fix bug of text cnn, add TextCNN component and model

docs/source/component/backbone.md

@@ -951,7 +951,7 @@ DBMTL模型需要在`model_params`里为每个子任务的Tower配置`relation_d
 ```protobuf
 model_config: {
   model_name: 'MaskNet + PPNet + MMoE'
-  model_class: 'RankModel'
+  model_class: "MultiTaskModel"
   feature_groups: {
     group_name: 'memorize'
     feature_names: 'user_id'
@@ -971,6 +971,7 @@ model_config: {
       name: "mask_net"
       inputs {
         feature_group_name: "general"
+        input_fn: "lambda x: [x, x]"
       }
       repeat {
         num_repeat: 3
@@ -1104,6 +1105,7 @@ MovieLens-1M数据集效果：
 | Gate              | 门控     | 多个输入的加权求和                       | [Cross Decoupling Network](../models/cdn.html#id2)                                                                                       |
 | PeriodicEmbedding | 周期激活函数 | 数值特征Embedding                   | [案例5](#dlrm-embedding)                                                                                                                   |
 | AutoDisEmbedding  | 自动离散化  | 数值特征Embedding                   | [dlrm_on_criteo_with_autodis.config](https://github.com/alibaba/EasyRec/tree/master/examples/configs/dlrm_on_criteo_with_autodis.config) |
+| TextCNN           | 文本卷积   | 提取文本序列的特征                       | [text_cnn_on_movielens.config](https://github.com/alibaba/EasyRec/tree/master/examples/configs/text_cnn_on_movielens.config)             |
 
 **备注**：Gate组件的第一个输入是权重向量，后面的输入拼凑成一个列表，权重向量的长度应等于列表的长度
 

docs/source/component/component.md

@@ -49,6 +49,18 @@
 | output_tensor_list | bool   | false | 是否同时输出embedding列表                                 |
 | output_3d_tensor   | bool   | false | 是否同时输出3d tensor, `output_tensor_list=true`时该参数不生效 |
 
+- TextCNN
+
+| 参数                  | 类型           | 默认值  | 说明               |
+| ------------------- | ------------ | ---- | ---------------- |
+| num_filters         | list<uint32> |      | 卷积核个数列表          |
+| filter_sizes        | list<uint32> |      | 卷积核步长列表          |
+| activation          | string       | relu | 卷积操作的激活函数        |
+| pad_sequence_length | uint32       |      | 序列补齐或截断的长度       |
+| mlp                 | MLP          |      | protobuf message |
+
+备注：pad_sequence_length 参数必须要配置，否则模型predict的分数可能不稳定
+
 ## 2.特征交叉组件
 
 - Bilinear

docs/source/feature/feature.rst

@@ -340,12 +340,16 @@ Sequence类特征格式一般为“XX\|XX\|XX”，如用户行为序列特征
       text_cnn: {
         filter_sizes: [2, 3, 4]
         num_filters: [16, 8, 8]
+        pad_sequence_length: 128
+        activation: 'relu'
       }
     }
   }
 
 - num_filters: 卷积核个数列表
 - filter_sizes: 卷积核步长列表
+- pad_sequence_length: 序列补齐或截断的长度
+- activation: 卷积操作的激活函数，默认为relu
 
 TextCNN网络是2014年提出的用来做文本分类的卷积神经网络，由于其结构简单、效果好，在文本分类、推荐等NLP领域应用广泛。
 从直观上理解，TextCNN通过一维卷积来获取句子中`N gram`的特征表示。

docs/source/models/text_cnn.md

@@ -0,0 +1,79 @@
+# TextCNN
+
+### 简介
+
+TextCNN网络是2014年提出的用来做文本分类的卷积神经网络，由于其结构简单、效果好，在文本分类、推荐等NLP领域应用广泛。
+从直观上理解，TextCNN通过一维卷积来获取句子中`N gram`的特征表示。
+
+在推荐模型中，可以用TextCNN网络来提取文本类型的特征。
+
+### 配置说明
+
+```protobuf
+model_config: {
+  model_name: 'TextCNN'
+  model_class: 'RankModel'
+  feature_groups: {
+    group_name: 'text_seq'
+    feature_names: 'title'
+    wide_deep: DEEP
+  }
+  backbone {
+    blocks {
+      name: 'text_seq'
+      inputs {
+        feature_group_name: 'text_seq'
+      }
+      input_layer {
+        output_seq_and_normal_feature: true
+      }
+    }
+    blocks {
+      name: 'textcnn'
+      inputs {
+        block_name: 'text_seq'
+      }
+      keras_layer {
+        class_name: 'TextCNN'
+        text_cnn {
