[feature]add xdeepfm model and docs

docs/source/models/rank.rst

@@ -6,6 +6,7 @@
 
    multi_tower
    deepfm
+   xdeepfm
    fm
    wide_and_deep
    dlrm

docs/source/models/xdeepfm.md

@@ -0,0 +1,154 @@
+# xDeepFM
+
+### 简介
+
+xDeepFM模型延续了deep&cross network(参考[DCN](https://easyrec.readthedocs.io/en/latest/models/dcn.html))模型的建模思想。不过，在建模显式高阶交叉特征时，采用了不同于deep&cross network的方式，文章称为compressed interaction network(CIN)，并将CIN网络与深度神经网络结合，最后输入到输出层。
+
+![xdeepfm.png](../../images/models/xdeepfm.png)
+
+### 配置说明
+
+组件化模型
+
+```protobuf
+model_config: {
+  model_name: 'xDeepFM'
+  model_class: 'RankModel'
+  feature_groups: {
+    group_name: 'features'
+    feature_names: 'user_id'
+    feature_names: 'cms_segid'
+    feature_names: 'cms_group_id'
+    feature_names: 'age_level'
+    feature_names: 'pvalue_level'
+    feature_names: 'shopping_level'
+    feature_names: 'occupation'
+    feature_names: 'new_user_class_level'
+    feature_names: 'tag_category_list'
+    feature_names: 'tag_brand_list'
+    feature_names: 'adgroup_id'
+    feature_names: 'cate_id'
+    feature_names: 'campaign_id'
+    feature_names: 'customer'
+    feature_names: 'brand'
+    feature_names: 'price'
+    feature_names: 'pid'
+    wide_deep:DEEP
+  }
+
+  feature_groups: {
+    group_name: "wide"
+    feature_names: 'user_id'
+    feature_names: 'cms_segid'
+    feature_names: 'cms_group_id'
+    feature_names: 'age_level'
+    feature_names: 'pvalue_level'
+    feature_names: 'shopping_level'
+    feature_names: 'occupation'
+    feature_names: 'new_user_class_level'
+    feature_names: 'tag_category_list'
+    feature_names: 'tag_brand_list'
+    feature_names: 'adgroup_id'
+    feature_names: 'cate_id'
+    feature_names: 'campaign_id'
+    feature_names: 'customer'
+    feature_names: 'brand'
+    feature_names: 'price'
+    feature_names: 'pid'
+    wide_deep:WIDE
+  }
+  backbone {
+    blocks {
+      name: 'wide'
+      inputs {
+        feature_group_name: 'wide'
+      }
+      input_layer {
+        only_output_feature_list: true
+        wide_output_dim: 1
+      }
+    }
+
+    blocks {
+      name: 'features'
+      inputs {
+        feature_group_name: 'features'
+      }
+      input_layer {
+        output_2d_tensor_and_feature_list: true
+      }
+    }
+    blocks {
+      name: 'cin'
+      inputs {
+        block_name: 'features'
+        input_slice: '[1]'
+      }
+      extra_input_fn: 'lambda x: tf.stack(x, axis=1)'
+      keras_layer {
+        class_name: 'CIN'
+        cin {
+          hidden_feature_sizes: [64, 64, 64]
+        }
+      }
+    }
+
+    blocks {
+      name: 'dnn'
+      inputs {
+        block_name: 'features'
+        input_slice: '[0]'
+      }
+      keras_layer {
+        class_name: 'MLP'
+        mlp {
+          hidden_units: [128, 64]
+        }
+      }
+    }
+
+    blocks {
+      name: 'final_logit'
+      inputs {
+        block_name: 'wide'
+        input_fn: 'lambda x: tf.add_n(x)'
+      }
+      inputs {
+        block_name: 'cin'
+      }
+      inputs {
+        block_name: 'dnn'
+      }
+
+      keras_layer {
+        class_name: 'MLP'
+        mlp {
+          hidden_units: [32, 1]
+          use_final_bn: false
+          final_activation: 'linear'
+        }
+      }
+    }
+    concat_blocks: 'final_logit'
+  }
+}
+```
+
+- model_name: 任意自定义字符串，仅有注释作用, 本模型命名为xDeepFM
+- model_class: 'RankModel', 不需要修改
+- feature_groups: 特征组
+  - 包含两个feature_group: wide 和 deep features group
+- backbone: 通过组件化的方式搭建的主干网络，[参考文档](../component/backbone.md)
+  - wide block: 输入wide特征，以list形式输出wide特征
+  - features block: 输入deep features特征，以2Dtensor和list形式同时输出deep features特征
+  - cin block: CIN模块
+  - dnn block: DNN模块
+  - final_logit block: 拼接wide输出、cin输出、dnn输出，叠加一个顶层的MLP，输出最终的预测结果
+
+### 示例Config
+
+1. 组件化模型：[xdeepfm_on_taobao_backbone.config](https://github.com/alibaba/EasyRec/tree/master/samples/model_config/xdeepfm_on_taobao_backbone.config)
+
+### 参考论文
+
+[xDeepFM](https://arxiv.org/pdf/1803.05170)

easy_rec/python/test/train_eval_test.py

@@ -1274,6 +1274,12 @@ def test_parallel_dssm_backbone_on_taobao(self):
         self._test_dir)
     self.assertTrue(self._success)
 
+  def test_xdeefm_backbone_on_taobao(self):
+    self._success = test_utils.test_single_train_eval(
+        'samples/model_config/xdeepfm_on_taobao_backbone.config',
+        self._test_dir)
+    self.assertTrue(self._success)
+
 
 if __name__ == '__main__':
   tf.test.main()