dataset.proto

syntax = "proto2";
package protos;

// Weighted Random Sampling ItemID not in Batch
message NegativeSampler {
    // sample data path
    // itemid weight attrs
    required string input_path = 1;
    // number of negative sample
    required uint32 num_sample = 2;
    // field names of attrs in train data or eval data
    repeated string attr_fields = 3;
    // field name of item_id in train data or eval data
    required string item_id_field = 4;

    optional string attr_delimiter = 5 [default=":"];

    optional uint32 num_eval_sample = 6 [default=0];

    // only works on DataScience/Local
    optional string field_delimiter = 7 [default="\001"];
}

message NegativeSamplerInMemory {
    // sample data path
    // itemid weight attrs
    required string input_path = 1;
    // number of negative sample
    required uint32 num_sample = 2;
    // field names of attrs in train data or eval data
    repeated string attr_fields = 3;
    // field name of item_id in train data or eval data
    required string item_id_field = 4;

    optional string attr_delimiter = 5 [default=":"];

    optional uint32 num_eval_sample = 6 [default=0];

    // only works on DataScience/Local
    optional string field_delimiter = 7 [default="\001"];
}

// Weighted Random Sampling ItemID not with Edge
message NegativeSamplerV2 {
    // user data path
    // userid weight
    required string user_input_path = 1;
    // item data path
    // itemid weight attrs
    required string item_input_path = 2;
    // positive edge path
    // userid itemid weight
    required string pos_edge_input_path = 3;
    // number of negative sample
    required uint32 num_sample = 4;
    // field names of attrs in train data or eval data
    repeated string attr_fields = 5;
    // field name of item_id in train data or eval data
    required string item_id_field = 6;
    // field name of user_id in train data or eval data
    required string user_id_field = 7;

    optional string attr_delimiter = 8 [default=":"];

    optional uint32 num_eval_sample = 9 [default=0];

    // only works on DataScience/Local
    optional string field_delimiter = 10 [default="\001"];
}

// Weighted Random Sampling ItemID not in Batch and Sampling Hard Edge
message HardNegativeSampler {
    // user data path
    // userid weight
    required string user_input_path = 1;
    // item data path
    // itemid weight attrs
    required string item_input_path = 2;
    // hard negative edge path
    // userid itemid weight
    required string hard_neg_edge_input_path = 3;
    // number of negative sample
    required uint32 num_sample = 4;
    // max number of hard negative sample
    required uint32 num_hard_sample = 5;
    // field names of attrs in train data or eval data
    repeated string attr_fields = 6;
    // field name of item_id in train data or eval data
    required string item_id_field = 7;
    // field name of user_id in train data or eval data
    required string user_id_field = 8;

    optional string attr_delimiter = 9 [default=":"];

    optional uint32 num_eval_sample = 10 [default=0];

    // only works on DataScience/Local
    optional string field_delimiter = 11 [default="\001"];
}

// Weighted Random Sampling ItemID not with Edge and Sampling Hard Edge
message HardNegativeSamplerV2 {
    // user data path
    // userid weight
    required string user_input_path = 1;
    // item data path
    // itemid weight attrs
    required string item_input_path = 2;
    // positive edge path
    // userid itemid weight
    required string pos_edge_input_path = 3;
    // hard negative edge path
    // userid itemid weight
    required string hard_neg_edge_input_path = 4;
    // number of negative sample
    required uint32 num_sample = 5;
    // max number of hard negative sample
    required uint32 num_hard_sample = 6;
    // field names of attrs in train data or eval data
    repeated string attr_fields = 7;
    // field name of item_id in train data or eval data
    required string item_id_field = 8;
    // field name of user_id in train data or eval data
    required string user_id_field = 9;

    optional string attr_delimiter = 10 [default=":"];

    optional uint32 num_eval_sample = 11 [default=0];

    // only works on DataScience/Local
    optional string field_delimiter = 12 [default="\001"];
}

message DatasetConfig {
    // mini batch size to use for training and evaluation.
    optional uint32 batch_size = 1 [default = 32];

    enum FieldType {
        INT32 = 0;
        INT64 = 1;
        STRING = 2;
        FLOAT = 4;
        DOUBLE = 5;
        BOOL = 6;
    }

    message Field {
        required string input_name = 1;
        required FieldType input_type = 2 [default = STRING];
        optional string default_val = 3;
        optional uint32 input_dim = 4 [default=1];
        optional uint32 input_shape = 5 [default = 1];
        // user-defined function for label. eg: tf.math.log1p, remap_lbl
        optional string user_define_fn = 6;
        // user-defined function path. eg: /samples/demo_script/process_lbl.py
        optional string user_define_fn_path = 7;
        // output field type of user-defined function.
        optional FieldType user_define_fn_res_type = 8;
        // ignore value
        optional string ignore_val = 9;
    }

    // set auto_expand_input_fields to true to
    // auto_expand field[1-21] to field1, field2, ..., field21
    optional bool auto_expand_input_fields = 3 [default = false];

    // label fields, normally only one field is used.
    // For multiple target models such as MMOE
    // multiple label_fields will be set.
    repeated string label_fields = 4;

    // label separator
    repeated string label_sep = 41;

