"""
Author:
Mincai Lai, laimc@shanghaitech.edu.cn
Weichen Shen, weichenswc@163.com
Reference:
[1] Ma J, Zhao Z, Yi X, et al. Modeling task relationships in multi-task learning with multi-gate mixture-of-experts[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018.(https://dl.acm.org/doi/abs/10.1145/3219819.3220007)
"""
import tensorflow as tf
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import Dense, Lambda
from ...feature_column import build_input_features, input_from_feature_columns
from ...layers.core import PredictionLayer, DNN
from ...layers.utils import combined_dnn_input, reduce_sum
[docs]def MMOE(dnn_feature_columns, num_experts=3, expert_dnn_hidden_units=(256, 128), tower_dnn_hidden_units=(64,),
gate_dnn_hidden_units=(), l2_reg_embedding=0.00001, l2_reg_dnn=0, seed=1024, dnn_dropout=0,
dnn_activation='relu',
dnn_use_bn=False, task_types=('binary', 'binary'), task_names=('ctr', 'ctcvr')):
"""Instantiates the Multi-gate Mixture-of-Experts multi-task learning architecture.
:param dnn_feature_columns: An iterable containing all the features used by deep part of the model.
:param num_experts: integer, number of experts.
:param expert_dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of expert DNN.
:param tower_dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of task-specific DNN.
:param gate_dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of gate DNN.
:param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector
:param l2_reg_dnn: float. L2 regularizer strength applied to DNN
:param seed: integer ,to use as random seed.
:param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
:param dnn_activation: Activation function to use in DNN
:param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in DNN
:param task_types: list of str, indicating the loss of each tasks, ``"binary"`` for binary logloss, ``"regression"`` for regression loss. e.g. ['binary', 'regression']
:param task_names: list of str, indicating the predict target of each tasks
:return: a Keras model instance
"""
num_tasks = len(task_names)
if num_tasks <= 1:
raise ValueError("num_tasks must be greater than 1")
if num_experts <= 1:
raise ValueError("num_experts must be greater than 1")
if len(task_types) != num_tasks:
raise ValueError("num_tasks must be equal to the length of task_types")
for task_type in task_types:
if task_type not in ['binary', 'regression']:
raise ValueError("task must be binary or regression, {} is illegal".format(task_type))
features = build_input_features(dnn_feature_columns)
inputs_list = list(features.values())
sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns,
l2_reg_embedding, seed)
dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list)
# build expert layer
expert_outs = []
for i in range(num_experts):
expert_network = DNN(expert_dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed,
name='expert_' + str(i))(dnn_input)
expert_outs.append(expert_network)
expert_concat = Lambda(lambda x: tf.stack(x, axis=1))(expert_outs) # None,num_experts,dim
mmoe_outs = []
for i in range(num_tasks): # one mmoe layer: nums_tasks = num_gates
# build gate layers
gate_input = DNN(gate_dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed,
name='gate_' + task_names[i])(dnn_input)
gate_out = Dense(num_experts, use_bias=False, activation='softmax',
name='gate_softmax_' + task_names[i])(gate_input)
gate_out = Lambda(lambda x: tf.expand_dims(x, axis=-1))(gate_out)
# gate multiply the expert
gate_mul_expert = Lambda(lambda x: reduce_sum(x[0] * x[1], axis=1, keep_dims=False),
name='gate_mul_expert_' + task_names[i])([expert_concat, gate_out])
mmoe_outs.append(gate_mul_expert)
task_outs = []
for task_type, task_name, mmoe_out in zip(task_types, task_names, mmoe_outs):
# build tower layer
tower_output = DNN(tower_dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed,
name='tower_' + task_name)(mmoe_out)
logit = Dense(1, use_bias=False)(tower_output)
output = PredictionLayer(task_type, name=task_name)(logit)
task_outs.append(output)
model = Model(inputs=inputs_list, outputs=task_outs)
return model