feature/DC asgd (#12722)

* wip

* add ref_by_trainer_id op

* ready to test

* fix ref inputs

* refine rpc_op_handle

* fix merge bug

paddle/fluid/framework/details/rpc_op_handle.cc

@@ -29,22 +29,19 @@ RPCOpHandle::RPCOpHandle(ir::Node *node, const framework::OpDesc &op_desc,
       place_(place) {}
 
 void RPCOpHandle::RunImpl() {
-  // TODO(wuyi): need further analysis whether wait VarDummyHandle.
-  // Wait input done
   for (auto *in : inputs_) {
     auto &p = static_cast<VarHandle *>(in)->place_;
-    // FIXME(Yancey1989): need a better solution instead of use DebugString()
-    if (ir::IsControlDepVar(*in->Node())) {  // HACK
+    if (ir::IsControlDepVar(*in->Node())) {
       continue;
     }
     if (in->GeneratedOp()) {
       in->GeneratedOp()->RecordWaitEventOnCtx(dev_ctxes_.at(p));
     }
   }
-  auto &tmp_scope = local_scope_->FindVar(kLocalExecScopeName)->Get<Scope *>();
-  // FIXME(wuyi): can not use RunAndRecordEvent here, for it will cause dead
-  // lock.
-  op_->Run(*tmp_scope, place_);
+  this->RunAndRecordEvent([this] {
+    op_->Run(*local_scope_->FindVar(kLocalExecScopeName)->Get<Scope *>(),
+             place_);
+  });
 }
 
 std::string RPCOpHandle::Name() const { return name_; }

paddle/fluid/framework/executor.cc

@@ -85,8 +85,10 @@ Executor::Executor(const platform::Place& place) : place_(place) {}
 
 void Executor::Close() {
 #ifdef PADDLE_WITH_DISTRIBUTE
+  // TODO(typhoonzero): complete message will need to use real trainer_id,
+  // except 0.
   ::paddle::operators::distributed::RPCClient::GetInstance<
-      ::paddle::operators::distributed::GRPCClient>()
+      ::paddle::operators::distributed::GRPCClient>(0)
       ->SendComplete();
 #endif
 }

paddle/fluid/operators/checkpoint_notify_op.cc

@@ -38,9 +38,10 @@ class CheckpointNotifyOp : public framework::OperatorBase {
     std::vector<std::string> epmap = Attr<std::vector<std::string>>("epmap");
     std::string dir = Attr<std::string>("dir");
     std::string lookup_table_name = Attr<std::string>("lookup_table");
+    int trainer_id = Attr<int>("trainer_id");
 
     distributed::RPCClient* rpc_client =
-        distributed::RPCClient::GetInstance<RPCCLIENT_T>();
+        distributed::RPCClient::GetInstance<RPCCLIENT_T>(trainer_id);
     for (size_t i = 0; i < epmap.size(); i++) {
       auto lookup_table_save_dir =
           string::Sprintf("%s/%s_%d", dir, lookup_table_name, i);
@@ -63,6 +64,7 @@ class CheckpointNotifyOpMaker : public framework::OpProtoAndCheckerMaker {
         "dir", "(string, default '') indicate the folder checkpoint will use");
     AddAttr<std::string>("lookup_table",
                          "(string, default '') the lookup table name");
+    AddAttr<int>("trainer_id", "trainer id from 0 ~ worker_num.").SetDefault(0);
     AddComment(R"DOC(
 CheckpointNotify operator
 

paddle/fluid/operators/distributed/grpc_client.cc

@@ -79,7 +79,7 @@ VarHandlePtr GRPCClient::AsyncSendVar(const std::string& ep,
     auto* var = p_scope->FindVar(var_name_val);
 
     ::grpc::ByteBuffer req;
-    SerializeToByteBuffer(var_name_val, var, *p_ctx, &req);
+    SerializeToByteBuffer(var_name_val, var, *p_ctx, &req, "", trainer_id_);
 
     VLOG(3) << s->GetVarHandlePtr()->String() << " begin";
 
@@ -105,7 +105,10 @@ VarHandlePtr GRPCClient::AsyncSendVar(const std::string& ep,
 void ProcGetResponse(const VarHandle& var_h,
                      const ::grpc::ByteBuffer& ret_msg) {
   framework::Variable* outvar = nullptr;
-  DeserializeFromByteBuffer(ret_msg, *var_h.ctx(), var_h.scope(), &outvar);
+  // get response's trainer_id is not used
+  int trainer_id;
+  DeserializeFromByteBuffer(ret_msg, *var_h.ctx(), var_h.scope(), &outvar,
+                            &trainer_id);
 }
 
 template <typename T>
@@ -135,6 +138,7 @@ VarHandlePtr GRPCClient::AsyncGetVar(const std::string& ep,
     // prepare input
     sendrecv::VariableMessage req;
     req.set_varname(var_name_val);
+    req.set_trainer_id(trainer_id_);
     ::grpc::ByteBuffer buf;
     RequestToByteBuffer<sendrecv::VariableMessage>(req, &buf);
 

paddle/fluid/operators/distributed/grpc_serde.cc

@@ -34,8 +34,8 @@ namespace distributed {
 
 void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
                            const platform::DeviceContext& ctx,
-                           ::grpc::ByteBuffer* msg,
-                           const std::string& out_name) {
+                           ::grpc::ByteBuffer* msg, const std::string& out_name,
+                           const int trainer_id) {
   platform::RecordRPCEvent record_event("serial", &ctx);
   // Default DestroyCallback does nothing, When using GPU
   // the CPU buffer need to be freed.
@@ -45,6 +45,7 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
   size_t payload_size;
 
   request.set_varname(name);
+  request.set_trainer_id(trainer_id);
   // Note: normally the profiler is enabled in 1 trainer, hence only
   // 1 trainer returns true for ShouldSendProfileState(). It tells PS
   // servers the trainer's profiling state so that PS can follow the
@@ -147,11 +148,12 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
 void DeserializeFromByteBuffer(const ::grpc::ByteBuffer& msg,
                                const platform::DeviceContext& ctx,
                                const framework::Scope* scope,
-                               framework::Variable** var) {
+                               framework::Variable** var, int* trainer_id) {
   platform::RecordRPCEvent record_event("deserial", &ctx);
   operators::distributed::GRPCVariableResponse resp(scope, &ctx);
   PADDLE_ENFORCE(resp.Parse(msg) == 0, "parse bytebuffer to tensor error!");
   *var = resp.GetVar();
+  *trainer_id = resp.GetTrainerId();
 }
 
 }  // namespace distributed

paddle/fluid/operators/distributed/grpc_serde.h

@@ -38,12 +38,13 @@ typedef void (*DestroyCallback)(void*);
 void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
                            const platform::DeviceContext& ctx,
                            ::grpc::ByteBuffer* msg,
-                           const std::string& out_varname = std::string());
+                           const std::string& out_varname = std::string(),
+                           const int trainer_id = 0);
 
 void DeserializeFromByteBuffer(const ::grpc::ByteBuffer& msg,
                                const platform::DeviceContext& ctx,
                                const framework::Scope* scope,
-                               framework::Variable** var);
+                               framework::Variable** var, int* trainer_id);
 
 }  // namespace distributed
 }  // namespace operators

paddle/fluid/operators/distributed/grpc_server.cc

@@ -102,9 +102,10 @@ class RequestSend final : public RequestBase {
 
     auto scope = request_->GetMutableLocalScope();
     auto invar = request_->GetVar();
+    int trainer_id = request_->GetTrainerId();
     framework::Variable* outvar = nullptr;
 
-    request_handler_->Handle(varname, scope, invar, &outvar);
+    request_handler_->Handle(varname, scope, invar, &outvar, trainer_id);
     Finish(reply_, &responder_);
   }
 
@@ -133,13 +134,14 @@ class RequestGet final : public RequestBase {
   void Process() override {
     // proc request.
     std::string varname = request_.varname();
+    int trainer_id = request_.trainer_id();
     VLOG(4) << "RequestGet " << varname;
 
     auto scope = request_handler_->scope();
     auto invar = scope->FindVar(varname);
     framework::Variable* outvar = nullptr;
 
-    request_handler_->Handle(varname, scope, invar, &outvar);
+    request_handler_->Handle(varname, scope, invar, &outvar, trainer_id);
 
     if (outvar) {
       SerializeToByteBuffer(varname, outvar, *request_handler_->dev_ctx(),
@@ -179,6 +181,7 @@ class RequestPrefetch final : public RequestBase {
     // prefetch process...
     std::string in_var_name = request_->Varname();
     std::string out_var_name = request_->OutVarname();
+    int trainer_id = request_->GetTrainerId();
     VLOG(4) << "RequestPrefetch, in_var_name: " << in_var_name
             << " out_var_name: " << out_var_name;
 
@@ -187,7 +190,8 @@ class RequestPrefetch final : public RequestBase {
     // out var must be created in local scope!
     framework::Variable* outvar = scope->Var(out_var_name);
 
-    request_handler_->Handle(in_var_name, scope, invar, &outvar, out_var_name);
+    request_handler_->Handle(in_var_name, scope, invar, &outvar, trainer_id,
+                             out_var_name);
 
     SerializeToByteBuffer(out_var_name, outvar, *request_handler_->dev_ctx(),
                           &reply_);
@@ -225,12 +229,13 @@ class RequestCheckpointNotify final : public RequestBase {
 
     std::string checkpoint_notify = request_->Varname();
     std::string checkpoint_dir = request_->OutVarname();
+    int trainer_id = request_->GetTrainerId();
 
     VLOG(4) << "RequestCheckpointNotify notify: " << checkpoint_notify
             << ", dir: " << checkpoint_dir;
 
     request_handler_->Handle(checkpoint_notify, scope, nullptr, nullptr,
-                             checkpoint_dir);
+                             trainer_id, checkpoint_dir);
     Finish(reply_, &responder_);
   }
 

paddle/fluid/operators/distributed/grpc_variable_response.cc

@@ -293,6 +293,14 @@ int GRPCVariableResponse::Parse(Source* source) {
         }
         break;
       }
+      case sendrecv::VariableMessage::kTrainerIdFieldNumber: {
+        uint64_t trainer_id = 0;
+        if (!input.ReadVarint64(&trainer_id)) {
+          return tag;
+        }
+        meta_.set_trainer_id(trainer_id);
+        break;
+      }
       default: {
         // Unknown tag, return unknown error.
         return -1;

paddle/fluid/operators/distributed/request_handler.h

@@ -190,6 +190,7 @@ class RequestHandler {
   //    }
   virtual bool Handle(const std::string& varname, framework::Scope* scope,
                       framework::Variable* var, framework::Variable** outvar,
+                      const int trainer_id,
                       const std::string& out_var_name = "") = 0;
 
  protected:

paddle/fluid/operators/distributed/request_handler_impl.cc

@@ -36,6 +36,7 @@ bool RequestSendHandler::Handle(const std::string& varname,
                                 framework::Scope* scope,
                                 framework::Variable* invar,
                                 framework::Variable** outvar,
+                                const int trainer_id,
                                 const std::string& out_var_name) {
   VLOG(4) << "RequestSendHandler:" << varname;
 
