refine executor_thread_worker.cc & executor_thread_worker.h code style

paddle/fluid/framework/executor_thread_worker.cc

@@ -303,7 +303,7 @@ void ExecutorThreadWorker::SetRootScope(Scope* g_scope) {
   root_scope_ = g_scope;
 }
 
-//AsyncExecutor
+//  AsyncExecutor
 void AsyncExecutorThreadWorker::TrainFiles() {
   SetDevice();
 
@@ -330,7 +330,6 @@ void AsyncExecutorThreadWorker::TrainFiles() {
       print_fetch_var(thread_scope_, fetch_var_names_[i]);
     }  // end for (int i = 0...)
   }    // end while ()
-  LOG(ERROR) << "TRAIN DONE";
 }
 
 void AsyncExecutorThreadWorker::SetPSlibPtr(std::shared_ptr<paddle::distributed::PSlib> pslib_ptr) {
@@ -360,44 +359,12 @@ void AsyncExecutorThreadWorker::TrainOneNetwork() {
     UpdateParams();
 }
 
-void AsyncExecutorThreadWorker::BindingSlotVariableMemory() {
-    /*
-    std::vector<int> ins_slot_offset(batch_size + 1, 0);
-    for (auto i = 1u; i <= batch_size; ++i) {
-        ins_slot_offset[i] += ins_slot_offset[i - 1] + slot_dim;
-    }
-
-    std::vector<int> tensor_lod(batch_size + 1, 0);
-    for (auto i = 1u; i <= batch_size; ++i) {
-        tensor_lod[i] += tensor_lod[i - 1] + 1;
-    }
-
-    auto& used_slots = reader->get_use_slot_alias();
-    slot_input_vec.resize(used_slots.size() - 1);
-    for (auto slot_idx = 1u; slot_idx < used_slots.size(); ++slot_idx) {
-        auto var = slot_input_variable_name[slot_idx];
-
-        auto v = thread_scope->FindVar(var);
-        CHECK(v != nullptr) << "var[" << var << "] not found";
-
-        LoDTensor* tensor = v->GetMutable<LoDTensor>();
-        float* tensor_ptr = tensor->mutable_data<float>({batch_size, slot_dim}, platform::CPUPlace());
-        memset(tensor_ptr, 0, sizeof(float) * ins_slot_offset.back());
-
-        LoD data_lod{tensor_lod};
-        tensor->set_lod(data_lod);
-
-        slot_input_vec[slot_idx - 1].reset(tensor);
-    }
-    */
-}
 
 void AsyncExecutorThreadWorker::SetParamConfig(AsyncWorkerParamConfig* param_config) {
     _param_config = param_config;
 }
 
 void AsyncExecutorThreadWorker::PrepareParams() {
-    //int table_id = 0; //TODO
     for (auto table_id: _param_config->sparse_table_id) {
         PullSparse(table_id);
         for (auto& t : _pull_sparse_status) {
@@ -423,9 +390,7 @@ void AsyncExecutorThreadWorker::UpdateParams() {
   for (auto i : _param_config->dense_table_id) {
     PushDense(i);
   }
-  // _param_config->tmp_push_dense_wait_times
   int32_t tmp_push_dense_wait_times = -1;
-  // _param_config->tmp_push_sparse_wait_times
   int32_t tmp_push_sparse_wait_times = -1;
   static uint32_t push_dense_wait_times =
       static_cast<uint32_t>(tmp_push_dense_wait_times);
@@ -509,17 +474,15 @@ void AsyncExecutorThreadWorker::PullSparse(int table_id) {
             pull_feature_value.data(), table_id, features.data(), features.size());
     _pull_sparse_status.push_back(std::move(status));
 
-    //to save time
     auto& push_g = _feature_push_value[table_id];
     check_pull_push_memory(features, push_g, fea_dim);
 
-    //binding_slot_embed_with_concat(); TODO
-    collect_feasign_info(table_id); //TODO
+    collect_feasign_info(table_id); 
 }
 
 void AsyncExecutorThreadWorker::FillSparse(int table_id) {
-    auto slot_dim = _param_config->slot_dim; // TODO
-    auto fea_dim = _param_config->fea_dim; //TODO
+    auto slot_dim = _param_config->slot_dim; 
+    auto fea_dim = _param_config->fea_dim; 
     auto& features = _features[table_id];
     auto& fea_value = _feature_value[table_id];
 
