Introduce logging_strategy training argument (#10267) (#10267)

Introduce logging_strategy training argument
in TrainingArguments and TFTrainingArguments. (#9838)

src/transformers/trainer_callback.py

@@ -24,7 +24,7 @@ from typing import Dict, List, Optional, Union
 import numpy as np
 from tqdm.auto import tqdm
 
-from .trainer_utils import EvaluationStrategy
+from .trainer_utils import EvaluationStrategy, LoggingStrategy
 from .training_args import TrainingArguments
 from .utils import logging
 
@@ -403,7 +403,11 @@ class DefaultFlowCallback(TrainerCallback):
         # Log
         if state.global_step == 1 and args.logging_first_step:
             control.should_log = True
-        if args.logging_steps > 0 and state.global_step % args.logging_steps == 0:
+        if (
+            args.logging_strategy == LoggingStrategy.STEPS
+            and args.logging_steps > 0
+            and state.global_step % args.logging_steps == 0
+        ):
             control.should_log = True
 
         # Evaluate
@@ -423,6 +427,11 @@ class DefaultFlowCallback(TrainerCallback):
         return control
 
     def on_epoch_end(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs):
+        # Log
+        if args.logging_strategy == LoggingStrategy.EPOCH:
+            control.should_log = True
+
+        # Evaluate
         if args.evaluation_strategy == EvaluationStrategy.EPOCH:
             control.should_evaluate = True
             if args.load_best_model_at_end: