Optimize decay (#20816)

* update pserver decay blocks

* update distributed notify handler

paddle/fluid/framework/details/async_ssa_graph_executor.cc

@@ -62,8 +62,16 @@ void ProcessGraph(std::vector<ir::Graph *> graphs, Scope *scope) {
             node->Op()->GetNullableAttr("sections"));
         auto trainer_id =
             boost::get<int>(node->Op()->GetNullableAttr("trainer_id"));
+        auto merge_add =
+            boost::get<bool>(node->Op()->GetNullableAttr("merge_add"));
+        if (!merge_add) {
+          merge_add = FLAGS_communicator_is_sgd_optimizer;
+        }
+        auto use_send_handler =
+            boost::get<bool>(node->Op()->GetNullableAttr("use_send_handler"));
         send_varname_to_ctx[send_var_name] = operators::distributed::RpcContext(
-            send_var_name, send_varnames, epmap, height_section, trainer_id);
+            send_var_name, send_varnames, epmap, height_section, trainer_id,
+            merge_add, use_send_handler);
         VLOG(3) << "find and init an send op: "
                 << send_varname_to_ctx[send_var_name];
       } else if (node->Name() == "recv") {

paddle/fluid/operators/distributed/communicator.cc

@@ -130,8 +130,15 @@ void AsyncCommunicator::InitImpl(const paddle::framework::ProgramDesc &program,
       auto height_section =
           boost::get<std::vector<int64_t>>(op->GetNullableAttr("sections"));
       auto trainer_id = boost::get<int>(op->GetNullableAttr("trainer_id"));
+      auto merge_add = boost::get<bool>(op->GetNullableAttr("merge_add"));
+      if (!merge_add) {
+        merge_add = FLAGS_communicator_is_sgd_optimizer;
+      }
+      auto use_send_handler =
+          boost::get<bool>(op->GetNullableAttr("use_send_handler"));
       send_varname_to_ctx[send_var_name] = operators::distributed::RpcContext(
-          send_var_name, send_varnames, epmap, height_section, trainer_id);
+          send_var_name, send_varnames, epmap, height_section, trainer_id,
+          merge_add, use_send_handler);
       VLOG(3) << "find and init an send op: "
               << send_varname_to_ctx[send_var_name];
     } else if (op->Type() == "recv") {
@@ -208,12 +215,17 @@ void AsyncCommunicator::SendThread() {
             }
           }
           auto before_merge = GetCurrentUS();
-          MergeVars(var_name, vars, send_scope_.get());
+          auto &ctx = send_varname_to_ctx_.at(var_name);
+          if (ctx.use_send_handler) {
+            MergeVars<float>(var_name, vars, send_scope_.get(), ctx.merge_add);
+          } else {
+            MergeVars<int64_t>(var_name, vars, send_scope_.get(),
+                               ctx.merge_add);
+          }
           auto after_merge = GetCurrentUS();
           VLOG(3) << "merge " << merged_var_num << " " << var_name
                   << " use time " << after_merge - before_merge;
           auto send_functor = distributed::ParameterSend<float>();
-          auto &ctx = send_varname_to_ctx_.at(var_name);
           if (!FLAGS_communicator_fake_rpc) {
             send_functor(ctx, *send_scope_, true, 1);
           }

paddle/fluid/operators/distributed/communicator.h

@@ -107,21 +107,21 @@ template <typename T, int MajorType = Eigen::RowMajor,
           typename IndexType = Eigen::DenseIndex>
 using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
 
+template <typename T>
 inline void MergeVars(const std::string& var_name,
                       const std::vector<std::shared_ptr<Variable>>& vars,
-                      Scope* scope) {
+                      Scope* scope, bool merge_add = true) {
   PADDLE_ENFORCE(!vars.empty(), "should have value to merge!");
   auto cpu_place = platform::CPUPlace();
   auto& var0 = vars[0];
   auto* out_var = scope->Var(var_name);
   if (var0->IsType<framework::LoDTensor>()) {
     auto dims = var0->Get<framework::LoDTensor>().dims();
-    VLOG(3) << "merge " << var_name << " LoDTensor dims " << dims;
-
+    VLOG(3) << "merge " << var_name << " LoDTensor dims " << dims
+            << "; merge add: " << merge_add;
     // init output tensor
     auto* out_t = out_var->GetMutable<framework::LoDTensor>();
-    out_t->mutable_data<float>(dims, cpu_place);
-
+    out_t->mutable_data<T>(dims, cpu_place);
     // check the input dims
     for (auto& var : vars) {
       auto& var_t = var->Get<framework::LoDTensor>();
@@ -130,44 +130,41 @@ inline void MergeVars(const std::string& var_name,
 
