RTP FG¶
RTP FG能够以比较高的效率生成一些复杂的特征，如MatchFeature和LookupFeature, 线上线下使用同一套代码保证一致性.
其生成的特征可以接入EasyRec进行训练，从RTP FG的配置(fg.json)可以生成EasyRec的配置文件(pipeline.config).
线上部署的时候提供带FG功能的EAS processor，一键部署.
训练¶
编写配置 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"
],
"multi_val_sep": "|"
}
Feature配置说明：
-
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+].
vocab_file: 词典文件路径，根据词典将对应的输入映射成ID.
vocab_list: 词典list，根据词典将对应的输入映射成ID.
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.
和vocab_file, vocab_list相比，优势是不需要词典，词典可以是不固定的.
劣势是需要设置的容量比较大，容易导致hash冲突.
embedding_dimension/embedding_dim: 对应EasyRec feature_config.features里面的embedding_dim.
-
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.
-
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.
-
根据id查找对应的value.
-
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.
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
separator: sparse格式里面，特征之间的分隔符，不指定默认是”,”，
训练时，对稠密格式没有影响，对稀疏格式有影响
预测时，item feature在redis里面存储的是稀疏格式，因此是有影响的
i_item_id:10539078362,i_seller_id:21776327,...
multi_val_sep: 多值特征的分隔符，不指定默认是chr(29) 即”\u001D”
kv_separator: 多值有权重特征的分隔符，如”体育:0.3|娱乐:0.2|军事:0.5”，不指定默认None，即没有权重
model_dir: 模型目录，仅仅影响EasyRec config生成.
num_steps: 训练的轮数，仅仅影响EasyRec config生成.
embedding_dim: 全局的embedding dimension.
适合DeepFM等需要所有的feature都使用统一的embedding_dim.
如果feature字段没有单独设置embedding_dimension, 将使用统一的embedding_dim.
配置里面的embedding_dim会覆盖从命令行easy_rec.python.tools.convert_rtp_fg传入的embedding_dim.
model_type: 模型的类型，当前支持WideAndDeep/MultiTower/DeepFM.
暂未支持的EasyRec模型，可以不指定model_type，在生成EasyRec config之后添加相应的部分.
label_fields: label数组，针对多目标模型需要设置多个label fields.
model_path: 定义模型部分的config文件, 适用于暂未支持的EasyRec模型或自定义模型.
edit_config_json: 对EasyRec config的修改, 如修改dnn的hidden_units
"edit_config_json": [{"model_config.wide_and_deep.dnn.hidden_units": [48, 24]}]
上传数据(如果已经有数据，可以跳过这一步)¶
支持两种格式: 稀疏格式和稠密格式, 根据表的schema自动识别是哪一种格式, 包含user_feature和item_feature则识别成稀疏格式.
稀疏格式的数据: user特征, item特征, context特征各放一列；特征在列内以kv形式存储, 如：
| label | user_id | item_id | context_feature | user_feature | item_feature |
|---|---|---|---|---|---|
| 0 | 122017 | 389957 | tag_category_list:4589,new_user_class_level:,...,user_id:122017 | adgroup_id:539227,pid:430548_1007,...,cate_id:4281 |
-- taobao_train_input.txt oss://easyrec/data/rtp/
-- wget http://easyrec.oss-cn-beijing.aliyuncs.com/data/rtp/taobao_train_input.txt
-- wget http://easyrec.oss-cn-beijing.aliyuncs.com/data/rtp/taobao_test_input.txt
drop table if exists taobao_train_input;
create table if not exists taobao_train_input(`label` BIGINT,user_id STRING,item_id STRING,context_feature STRING,user_feature STRING,item_feature STRING);
tunnel upload taobao_train_input.txt taobao_train_input -fd=';';
drop table if exists taobao_test_input;
create table if not exists taobao_test_input(`label` BIGINT,user_id STRING,item_id STRING,context_feature STRING,user_feature STRING,item_feature STRING);
tunnel upload taobao_test_input.txt taobao_test_input -fd=';';
稠密格式的数据，每个特征是单独的一列，如：
| label | user_id | item_id | tag_category_list | new_user_class_level | age_level |
|---|---|---|---|---|---|
| 1 | 122017 | 389957 | 4589 | 0 |
drop table if exists taobao_train_input;
create table taobao_train_input_dense(label bigint, user_id string, item_id string, tag_category_list bigint, ...);
Note: 特征列名可以加上prefix: “user__”, “item__”, “context__”
如: 列名ctx_position也可以写成 context__ctx_position
生成样本¶
下载rtp_fg jar 包
生成特征
add jar target/fg_on_odps-1.3.57-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 taobao_fg_train_out;
create table taobao_fg_train_out(label bigint, user_id string, item_id string, features string);
jar -resources fg_on_odps-1.3.57-jar-with-dependencies.jar,fg.json -classpath fg_on_odps-1.3.57-jar-with-dependencies.jar com.taobao.fg_on_odps.EasyRecFGMapper -i taobao_train_input -o taobao_fg_train_out -f fg.json;
drop table if exists taobao_fg_test_out;
create table taobao_fg_test_out(label bigint, user_id string, item_id string, features string);
jar -resources fg_on_odps-1.3.57-jar-with-dependencies.jar,fg.json -classpath fg_on_odps-1.3.57-jar-with-dependencies.jar com.taobao.fg_on_odps.EasyRecFGMapper -i taobao_test_input -o taobao_fg_test_out -f fg.json;
--下载查看数据(可选)
tunnel download taobao_fg_test_out taobao_fg_test_out.txt -fd=';';
EasyRecFGMapper参数格式:
-i, 输入表
支持分区表，分区表可以指定partition，也可以不指定partition，不指定partition时使用所有partition
分区格式示例: my_table/day=20201010,sex=male
可以用多个-i指定多个表的多个分区
-o, 输出表，如果是分区表，一定要指定分区，只能指定一个输出表
-f, fg.json
-m, mapper memory的大小，默认可以不设置
EasyRecFGMapper会自动判断是稠密格式还是稀疏格式
如果表里面有user_feature和item_feature字段，那么判定是稀疏格式
否则，判定是稠密格式
生成的特征示例(taobao_fg_train_out):
| label | user_id | item_id | features |
|---|---|---|---|
| 0 | 336811 | 100002 | user_id_100002^Bcms_segid_5^Bcms_group_id_2^Bage_level_2^Bpvalue_level_1^Bshopping_level_3^Boccupation_1^B... |
从配置文件[fg.json]生成EasyRec的config¶
从Git克隆EasyRec
git clone https://github.com/alibaba/EasyRec.git
python -m easy_rec.python.tools.convert_rtp_fg --label clk --rtp_fg fg.json --model_type multi_tower --embedding_dim 10 --output_path fg.config --selected_cols "label,features"
多目标模型写法
python -m easy_rec.python.tools.convert_rtp_fg --label is_product_detail is_purchase --rtp_fg fg.json --model_type dbmtl --embedding_dim 10 --output_path fg.config --selected_cols "is_product_detail,is_purchase,features"
–model_type: 模型类型, 可选: multi_tower, deepfm, essm, dbmtl 其它模型暂时不能设置，需要在生成的config里面增加model_config的部分
–embedding_dim: embedding dimension, 如果fg.json里面的feature没有指定embedding_dimension, 那么将使用该选项指定的值
–batch_size: batch_size, 训练时使用的batch_size
–label: label字段, 可以指定多个
–num_steps: 训练的步数,默认1000
–output_path: 输出的EasyRec config路径
–separator: feature之间的分隔符, 默认是CTRL_B(\u0002)
–selected_cols: 指定输入列，包括label和features，其中label可以指定多列，表示要使用多个label(一般是多任务模型), 最后一列必须是features, 如:
label0,label1,features
注意不要有空格
–incol_separator: feature内部的分隔符，即多值分隔符，默认是CTRL_C(\u0003)
–input_type: 输入类型，默认是OdpsRTPInput, 如果在EMR上使用或者本地使用，应该用RTPInput, 如果使用RTPInput那么–selected_cols也需要进行修改, 使用对应的列的id:
0,4
其中第0列是label, 第4列是features
还需要指定–rtp_separator，表示label和features之间的分隔符, 默认是”;”
–train_input_path, 训练数据路径
MaxCompute上不用指定，在训练的时候指定
–eval_input_path, 评估数据路径
MaxCompute上不用指定，在训练的时候指定
启动训练¶
上传fg.config到oss
启动训练
pai -name easy_rec_ext
-Dconfig=oss://bucket-name/easy_rec_test/fg.config
-Dcmd=train
-Dtables='odps://project-name/tables/taobao_fg_train_out,odps://project-name/tables/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
-Deval_method=separate;
环境里没有安装easy_rec_ext ，则上传easy_rec.tar.gz包
pai -name tensorflow1120_cpu_ext
-Dscript='oss://<path>/easy_rec.tar.gz'
-DentryFile='run.py'
-Dbuckets='oss://<bucket-name>/'
-Dtables='odps://<project-name>/tables/<train_table_name>/dt=${bizdate},odps://<project-name>/tables/<test_table_name>/dt=${bizdate}'
-Darn='acs:ram::xxx:role/aliyunodpspaidefaultrole'
-DossHost='oss-us-west-1-internal.aliyuncs.com'
-Dcluster='{
\"ps\": {
\"count\" : 4,
\"cpu\" : 600,
\"memory\" : 30000
},
\"worker\" : {
\"count\" : 33,
\"cpu\" : 800,
\"memory\" : 30000
}
}'
-DuserDefinedParameters='--cmd train --config oss://<path>/fg.config --model_dir oss://<model_path>/ --train_tables odps://<project-name>/tables/<train_table_name>/dt=${bizdate} --eval_tables odps://<project-name>/tables/<test_table_name>/dt=${bizdate} --with_evaluator'
;
模型导出¶
pai -name tensorflow1120_cpu_ext
