Merge branch 'master' into mod_yaml

models/multitask/esmm/config.yaml

@@ -12,40 +12,55 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-evaluate:
-  reader:
-    batch_size: 1
-    class: "{workspace}/esmm_infer_reader.py"
-    test_data_path: "{workspace}/data/train"
-
-train:
-  trainer:
-    # for cluster training
-    strategy: "async"
 
-  epochs: 3
-  workspace: "paddlerec.models.multitask.esmm"
-  device: cpu
+workspace: "paddlerec.models.multitask.esmm"
 
-  reader:
-    batch_size: 2
-    class: "{workspace}/esmm_reader.py"
-    train_data_path: "{workspace}/data/train"
+dataset:
+- name: dataset_train
+  batch_size: 1
+  type: QueueDataset
+  data_path: "{workspace}/data/train"
+  data_converter: "{workspace}/esmm_reader.py"
+- name: dataset_infer
+  batch_size: 1
+  type: QueueDataset
+  data_path: "{workspace}/data/test"
+  data_converter: "{workspace}/esmm_reader.py"
 
-  model:
-    models: "{workspace}/model.py"
-    hyper_parameters:
+hyper_parameters:
   vocab_size: 10000
   embed_size: 128
+  optimizer: 
+    class: adam
     learning_rate: 0.001
-      optimizer: adam
+    strategy: async
+
+#use infer_runner mode and modify 'phase' below if infer
+mode: train_runner
+#mode: infer_runner
+
+runner:
+- name: train_runner
+  class: single_train
+  device: cpu
+  epochs: 3
+  save_checkpoint_interval: 2
+  save_inference_interval: 4
+  save_checkpoint_path: "increment"
+  save_inference_path: "inference"
+  print_interval: 10
+- name: infer_runner
+  class: single_infer
+  init_model_path: "increment/0"
+  device: cpu
+  epochs: 3
 
-  save:
-    increment:
-      dirname: "increment"
-      epoch_interval: 2
-      save_last: True
-    inference:
-      dirname: "inference"
-      epoch_interval: 4
-      save_last: True
+phase:
+- name: train
+  model: "{workspace}/model.py"
+  dataset_name: dataset_train
+  thread_num: 1
+    #- name: infer
+    #  model: "{workspace}/model.py"
+    #  dataset_name: dataset_infer
+    #  thread_num: 1

models/multitask/esmm/esmm_infer_reader.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.
-from __future__ import print_function
-
-from collections import defaultdict
-
-from paddlerec.core.reader import Reader
-
-
-class EvaluateReader(Reader):
-    def init(self):
-        all_field_id = [
-            '101', '109_14', '110_14', '127_14', '150_14', '121', '122', '124',
-            '125', '126', '127', '128', '129', '205', '206', '207', '210',
-            '216', '508', '509', '702', '853', '301'
-        ]
-        self.all_field_id_dict = defaultdict(int)
-        for i, field_id in enumerate(all_field_id):
-            self.all_field_id_dict[field_id] = [False, i]
-
-    def generate_sample(self, line):
-        """
-        Read the data line by line and process it as a dictionary
-        """
-
-        def reader():
-            """
-            This function needs to be implemented by the user, based on data format
-            """
-            features = line.strip().split(',')
-            ctr = int(features[1])
-            cvr = int(features[2])
-
-            padding = 0
-            output = [(field_id, []) for field_id in self.all_field_id_dict]
-
-            for elem in features[4:]:
-                field_id, feat_id = elem.strip().split(':')
-                if field_id not in self.all_field_id_dict:
-                    continue
-                self.all_field_id_dict[field_id][0] = True
-                index = self.all_field_id_dict[field_id][1]
-                output[index][1].append(int(feat_id))
-
-            for field_id in self.all_field_id_dict:
-                visited, index = self.all_field_id_dict[field_id]
-                if visited:
-                    self.all_field_id_dict[field_id][0] = False
-                else:
-                    output[index][1].append(padding)
-            output.append(('ctr', [ctr]))
-            output.append(('cvr', [cvr]))
-            yield output
-
-        return reader

models/multitask/esmm/esmm_reader.py

@@ -40,8 +40,6 @@ class TrainReader(Reader):
             This function needs to be implemented by the user, based on data format
             """
             features = line.strip().split(',')
-            # ctr = list(map(int, features[1]))
-            # cvr = list(map(int, features[2]))
             ctr = int(features[1])
             cvr = int(features[2])
 
@@ -54,7 +52,6 @@ class TrainReader(Reader):
                     continue
                 self.all_field_id_dict[field_id][0] = True
                 index = self.all_field_id_dict[field_id][1]
-                # feat_id = list(map(int, feat_id))
                 output[index][1].append(int(feat_id))
 
             for field_id in self.all_field_id_dict:

models/multitask/esmm/model.py

@@ -23,28 +23,11 @@ class Model(ModelBase):
     def __init__(self, config):
         ModelBase.__init__(self, config)
 
-    def fc(self, tag, data, out_dim, active='prelu'):
+    def _init_hyper_parameters(self):
+        self.vocab_size = envs.get_global_env("hyper_parameters.vocab_size")
+        self.embed_size = envs.get_global_env("hyper_parameters.embed_size")
 
-        init_stddev = 1.0
-        scales = 1.0 / np.sqrt(data.shape[1])
-
-        p_attr = fluid.param_attr.ParamAttr(
-            name='%s_weight' % tag,
-            initializer=fluid.initializer.NormalInitializer(
-                loc=0.0, scale=init_stddev * scales))
-
-        b_attr = fluid.ParamAttr(
-            name='%s_bias' % tag, initializer=fluid.initializer.Constant(0.1))
-
-        out = fluid.layers.fc(input=data,
-                              size=out_dim,
-                              act=active,
-                              param_attr=p_attr,
-                              bias_attr=b_attr,
-                              name=tag)
-        return out
-
-    def input_data(self):
+    def input_data(self, is_infer=False, **kwargs):
         sparse_input_ids = [
             fluid.data(
                 name="field_" + str(i),
@@ -55,26 +38,24 @@ class Model(ModelBase):
         label_ctr = fluid.data(name="ctr", shape=[-1, 1], dtype="int64")
         label_cvr = fluid.data(name="cvr", shape=[-1, 1], dtype="int64")
         inputs = sparse_input_ids + [label_ctr] + [label_cvr]
-        self._data_var.extend(inputs)
-
+        if is_infer:
+            return inputs
+        else:
             return inputs
 
     def net(self, inputs, is_infer=False):
 
-        vocab_size = envs.get_global_env("hyper_parameters.vocab_size", None,
-                                         self._namespace)
-        embed_size = envs.get_global_env("hyper_parameters.embed_size", None,
-                                         self._namespace)
         emb = []
+        # input feature data
         for data in inputs[0:-2]:
             feat_emb = fluid.embedding(
                 input=data,
-                size=[vocab_size, embed_size],
+                size=[self.vocab_size, self.embed_size],
                 param_attr=fluid.ParamAttr(
                     name='dis_emb',
                     learning_rate=5,
                     initializer=fluid.initializer.Xavier(
-                        fan_in=embed_size, fan_out=embed_size)),
+                        fan_in=self.embed_size, fan_out=self.embed_size)),
                 is_sparse=True)
             field_emb = fluid.layers.sequence_pool(
                 input=feat_emb, pool_type='sum')
@@ -83,14 +64,14 @@ class Model(ModelBase):
 
         # ctr
         active = 'relu'
-        ctr_fc1 = self.fc('ctr_fc1', concat_emb, 200, active)
-        ctr_fc2 = self.fc('ctr_fc2', ctr_fc1, 80, active)
-        ctr_out = self.fc('ctr_out', ctr_fc2, 2, 'softmax')
+        ctr_fc1 = self._fc('ctr_fc1', concat_emb, 200, active)
+        ctr_fc2 = self._fc('ctr_fc2', ctr_fc1, 80, active)
+        ctr_out = self._fc('ctr_out', ctr_fc2, 2, 'softmax')
 
         # cvr
-        cvr_fc1 = self.fc('cvr_fc1', concat_emb, 200, active)
-        cvr_fc2 = self.fc('cvr_fc2', cvr_fc1, 80, active)
-        cvr_out = self.fc('cvr_out', cvr_fc2, 2, 'softmax')
+        cvr_fc1 = self._fc('cvr_fc1', concat_emb, 200, active)
+        cvr_fc2 = self._fc('cvr_fc2', cvr_fc1, 80, active)
+        cvr_out = self._fc('cvr_out', cvr_fc2, 2, 'softmax')
 
         ctr_clk = inputs[-2]
         ctcvr_buy = inputs[-1]
@@ -127,15 +108,23 @@ class Model(ModelBase):
         self._metrics["AUC_ctcvr"] = auc_ctcvr
         self._metrics["BATCH_AUC_ctcvr"] = batch_auc_ctcvr
 
-    def train_net(self):
-        input_data = self.input_data()
-        self.net(input_data)
-
-    def infer_net(self):
-        self._infer_data_var = self.input_data()
-        self._infer_data_loader = fluid.io.DataLoader.from_generator(
-            feed_list=self._infer_data_var,
-            capacity=64,
-            use_double_buffer=False,
-            iterable=False)
-        self.net(self._infer_data_var, is_infer=True)
+    def _fc(self, tag, data, out_dim, active='prelu'):
+
+        init_stddev = 1.0
+        scales = 1.0 / np.sqrt(data.shape[1])
+
+        p_attr = fluid.param_attr.ParamAttr(
+            name='%s_weight' % tag,
+            initializer=fluid.initializer.NormalInitializer(
+                loc=0.0, scale=init_stddev * scales))
+
+        b_attr = fluid.ParamAttr(
+            name='%s_bias' % tag, initializer=fluid.initializer.Constant(0.1))
+
+        out = fluid.layers.fc(input=data,
+                              size=out_dim,
+                              act=active,
+                              param_attr=p_attr,
+                              bias_attr=b_attr,
+                              name=tag)
+        return out

models/multitask/mmoe/census_infer_reader.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.
-
-from __future__ import print_function
-
-from paddlerec.core.reader import Reader
-
-
-class EvaluateReader(Reader):
-    def init(self):
-
-        pass
-
-    def generate_sample(self, line):
-        """
-        Read the data line by line and process it as a dictionary
-        """
-
-        def reader():
-            """
-            This function needs to be implemented by the user, based on data format
-            """
-            l = line.strip().split(',')
-            l = list(map(float, l))
-            label_income = []
-            label_marital = []
-            data = l[2:]
-            if int(l[1]) == 0:
-                label_income = [1, 0]
-            elif int(l[1]) == 1:
-                label_income = [0, 1]
-            if int(l[0]) == 0:
-                label_marital = [1, 0]
-            elif int(l[0]) == 1:
-                label_marital = [0, 1]
-            feature_name = ["input", "label_income", "label_marital"]
-            yield zip(feature_name, [data] + [label_income] + [label_marital])
-
-        return reader

models/multitask/mmoe/config.yaml

@@ -12,43 +12,57 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-evaluate:
-  reader:
-    batch_size: 1
-    class: "{workspace}/census_infer_reader.py"
-    test_data_path: "{workspace}/data/train"
-
-train:
-  trainer:
-    # for cluster training
-    strategy: "async"
-
-  epochs: 3
-  workspace: "paddlerec.models.multitask.mmoe"
-  device: cpu
+workspace: "paddlerec.models.multitask.mmoe"
 
-  reader:
+dataset:
+- name: dataset_train
+  batch_size: 1
+  type: QueueDataset
+  data_path: "{workspace}/data/train"
+  data_converter: "{workspace}/census_reader.py"
+- name: dataset_infer
   batch_size: 1
-    class: "{workspace}/census_reader.py"
-    train_data_path: "{workspace}/data/train"
+  type: QueueDataset
+  data_path: "{workspace}/data/train"
+  data_converter: "{workspace}/census_reader.py"
 
-  model:
-    models: "{workspace}/model.py"
-    hyper_parameters:
+hyper_parameters:
   feature_size: 499
   expert_num: 8
   gate_num: 2
   expert_size: 16
   tower_size: 8
+  optimizer: 
+    class: adam
     learning_rate: 0.001
-      optimizer: adam
+    strategy: async
+
+#use infer_runner mode and modify 'phase' below if infer
+mode: train_runner
+#mode: infer_runner
+
+runner:
+- name: train_runner
+  class: single_train
+  device: cpu
+  epochs: 3
+  save_checkpoint_interval: 2
+  save_inference_interval: 4
+  save_checkpoint_path: "increment"
+  save_inference_path: "inference"
+  print_interval: 10
+- name: infer_runner
+  class: single_infer
+  init_model_path: "increment/0"
+  device: cpu
+  epochs: 3
 
-  save:
-    increment:
-      dirname: "increment"
-      epoch_interval: 2
-      save_last: True
-    inference:
-      dirname: "inference"
-      epoch_interval: 4
-      save_last: True
+phase:
+- name: train
+  model: "{workspace}/model.py"
+  dataset_name: dataset_train
+  thread_num: 1
+    #- name: infer
+    #  model: "{workspace}/model.py"
+    #  dataset_name: dataset_infer
+    #  thread_num: 1

models/multitask/mmoe/data/run.sh

+mkdir train_data
+mkdir test_data
+mkdir data
+train_path="data/census-income.data"
+test_path="data/census-income.test"
+train_data_path="train_data/"
+test_data_path="test_data/"
+pip install -r requirements.txt
+
+wget -P data/ https://archive.ics.uci.edu/ml/machine-learning-databases/census-income-mld/census.tar.gz
+tar -zxvf data/census.tar.gz -C data/
+
+python data_preparation.py --train_path ${train_path} \
+                           --test_path ${test_path} \
+                           --train_data_path ${train_data_path}\
+                           --test_data_path ${test_data_path}

models/multitask/mmoe/model.py

@@ -22,53 +22,51 @@ class Model(ModelBase):
     def __init__(self, config):
         ModelBase.__init__(self, config)
 
-    def MMOE(self, is_infer=False):
-        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")
+    def _init_hyper_parameters(self):
+        self.feature_size = envs.get_global_env(
+            "hyper_parameters.feature_size")
+        self.expert_num = envs.get_global_env("hyper_parameters.expert_num")
+        self.gate_num = envs.get_global_env("hyper_parameters.gate_num")
+        self.expert_size = envs.get_global_env("hyper_parameters.expert_size")
+        self.tower_size = envs.get_global_env("hyper_parameters.tower_size")
+
+    def input_data(self, is_infer=False, **kwargs):
+        inputs = fluid.data(
+            name="input", shape=[-1, self.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)
         if is_infer:
-            self._infer_data_var = [input_data, label_income, label_marital]
-            self._infer_data_loader = fluid.io.DataLoader.from_generator(
-                feed_list=self._infer_data_var,
-                capacity=64,
-                use_double_buffer=False,
-                iterable=False)
-
-        self._data_var.extend([input_data, label_income, label_marital])
+            return [inputs, label_income, label_marital]
+        else:
+            return [inputs, label_income, label_marital]
+
+    def net(self, inputs, is_infer=False):
+        input_data = inputs[0]
+        label_income = inputs[1]
+        label_marital = inputs[2]
+
         # 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):
+        for i in range(0, self.expert_num):
             expert_output = fluid.layers.fc(
                 input=input_data,
-                size=expert_size,
+                size=self.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])
+        expert_concat = fluid.layers.reshape(
+            expert_concat, [-1, self.expert_num, self.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):
+        for i in range(0, self.gate_num):
             cur_gate = fluid.layers.fc(
                 input=input_data,
-                size=expert_num,
+                size=self.expert_num,
                 act='softmax',
                 bias_attr=fluid.ParamAttr(learning_rate=1.0),
                 name='gate_' + str(i))
@@ -78,7 +76,7 @@ class Model(ModelBase):
             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,
+                                        size=self.tower_size,
                                         act='relu',
                                         name='task_layer_' + str(i))
             out = fluid.layers.fc(input=cur_tower,
@@ -127,8 +125,5 @@ class Model(ModelBase):
         self._metrics["AUC_marital"] = auc_marital
         self._metrics["BATCH_AUC_marital"] = batch_auc_2
 
-    def train_net(self):
-        self.MMOE()
-
     def infer_net(self):
-        self.MMOE(is_infer=True)
+        pass

models/multitask/readme.md

@@ -9,7 +9,9 @@
 * [整体介绍](#整体介绍)
     * [多任务模型列表](#多任务模型列表)
 * [使用教程](#使用教程)
-    * [训练&预测](#训练&预测)
+    * [数据处理](#数据处理)
+    * [训练](#训练)
+    * [预测](#预测)
 * [效果对比](#效果对比)
     * [模型效果列表](#模型效果列表)
 
@@ -40,14 +42,49 @@
 <img align="center" src="../../doc/imgs/mmoe.png">
 <p>
 
-## 使用教程
-### 训练&预测
+## 使用教程(快速开始)
 ```shell
 python -m paddlerec.run -m paddlerec.models.multitask.mmoe # mmoe
 python -m paddlerec.run -m paddlerec.models.multitask.share-bottom # share-bottom
 python -m paddlerec.run -m paddlerec.models.multitask.esmm # esmm
 ```
 
+## 使用教程（复现论文）
+### 注意
+为了方便使用者能够快速的跑通每一个模型，我们在每个模型下都提供了样例数据，并且调整了batch_size等超参以便在样例数据上更加友好的显示训练&测试日志。如果需要复现readme中的效果请按照如下表格调整batch_size等超参，并使用提供的脚本下载对应数据集以及数据预处理。
+
+|       模型       |       batch_size      |       thread_num      |       epoch_num      |
+| :------------------: | :--------------------: | :--------------------: | :--------------------: |
+|       Share-Bottom        |       32       |        1       |        400       |
+|       MMoE        |       32       |       1       |        400       |
+|       ESMM     |       64       |       2       |        100       |
+
+### 数据处理
+参考每个模型目录数据下载&预处理脚本
+
+```
+sh run.sh
+```
+
+### 训练
+```
+cd modles/multitask/mmoe # 进入选定好的排序模型的目录 以MMoE为例
+python -m paddlerec.run -m ./config.yaml # 自定义修改超参后，指定配置文件，使用自定义配置
+```
+
+### 预测
+```
+# 修改对应模型的config.yaml, workspace配置为当前目录的绝对路径
+# 修改对应模型的config.yaml，mode配置infer_runner
+# 示例: mode: train_runner -> mode: infer_runner
+# infer_runner中 class配置为 class: single_infer
+# 修改phase阶段为infer的配置，参照config注释
+
+# 修改完config.yaml后 执行:
+python -m paddlerec.run -m ./config.yaml # 以MMoE为例
+```
+
+
 ## 效果对比
 ### 模型效果列表
 

models/multitask/share-bottom/census_infer_reader.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.
-
-from __future__ import print_function
-
-from paddlerec.core.reader import Reader
-
-
-class EvaluateReader(Reader):
-    def init(self):
-        pass
-
-    def generate_sample(self, line):
-        """
-        Read the data line by line and process it as a dictionary
-        """
-
-        def reader():
-            """
-            This function needs to be implemented by the user, based on data format
-            """
-            l = line.strip().split(',')
-            l = list(map(float, l))
-            label_income = []
-            label_marital = []
-            data = l[2:]
-            if int(l[1]) == 0:
-                label_income = [1, 0]
-            elif int(l[1]) == 1:
-                label_income = [0, 1]
-            if int(l[0]) == 0:
-                label_marital = [1, 0]
-            elif int(l[0]) == 1:
-                label_marital = [0, 1]
-            feature_name = ["input", "label_income", "label_marital"]
-            yield zip(feature_name, [data] + [label_income] + [label_marital])
-
-        return reader

models/multitask/share-bottom/config.yaml

@@ -12,42 +12,56 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-evaluate:
-  reader:
-    batch_size: 1
-    class: "{workspace}/census_infer_reader.py"
-    test_data_path: "{workspace}/data/train"
-
-train:
-  trainer:
-    # for cluster training
-    strategy: "async"
+workspace: "paddlerec.models.multitask.share-bottom"
 
-  epochs: 3
-  workspace: "paddlerec.models.multitask.share-bottom"
-  device: cpu
-
-  reader:
-    batch_size: 2
-    class: "{workspace}/census_reader.py"
-    train_data_path: "{workspace}/data/train"
+dataset:
+- name: dataset_train
+  batch_size: 1
+  type: QueueDataset
+  data_path: "{workspace}/data/train"
+  data_converter: "{workspace}/census_reader.py"
+- name: dataset_infer
+  batch_size: 1
+  type: QueueDataset
+  data_path: "{workspace}/data/train"
+  data_converter: "{workspace}/census_reader.py"
 
-  model:
-    models: "{workspace}/model.py"
-    hyper_parameters:
+hyper_parameters:
   feature_size: 499
   bottom_size: 117
   tower_nums: 2
   tower_size: 8
+  optimizer: 
+    class: adam
     learning_rate: 0.001
-      optimizer: adam
+    strategy: async
+
+#use infer_runner mode and modify 'phase' below if infer
+mode: train_runner
+#mode: infer_runner
+
+runner:
+- name: train_runner
+  class: single_train
+  device: cpu
+  epochs: 3
+  save_checkpoint_interval: 2
+  save_inference_interval: 4
+  save_checkpoint_path: "increment"
+  save_inference_path: "inference"
+  print_interval: 5
+- name: infer_runner
+  class: single_infer
+  init_model_path: "increment/0"
+  device: cpu
+  epochs: 3
 
-  save:
-    increment:
-      dirname: "increment"
-      epoch_interval: 2
-      save_last: True
-    inference:
-      dirname: "inference"
-      epoch_interval: 4
-      save_last: True
+phase:
+- name: train
+  model: "{workspace}/model.py"
+  dataset_name: dataset_train
+  thread_num: 1
+    #- name: infer
+    #  model: "{workspace}/model.py"
+    #  dataset_name: dataset_infer
+    #  thread_num: 1

models/multitask/share-bottom/model.py

@@ -22,46 +22,42 @@ class Model(ModelBase):
     def __init__(self, config):
         ModelBase.__init__(self, config)
 
-    def model(self, is_infer=False):
-
-        feature_size = envs.get_global_env("hyper_parameters.feature_size",
-                                           None, self._namespace)
-        bottom_size = envs.get_global_env("hyper_parameters.bottom_size", None,
-                                          self._namespace)
-        tower_size = envs.get_global_env("hyper_parameters.tower_size", None,
-                                         self._namespace)
-        tower_nums = envs.get_global_env("hyper_parameters.tower_nums", None,
-                                         self._namespace)
-
-        input_data = fluid.data(
-            name="input", shape=[-1, feature_size], dtype="float32")
+    def _init_hyper_parameters(self):
+        self.feature_size = envs.get_global_env(
+            "hyper_parameters.feature_size")
+        self.bottom_size = envs.get_global_env("hyper_parameters.bottom_size")
+        self.tower_size = envs.get_global_env("hyper_parameters.tower_size")
+        self.tower_nums = envs.get_global_env("hyper_parameters.tower_nums")
+
+    def input_data(self, is_infer=False, **kwargs):
+        inputs = fluid.data(
+            name="input", shape=[-1, self.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)
-
         if is_infer:
-            self._infer_data_var = [input_data, label_income, label_marital]
-            self._infer_data_loader = fluid.io.DataLoader.from_generator(
-                feed_list=self._infer_data_var,
-                capacity=64,
-                use_double_buffer=False,
-                iterable=False)
+            return [inputs, label_income, label_marital]
+        else:
+            return [inputs, label_income, label_marital]
 
-        self._data_var.extend([input_data, label_income, label_marital])
+    def net(self, inputs, is_infer=False):
+        input_data = inputs[0]
+        label_income = inputs[1]
+        label_marital = inputs[2]
 
         bottom_output = fluid.layers.fc(
             input=input_data,
-            size=bottom_size,
+            size=self.bottom_size,
             act='relu',
             bias_attr=fluid.ParamAttr(learning_rate=1.0),
             name='bottom_output')
 
         # Build tower layer from bottom layer
         output_layers = []
-        for index in range(tower_nums):
+        for index in range(self.tower_nums):
             tower_layer = fluid.layers.fc(input=bottom_output,
-                                          size=tower_size,
+                                          size=self.tower_size,
                                           act='relu',
                                           name='task_layer_' + str(index))
             output_layer = fluid.layers.fc(input=tower_layer,
@@ -107,9 +103,3 @@ class Model(ModelBase):
         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.model()
-
-    def infer_net(self):
-        self.model(is_infer=True)

models/recall/gru4rec/config.yaml

@@ -12,31 +12,21 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-evaluate:
-  reader:
-    batch_size: 1
-    class: "{workspace}/rsc15_infer_reader.py"
-    test_data_path: "{workspace}/data/train"
-  is_return_numpy: False
+workspace: "paddlerec.models.recall.gru4rec"
 
-
-train:
-  trainer:
-    # for cluster training
-    strategy: "async"
-
-  epochs: 3
-  workspace: "paddlerec.models.recall.gru4rec"
-  device: cpu
-
-  reader:
+dataset:
+- name: dataset_train
+  batch_size: 5
+  type: QueueDataset
+  data_path: "{workspace}/data/train"
+  data_converter: "{workspace}/rsc15_reader.py"
+- name: dataset_infer
   batch_size: 5
-    class: "{workspace}/rsc15_reader.py"
-    train_data_path: "{workspace}/data/train"
+  type: QueueDataset
+  data_path: "{workspace}/data/test"
+  data_converter: "{workspace}/rsc15_reader.py"
 
-  model:
-    models: "{workspace}/model.py"
-    hyper_parameters:
+hyper_parameters:
   vocab_size: 1000
   hid_size: 100
   emb_lr_x: 10.0
@@ -44,15 +34,37 @@ train:
   fc_lr_x: 1.0
   init_low_bound: -0.04
   init_high_bound: 0.04
+  optimizer: 
+    class: adagrad
     learning_rate: 0.01
-      optimizer: adagrad
+    strategy: async
+
+#use infer_runner mode and modify 'phase' below if infer
+mode: train_runner
+#mode: infer_runner
+
+runner:
+- name: train_runner
+  class: single_train
+  device: cpu
+  epochs: 3
+  save_checkpoint_interval: 2
+  save_inference_interval: 4
+  save_checkpoint_path: "increment"
+  save_inference_path: "inference"
+  print_interval: 10
+- name: infer_runner
+  class: single_infer
+  init_model_path: "increment/0"
+  device: cpu
+  epochs: 3
 
-  save:
-    increment:
-      dirname: "increment"
-      epoch_interval: 2
-      save_last: True
-    inference:
-      dirname: "inference"
-      epoch_interval: 4
-      save_last: True
+phase:
+- name: train
+  model: "{workspace}/model.py"
+  dataset_name: dataset_train
+  thread_num: 1
+    #- name: infer
+    #  model: "{workspace}/model.py"
+    #  dataset_name: dataset_infer
+    #  thread_num: 1

models/recall/gru4rec/model.py

@@ -22,84 +22,72 @@ class Model(ModelBase):
     def __init__(self, config):
         ModelBase.__init__(self, config)
 
-    def all_vocab_network(self, is_infer=False):
-        """ network definition """
-        recall_k = envs.get_global_env("hyper_parameters.recall_k", None,
-                                       self._namespace)
-        vocab_size = envs.get_global_env("hyper_parameters.vocab_size", None,
-                                         self._namespace)
-        hid_size = envs.get_global_env("hyper_parameters.hid_size", None,
-                                       self._namespace)
-        init_low_bound = envs.get_global_env("hyper_parameters.init_low_bound",
-                                             None, self._namespace)
-        init_high_bound = envs.get_global_env(
-            "hyper_parameters.init_high_bound", None, self._namespace)
-        emb_lr_x = envs.get_global_env("hyper_parameters.emb_lr_x", None,
-                                       self._namespace)
-        gru_lr_x = envs.get_global_env("hyper_parameters.gru_lr_x", None,
-                                       self._namespace)
-        fc_lr_x = envs.get_global_env("hyper_parameters.fc_lr_x", None,
-                                      self._namespace)
+    def _init_hyper_parameters(self):
+        self.recall_k = envs.get_global_env("hyper_parameters.recall_k")
+        self.vocab_size = envs.get_global_env("hyper_parameters.vocab_size")
+        self.hid_size = envs.get_global_env("hyper_parameters.hid_size")
+        self.init_low_bound = envs.get_global_env(
+            "hyper_parameters.init_low_bound")
+        self.init_high_bound = envs.get_global_env(
+            "hyper_parameters.init_high_bound")
+        self.emb_lr_x = envs.get_global_env("hyper_parameters.emb_lr_x")
+        self.gru_lr_x = envs.get_global_env("hyper_parameters.gru_lr_x")
+        self.fc_lr_x = envs.get_global_env("hyper_parameters.fc_lr_x")
+
+    def input_data(self, is_infer=False, **kwargs):
+
         # Input data
         src_wordseq = fluid.data(
             name="src_wordseq", shape=[None, 1], dtype="int64", lod_level=1)
         dst_wordseq = fluid.data(
             name="dst_wordseq", shape=[None, 1], dtype="int64", lod_level=1)
 
-        if is_infer:
-            self._infer_data_var = [src_wordseq, dst_wordseq]
-            self._infer_data_loader = fluid.io.DataLoader.from_generator(
-                feed_list=self._infer_data_var,
-                capacity=64,
-                use_double_buffer=False,
-                iterable=False)
+        return [src_wordseq, dst_wordseq]
+
+    def net(self, inputs, is_infer=False):
+        src_wordseq = inputs[0]
+        dst_wordseq = inputs[1]
 
         emb = fluid.embedding(
             input=src_wordseq,
-            size=[vocab_size, hid_size],
+            size=[self.vocab_size, self.hid_size],
             param_attr=fluid.ParamAttr(
                 name="emb",
                 initializer=fluid.initializer.Uniform(
-                    low=init_low_bound, high=init_high_bound),
-                learning_rate=emb_lr_x),
+                    low=self.init_low_bound, high=self.init_high_bound),
+                learning_rate=self.emb_lr_x),
             is_sparse=True)
         fc0 = fluid.layers.fc(input=emb,
-                              size=hid_size * 3,
+                              size=self.hid_size * 3,
                               param_attr=fluid.ParamAttr(
                                   initializer=fluid.initializer.Uniform(
-                                      low=init_low_bound,
-                                      high=init_high_bound),
-                                  learning_rate=gru_lr_x))
+                                      low=self.init_low_bound,
+                                      high=self.init_high_bound),
+                                  learning_rate=self.gru_lr_x))
         gru_h0 = fluid.layers.dynamic_gru(
             input=fc0,
-            size=hid_size,
+            size=self.hid_size,
             param_attr=fluid.ParamAttr(
                 initializer=fluid.initializer.Uniform(
-                    low=init_low_bound, high=init_high_bound),
-                learning_rate=gru_lr_x))
+                    low=self.init_low_bound, high=self.init_high_bound),
+                learning_rate=self.gru_lr_x))
 
         fc = fluid.layers.fc(input=gru_h0,
-                             size=vocab_size,
+                             size=self.vocab_size,
                              act='softmax',
                              param_attr=fluid.ParamAttr(
                                  initializer=fluid.initializer.Uniform(
-                                     low=init_low_bound, high=init_high_bound),
-                                 learning_rate=fc_lr_x))
+                                     low=self.init_low_bound,
+                                     high=self.init_high_bound),
+                                 learning_rate=self.fc_lr_x))
         cost = fluid.layers.cross_entropy(input=fc, label=dst_wordseq)
-        acc = fluid.layers.accuracy(input=fc, label=dst_wordseq, k=recall_k)
+        acc = fluid.layers.accuracy(
+            input=fc, label=dst_wordseq, k=self.recall_k)
         if is_infer:
             self._infer_results['recall20'] = acc
             return
         avg_cost = fluid.layers.mean(x=cost)
 
