# 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 from collections import OrderedDict import paddle.fluid as fluid from paddlerec.core.utils import envs from paddlerec.core.model import ModelBase class Model(ModelBase): def __init__(self, config): ModelBase.__init__(self, config) def _init_hyper_parameters(self): self.is_distributed = True if envs.get_fleet_mode().upper( ) == "PSLIB" else False self.sparse_feature_number = envs.get_global_env( "hyper_parameters.sparse_feature_number", None) self.sparse_feature_dim = envs.get_global_env( "hyper_parameters.sparse_feature_dim", None) self.is_sparse = envs.get_global_env("hyper_parameters.is_sparse", False) self.reg = envs.get_global_env("hyper_parameters.reg", 1e-4) self.num_field = envs.get_global_env("hyper_parameters.num_field", None) self.hidden1_attention_size = envs.get_global_env( "hyper_parameters.hidden1_attention_size", 16) self.attention_act = envs.get_global_env("hyper_parameters.act", "relu") def net(self, inputs, is_infer=False): raw_feat_idx = self._sparse_data_var[1] # (batch_size * num_field) * 1 raw_feat_value = self._dense_data_var[0] # batch_size * num_field self.label = self._sparse_data_var[0] # batch_size * 1 init_value_ = 0.1 feat_idx = raw_feat_idx feat_value = fluid.layers.reshape( raw_feat_value, [-1, self.num_field, 1]) # batch_size * num_field * 1 # ------------------------- first order term -------------------------- first_weights_re = fluid.embedding( input=feat_idx, is_sparse=self.is_sparse, is_distributed=self.is_distributed, dtype='float32', size=[self.sparse_feature_number + 1, 1], padding_idx=0, param_attr=fluid.ParamAttr( initializer=fluid.initializer.TruncatedNormalInitializer( loc=0.0, scale=init_value_), regularizer=fluid.regularizer.L1DecayRegularizer(self.reg)) ) # (batch_size * num_field) * 1 * 1(embedding_size) first_weights = fluid.layers.reshape( first_weights_re, shape=[-1, self.num_field, 1]) # batch_size * num_field * 1 y_first_order = fluid.layers.reduce_sum((first_weights * feat_value), 1) # batch_size * 1 # ------------------------- Pair-wise Interaction Layer -------------------------- feat_embeddings_re = fluid.embedding( input=feat_idx, is_sparse=self.is_sparse, is_distributed=self.is_distributed, dtype='float32', size=[self.sparse_feature_number + 1, self.sparse_feature_dim], padding_idx=0, param_attr=fluid.ParamAttr( initializer=fluid.initializer.TruncatedNormalInitializer( loc=0.0, scale=init_value_ / math.sqrt(float(self.sparse_feature_dim)))) ) # (batch_size * num_field) * 1 * embedding_size feat_embeddings = fluid.layers.reshape( feat_embeddings_re, shape=[-1, self.num_field, self.sparse_feature_dim ]) # batch_size * num_field * embedding_size # batch_size * num_field * embedding_size feat_embeddings = feat_embeddings * feat_value element_wise_product_list = [] for i in range(self.num_field): for j in range(i + 1, self.num_field): element_wise_product_list.append( feat_embeddings[:, i, :] * feat_embeddings[:, j, :]) # list(batch_size * embedding_size) stack_element_wise_product = fluid.layers.stack( element_wise_product_list, axis=0) # (num_field*(num_field-1)/2) * batch_size * embedding_size stack_element_wise_product = fluid.layers.transpose( stack_element_wise_product, perm=[1, 0, 2] ) # batch_size * (num_field*(num_field-1)/2) * embedding_size # ------------------------- Attention-based Pooling -------------------------- attetion_mul = fluid.layers.fc( input=fluid.layers.reshape( stack_element_wise_product, shape=[-1, self.sparse_feature_dim]), size=self.hidden1_attention_size, act=self.attention_act, param_attr=fluid.ParamAttr( initializer=fluid.initializer.TruncatedNormalInitializer( loc=0.0, scale=init_value_)), bias_attr=fluid.ParamAttr( initializer=fluid.initializer.TruncatedNormalInitializer( loc=0.0, scale=init_value_)) ) # (batch_size * (num_field*(num_field-1)/2)) * hidden1_attention_size attention_h = fluid.layers.create_parameter( shape=[self.hidden1_attention_size, 1], dtype="float32") attention_out = fluid.layers.matmul( attetion_mul, attention_h) # (batch_size * (num_field*(num_field-1)/2)) * 1 attention_out = fluid.layers.softmax( attention_out) # (batch_size * (num_field*(num_field-1)/2)) * 1 num_interactions = int(self.num_field * (self.num_field - 1) / 2) attention_out = fluid.layers.reshape( attention_out, shape=[-1, num_interactions, 1]) # batch_size * (num_field*(num_field-1)/2) * 1 attention_pooling = fluid.layers.matmul( attention_out, stack_element_wise_product, transpose_x=True) # batch_size * 1 * embedding_size attention_pooling = fluid.layers.reshape( attention_pooling, shape=[-1, self.sparse_feature_dim]) # batch_size * embedding_size y_AFM = fluid.layers.fc( input=attention_pooling, size=1, act=None, param_attr=fluid.ParamAttr( initializer=fluid.initializer.TruncatedNormalInitializer( loc=0.0, scale=init_value_)), bias_attr=fluid.ParamAttr( initializer=fluid.initializer.TruncatedNormalInitializer( loc=0.0, scale=init_value_))) # batch_size * 1 # ------------------------- Predict -------------------------- self.predict = fluid.layers.sigmoid(y_first_order + y_AFM) cost = fluid.layers.log_loss( input=self.predict, label=fluid.layers.cast(self.label, "float32")) # log_loss avg_cost = fluid.layers.reduce_sum(cost) self._cost = avg_cost predict_2d = fluid.layers.concat([1 - self.predict, self.predict], 1) label_int = fluid.layers.cast(self.label, 'int64') auc_var, batch_auc_var, _ = fluid.layers.auc(input=predict_2d, label=label_int, slide_steps=0) self._metrics["AUC"] = auc_var self._metrics["BATCH_AUC"] = batch_auc_var if is_infer: self._infer_results["AUC"] = auc_var