gpusPerNode}
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, mDeviceIds{deviceIds.value_or(std::vector<SizeType>(mGpusPerNode))}
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{
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auto const numDevices = mDeviceIds.size();
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TLLM_CHECK(numDevices > 0);
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if (!deviceIds.has_value())
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{
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mDeviceIds.resize(mGpusPerNode);
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std::iota(mDeviceIds.begin(), mDeviceIds.end(), 0);
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}
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else
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{
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// total number is at most mGpusPerNode
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TLLM_CHECK_WITH_INFO(static_cast<SizeType>(numDevices) <= mGpusPerNode,
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"Number of device IDs %zu is greater than GPUs per node %d", numDevices, mGpusPerNode);
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// all deviceIds is within the range
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TLLM_CHECK(*std::max_element(mDeviceIds.begin(), mDeviceIds.end()) < mGpusPerNode);
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TLLM_CHECK(*std::min_element(mDeviceIds.begin(), mDeviceIds.end()) >= 0);
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// all ids are unique
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std::set<SizeType> const deviceIdSet(mDeviceIds.begin(), mDeviceIds.end());
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TLLM_CHECK_WITH_INFO(
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deviceIdSet.size() == numDevices, "Device IDs are not unique %zu != %zu", deviceIdSet.size(), numDevices);
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// log a warning if device ids are not contiguous
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if (std::adjacent_find(deviceIdSet.begin(), deviceIdSet.end(), [](auto x, auto y) { return y - x != 1; })
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!= deviceIdSet.end())
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{
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TLLM_LOG_WARNING("The user specified device IDs are not contiguous!");
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}
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TLLM_LOG_INFO("Using user-specified devices: %s", tc::arr2str(mDeviceIds.data(), numDevices).c_str());
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}
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TLLM_CHECK(mTensorParallelism > 0);
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TLLM_CHECK(mPipelineParallelism > 0);
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}
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bool WorldConfig::validConfig(SizeType tensorParallelism, SizeType pipelineParallelism)
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{
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auto const mpiSize = COMM_SESSION.getSize();
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return mpiSize == tensorParallelism * pipelineParallelism;
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}
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WorldConfig WorldConfig::mpi(SizeType gpusPerNode, std::optional<SizeType> tensorParallelism,
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std::optional<SizeType> pipelineParallelism, std::optional<std::vector<SizeType>> const& deviceIds)
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{
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auto& comm = COMM_SESSION;
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auto const mpiSize = comm.getSize();
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auto const mpiRank = comm.getRank();
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TLLM_LOG_INFO("MPI size: %d, rank: %d", mpiSize, mpiRank);
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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, deviceIds};
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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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