RTP部署

本文档介绍将EasyRec模型部署到RTP上的流程.

RTP目前仅支持checkpoint形式的模型部署，因此需要将EasyRec模型导出为checkpoint形式

编写RTP特征配置 fg.json

包含了features配置和全局配置两个部分, 示例:

{
  "features": [
     {"expression": "user:user_id", "feature_name": "user_id", "feature_type":"id_feature", "value_type":"String", "combiner":"mean", "hash_bucket_size": 100000, "embedding_dim": 16, "group":"user"},
     {"expression": "user:cms_segid", "feature_name": "cms_segid", "feature_type":"id_feature", "value_type":"String", "combiner":"mean", "hash_bucket_size": 100, "embedding_dim": 16, "group":"user"},
     ...
     {"expression": "item:price", "feature_name": "price", "feature_type":"raw_feature", "value_type":"Integer", "combiner":"mean", "group":"item"},
     {"expression": "item:pid", "feature_name": "pid", "feature_type":"id_feature", "value_type":"String", "combiner":"mean", "hash_bucket_size": 100000, "embedding_dim": 16, "group":"item"},
     {"expression": "user:tag_category_list", "feature_name": "user_tag_cate", "feature_type":"id_feature", "hash_bucket_size":100000, "group":"user"},
     {"map": "user:tag_brand_list", "key":"item:brand", "feature_name": "combo_brand", "feature_type":"lookup_feature",  "needDiscrete":true, "hash_bucket_size":100000, "group":"combo"},
     {"map": "user:tag_category_list", "key":"item:cate_id", "feature_name": "combo_cate_id", "feature_type":"lookup_feature",  "needDiscrete":true, "hash_bucket_size":10000, "group":"combo"}
 ],

 "reserves": [
   "user_id", "campaign_id", "clk"
 ]
}

Feature配置说明：
- IdFeature
  - is_multi: id_feature是否是多值属性
    - 默认是false, 转换成EasyRec的config时会转成IdFeature
    - 如果设成true, 转换成EasyRec的config时会转成TagFeature.
    - 多值分隔符使用chr(29)[ctrl+v ctrl+].
  - num_buckets: 当输入是unsigned int类型的时候，并且输入有界的时候，可以指定num_bucket为输入的最大值.
  - hash_bucket_size: 对应EasyRec feature_config.features的hash_bucket_size。hash_bucket方式是目前RTP唯一支持的embedding方式
  - embedding_dimension/embedding_dim: 对应EasyRec feature_config.features里面的embedding_dim.
- RawFeature
  - bucketize_boundaries: 会生成离散化的结果, 在生成EasyRec config的时候:
  - 设置feature_config.features.num_buckets = len(boundaries) + 1
  - value_dimension > 1时, feature_type = TagFeature
  - value_dimension = 1时, feature_type = IdFeature
  - boundaries: 生成的还是连续值，但在生成EasyRec config的时候:
```
会配置离散化的bucket, 如:
feature_config: {
  features: {
    input_names: "hour"
    feature_type: RawFeature
    boundaries: [1,5,9,15,19,23]
    embedding_dim: 16
  }
}
```
  - 设置bucketize_boundaries/boundaries的同时需要设置embedding_dimension.
  - value_dimension: 连续值的维度，>1时表示有多个连续值, 也就是一个向量.
    - 比如ctr_1d,ctr_2d,ctr_3d,ctr_12d可以放在一个RawFeature里面.
    - 该选项对生成数据有影响.
    - 该选项对生成EasyRec config也有影响.
- ComboFeature
  - 需要设置embedding_dimension和hash_bucket_size. 方法一：在fg中生成combo特征，见ComboFeature
```
{"expression": "user:user_id", "feature_name": "user_id", "feature_type":"id_feature", "value_type":"String", "combiner":"mean", "hash_bucket_size": 100000, "embedding_dim": 16, "group":"user"},
{"expression": "user:occupation", "feature_name": "occupation", "feature_type":"id_feature", "value_type":"String", "combiner":"mean", "hash_bucket_size": 10, "embedding_dim": 16, "group":"user"},
{"expression" : ["user:user_id", "user:occupation"], "feature_name" : "combo__occupation_age_level", "feature_type" : "combo_feature", "hash_bucket_size": 10, "embedding_dim": 16}
```
  - fg.json需进行三项配置，生成三列数据方法二：在参与combo的特征配置中加入extra_combo_info配置，fg会生成两列数据，在easyrec层面进行combo.
```
 {"expression": "user:user_id", "feature_name": "user_id", "feature_type":"id_feature", "value_type":"String", "combiner":"mean", "hash_bucket_size": 100000, "embedding_dim": 16, "group":"user"},
 {"expression": "user:occupation", "feature_name": "occupation", "feature_type":"id_feature", "value_type":"String", "combiner":"mean", "hash_bucket_size": 10, "embedding_dim": 16, "group":"user",
   "extra_combo_info": {
     "final_feature_name": "combo__occupation_age_level",
     "feature_names": ["user_id"],
     "combiner":"mean", "hash_bucket_size": 10, "embedding_dim": 16
   }
 }
```
  - 最终会生成两列数据（user_id和occupation），config中生成三个特征配置，分别是user_id，occupation，combo__occupation_age_level.
  - final_feature_name: 该combo特征的名字.
  - feature_names: 除当前特征外，参与combo的特征，至少一项.
  - combiner, hash_bucket_size, embedding_dim 配置与上述一致.
- LookupFeature
  - 根据id查找对应的value.
- MatchFeature
  - 双层查找, 根据category和item_id查找value.
  - match Feature里面多值分隔符可以使用chr(29) (ctrl+v ctrl+])或者逗号[,]，如:
```
  50011740^107287172:0.2^]36806676:0.3^]122572685:0.5|50006842^16788816:0.1^]10122:0.2^]29889:0.3^]30068:19
```
  - needWeighting: 生成特征权重，即kv格式, kv之间用[ctrl+v ctrl+e]分割, 转换成TagFeature.
- OverLapFeature
- 所有feature都需要的字段:
  - group: feature所属的分组
    - 对于WideAndDeep/DeepFM是wide/deep.
    - 对于MultiTower可以自定义分组名称，如user/item/combo.
  - combiner: 默认是mean, 也可以是sum.
    - 影响数据生成和 EasyRec feature_config 生成, 主要是多值Feature.
  - 多值类型说明
    - 多值feature使用chr(29)[ctrl+v ctrl+]]作为分隔符.
全局配置说明:
- reserves: 要在最终表里面要保存的字段，通常包括label, user_id, item_id等