@@ -76,6 +77,7 @@ bool RequestGetHandler::Handle(const std::string& varname,
                                framework::Scope* scope,
                                framework::Variable* invar,
                                framework::Variable** outvar,
+                               const int trainer_id,
                                const std::string& out_var_name) {
   VLOG(4) << "RequestGetHandler:" << varname;
   if (sync_mode_) {
@@ -88,6 +90,19 @@ bool RequestGetHandler::Handle(const std::string& varname,
     }
   } else {
     if (varname != FETCH_BARRIER_MESSAGE && varname != COMPLETE_MESSAGE) {
+      if (enable_dc_asgd_) {
+        // NOTE: the format is determined by distributed_transpiler.py
+        std::string param_bak_name =
+            string::Sprintf("%s.trainer_%d_bak", varname, trainer_id);
+        VLOG(3) << "getting " << param_bak_name << " trainer_id " << trainer_id;
+        auto var = scope_->FindVar(varname);
+        auto t_orig = var->Get<framework::LoDTensor>();
+        auto param_bak = scope_->Var(param_bak_name);
+        auto t = param_bak->GetMutable<framework::LoDTensor>();
+        t->mutable_data(dev_ctx_->GetPlace(), t_orig.type());
+        VLOG(3) << "copying " << varname << " to " << param_bak_name;
+        framework::TensorCopy(t_orig, dev_ctx_->GetPlace(), t);
+      }
       *outvar = scope_->FindVar(varname);
     }
   }
@@ -98,6 +113,7 @@ bool RequestPrefetchHandler::Handle(const std::string& varname,
                                     framework::Scope* scope,
                                     framework::Variable* invar,
                                     framework::Variable** outvar,
+                                    const int trainer_id,
                                     const std::string& out_var_name) {
   VLOG(4) << "RequestPrefetchHandler " << varname;
 
@@ -113,6 +129,7 @@ bool RequestCheckpointHandler::Handle(const std::string& varname,
                                       framework::Scope* scope,
                                       framework::Variable* invar,
                                       framework::Variable** outvar,
+                                      const int trainer_id,
                                       const std::string& out_var_name) {
   PADDLE_ENFORCE(
       checkpoint_notify_id != -1,

paddle/fluid/operators/distributed/request_handler_impl.h

@@ -36,20 +36,34 @@ namespace distributed {
 
 class RequestSendHandler final : public RequestHandler {
  public:
-  explicit RequestSendHandler(bool sync_mode) : RequestHandler(sync_mode) {}
+  explicit RequestSendHandler(bool sync_mode, bool enable_dc_asgd = false)
+      : RequestHandler(sync_mode) {
+    enable_dc_asgd_ = enable_dc_asgd;
+  }
   virtual ~RequestSendHandler() {}
   bool Handle(const std::string& varname, framework::Scope* scope,
               framework::Variable* var, framework::Variable** outvar,
+              const int trainer_id,
               const std::string& out_var_name = "") override;
+
+ private:
+  bool enable_dc_asgd_;
 };
 
 class RequestGetHandler final : public RequestHandler {
  public:
-  explicit RequestGetHandler(bool sync_mode) : RequestHandler(sync_mode) {}
+  explicit RequestGetHandler(bool sync_mode, bool enable_dc_asgd = false)
+      : RequestHandler(sync_mode) {
+    enable_dc_asgd_ = enable_dc_asgd;
+  }
   virtual ~RequestGetHandler() {}
   bool Handle(const std::string& varname, framework::Scope* scope,
               framework::Variable* var, framework::Variable** outvar,
+              const int trainer_id,
               const std::string& out_var_name = "") override;
+
+ private:
+  bool enable_dc_asgd_;
 };
 
 class RequestPrefetchHandler final : public RequestHandler {
@@ -58,6 +72,7 @@ class RequestPrefetchHandler final : public RequestHandler {
   virtual ~RequestPrefetchHandler() {}
   bool Handle(const std::string& varname, framework::Scope* scope,
               framework::Variable* var, framework::Variable** outvar,
+              const int trainer_id,
               const std::string& out_var_name = "") override;
 };
 
@@ -70,6 +85,7 @@ class RequestCheckpointHandler final : public RequestHandler {
   virtual ~RequestCheckpointHandler() {}
   bool Handle(const std::string& varname, framework::Scope* scope,
               framework::Variable* var, framework::Variable** outvar,
+              const int trainer_id,
               const std::string& out_var_name = "") override;
 
  private:

paddle/fluid/operators/distributed/rpc_client.cc

@@ -24,6 +24,7 @@ namespace distributed {
 
 std::once_flag RPCClient::init_flag_;
 std::unique_ptr<RPCClient> RPCClient::rpc_client_(nullptr);
+int RPCClient::trainer_id_ = 0;
 
 }  // namespace distributed
 }  // namespace operators

paddle/fluid/operators/distributed/rpc_client.h

@@ -72,14 +72,15 @@ class RPCClient {
   virtual bool Wait() = 0;
 
   template <typename T>
-  static RPCClient* GetInstance() {
-    std::call_once(init_flag_, &RPCClient::Init<T>);
+  static RPCClient* GetInstance(int trainer_id) {
+    std::call_once(init_flag_, &RPCClient::Init<T>, trainer_id);
     return rpc_client_.get();
   }
 
