OptimizerConfig.proto

syntax = "proto2";

option optimize_for = LITE_RUNTIME;

package paddle;

message SGDConfig {
  // SGD
  // momentum: float >= 0. Parameter updates momentum.
  // decay: float >= 0. Learning rate decay over each update.
  // nesterov: boolean. Whether to apply Nesterov momentum.
  optional double momentum = 21 [ default = 0.0 ];
  optional double decay = 23 [ default = 0.0 ];
  optional bool nesterov = 24 [ default = false ];
}

message AdadeltaConfig {
  // Adadelta
  // It is recommended to leave it at the default value.
  // rho: float >= 0.
  // epsilon: float >= 0. Fuzz factor.
  // decay: float >= 0. Learning rate decay over each update.

  // reference : [Adadelta - an adaptive learning rate
  // method](http://arxiv.org/abs/1212.5701)
  optional double rho = 33 [ default = 0.90 ];
  optional double epsilon = 31 [ default = 1e-5 ];
  optional double decay = 32 [ default = 0.0 ];
}

message AdagradConfig {
  // Adagrad
  // epsilon: float >= 0.
  // decay: float >= 0. Learning rate decay over each update.

  // reference : [Adaptive Subgradient Methods for Online Learning and
  // Stochastic
  // Optimization](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
  optional double epsilon = 41 [ default = 1e-5 ];
  optional double decay = 42 [ default = 0.0 ];
}

message AdamConfig {
  // Adaj
  // beta_1: float, 0 < beta < 1. Generally close to 1.
  // beta_2: float, 0 < beta < 1. Generally close to 1.
  // epsilon: float >= 0. Fuzz factor.
  // decay: float >= 0. Learning rate decay over each update.
  // reference : [Adam - A Method for Stochastic
  // Optimization](http://arxiv.org/abs/1412.6980v8)
  optional double beta_1 = 41;
  optional double beta_2 = 42;
  optional double epsilon = 43;
  optional double decay = 44;
}

message ConstLrConfig {
  // learninRate Policy
  optional double learning_rate = 1 [ default = 1.0 ];
}

message LinearLrConfig {
  // learninRate Policy
  optional double learning_rate = 1 [ default = 1.0 ];
  optional double lr_decay_a = 2;
  optional double lr_decay_b = 3;
}

message TensorProto {
  enum DataType {
    PADDLE_ELEMENT_TYPE_INT32 = 0;
    PADDLE_ELEMENT_TYPE_UINT32 = 1;
    PADDLE_ELEMENT_TYPE_INT64 = 2;
    PADDLE_ELEMENT_TYPE_UINT64 = 3;
    PADDLE_ELEMENT_TYPE_FLOAT32 = 4;
    PADDLE_ELEMENT_TYPE_FLOAT64 = 5;
  }
  optional DataType data_type = 1;
  repeated bytes content = 2;
}

message LrPolicyState {
  // learninRate Policy
  optional double learning_rate = 1 [ default = 1.0 ];
  optional double lr_decay_a = 2;
  optional double lr_decay_b = 3;
}

message SGDOptimizerState {
  optional LrPolicyState lr_state = 101;
  optional double num_sample_passed = 104;
  // state
  optional TensorProto parameter = 1;
  optional TensorProto momentums = 2;
}

message AdadeltaOptimizerState {
  // learning rate policy
  optional LrPolicyState lr_state = 101;
  optional double num_sample_passed = 104;
  // state
  optional TensorProto parameter = 1;
  optional TensorProto accum_gradient = 2;
  optional TensorProto accum_delta = 3;
  optional TensorProto update_delta = 4;
}

message AdagradOptimizerState {
  optional LrPolicyState lr_state = 101;
  optional double num_sample_passed = 104;
  // state
  optional TensorProto parameter = 1;
  optional TensorProto accum_gradient = 2;
}

message AdamOptimizerState {
  optional LrPolicyState lr_state = 101;
  optional double num_sample_passed = 104;
  // state
  optional TensorProto parameter = 1;
  optional TensorProto momentums = 2;
  optional TensorProto velocitys = 3;
}

message OptimizerConfig {
  enum Optimizer {
    SGD = 1;
    Adadelta = 2;
    Adagrad = 3;
    Adam = 4;
  }
  optional Optimizer optimizer = 1;
  optional SGDConfig sgd = 3;
  optional AdadeltaConfig adadelta = 4;
  optional AdagradConfig adagrad = 5;
  optional AdamConfig adam = 6;

  enum LrPolicy {
    Const = 0;
    Linear = 1;
  }
  optional LrPolicy lr_policy = 11;
  optional ConstLrConfig const_lr = 12;
  optional LinearLrConfig linear_lr = 13;

  // common config of optimizer
  // gradient clip when L2 exceeding value
  optional double clip_norm = 101;
  // gradient clip when L1 exceeding value
  optional double clip_value = 102;
}