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338 lines
13 KiB
338 lines
13 KiB
/* Copyright (c) 2020 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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#include "paddle/fluid/distributed/service/brpc_utils.h"
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#include <arpa/inet.h>
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#include <netdb.h>
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#include "paddle/fluid/platform/enforce.h"
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namespace paddle {
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namespace framework {
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class Variable;
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class LoDTensor;
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} // namespace framework
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} // namespace paddle
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namespace paddle {
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namespace distributed {
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framework::proto::VarType::Type VarMessageToVarType(
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VariableMessage::Type type) {
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switch (type) {
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case VariableMessage::FP32:
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return framework::proto::VarType::FP32; // NOLINT
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case VariableMessage::FP64:
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return framework::proto::VarType::FP64; // NOLINT
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case VariableMessage::INT32:
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return framework::proto::VarType::INT32; // NOLINT
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case VariableMessage::INT64:
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return framework::proto::VarType::INT64; // NOLINT
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case VariableMessage::BOOL:
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return framework::proto::VarType::BOOL; // NOLINT
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default:
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PADDLE_THROW(platform::errors::InvalidArgument(
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"VarMessageToVarType:Unsupported type %d", type));
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}
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}
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void SerializeToMultiVarMsgAndIOBuf(
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const std::string& message_name,
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const std::vector<std::string>& send_var_name_val,
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const std::vector<std::string>& recv_var_name_val,
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const platform::DeviceContext& ctx, const framework::Scope* scope,
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MultiVarMsg* request, butil::IOBuf* iobuf) {
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// 1. message_name
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request->set_message_name(message_name);
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// 2. var_names
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for (auto& send_var_name : send_var_name_val) {
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request->add_send_var_names(send_var_name);
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}
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for (auto& recv_var_name : recv_var_name_val) {
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request->add_recv_var_names(recv_var_name);
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}
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// 3. VarMessage
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for (auto& send_var_name : send_var_name_val) {
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auto* send_var_msg = request->add_var_messages();
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butil::IOBuf temp_iobuf;
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send_var_msg->set_varname(send_var_name);
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framework::Variable* var = scope->FindVar(send_var_name);
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if (var->IsType<framework::LoDTensor>()) {
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SerializeLodTensor(var, ctx, send_var_msg, &temp_iobuf);
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} else if (var->IsType<framework::SelectedRows>()) {
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SerializeSelectedRows(var, ctx, send_var_msg, &temp_iobuf);
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}
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iobuf->append(temp_iobuf);
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}
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}
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void SerializeLodTensor(framework::Variable* var,
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const platform::DeviceContext& ctx, VarMsg* var_msg,
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butil::IOBuf* iobuf) {
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auto* tensor = var->GetMutable<framework::LoDTensor>();
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var_msg->set_type(::paddle::distributed::LOD_TENSOR);
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const framework::LoD lod = tensor->lod();
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if (lod.size() > 0) {
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var_msg->set_lod_level(lod.size());
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for (auto& each : lod) {
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VarMsg::LodData* lod_inner = var_msg->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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var_msg->set_data_type(static_cast<VarMsg::Type>(tensor->type()));
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for (auto& dim : framework::vectorize(tensor->dims())) {
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var_msg->add_dims(dim);
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}
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// IO Buffer
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if (platform::is_cpu_place(tensor->place())) {
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auto data_len = tensor->numel() * framework::SizeOfType(tensor->type());
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iobuf->append(reinterpret_cast<const char*>(&data_len), 8);
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iobuf->append(reinterpret_cast<const char*>(tensor->data<void>()),
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data_len);
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} else {
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#ifdef PADDLE_WITH_CUDA
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char* temp_ptr =
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new char[tensor->numel() * framework::SizeOfType(tensor->type())];
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auto stream =
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reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream();
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memory::Copy(platform::CPUPlace(), temp_ptr,
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BOOST_GET_CONST(platform::CUDAPlace, tensor->place()),
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tensor->data<void>(),
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tensor->numel() * framework::SizeOfType(tensor->type()),
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stream);
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auto data_len = tensor->numel() * framework::SizeOfType(tensor->type());
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iobuf->append(reinterpret_cast<const char*>(&data_len), 8);
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iobuf->append(reinterpret_cast<const char*>(temp_ptr), data_len);
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delete[] temp_ptr;
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#endif
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}
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}