-Dscript='oss://<path>/easy_rec.tar.gz'
-DentryFile='run.py'
-Dbuckets='oss://<bucket-name>/'
-Darn='acs:ram::xxx:role/aliyunodpspaidefaultrole'
-DossHost='oss-us-west-1-internal.aliyuncs.com'
-DuserDefinedParameters='--cmd export --config=oss://<model_path>/pipeline.config --export_dir=oss://<export_path>/ --asset_files=oss://<path>/fg.json';
;
增加特征¶
增加特征可以用原来的样本表A left outer join 新增的特征表B 生成表C
create table C
as select * from A
left outer join B
on A.req_id = B.req_id and A.item_id = B.item_id
表C使用增量incre_fg.json生成表incre_fea_table, incre_fg.json定义了新增的特征
jar -resources fg_on_odps-1.3.57-jar-with-dependencies.jar,incre_fg.json -classpath fg_on_odps-1.3.57-jar-with-dependencies.jar com.taobao.fg_on_odps.EasyRecFGMapper -i taobao_test_input -o taobao_fg_test_out -f incre_fg.json;
生成新的样本表D:
create new_feature_table as
select A.*, wm_concat(fea_table.features, chr(2), incre_fea_table.features) as features
from A
inner join fea_table
on A.req_id = fea_table.req_id and A.item_id = fea_table.item_id
inner join incre_fea_table
on A.req_id = incre_fea_table.req_id and A.req_id = incre_fea_table.item_id
特征筛选¶
可以筛选fg.json里面的部分特征用于训练
方法: 在fg.config的model_config.feature_groups里面把不需要的特征注释掉即可
预测¶
服务部署¶
部署的 service.json 示例如下
bizdate=$1
cat << EOF > echo.json
{
"name":"easyrec_processor",
"baseimage": "registry.cn-shanghai.aliyuncs.com/eas/eas-worker-amd64:0.4.22",
"metadata": {
"region": "us-west-1",
"cpu": 6,
"memory": 20000,
"instance": 3
},
"model_config":"{\"holo-conf\":{\"url\":\"postgresql://<AccessKeyID>:<AccessKeySecret>@<域名>:<port>/<database>\",\"prefix\":\"fg_*\",\"table\" : [{\"name\": \"<schema>.<table_name>\",\"key\" : \"<index_column_name>\",\"value\": \"<column_name>\",\"period\": 2880}]},\"period\": 2880,\"fg\":true,\"multitargets\":true,\"outputs\":\"probs_ctr,probs_cvr\",\"inter_op_parallelism_threads\": 6, \"intra_op_parallelism_threads\": 6, \"fg_ins_num\":2}",
"model_path": "oss://<model_path>/",
"processor_path": "oss://easyrec/deploy/processor/easyrec_holo_broadwell.tar.gz",
"processor_entry": "libtf_predictor.so",
"token": "Y2E4OGY2MTBkODFhMzJhMDUzODM0YmE4OGRjZTI2MTgxYWNhOWRkNw==",
"processor_type": "cpp"
}
EOF
# 执行部署命令。
#/home/admin/usertools/tools/eascmd -i <AccessKeyID> -k <AccessKeySecret> -e pai-eas.us-west-1.aliyuncs.com create echo.json
/home/admin/usertools/tools/eascmd -i <AccessKeyID> -k <AccessKeySecret> -e pai-eas.us-west-1.aliyuncs.com update easyrec_processor -s echo.json
训练导出的时候需要修改fg.config ，保证导出的模型是支持多个place_holder 的输入
export_config {
multi_placeholder: true
}
processor_path， processor_entry， processor_type 自定义 easyrec processor 设置，与示例保持一致即可
model_config: eas 部署配置。主要控制把 item 特征加载到内存中。目前数据源支持redis和holo
redis-conf: 配置redis 访问的相关配置，包括 url, password
prefix: item_id key的前缀, 为了和其它的key(如user_id等)区分开来
cluster: cluster模式访问redis, 默认是false, 使用单例模式
pool_size: redis connection pool size
period: item feature reload period, 单位minutes
更多选项:
model_config:
fg_ins_num: fg并行数，可以加快fg的计算速度
multitargets: 是否多目标模型
outputs: saved_model output signatures, 如果有多个，之间用,分割
"model_config":{ "fg_ins_num": 4, "multitargets": true, "outputs": "probs_ctr,probs_cvr", ... }
holo-conf: 也支持使用holo存储item feature, 好处是支持增量更新
需要创建一张holo表, 包含3列:
｜item_id｜item_features｜update_time｜
url: holo url
user: holo db username
password: holo db password
dbname: holo dbname
table: holo table name
key: name of the column store item_ids
value: name of the column store item features
{
"model_config":{
"holo-conf":{
"url":"hgprecn-cn-09k22ikm5008-cn-hangzhou.hologres.aliyuncs.com",
"user":"admin",
"password":"1234567",
"dbname":"easyrec_test",
"table":"test_table",
"key":"item_id",
"value":"item_features"
}
...