   // Init is called by GetInstance.
   template <typename T>
-  static void Init() {
+  static void Init(int trainer_id) {
+    trainer_id_ = trainer_id;
     if (rpc_client_.get() == nullptr) {
       rpc_client_.reset(new T());
       rpc_client_->InitImpl();
@@ -88,6 +89,8 @@ class RPCClient {
 
  protected:
   virtual void InitImpl() {}
+  // each trainer have exact one trainer id, it should be static
+  static int trainer_id_;
 
  private:
   static std::once_flag init_flag_;

paddle/fluid/operators/distributed/rpc_server_test.cc

@@ -125,7 +125,7 @@ TEST(PREFETCH, CPU) {
   g_req_handler.reset(new distributed::RequestPrefetchHandler(true));
   g_rpc_service.reset(new RPCSERVER_T("127.0.0.1:0", 1));
   distributed::RPCClient* client =
-      distributed::RPCClient::GetInstance<RPCCLIENT_T>();
+      distributed::RPCClient::GetInstance<RPCCLIENT_T>(0);
 
   std::thread server_thread(StartServer, distributed::kRequestPrefetch);
   g_rpc_service->WaitServerReady();
@@ -165,7 +165,7 @@ TEST(COMPLETE, CPU) {
   g_req_handler.reset(new distributed::RequestSendHandler(true));
   g_rpc_service.reset(new RPCSERVER_T("127.0.0.1:0", 2));
   distributed::RPCClient* client =
-      distributed::RPCClient::GetInstance<RPCCLIENT_T>();
+      distributed::RPCClient::GetInstance<RPCCLIENT_T>(0);
   PADDLE_ENFORCE(client != nullptr);
   std::thread server_thread(StartServer, distributed::kRequestSend);
   g_rpc_service->WaitServerReady();

paddle/fluid/operators/distributed/send_recv.proto.in

@@ -79,6 +79,7 @@ message VariableMessage {
   // server stops profiling and generates a profile to /tmp/profile_ps_*
   // when profile switches from 1 to 2.
   int64 profile = 11;
+  int64 trainer_id = 12;
 }
 
 message VoidMessage {}

paddle/fluid/operators/distributed/variable_response.h

@@ -92,6 +92,8 @@ class VariableResponse {
     return scope_->FindVar(meta_.varname());
   }
 
+  int GetTrainerId() { return static_cast<int>(meta_.trainer_id()); }
+
  protected:
   bool ReadRaw(::google::protobuf::io::CodedInputStream* input,
                const platform::DeviceContext& dev_ctx, platform::Place place,

paddle/fluid/operators/fetch_barrier_op.cc

@@ -37,7 +37,8 @@ class FetchBarrierOp : public framework::OperatorBase {
                const platform::Place& place) const override {
     std::vector<std::string> eps = Attr<std::vector<std::string>>("endpoints");
     distributed::RPCClient* rpc_client =
-        distributed::RPCClient::GetInstance<RPCCLIENT_T>();
+        distributed::RPCClient::GetInstance<RPCCLIENT_T>(
+            Attr<int>("trainer_id"));
 
     PADDLE_ENFORCE(rpc_client->Wait(), "internal error in RPCClient");
 
@@ -61,6 +62,7 @@ This operator will send a send barrier signal to list_and_serv op, so that
 the Parameter Server would knew all variables have been sent.
 )DOC");
 
+    AddAttr<int>("trainer_id", "trainer id from 0 ~ worker_num.").SetDefault(0);
     AddAttr<std::vector<std::string>>("endpoints",
                                       "(string vector, default 127.0.0.1:6164)"
                                       "Server endpoints to send variables to.")

paddle/fluid/operators/gen_nccl_id_op.cc

@@ -61,7 +61,7 @@ class GenNCCLIdOp : public framework::OperatorBase {
     std::vector<std::string> endpoint_list =
         Attr<std::vector<std::string>>("endpoint_list");
     distributed::RPCClient* client =
-        distributed::RPCClient::GetInstance<RPCCLIENT_T>();
+        distributed::RPCClient::GetInstance<RPCCLIENT_T>(0);
 
     for (auto& ep : endpoint_list) {
       VLOG(3) << "sending nccl id to " << ep;

paddle/fluid/operators/listen_and_serv_op.cc

@@ -218,23 +218,26 @@ void ListenAndServOp::RunAsyncLoop(framework::Executor *executor,
                                    framework::ProgramDesc *program,
                                    framework::Scope *recv_scope) const {
   VLOG(2) << "RunAsyncLoop";
-  // grad name to block id
-  std::unordered_map<std::string, int32_t> grad_to_block_id;
-  std::unordered_map<int32_t, std::string> id_to_grad;
-
   auto grad_to_block_id_str =
       Attr<std::vector<std::string>>("grad_to_block_id");
-  for (const auto &grad_and_id : grad_to_block_id_str) {
+  DoubleFindMap<std::string, int32_t> grad_to_block_id;
+
+  auto append_block_maps = [](DoubleFindMap<std::string, int32_t> *out_map,
+                              const std::string &grad_and_id) {
     std::vector<std::string> pieces;
     split(grad_and_id, ':', &pieces);
-    VLOG(3) << "after split, grad = " << pieces[0] << ", id=" << pieces[1];
+    VLOG(3) << "after split, key = " << pieces[0] << ", id=" << pieces[1];
     PADDLE_ENFORCE_EQ(pieces.size(), 2);
-    PADDLE_ENFORCE_EQ(grad_to_block_id.count(pieces[0]), 0);
+    PADDLE_ENFORCE_EQ(out_map->count(pieces[0]), 0);
 
     int block_id = std::stoi(pieces[1]);
-    grad_to_block_id[pieces[0]] = block_id;
-    id_to_grad[block_id] = pieces[0];
+    (*out_map)[pieces[0]] = block_id;
+  };
+
+  for (const auto &grad_and_id : grad_to_block_id_str) {
+    append_block_maps(&grad_to_block_id, grad_and_id);
   }
+
   size_t num_blocks = program->Size();
   PADDLE_ENFORCE_GE(num_blocks, 2,
                     "server program should have at least 2 blocks");
@@ -244,15 +247,22 @@ void ListenAndServOp::RunAsyncLoop(framework::Executor *executor,
     block_list.push_back(blkid);
   }
   auto optimize_prepared = executor->Prepare(*program, block_list);
-  // execute global block if needed
-  if (block_list[0] == 1 && id_to_grad.count(1) == 0) {
+  // execute global block if needed, block id 1 in the program is global
+  // block if it's not bind to a grad var for it's update.
+  if (block_list[0] == 1 &&
+      grad_to_block_id.find_value(static_cast<int32_t>(1)) ==
+          grad_to_block_id.end()) {
     executor->RunPreparedContext(optimize_prepared[0].get(), recv_scope);
   }
   std::unordered_map<std::string,
                      std::shared_ptr<framework::ExecutorPrepareContext>>
-      grad_to_prepared_ctx;
+      grad_to_prepared_ctx, param_to_prepared_ctx;
   for (size_t i = 0; i < block_list.size(); ++i) {
-    grad_to_prepared_ctx[id_to_grad[block_list[i]]] = optimize_prepared[i];
+    auto blkid = block_list[i];
+    auto it = grad_to_block_id.find_value(blkid);
+    if (it != grad_to_block_id.end()) {
+      grad_to_prepared_ctx[it->first] = optimize_prepared[i];
+    }
   }
 