-        self._data_var.append(src_wordseq)
-        self._data_var.append(dst_wordseq)
         self._cost = avg_cost
         self._metrics["cost"] = avg_cost
         self._metrics["acc"] = acc
-
-    def train_net(self):
-        self.all_vocab_network()
-
-    def infer_net(self):
-        self.all_vocab_network(is_infer=True)

models/recall/gru4rec/rsc15_infer_reader.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.
-
-from __future__ import print_function
-
-from paddlerec.core.reader import Reader
-
-
-class EvaluateReader(Reader):
-    def init(self):
-        pass
-
-    def generate_sample(self, line):
-        """
-        Read the data line by line and process it as a dictionary
-        """
-
-        def reader():
-            """
-            This function needs to be implemented by the user, based on data format
-            """
-            l = line.strip().split()
-            l = [w for w in l]
-            src_seq = l[:len(l) - 1]
-            src_seq = [int(e) for e in src_seq]
-            trg_seq = l[1:]
-            trg_seq = [int(e) for e in trg_seq]
-            feature_name = ["src_wordseq", "dst_wordseq"]
-            yield zip(feature_name, [src_seq] + [trg_seq])
-
-        return reader

models/recall/ncf/config.yaml

@@ -12,42 +12,56 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-evaluate:
-  reader:
-    batch_size: 1
-    class: "{workspace}/movielens_infer_reader.py"
-    test_data_path: "{workspace}/data/test"
+workspace: "paddlerec.models.recall.ncf"
 
-train:
-  trainer:
-    # for cluster training
-    strategy: "async"
+dataset:
+- name: dataset_train
+  batch_size: 5
+  type: QueueDataset
+  data_path: "{workspace}/data/train"
+  data_converter: "{workspace}/movielens_reader.py"
+- name: dataset_infer
+  batch_size: 5
+  type: QueueDataset
+  data_path: "{workspace}/data/test"
+  data_converter: "{workspace}/movielens_infer_reader.py"
 
-  epochs: 3
-  workspace: "paddlerec.models.recall.ncf"
-  device: cpu
-
-  reader:
-    batch_size: 2
-    class: "{workspace}/movielens_reader.py"
-    train_data_path: "{workspace}/data/train"
-
-  model:
-    models: "{workspace}/model.py"
-    hyper_parameters:
+hyper_parameters:
   num_users: 6040
   num_items: 3706
   latent_dim: 8
-      layers: [64, 32, 16, 8]
+  fc_layers: [64, 32, 16, 8]
+  optimizer: 
+    class: adam
     learning_rate: 0.001
-      optimizer: adam
+    strategy: async
+
+#use infer_runner mode and modify 'phase' below if infer
+mode: train_runner
+#mode: infer_runner
+
+runner:
+- name: train_runner
+  class: single_train
+  device: cpu
+  epochs: 3
+  save_checkpoint_interval: 2
+  save_inference_interval: 4
+  save_checkpoint_path: "increment"
+  save_inference_path: "inference"
+  print_interval: 10
+- name: infer_runner
+  class: single_infer
+  init_model_path: "increment/0"
+  device: cpu
+  epochs: 3
 
-  save:
-    increment:
-      dirname: "increment"
-      epoch_interval: 2
-      save_last: True
-    inference:
-      dirname: "inference"
-      epoch_interval: 4
-      save_last: True
+phase:
+- name: train
+  model: "{workspace}/model.py"
+  dataset_name: dataset_train
+  thread_num: 1
+    #- name: infer
+    #  model: "{workspace}/model.py"
+    #  dataset_name: dataset_infer
+    #  thread_num: 1

models/recall/ncf/model.py

@@ -24,7 +24,13 @@ class Model(ModelBase):
     def __init__(self, config):
         ModelBase.__init__(self, config)
 
-    def input_data(self, is_infer=False):
+    def _init_hyper_parameters(self):
+        self.num_users = envs.get_global_env("hyper_parameters.num_users")
+        self.num_items = envs.get_global_env("hyper_parameters.num_items")
+        self.latent_dim = envs.get_global_env("hyper_parameters.latent_dim")
+        self.layers = envs.get_global_env("hyper_parameters.fc_layers")
+
+    def input_data(self, is_infer=False, **kwargs):
         user_input = fluid.data(
             name="user_input", shape=[-1, 1], dtype="int64", lod_level=0)
         item_input = fluid.data(
@@ -35,45 +41,35 @@ class Model(ModelBase):
             inputs = [user_input] + [item_input]
         else:
             inputs = [user_input] + [item_input] + [label]
-            self._data_var = inputs
 
         return inputs
 
     def net(self, inputs, is_infer=False):
 
-        num_users = envs.get_global_env("hyper_parameters.num_users", None,
-                                        self._namespace)
-        num_items = envs.get_global_env("hyper_parameters.num_items", None,
-                                        self._namespace)
-        latent_dim = envs.get_global_env("hyper_parameters.latent_dim", None,
-                                         self._namespace)
-        layers = envs.get_global_env("hyper_parameters.layers", None,
-                                     self._namespace)
-
-        num_layer = len(layers)  #Number of layers in the MLP
+        num_layer = len(self.layers)  #Number of layers in the MLP
 
         MF_Embedding_User = fluid.embedding(
             input=inputs[0],
-            size=[num_users, latent_dim],
+            size=[self.num_users, self.latent_dim],
             param_attr=fluid.initializer.Normal(
                 loc=0.0, scale=0.01),
             is_sparse=True)
         MF_Embedding_Item = fluid.embedding(
             input=inputs[1],
-            size=[num_items, latent_dim],
+            size=[self.num_items, self.latent_dim],
             param_attr=fluid.initializer.Normal(
                 loc=0.0, scale=0.01),
             is_sparse=True)
 
         MLP_Embedding_User = fluid.embedding(
             input=inputs[0],
-            size=[num_users, int(layers[0] / 2)],
+            size=[self.num_users, int(self.layers[0] / 2)],
             param_attr=fluid.initializer.Normal(
                 loc=0.0, scale=0.01),
             is_sparse=True)
         MLP_Embedding_Item = fluid.embedding(
             input=inputs[1],
-            size=[num_items, int(layers[0] / 2)],
+            size=[self.num_items, int(self.layers[0] / 2)],
             param_attr=fluid.initializer.Normal(
                 loc=0.0, scale=0.01),
             is_sparse=True)
@@ -94,7 +90,7 @@ class Model(ModelBase):
         for i in range(1, num_layer):
             mlp_vector = fluid.layers.fc(
                 input=mlp_vector,
-                size=layers[i],
+                size=self.layers[i],
                 act='relu',
                 param_attr=fluid.ParamAttr(
                     initializer=fluid.initializer.TruncatedNormal(
@@ -126,16 +122,3 @@ class Model(ModelBase):
 
         self._cost = avg_cost
         self._metrics["cost"] = avg_cost
-
-    def train_net(self):
-        input_data = self.input_data()
-        self.net(input_data)
-
-    def infer_net(self):
-        self._infer_data_var = self.input_data(is_infer=True)
-        self._infer_data_loader = fluid.io.DataLoader.from_generator(
-            feed_list=self._infer_data_var,
-            capacity=64,
-            use_double_buffer=False,
-            iterable=False)
-        self.net(self._infer_data_var, is_infer=True)

models/recall/ncf/movielens_infer_reader.py

@@ -19,7 +19,7 @@ from collections import defaultdict
 import numpy as np
 
 
-class EvaluateReader(Reader):
+class TrainReader(Reader):
     def init(self):
         pass
 

models/recall/ssr/config.yaml

@@ -12,43 +12,55 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+workspace: "paddlerec.models.recall.ssr"
 
-evaluate:
-  reader:
-    batch_size: 1
-    class: "{workspace}/ssr_infer_reader.py"
-    test_data_path: "{workspace}/data/train"
-  is_return_numpy: True
-
-train:
-  trainer:
-    # for cluster training
-    strategy: "async"
-
-  epochs: 3
-  workspace: "paddlerec.models.recall.ssr"
-  device: cpu
-
-  reader:
+dataset:
+- name: dataset_train
+  batch_size: 5
+  type: QueueDataset
+  data_path: "{workspace}/data/train"
+  data_converter: "{workspace}/ssr_reader.py"
+- name: dataset_infer
   batch_size: 5
-    class: "{workspace}/ssr_reader.py"
-    train_data_path: "{workspace}/data/train"
+  type: QueueDataset
+  data_path: "{workspace}/data/test"
+  data_converter: "{workspace}/ssr_infer_reader.py"
 
