# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math import paddle.fluid as fluid from fleetrec.core.utils import envs from fleetrec.core.model import Model as ModelBase class Model(ModelBase): def __init__(self, config): ModelBase.__init__(self, config) def MMOE(self): feature_size = envs.get_global_env("hyper_parameters.feature_size", None, self._namespace) expert_num = envs.get_global_env("hyper_parameters.expert_num", None, self._namespace) gate_num = envs.get_global_env("hyper_parameters.gate_num", None, self._namespace) expert_size = envs.get_global_env("hyper_parameters.expert_size", None, self._namespace) tower_size = envs.get_global_env("hyper_parameters.tower_size", None, self._namespace) input_data = fluid.data(name="input", shape=[-1, feature_size], dtype="float32") label_income = fluid.data(name="label_income", shape=[-1, 2], dtype="float32", lod_level=0) label_marital = fluid.data(name="label_marital", shape=[-1, 2], dtype="float32", lod_level=0) self._data_var.extend([input_data, label_income, label_marital]) # f_{i}(x) = activation(W_{i} * x + b), where activation is ReLU according to the paper expert_outputs = [] for i in range(0, expert_num): expert_output = fluid.layers.fc(input=input_data, size=expert_size, act='relu', bias_attr=fluid.ParamAttr(learning_rate=1.0), name='expert_' + str(i)) expert_outputs.append(expert_output) expert_concat = fluid.layers.concat(expert_outputs, axis=1) expert_concat = fluid.layers.reshape(expert_concat,[-1, expert_num, expert_size]) # g^{k}(x) = activation(W_{gk} * x + b), where activation is softmax according to the paper output_layers = [] for i in range(0, gate_num): cur_gate = fluid.layers.fc(input=input_data, size=expert_num, act='softmax', bias_attr=fluid.ParamAttr(learning_rate=1.0), name='gate_' + str(i)) # f^{k}(x) = sum_{i=1}^{n}(g^{k}(x)_{i} * f_{i}(x)) cur_gate_expert = fluid.layers.elementwise_mul(expert_concat, cur_gate, axis=0) cur_gate_expert = fluid.layers.reduce_sum(cur_gate_expert, dim=1) # Build tower layer cur_tower = fluid.layers.fc(input=cur_gate_expert, size=tower_size, act='relu', name='task_layer_' + str(i)) out = fluid.layers.fc(input=cur_tower, size=2, act='softmax', name='out_' + str(i)) output_layers.append(out) pred_income = fluid.layers.clip(output_layers[0], min=1e-15, max=1.0 - 1e-15) pred_marital = fluid.layers.clip(output_layers[1], min=1e-15, max=1.0 - 1e-15) cost_income = fluid.layers.cross_entropy(input=pred_income, label=label_income,soft_label = True) cost_marital = fluid.layers.cross_entropy(input=pred_marital, label=label_marital,soft_label = True) label_income_1 = fluid.layers.slice(label_income, axes=[1], starts=[1], ends=[2]) label_marital_1 = fluid.layers.slice(label_marital, axes=[1], starts=[1], ends=[2]) auc_income, batch_auc_1, auc_states_1 = fluid.layers.auc(input=pred_income, label=fluid.layers.cast(x=label_income_1, dtype='int64')) auc_marital, batch_auc_2, auc_states_2 = fluid.layers.auc(input=pred_marital, label=fluid.layers.cast(x=label_marital_1, dtype='int64')) avg_cost_income = fluid.layers.mean(x=cost_income) avg_cost_marital = fluid.layers.mean(x=cost_marital) cost = avg_cost_income + avg_cost_marital self._cost = cost self._metrics["AUC_income"] = auc_income self._metrics["BATCH_AUC_income"] = batch_auc_1 self._metrics["AUC_marital"] = auc_marital self._metrics["BATCH_AUC_marital"] = batch_auc_2 def train_net(self): self.MMOE() def infer_net(self): pass