生成样本

请参见RTP文档规范，用你喜欢的方式生成样本。

举例：

准备 fg.json 配置：

{
  "features": [
    {"expression": "user:user_id", "feature_name": "user_id", "feature_type":"id_feature", "value_type":"String", "combiner":"mean", "hash_bucket_size": 100000, "embedding_dim": 16, "group":"user"},
    {"expression": "user:cms_segid", "feature_name": "cms_segid", "feature_type":"id_feature", "value_type":"String", "combiner":"mean", "hash_bucket_size": 100, "embedding_dim": 16, "group":"user"},
    ...
    {"expression": "item:price", "feature_name": "price", "feature_type":"raw_feature", "value_type":"Integer", "combiner":"mean", "group":"item"},
    {"expression": "item:item_id", "feature_name": "item_id", "feature_type":"id_feature", "value_type":"String", "combiner":"mean", "hash_bucket_size": 100000, "embedding_dim": 16, "group":"item"},
    {"expression": "user:tag_category_list", "feature_name": "user_tag_cate", "feature_type":"id_feature", "hash_bucket_size":100000, "group":"user"},
    {"map": "user:tag_brand_list", "key":"item:brand", "feature_name": "combo_brand", "feature_type":"lookup_feature",  "needDiscrete":true, "hash_bucket_size":100000, "group":"combo"},
    {"map": "user:tag_category_list", "key":"item:cate_id", "feature_name": "combo_cate_id", "feature_type":"lookup_feature",  "needDiscrete":true, "hash_bucket_size":10000, "group":"combo"}
  ],
  "reserves": [
    "user_id", "item_id", "clk"
  ]
}

准备数据，例如：

clk	user_id	item_id	tag_category_list	price	age_level	...
1	122017	389957	4589	10	0	...

下载 fg_on_odps 的 jar包 fg_on_odps_nodep_5u_file20-1.4.20-jar-with-dependencies.jar

生成样本：

add jar fg_on_odps_nodep_5u_file20-1.4.20-jar-with-dependencies.jar -f;
add file fg.json -f;

set odps.sql.planner.mode=sql;
set odps.isolation.session.enable=true;
set odps.sql.counters.dynamic.limit=true;

drop table if exists dssm_taobao_fg_train_out;
create table dssm_taobao_fg_train_out(clk bigint, user_id string, item_id string, features string);
jar -libjars fg_on_odps_nodep_5u_file20-1.4.20-jar-with-dependencies.jar
    -resources fg.json
    -classpath fg_on_odps_nodep_5u_file20-1.4.20-jar-with-dependencies.jar com.taobao.fg_on_odps.FGMapperTF
	dssm_test_feature_table
    dssm_taobao_fg_train_out
    fg.json;

drop table if exists dssm_taobao_fg_test_out;
create table dssm_taobao_fg_test_out(clk bigint, user_id string, item_id string, features string);
jar -libjars fg_on_odps_nodep_5u_file20-1.4.20-jar-with-dependencies.jar
    -resources fg.json
    -classpath fg_on_odps_nodep_5u_file20-1.4.20-jar-with-dependencies.jar com.taobao.fg_on_odps.FGMapperTF
	dssm_test_feature_table_test
    dssm_taobao_fg_test_out
    fg.json;

--下载查看数据(可选)
tunnel download dssm_taobao_fg_test_out dssm_taobao_fg_test_out.txt -fd=';';

这会生成这样的样本表：

clk	user_id	item_id	features
1	122017	389957	tag_category_list^C4589^Bprice^C10^Bage_level^C0^B...

