"fix parameter accumulate size"

paddle/optimizer/adadelta_optimizer.cc

@@ -27,22 +27,24 @@ void AdadeltaOptimizer::Update(const Tensor* gradient) {
 
 const char* AdadeltaOptimizer::SerializeState(int* state_len) {
   AdadeltaOptimizerState state;
-  // TODO(zhihong) : add lr_policy serialization
   state.set_num_sample_passed(num_sample_passed_);
+  std::string lr_str = this->lr_policy_->SerializeState(state_len);
+  state.mutable_lr_state()->ParseFromString(lr_str);
 
   TensorToProto(*parameter_, state.mutable_parameter());
   TensorToProto(*accum_gradient_, state.mutable_accum_gradient());
   TensorToProto(*accum_delta_, state.mutable_accum_delta());
   TensorToProto(*update_delta_, state.mutable_update_delta());
   auto str = state.SerializeAsString();
-  *state_len = str.size();
+  *state_len += str.size();
   return str.c_str();
 }
 
 void AdadeltaOptimizer::DeserializeState(const std::string& str) {
   AdadeltaOptimizerState state;
   state.ParseFromString(str);
-  // TODO(zhihong) : add lr_policy DeserializeState
+  auto lr_state = state.lr_state();
+  this->lr_policy_->DeserializeState(lr_state.SerializeAsString());
   num_sample_passed_ = state.num_sample_passed();
 
   ProtoToTensor(state.parameter(), parameter_);

paddle/optimizer/adagrad_optimizer.cc

@@ -19,20 +19,23 @@ void AdagradOptimizer::Update(const Tensor* gradient) {
 }
 const char* AdagradOptimizer::SerializeState(int* state_len) {
   AdagradOptimizerState state;
-  // TODO(zhihong) : add lr_policy serialization
   state.set_num_sample_passed(num_sample_passed_);
+  std::string lr_str = this->lr_policy_->SerializeState(state_len);
+  state.mutable_lr_state()->ParseFromString(lr_str);
 
   TensorToProto(*parameter_, state.mutable_parameter());
   TensorToProto(*accum_gradient_, state.mutable_accum_gradient());
   auto str = state.SerializeAsString();
-  *state_len = str.size();
+  *state_len += str.size();
   return str.c_str();
 }
 
 void AdagradOptimizer::DeserializeState(const std::string& str) {
   AdagradOptimizerState state;
   state.ParseFromString(str);
-  // TODO(zhihong) : add lr_policy DeserializeState
+  auto lr_state = state.lr_state();
+  this->lr_policy_->DeserializeState(lr_state.SerializeAsString());
+
   num_sample_passed_ = state.num_sample_passed();
   ProtoToTensor(state.parameter(), parameter_);
   ProtoToTensor(state.accum_gradient(), accum_gradient_);

paddle/optimizer/adam_optimizer.cc

@@ -24,20 +24,23 @@ void AdamOptimizer::Update(const Tensor *gradient) {
 
 const char *AdamOptimizer::SerializeState(int *state_len) {
   AdamOptimizerState state;
-  // TODO(zhihong) : add lr_policy serialization
+  std::string lr_str = this->lr_policy_->SerializeState(state_len);
+  state.mutable_lr_state()->ParseFromString(lr_str);
   state.set_num_sample_passed(num_sample_passed_);
+
   TensorToProto(*parameter_, state.mutable_parameter());
   TensorToProto(*momentums_, state.mutable_momentums());
   TensorToProto(*velocitys_, state.mutable_velocitys());
   auto str = state.SerializeAsString();
-  *state_len = str.size();
+  *state_len += str.size();
   return str.c_str();
 }
 
 void AdamOptimizer::DeserializeState(const std::string &str) {
   AdamOptimizerState state;
   state.ParseFromString(str);
-  // TODO(zhihong) : add lr_policy DeserializeState
+  auto lr_state = state.lr_state();
+  this->lr_policy_->DeserializeState(lr_state.SerializeAsString());
   num_sample_passed_ = state.num_sample_passed();
 
   ProtoToTensor(state.parameter(), parameter_);

paddle/optimizer/sgd_optimizer.cc

@@ -31,8 +31,6 @@ const char *SGDOptimizer::SerializeState(int *state_len) {
   SGDOptimizerState state;
   state.set_num_sample_passed(num_sample_passed_);
   std::string lr_str = this->lr_policy_->SerializeState(state_len);
-  LrPolicyState lr_state;
-  lr_state.ParseFromString(lr_str);
   state.mutable_lr_state()->ParseFromString(lr_str);
   TensorToProto(*parameter_, state.mutable_parameter());
   if (momentum_ != 0.0) TensorToProto(*momentums_, state.mutable_momentums());