   request_send_handler_->SetGradToPreparedCtx(&grad_to_prepared_ctx);
@@ -315,6 +325,7 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
   framework::Scope &recv_scope = scope.NewScope();
 
   bool sync_mode = Attr<bool>("sync_mode");
+  bool dc_sgd = Attr<bool>("dc_asgd");
   auto fan_in = Attr<int>("Fanin");
   auto inputs = Inputs("X");
 
@@ -328,8 +339,10 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
 
   rpc_service_.reset(new RPCSERVER_T(endpoint, fan_in));
 
-  request_send_handler_.reset(new distributed::RequestSendHandler(sync_mode));
-  request_get_handler_.reset(new distributed::RequestGetHandler(sync_mode));
+  request_send_handler_.reset(
+      new distributed::RequestSendHandler(sync_mode, dc_sgd));
+  request_get_handler_.reset(
+      new distributed::RequestGetHandler(sync_mode, dc_sgd));
   request_prefetch_handler_.reset(
       new distributed::RequestPrefetchHandler(sync_mode));
   request_checkpoint_handler_.reset(new distributed::RequestCheckpointHandler(
@@ -443,6 +456,8 @@ class ListenAndServOpMaker : public framework::OpProtoAndCheckerMaker {
         "a map from grad name to it's optimize block id")
         .SetDefault({});
     AddAttr<bool>("sync_mode", "if works at sync_mode or not").SetDefault(true);
+    AddAttr<bool>("dc_asgd", "set to true will enable DC-ASGD training.")
+        .SetDefault(false);
     AddAttr<std::vector<framework::BlockDesc *>>(
         kOptimizeBlocks, "Optimize blocks to run on server side.")
         .SetDefault({});

paddle/fluid/operators/listen_and_serv_op.h

@@ -18,6 +18,7 @@ limitations under the License. */
 #include <atomic>
 #include <set>
 #include <string>
+#include <utility>
 #include <vector>
 
 #include "paddle/fluid/framework/executor.h"
@@ -37,6 +38,17 @@ constexpr char kCheckpointBlockId[] = "checkpint_block_id";
 
 void RunServer(std::shared_ptr<distributed::RPCServer> service);
 
+template <class TKey, class TValue>
+class DoubleFindMap : public std::unordered_map<TKey, TValue> {
+ public:
+  typename std::unordered_map<TKey, TValue>::iterator find_value(TValue v) {
+    return std::find_if(this->begin(), this->end(),
+                        [&v](const std::pair<const std::string, int> p) {
+                          return p.second == v;
+                        });
+  }
+};
+
 class ListenAndServOp : public framework::OperatorBase {
  public:
   ListenAndServOp(const std::string& type,

paddle/fluid/operators/prefetch_op.cc

@@ -42,7 +42,8 @@ class PrefetchOp : public framework::OperatorBase {
     auto& ctx = *pool.Get(place);
 
     distributed::RPCClient* rpc_client =
-        distributed::RPCClient::GetInstance<RPCCLIENT_T>();
+        distributed::RPCClient::GetInstance<RPCCLIENT_T>(
+            Attr<int>("trainer_id"));
 
     std::vector<distributed::VarHandlePtr> rets;
     for (size_t i = 0; i < ins.size(); i++) {
@@ -69,6 +70,7 @@ class PrefetchOpMaker : public framework::OpProtoAndCheckerMaker {
               "(LoDTensor) result "
               "to be fetched from parameter server")
         .AsDuplicable();
+    AddAttr<int>("trainer_id", "trainer id from 0 ~ worker_num.").SetDefault(0);
     AddAttr<std::vector<std::string>>(
         "epmap",
         "(string vector, default 127.0.0.1:6164)"

paddle/fluid/operators/recv_op.cc

@@ -42,7 +42,8 @@ class RecvOp : public framework::OperatorBase {
     auto& ctx = *pool.Get(place);
 
     distributed::RPCClient* rpc_client =
-        distributed::RPCClient::GetInstance<RPCCLIENT_T>();
+        distributed::RPCClient::GetInstance<RPCCLIENT_T>(
+            Attr<int>("trainer_id"));
 
     std::vector<distributed::VarHandlePtr> rets;
     for (size_t i = 0; i < outs.size(); i++) {
@@ -73,6 +74,7 @@ This operator can get variables from server side.
                                       "Server endpoints in the order of input "
                                       "variables for mapping")
         .SetDefault({});
+    AddAttr<int>("trainer_id", "trainer id from 0 ~ worker_num.").SetDefault(0);
     AddAttr<int>("sync_mode",
                  "(int, default 0)"
                  "sync recv or async recv.")