     // set output tensor to 0.
     auto cpu_ctx = paddle::platform::CPUDeviceContext();
-    math::SetConstant<paddle::platform::CPUDeviceContext, float>
-        constant_functor;
-    constant_functor(cpu_ctx, out_t, static_cast<float>(0));
-
+    math::SetConstant<paddle::platform::CPUDeviceContext, T> constant_functor;
+    constant_functor(cpu_ctx, out_t, static_cast<T>(0));
     // sum all vars to out
-    auto result = EigenVector<float>::Flatten(*out_t);
+    auto result = EigenVector<T>::Flatten(*out_t);
     for (auto& var : vars) {
       auto& in_t = var->Get<framework::LoDTensor>();
-      auto in = EigenVector<float>::Flatten(in_t);
+      auto in = EigenVector<T>::Flatten(in_t);
       result.device(*cpu_ctx.eigen_device()) = result + in;
     }
-    if (!FLAGS_communicator_is_sgd_optimizer) {
+    if (!merge_add) {
       result.device(*cpu_ctx.eigen_device()) =
-          result / static_cast<float>(vars.size());
+          result / static_cast<T>(vars.size());
     }
   } else if (var0->IsType<framework::SelectedRows>()) {
     auto& slr0 = var0->Get<framework::SelectedRows>();
     auto* out_slr = out_var->GetMutable<framework::SelectedRows>();
     out_slr->mutable_rows()->clear();
-    out_slr->mutable_value()->mutable_data<float>({{}}, cpu_place);
+    out_slr->mutable_value()->mutable_data<T>({{}}, cpu_place);
     std::vector<const paddle::framework::SelectedRows*> inputs;
     inputs.reserve(vars.size());
     for (auto& var : vars) {
       inputs.push_back(&var->Get<framework::SelectedRows>());
     }
     auto dev_ctx = paddle::platform::CPUDeviceContext();
-    if (FLAGS_communicator_is_sgd_optimizer) {
-      math::scatter::MergeAdd<paddle::platform::CPUDeviceContext, float>
-          merge_add;
+    if (merge_add) {
+      math::scatter::MergeAdd<paddle::platform::CPUDeviceContext, T> merge_add;
       merge_add(dev_ctx, inputs, out_slr);
     } else {
-      math::scatter::MergeAverage<paddle::platform::CPUDeviceContext, float>
+      math::scatter::MergeAverage<paddle::platform::CPUDeviceContext, T>
           merge_average;
       merge_average(dev_ctx, inputs, out_slr);
     }
 
     VLOG(3) << "merge " << var_name << " SelectedRows height: " << slr0.height()
-            << " dims: " << slr0.value().dims();
+            << " dims: " << slr0.value().dims() << "; merge add: " << merge_add;
   } else {
     PADDLE_THROW("unsupported var type!");
   }

paddle/fluid/operators/distributed/communicator_test.cc

@@ -47,7 +47,7 @@ TEST(communicator, merge_lod_tensors) {
   scope.reset(new framework::Scope());
   scope->Var(out_name);
   for (auto i = 0; i < 10; ++i) {
-    MergeVars(out_name, in_vars, scope.get());
+    MergeVars<float>(out_name, in_vars, scope.get());
   }
   auto &out_tensor = scope->FindVar(out_name)->Get<LoDTensor>();
   auto *out_data = out_tensor.data<float>();
@@ -86,7 +86,7 @@ TEST(communicator, merge_selected_rows) {
   scope.reset(new framework::Scope());
   scope->Var(out_name);
   for (auto i = 0; i < 10; ++i) {
-    MergeVars(out_name, in_vars, scope.get());
+    MergeVars<float>(out_name, in_vars, scope.get());
   }
   auto &out_slr = scope->FindVar(out_name)->Get<SelectedRows>();
   auto &out_t = out_slr.value();

paddle/fluid/operators/distributed/grpc/grpc_client.cc

@@ -438,26 +438,40 @@ VarHandlePtr GRPCClient::AsyncCheckpointNotify(const std::string& ep,
   return h;
 }
 
-VarHandlePtr GRPCClient::AsyncDistributeNotify(const std::string& ep,
-                                               const std::string& type,
+VarHandlePtr GRPCClient::AsyncDistributeNotify(
+    const std::string& ep, const platform::DeviceContext& ctx,
+    const framework::Scope& scope, const std::string& var_name,
     int64_t time_out) {
-  const auto ch = GetChannel(ep);
-
-  DistributeNotifyProcessor* s = new DistributeNotifyProcessor(ch);
-
+  const platform::DeviceContext* p_ctx = &ctx;
+  const std::string ep_val = ep;
+  const std::string var_name_val = var_name;
+  const framework::Scope* p_scope = &scope;
+  const auto ch = GetChannel(ep_val);
   const std::string method = kRequestNotify;
 
-  VarHandlePtr h(
-      new VarHandle(ep, method, LEARNING_RATE_DECAY_MESSAGE, nullptr, nullptr));
+  SendProcessor* s = new SendProcessor(ch);
+  VarHandlePtr h(new VarHandle(ep, method, var_name_val, p_ctx, p_scope));
   s->Prepare(h, time_out);
 
-  sendrecv::VariableMessage req;
-  req.set_varname(type);
+  framework::AsyncIO([var_name_val, p_scope, p_ctx, s, method, h, this] {
+    auto* var = p_scope->FindVar(var_name_val);
+
+    ::grpc::ByteBuffer req;
+    SerializeToByteBuffer(var_name_val, var, *p_ctx, &req, "", trainer_id_);
+
+    VLOG(3) << s->GetVarHandlePtr()->String() << " begin";
+
+    // stub context
+    s->response_call_back_ = nullptr;
 
     platform::RecordRPCEvent record_event(method);
 
-  auto rpc = s->stub_->AsyncDistributeNotify(s->context_.get(), req, &cq_);
-  rpc->Finish(&s->reply_, &s->status_, reinterpret_cast<void*>(s));
+    auto call = s->stub_g_.PrepareUnaryCall(
+        s->context_.get(), "/sendrecv.SendRecvService/DistributeNotify", req,
+        &cq_);
+    call->StartCall();
+    call->Finish(&s->reply_, &s->status_, reinterpret_cast<void*>(s));
+  });
   req_count_++;
 
   if (UNLIKELY(platform::IsProfileEnabled())) {

paddle/fluid/operators/distributed/grpc/grpc_client.h

@@ -173,20 +173,6 @@ class CheckpointNotifyProcessor : public BaseProcessor {
   std::unique_ptr<sendrecv::SendRecvService::Stub> stub_;
 };
 
-class DistributeNotifyProcessor : public BaseProcessor {
- public:
-  explicit DistributeNotifyProcessor(std::shared_ptr<grpc::Channel> ch)
-      : BaseProcessor() {
-    stub_ = sendrecv::SendRecvService::NewStub(ch);
-  }
-
-  virtual ~DistributeNotifyProcessor() {}
-
-  void ProcessImpl() override {}
-  sendrecv::VoidMessage reply_;
-  std::unique_ptr<sendrecv::SendRecvService::Stub> stub_;
-};
-
 class GRPCClient : public RPCClient {
  public:
   GRPCClient() : ok_(true), completed_(false), stopped_(false) {}
@@ -240,7 +226,8 @@ class GRPCClient : public RPCClient {
       int64_t time_out = FLAGS_rpc_deadline) override;
 
   VarHandlePtr AsyncDistributeNotify(
-      const std::string& ep, const std::string& type,
+      const std::string& ep, const platform::DeviceContext& ctx,
+      const framework::Scope& scope, const std::string& var_name,
       int64_t time_out = FLAGS_rpc_deadline) override;
 
   VarHandlePtr AsyncSendComplete(

paddle/fluid/operators/distributed/grpc/grpc_server.cc

@@ -402,33 +402,31 @@ class RequestNotify final : public RequestBase {
                          RequestHandler* request_handler, int req_id)
       : RequestBase(service, cq, request_handler, req_id), responder_(&ctx_) {
     request_.reset(new GRPCVariableResponse(request_handler->scope(),
-                                            request_handler->dev_ctx()));
+                                            request_handler->dev_ctx(),
+                                            !request_handler->sync_mode()));
     int method_id = static_cast<int>(distributed::GrpcMethod::kRequestNotify);
     service_->RequestAsyncUnary(
         method_id, &ctx_, request_.get(), &responder_, cq_, cq_,
         reinterpret_cast<void*>(static_cast<intptr_t>(req_id)));
   }
-
   virtual ~RequestNotify() {}
-
   std::string GetReqName() override { return request_->Varname(); }
 
   void Process() override {
-    auto scope = request_->GetMutableLocalScope();
+    std::string varname = GetReqName();
+    VLOG(4) << "RequestNotify var_name:" << varname;
 
-    std::string varname = request_->Varname();
+    auto scope = request_->GetMutableLocalScope();
+    auto invar = request_->GetVar();
     int trainer_id = request_->GetTrainerId();
-
-    VLOG(4) << "RequestNotify notify: " << varname
-            << ", trainer id: " << trainer_id;
-
-    request_handler_->Handle(varname, scope, nullptr, nullptr, trainer_id);
+    framework::Variable* outvar = nullptr;
+    request_handler_->Handle(varname, scope, invar, &outvar, trainer_id);
     Finish(reply_, &responder_);
   }
 
  protected:
-  std::shared_ptr<GRPCVariableResponse> request_;
   sendrecv::VoidMessage reply_;
+  std::shared_ptr<GRPCVariableResponse> request_;
   ServerAsyncResponseWriter<sendrecv::VoidMessage> responder_;
 };
 

paddle/fluid/operators/distributed/parameter_send.cc

@@ -116,7 +116,7 @@ void ParameterSend<T>::operator()(const RpcContext &rpc_ctx,
         row_offset += outs_dims[i][0];
       }
     }
-
+    if (rpc_ctx.use_send_handler) {
       for (size_t i = 0; i < rpc_ctx.splited_var_names.size(); i++) {
         auto &send_var_name = rpc_ctx.splited_var_names[i];
         VLOG(4) << "send var name: " << send_var_name;
@@ -133,7 +133,27 @@ void ParameterSend<T>::operator()(const RpcContext &rpc_ctx,
                   << rpc_ctx.splited_var_names[i];
         }
       }
-
+    } else {
+      for (size_t i = 0; i < rpc_ctx.splited_var_names.size(); i++) {
+        for (size_t j = 0; j < rpc_ctx.epmap.size(); j++) {
+          auto &send_var_name = rpc_ctx.splited_var_names[i];
+          VLOG(4) << "send var name: " << send_var_name;
+          auto &endpoint = rpc_ctx.epmap[j];
+          VLOG(4) << "send var endpoint: " << endpoint;
+          VLOG(4) << "need send: "
+                  << NeedSend(*local_scope.get(), send_var_name);
+          if (NeedSend(*local_scope.get(), send_var_name)) {
+            VLOG(3) << "sending " << send_var_name << " to " << endpoint;
+            rets.push_back(rpc_client->AsyncDistributeNotify(
+                endpoint, cpu_ctx, *local_scope.get(), send_var_name));
+            VLOG(4) << "send var " << send_var_name << " async handle done";
+          } else {
+            VLOG(3) << "don't send non-initialized variable: "
+                    << rpc_ctx.splited_var_names[i];
+          }
+        }
+      }
+    }
   } else if (send_var->IsType<framework::SelectedRows>()) {
     auto &send_slr = send_var->Get<framework::SelectedRows>();
     auto abs_sections = ToAbsoluteSection(rpc_ctx.height_sections);

paddle/fluid/operators/distributed/request_handler.h

@@ -63,7 +63,7 @@ constexpr char kCheckPointNotifyRPC[] = "CheckPointNotifyRPC";
 #define FETCH_BARRIER_MESSAGE "FETCH_BARRIER@RECV"
 #define COMPLETE_MESSAGE "COMPLETE@RECV"
 #define WITHOUT_BARRIER_MESSAGE "@WITHOUT_BARRIER@RECV"
-#define LEARNING_RATE_DECAY_MESSAGE "LRDECAY@RECV"
+#define LEARNING_RATE_DECAY_COUNTER "@LR_DECAY_COUNTER@"
 
 #define CHECKPOINT_SAVE_MESSAGE "SAVE@CHECKPOINTNOTIFY"
 #define CHECKPOINT_LOAD_MESSAGE "LOAD@CHECKPOINTNOTIFY"

paddle/fluid/operators/distributed/request_handler_impl.cc

@@ -262,11 +262,25 @@ bool RequestNotifyHandler::Handle(const std::string& varname,
                                   const int trainer_id,
                                   const std::string& out_var_name,
                                   const std::string& table_name) {
-  VLOG(4) << "RequestNotifyHandler" << varname;
-  if (varname == LEARNING_RATE_DECAY_MESSAGE) {
+  VLOG(4) << "RequestNotifyHandler: " << varname;
+  VLOG(3) << "async process var: " << varname << ", trainer_id: " << trainer_id;
+
+  string::Piece decay_piece(LEARNING_RATE_DECAY_COUNTER);
+  string::Piece var_name_piece = string::Piece(varname);
+  if (string::Contains(var_name_piece, decay_piece)) {
+    VLOG(3) << "LearningRate Decay Counter Update";
     PADDLE_ENFORCE_NE(
         lr_decay_block_id, -1,
         "when lr_decay_block_id = -1, there should be no RPC invoke.");
+    auto* origin_var = scope_->FindVar(varname);
+    auto origin_var_tensor = origin_var->Get<framework::LoDTensor>();
+    auto* send_var = scope->FindVar(varname);
+    auto send_var_tensor = send_var->Get<framework::LoDTensor>();
+    int64_t* origin_value =
+        origin_var_tensor.mutable_data<int64_t>(origin_var_tensor.place());
+    int64_t* send_value =
+        send_var_tensor.mutable_data<int64_t>(send_var_tensor.place());
+    origin_value[0] += send_value[0];
     executor_->RunPreparedContext(lr_decay_prepared_ctx_.get(), scope_);
   }
   return true;