-  model:
-    models: "{workspace}/model.py"
-    hyper_parameters:
+hyper_parameters:
   vocab_size: 1000
   emb_dim: 128
   hidden_size: 100
+  optimizer: 
+    class: adagrad
     learning_rate: 0.01
-      optimizer: adagrad
+    strategy: async
+
+#use infer_runner mode and modify 'phase' below if infer
+mode: train_runner
+#mode: infer_runner
+
+runner:
+- name: train_runner
+  class: single_train
+  device: cpu
+  epochs: 3
+  save_checkpoint_interval: 2
+  save_inference_interval: 4
+  save_checkpoint_path: "increment"
+  save_inference_path: "inference"
+  print_interval: 10
+- name: infer_runner
+  class: single_infer
+  init_model_path: "increment/0"
+  device: cpu
+  epochs: 3
 
-  save:
-    increment:
-      dirname: "increment"
-      epoch_interval: 2
-      save_last: True
-    inference:
-      dirname: "inference"
-      epoch_interval: 4
-      save_last: True
+phase:
+- name: train
+  model: "{workspace}/model.py"
+  dataset_name: dataset_train
+  thread_num: 1
+    #- name: infer
+    #  model: "{workspace}/model.py"
+    #  dataset_name: dataset_infer
+    #  thread_num: 1

models/recall/ssr/model.py

@@ -20,84 +20,44 @@ from paddlerec.core.utils import envs
 from paddlerec.core.model import Model as ModelBase
 
 
-class BowEncoder(object):
-    """ bow-encoder """
-
-    def __init__(self):
-        self.param_name = ""
-
-    def forward(self, emb):
-        return fluid.layers.sequence_pool(input=emb, pool_type='sum')
-
-
-class GrnnEncoder(object):
-    """ grnn-encoder """
-
-    def __init__(self, param_name="grnn", hidden_size=128):
-        self.param_name = param_name
-        self.hidden_size = hidden_size
-
-    def forward(self, emb):
-        fc0 = fluid.layers.fc(input=emb,
-                              size=self.hidden_size * 3,
-                              param_attr=self.param_name + "_fc.w",
-                              bias_attr=False)
-
-        gru_h = fluid.layers.dynamic_gru(
-            input=fc0,
-            size=self.hidden_size,
-            is_reverse=False,
-            param_attr=self.param_name + ".param",
-            bias_attr=self.param_name + ".bias")
-        return fluid.layers.sequence_pool(input=gru_h, pool_type='max')
-
-
-class PairwiseHingeLoss(object):
-    def __init__(self, margin=0.8):
-        self.margin = margin
-
-    def forward(self, pos, neg):
-        loss_part1 = fluid.layers.elementwise_sub(
-            tensor.fill_constant_batch_size_like(
-                input=pos, shape=[-1, 1], value=self.margin, dtype='float32'),
-            pos)
-        loss_part2 = fluid.layers.elementwise_add(loss_part1, neg)
-        loss_part3 = fluid.layers.elementwise_max(
-            tensor.fill_constant_batch_size_like(
-                input=loss_part2, shape=[-1, 1], value=0.0, dtype='float32'),
-            loss_part2)
-        return loss_part3
-
-
 class Model(ModelBase):
     def __init__(self, config):
         ModelBase.__init__(self, config)
 
-    def get_correct(self, x, y):
-        less = tensor.cast(cf.less_than(x, y), dtype='float32')
-        correct = fluid.layers.reduce_sum(less)
-        return correct
-
-    def train(self):
-        vocab_size = envs.get_global_env("hyper_parameters.vocab_size", None,
-                                         self._namespace)
-        emb_dim = envs.get_global_env("hyper_parameters.emb_dim", None,
-                                      self._namespace)
-        hidden_size = envs.get_global_env("hyper_parameters.hidden_size", None,
-                                          self._namespace)
-        emb_shape = [vocab_size, emb_dim]
-
-        self.user_encoder = GrnnEncoder()
-        self.item_encoder = BowEncoder()
-        self.pairwise_hinge_loss = PairwiseHingeLoss()
+    def _init_hyper_parameters(self):
+        self.vocab_size = envs.get_global_env("hyper_parameters.vocab_size")
+        self.emb_dim = envs.get_global_env("hyper_parameters.emb_dim")
+        self.hidden_size = envs.get_global_env("hyper_parameters.hidden_size")
 
+    def input_data(self, is_infer=False, **kwargs):
+        if is_infer:
+            user_data = fluid.data(
+                name="user", shape=[None, 1], dtype="int64", lod_level=1)
+            all_item_data = fluid.data(
+                name="all_item", shape=[None, self.vocab_size], dtype="int64")
+            pos_label = fluid.data(
+                name="pos_label", shape=[None, 1], dtype="int64")
+            return [user_data, all_item_data, pos_label]
+        else:
             user_data = fluid.data(
                 name="user", shape=[None, 1], dtype="int64", lod_level=1)
             pos_item_data = fluid.data(
                 name="p_item", shape=[None, 1], dtype="int64", lod_level=1)
             neg_item_data = fluid.data(
                 name="n_item", shape=[None, 1], dtype="int64", lod_level=1)
-        self._data_var.extend([user_data, pos_item_data, neg_item_data])
+            return [user_data, pos_item_data, neg_item_data]
+
+    def net(self, inputs, is_infer=False):
+        if is_infer:
+            self._infer_net(inputs)
+            return
+        user_data = inputs[0]
+        pos_item_data = inputs[1]
+        neg_item_data = inputs[2]
+        emb_shape = [self.vocab_size, self.emb_dim]
+        self.user_encoder = GrnnEncoder()
+        self.item_encoder = BowEncoder()
+        self.pairwise_hinge_loss = PairwiseHingeLoss()
 
         user_emb = fluid.embedding(
             input=user_data, size=emb_shape, param_attr="emb.item")
@@ -109,79 +69,115 @@ class Model(ModelBase):
         pos_item_enc = self.item_encoder.forward(pos_item_emb)
         neg_item_enc = self.item_encoder.forward(neg_item_emb)
         user_hid = fluid.layers.fc(input=user_enc,
-                                   size=hidden_size,
+                                   size=self.hidden_size,
                                    param_attr='user.w',
                                    bias_attr="user.b")
         pos_item_hid = fluid.layers.fc(input=pos_item_enc,
-                                       size=hidden_size,
+                                       size=self.hidden_size,
                                        param_attr='item.w',
                                        bias_attr="item.b")
         neg_item_hid = fluid.layers.fc(input=neg_item_enc,
-                                       size=hidden_size,
+                                       size=self.hidden_size,
                                        param_attr='item.w',
                                        bias_attr="item.b")
         cos_pos = fluid.layers.cos_sim(user_hid, pos_item_hid)
         cos_neg = fluid.layers.cos_sim(user_hid, neg_item_hid)
         hinge_loss = self.pairwise_hinge_loss.forward(cos_pos, cos_neg)
         avg_cost = fluid.layers.mean(hinge_loss)
-        correct = self.get_correct(cos_neg, cos_pos)
+        correct = self._get_correct(cos_neg, cos_pos)
 
         self._cost = avg_cost
         self._metrics["correct"] = correct
         self._metrics["hinge_loss"] = hinge_loss
 
-    def train_net(self):
-        self.train()
-
-    def infer(self):
-        vocab_size = envs.get_global_env("hyper_parameters.vocab_size", None,
-                                         self._namespace)
-        emb_dim = envs.get_global_env("hyper_parameters.emb_dim", None,
-                                      self._namespace)
-        hidden_size = envs.get_global_env("hyper_parameters.hidden_size", None,
-                                          self._namespace)
-
-        user_data = fluid.data(
-            name="user", shape=[None, 1], dtype="int64", lod_level=1)
-        all_item_data = fluid.data(
-            name="all_item", shape=[None, vocab_size], dtype="int64")
-        pos_label = fluid.data(
-            name="pos_label", shape=[None, 1], dtype="int64")
-        self._infer_data_var = [user_data, all_item_data, pos_label]
-        self._infer_data_loader = fluid.io.DataLoader.from_generator(
-            feed_list=self._infer_data_var,
-            capacity=64,
-            use_double_buffer=False,
-            iterable=False)
+    def _infer_net(self, inputs):
+        user_data = inputs[0]
+        all_item_data = inputs[1]
+        pos_label = inputs[2]
 
