easy_rec/python/protos/easy_rec_model.proto

syntax = "proto2";
package protos;

import "easy_rec/python/protos/backbone.proto";
import "easy_rec/python/protos/fm.proto";
import "easy_rec/python/protos/deepfm.proto";
import "easy_rec/python/protos/wide_and_deep.proto";
import "easy_rec/python/protos/multi_tower.proto";
import "easy_rec/python/protos/dlrm.proto";
import "easy_rec/python/protos/feature_config.proto";
import "easy_rec/python/protos/dropoutnet.proto";
import "easy_rec/python/protos/dssm.proto";
import "easy_rec/python/protos/collaborative_metric_learning.proto";
import "easy_rec/python/protos/mmoe.proto";
import "easy_rec/python/protos/esmm.proto";
import "easy_rec/python/protos/dbmtl.proto";
import "easy_rec/python/protos/ple.proto";
import "easy_rec/python/protos/simple_multi_task.proto";
import "easy_rec/python/protos/dcn.proto";
import "easy_rec/python/protos/cmbf.proto";
import "easy_rec/python/protos/uniter.proto";
import "easy_rec/python/protos/autoint.proto";
import "easy_rec/python/protos/mind.proto";
import "easy_rec/python/protos/loss.proto";
import "easy_rec/python/protos/rocket_launching.proto";
import "easy_rec/python/protos/variational_dropout.proto";
import "easy_rec/python/protos/multi_tower_recall.proto";
import "easy_rec/python/protos/tower.proto";
import "easy_rec/python/protos/pdn.proto";
import "easy_rec/python/protos/dssm_senet.proto";
import "easy_rec/python/protos/simi.proto";
import "easy_rec/python/protos/dat.proto";
// for input performance test
message DummyModel {
}

// configure backbone network common parameters
message ModelParams {
  optional float l2_regularization = 1;
  repeated string outputs = 2;
  repeated BayesTaskTower task_towers = 3;
  optional int32 user_tower_idx_in_output = 4 [default = 0];
  optional int32 item_tower_idx_in_output = 5 [default = 1];
  optional Similarity simi_func = 6 [default = COSINE];
  optional float temperature = 7 [default = 1.0];
  optional bool scale_simi = 8 [default = false];

}

// for knowledge distillation
message KD {
  optional string loss_name = 10;
  required string pred_name = 11;
  // default to be logits
  optional bool pred_is_logits = 12 [default=true];
  // for CROSS_ENTROPY_LOSS, soft_label must be logits instead of probs
  required string soft_label_name = 21;
  // default to be logits
  optional bool label_is_logits = 22 [default=true];
  required LossType loss_type = 3;
  optional float loss_weight = 4 [default=1.0];
  // only for loss_type == CROSS_ENTROPY_LOSS or BINARY_CROSS_ENTROPY_LOSS or KL_DIVERGENCE_LOSS
  optional float temperature = 5 [default=1.0];
  // field name for indicating the sample space for this task
  optional string task_space_indicator_name = 6;
  // field value for indicating the sample space for this task
  optional string task_space_indicator_value = 7;
  // the loss weight for sample in the task space
  optional float in_task_space_weight = 8 [default = 1.0];
  // the loss weight for sample out the task space
  optional float out_task_space_weight = 9 [default = 1.0];

  oneof loss_param {
    F1ReweighedLoss f1_reweighted_loss = 101;
    SoftmaxCrossEntropyWithNegativeMining softmax_loss = 102;
    CircleLoss circle_loss = 103;
    MultiSimilarityLoss multi_simi_loss = 104;
    BinaryFocalLoss binary_focal_loss = 105;
    PairwiseLoss pairwise_loss = 106;
    PairwiseFocalLoss pairwise_focal_loss = 107;
    PairwiseLogisticLoss pairwise_logistic_loss = 108;
    JRCLoss jrc_loss = 109;
    PairwiseHingeLoss pairwise_hinge_loss = 110;
    ListwiseRankLoss listwise_rank_loss = 111;
    ListwiseDistillLoss listwise_distill_loss = 112;
  }
}

message EasyRecModel {
    required string model_class = 1;
    // just a name for backbone config
    optional string model_name = 99;

    // actually input layers, each layer produce a group of feature
    repeated FeatureGroupConfig feature_groups = 2;

    // model parameters
    oneof model {
        ModelParams model_params = 100;
        DummyModel dummy = 101;
        WideAndDeep wide_and_deep = 102;
        DeepFM deepfm = 103;
        MultiTower multi_tower = 104;
        FM fm = 105;
        DCN dcn = 106;
        AutoInt autoint = 107;
        DLRM dlrm = 108;
        CMBF cmbf = 109;
        Uniter uniter = 110;

        MultiTowerRecall multi_tower_recall = 200;
        DSSM dssm = 201;
        MIND mind = 202;
        DropoutNet dropoutnet = 203;
        CoMetricLearningI2I metric_learning = 204;
        PDN pdn = 205;
        DSSM_SENet dssm_senet = 206;
        DAT dat = 207;

        MMoE mmoe = 301;
        ESMM esmm = 302;
        DBMTL dbmtl = 303;
        SimpleMultiTask simple_multi_task = 304;
        PLE ple = 305;

        RocketLaunching rocket_launching = 401;
    }
    repeated SeqAttGroupConfig seq_att_groups = 7;
    // implemented in easy_rec/python/model/easy_rec_estimator
    // add regularization to all variables with "embedding_weights:"
    // in name
    optional float embedding_regularization = 8 [default = 0.0];

    optional LossType loss_type = 9 [default = CLASSIFICATION];

    optional uint32 num_class = 10 [default = 1];

    optional EVParams ev_params = 11;

    repeated KD kd = 12;

    // filter variables matching any pattern in restore_filters
    // common filters are Adam, Momentum, etc.
    repeated string restore_filters = 13;

    optional VariationalDropoutLayer variational_dropout = 14;

    repeated Loss losses = 15;

    enum LossWeightStrategy {
        Fixed = 0;
        Uncertainty = 1;
        Random = 2;
    }
    required LossWeightStrategy loss_weight_strategy = 16 [default = Fixed];

    optional BackboneTower backbone = 17;

    // label name for rank_model to select one label between multiple labels
    optional string label_name = 18;
}