paddle/fluid/operators/distributed/rpc_client.h

@@ -81,7 +81,8 @@ class RPCClient {
       int64_t time_out = FLAGS_rpc_deadline) = 0;
 
   virtual VarHandlePtr AsyncDistributeNotify(
-      const std::string& ep, const std::string& type,
+      const std::string& ep, const platform::DeviceContext& ctx,
+      const framework::Scope& scope, const std::string& var_name,
       int64_t time_out = FLAGS_rpc_deadline) = 0;
 
   virtual VarHandlePtr AsyncSendComplete(

paddle/fluid/operators/distributed/rpc_common.h

@@ -27,12 +27,15 @@ struct RpcContext {
 
   RpcContext(const std::string &name, const std::vector<std::string> &names,
              const std::vector<std::string> &emap,
-             const std::vector<int64_t> &sections, int id)
+             const std::vector<int64_t> &sections, int id,
+             bool merge_add_ = true, bool use_send_handler_ = true)
       : var_name(name),
         splited_var_names(names),
         epmap(emap),
         height_sections(sections),
-        trainer_id(id) {}
+        trainer_id(id),
+        merge_add(merge_add_),
+        use_send_handler(use_send_handler_) {}
 
   RpcContext(const RpcContext &ctx) {
     var_name = ctx.var_name;
@@ -40,6 +43,8 @@ struct RpcContext {
     epmap = ctx.epmap;
     height_sections = ctx.height_sections;
     trainer_id = ctx.trainer_id;
+    merge_add = ctx.merge_add;
+    use_send_handler = ctx.use_send_handler;
   }
 
   std::string var_name;
@@ -47,6 +52,8 @@ struct RpcContext {
   std::vector<std::string> epmap;
   std::vector<int64_t> height_sections;
   int trainer_id;
+  bool merge_add;
+  bool use_send_handler;
 };
 
 inline std::ostream &operator<<(std::ostream &os, const RpcContext &rpc_ctx) {
@@ -70,6 +77,9 @@ inline std::ostream &operator<<(std::ostream &os, const RpcContext &rpc_ctx) {
     os << section << ", ";
   }
   os << "]\n";
+
+  os << "merge add: " << rpc_ctx.merge_add;
+  os << "; send handler: " << rpc_ctx.use_send_handler << "\n";
   os << "}";
   return os;
 }

paddle/fluid/operators/distributed_ops/distributed_notify_op.cc

-/* Copyright (c) 2018 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 <future>  // NOLINT
-#include <ostream>
-
-#include "paddle/fluid/framework/data_type.h"
-#include "paddle/fluid/framework/lod_tensor.h"
-#include "paddle/fluid/framework/op_registry.h"
-#include "paddle/fluid/operators/distributed/distributed.h"
-#include "paddle/fluid/operators/distributed_ops/send_recv_util.h"
-#include "paddle/fluid/string/printf.h"
-
-namespace paddle {
-namespace operators {
-
-class DistributedNotifyOp : public framework::OperatorBase {
- public:
-  DistributedNotifyOp(const std::string& type,
-                      const framework::VariableNameMap& inputs,
-                      const framework::VariableNameMap& outputs,
-                      const framework::AttributeMap& attrs)
-      : OperatorBase(type, inputs, outputs, attrs) {}
-
-  void RunImpl(const framework::Scope& scope,
-               const platform::Place& place) const override {
-    std::vector<std::string> epmap = Attr<std::vector<std::string>>("epmap");
-    std::string type = Attr<std::string>("type");
-    int trainer_id = Attr<int>("trainer_id");
-
-    distributed::RPCClient* rpc_client =
-        distributed::RPCClient::GetInstance<RPCCLIENT_T>(trainer_id);
-    for (size_t i = 0; i < epmap.size(); i++) {
-      rpc_client->AsyncDistributeNotify(epmap[i], type);
-      VLOG(4) << "distribute notify sending : " << type << " to " << epmap[i];
-    }
-    PADDLE_ENFORCE_EQ(rpc_client->Wait(), true, "internal error in RPCClient");
-  }
-};
-
-class DistributedNotifyOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  void Make() {
-    AddAttr<std::vector<std::string>>("epmap",
-                                      "(string vector, default  127.0.0.1:6164)"
-                                      "Parameter Server endpoints in the order")
-        .SetDefault({"127.0.0.1:6164"});
-    AddAttr<std::string>("type",
-                         "(string, default '') indicate the action type");
-    AddAttr<int>("trainer_id", "trainer id from 0 ~ worker_num.").SetDefault(0);
-    AddComment(R"DOC(
-DistributeNotify operator
-
-This operator will send a signal to listen_and_serve op at
-the parameter server.
-)DOC");
-  }
-};
-
-class DistributedNotifyOpShapeInference : public framework::InferShapeBase {
- public:
-  void operator()(framework::InferShapeContext* ctx) const override {}
-};
-
-}  // namespace operators
-}  // namespace paddle
-
-namespace ops = paddle::operators;
-
-REGISTER_OPERATOR(
-    distributed_notify, ops::DistributedNotifyOp,
-    paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
-    paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>,
-    ops::DistributedNotifyOpMaker, ops::DistributedNotifyOpShapeInference);