         user_emb = fluid.embedding(
-            input=user_data, size=[vocab_size, emb_dim], param_attr="emb.item")
+            input=user_data,
+            size=[self.vocab_size, self.emb_dim],
+            param_attr="emb.item")
         all_item_emb = fluid.embedding(
             input=all_item_data,
-            size=[vocab_size, emb_dim],
+            size=[self.vocab_size, self.emb_dim],
             param_attr="emb.item")
         all_item_emb_re = fluid.layers.reshape(
-            x=all_item_emb, shape=[-1, emb_dim])
+            x=all_item_emb, shape=[-1, self.emb_dim])
 
         user_encoder = GrnnEncoder()
         user_enc = user_encoder.forward(user_emb)
         user_hid = fluid.layers.fc(input=user_enc,
-                                   size=hidden_size,
+                                   size=self.hidden_size,
                                    param_attr='user.w',
                                    bias_attr="user.b")
         user_exp = fluid.layers.expand(
-            x=user_hid, expand_times=[1, vocab_size])
-        user_re = fluid.layers.reshape(x=user_exp, shape=[-1, hidden_size])
+            x=user_hid, expand_times=[1, self.vocab_size])
+        user_re = fluid.layers.reshape(
+            x=user_exp, shape=[-1, self.hidden_size])
 
         all_item_hid = fluid.layers.fc(input=all_item_emb_re,
-                                       size=hidden_size,
+                                       size=self.hidden_size,
                                        param_attr='item.w',
                                        bias_attr="item.b")
         cos_item = fluid.layers.cos_sim(X=all_item_hid, Y=user_re)
-        all_pre_ = fluid.layers.reshape(x=cos_item, shape=[-1, vocab_size])
+        all_pre_ = fluid.layers.reshape(
+            x=cos_item, shape=[-1, self.vocab_size])
         acc = fluid.layers.accuracy(input=all_pre_, label=pos_label, k=20)
 
         self._infer_results['recall20'] = acc
 
-    def infer_net(self):
-        self.infer()
+    def _get_correct(self, x, y):
+        less = tensor.cast(cf.less_than(x, y), dtype='float32')
+        correct = fluid.layers.reduce_sum(less)
+        return correct
+
+
+class BowEncoder(object):
+    """ bow-encoder """
+
+    def __init__(self):
+        self.param_name = ""
+
+    def forward(self, emb):
+        return fluid.layers.sequence_pool(input=emb, pool_type='sum')
+
+
+class GrnnEncoder(object):
+    """ grnn-encoder """
+
+    def __init__(self, param_name="grnn", hidden_size=128):
+        self.param_name = param_name
+        self.hidden_size = hidden_size
+
+    def forward(self, emb):
+        fc0 = fluid.layers.fc(input=emb,
+                              size=self.hidden_size * 3,
+                              param_attr=self.param_name + "_fc.w",
+                              bias_attr=False)
+
+        gru_h = fluid.layers.dynamic_gru(
+            input=fc0,
+            size=self.hidden_size,
+            is_reverse=False,
+            param_attr=self.param_name + ".param",
+            bias_attr=self.param_name + ".bias")
+        return fluid.layers.sequence_pool(input=gru_h, pool_type='max')
+
+
+class PairwiseHingeLoss(object):
+    def __init__(self, margin=0.8):
+        self.margin = margin
+
+    def forward(self, pos, neg):
+        loss_part1 = fluid.layers.elementwise_sub(
+            tensor.fill_constant_batch_size_like(
+                input=pos, shape=[-1, 1], value=self.margin, dtype='float32'),
+            pos)
+        loss_part2 = fluid.layers.elementwise_add(loss_part1, neg)
+        loss_part3 = fluid.layers.elementwise_max(
+            tensor.fill_constant_batch_size_like(
+                input=loss_part2, shape=[-1, 1], value=0.0, dtype='float32'),
+            loss_part2)
+        return loss_part3

models/recall/youtube_dnn/config.yaml

@@ -13,37 +13,42 @@
 # limitations under the License.
 
 
-train:
-  trainer:
-    # for cluster training
-    strategy: "async"
+workspace: "paddlerec.models.recall.youtube_dnn"
 
-  epochs: 3
-  workspace: "paddlerec.models.recall.youtube_dnn"
-  device: cpu
+dataset:
+- name: dataset_train
+  batch_size: 5
+  type: DataLoader
+  #type: QueueDataset
+  data_path: "{workspace}/data/train"
+  data_converter: "{workspace}/random_reader.py"
 
-  reader:
-    batch_size: 2
-    class: "{workspace}/random_reader.py"
-    train_data_path: "{workspace}/data/train"
-
-  model:
-    models: "{workspace}/model.py"
-    hyper_parameters:
+hyper_parameters:
   watch_vec_size: 64
   search_vec_size: 64
   other_feat_size: 64
   output_size: 100
   layers: [128, 64, 32]
-      learning_rate: 0.01
-      optimizer: sgd
+  optimizer: 
+    class: adam
+    learning_rate: 0.001
+    strategy: async
+
+mode: train_runner
+
+runner:
+- name: train_runner
+  class: single_train
+  device: cpu
+  epochs: 3
+  save_checkpoint_interval: 2
+  save_inference_interval: 4
+  save_checkpoint_path: "increment"
+  save_inference_path: "inference"
+  print_interval: 10
 
-  save:
-    increment:
-      dirname: "increment"
-      epoch_interval: 2
-      save_last: True
-    inference:
-      dirname: "inference"
-      epoch_interval: 4
-      save_last: True
+phase:
+- name: train
+  model: "{workspace}/model.py"
+  dataset_name: dataset_train
+  thread_num: 1

models/recall/youtube_dnn/model.py

@@ -13,39 +13,64 @@
 # limitations under the License.
 
 import math
+import numpy as np
 import paddle.fluid as fluid
 
 from paddlerec.core.utils import envs
 from paddlerec.core.model import Model as ModelBase
-import numpy as np
 
 
 class Model(ModelBase):
     def __init__(self, config):
         ModelBase.__init__(self, config)
 
-    def input_data(self, is_infer=False):
+    def _init_hyper_parameters(self):
+        self.watch_vec_size = envs.get_global_env(
+            "hyper_parameters.watch_vec_size")
+        self.search_vec_size = envs.get_global_env(
+            "hyper_parameters.search_vec_size")
+        self.other_feat_size = envs.get_global_env(
+            "hyper_parameters.other_feat_size")
+        self.output_size = envs.get_global_env("hyper_parameters.output_size")
+        self.layers = envs.get_global_env("hyper_parameters.layers")
 
-        watch_vec_size = envs.get_global_env("hyper_parameters.watch_vec_size",
-                                             None, self._namespace)
-        search_vec_size = envs.get_global_env(
-            "hyper_parameters.search_vec_size", None, self._namespace)
-        other_feat_size = envs.get_global_env(
-            "hyper_parameters.other_feat_size", None, self._namespace)
+    def input_data(self, is_infer=False, **kwargs):
 
         watch_vec = fluid.data(
-            name="watch_vec", shape=[None, watch_vec_size], dtype="float32")
+            name="watch_vec",
+            shape=[None, self.watch_vec_size],
+            dtype="float32")
         search_vec = fluid.data(
-            name="search_vec", shape=[None, search_vec_size], dtype="float32")
+            name="search_vec",
+            shape=[None, self.search_vec_size],
+            dtype="float32")
         other_feat = fluid.data(
-            name="other_feat", shape=[None, other_feat_size], dtype="float32")
+            name="other_feat",
+            shape=[None, self.other_feat_size],
+            dtype="float32")
         label = fluid.data(name="label", shape=[None, 1], dtype="int64")
         inputs = [watch_vec] + [search_vec] + [other_feat] + [label]
-        self._data_var = inputs
 
         return inputs
 
-    def fc(self, tag, data, out_dim, active='relu'):
+    def net(self, inputs, is_infer=False):
+        concat_feats = fluid.layers.concat(input=inputs[:-1], axis=-1)
+
+        l1 = self._fc('l1', concat_feats, self.layers[0], 'relu')
+        l2 = self._fc('l2', l1, self.layers[1], 'relu')
+        l3 = self._fc('l3', l2, self.layers[2], 'relu')
+        l4 = self._fc('l4', l3, self.output_size, 'softmax')
+
+        num_seqs = fluid.layers.create_tensor(dtype='int64')
+        acc = fluid.layers.accuracy(input=l4, label=inputs[-1], total=num_seqs)
+
+        cost = fluid.layers.cross_entropy(input=l4, label=inputs[-1])
+        avg_cost = fluid.layers.mean(cost)
+
+        self._cost = avg_cost
+        self._metrics["acc"] = acc
+
+    def _fc(self, tag, data, out_dim, active='relu'):
         init_stddev = 1.0
         scales = 1.0 / np.sqrt(data.shape[1])
 
