package monolith.hash_table

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Used in: OptimizerConfig

• optional int32 dim_size = 1

• optional float learning_rate = 2

• optional float weight_decay_factor = 3

• optional float averaging_ratio = 4

• optional float epsilon = 5

• optional int64 warmup_steps = 7

Used in: SingleOptimizerDump

• repeated float accum = 1

• repeated float accum_update = 2

Used in: OptimizerConfig

• optional int32 dim_size = 1

• optional float learning_rate = 2

• optional float initial_accumulator_value = 3

• optional int32 hessian_compression_times = 4

• optional float weight_decay_factor = 5

• optional int64 warmup_steps = 6

Used in: SingleOptimizerDump

• repeated float norm = 1

Used in: OptimizerConfig

• optional int32 dim_size = 1

• optional float learning_rate = 2

• optional float beta1 = 3

• optional float beta2 = 4

• optional bool use_beta1_warmup = 5

• optional float weight_decay_factor = 6

• optional bool use_nesterov = 7

• optional float epsilon = 8

• optional int64 warmup_steps = 9

Used in: SingleOptimizerDump

• repeated float m = 1

• repeated float v = 2

• optional float beta1_power = 3

• optional float beta2_power = 4

Used in: OptimizerConfig

• optional int32 dim_size = 1

• optional float learning_rate = 2

• optional float beta1 = 3

• optional float beta2 = 4

• optional float weight_decay_factor = 6

• optional bool use_nesterov = 7

• optional float epsilon = 8

• optional int64 warmup_steps = 9

Used in: SingleOptimizerDump

• repeated float m = 1

• repeated float v = 2

• repeated float vhat = 3

• optional float beta1_power = 4

• optional float beta2_power = 5

Used in: OptimizerConfig

• optional int32 dim_size = 1

• optional float learning_rate = 2

Used in: SingleOptimizerDump

• optional int64 global_step = 1

Used in: InitializerConfig

• optional int32 dim_size = 1

• optional float constant = 2

Used in: EmbeddingHashTableConfig

(message has no fields)

Used in: OptimizerConfig

• optional int32 dim_size = 1

• optional float lambda_ = 2

Used in: OptimizerConfig

• optional int32 dim_size = 1

• optional float learning_rate = 2

• optional float initial_accumulator_value = 3

• optional int32 hessian_compression_times = 4

• optional float weight_decay_factor = 5

• optional int64 warmup_steps = 6

• optional bool decouple_weight_decay = 7

• optional bool enable_dynamic_wd = 8

• optional float dynamic_wd_temperature = 9

• optional bool flip_direction = 10

Used in: SingleOptimizerDump

• repeated float norm = 1

• optional int64 last_update_step = 2

Used in: MultiEmbeddingHashTableConfig

• optional EntryConfig entry_config = 1

• optional EmbeddingHashTableConfig.EntryType entry_type = 6

• optional uint64 initial_capacity = 2

• optional SlotExpireTimeConfig slot_expire_time_config = 3

• oneof type

  • CuckooEmbeddingHashTableConfig cuckoo = 5

• optional bool enable_feature_eviction = 7

  Whether to evict features periodically during training and serving.

• optional int32 feature_evict_every_n_hours = 8

  Trigger features eviction every n hours.

• optional bool skip_zero_embedding = 10

  Whether to erase zero embeddings(l2norm = 0) when serving

Used in: EmbeddingHashTableConfig

• PACKED = 1

  Memory efficient, but slower.

• RAW = 2

  Fastest

Used in: EmbeddingHashTableConfig

• repeated EntryConfig.Segment segments = 1

• optional EntryConfig.EntryType entry_type = 2

  If this entry is for serving: For training entry, comp_config is not used. For serving entry, init_config & opt_config is not used.