paddle/fluid/operators/ref_by_trainer_id_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/operators/ref_by_trainer_id_op.h"
+#include <string>
+
+namespace paddle {
+namespace operators {
+
+class RefByTrainerIdOp : public framework::OperatorWithKernel {
+ public:
+  RefByTrainerIdOp(const std::string &type,
+                   const framework::VariableNameMap &inputs,
+                   const framework::VariableNameMap &outputs,
+                   const framework::AttributeMap &attrs)
+      : OperatorWithKernel(type, inputs, outputs, attrs) {}
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInputs("X"),
+                   "Input(X) of RefByTrainerIdOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("TrainerId"),
+                   "Input(TrainerId) of RefByTrainerIdOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of RefByTrainerIdOp should not be null.");
+    PADDLE_ENFORCE_EQ(ctx->GetInputDim("TrainerId").size(), 1,
+                      "TrainerId should be a scalar.");
+    // Out's shape is determined at runtime.
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(
+            ctx.MultiInput<framework::Tensor>("X")[0]->type()),
+        ctx.GetPlace());
+  }
+};
+
+class RefByTrainerIdOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X", "(Tensor) Input tensor list.").AsDuplicable();
+    AddInput("TrainerId", "(Tensor) Scalar int, the trainer id runtime value.");
+    AddOutput("Out", "(Tensor) Return one tensor reference of X[trainer_id]");
+    AddComment(R"DOC(
+**RefByTrainerId operator**
+
+Return a reference of a tensor, using trainer_id as the index to find from the input.
+
+$$Out = X[TrainerId]$$
+)DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_WITHOUT_GRADIENT(ref_by_trainer_id, ops::RefByTrainerIdOp,
+                             ops::RefByTrainerIdOpMaker);
+REGISTER_OP_CPU_KERNEL(
+    ref_by_trainer_id,
+    ops::RefByTrainerIdKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::RefByTrainerIdKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::RefByTrainerIdKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::RefByTrainerIdKernel<paddle::platform::CPUDeviceContext, int64_t>);

paddle/fluid/operators/ref_by_trainer_id_op.cu.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/operators/ref_by_trainer_id_op.h"
+
+REGISTER_OP_CUDA_KERNEL(
+    ref_by_trainer_id,
+    paddle::operators::RefByTrainerIdKernel<paddle::platform::CUDADeviceContext,
+                                            float>,
+    paddle::operators::RefByTrainerIdKernel<paddle::platform::CUDADeviceContext,
+                                            double>,
+    paddle::operators::RefByTrainerIdKernel<paddle::platform::CUDADeviceContext,
+                                            int>,
+    paddle::operators::RefByTrainerIdKernel<paddle::platform::CUDADeviceContext,
+                                            int64_t>);

paddle/fluid/operators/ref_by_trainer_id_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include <stdio.h>
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+template <typename DeviceContext, typename T>
+class RefByTrainerIdKernel : public framework::OpKernel<T> {
+ public:
+  virtual void Compute(const framework::ExecutionContext& context) const {
+    auto* out = context.Output<framework::Tensor>("Out");
+    auto in_list = context.MultiInput<framework::Tensor>("X");
+    auto* trainer_id_t = context.Input<framework::Tensor>("TrainerId");
+    int64_t trainer_id;
+    auto* trainer_id_data = trainer_id_t->data<int64_t>();
+    if (platform::is_gpu_place(context.GetPlace())) {
+#ifdef PADDLE_WITH_CUDA
+      auto stream = context.cuda_device_context().stream();
+      memory::Copy<>(platform::CPUPlace(), &trainer_id,
+                     boost::get<platform::CUDAPlace>(context.GetPlace()),
+                     trainer_id_data, sizeof(int64_t), stream);
+#endif
+    } else {
+      trainer_id = *trainer_id_data;
+    }
+    printf("after get trainer_id %lu\n", trainer_id);
+    PADDLE_ENFORCE_LT(trainer_id, in_list.size());
+    out->mutable_data<T>(context.GetPlace());
+    out->ShareDataWith(*(in_list[trainer_id]));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/send_barrier_op.cc

@@ -39,7 +39,8 @@ class SendBarrierOp : public framework::OperatorBase {
     std::vector<std::string> eps = Attr<std::vector<std::string>>("endpoints");
 
     distributed::RPCClient* rpc_client =
-        distributed::RPCClient::GetInstance<RPCCLIENT_T>();
+        distributed::RPCClient::GetInstance<RPCCLIENT_T>(
+            Attr<int>("trainer_id"));
 
     VLOG(3) << "SendBarrierOp sync";
 
@@ -67,6 +68,7 @@ This operator will send a send barrier signal to list_and_serv op, so that
 the Parameter Server would knew all variables have been sent.
 )DOC");
 
+    AddAttr<int>("trainer_id", "trainer id from 0 ~ worker_num.").SetDefault(0);
     AddAttr<std::vector<std::string>>("endpoints",
                                       "(string vector, default 127.0.0.1:6164)"
                                       "Server endpoints to send variables to.")

paddle/fluid/operators/send_op.cc

@@ -44,7 +44,8 @@ class SendOp : public framework::OperatorBase {
     auto& ctx = *pool.Get(place);
 
     distributed::RPCClient* rpc_client =
-        distributed::RPCClient::GetInstance<RPCCLIENT_T>();
+        distributed::RPCClient::GetInstance<RPCCLIENT_T>(
+            Attr<int>("trainer_id"));
 
     std::vector<distributed::VarHandlePtr> rets;
     for (size_t i = 0; i < ins.size(); i++) {
@@ -79,6 +80,7 @@ This operator will send variables to listen_and_serve op at the parameter server
                  "(int, default 0)"
                  "sync send or async send.")
         .SetDefault(0);
+    AddAttr<int>("trainer_id", "trainer id from 0 ~ worker_num.").SetDefault(0);
     AddAttr<std::vector<std::string>>("epmap",
                                       "(string vector, default 127.0.0.1:6164)"
                                       "Server endpoints in the order of input "

paddle/fluid/operators/test_send_nccl_id.cc

@@ -92,7 +92,7 @@ TEST(SendNcclId, RPCServer) {
   std::string ep = string::Sprintf("127.0.0.1:%d", port);
 
   distributed::RPCClient* client =
-      distributed::RPCClient::GetInstance<RPCCLIENT_T>();
+      distributed::RPCClient::GetInstance<RPCCLIENT_T>(0);
 
   LOG(INFO) << "connect to server" << ep;
   client->AsyncSendVar(ep, dev_ctx, scope, NCCL_ID_VARNAME);

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

@@ -37,10 +37,15 @@ class TestDistRunnerBase(object):
             "get_model should be implemented by child classes.")
 