@@ -67,31 +92,3 @@ class Model(ModelBase):
                               bias_attr=b_attr,
                               name=tag)
         return out
-
-    def net(self, inputs):
-        output_size = envs.get_global_env("hyper_parameters.output_size", None,
-                                          self._namespace)
-        layers = envs.get_global_env("hyper_parameters.layers", None,
-                                     self._namespace)
-        concat_feats = fluid.layers.concat(input=inputs[:-1], axis=-1)
-
-        l1 = self.fc('l1', concat_feats, layers[0], 'relu')
-        l2 = self.fc('l2', l1, layers[1], 'relu')
-        l3 = self.fc('l3', l2, layers[2], 'relu')
-        l4 = self.fc('l4', l3, output_size, 'softmax')
-
-        num_seqs = fluid.layers.create_tensor(dtype='int64')
-        acc = fluid.layers.accuracy(input=l4, label=inputs[-1], total=num_seqs)
-
-        cost = fluid.layers.cross_entropy(input=l4, label=inputs[-1])
-        avg_cost = fluid.layers.mean(cost)
-
-        self._cost = avg_cost
-        self._metrics["acc"] = acc
-
-    def train_net(self):
-        input_data = self.input_data()
-        self.net(input_data)
-
-    def infer_net(self):
-        pass

models/recall/youtube_dnn/random_reader.py

@@ -13,22 +13,22 @@
 # limitations under the License.
 from __future__ import print_function
 
+import numpy as np
+
 from paddlerec.core.reader import Reader
 from paddlerec.core.utils import envs
 from collections import defaultdict
-import numpy as np
 
 
 class TrainReader(Reader):
     def init(self):
         self.watch_vec_size = envs.get_global_env(
-            "hyper_parameters.watch_vec_size", None, "train.model")
+            "hyper_parameters.watch_vec_size")
         self.search_vec_size = envs.get_global_env(
-            "hyper_parameters.search_vec_size", None, "train.model")
+            "hyper_parameters.search_vec_size")
         self.other_feat_size = envs.get_global_env(
-            "hyper_parameters.other_feat_size", None, "train.model")
-        self.output_size = envs.get_global_env("hyper_parameters.output_size",
-                                               None, "train.model")
+            "hyper_parameters.other_feat_size")
+        self.output_size = envs.get_global_env("hyper_parameters.output_size")
 
     def generate_sample(self, line):
         """

models/rerank/listwise/config.yaml

@@ -12,44 +12,56 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-evaluate:
-  reader:
-    batch_size: 1
-    class: "{workspace}/random_infer_reader.py"
-    test_data_path: "{workspace}/data/train"
-
-train:
-  trainer:
-    # for cluster training
-    strategy: "async"
 
-  epochs: 3
-  workspace: "paddlerec.models.rerank.listwise"
-  device: cpu
+workspace: "paddlerec.models.rerank.listwise"
 
-  reader:
-    batch_size: 2
-    class: "{workspace}/random_reader.py"
-    train_data_path: "{workspace}/data/train"
-    dataset_class: "DataLoader"
+dataset:
+- name: dataset_train
+  type: DataLoader
+  data_path: "{workspace}/data/train"
+  data_converter: "{workspace}/random_reader.py"
+- name: dataset_infer
+  type: DataLoader
+  data_path: "{workspace}/data/test"
+  data_converter: "{workspace}/random_reader.py"
 
-  model:
-    models: "{workspace}/model.py"
-    hyper_parameters:
+hyper_parameters:
   hidden_size: 128
   user_vocab: 200
   item_vocab: 1000
   item_len: 5
   embed_size: 16
+  batch_size: 1
+  optimizer: 
+    class: sgd
     learning_rate: 0.01
-      optimizer: sgd
+    strategy: async
+
+#use infer_runner mode and modify 'phase' below if infer
+mode: train_runner
+#mode: infer_runner
+
+runner:
+- name: train_runner
+  class: single_train
+  device: cpu
+  epochs: 3
+  save_checkpoint_interval: 2
+  save_inference_interval: 4
+  save_checkpoint_path: "increment"
+  save_inference_path: "inference"
+- name: infer_runner
+  class: single_infer
+  init_model_path: "increment/0"
+  device: cpu
+  epochs: 3
 
-  save:
-    increment:
-      dirname: "increment"
-      epoch_interval: 2
-      save_last: True
-    inference:
-      dirname: "inference"
-      epoch_interval: 4
-      save_last: True
+phase:
+- name: train
+  model: "{workspace}/model.py"
+  dataset_name: dataset_train
+  thread_num: 1
+    #- name: infer
+    #  model: "{workspace}/model.py"
+    #  dataset_name: dataset_infer
+    #  thread_num: 1

models/rerank/listwise/model.py

@@ -25,18 +25,13 @@ class Model(ModelBase):
         ModelBase.__init__(self, config)
 
     def _init_hyper_parameters(self):
-        self.item_len = envs.get_global_env("hyper_parameters.self.item_len",
-                                            None, self._namespace)
-        self.hidden_size = envs.get_global_env("hyper_parameters.hidden_size",
-                                               None, self._namespace)
-        self.user_vocab = envs.get_global_env("hyper_parameters.user_vocab",
-                                              None, self._namespace)
-        self.item_vocab = envs.get_global_env("hyper_parameters.item_vocab",
-                                              None, self._namespace)
-        self.embed_size = envs.get_global_env("hyper_parameters.embed_size",
-                                              None, self._namespace)
-
-    def input_data(self, is_infer=False):
+        self.item_len = envs.get_global_env("hyper_parameters.self.item_len")
+        self.hidden_size = envs.get_global_env("hyper_parameters.hidden_size")
+        self.user_vocab = envs.get_global_env("hyper_parameters.user_vocab")
+        self.item_vocab = envs.get_global_env("hyper_parameters.item_vocab")
+        self.embed_size = envs.get_global_env("hyper_parameters.embed_size")
+
+    def input_data(self, is_infer=False, **kwargs):
         user_slot_names = fluid.data(
             name='user_slot_names',
             shape=[None, 1],

models/rerank/listwise/random_reader.py

@@ -23,14 +23,10 @@ from collections import defaultdict
 
 class TrainReader(Reader):
     def init(self):
-        self.user_vocab = envs.get_global_env("hyper_parameters.user_vocab",
-                                              None, "train.model")
-        self.item_vocab = envs.get_global_env("hyper_parameters.item_vocab",
-                                              None, "train.model")
-        self.item_len = envs.get_global_env("hyper_parameters.item_len", None,
-                                            "train.model")
-        self.batch_size = envs.get_global_env("batch_size", None,
-                                              "train.reader")
+        self.user_vocab = envs.get_global_env("hyper_parameters.user_vocab")
+        self.item_vocab = envs.get_global_env("hyper_parameters.item_vocab")
+        self.item_len = envs.get_global_env("hyper_parameters.item_len")
+        self.batch_size = envs.get_global_env("hyper_parameters.batch_size")
 
     def reader_creator(self):
         def reader():

models/rerank/readme.md

@@ -9,9 +9,6 @@
 * [整体介绍](#整体介绍)
     * [重排序模型列表](#重排序模型列表)
 * [使用教程](#使用教程)
-    * [训练 预测](#训练 预测)
-* [效果对比](#效果对比)
-    * [模型效果列表](#模型效果列表)
 
 ## 整体介绍
 ### 融合模型列表
@@ -29,15 +26,11 @@
 <p>
 
 
-## 使用教程
-### 训练 预测
+## 使用教程(快速开始)
 ```shell
 python -m paddlerec.run -m paddlerec.models.rerank.listwise # listwise
 ```
 
-## 效果对比
-### 模型效果列表
+## 使用教程（复现论文）
 
-|       数据集        |       模型       |       loss        |       auc       | 
-| :------------------: | :--------------------: | :---------: |:---------: |
-|    --     |       Listwise       |       --        |     --         |
+listwise原论文没有给出训练数据，我们使用了随机的数据，可参考快速开始