    // label dimensions which need to be set when there
    // are labels have dimension > 1
    repeated uint32 label_dim = 42;

    // whether to shuffle data
    optional bool shuffle = 5 [default = true];

    // shufffle buffer for better performance, even shuffle buffer is set,
    // it is suggested to do full data shuffle before training
    // especially when the performance of models is not good.
    optional int32 shuffle_buffer_size = 11 [default = 32];

    // The number of times a data source is read. If set to zero, the data source
    // will be reused indefinitely.
    optional uint32 num_epochs = 6 [default = 0];

    // Number of decoded batches to prefetch.
    optional uint32 prefetch_size = 7 [default = 32];

    // shard dataset to 1/num_workers in distribute mode
    // this param is not used anymore
    optional bool shard = 801 [default = false];

    // shard by file, not by sample, valid only for CSVInput
    optional bool file_shard = 802 [default = false];

    enum InputType {
        // csv format input, could be used in local or hdfs
        // support .gz compression(but not .tar.gz files)
        CSVInput = 10;
        // @Depreciated
        CSVInputV2 = 11;
        // extended csv format, allow quote in fields
        CSVInputEx = 12;
        // @Depreciated, has memory leak problem
        OdpsInput = 2;
        // odps input, used on pai
        OdpsInputV2 = 3;
        DataHubInput = 15;
        OdpsInputV3 = 9;
        RTPInput = 4;
        RTPInputV2 = 5;
        OdpsRTPInput = 601;
        OdpsRTPInputV2 = 602;
        TFRecordInput = 7;
        BatchTFRecordInput = 14;
        // for the purpose to debug performance bottleneck of
        // input pipelines
        DummyInput = 8;
        KafkaInput = 13;
        HiveInput = 16;
        HiveRTPInput = 17;
        HiveParquetInput = 18;

        // All features are packed into one field for fast copying to gpu,
        // and there are no feature preprocessing step, it is assumed that
        // features are preprocessed before training.
        // Requirements: python3 and tf2.x due to multiprocssing spawn and
        //    RaggedTensor apis.
        ParquetInput = 19;

        // Features are not packed, and are preprocessing separately.
        // Requirements: python3 and tf2.x due to multiprocssing spawn and
        //    RaggedTensor apis.
        ParquetInputV2 = 20;

        // c++ version of parquet dataset which currently are only available
        // with deeprec.
        ParquetInputV3 = 21;
        CriteoInput = 1001;
    }
    required InputType input_type = 10;

    // separator of column features, only used for CSVInput*
    // not used in OdpsInput*
    // binary separators are supported:
    //   CTRL+A could be set as '\001'
    //   CTRL+B could be set as '\002'
    //   CTRL+C could be set as '\003'
    // for RTPInput and OdpsRTPInput it is usually set
    // to '\002'
    optional string separator = 12 [default = ','];

    // parallel preproces of raw data, avoid using too small
    // or too large numbers(suggested be to small than
    // number of the cores)
    optional uint32 num_parallel_calls = 13 [default = 8];

    // only used for OdpsInput/OdpsInputV2/OdpsRTPInput, comma separated
    // for RTPInput, selected_cols use indices as column names
    //  such as '1,2,4', where 1,2 are label columns, and
    //  4 is the feature column, column 0,3 are not used,
    optional string selected_cols = 14 [default = ''];

    // selected col types, only used for OdpsInput/OdpsInputV2
    // to avoid error setting of data types
    optional string selected_col_types = 15 [default = ''];

    // the input fields must be the same number and in the
    // same order as data in csv files or odps tables
    repeated Field input_fields = 16;

    // for RTPInput only
    optional string rtp_separator = 17 [default = ';'];

    // ignore some data errors
    // it is not suggested to set this parameter
    optional bool ignore_error = 18 [default=false];

    // whether to use pai global shuffle queue, only for OdpsInput,
    // OdpsInputV2, OdpsRTPInputV2
    optional bool pai_worker_queue = 19 [default = false];
    optional int32 pai_worker_slice_num = 20 [default = 100];

    // if true, one worker will duplicate the data of the chief node
    // and undertake the gradient computation of the chief node
    optional bool chief_redundant = 21 [default = false];

    // input field for sample weight
    optional string sample_weight = 22;
    // input field for label dynimic weight
    repeated string label_dynamic_weight = 27;
    // the compression type of tfrecord
    optional string data_compression_type = 23 [default = ''];

    // n data for one feature in tfrecord
    optional uint32 n_data_batch_tfrecord = 24;

    // for csv files, may optionally with an header
    // in that case, input_name must match header name,
    // and the number and the order of input_fields
    // may not be the same as that in csv files.
    optional bool with_header = 25 [default = false];

    repeated string feature_fields = 26;

    oneof sampler {
        NegativeSampler negative_sampler = 101;
        NegativeSamplerV2 negative_sampler_v2 = 102;
        HardNegativeSampler hard_negative_sampler = 103;
        HardNegativeSamplerV2 hard_negative_sampler_v2 = 104;
        NegativeSamplerInMemory negative_sampler_in_memory = 105;
    }
    optional uint32 eval_batch_size = 1001 [default = 4096];

    optional bool drop_remainder = 1002 [default = false];
}