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117 lines
4.0 KiB
117 lines
4.0 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#ifdef PADDLE_WITH_CUDA
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#include <nccl.h>
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#endif
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#include <sys/time.h>
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#include <thread> // NOLINT
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#include "paddle/fluid/framework/data_type.h"
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#include "paddle/fluid/operators/distributed/sendrecvop_utils.h"
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#include "paddle/fluid/operators/distributed/variable_response.h"
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namespace paddle {
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namespace operators {
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namespace distributed {
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using VarMsg = sendrecv::VariableMessage;
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#ifdef PADDLE_WITH_CUDA
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void* GetVarPayLoad(const std::string varname, int64_t size) {
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platform::CUDAPinnedPlace cuda_pinned;
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return memory::Alloc(cuda_pinned, size);
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}
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#endif
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void GetTensorPayload(framework::Variable* var,
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const platform::DeviceContext& ctx, VarMsg* request,
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void** payload, size_t* payload_size) {
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auto tensor = var->Get<framework::LoDTensor>();
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// FIXME(wuyi): data types in send_recv.proto is copied from
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// framework.proto
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request->set_data_type(
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static_cast<VarMsg::Type>(framework::ToDataType(tensor.type())));
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for (auto& dim : framework::vectorize(tensor.dims())) {
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request->add_dims(dim);
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}
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const framework::LoD lod = tensor.lod();
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if (lod.size() > 0) {
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request->set_lod_level(lod.size());
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for (auto& each : lod) {
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VarMsg::LodData* lod_inner = request->add_lod();
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for (auto& d : each) {
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lod_inner->add_lod_data(d);
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}
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}
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}
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if (platform::is_gpu_place(ctx.GetPlace())) {
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#ifdef PADDLE_WITH_CUDA
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PADDLE_ENFORCE(platform::is_gpu_place(tensor.place()));
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// platform::CUDAPinnedPlace cuda_pinned;
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auto& gpu_dev_ctx = static_cast<const platform::CUDADeviceContext&>(ctx);
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auto copy_size = tensor.numel() * framework::SizeOfType(tensor.type());
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*payload = GetVarPayLoad(request->varname(), copy_size);
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platform::CUDAPinnedPlace cuda_pinned;
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memory::Copy(cuda_pinned, *payload,
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boost::get<platform::CUDAPlace>(tensor.place()),
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reinterpret_cast<const void*>(tensor.data<void>()), copy_size,
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gpu_dev_ctx.stream());
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ctx.Wait();
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#endif
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} else {
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*payload = tensor.data<void>();
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}
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*payload_size = tensor.numel() * framework::SizeOfType(tensor.type());
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}
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void GetSelectedRowsPayload(framework::Variable* var,
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const platform::DeviceContext& ctx, VarMsg* request,
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void** payload, size_t* payload_size) {
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auto* slr = var->GetMutable<framework::SelectedRows>();
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request->set_data_type(
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static_cast<VarMsg::Type>(framework::ToDataType(slr->value().type())));
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request->set_lod_level(0);
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request->set_slr_height(slr->height());
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for (auto& dim : framework::vectorize(slr->value().dims())) {
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request->add_dims(dim);
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}
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auto* tensor = slr->mutable_value();
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if (platform::is_gpu_place(ctx.GetPlace())) {
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#ifdef PADDLE_WITH_CUDA
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auto& gpu_dev_ctx = static_cast<const platform::CUDADeviceContext&>(ctx);
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auto copy_size = tensor->numel() * framework::SizeOfType(tensor->type());
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*payload = GetVarPayLoad(request->varname(), copy_size);
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platform::CUDAPinnedPlace cuda_pinned;
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memory::Copy(cuda_pinned, *payload,
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boost::get<platform::CUDAPlace>(tensor->place()),
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reinterpret_cast<const void*>(tensor->data<void>()), copy_size,
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gpu_dev_ctx.stream());
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ctx.Wait();
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#endif
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} else {
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*payload = slr->mutable_value()->data<void>();
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}
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*payload_size = tensor->numel() * framework::SizeOfType(tensor->type());
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}
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} // namespace distributed
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} // namespace operators
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} // namespace paddle
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