[feat] move tdm export model to subdir and add use_tensorboard param to train config

docs/source/models/feature_group.md

@@ -18,6 +18,8 @@ DEEP: 深度特征组。其中feature_names只能包含非序列特征（IdFeatu
 
 - SimpleAttention: 类似于DINEncoder,其中的**score**是`query`和`sequence`矩阵相乘，得到序列特征中对用的权重，然后对权重和向量乘积求和得到池化向量。由于要做矩阵相乘，要求`query`和`sequence`的特征输数量和顺序一定相同。
 
+- PoolingEncoder: 对变长的`sequence`的Emebdding做池化，`pooling_type`支持mean和sum两种，常用于向量召回模型的user侧特征组
+
 #### WIDE
 
 WIDE: 广度特征组，主要用于WideAndDeep/DeepFM模型。其中feature_names只能包含非序列特征IdFeature/RawFeature/ComboFeature等，embedding_dim固定为4，不根据feature_group中的embedding_dim的配置变化而变化，开发者可以根据group_name从EmbeddingGroup的输出字典中获取到相应的特征组Embedding。不可以包含sequence_groups。

docs/source/quick_start/local_tutorial_tdm.md

@@ -70,7 +70,7 @@ torchrun --master_addr=localhost --master_port=32555 \
 
 #### 导出模型和embedding模块
 
-导出命令会自动导出模型模块和embedding模块, embedding模块会存在embedding子目录下
+导出命令会自动导出模型和embedding模块, 模型模块会存在model子目录下，embedding模块会存在embedding子目录下
 
 ```bash
 torchrun --master_addr=localhost --master_port=32555 \
@@ -169,7 +169,7 @@ torchrun --master_addr=localhost --master_port=32555 \
 torchrun --master_addr=localhost --master_port=32555 \
     --nnodes=1 --nproc-per-node=8 --node_rank=0 \
     -m tzrec.tools.tdm.retrieval \
-    --scripted_model_path experiments/tdm_taobao_local/export/ \
+    --scripted_model_path experiments/tdm_taobao_local/export/model/ \
     --predict_input_path data/taobao_data_recall_eval_transformed/\*.parquet \
     --predict_output_path data/init_tree/taobao_data_eval_recall \
     --recall_num 200 \

docs/source/usage/train.md

@@ -86,6 +86,7 @@ LR策略可以支持按epoch更新或者按step更新
       ```
 - log_step_count_steps: 打印log和summary的步数间隔（如果打印时间间隔小于1s，会跳过打印）
 - is_profiling: 是否做训练性能分析，设置为true，会在模型目录下记录trace文件
+- use_tensorboard: 是否使用tensorboard，默认为true
 
 ## 训练性能优化
 

tzrec/main.py

@@ -350,7 +350,7 @@ def _train_and_evaluate(
     eval_summary_writer = None
     if is_local_rank_zero:
         plogger = ProgressLogger(desc="Training Epoch 0", start_n=skip_steps)
-    if is_rank_zero:
+    if is_rank_zero and train_config.use_tensorboard:
         summary_writer = SummaryWriter(model_dir)
         eval_summary_writer = SummaryWriter(os.path.join(model_dir, "eval_val"))
 
@@ -873,15 +873,16 @@ def export(
                     dataloader,
                     os.path.join(export_dir, "embedding"),
                 )
+                break
         _script_model(
             ori_pipeline_config,
             ScriptWrapper(cpu_model),
             cpu_state_dict,
             dataloader,
-            export_dir,
+            os.path.join(export_dir, "model"),
         )
         for asset in assets:
-            shutil.copy(asset, export_dir)
+            shutil.copy(asset, os.path.join(export_dir, "model"))
     else:
         _script_model(
             ori_pipeline_config,

tzrec/protos/train.proto

@@ -22,5 +22,7 @@ message TrainConfig {
     optional uint32 log_step_count_steps = 8 [default = 100];
     // profiling or not
     optional bool is_profiling = 9 [default = false];
+    // use tensorboard or not.
+    optional bool use_tensorboard = 10 [default = true];
     // TBD: qcomm config
 }

tzrec/tests/train_eval_export_test.py

@@ -583,7 +583,7 @@ def test_tdm_train_eval_export(self):
             )
         if self.success:
             self.success = utils.test_tdm_retrieval(
-                scripted_model_path=os.path.join(self.test_dir, "export"),
+                scripted_model_path=os.path.join(self.test_dir, "export/model"),
                 eval_data_path=os.path.join(self.test_dir, r"eval_data/\*.parquet"),
                 retrieval_output_path=os.path.join(self.test_dir, "retrieval_result"),
                 reserved_columns="user_id,item_id",
@@ -601,10 +601,12 @@ def test_tdm_train_eval_export(self):
             )
         )
         self.assertTrue(
-            os.path.exists(os.path.join(self.test_dir, "export/scripted_model.pt"))
+            os.path.exists(
+                os.path.join(self.test_dir, "export/model/scripted_model.pt")
+            )
         )
         self.assertTrue(
-            os.path.exists(os.path.join(self.test_dir, "export/serving_tree"))
+            os.path.exists(os.path.join(self.test_dir, "export/model/serving_tree"))
         )
         self.assertTrue(os.path.exists(os.path.join(self.test_dir, "retrieval_result")))