diff --git a/core/utils/envs.py b/core/utils/envs.py index 79168e83b742466e27c1f7db846706185adfad06..f768e14ab6d412dc6d834ffaf99cac6c8b95f3d8 100755 --- a/core/utils/envs.py +++ b/core/utils/envs.py @@ -13,6 +13,7 @@ # limitations under the License. from contextlib import closing +import yaml import copy import os import socket diff --git a/core/utils/validation.py b/core/utils/validation.py index 6f800ed27fb936839143dc254dc139c017b91c54..6d1381cf23fae7cc79b76da96968b7f035de40d8 100644 --- a/core/utils/validation.py +++ b/core/utils/validation.py @@ -120,9 +120,7 @@ def register(): validations["train.engine"] = ValueFormat( "str", ["single", "local_cluster", "cluster"], in_value_handler) - requires = [ - "train.namespace", "train.device", "train.epochs", "train.engine" - ] + requires = ["workspace", "dataset", "mode", "runner", "mode"] return validations, requires diff --git a/models/rank/afm/__init__.py b/models/rank/afm/__init__.py new file mode 100755 index 0000000000000000000000000000000000000000..abf198b97e6e818e1fbe59006f98492640bcee54 --- /dev/null +++ b/models/rank/afm/__init__.py @@ -0,0 +1,13 @@ +# 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. diff --git a/models/rank/afm/config.yaml b/models/rank/afm/config.yaml new file mode 100755 index 0000000000000000000000000000000000000000..2c1035421bed448ffe037f16583f342ab1bf992e --- /dev/null +++ b/models/rank/afm/config.yaml @@ -0,0 +1,76 @@ +# 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. + + +# global settings +debug: false +workspace: "paddlerec.models.rank.afm" + +dataset: + - name: train_sample + type: QueueDataset + batch_size: 5 + data_path: "{workspace}/../dataset/Criteo_data/sample_data/train" + sparse_slots: "label feat_idx" + dense_slots: "feat_value:39" + - name: infer_sample + type: QueueDataset + batch_size: 5 + data_path: "{workspace}/../dataset/Criteo_data/sample_data/train" + sparse_slots: "label feat_idx" + dense_slots: "feat_value:39" + +# 用户自定义配置 +hyper_parameters: + optimizer: + class: Adam + learning_rate: 0.0001 + sparse_feature_number: 1086460 + sparse_feature_dim: 16 + is_sparse: False + reg: 0.001 + num_field: 39 + act: "relu" + hidden1_attention_size: 16 + +mode: train_runner +# if infer, change mode to "infer_runner" and change phase to "infer_phase" + +runner: + - name: train_runner + trainer_class: single_train + epochs: 1 + device: cpu + init_model_path: "" + save_checkpoint_interval: 1 + save_inference_interval: 1 + save_checkpoint_path: "increment" + save_inference_path: "inference" + print_interval: 1 + - name: infer_runner + trainer_class: single_infer + epochs: 1 + device: cpu + init_model_path: "increment/0" + print_interval: 1 + +phase: +- name: phase1 + model: "{workspace}/model.py" + dataset_name: train_sample + thread_num: 1 +#- name: infer_phase +# model: "{workspace}/model.py" +# dataset_name: infer_sample +# thread_num: 1 diff --git a/models/rank/afm/model.py b/models/rank/afm/model.py new file mode 100755 index 0000000000000000000000000000000000000000..0cd8a87f4bb05c00d7de14258ed216e61c5a7ecf --- /dev/null +++ b/models/rank/afm/model.py @@ -0,0 +1,176 @@ +# 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 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", 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 + feat_embeddings = feat_embeddings * feat_value # batch_size * num_field * embedding_size + + 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 = 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