/* Copyright (c) 2019 PaddlePaddle Authors. 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 "paddle/fluid/operators/collective/partial_allgather_op.h" #if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL) #include "paddle/fluid/distributed/collective/ProcessGroup.h" #include "paddle/fluid/platform/collective_helper.h" #include "paddle/fluid/platform/device/gpu/nccl_helper.h" #endif namespace paddle { namespace operators { template class PartialAllGatherOpCUDAKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { #if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL) auto in = ctx.Input("X"); auto out = ctx.Output("Out"); int64_t numel = in->numel(); ncclDataType_t dtype = platform::ToNCCLDataType(framework::TransToProtoVarType(in->dtype())); int nranks = ctx.Attr("nranks"); int rank = ctx.Attr("rank"); int rid = ctx.Attr("ring_id"); auto place = ctx.GetPlace(); auto comm = platform::NCCLCommContext::Instance().Get(rid, place); PADDLE_ENFORCE_EQ( nranks, comm->nranks(), platform::errors::InvalidArgument( "nranks: %s should equal to %s", nranks, comm->nranks())); PADDLE_ENFORCE_EQ(rank, comm->rank(), platform::errors::InvalidArgument( "rank: %s should equal to %s", rank, comm->rank())); PADDLE_ENFORCE_EQ( (numel % nranks), 0, platform::errors::InvalidArgument( "The input numel (%d) must be divisible by nranks(%d)", numel, nranks)); framework::DDim dims = in->dims(); out->mutable_data(dims, place); int64_t send_numel = numel / nranks; int offset = send_numel * rank; auto map = distributed::ProcessGroupMapFromGid::getInstance(); if (map->has(rid)) { // Use ProcessGroup distributed::ProcessGroup* pg = map->get(rid); std::vector in_tensors; std::vector out_tensors; in_tensors.push_back(*in); out_tensors.push_back(*out); auto task = pg->AllGather_Partial(in_tensors, out_tensors, offset, send_numel); task->Wait(); } else { const T* send_buff = in->data() + offset; T* recv_buff = out->data(); gpuStream_t stream = nullptr; if (ctx.Attr("use_calc_stream")) { auto dev_ctx = platform::DeviceContextPool::Instance().Get(place); stream = static_cast(dev_ctx)->stream(); } else { stream = comm->stream(); } PADDLE_ENFORCE_GPU_SUCCESS( platform::dynload::ncclAllGather(send_buff, recv_buff, send_numel, static_cast(dtype), comm->comm(), stream)); } #else PADDLE_THROW(platform::errors::PreconditionNotMet( "PaddlePaddle should compile with GPU.")); #endif } }; } // namespace operators } // namespace paddle namespace ops = paddle::operators; namespace plat = paddle::platform; REGISTER_OP_CUDA_KERNEL(partial_allgather, ops::PartialAllGatherOpCUDAKernel, ops::PartialAllGatherOpCUDAKernel, ops::PartialAllGatherOpCUDAKernel, ops::PartialAllGatherOpCUDAKernel, ops::PartialAllGatherOpCUDAKernel);