paddle/fluid/operators/distributed_ops/listen_and_serv_op.cc

@@ -383,7 +383,8 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
   rpc_service_->RegisterRPC(distributed::kRequestGetNoBarrier,
                             request_get_no_barrier_handler_.get());
   rpc_service_->RegisterRPC(distributed::kRequestNotify,
-                            request_notify_handler_.get(), 1);
+                            request_notify_handler_.get(),
+                            FLAGS_rpc_send_thread_num);
 
   auto optimize_blocks =
       Attr<std::vector<framework::BlockDesc *>>(kOptimizeBlocks);

paddle/fluid/operators/distributed_ops/send_op.cc

@@ -45,6 +45,7 @@ class SendOp : public framework::OperatorBase {
 
     auto send_varnames = Attr<std::vector<std::string>>("send_varnames");
     auto height_sections = Attr<std::vector<int64_t>>("sections");
+    auto use_send_handler = Attr<bool>("use_send_handler");
 
     if (send_varnames.size() > 0) {
       if (ins.size() > 1) {
@@ -62,6 +63,7 @@ class SendOp : public framework::OperatorBase {
           distributed::RPCClient::GetInstance<RPCCLIENT_T>(trainer_id);
 
       std::vector<distributed::VarHandlePtr> rets;
+      if (use_send_handler) {
         for (size_t i = 0; i < ins.size(); i++) {
           if (NeedSend(scope, ins[i])) {
             VLOG(3) << "sending " << ins[i] << " to " << epmap[i];
@@ -71,6 +73,19 @@ class SendOp : public framework::OperatorBase {
             VLOG(3) << "don't send no-initialied variable: " << ins[i];
           }
         }
+      } else {
+        for (size_t i = 0; i < ins.size(); i++) {
+          for (size_t j = 0; j < epmap.size(); j++) {
+            if (NeedSend(scope, ins[i])) {
+              VLOG(3) << "sending " << ins[i] << " to " << epmap[j];
+              rets.push_back(rpc_client->AsyncDistributeNotify(epmap[j], ctx,
+                                                               scope, ins[i]));
+            } else {
+              VLOG(3) << "don't send no-initialied variable: " << ins[i];
+            }
+          }
+        }
+      }
       for (size_t i = 0; i < rets.size(); i++) {
         VLOG(7) << "before sync_send " << ins[i] << "from " << epmap[i];
         PADDLE_ENFORCE_NE(rets[i]->Wait(), 0U, "internal error in RPCClient");
@@ -113,6 +128,15 @@ This operator will send variables to listen_and_serve op at the parameter server
                  "Number of sub-tensors. This must evenly divide "
                  "Input.dims()[axis]")
         .SetDefault(0);
+    AddAttr<bool>("merge_add",
+                  "(bool, default 0)"
+                  "merge method, true represent add, false represent average")
+        .SetDefault(false);
+    AddAttr<bool>(
+        "use_send_handler",
+        "(bool, default 1)"
+        "if it's true, use send handler, other wise, use notify handler")
+        .SetDefault(true);
   }
 };
 