+          filter_sizes: [2, 3, 4]
+          num_filters: [16, 8, 8]
+          pad_sequence_length: 14
+          mlp {
+            hidden_units: [256, 128, 64]
+          }
+        }
+      }
+    }
+  }
+  model_params {
+    l2_regularization: 1e-6
+  }
+  embedding_regularization: 1e-6
+}
+```
+
+- model_name: 任意自定义字符串，仅有注释作用
+- model_class: 'RankModel', 不需要修改, 通过组件化方式搭建的单目标排序模型都叫这个名字
+- feature_groups: 配置一组特征。
+- backbone: 通过组件化的方式搭建的主干网络，[参考文档](../component/backbone.md)
+  - blocks: 由多个`组件块`组成的一个有向无环图（DAG），框架负责按照DAG的拓扑排序执行个`组件块`关联的代码逻辑，构建TF Graph的一个子图
+  - name/inputs: 每个`block`有一个唯一的名字（name），并且有一个或多个输入(inputs)和输出
+  - keras_layer: 加载由`class_name`指定的自定义或系统内置的keras layer，执行一段代码逻辑；[参考文档](../component/backbone.md#keraslayer)
+  - TextCNN: 调用TextCNN组件。组件的参数，详见[参考文档](../component/component.md#id2)
+    - num_filters: 卷积核个数列表
+    - filter_sizes: 卷积核步长列表
+    - pad_sequence_length: 序列补齐或截断的长度
+    - activation: 卷积操作的激活函数，默认为relu
+  - concat_blocks: DAG的输出节点由`concat_blocks`配置项定义，如果不配置`concat_blocks`，框架会自动拼接DAG的所有叶子节点并输出。
+- model_params:
+  - l2_regularization: (可选) 对DNN参数的regularization, 减少overfit
+- embedding_regularization: 对embedding部分加regularization, 减少overfit
+
+### 示例Config
+
+[text_cnn_on_movielens.config](https://github.com/alibaba/EasyRec/tree/master/samples/model_config/text_cnn_on_movielens.config)
+
+### 参考论文
+
+[Convolutional Neural Networks for Sentence Classification](http://arxiv.org/abs/1408.5882)

easy_rec/python/layers/input_layer.py

@@ -14,7 +14,8 @@
 from easy_rec.python.feature_column.feature_group import FeatureGroup
 from easy_rec.python.layers import sequence_feature_layer
 from easy_rec.python.layers import variational_dropout_layer
-from easy_rec.python.layers.common_layers import text_cnn
+from easy_rec.python.layers.keras import TextCNN
+from easy_rec.python.layers.utils import Parameter
 from easy_rec.python.protos.feature_config_pb2 import WideOrDeep
 from easy_rec.python.utils import conditional
 from easy_rec.python.utils import shape_utils
@@ -285,9 +286,9 @@ def single_call_input_layer(self,
           seq_features.append(seq_feature)
           cols_to_output_tensors[column] = seq_feature
         elif sequence_combiner.WhichOneof('combiner') == 'text_cnn':
-          num_filters = sequence_combiner.text_cnn.num_filters
-          filter_sizes = sequence_combiner.text_cnn.filter_sizes
-          cnn_feature = text_cnn(seq_feature, filter_sizes, num_filters)
+          params = Parameter.make_from_pb(sequence_combiner.text_cnn)
+          text_cnn_layer = TextCNN(params, name=column.name + '_text_cnn')
+          cnn_feature = text_cnn_layer((seq_feature, seq_len))
           seq_features.append(cnn_feature)
           cols_to_output_tensors[column] = cnn_feature
         else:

easy_rec/python/layers/keras/__init__.py

@@ -2,6 +2,7 @@
 from .blocks import MLP
 from .blocks import Gate
 from .blocks import Highway
+from .blocks import TextCNN
 from .bst import BST
 from .data_augment import SeqAugment
 from .din import DIN

easy_rec/python/layers/keras/blocks.py

@@ -6,6 +6,8 @@
 import tensorflow as tf
 
 from easy_rec.python.layers.keras.activation import activation_layer
+from easy_rec.python.layers.utils import Parameter
+from easy_rec.python.utils.shape_utils import pad_or_truncate_sequence
 from easy_rec.python.utils.tf_utils import add_elements_to_collection
 
 if tf.__version__ >= '2.0':
@@ -161,3 +163,56 @@ def call(self, inputs, **kwargs):
         output += weights[:, j, None] * x
       j += 1
     return output
+
+
+class TextCNN(tf.keras.layers.Layer):
+  """Text CNN Model.
