mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
312 lines
10 KiB
C++
312 lines
10 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 "tensorrt_llm/runtime/utils/pgUtils.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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#include <torch/csrc/distributed/c10d/FileStore.hpp>
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#include <torch/csrc/distributed/c10d/ProcessGroupNCCL.hpp>
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#endif // ENABLE_MULTI_DEVICE
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using tensorrt_llm::pg_utils::PgHelper;
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TRTLLM_NAMESPACE_BEGIN
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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)
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: mGroup(std::move(group))
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{
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}
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~AllgatherOp() = default;
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int initialize()
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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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std::vector<torch::Tensor> run_list(torch::TensorList input_list, torch::optional<torch::List<int64_t>> sizes)
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{
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TLLM_CHECK_WITH_INFO(mNcclComm.get() != nullptr, "mNcclComm should be initialized before used");
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bool use_nccl_allgather = !sizes.has_value()
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|| std::all_of(sizes.value().begin(), sizes.value().end(),
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[&sizes](int64_t size) { return size == sizes.value()[0]; });
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int64_t sum_sizes
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= sizes.has_value() ? std::accumulate(sizes.value().begin(), sizes.value().end(), 0, std::plus<>{}) : 0;
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std::vector<torch::Tensor> output_list;
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output_list.reserve(input_list.size());
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ncclGroupStart();
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for (auto const& input : input_list)
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{
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auto stream = at::cuda::getCurrentCUDAStream(input.get_device());
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auto type = tensorrt_llm::runtime::TorchUtils::dataType(input.scalar_type());
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std::vector<int64_t> outputShape = input.sizes().vec();
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if (sizes.has_value())
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{
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outputShape[0] = sum_sizes;
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}
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else
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{
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outputShape[0] *= mGroup.size();
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}
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auto output = torch::empty(outputShape, input.options());
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if (use_nccl_allgather)
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{
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ncclAllGather(input.data_ptr(), output.mutable_data_ptr(), input.numel(), (*getDtypeMap())[type],
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*mNcclComm, stream);
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}
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else
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{
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size_t numel_base
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= std::accumulate(outputShape.cbegin() + 1, outputShape.cend(), 1, std::multiplies<>{});
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int64_t split_offset = 0;
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for (int root = 0; root < static_cast<int>(mGroup.size()); ++root)
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{
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auto split_size = sizes.value()[root];
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ncclBroadcast(input.data_ptr(),
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output.index({torch::indexing::Slice(split_offset, torch::indexing::None)}).mutable_data_ptr(),
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numel_base * split_size, (*getDtypeMap())[type], root, *mNcclComm, stream);
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split_offset += split_size;
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}
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}
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output_list.push_back(output);
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}
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NCCLCHECK_THROW(ncclGroupEnd());
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return output_list;
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}
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torch::Tensor run(torch::Tensor input, torch::optional<torch::List<int64_t>> sizes)
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{
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return run_list({input}, sizes)[0];
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}
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private:
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std::set<int> mGroup;
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std::shared_ptr<ncclComm_t> mNcclComm;
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};
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class AllgatherPgOp
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{
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public:
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AllgatherPgOp(std::set<int> group, c10::intrusive_ptr<c10d::ProcessGroup> const& process_group_)
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: mGroup(std::move(group))
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, mProcessGroup(process_group_)
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{
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}
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~AllgatherPgOp() = 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__, mProcessGroup->getRank());
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return 0;
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}
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std::pair<torch::Tensor, c10::intrusive_ptr<c10d::Work>> run(
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torch::Tensor input, torch::optional<torch::List<int64_t>> sizes, bool coalescing = false)
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{
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TLLM_CHECK_WITH_INFO(mProcessGroup.get() != nullptr, "mProcessGroup should be initialized before used");
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std::vector<int64_t> outputShape = input.sizes().vec();
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if (sizes.has_value())
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{
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outputShape[0] = std::accumulate(sizes.value().begin(), sizes.value().end(), 0, std::plus<>{});
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}
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else
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{
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outputShape[0] *= mGroup.size();
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}
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auto output = torch::empty(outputShape, input.options());
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PgHelper pgh{mProcessGroup};
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c10::intrusive_ptr<c10d::Work> work;
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if (sizes.has_value())
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{
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std::vector inputs{input};
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int64_t split_offset = 0;
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std::vector<torch::Tensor> outputTensors{};
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for (int root = 0; root < static_cast<int>(mGroup.size()); ++root)