python/paddle/fluid/tests/unittests/test_dist_transpiler_async_decay.py

+#   Copyright (c) 2018 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.
+
+from __future__ import print_function
+
+import unittest
+import gc
+import paddle.fluid as fluid
+
+
+class TranspilerAsyncLRDecayTest(unittest.TestCase):
+    def setUp(self):
+        self.trainer_id = 0
+        self.trainers = 2
+        self.pservers = 2
+        # NOTE: we do not actually bind this port
+        self.pserver_eps = "127.0.0.1:6174,127.0.0.1:6175"
+        self.pserver1_ep = "127.0.0.1:6174"
+        self.pserver2_ep = "127.0.0.1:6175"
+        self.sync_mode = False
+        self.transpiler = None
+
+    def net_conf(self):
+        x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
+        y_predict = fluid.layers.fc(input=x,
+                                    size=1000,
+                                    act=None,
+                                    param_attr=fluid.ParamAttr(name='fc_w'),
+                                    bias_attr=fluid.ParamAttr(name='fc_b'))
+        y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+        cost = fluid.layers.square_error_cost(input=y_predict, label=y)
+        avg_cost = fluid.layers.mean(cost)
+        sgd_optimizer = fluid.optimizer.SGD(
+            learning_rate=fluid.layers.exponential_decay(
+                learning_rate=0.1,
+                decay_steps=100,
+                decay_rate=0.99,
+                staircase=True))
+        sgd_optimizer.minimize(avg_cost)
+
+    def get_main_program(self):
+        main = fluid.Program()
+        main.random_seed = 1
+        with fluid.program_guard(main):
+            self.net_conf()
+        self.origin_prog = main.clone()
+        return main
+
+    def get_trainer(self, config=None):
+        src = fluid.default_startup_program().clone()
+
+        t = self._transpiler_instance(config)
+
+        trainer_main = t.get_trainer_program(wait_port=False)
+        trainer_startup = fluid.default_startup_program()
+
+        assert (src.num_blocks == 1)
+        assert (trainer_startup.num_blocks == src.num_blocks)
+
+        return trainer_main, trainer_startup
+
+    def get_pserver(self, ep, config=None, sync_mode=True):
+        t = self._transpiler_instance(config, sync_mode)
+        pserver = t.get_pserver_program(ep)
+        startup = t.get_startup_program(ep, pserver)
+        return pserver, startup
+
+    def _transpiler_instance(self, config=None, sync_mode=True):
+        if not self.transpiler:
+            main = self.get_main_program()
+            self.transpiler = fluid.DistributeTranspiler(config=config)
+            self.transpiler.transpile(
+                self.trainer_id,
+                program=main,
+                pservers=self.pserver_eps,
+                trainers=self.trainers,
+                sync_mode=sync_mode)
+
+        return self.transpiler
+
+    def transpiler_test_impl(self):
+        pserver, startup = self.get_pserver(self.pserver1_ep, sync_mode=False)
+        pserver2, startup2 = self.get_pserver(self.pserver2_ep, sync_mode=False)
+
+        trainer, trainer_startup = self.get_trainer()
+
+        src = [op.type for op in trainer_startup.global_block().ops]
+        dst = ['fill_constant', 'fill_constant', 'fill_constant', 'fill_constant', \
+               'uniform_random', 'recv', 'recv', 'fetch_barrier', 'concat']
+        self.assertEqual(src, dst)
+
+        self.assertEqual([op.type for op in trainer.global_block().ops], [
+            'mul', 'elementwise_add', 'elementwise_sub', 'square', 'mean',
+            'fill_constant', 'mean_grad', 'square_grad', 'elementwise_sub_grad',
+            'elementwise_add_grad', 'send', 'mul_grad', 'split_byref', 'send',
+            'send', 'recv', 'recv', 'concat'
+        ])
+
+        self.assertEqual(len(pserver.blocks), 4)
+        # block0: listen_and_serv
+        self.assertEqual([op.type for op in pserver.blocks[0].ops],
+                         ["listen_and_serv"])
+        # block1: sum,cast,scale,floor,fill_constant,elementwise_pow,scale
+        self.assertEqual([op.type for op in pserver.blocks[1].ops], [
+            "sum", "cast", "scale", "floor", "fill_constant", "elementwise_pow",
+            "scale"
+        ])
+
+        # block1~2: optimize pass
+        self.assertEqual([op.type for op in pserver.blocks[2].ops], ["sgd"])
+        # confirm startup program
+        self.assertEqual([op.type for op in startup.global_block().ops], [
+            "fill_constant", "fill_constant", "fill_constant", "fill_constant",
+            "uniform_random"
+        ])
+
+    def test_transpiler(self):
+        main = fluid.Program()
+        startup = fluid.Program()
+        with fluid.unique_name.guard():
+            with fluid.program_guard(main, startup):
+                self.transpiler_test_impl()
+        # NOTE: run gc.collect to eliminate pybind side objects to
+        # prevent random double-deallocate when inherited in python.
+        del self.transpiler
+        del main
+        del startup
+        gc.collect()
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -41,7 +41,7 @@ import logging
 import numpy as np
 
 from .ps_dispatcher import RoundRobin, PSDispatcher
-from .. import core, framework, unique_name
+from .. import core, framework, unique_name, initializer
 from ..framework import Program, default_main_program, \
     default_startup_program, Block, Parameter, grad_var_name
 from .details import wait_server_ready, UnionFind, VarStruct, VarsDistributed
@@ -304,6 +304,7 @@ class DistributeTranspiler(object):
             PRINT_LOG = True
         assert (self.config.min_block_size >= 8192)
         assert (self.config.split_method.__bases__[0] == PSDispatcher)
+        self.counter_var = None
 
     def _transpile_nccl2(self,
                          trainer_id,
@@ -631,6 +632,7 @@ class DistributeTranspiler(object):
             np.random.shuffle(grad_var_mapping_items)
 
         self.grad_name_to_send_dummy_out = dict()
+
         for grad_varname, splited_vars in grad_var_mapping_items:
             eplist = ps_dispatcher.dispatch(splited_vars)
 