     @staticmethod
-    def get_transpiler(trainer_id, main_program, pserver_endpoints, trainers,
-                       sync_mode):
+    def get_transpiler(trainer_id,
+                       main_program,
+                       pserver_endpoints,
+                       trainers,
+                       sync_mode,
+                       dc_asgd=False):
         # NOTE: import fluid until runtime, or else forking processes will cause error.
         config = fluid.DistributeTranspilerConfig()
+        config.enable_dc_asgd = dc_asgd
         t = fluid.DistributeTranspiler(config=config)
         t.transpile(
             trainer_id=trainer_id,
@@ -55,7 +60,7 @@ class TestDistRunnerBase(object):
         # NOTE: pserver should not call memory optimize
         t = self.get_transpiler(args.trainer_id,
                                 fluid.default_main_program(), args.endpoints,
-                                args.trainers, args.sync_mode)
+                                args.trainers, args.sync_mode, args.dc_asgd)
         pserver_prog = t.get_pserver_program(args.current_endpoint)
         startup_prog = t.get_startup_program(args.current_endpoint,
                                              pserver_prog)
@@ -75,8 +80,7 @@ class TestDistRunnerBase(object):
             t = self.get_transpiler(args.trainer_id,
                                     fluid.default_main_program(),
                                     args.endpoints, args.trainers,
-                                    args.sync_mode)
-
+                                    args.sync_mode, args.dc_asgd)
             trainer_prog = t.get_trainer_program()
         else:
             trainer_prog = fluid.default_main_program()
@@ -155,6 +159,7 @@ def runtime_main(test_class):
     parser.add_argument('--mem_opt', action='store_true')
     parser.add_argument('--use_cuda', action='store_true')
     parser.add_argument('--use_reduce', action='store_true')
+    parser.add_argument('--dc_asgd', action='store_true')
     parser.add_argument(
         '--use_reader_alloc', action='store_true', required=False)
     parser.add_argument('--batch_size', required=False, type=int, default=2)
@@ -200,6 +205,7 @@ class TestDistBase(unittest.TestCase):
         self._enforce_place = None
         self._mem_opt = False
         self._use_reduce = False
+        self._dc_asgd = False  # must use with async mode
         self._use_reader_alloc = True
         self._setup_config()
         self._after_setup_config()

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

@@ -53,6 +53,15 @@ class TestDistMnistAsync(TestDistBase):
         self.check_with_place("dist_mnist.py", delta=200)
 
 
+class TestDistMnistDcAsgd(TestDistBase):
+    def _setup_config(self):
+        self._sync_mode = False
+        self._dc_asgd = True
+
+    def test_se_resnext(self):
+        self.check_with_place("dist_mnist.py", delta=200)
+
+
 # FIXME(typhoonzero): enable these tests once we have 4
 # 4 GPUs on CI machine, and the base class should be updated.
 #

python/paddle/fluid/tests/unittests/test_ref_by_trainer_id_op.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.
+
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestRefByTrainerIdOp(OpTest):
+    def setUp(self):
+        self.op_type = "ref_by_trainer_id"
+        param_baks = [("x%d" % x, np.random.random((10, 10)).astype("float32"))
+                      for x in range(10)]
+        self.inputs = {
+            'X': param_baks,
+            'TrainerId': np.array([8]).astype("int64")
+        }
+        self.outputs = {'Out': param_baks[8][1]}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -38,7 +38,7 @@ import six
 import logging
 
 from .ps_dispatcher import RoundRobin, HashName, PSDispatcher
-from .. import core, framework
+from .. import core, framework, unique_name
 from ..framework import Program, default_main_program, \
     default_startup_program, Block, \
     Parameter, grad_var_name
@@ -138,6 +138,7 @@ class DistributeTranspilerConfig(object):
     slice_var_up = True
     split_method = None
     min_block_size = 8192
+    enable_dc_asgd = False
     # supported modes: pserver, nccl2
     mode = "pserver"
     print_log = False
@@ -252,6 +253,8 @@ class DistributeTranspiler(object):
                 n workers, the id may range from 0 ~ n-1
             program (Program|None): program to transpile,
                 default is fluid.default_main_program().
+            startup_program (Program|None): startup_program to transpile,
+                default is fluid.default_startup_program().
             pservers (str): comma separated ip:port string for the pserver
                 list.
             trainers (int|str): in pserver mode this is the number of
@@ -383,6 +386,8 @@ class DistributeTranspiler(object):
                 outputs={"Out": send_barrier_out},
                 attrs={
                     "endpoints": pserver_endpoints,
+                    "sync_mode": self.sync_mode,
+                    "trainer_id": self.trainer_id,
                     RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
                 })
 
@@ -426,6 +431,7 @@ class DistributeTranspiler(object):
                 outputs={"Out": splited_var},
                 attrs={
                     "epmap": eps,
+                    "trainer_id": self.trainer_id,
                     RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE,
                     OP_ROLE_VAR_ATTR_NAME:
                     [param_varname, recv_op_role_var_name],
@@ -440,6 +446,7 @@ class DistributeTranspiler(object):
                 outputs={"Out": all_recv_outputs},
                 attrs={
                     "endpoints": pserver_endpoints,
+                    "trainer_id": self.trainer_id,
                     RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
                 })
 
