mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
59 lines
2.3 KiB
C++
59 lines
2.3 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/thop/ncclCommunicatorOp.h"
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#include "tensorrt_llm/runtime/iBuffer.h"
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namespace tr = tensorrt_llm::runtime;
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TRTLLM_NAMESPACE_BEGIN
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namespace torch_ext
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{
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NcclCommunicatorOp::NcclCommunicatorOp(int64_t worldSize, int64_t rank)
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: mRank(static_cast<int32_t>(rank))
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{
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mPipelineComm = std::make_shared<tensorrt_llm::runtime::NcclCommunicator>(worldSize, rank);
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}
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void NcclCommunicatorOp::send(th::Tensor tensor, int64_t toRank) const
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{
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auto ptr = static_cast<std::uint8_t*>(tensor.data_ptr());
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size_t const size = tensor.numel() * th::elementSize(th::typeMetaToScalarType(tensor.dtype()));
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tensorrt_llm::runtime::CudaStream cudaStream{at::cuda::getCurrentCUDAStream().stream(), mRank, false};
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mPipelineComm->send(*tr::IBuffer::wrap(ptr, size), static_cast<int>(toRank), cudaStream);
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}
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void NcclCommunicatorOp::recv(th::Tensor& tensor, int64_t fromRank) const
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{
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auto ptr = static_cast<std::uint8_t*>(tensor.data_ptr());
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size_t const size = tensor.numel() * th::elementSize(th::typeMetaToScalarType(tensor.dtype()));
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tensorrt_llm::runtime::CudaStream cudaStream{at::cuda::getCurrentCUDAStream().stream(), mRank, false};
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mPipelineComm->receive(*tr::IBuffer::wrap(ptr, size), static_cast<int>(fromRank), cudaStream);
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}
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} // namespace torch_ext
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TRTLLM_NAMESPACE_END
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static auto trtllmNcclCommunicator
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= torch::jit::class_<tensorrt_llm::torch_ext::NcclCommunicatorOp>("trtllm", "NcclCommunicatorOp")
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.def(torch::jit::init<int64_t, int64_t>())
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.def("send", &tensorrt_llm::torch_ext::NcclCommunicatorOp::send)
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.def("recv", &tensorrt_llm::torch_ext::NcclCommunicatorOp::recv);
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