/* Copyright (c) 2016 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. */ #ifdef PADDLE_WITH_CUDA #include #endif #include #include // NOLINT #include "paddle/fluid/framework/data_type.h" #include "paddle/fluid/operators/distributed/sendrecvop_utils.h" #include "paddle/fluid/operators/distributed/variable_response.h" namespace paddle { namespace operators { namespace distributed { using VarMsg = sendrecv::VariableMessage; #ifdef PADDLE_WITH_CUDA void* GetVarPayLoad(const std::string varname, int64_t size) { platform::CUDAPinnedPlace cuda_pinned; return memory::Alloc(cuda_pinned, size); } #endif void GetTensorPayload(framework::Variable* var, const platform::DeviceContext& ctx, VarMsg* request, void** payload, size_t* payload_size) { auto tensor = var->Get(); // FIXME(wuyi): data types in send_recv.proto is copied from // framework.proto request->set_data_type( static_cast(framework::ToDataType(tensor.type()))); for (auto& dim : framework::vectorize(tensor.dims())) { request->add_dims(dim); } const framework::LoD lod = tensor.lod(); if (lod.size() > 0) { request->set_lod_level(lod.size()); for (auto& each : lod) { VarMsg::LodData* lod_inner = request->add_lod(); for (auto& d : each) { lod_inner->add_lod_data(d); } } } if (platform::is_gpu_place(ctx.GetPlace())) { #ifdef PADDLE_WITH_CUDA PADDLE_ENFORCE(platform::is_gpu_place(tensor.place())); // platform::CUDAPinnedPlace cuda_pinned; auto& gpu_dev_ctx = static_cast(ctx); auto copy_size = tensor.numel() * framework::SizeOfType(tensor.type()); *payload = GetVarPayLoad(request->varname(), copy_size); platform::CUDAPinnedPlace cuda_pinned; memory::Copy(cuda_pinned, *payload, boost::get(tensor.place()), reinterpret_cast(tensor.data()), copy_size, gpu_dev_ctx.stream()); ctx.Wait(); #endif } else { *payload = tensor.data(); } *payload_size = tensor.numel() * framework::SizeOfType(tensor.type()); } void GetSelectedRowsPayload(framework::Variable* var, const platform::DeviceContext& ctx, VarMsg* request, void** payload, size_t* payload_size) { auto* slr = var->GetMutable(); request->set_data_type( static_cast(framework::ToDataType(slr->value().type()))); request->set_lod_level(0); request->set_slr_height(slr->height()); for (auto& dim : framework::vectorize(slr->value().dims())) { request->add_dims(dim); } auto* tensor = slr->mutable_value(); if (platform::is_gpu_place(ctx.GetPlace())) { #ifdef PADDLE_WITH_CUDA auto& gpu_dev_ctx = static_cast(ctx); auto copy_size = tensor->numel() * framework::SizeOfType(tensor->type()); *payload = GetVarPayLoad(request->varname(), copy_size); platform::CUDAPinnedPlace cuda_pinned; memory::Copy(cuda_pinned, *payload, boost::get(tensor->place()), reinterpret_cast(tensor->data()), copy_size, gpu_dev_ctx.stream()); ctx.Wait(); #endif } else { *payload = slr->mutable_value()->data(); } *payload_size = tensor->numel() * framework::SizeOfType(tensor->type()); } } // namespace distributed } // namespace operators } // namespace paddle