Used in: EntryConfig

• UNKNOWN = 0

• TRAINING = 1

• SERVING = 2

Used in: EntryConfig

• optional InitializerConfig init_config = 1

• optional OptimizerConfig opt_config = 2

• optional FloatCompressorConfig comp_config = 3

• optional int32 dim_size = 7

  Will overwrite dim_size in init_config, opt_config and comp_config.

• optional sfixed64 id = 1

• repeated float num = 2

• optional OptimizerDump opt = 3

• optional int64 last_update_ts_sec = 4

Used in: EntryConfig.Segment

• oneof type

  • FloatCompressorConfig.Fp32 fp32 = 1

  • FloatCompressorConfig.Fp16 fp16 = 2

  • FloatCompressorConfig.FixedR8 fixed_r8 = 3

  • FloatCompressorConfig.OneBit one_bit = 4

Corresponding to qat8 in Bytedance PS.

Used in: FloatCompressorConfig

• optional int32 dim_size = 1

• optional float r = 2

Using half-precision floating-point format.

Used in: FloatCompressorConfig

• optional int32 dim_size = 1

Not compressed. Useful in the test.

Used in: FloatCompressorConfig

• optional int32 dim_size = 1

HashNet

Used in: FloatCompressorConfig

• optional int32 dim_size = 1

• optional int64 step_size = 2

• optional float init_scale = 3

• optional float max_scale = 4

• optional float amplitude = 5

Used in: OptimizerConfig

• optional int32 dim_size = 1

• optional float learning_rate = 2

• optional float beta = 3

• optional float initial_accumulator_value = 4

• optional float l1_regularization_strength = 5

• optional float l2_regularization_strength = 6

• optional int64 warmup_steps = 7

Used in: SingleOptimizerDump

• repeated float zero = 1

• repeated float norm = 2

Used in: OptimizerConfig

• optional int32 dim_size = 1

• optional float learning_rate = 2

• optional float beta = 3

• optional float initial_accumulator_value = 4

• optional float l2_regularization_strength = 5

• optional float weight_decay_factor = 6

• optional int64 warmup_steps = 7

Used in: SingleOptimizerDump

• optional float grad_square_sum = 1

Used in: OptimizerConfig

• optional int32 dim_size = 1

• optional float learning_rate = 2

• optional float beta = 3

• optional float initial_accumulator_value = 4

• optional float l1_regularization_strength = 5

• optional float l2_regularization_strength = 6

• optional int64 warmup_steps = 7

Used in: SingleOptimizerDump

• repeated float zero = 1

• repeated float norm = 2

• optional uint32 offset = 1

• repeated uint32 data = 2

Here we make each hash filter split keep the shared meta dump. This meta is small and it can help simplify the design to store the shared meta in a seperate file. We will consider to refine this in future.

