Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into zeros_like
zhoukunsheng
6 years ago
commit
380df8281f
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// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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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#pragma once
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#include <algorithm>
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#include <string>
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#include <unordered_map>
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#include <utility>
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#include <vector>
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#include "paddle/fluid/framework/details/build_strategy.h"
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#include "paddle/fluid/framework/details/multi_devices_helper.h"
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#include "paddle/fluid/framework/ir/graph_helper.h"
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namespace paddle {
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namespace framework {
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namespace details {
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void SetFuseParameterGroupsSize(int group_size);
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int GetFuseParameterGroupsSize();
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void SetFuseParameterMemorySize(uint64_t memory_size);
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uint64_t GetFuseParameterMemorySize();
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class AllocContinuousSpaceForGradPass : public ir::Pass {
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protected:
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void ApplyImpl(ir::Graph *graph) const override;
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template <typename AttrType>
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void ResetAttribute(const std::string &attr_name, ir::Graph *graph) const;
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void SetGroupGradsAndParams(
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const std::unordered_map<std::string, ir::Node *> &var_nodes,
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const ParamsAndGrads ¶ms_grads,
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GroupGradsAndParams *group_grads_params) const;
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void SetGroupAccordingToLayers(
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const std::unordered_map<std::string, ir::Node *> &var_nodes,
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const ParamsAndGrads ¶ms_grads,
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GroupGradsAndParams *group_grads_params) const;
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void SetGroupAccordingToMemorySize(
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const std::unordered_map<std::string, ir::Node *> &var_nodes,
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GroupGradsAndParams *group_grads_params) const;
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void SetGroupAccordingToGroupSize(
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const std::unordered_map<std::string, ir::Node *> &var_nodes,
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GroupGradsAndParams *group_grads_params) const;
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private:
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bool IsSupportedVarType(const proto::VarType::Type &type) const;
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void RecordParamsAndGrads(ir::Node *node, ParamsAndGrads *params_grads) const;
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void InitFusedVarsAndAllocSpaceForVars(
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const std::vector<platform::Place> &places,
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const std::vector<Scope *> &local_scopes,
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const std::unordered_map<std::string, ir::Node *> &vars,
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const std::string &fused_var_name,
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const ParamsAndGrads ¶ms_grads) const;
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void AppendAllocSpaceForVarsOp(const std::vector<std::string> ¶ms_name,
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const std::vector<std::string> &grads_name,
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const std::string &fused_var_name,
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BlockDesc *global_block) const;
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};
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} // namespace details
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} // namespace framework
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} // namespace paddle
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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/framework/details/async_ssa_graph_executor.h"
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#include "paddle/fluid/framework/variable_helper.h"
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#ifdef PADDLE_WITH_DISTRIBUTE
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#include "paddle/fluid/operators/distributed/communicator.h"
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#endif
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namespace paddle {
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namespace framework {
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namespace details {
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inline void NewTempScopeAndInitVars(const std::vector<VarInfo> &var_infos,
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Scope *scope) {
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VLOG(3) << "NewTempScopeAndInitVars";
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Scope &local_scope = scope->NewScope();
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*scope->Var(details::kLocalExecScopeName)->GetMutable<Scope *>() =
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&local_scope;
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for (auto &info : var_infos) {
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if (scope->FindVar(info.name_) != nullptr) {
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continue;
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}
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if (info.persistable_) { // Persistable
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InitializeVariable(scope->Var(info.name_), info.type_);
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} else {
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InitializeVariable(local_scope.Var(info.name_), info.type_);
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}
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}
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}
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// get RpcContext and remote send and recv op
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void ProcessGraph(std::vector<ir::Graph *> graphs, Scope *scope) {
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#ifdef PADDLE_WITH_DISTRIBUTE
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using RpcCtxMap = operators::distributed::RpcCtxMap;
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VLOG(3) << "ProcessGraph";
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RpcCtxMap send_varname_to_ctx;
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RpcCtxMap recv_varname_to_ctx;
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for (auto i = 0; i < graphs.size(); ++i) {
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std::vector<ir::Node *> nodes_to_delete;
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for (auto &node : graphs[i]->Nodes()) {
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VLOG(3) << "node name " << node->Name();
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if (node && node->IsOp()) {
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if (node->Name() == "send") {
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auto send_var_name = node->Op()->Input("X")[0];
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auto send_varnames = boost::get<std::vector<std::string>>(
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node->Op()->GetNullableAttr("send_varnames"));
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auto epmap = boost::get<std::vector<std::string>>(
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node->Op()->GetNullableAttr("epmap"));
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auto height_section = boost::get<std::vector<int64_t>>(
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node->Op()->GetNullableAttr("sections"));
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send_varname_to_ctx[send_var_name] =
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operators::distributed::RpcContext(send_var_name, send_varnames,
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epmap, height_section);
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VLOG(3) << "find and init an send op: "
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<< send_varname_to_ctx[send_var_name];
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} else if (node->Name() == "recv") {
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auto recv_var_name = node->Op()->Output("Out")[0];
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auto recv_varnames = boost::get<std::vector<std::string>>(
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node->Op()->GetNullableAttr("recv_varnames"));
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auto epmap = boost::get<std::vector<std::string>>(
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node->Op()->GetNullableAttr("epmap"));
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recv_varname_to_ctx[recv_var_name] =
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operators::distributed::RpcContext(recv_var_name, recv_varnames,
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epmap, {});
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nodes_to_delete.push_back(node);
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VLOG(3) << "find and remove an recv op: "
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<< recv_varname_to_ctx[recv_var_name];
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}
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}
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}
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}
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// init communicator here
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if (send_varname_to_ctx.size() > 0) {
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VLOG(3) << "this is distribute mode, will use communicator";
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operators::distributed::Communicator::Init(send_varname_to_ctx,
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recv_varname_to_ctx, scope);
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operators::distributed::Communicator::GetInstance()->Start();
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}
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#endif
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}
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AsyncSSAGraphExecutor::AsyncSSAGraphExecutor(
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const ExecutionStrategy &strategy, const std::vector<Scope *> &local_scopes,
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const std::vector<platform::Place> &places, std::vector<ir::Graph *> graphs)
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: strategy_(std::move(strategy)),
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local_scopes_(std::move(local_scopes)),
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pool_(places.size() >= 2 ? new ::ThreadPool(places.size()) : nullptr),
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places_(std::move(places)),
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graphs_(std::move(graphs)) {
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VLOG(3) << "build AsyncSSAGraphExecutor";
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PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size());
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// set the correct size of thread pool to each device.