编写EasyRec配置 fg.config

示例

model_dir: "oss://easyrec/rtp_fg_demo"

train_config {
  optimizer_config {
    use_moving_average: false
    adam_optimizer {
      learning_rate {
        exponential_decay_learning_rate {
          initial_learning_rate: 0.0001
          decay_steps: 100000
          decay_factor: 0.5
          min_learning_rate: 1e-07
        }
      }
    }
  }
  num_steps: 1000
  sync_replicas: false
  log_step_count_steps: 200
}

fg_json_path: "oss://easyrec/rtp_fg/fg.json"

data_config {
  batch_size: 1024
  label_fields: "clk"
  input_type: OdpsRTPInputV2
  separator: "�"
  selected_cols: "clk,features"
  rtp_separator: ";"
}

model_config:{
  model_class: "DeepFM"
  feature_groups: {
    group_name: 'deep'
    feature_names: 'user_id'
    feature_names: 'cms_segid'
    ...
    feature_names: 'brand'
    wide_deep:DEEP
  }
  feature_groups: {
    group_name: 'wide'
    feature_names: 'user_id'
    feature_names: 'cms_segid'
    ...
    feature_names: 'brand'
    wide_deep:WIDE
  }
  deepfm {
    wide_output_dim: 16
    dnn {
      hidden_units: [128, 64, 32]
    }
    final_dnn {
      hidden_units: [128, 64]
    }
    l2_regularization: 1e-6
  }
  embedding_regularization: 5e-5
}

export_config {
  multi_placeholder: false
  export_rtp_outputs: true
}

fg_json_path: RTP FG 配置文件即 fg.json 的路径
data_config
- input_fields: 输入字段配置，无需设置，EasyRec 会根据 fg.json 自动生成
- input_type: 须填写OdpsRTPInputV2，仅此输入类型与RTP在线服务兼容
feature_configs: 特征配置，无需设置，EasyRec 会根据 fg.json 自动生成
export_config
- export_rtp_outputs: 须设置为true，令 EasyRec 在输出图中加入 RTP 预测节点

启动训练

上传fg.config和fg.json到oss
启动训练

pai -name easy_rec_ext -project algo_public
-Dconfig=oss://bucket-name/easy_rec_test/fg.config
-Dcmd=train
-Dtables='odps://project-name/tables/dssm_taobao_fg_train_out,odps://project-name/tables/dssm_taobao_fg_test_out'
-Dcluster='{"ps":{"count":1, "cpu":1000}, "worker" : {"count":3, "cpu":1000, "gpu":100, "memory":40000}}'
-Darn='acs:ram::xxx:role/ev-ext-test-oss'
-Dbuckets='oss://bucket-name/'
-DossHost='oss-cn-xxx.aliyuncs.com'
-Dmodel_dir='oss://bucket-name/easy_rect_test_model/202203031730/data'
-Dselected_cols='clk,features';

模型导出

pai -name easy_rec_ext -project algo_public
-Dconfig='oss://bucket-name/easy_rec_test/fg.config'
-Dcmd=export_checkpoint
-Dcluster='{"worker" : {"count":1, "cpu":1000, "gpu":0, "memory":40000}}'
-Darn='acs:ram::xxx:role/ev-ext-test-oss'
-Dbuckets='oss://bucket-name/'
-DossHost='oss-cn-xxx.aliyuncs.com'
-Dmodel_dir='oss://bucket-name/easy_rect_test_model/202203031730/data'
-Dexport_dir='oss://bucket-name/easy_rect_test_model_export/202203031730/data'
-Dselected_cols='clk,features'
-Dbatch_size=256;

在export_dir指定的目录下生成名为 model.ckpt.* 的checkpoint文件，将export_dir指定的目录所对应的主目录指定为RTP模型表的模型目录即可, 例如:
export_dir为 oss://bucket-name/easy_rect_test_model_export/202203031730/data，则将RTP模型目录指定为 oss://bucket-name/easy_rect_test_model_export
Note: 弹内pai版本ossHost, arn, buckets参数指定方式和公有云版本有差异，具体见使用文档 RTP Serving部分.

EAS部署

还可以将模型部署到EAS上，参考文档.

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rtp_native.md

rtp_native.md

RTP部署

编写RTP特征配置 fg.json

生成样本

编写EasyRec配置 fg.config

启动训练

模型导出

EAS部署

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rtp_native.md

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RTP部署

编写RTP特征配置 fg.json

生成样本

编写EasyRec配置 fg.config

启动训练

模型导出

EAS部署