@@ -720,6 +722,31 @@ class DistributeTranspiler(object):
                     RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
                 })
             fetch_barrier_input.append(send_barrier_out)
+        else:
+            lr_ops = self._get_lr_ops()
+            if len(lr_ops) > 0 and self.counter_var:
+                decay_dummy_output = program.global_block().create_var(
+                    name=framework.generate_control_dev_var_name())
+                if self.config.runtime_split_send_recv:
+                    ## async mode, using communicator to merge and send
+                    send_varnames = [self.counter_var.name]
+                else:
+                    send_varnames = []
+                sections = []
+                program.global_block().append_op(
+                    type="send",
+                    inputs={"X": self.counter_var},
+                    outputs={"Out": decay_dummy_output},
+                    attrs={
+                        "epmap": pserver_endpoints,
+                        "sections": sections,
+                        "send_varnames": send_varnames,
+                        "merge_add": True,
+                        "use_send_handler": False,
+                        RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE,
+                        OP_ROLE_VAR_ATTR_NAME:
+                        [self.counter_var.name, self.counter_var.name]
+                    })
 
         # step 3: insert recv op to receive parameters from parameter server
         recv_vars = []
@@ -821,19 +848,6 @@ class DistributeTranspiler(object):
                             RPC_OP_ROLE_ATTR_NAME: DIST_OP_ROLE_ATTR_VALUE
                         })
 
-        if not self.sync_mode:
-            lr_ops = self._get_lr_ops()
-            if len(lr_ops) > 0:
-                program.global_block().append_op(
-                    type="distributed_notify",
-                    inputs={},
-                    outputs={},
-                    attrs={
-                        "epmap": pserver_endpoints,
-                        "trainer_id": self.trainer_id,
-                        "type": "LRDECAY@RECV"
-                    })
-
         self._get_trainer_startup_program(recv_vars=recv_vars, eplist=eplist)
 
         if self.has_distributed_lookup_table:
@@ -2380,11 +2394,57 @@ class DistributeTranspiler(object):
     def _get_lr_ops(self):
         lr_ops = []
         block = self.origin_program.global_block()
-        for op in block.ops:
+        for index, op in enumerate(block.ops):
             role_id = int(op.attr(RPC_OP_ROLE_ATTR_NAME))
             if role_id == int(LR_SCHED_OP_ROLE_ATTR_VALUE) or \
                 role_id == int(LR_SCHED_OP_ROLE_ATTR_VALUE) | \
                     int(OPT_OP_ROLE_ATTR_VALUE):
+                if self.sync_mode == False and op.type == 'increment':
+                    inputs = self._get_input_map_from_op(
+                        self.origin_program.global_block().vars, op)
+                    outputs = self._get_output_map_from_op(
+                        self.origin_program.global_block().vars, op)
+                    for key in outputs:
+                        counter_var = outputs[key]
+                    all_trainer_counter_inputs = [
+                        self.origin_program.global_block().create_var(
+                            name="%s.trainer_%d" % (counter_var.name, id_),
+                            type=counter_var.type,
+                            shape=counter_var.shape,
+                            dtype=counter_var.dtype,
+                            persistable=counter_var.persistable)
+                        for id_ in range(self.trainer_num)
+                    ]
+                    for i, op in enumerate(self.startup_program.global_block()
+                                           .ops):
+                        if op.type == 'fill_constant':
+                            for key in op.output_names:
+                                if len(op.output(key)) == 1 and op.output(key)[
+                                        0] == counter_var.name:
+                                    self.startup_program.global_block().ops[
+                                        i]._set_attr(
+                                            'value',
+                                            float(0.0 - self.trainer_num))
+                    for var in all_trainer_counter_inputs:
+                        if var.name == "%s.trainer_%d" % (counter_var.name,
+                                                          self.trainer_id):
+                            self.counter_var = var
+                        self.startup_program.global_block().create_var(
+                            name=var.name,
+                            type=var.type,
+                            dtype=var.dtype,
+                            shape=var.shape,
+                            persistable=var.persistable,
+                            initializer=initializer.Constant(1))
+                    op_role_attr_name = core.op_proto_and_checker_maker.kOpRoleAttrName(
+                    )
+                    block._remove_op(index)
+                    op = block._insert_op(
+                        index,
+                        type='sum',
+                        inputs={'X': all_trainer_counter_inputs},
+                        outputs=outputs,
+                        attrs={op_role_attr_name: LR_SCHED_OP_ROLE_ATTR_VALUE})
                 lr_ops.append(op)
                 log("append lr op: ", op.type)
         return lr_ops