// Copyright (c) 2023 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/phi/kernels/p_send_kernel.h" #include "glog/logging.h" #include "paddle/phi/backends/all_context.h" #include "paddle/phi/common/memory_utils.h" #include "paddle/phi/core/kernel_registry.h" #include "paddle/phi/core/utils/data_type.h" #include "paddle/phi/kernels/funcs/send_recv_functor.h" #if defined(PADDLE_WITH_NCCL) || \ defined(PADDLE_WITH_RCCL) && NCCL_VERSION_CODE >= 2703 #include "paddle/phi/core/distributed/nccl_comm_context.h" #endif namespace phi { template void PSendKernel(const Context& dev_ctx, const DenseTensor& x, int peer, bool dynamic_shape) { #if defined(PADDLE_WITH_NCCL) || \ defined(PADDLE_WITH_RCCL) && NCCL_VERSION_CODE >= 2703 auto comm_ctx = GetCommContext(dev_ctx, peer); gpuStream_t stream = dev_ctx.stream(); if (dynamic_shape) { send_shape_info( dev_ctx, x, comm_ctx, peer, stream); } comm_ctx->Send(x, x.numel(), peer, stream); #else PADDLE_THROW( errors::PreconditionNotMet("PaddlePaddle should compile with GPU." "and NCCL version >= 2.7.3 is needed.")); #endif } template void PSendArrayKernel(const Context& dev_ctx, const TensorArray& x_array, int peer) { #if defined(PADDLE_WITH_NCCL) || \ defined(PADDLE_WITH_RCCL) && NCCL_VERSION_CODE >= 2703 auto comm_ctx = GetCommContext(dev_ctx, peer); gpuStream_t stream = dev_ctx.stream(); for (size_t idx = 0; idx < x_array.size(); idx++) { VLOG(3) << "DenseTensorArray: idx(" << idx << ")"; auto x = x_array.at(idx); int64_t numel = x.numel(); ncclDataType_t dtype = ToNCCLDataType(x.type()); comm_ctx->Send(x, x.numel(), peer, stream); VLOG(3) << "rank " << comm_ctx->GetRank() << " send " << common::product(x.dims()) << " to " << peer; } #else PADDLE_THROW( errors::PreconditionNotMet("PaddlePaddle should compile with GPU." "and NCCL version >= 2.7.3 is needed.")); #endif } } // namespace phi #if NCCL_VERSION_CODE >= 21000 PD_REGISTER_KERNEL(p_send, GPU, ALL_LAYOUT, phi::PSendKernel, float, double, int, bool, int8_t, uint8_t, int16_t, int64_t, phi::bfloat16, phi::float16) {} PD_REGISTER_KERNEL(p_send_array, GPU, ALL_LAYOUT, phi::PSendArrayKernel, float, double, int, bool, int8_t, uint8_t, int64_t, phi::bfloat16, phi::float16) {} #else PD_REGISTER_KERNEL(p_send, GPU, ALL_LAYOUT, phi::PSendKernel, float, double, int, bool, int8_t, uint8_t, int16_t, int64_t, phi::float16) {} PD_REGISTER_KERNEL(p_send_array, GPU, ALL_LAYOUT, phi::PSendArrayKernel, float, double, int, bool, int8_t, uint8_t, int64_t, phi::float16) {} #endif