• optional uint64 failure_count = 1

• optional uint64 total_size = 2

• optional uint64 num_elements = 3

• optional double fill_rate = 4

• optional SlidingHashFilterMetaDump sliding_hash_filter_meta = 5

Used in: EntryConfig.Segment

• oneof type

  • ZerosInitializerConfig zeros = 1

  • RandomUniformInitializerConfig random_uniform = 2

  • OnesInitializerConfig ones = 3

  • ConstantsInitializerConfig constants = 15

Used in: OptimizerConfig

• optional int32 dim_size = 1

• optional float learning_rate = 2

• optional float weight_decay_factor = 3

• optional bool use_nesterov = 4

• optional float momentum = 5

• optional int64 warmup_steps = 6

Used in: SingleOptimizerDump

• repeated float n = 1

Used in: OptimizerConfig

• optional int32 dim_size = 1

• optional float momentum = 2

• repeated string names = 1

• repeated EmbeddingHashTableConfig configs = 2

• optional string table_name = 1

• optional uint64 num_entries = 2

Used in: InitializerConfig

• optional int32 dim_size = 1

Used in: EntryConfig.Segment

• oneof type

  • AdagradOptimizerConfig adagrad = 1

  • SgdOptimizerConfig sgd = 2

  • FtrlOptimizerConfig ftrl = 3

  • DynamicWdAdagradOptimizerConfig dynamic_wd_adagrad = 5

  • AdadeltaOptimizerConfig adadelta = 6

  • AdamOptimizerConfig adam = 7

  • AmsgradOptimizerConfig amsgrad = 8

  • MomentumOptimizerConfig momentum = 9

  • MovingAverageOptimizerConfig moving_average = 10

  • RmspropOptimizerConfig rmsprop = 11

  • RmspropV2OptimizerConfig rmspropv2 = 12

  • DcOptimizerConfig dc = 13

  • GroupFtrlOptimizerConfig group_ftrl = 14

  • BatchSoftmaxOptimizerConfig batch_softmax = 15

  • GroupAdaGradOptimizerConfig group_adagrad = 16

• optional bool stochastic_rounding_float16 = 4

  Default false.

TODO(leqi.zou): Consider about adding Arena to improve the performance.

Used in: EntryDump

• repeated SingleOptimizerDump dump = 1

Used in: InitializerConfig

• optional int32 dim_size = 1

• optional float minval = 2

• optional float maxval = 3

Used in: OptimizerConfig

• optional int32 dim_size = 1

• optional float learning_rate = 2

• optional float weight_decay_factor = 3

• optional float momentum = 4

Used in: SingleOptimizerDump

• repeated float n = 1

Used in: OptimizerConfig

• optional int32 dim_size = 1

• optional float learning_rate = 2

• optional float weight_decay_factor = 3

• optional float momentum = 4

Used in: SingleOptimizerDump

• repeated float n = 1

Used in: OptimizerConfig

• optional int32 dim_size = 1

• optional float learning_rate = 2

• optional int64 warmup_steps = 6

Used in: SingleOptimizerDump

(message has no fields)

Used in: OptimizerDump

• oneof type

  • AdagradOptimizerDump adagrad = 1

  • SgdOptimizerDump sgd = 2

  • FtrlOptimizerDump ftrl = 3

  • DynamicWdAdagradOptimizerDump dynamic_wd_adagrad = 4

  • AdadeltaOptimizerDump adadelta = 6

  • AdamOptimizerDump adam = 7

  • AmsgradOptimizerDump amsgrad = 8

  • MomentumOptimizerDump momentum = 9

  • RmspropOptimizerDump rmsprop = 11

  • RmspropV2OptimizerDump rmspropv2 = 12

  • GroupFtrlOptimizerDump group_ftrl = 13

  • BatchSoftmaxOptimizerDump batch_softmax = 14

  • GroupAdaGradOptimizerDump group_adagrad = 15

Used in: HashFilterSplitMetaDump

• optional uint32 split_num = 1

• optional uint32 max_forward_step = 2

• optional uint32 max_backward_step = 3

• optional uint32 max_step = 4

• optional uint32 head = 5

• optional uint32 head_increment = 6

• optional uint64 failure_count = 7

Use per slot expire time (in days) to align with sail requirement. In future, slot settings might be deprecated.

Used in: EmbeddingHashTableConfig

• repeated SlotExpireTimeConfig.SlotExpireTime slot_expire_times = 1

• optional uint32 default_expire_time = 2

  default expire time is 100 years.

These slot and expire time are used to overwrite default slot expire time.

Used in: SlotExpireTimeConfig

• optional uint32 slot = 1

• optional uint32 expire_time = 2

Use per slot occurrence threshold config to align with sail requirement. In future, slot settings might be deprecated.

• repeated SlotOccurrenceThresholdConfig.SlotOccurrenceThreshold slot_occurrence_thresholds = 1

• optional uint32 default_occurrence_threshold = 2

These slot and occurrence threshold are used to overwrite default occurrence thresholds.

Used in: SlotOccurrenceThresholdConfig

• optional uint32 slot = 1

• optional uint32 occurrence_threshold = 2

Used in: InitializerConfig

• optional int32 dim_size = 1