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strategy_.num_threads_ = strategy_.num_threads_ < places_.size()
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? 1UL
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: strategy_.num_threads_ / places_.size();
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VLOG(1) << "set num_threads: " << strategy_.num_threads_
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<< " to run the operators of the graph on each device.";
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for (size_t i = 0; i < places.size(); ++i) {
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executors_.emplace_back(new details::ThreadedSSAGraphExecutor(
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strategy_, {local_scopes_[i]}, {places_[i]}, graphs_[i]));
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}
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for (auto &node : graphs_[0]->Nodes()) {
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if (node->IsVar() && !node->IsCtrlVar() && node->Var()) {
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var_infos_.emplace_back();
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var_infos_.back().name_ = node->Var()->Name();
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var_infos_.back().type_ = node->Var()->GetType();
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var_infos_.back().persistable_ = node->Var()->Persistable();
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}
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}
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for (auto *scope : local_scopes_) {
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NewTempScopeAndInitVars(var_infos_, scope);
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}
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ProcessGraph(graphs_, local_scopes_[0]);
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}
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void AsyncSSAGraphExecutor::StartOffPythonTrainLoop() {
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VLOG(3) << "StartOffPythonTrainLoop size = " << places_.size();
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for (size_t i = 1; i < places_.size(); ++i) {
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auto call = [this, i]() -> void {
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VLOG(3) << "start off python thread " << i;
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try {
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while (true) {
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executors_[i]->Run({});
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}
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} catch (...) {
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exception_holder_.Catch(std::current_exception());
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VLOG(3) << "get exception type = " << exception_holder_.Type();
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}
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VLOG(3) << "thread " << i << " exited!";
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};
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run_futures_.emplace_back(pool_->enqueue(std::move(call)));
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}
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}
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void AsyncSSAGraphExecutor::HandleException() {
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if (exception_holder_.IsCaught()) {
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for (auto &f : run_futures_) {
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VLOG(3) << "wait future";
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f.wait();
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}
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VLOG(3) << "caught exception " << exception_holder_.Type()
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<< ", rethrow it";
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run_futures_.clear();
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exception_holder_.ReThrow();
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}
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}
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FeedFetchList AsyncSSAGraphExecutor::Run(
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const std::vector<std::string> &fetch_tensors) {
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// init once
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if (run_futures_.size() == 0 && places_.size() > 1) {
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exception_holder_.Clear();
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StartOffPythonTrainLoop();
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}
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if (places_.size() == 1) {
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exception_holder_.Clear();
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} else {
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HandleException();
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}
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FeedFetchList fetch_data;
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fetch_data.reserve(fetch_tensors.size());
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try {
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fetch_data = executors_[0]->Run(fetch_tensors);
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} catch (...) {
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exception_holder_.Catch(std::current_exception());
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}
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HandleException();
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FeedFetchList ret;
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for (size_t fetch_idx = 0; fetch_idx < fetch_tensors.size(); ++fetch_idx) {
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std::vector<const LoDTensor *> lodtensor_ptrs;
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lodtensor_ptrs.push_back(&fetch_data.at(fetch_idx));
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ret.emplace_back();
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ret.back().MergeLoDTensor(lodtensor_ptrs, platform::CPUPlace());
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}
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return ret;
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}
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} // namespace details
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} // namespace framework
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} // namespace paddle
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@ -0,0 +1,65 @@
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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.
|
||||
// See the License for the specific language governing permissions and
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||||
// limitations under the License.
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||||
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#pragma once
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#include <memory>
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#include <string>
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#include <utility>
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#include <vector>
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#include "ThreadPool.h"
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#include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
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namespace paddle {
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namespace framework {
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namespace details {
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struct VarInfo {
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std::string name_;
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proto::VarType::Type type_;
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bool persistable_;
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};
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class AsyncSSAGraphExecutor : public SSAGraphExecutor {
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public:
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AsyncSSAGraphExecutor(const ExecutionStrategy &strategy,
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const std::vector<Scope *> &local_scopes,
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const std::vector<platform::Place> &places,
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std::vector<ir::Graph *> graphs);
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~AsyncSSAGraphExecutor() final = default;
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const ir::Graph &Graph() const override { return *graphs_[0]; }
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FeedFetchList Run(const std::vector<std::string> &fetch_tensors) override;
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private:
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void StartOffPythonTrainLoop();
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void HandleException();
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private:
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ExecutionStrategy strategy_;
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std::vector<Scope *> local_scopes_;
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std::unique_ptr<::ThreadPool> pool_{nullptr};
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std::vector<platform::Place> places_;
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std::vector<ir::Graph *> graphs_;
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std::vector<std::unique_ptr<details::ThreadedSSAGraphExecutor>> executors_;
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ExceptionHolder exception_holder_;
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std::vector<std::future<void>> run_futures_;
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std::vector<VarInfo> var_infos_;
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};
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} // namespace details
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} // namespace framework
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} // namespace paddle
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