# 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 numpy as np import paddle.fluid as fluid from paddlerec.core.utils import envs from paddlerec.core.model import Model as ModelBase class Model(ModelBase): def __init__(self, config): ModelBase.__init__(self, config) def _init_hyper_parameters(self): self.is_distributed = True if envs.get_trainer( ) == "CtrTrainer" else False self.sparse_feature_number = envs.get_global_env( "hyper_parameters.sparse_feature_number") self.sparse_feature_dim = envs.get_global_env( "hyper_parameters.sparse_feature_dim") self.neg_num = envs.get_global_env("hyper_parameters.neg_num") self.with_shuffle_batch = envs.get_global_env( "hyper_parameters.with_shuffle_batch") self.learning_rate = envs.get_global_env( "hyper_parameters.optimizer.learning_rate") self.decay_steps = envs.get_global_env( "hyper_parameters.optimizer.decay_steps") self.decay_rate = envs.get_global_env( "hyper_parameters.optimizer.decay_rate") def input_data(self, is_infer=False, **kwargs): if is_infer: analogy_a = fluid.data( name="analogy_a", shape=[None], dtype='int64') analogy_b = fluid.data( name="analogy_b", shape=[None], dtype='int64') analogy_c = fluid.data( name="analogy_c", shape=[None], dtype='int64') analogy_d = fluid.data( name="analogy_d", shape=[None], dtype='int64') return [analogy_a, analogy_b, analogy_c, analogy_d] input_word = fluid.data( name="input_word", shape=[None, 1], dtype='int64') true_word = fluid.data( name='true_label', shape=[None, 1], dtype='int64') if self.with_shuffle_batch: return [input_word, true_word] neg_word = fluid.data( name="neg_label", shape=[None, self.neg_num], dtype='int64') return [input_word, true_word, neg_word] def net(self, inputs, is_infer=False): if is_infer: self.infer_net(inputs) return def embedding_layer(input, table_name, emb_dim, initializer_instance=None, squeeze=False): emb = fluid.embedding( input=input, is_sparse=True, is_distributed=self.is_distributed, size=[self.sparse_feature_number, emb_dim], param_attr=fluid.ParamAttr( name=table_name, initializer=initializer_instance), ) if squeeze: return fluid.layers.squeeze(input=emb, axes=[1]) else: return emb init_width = 0.5 / self.sparse_feature_dim emb_initializer = fluid.initializer.Uniform(-init_width, init_width) emb_w_initializer = fluid.initializer.Constant(value=0.0) input_emb = embedding_layer(inputs[0], "emb", self.sparse_feature_dim, emb_initializer, True) true_emb_w = embedding_layer(inputs[1], "emb_w", self.sparse_feature_dim, emb_w_initializer, True) true_emb_b = embedding_layer(inputs[1], "emb_b", 1, emb_w_initializer, True) if self.with_shuffle_batch: neg_emb_w_list = [] for i in range(self.neg_num): neg_emb_w_list.append( fluid.contrib.layers.shuffle_batch( true_emb_w)) # shuffle true_word neg_emb_w_concat = fluid.layers.concat(neg_emb_w_list, axis=0) neg_emb_w = fluid.layers.reshape( neg_emb_w_concat, shape=[-1, self.neg_num, self.sparse_feature_dim]) neg_emb_b_list = [] for i in range(self.neg_num): neg_emb_b_list.append( fluid.contrib.layers.shuffle_batch( true_emb_b)) # shuffle true_word neg_emb_b = fluid.layers.concat(neg_emb_b_list, axis=0) neg_emb_b_vec = fluid.layers.reshape( neg_emb_b, shape=[-1, self.neg_num]) else: neg_emb_w = embedding_layer( inputs[2], "emb_w", self.sparse_feature_dim, emb_w_initializer) neg_emb_b = embedding_layer(inputs[2], "emb_b", 1, emb_w_initializer) neg_emb_b_vec = fluid.layers.reshape( neg_emb_b, shape=[-1, self.neg_num]) true_logits = fluid.layers.elementwise_add( fluid.layers.reduce_sum( fluid.layers.elementwise_mul(input_emb, true_emb_w), dim=1, keep_dim=True), true_emb_b) input_emb_re = fluid.layers.reshape( input_emb, shape=[-1, 1, self.sparse_feature_dim]) neg_matmul = fluid.layers.matmul( input_emb_re, neg_emb_w, transpose_y=True) neg_matmul_re = fluid.layers.reshape( neg_matmul, shape=[-1, self.neg_num]) neg_logits = fluid.layers.elementwise_add(neg_matmul_re, neg_emb_b_vec) #nce loss label_ones = fluid.layers.fill_constant( shape=[fluid.layers.shape(true_logits)[0], 1], value=1.0, dtype='float32') label_zeros = fluid.layers.fill_constant( shape=[fluid.layers.shape(true_logits)[0], self.neg_num], value=0.0, dtype='float32') true_xent = fluid.layers.sigmoid_cross_entropy_with_logits(true_logits, label_ones) neg_xent = fluid.layers.sigmoid_cross_entropy_with_logits(neg_logits, label_zeros) cost = fluid.layers.elementwise_add( fluid.layers.reduce_sum( true_xent, dim=1), fluid.layers.reduce_sum( neg_xent, dim=1)) avg_cost = fluid.layers.reduce_mean(cost) self._cost = avg_cost global_right_cnt = fluid.layers.create_global_var( name="global_right_cnt", persistable=True, dtype='float32', shape=[1], value=0) global_total_cnt = fluid.layers.create_global_var( name="global_total_cnt", persistable=True, dtype='float32', shape=[1], value=0) global_right_cnt.stop_gradient = True global_total_cnt.stop_gradient = True self._metrics["LOSS"] = avg_cost def optimizer(self): optimizer = fluid.optimizer.SGD( learning_rate=fluid.layers.exponential_decay( learning_rate=self.learning_rate, decay_steps=self.decay_steps, decay_rate=self.decay_rate, staircase=True)) return optimizer def infer_net(self, inputs): def embedding_layer(input, table_name, initializer_instance=None): emb = fluid.embedding( input=input, size=[self.sparse_feature_number, self.sparse_feature_dim], param_attr=table_name) return emb all_label = np.arange(self.sparse_feature_number).reshape( self.sparse_feature_number).astype('int32') self.all_label = fluid.layers.cast( x=fluid.layers.assign(all_label), dtype='int64') emb_all_label = embedding_layer(self.all_label, "emb") emb_a = embedding_layer(inputs[0], "emb") emb_b = embedding_layer(inputs[1], "emb") emb_c = embedding_layer(inputs[2], "emb") target = fluid.layers.elementwise_add( fluid.layers.elementwise_sub(emb_b, emb_a), emb_c) emb_all_label_l2 = fluid.layers.l2_normalize(x=emb_all_label, axis=1) dist = fluid.layers.matmul( x=target, y=emb_all_label_l2, transpose_y=True) values, pred_idx = fluid.layers.topk(input=dist, k=4) label = fluid.layers.expand( fluid.layers.unsqueeze( inputs[3], axes=[1]), expand_times=[1, 4]) label_ones = fluid.layers.fill_constant_batch_size_like( label, shape=[-1, 1], value=1.0, dtype='float32') right_cnt = fluid.layers.reduce_sum(input=fluid.layers.cast( fluid.layers.equal(pred_idx, label), dtype='float32')) total_cnt = fluid.layers.reduce_sum(label_ones) global_right_cnt = fluid.layers.create_global_var( name="global_right_cnt", persistable=True, dtype='float32', shape=[1], value=0) global_total_cnt = fluid.layers.create_global_var( name="global_total_cnt", persistable=True, dtype='float32', shape=[1], value=0) global_right_cnt.stop_gradient = True global_total_cnt.stop_gradient = True tmp1 = fluid.layers.elementwise_add(right_cnt, global_right_cnt) fluid.layers.assign(tmp1, global_right_cnt) tmp2 = fluid.layers.elementwise_add(total_cnt, global_total_cnt) fluid.layers.assign(tmp2, global_total_cnt) acc = fluid.layers.elementwise_div( global_right_cnt, global_total_cnt, name="total_acc") self._infer_results['acc'] = acc