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{
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auto split_size = sizes.value()[root];
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outputTensors.push_back(
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output.index({torch::indexing::Slice(split_offset, split_offset + split_size)}));
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split_offset += split_size;
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}
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std::vector<std::vector<torch::Tensor>> outputs{outputTensors};
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work = mProcessGroup->allgather(outputs, inputs, {});
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}
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else
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{
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work = pgh.allgather(input, output, {});
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}
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if (!coalescing)
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{
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PGCHECK_THROW_WITH_INFO(work, "ProcessGroup: allgather");
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return {output, nullptr};
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}
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return {output, work};
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}
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std::vector<torch::Tensor> run_list(torch::TensorList input_list, torch::optional<torch::List<int64_t>> sizes)
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{
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std::vector<torch::Tensor> output_list;
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std::vector<c10::intrusive_ptr<c10d::Work>> work_list;
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output_list.reserve(input_list.size());
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work_list.reserve(input_list.size());
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mProcessGroup->startCoalescing(c10::DeviceType::CUDA);
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for (auto const& input : input_list)
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{
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auto [output, work] = run(input, sizes, true);
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output_list.push_back(output);
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work_list.push_back(work); // Hold work objects (input & output tensors) until endCoalescing wait finished
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}
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if (auto work = mProcessGroup->endCoalescing(c10::DeviceType::CUDA))
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{
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PGCHECK_THROW_WITH_INFO(work, "ProcessGroup: allgather, end coalescing");
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}
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return output_list;
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}
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private:
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std::set<int> mGroup;
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c10::intrusive_ptr<c10d::ProcessGroup> mProcessGroup;
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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::optional<torch::List<int64_t>> sizes, torch::List<int64_t> group_)
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{
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#if ENABLE_MULTI_DEVICE
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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);
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op.initialize();
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auto output = op.run(input, sizes);
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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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torch::Tensor allgather_pg(torch::Tensor input, torch::optional<torch::List<int64_t>> sizes,
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torch::List<int64_t> group_, c10::intrusive_ptr<c10d::ProcessGroup> const& process_group_)
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{
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#if ENABLE_MULTI_DEVICE
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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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AllgatherPgOp op(group, process_group_);
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op.initialize();
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auto [output, _] = op.run(input, sizes);
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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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std::vector<torch::Tensor> allgather_list(
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torch::TensorList input_list, torch::optional<torch::List<int64_t>> sizes, torch::List<int64_t> group_)
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{
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#if ENABLE_MULTI_DEVICE
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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);
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op.initialize();
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auto output_list = op.run_list(input_list, sizes);
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return output_list;
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#else
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return input_list.vec();
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#endif // ENABLE_MULTI_DEVICE
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}
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std::vector<torch::Tensor> allgather_list_pg(torch::TensorList input_list, torch::optional<torch::List<int64_t>> sizes,
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torch::List<int64_t> group_, c10::intrusive_ptr<c10d::ProcessGroup> const& process_group_)
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{
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#if ENABLE_MULTI_DEVICE
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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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AllgatherPgOp op(group, process_group_);
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op.initialize();
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auto output_list = op.run_list(input_list, sizes);
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return output_list;
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#else
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return input_list.vec();
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#endif // ENABLE_MULTI_DEVICE
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}
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} // namespace torch_ext
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TRTLLM_NAMESPACE_END
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TORCH_LIBRARY_FRAGMENT(trtllm, m)
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{
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m.def("allgather(Tensor input, SymInt[]? sizes, int[] group) -> Tensor");
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m.def(
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"allgather_pg(Tensor input, SymInt[]? sizes, int[] group, __torch__.torch.classes.c10d.ProcessGroup "
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"process_group) -> Tensor");
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m.def("allgather_list(Tensor[] input_list, SymInt[]? sizes, int[] group) -> Tensor[]");
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m.def(
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"allgather_list_pg(Tensor[] input_list, SymInt[]? sizes, int[] group, "
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"__torch__.torch.classes.c10d.ProcessGroup process_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", &tensorrt_llm::torch_ext::allgather);
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m.impl("allgather_pg", &tensorrt_llm::torch_ext::allgather_pg);
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m.impl("allgather_list", &tensorrt_llm::torch_ext::allgather_list);
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m.impl("allgather_list_pg", &tensorrt_llm::torch_ext::allgather_list_pg);
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}
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