!3729 modify readme and timemoniter steps

Merge pull request !3729 from wanghua/c75_tinybert

model_zoo/official/nlp/tinybert/README.md

@@ -1,6 +1,6 @@
 # TinyBERT Example
 ## Description
-This example implements general distill and task distill of [BERT-base](https://github.com/google-research/bert)(the base version of BERT model).
+[TinyBERT](https://github.com/huawei-noah/Pretrained-Model/tree/master/TinyBERT) is 7.5x smalller and 9.4x faster on inference than [BERT-base](https://github.com/google-research/bert)(the base version of BERT model) and achieves competitive performances in the tasks of natural language understanding. It performs a novel transformer distillation at both the pre-training and task-specific learning stages.
 
 ## Requirements
 - Install [MindSpore](https://www.mindspore.cn/install/en).

model_zoo/official/nlp/tinybert/run_general_distill.py

@@ -87,8 +87,10 @@ def run_general_distill():
 
     if args_opt.enable_data_sink == "true":
         repeat_count = args_opt.epoch_size * dataset.get_dataset_size() // args_opt.data_sink_steps
+        time_monitor_steps = args_opt.data_sink_steps
     else:
         repeat_count = args_opt.epoch_size
+        time_monitor_steps = dataset_size
 
     lr_schedule = BertLearningRate(learning_rate=common_cfg.AdamWeightDecay.learning_rate,
                                    end_learning_rate=common_cfg.AdamWeightDecay.end_learning_rate,
@@ -104,10 +106,10 @@ def run_general_distill():
 
     optimizer = AdamWeightDecay(group_params, learning_rate=lr_schedule, eps=common_cfg.AdamWeightDecay.eps)
 
-    callback = [TimeMonitor(dataset_size), LossCallBack(), ModelSaveCkpt(netwithloss.bert,
-                                                                         args_opt.save_ckpt_step,
-                                                                         args_opt.max_ckpt_num,
-                                                                         save_ckpt_dir)]
+    callback = [TimeMonitor(time_monitor_steps), LossCallBack(), ModelSaveCkpt(netwithloss.bert,
+                                                                               args_opt.save_ckpt_step,
+                                                                               args_opt.max_ckpt_num,
+                                                                               save_ckpt_dir)]
 
     update_cell = DynamicLossScaleUpdateCell(loss_scale_value=common_cfg.loss_scale_value,
                                              scale_factor=common_cfg.scale_factor,

model_zoo/official/nlp/tinybert/run_task_distill.py

@@ -92,8 +92,10 @@ def run_predistill():
     dataset_size = dataset.get_dataset_size()
     if args_opt.enable_data_sink == 'true':
         repeat_count = args_opt.td_phase1_epoch_size * dataset.get_dataset_size() // args_opt.data_sink_steps
+        time_monitor_steps = args_opt.data_sink_steps
     else:
         repeat_count = args_opt.td_phase1_epoch_size
+        time_monitor_steps = dataset_size
 
     optimizer_cfg = cfg.optimizer_cfg
 
@@ -110,10 +112,10 @@ def run_predistill():
                     {'order_params': params}]
 
     optimizer = AdamWeightDecay(group_params, learning_rate=lr_schedule, eps=optimizer_cfg.AdamWeightDecay.eps)
-    callback = [TimeMonitor(dataset_size), LossCallBack(), ModelSaveCkpt(netwithloss.bert,
-                                                                         args_opt.save_ckpt_step,
-                                                                         args_opt.max_ckpt_num,
-                                                                         td_phase1_save_ckpt_dir)]
+    callback = [TimeMonitor(time_monitor_steps), LossCallBack(), ModelSaveCkpt(netwithloss.bert,
+                                                                               args_opt.save_ckpt_step,
+                                                                               args_opt.max_ckpt_num,
+                                                                               td_phase1_save_ckpt_dir)]
     update_cell = DynamicLossScaleUpdateCell(loss_scale_value=cfg.loss_scale_value,
                                              scale_factor=cfg.scale_factor,
                                              scale_window=cfg.scale_window)
@@ -147,8 +149,10 @@ def run_task_distill(ckpt_file):
     dataset_size = train_dataset.get_dataset_size()
     if args_opt.enable_data_sink == 'true':
         repeat_count = args_opt.td_phase2_epoch_size * train_dataset.get_dataset_size() // args_opt.data_sink_steps
+        time_monitor_steps = args_opt.data_sink_steps
     else:
         repeat_count = args_opt.td_phase2_epoch_size
+        time_monitor_steps = dataset_size
 
     optimizer_cfg = cfg.optimizer_cfg
 
@@ -170,14 +174,14 @@ def run_task_distill(ckpt_file):
                                            device_num, rank, args_opt.do_shuffle,
                                            args_opt.eval_data_dir, args_opt.schema_dir)
     if args_opt.do_eval.lower() == "true":
-        callback = [TimeMonitor(dataset_size), LossCallBack(),
+        callback = [TimeMonitor(time_monitor_steps), LossCallBack(),
                     ModelSaveCkpt(netwithloss.bert,
                                   args_opt.save_ckpt_step,
                                   args_opt.max_ckpt_num,
                                   td_phase2_save_ckpt_dir),
                     EvalCallBack(netwithloss.bert, eval_dataset)]
     else:
-        callback = [TimeMonitor(dataset_size), LossCallBack(),
+        callback = [TimeMonitor(time_monitor_steps), LossCallBack(),
                     ModelSaveCkpt(netwithloss.bert,
                                   args_opt.save_ckpt_step,
                                   args_opt.max_ckpt_num,