[HeterPs] merge dense && data norm && g2sum

paddle/fluid/framework/fleet/heter_ps/optimizer.cuh.h

@@ -44,7 +44,7 @@ class Optimizer {
     if (w < optimizer_config::min_bound) w = optimizer_config::min_bound;
     if (w > optimizer_config::max_bound) w = optimizer_config::max_bound;
 
-    add_g2sum = scaled_grad * scaled_grad;
+    add_g2sum += scaled_grad * scaled_grad;
 
     g2sum += add_g2sum;
   }
@@ -64,7 +64,7 @@ class Optimizer {
         w[i] = optimizer_config::mf_min_bound;
       if (w[i] > optimizer_config::mf_max_bound)
         w[i] = optimizer_config::mf_max_bound;
-      add_g2sum = scaled_grad * scaled_grad;
+      add_g2sum += scaled_grad * scaled_grad;
     }
 
     g2sum += add_g2sum / n;

paddle/fluid/framework/ps_gpu_trainer.cc

@@ -57,8 +57,6 @@ void PSGPUTrainer::Initialize(const TrainerDesc& trainer_desc,
         trainer_desc.downpour_param().stat_var_names(i));
   }
   VLOG(3) << "going to initialize pull dense worker";
-  pull_dense_worker_ = PullDenseWorker::GetInstance();
-  pull_dense_worker_->Initialize(trainer_desc);
   SetDebug(trainer_desc.debug());
   trainer_desc_ = trainer_desc;
   workers_.resize(place_num);
@@ -112,15 +110,21 @@ void PSGPUTrainer::InitTrainerEnv(const ProgramDesc& main_program,
       }
     }
   }
+  for (auto& var : main_program.Block(0).AllVars()) {
+    if (var->Persistable()) {
+      auto it = std::find(need_merge_var_names_.begin(),
+                          need_merge_var_names_.end(), var->Name());
+      if (it == need_merge_var_names_.end()) {
+        VLOG(2) << "train param: " << var->Name();
+        trainable_param_.push_back(var->Name());
+      }
+    }
+  }
   place_ = place;
   return;
 }
 
 void PSGPUTrainer::InitOtherEnv(const ProgramDesc& main_program) {
-  pull_dense_worker_->SetRootScope(root_scope_);
-  for (size_t i = 0; i < places_.size(); ++i) {
-    pull_dense_worker_->AddThreadScope(workers_[i]->GetThreadScope());
-  }
   VLOG(3) << "init other env done.";
 }
 
@@ -141,15 +145,27 @@ Scope* PSGPUTrainer::GetWorkerScope(int thread_id) { return nullptr; }
 template <typename T>
 void PSGPUTrainer::MergeToRootScope(LoDTensor* root_tensor, LoDTensor* tensor) {
   LoDTensor tmp_root;
-  TensorCopy(*root_tensor, platform::CPUPlace(), &tmp_root);
+  TensorCopySync(*root_tensor, platform::CPUPlace(), &tmp_root);
   T* tmp_root_data = tmp_root.data<T>();
   LoDTensor tmp_tensor;
-  TensorCopy(*tensor, platform::CPUPlace(), &tmp_tensor);
+  TensorCopySync(*tensor, platform::CPUPlace(), &tmp_tensor);
   T* data = tmp_tensor.data<T>();
   for (int i = 0; i < tmp_tensor.numel(); i++) {
     tmp_root_data[i] += data[i];
   }
-  TensorCopy(tmp_root, platform::CPUPlace(), root_tensor);
+  TensorCopySync(tmp_root, platform::CPUPlace(), root_tensor);
+}
+
+void PSGPUTrainer::MergeDenseParam() {
+  auto thread_scope = workers_[0]->GetThreadScope();
+  for (auto& name : trainable_param_) {
+    VLOG(2) << "merge var " << name << " to root scope";
+    Variable* root_var = root_scope_->FindVar(name);
+    LoDTensor* root_tensor = root_var->GetMutable<LoDTensor>();
+    Variable* var = thread_scope->FindVar(name);
+    LoDTensor* tensor = var->GetMutable<LoDTensor>();
+    TensorCopySync((*tensor), root_tensor->place(), root_tensor);
+  }
 }
 
 void PSGPUTrainer::Finalize() {
@@ -187,7 +203,7 @@ void PSGPUTrainer::Finalize() {
       _ForEachDataType_(MergeCallback);
     }
   }
-  pull_dense_worker_->MergeDenseParam();
+  MergeDenseParam();
   root_scope_->DropKids();
 }
 }  // namespace framework

paddle/fluid/framework/trainer.h

@@ -265,6 +265,7 @@ class PSGPUTrainer : public TrainerBase {
   }
   virtual std::string GetDumpPath(int tid) { return ""; }
   virtual void InitDumpEnv() {}
+  virtual void MergeDenseParam();
 
   template <typename T>
   void MergeToRootScope(LoDTensor* root_tensor, LoDTensor* thread_tensor);
@@ -274,6 +275,7 @@ class PSGPUTrainer : public TrainerBase {
   DownpourWorkerParameter param_;
   std::map<uint64_t, std::vector<std::string>> dense_grad_names_;
   std::vector<std::string> need_merge_var_names_;
+  std::vector<std::string> trainable_param_;
   float scale_datanorm_;
   paddle::platform::Place place_;
   ProgramDesc program_;

python/paddle/fluid/incubate/fleet/parameter_server/pslib/optimizer_factory.py

@@ -412,11 +412,13 @@ def _minimize(self,
         sparse_table_index = 0
         for num in range(len(losses)):
             loss = losses[num]
+            parameters = None
+            if parameter_list != None:
+                parameters = parameter_list[num]
             prog_id = str(id(loss.block.program))
             # param_grads of program
             params_grads = sorted(
-                fluid.backward.append_backward(loss, parameter_list,
-                                               no_grad_set),
+                fluid.backward.append_backward(loss, parameters, no_grad_set),
                 key=lambda x: x[0].name)
 
             flag_use_ps_gpu = strategy.get("use_ps_gpu", False)