@@ -651,6 +658,24 @@ in a single call.")
                     endpoint, op):
                 opt_op_on_pserver.append(op)
         # step 3.3
+        # prepare if dc asgd is enabled
+        if self.config.enable_dc_asgd == True:
+            assert (self.sync_mode == False)
+            self.param_bak_list = []
+            # add param_bak for each trainer
+            for p in self.param_grad_ep_mapping[endpoint]["params"]:
+                # each parameter should have w_bak for each trainer id
+                for i in range(self.trainer_num):
+                    param_bak_name = "%s.trainer_%d_bak" % (p.name, i)
+                    tmpvar = pserver_program.global_block().create_var(
+                        # NOTE: this var name format is used in `request_get_handler`
+                        name=param_bak_name,
+                        type=p.type,
+                        shape=p.shape,
+                        dtype=p.dtype)
+                    self.param_bak_list.append((p, tmpvar))
+
+        # step 3.4
         # Iterate through the ops, and if an op and the optimize ops
         # which located on current pserver are in one set, then
         # append it into the sub program.
@@ -741,7 +766,7 @@ in a single call.")
                                                grad_to_block_id, merged_var,
                                                lr_ops)
 
-        # dedup grad to ids list
+# dedup grad to ids list
         grad_to_block_id = list(set(grad_to_block_id))
         # append global ops
         if global_ops:
@@ -787,6 +812,8 @@ in a single call.")
 
         if self.has_distributed_lookup_table:
             attrs['checkpint_block_id'] = checkpoint_block_id
+        if self.config.enable_dc_asgd:
+            attrs['dc_asgd'] = True
 
         if len(prefetch_var_name_to_block_id) > 0:
             attrs[
@@ -903,6 +930,15 @@ to transpile() call.")
                     inputs=new_inputs,
                     outputs=new_outputs,
                     attrs=op.all_attrs())
+        if self.config.enable_dc_asgd:
+            for p, p_bak in self.param_bak_list:
+                startup_param_var = s_prog.global_block().vars[p.name]
+                startup_tmpvar = s_prog.global_block().vars[p_bak.name]
+                # copy init random value to param_bak
+                s_prog.global_block().append_op(
+                    type="assign",
+                    inputs={"X": startup_param_var},
+                    outputs={"Out": startup_tmpvar})
 
         # add slice vars
         s_prog._slice_vars_and_attrs = self._get_slice_vars_and_attrs(endpoint)
@@ -1175,6 +1211,7 @@ to transpile() call.")
                     attrs={
                         "sync_mode": not self.sync_mode,
                         "epmap": pserver_endpoints,
+                        "trainer_id": self.trainer_id,
                         RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE,
                         OP_ROLE_VAR_ATTR_NAME: [
                             self.grad_name_to_param_name[table_grad_name],
@@ -1531,6 +1568,69 @@ to transpile() call.")
                 attrs={"scale": 1.0 / float(self.trainer_num)})
         return merged_var
 
+    def _append_dc_asgd_ops(self, block, param_var, grad_var):
+        # NOTE: can not use grammar candy here, should put ops in specific block
+        local_param_bak = block.create_var(
+            name="%s.local_bak" % param_var.name,
+            shape=param_var.shape,
+            type=param_var.type,
+            dtype=param_var.dtype,
+            persistable=False)
+        # trainer_id_var is block local
+        trainer_id_var = block.create_var(
+            name="@TRAINER_ID@",
+            type=core.VarDesc.VarType.LOD_TENSOR,
+            dtype=core.VarDesc.VarType.INT64,
+            shape=[1],
+            persistable=False)
+
+        # ref_inputs = [x[1] for x in self.param_bak_list]
+        ref_inputs = []
+        for p, p_bak in self.param_bak_list:
+            if p.name == param_var.name:
+                print("#### ref inputs: ", param_var.name, p_bak.name)
+                ref_inputs.append(p_bak)
+        block.append_op(
+            type="ref_by_trainer_id",
+            inputs={"X": ref_inputs,
+                    "TrainerId": trainer_id_var},
+            outputs={"Out": local_param_bak})
+
+        def __create_temp_var__():
+            return block.create_var(
+                name=unique_name.generate("tmp_dc_output"),
+                shape=param_var.shape,
+                type=param_var.type,
+                dtype=param_var.dtype,
+                persistable=False)
+
+        o1 = __create_temp_var__()
+        block.append_op(
+            type="elementwise_sub",
+            inputs={"X": param_var,
+                    "Y": local_param_bak},
+            outputs={"Out": o1})
+        o2 = __create_temp_var__()
+        block.append_op(
+            type="elementwise_mul",
+            inputs={"X": o1,
+                    "Y": grad_var},
+            outputs={"Out": o2})
+        o3 = __create_temp_var__()
+        block.append_op(
+            type="elementwise_mul",
+            inputs={"X": o2,
+                    "Y": grad_var},
+            outputs={"Out": o3})
+        # TODO(typhoonzero): append scale
+        o4 = __create_temp_var__()
+        block.append_op(
+            type="elementwise_add",
+            inputs={"X": grad_var,
+                    "Y": o3},
+            outputs={"Out": o4})
+        return o4
+
     def _append_pserver_ops(self, optimize_block, opt_op, endpoint,
                             grad_to_block_id, origin_program, merged_var):
         program = optimize_block.program
@@ -1546,8 +1646,15 @@ to transpile() call.")
                     break
             return param_block
 
+        if self.config.enable_dc_asgd:
+            param_var = _get_param_block(opt_op)
+            dc = self._append_dc_asgd_ops(optimize_block, param_var, merged_var)
+
         for key in opt_op.input_names:
             if key == "Grad":
+                if self.config.enable_dc_asgd:
+                    new_inputs[key] = dc
+                else:
                     new_inputs[key] = merged_var
             elif key == "Param":
                 param_block = _get_param_block(opt_op)