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Add support for componet based Custom op (#430)
* add custom op & kd loss & add support for rank distillation
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| # 使用自定义 OP | ||
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| 当内置的tf算子不能满足业务需求，或者通过组合现有算子实现需求的性能较差时，可以考虑自定义tf的OP。 | ||
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| 1. 实现自定义算子，编译为动态库 | ||
| - 参考官方示例：[TensorFlow Custom Op](https://github.com/tensorflow/custom-op/) | ||
| - 注意：自定义Op的编译依赖tf版本需要与执行时的tf版本保持一致 | ||
| - 您可能需要为离线训练 与 在线推理服务 编译两个不同依赖环境的动态库 | ||
| - 在PAI平台上需要依赖 tf 1.12 版本编译（先下载pai-tf的官方镜像） | ||
| - 在EAS的 [EasyRec Processor](https://help.aliyun.com/zh/pai/user-guide/easyrec) 中使用自定义Op需要依赖 tf 2.10.1 编译 | ||
| 1. 在`EasyRec`中使用自定义Op的步骤 | ||
| 1. 下载EasyRec的最新[源代码](https://github.com/alibaba/EasyRec) | ||
| 1. 把上一步编译好的动态库放到`easy_rec/python/ops/${tf_version}`目录，注意版本要子目录名一致 | ||
| 1. 开发一个使用自定义Op的组件 | ||
| - 新组件的代码添加到 `easy_rec/python/layers/keras/custom_ops.py` | ||
| - `custom_ops.py` 提供了一个自定义Op组件的示例 | ||
| - 声明新组件，在`easy_rec/python/layers/keras/__init__.py`文件中添加导出语句 | ||
| 1. 编写模型配置文件，使用组件化的方式搭建模型，包含新定义的组件（参考下文） | ||
| 1. 运行`pai_jobs/deploy_ext.sh`脚本，打包EasyRec，并把打好的资源包（`easy_rec_ext_${version}_res.tar.gz`）上传到MaxCompute项目空间 | ||
| 1. (在DataWorks里 or 用odpscmd客户端工具) 训练 & 评估 & 导出 模型 | ||
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| ## 导出自定义Op的动态库到 saved_model 的 assets 目录 | ||
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| ```bash | ||
| pai -name easy_rec_ext | ||
| -Dcmd='export' | ||
| -Dconfig='oss://cold-start/EasyRec/custom_op/pipeline.config' | ||
| -Dexport_dir='oss://cold-start/EasyRec/custom_op/export/final_with_lib' | ||
| -Dextra_params='--asset_files oss://cold-start/EasyRec/config/libedit_distance.so' | ||
| -Dres_project='pai_rec_test_dev' | ||
| -Dversion='0.7.5' | ||
| -Dbuckets='oss://cold-start/' | ||
| -Darn='acs:ram::XXXXXXXXXX:role/aliyunodpspaidefaultrole' | ||
| -DossHost='oss-cn-beijing-internal.aliyuncs.com' | ||
| ; | ||
| ``` | ||
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| **注意**： | ||
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| 1. 在 训练、评估、导出 命令中需要用`-Dres_project`指定上传easyrec资源包的MaxCompute项目空间名 | ||
| 1. 在 训练、评估、导出 命令中需要用`-Dversion`指定资源包的版本 | ||
| 1. asset_files参数指定的动态库会被线上推理服务加载，因此需要在与线上推理服务一致的tf版本上编译。（目前是EAS平台的EasyRec Processor依赖 tf 2.10.1版本）。 | ||
| - 如果 asset_files 参数还需要指定其他文件路径（比如 fg.json），多个路径之间用英文逗号隔开。 | ||
| 1. 再次强调一遍，**导出的动态库依赖的tf版本需要与推理服务依赖的tf版本保持一致** | ||
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| ## 自定义Op的示例 | ||
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| 使用自定义OP求两段输入文本的Term匹配率 | ||
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| ```protobuf | ||
| feature_config: { | ||
| ... | ||
| features: { | ||
| feature_name: 'raw_genres' | ||
| input_names: 'genres' | ||
| feature_type: PassThroughFeature | ||
| } | ||
| features: { | ||
| feature_name: 'raw_title' | ||
| input_names: 'title' | ||
| feature_type: PassThroughFeature | ||
| } | ||
| } | ||
| model_config: { | ||
| model_class: 'RankModel' | ||
| model_name: 'MLP' | ||
| feature_groups: { | ||
| group_name: 'text' | ||
| feature_names: 'raw_genres' | ||
| feature_names: 'raw_title' | ||
| wide_deep: DEEP | ||
| } | ||
| feature_groups: { | ||
| group_name: 'features' | ||
| feature_names: 'user_id' | ||
| feature_names: 'movie_id' | ||
| feature_names: 'gender' | ||
| feature_names: 'age' | ||
| feature_names: 'occupation' | ||
| feature_names: 'zip_id' | ||
| feature_names: 'movie_year_bin' | ||
| wide_deep: DEEP | ||
| } | ||
| backbone { | ||
| blocks { | ||
| name: 'text' | ||
| inputs { | ||
| feature_group_name: 'text' | ||
| } | ||
| raw_input { | ||
| } | ||
| } | ||
| blocks { | ||
| name: 'match_ratio' | ||
| inputs { | ||
| block_name: 'text' | ||
| } | ||
| keras_layer { | ||
| class_name: 'OverlapFeature' | ||
| overlap { | ||
| separator: " " | ||
| default_value: "0" | ||
| methods: "query_common_ratio" | ||
| } | ||
| } | ||
| } | ||
| blocks { | ||
| name: 'mlp' | ||
| inputs { | ||
| feature_group_name: 'features' | ||
| } | ||
| inputs { | ||
| block_name: 'match_ratio' | ||
| } | ||
| keras_layer { | ||
| class_name: 'MLP' | ||
| mlp { | ||
| hidden_units: [256, 128] | ||
| } | ||
| } | ||
| } | ||
| } | ||
| model_params { | ||
| l2_regularization: 1e-5 | ||
| } | ||
| embedding_regularization: 1e-6 | ||
| } | ||
| ``` | ||
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| 1. 如果自定义Op需要处理原始输入特征，则在定义特征时指定 `feature_type: PassThroughFeature` | ||
| - 非 `PassThroughFeature` 类型的特征会在预处理阶段做一些变换，组件代码里拿不到原始值 | ||
| 1. 自定义Op需要处理的原始输入特征按照顺序放置到同一个`feature group`内 | ||
| 1. 配置一个类型为`raw_input`的输入组件，获取原始输入特征 | ||
| - 这是目前EasyRec支持的读取原始输入特征的唯一方式 |
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