mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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111 lines
3.0 KiB
C++
111 lines
3.0 KiB
C++
/*
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* SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: Apache-2.0
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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/common/opUtils.h"
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#include "tensorrt_llm/runtime/torchUtils.h"
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#include "tensorrt_llm/runtime/utils/mpiUtils.h"
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#include <NvInferRuntime.h>
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#include <c10/cuda/CUDAStream.h>
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#include <cassert>
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#include <set>
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#include <string>
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#include <torch/extension.h>
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#include <vector>
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#if ENABLE_MULTI_DEVICE
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#include <nccl.h>
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#endif // ENABLE_MULTI_DEVICE
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namespace torch_ext
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{
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#if ENABLE_MULTI_DEVICE
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namespace
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{
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class AllgatherOp
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{
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public:
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AllgatherOp(std::set<int> group, nvinfer1::DataType type)
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: mGroup(std::move(group))
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, mType(type)
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{
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}
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~AllgatherOp() = default;
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int initialize() noexcept
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{
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TLLM_LOG_TRACE("%s start for rank %d", __PRETTY_FUNCTION__, COMM_SESSION.getRank());
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mNcclComm = getComm(mGroup);
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TLLM_LOG_TRACE("%s stop for rank %d", __PRETTY_FUNCTION__, COMM_SESSION.getRank());
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return 0;
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}
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torch::Tensor run(torch::Tensor input) noexcept
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{
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auto stream = at::cuda::getCurrentCUDAStream(input.get_device());
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std::vector<int64_t> outputShape = input.sizes().vec();
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outputShape.insert(outputShape.begin(), mGroup.size());
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auto output = torch::empty(outputShape, input.options());
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size_t size = input.numel();
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TLLM_CHECK_WITH_INFO(mNcclComm.get() != nullptr, "mNcclComm should be initialized before used");
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NCCLCHECK(ncclAllGather(
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input.data_ptr(), output.mutable_data_ptr(), size, (*getDtypeMap())[mType], *mNcclComm, stream));
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return output;
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}
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private:
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std::set<int> mGroup;
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nvinfer1::DataType mType;
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std::shared_ptr<ncclComm_t> mNcclComm;
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};
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} // namespace
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#endif // ENABLE_MULTI_DEVICE
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torch::Tensor allgather(torch::Tensor input, torch::List<int64_t> group_)
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{
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#if ENABLE_MULTI_DEVICE
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auto const type = tensorrt_llm::runtime::TorchUtils::dataType(input.scalar_type());
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std::set<int> group;
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for (int64_t rank : group_)
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{
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group.insert(static_cast<int>(rank));
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}
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AllgatherOp op(group, type);
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op.initialize();
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auto output = op.run(input);
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return output;
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#else
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return input;
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#endif // ENABLE_MULTI_DEVICE
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}
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} // namespace torch_ext
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TORCH_LIBRARY_FRAGMENT(trtllm, m)
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{
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m.def("allgather(Tensor input, int[] group) -> Tensor");
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}
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TORCH_LIBRARY_IMPL(trtllm, CUDA, m)
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{
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m.impl("allgather", &torch_ext::allgather);
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}
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