+
+  References
+  - [Convolutional Neural Networks for Sentence Classification](https://arxiv.org/abs/1408.5882)
+  """
+
+  def __init__(self, params, name='text_cnn', reuse=None, **kwargs):
+    super(TextCNN, self).__init__(name, **kwargs)
+    self.config = params.get_pb_config()
+    self.pad_seq_length = self.config.pad_sequence_length
+    if self.pad_seq_length <= 0:
+      logging.warning(
+          'run text cnn with pad_sequence_length <= 0, the predict of model may be unstable'
+      )
+    self.conv_layers = []
+    self.pool_layer = tf.keras.layers.GlobalMaxPool1D()
+    self.concat_layer = tf.keras.layers.Concatenate(axis=-1)
+    for size, filters in zip(self.config.filter_sizes, self.config.num_filters):
+      conv = tf.keras.layers.Conv1D(
+          filters=int(filters),
+          kernel_size=int(size),
+          activation=self.config.activation)
+      self.conv_layers.append(conv)
+    if self.config.HasField('mlp'):
+      p = Parameter.make_from_pb(self.config.mlp)
+      p.l2_regularizer = params.l2_regularizer
+      self.mlp = MLP(p, name='mlp', reuse=reuse)
+    else:
+      self.mlp = None
+
+  def call(self, inputs, training=None, **kwargs):
+    """Input shape: 3D tensor with shape: `(batch_size, steps, input_dim)."""
+    assert isinstance(inputs, (list, tuple))
+    assert len(inputs) >= 2
+    seq_emb, seq_len = inputs[:2]
+
+    if self.pad_seq_length > 0:
+      seq_emb, seq_len = pad_or_truncate_sequence(seq_emb, seq_len,
+                                                  self.pad_seq_length)
+    pooled_outputs = []
+    for layer in self.conv_layers:
+      conv = layer(seq_emb)
+      pooled = self.pool_layer(conv)
+      pooled_outputs.append(pooled)
+    net = self.concat_layer(pooled_outputs)
+    if self.mlp is not None:
+      output = self.mlp(net)
+    else:
+      output = net
+    return output

easy_rec/python/protos/feature_config.proto

@@ -3,6 +3,7 @@ package protos;
 
 import "easy_rec/python/protos/hyperparams.proto";
 import "easy_rec/python/protos/dnn.proto";
+import "easy_rec/python/protos/layer.proto";
 enum WideOrDeep {
     DEEP = 0;
     WIDE = 1;
@@ -15,16 +16,11 @@ message AttentionCombiner {
 message MultiHeadAttentionCombiner {
 }
 
-message TextCnnCombiner {
-    repeated uint32 filter_sizes = 1;
-    repeated uint32 num_filters = 2;
-}
-
 message SequenceCombiner {
     oneof combiner {
         AttentionCombiner attention = 1;
         MultiHeadAttentionCombiner multi_head_attention = 2;
-        TextCnnCombiner text_cnn = 3;
+        TextCNN text_cnn = 3;
     }
 }
 

easy_rec/python/protos/keras_layer.proto

@@ -25,5 +25,6 @@ message KerasLayer {
         MMoELayer mmoe = 14;
         SequenceAugment seq_aug = 15;
         PPNet ppnet = 16;
+        TextCNN text_cnn = 17;
     }
 }

easy_rec/python/protos/layer.proto

@@ -88,3 +88,11 @@ message PPNet {
     required string mode = 3 [default = 'eager'];
     optional bool full_gate_input = 4 [default = true];
 }
+
+message TextCNN {
+    repeated uint32 filter_sizes = 1;
+    repeated uint32 num_filters = 2;
+    required uint32 pad_sequence_length = 3;
+    optional string activation = 4 [default = 'relu'];
+    optional MLP mlp = 5;
+}

easy_rec/python/utils/shape_utils.py

@@ -408,3 +408,25 @@ def keep(seq_embed, seq_length):
 
