add afm in rank and fix bugs

core/utils/envs.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 from contextlib import closing
+import yaml
 import copy
 import os
 import socket

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
 
 

models/rank/afm/__init__.py

+# 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.

models/rank/afm/config.yaml

+# 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

models/rank/afm/model.py

+#   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