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void SerializeSelectedRows(framework::Variable* var,
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const platform::DeviceContext& ctx, VarMsg* var_msg,
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butil::IOBuf* iobuf) {
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framework::SelectedRows* slr = var->GetMutable<framework::SelectedRows>();
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auto* tensor = slr->mutable_value();
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auto* rows = slr->mutable_rows();
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var_msg->set_type(::paddle::distributed::SELECTED_ROWS);
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var_msg->set_slr_height(slr->height());
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auto* var_data = var_msg->mutable_data();
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var_data->clear();
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var_data->resize(rows->size() * sizeof(int64_t));
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char* data_ptr = const_cast<char*>(var_data->data());
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if (platform::is_cpu_place(tensor->place())) {
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memcpy(data_ptr, &(*rows)[0], rows->size() * sizeof(int64_t));
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} else {
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#ifdef PADDLE_WITH_CUDA
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auto stream =
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reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream();
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memory::Copy(platform::CPUPlace(), data_ptr,
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BOOST_GET_CONST(platform::CUDAPlace, tensor->place()),
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&(*rows)[0], rows->size() * sizeof(int64_t), stream);
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#endif
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}
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var_msg->set_data_type(static_cast<VarMsg::Type>(tensor->type()));
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for (auto& dim : framework::vectorize(tensor->dims())) {
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var_msg->add_dims(dim);
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}
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// IO Buffer
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if (platform::is_cpu_place(tensor->place())) {
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auto data_len = tensor->numel() * framework::SizeOfType(tensor->type());
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iobuf->append(reinterpret_cast<const char*>(&data_len), 8);
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iobuf->append(reinterpret_cast<const char*>(tensor->data<void>()),
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data_len);
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} else {
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#ifdef PADDLE_WITH_CUDA
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char* temp_ptr =
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new char[tensor->numel() * framework::SizeOfType(tensor->type())];
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auto stream =
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reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream();
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memory::Copy(platform::CPUPlace(), temp_ptr,
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BOOST_GET_CONST(platform::CUDAPlace, tensor->place()),
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tensor->data<void>(),
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tensor->numel() * framework::SizeOfType(tensor->type()),
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stream);
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auto data_len = tensor->numel() * framework::SizeOfType(tensor->type());
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iobuf->append(reinterpret_cast<const char*>(&data_len), 8);
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iobuf->append(reinterpret_cast<const char*>(temp_ptr), data_len);
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delete[] temp_ptr;
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#endif
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}
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}
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void DeserializeFromMultiVarMsgAndIOBuf(const MultiVarMsg& multi_msg,
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const butil::IOBuf* iobuf,
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const platform::DeviceContext& ctx,
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framework::Scope* scope) {
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butil::IOBufBytesIterator io_buffer_itr(*iobuf);
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// size_t shard_buffer_remain = res_io_buffer.size();
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for (int recv_var_index = 0; recv_var_index < multi_msg.send_var_names_size();
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++recv_var_index) {
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const auto& msg = multi_msg.var_messages(recv_var_index);
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auto* var = scope->Var(msg.varname());
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if (msg.type() == ::paddle::distributed::LOD_TENSOR) {
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DeserializeLodTensor(var, msg, io_buffer_itr, ctx);
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} else if (msg.type() == ::paddle::distributed::SELECTED_ROWS) {
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DeserializeSelectedRows(var, msg, io_buffer_itr, ctx);
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}
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}
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}
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void DeserializeFromMultiVarMsgAndIOBuf(const MultiVarMsg& multi_msg,
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const butil::IOBuf* iobuf,
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const platform::DeviceContext& ctx,
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const framework::Scope* scope) {
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butil::IOBufBytesIterator io_buffer_itr(*iobuf);
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// size_t shard_buffer_remain = res_io_buffer.size();
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for (int recv_var_index = 0; recv_var_index < multi_msg.send_var_names_size();
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++recv_var_index) {
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const auto& msg = multi_msg.var_messages(recv_var_index);
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auto* var = scope->FindVar(msg.varname());
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PADDLE_ENFORCE_NE(var, nullptr,
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platform::errors::InvalidArgument(
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"Not find variable %s in scope.", msg.varname()));
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if (msg.type() == ::paddle::distributed::LOD_TENSOR) {
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DeserializeLodTensor(var, msg, io_buffer_itr, ctx);
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} else if (msg.type() == ::paddle::distributed::SELECTED_ROWS) {
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DeserializeSelectedRows(var, msg, io_buffer_itr, ctx);
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}
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}
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}
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void DeserializeLodTensor(framework::Variable* var, const VarMsg& msg,
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butil::IOBufBytesIterator& io_buffer_itr,
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const platform::DeviceContext& ctx) {
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const auto place = ctx.GetPlace();
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framework::LoDTensor* tensor = var->GetMutable<framework::LoDTensor>();
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std::vector<int> vec_dim;
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for (auto& x : msg.dims()) {