   return tf.cond(max_seq_len > limited_len, lambda: truncate(seq_emb, seq_len),
                  lambda: keep(seq_emb, seq_len))
+
+
+def pad_or_truncate_sequence(seq_emb, seq_len, fixed_len):
+  padding_length = fixed_len - tf.shape(seq_emb)[1]
+
+  def padding():
+    paddings = tf.stack([[0, 0], [0, padding_length], [0, 0]])
+    padded = tf.pad(seq_emb, paddings)
+    return padded, seq_len
+
+  def truncate():
+    sliced = tf.slice(seq_emb, [0, 0, 0], [-1, fixed_len, -1])
+    length = tf.where(seq_len < fixed_len, seq_len,
+                      tf.ones_like(seq_len) *
+                      fixed_len) if seq_len is not None else None
+    return sliced, length
+
+  def keep():
+    return seq_emb, seq_len
+
+  return tf.cond(padding_length > 0, padding,
+                 lambda: tf.cond(padding_length < 0, truncate, keep))

examples/configs/contrastive_learning_on_movielens.config

@@ -137,6 +137,7 @@ feature_config: {
       text_cnn: {
         filter_sizes: [2, 3, 4]
         num_filters: [16, 8, 8]
+        pad_sequence_length: 14
       }
     }
   }

examples/configs/dcn_backbone_on_movielens.config

@@ -137,6 +137,7 @@ feature_config: {
       text_cnn: {
         filter_sizes: [2, 3, 4]
         num_filters: [16, 8, 8]
+        pad_sequence_length: 14
       }
     }
   }

examples/configs/dcn_on_movielens.config

@@ -138,6 +138,7 @@ feature_config: {
       text_cnn: {
         filter_sizes: [2, 3, 4]
         num_filters: [16, 8, 8]
+        pad_sequence_length: 14
       }
     }
   }

examples/configs/deepfm_on_movielens.config

@@ -138,6 +138,7 @@ feature_config: {
       text_cnn: {
         filter_sizes: [2, 3, 4]
         num_filters: [8, 4, 4]
+        pad_sequence_length: 14
       }
     }
   }

examples/configs/fibinet_on_movielens.config

@@ -137,6 +137,7 @@ feature_config: {
       text_cnn: {
         filter_sizes: [2, 3, 4]
         num_filters: [16, 8, 8]
+        pad_sequence_length: 14
       }
     }
   }

examples/configs/masknet_on_movielens.config

@@ -137,6 +137,7 @@ feature_config: {
       text_cnn: {
         filter_sizes: [2, 3, 4]
         num_filters: [16, 8, 8]
+        pad_sequence_length: 14
       }
     }
   }

examples/configs/mlp_on_movielens.config

@@ -137,6 +137,7 @@ feature_config: {
       text_cnn: {
         filter_sizes: [2, 3, 4]
         num_filters: [16, 8, 8]
+        pad_sequence_length: 14
       }
     }
   }
@@ -158,6 +159,7 @@ model_config: {
     feature_names: 'gender'
     feature_names: 'year'
     feature_names: 'genres'
+    feature_names: 'title'
     wide_deep: DEEP
   }
   backbone {

examples/configs/multi_tower_on_movielens.config

@@ -137,6 +137,7 @@ feature_config: {
       text_cnn: {
         filter_sizes: [2, 3, 4]
         num_filters: [16, 8, 8]
+        pad_sequence_length: 14
       }
     }
   }

examples/configs/wide_and_deep_backbone_on_movielens.config

@@ -137,6 +137,7 @@ feature_config: {
       text_cnn: {
         filter_sizes: [2, 3, 4]
         num_filters: [16, 8, 8]
+        pad_sequence_length: 14
       }
     }
   }

examples/configs/wide_and_deep_on_movielens.config

@@ -138,6 +138,7 @@ feature_config: {
       text_cnn: {
         filter_sizes: [2, 3, 4]
         num_filters: [16, 8, 8]
+        pad_sequence_length: 14
       }
     }
   }