}
}
客户端访问¶
同eas sdk 中的TFRequest类似，easyrec 也是使用ProtoBuffer 作为传输协议. proto 文件定义：
syntax = "proto3";
package com.alibaba.pairec.processor;
option cc_enable_arenas = true;
option java_package = "com.alibaba.pairec.processor";
option java_outer_classname = "PredictProtos";
// context features
message ContextFeatures {
repeated string features = 1;
}
// PBRequest specifies the request for aggregator
message PBRequest {
// debug mode
bool debug_mode = 1;
// user features
map<string, string> user_features = 2;
// item ids
repeated string item_ids = 3;
// context features for each item
map<string, ContextFeatures> context_features = 4;
}
// return results
message Results {
# use repeated to be compatiable for multi-target models.
repeated double scores = 1 [packed = true];
}
enum StatusCode {
OK = 0;
INPUT_EMPTY = 1;
EXCEPTION = 2;
}
// PBResponse specifies the response for aggregator
message PBResponse {
// results
map<string, Results> results = 1;
// item features
map<string, string> item_features = 2;
// generate features
map<string, string> generate_features = 3;
// context features
map<string, ContextFeatures> context_features = 4;
string error_msg = 5;
StatusCode status_code = 6;
}
提供了 java 的客户端实例，客户端 jar 包地址. 下载后的 jar 通过下面命令安装到本地 mvn 库里.
mvn install:install-file -Dfile=easyrec-eas-client-0.0.1-jar-with-dependencies.jar -DgroupId=com.alibaba.pairec -DartifactId=easyrec-eas-client -Dversion=0.0.1 -Dpackaging=jar
然后在pom.xml里面加入:
<dependency>
<groupId>com.alibaba.pairec</groupId>
<artifactId>easyrec-eas-client</artifactId>
<version>0.0.1</version>
</dependency>
java 客户端测试代码参考：
import com.alibaba.pairec.processor.client.*;
PaiPredictClient client = new PaiPredictClient(new HttpConfig());
client.setEndpoint(cmd.getOptionValue("e"));
client.setModelName(cmd.getOptionValue("m"));
EasyrecRequest easyrecRequest = new EasyrecRequest(separator);
easyrecRequest.appendUserFeatureString(userFeatures);
easyrecRequest.appendContextFeatureString(contextFeatures);
easyrecRequest.appendItemStr(itemIdStr, ",");
PredictProtos.PBResponse response = client.predict(easyrecRequest);
for (Map.Entry<String, PredictProtos.Results> entry : response.getResultsMap().entrySet()) {
String key = entry.getKey();
PredictProtos.Results value = entry.getValue();
System.out.print("key: " + key);
for (int i = 0; i < value.getScoresCount(); i++) {
System.out.format(" value: %.4f ", value.getScores(i));
}
}
验证特征一致性
...
easyrecRequest.setDebug();
PredictProtos.PBResponse response = client.predict(easyrecRequest);
for (Map.Entry<String, PredictProtos.Results> entry :
response.getResultsMap().entrySet()) {
Map<String, String> itemFeas = response.getItemFeatures();
for(String itemId: itemFeas.keySet()) {
System.out.println(itemId);
System.out.println(itemFeas.get(itemId));
}
}
Note: 生产环境调用的时候不要设置debug，会导致rt升高，qps下降.