@@ -544,53 +507,35 @@ void AsyncExecutorThreadWorker::FillSparse(int table_id) {
         
         LoD data_lod{tensor_lod};
         tensor_emb->set_lod(data_lod);
-        //float* ptr = tensor_emb->data<float>();
 
         for (auto index = 0u; index < len; ++index){
-            //if (_current_train_job.use_cvm_feature()) {
-            //    if (ids[index] == 0u) {
-            //        memcpy(ptr + slot_dim * index, init_value.data(), sizeof(float) * slot_dim);
-            //        continue;
-            //    }
-            //    memcpy(ptr + slot_dim * index, fea_value[fea_idx].data(), sizeof(float) * slot_dim);
-            //    (ptr + slot_dim * index)[0] = log((ptr + slot_dim * index)[0] + 1);
-            //    (ptr + slot_dim * index)[1] = log((ptr + slot_dim * index)[1] + 1) - (ptr + slot_dim * index)[0];
-            //    fea_idx++;
-            //} else {
-                if (ids[index] == 0u) {
-                    memcpy(ptr + slot_dim * index, init_value.data() + 2, sizeof(float) * slot_dim);
-                    continue;
-                }
-                memcpy(ptr + slot_dim * index, fea_value[fea_idx].data() + 2, sizeof(float) * slot_dim);
-                fea_idx++;
-            //}
+            if (ids[index] == 0u) {
+                memcpy(ptr + slot_dim * index, init_value.data() + 2, sizeof(float) * slot_dim);
+                continue;
+            }
+            memcpy(ptr + slot_dim * index, fea_value[fea_idx].data() + 2, sizeof(float) * slot_dim);
+            fea_idx++;
         }
     }
 }
 
 void AsyncExecutorThreadWorker::PushSparse(int table_id) {
-    auto slot_dim = _param_config->slot_dim; //TODO
-    auto fea_dim = _param_config->fea_dim;//_current_train_job.fea_dim();TODO
+    auto slot_dim = _param_config->slot_dim; 
+    auto fea_dim = _param_config->fea_dim;
     auto& features = _features[table_id]; 
-    CHECK(features.size() < 1000000) << "features size:" << features.size();
-    //std::vector<std::string> gradient_var;
-    //auto& gradient_var = GlobalConfig::instance().input_gradient_variable_name; //TODO
+    CHECK(features.size() < 1000000) << "features size is too big, may be wrong:" << features.size();
     auto& push_g = _feature_push_value[table_id]; 
     check_pull_push_memory(features, push_g, fea_dim);
     CHECK(push_g.size() == features.size() + 1) << "push_g size:" << push_g.size() << " features size:" << features.size();
     uint64_t fea_idx = 0u;
     auto& fea_info = _fea_info[table_id]; 
-    int offset = 0;
-    //if (!_current_train_job.use_cvm_feature()) { //TODO
-        offset = 2;
-    //}
+    int offset = 2;
     const std::vector<std::string>& feed_vec = thread_reader_->GetUseSlotAlias();
-    // slot_idx = 0 is label TODO
+    // slot_idx = 0 is label 
     for (auto slot_idx = 1u; slot_idx < feed_vec.size(); ++slot_idx) {
         if (_param_config->slot_alias_to_table.find(feed_vec[slot_idx]) == _param_config->slot_alias_to_table.end()) { 
             LOG(ERROR) << "ERROR slot_idx:" << slot_idx << " name:" << feed_vec[slot_idx];
         } else if (_param_config->slot_alias_to_table[feed_vec[slot_idx]] != table_id) {
-            LOG(ERROR) << "ERROR continue";
             continue;
         }
         Variable* g_var = thread_scope_->FindVar(_param_config->gradient_var[table_id][slot_idx - 1]); 
@@ -609,7 +554,6 @@ void AsyncExecutorThreadWorker::PushSparse(int table_id) {
             LOG(ERROR) << "var[" << feed_vec[slot_idx] << "] not found";
             exit(-1);
         }
-        //int len = tensor->lod()[0].back();
         int len = tensor->numel();
         CHECK(slot_dim * len == g_tensor->numel()) << "len:" << len << " g_numel:" << g_tensor->numel();
         CHECK(len == tensor->numel()) << "len:" << len << "t_numel:" << tensor->numel();

paddle/fluid/framework/executor_thread_worker.h

@@ -155,7 +155,6 @@ class ExecutorThreadWorker {
   void SetFetchVarNames(const std::vector<std::string>& fetch_var_names);
   virtual void SetPSlibPtr(std::shared_ptr<paddle::distributed::PSlib> pslib_ptr);
   virtual void SetPullDenseThread(std::shared_ptr<DensePullThread>  dpt) {};
-  virtual void BindingSlotVariableMemory() {};
   virtual void SetParamConfig(AsyncWorkerParamConfig* param_config) {};
  private:
   void CreateThreadScope(const framework::ProgramDesc& program);
@@ -191,7 +190,6 @@ public:
     virtual ~AsyncExecutorThreadWorker() {}
     void SetPSlibPtr(std::shared_ptr<paddle::distributed::PSlib> pslib_ptr);
     void SetPullDenseThread(std::shared_ptr<DensePullThread> dpt);
-    void BindingSlotVariableMemory();
     void SetParamConfig(AsyncWorkerParamConfig* param_config);
     void TrainFiles();  
     void TrainOneNetwork();