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vec_dim.push_back(x);
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}
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tensor->Resize(framework::make_ddim(vec_dim));
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framework::LoD lod;
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for (int i = 0; i < msg.lod_level(); ++i) {
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framework::Vector<size_t> v;
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for (int j = 0; j < msg.lod(i).lod_data_size(); ++j) {
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v.push_back(msg.lod(i).lod_data(j));
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}
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lod.push_back(v);
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}
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tensor->set_lod(lod);
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void* tensor_data =
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tensor->mutable_data(place, VarMessageToVarType(msg.data_type()));
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// IO Buffer
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if (platform::is_cpu_place(place)) {
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unsigned long data_len;
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io_buffer_itr.copy_and_forward((void*)(&data_len), 8);
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io_buffer_itr.copy_and_forward(tensor_data, data_len);
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} else if (platform::is_gpu_place(place)) {
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#ifdef PADDLE_WITH_CUDA
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unsigned long data_len;
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char* temp_ptr =
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new char[tensor->numel() * framework::SizeOfType(tensor->type())];
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io_buffer_itr.copy_and_forward((void*)(&data_len), 8);
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io_buffer_itr.copy_and_forward((void*)temp_ptr, data_len);
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auto stream =
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reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream();
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memory::Copy(BOOST_GET_CONST(platform::CUDAPlace, place), tensor_data,
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platform::CPUPlace(), (void*)temp_ptr,
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tensor->numel() * framework::SizeOfType(tensor->type()),
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stream);
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delete[] temp_ptr;
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#endif
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}
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}
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void DeserializeSelectedRows(framework::Variable* var, const VarMsg& msg,
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butil::IOBufBytesIterator& io_buffer_itr,
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const platform::DeviceContext& ctx) {
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const auto place = ctx.GetPlace();
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auto* slr = var->GetMutable<framework::SelectedRows>();
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framework::Tensor* tensor = slr->mutable_value();
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slr->set_height(msg.slr_height());
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std::vector<int64_t> tmp_rows(msg.slr_height());
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memcpy(&tmp_rows[0], msg.data().data(), msg.slr_height() * sizeof(int64_t));
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slr->set_rows(tmp_rows);
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std::vector<int> vec_dim;
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for (auto& x : msg.dims()) {
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vec_dim.push_back(x);
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}
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tensor->Resize(framework::make_ddim(vec_dim));
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void* tensor_data =
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tensor->mutable_data(place, VarMessageToVarType(msg.data_type()));
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// IO Buffer
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if (platform::is_cpu_place(place)) {
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unsigned long data_len;
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io_buffer_itr.copy_and_forward((void*)(&data_len), 8);
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io_buffer_itr.copy_and_forward(tensor_data, data_len);
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} else if (platform::is_gpu_place(place)) {
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#ifdef PADDLE_WITH_CUDA
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char* temp_ptr =
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new char[tensor->numel() * framework::SizeOfType(tensor->type())];
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unsigned long data_len;
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io_buffer_itr.copy_and_forward((void*)(&data_len), 8);
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io_buffer_itr.copy_and_forward(temp_ptr, data_len);
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auto stream =
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reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream();
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memory::Copy(BOOST_GET_CONST(platform::CUDAPlace, place), tensor_data,
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platform::CPUPlace(), temp_ptr,
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tensor->numel() * framework::SizeOfType(tensor->type()),
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stream);
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delete[] temp_ptr;
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#endif
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}
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}
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std::string GetIntTypeEndpoint(const std::string& ip, const uint32_t& port) {
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// There are usually two forms of IP address: ip(int) / ip (hostname)
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// If there're some problem with DNS, or ip triggers the bug of Brpc
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// We will try to get the IP address of the domain name manually again
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std::string ip_port = ip + ":" + std::to_string(port);
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struct hostent* hp = NULL;
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hp = gethostbyname(ip.c_str());
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if (NULL == hp) {
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LOG(ERROR) << "Brpc Start failed, ip_port= " << ip_port
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<< " , Error infomation: " << hstrerror(h_errno);
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}
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int i = 0;
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char* int_ip = NULL;
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while (hp->h_addr_list[i] != NULL) {
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int_ip = inet_ntoa(*(struct in_addr*)hp->h_addr_list[i]);
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VLOG(0) << "Brpc Get host by name, host:" << ip << " -> ip: " << int_ip;
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break;
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}
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std::string str_ip = int_ip;
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std::string int_ip_port = str_ip + ":" + std::to_string(port);
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return int_ip_port;
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}
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} // namespace distributed
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} // namespace paddle
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