Introduce logging_strategy training argument

src/transformers/trainer_callback.py

@@ -24,7 +24,7 @@
 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 @@ def on_step_end(self, args: TrainingArguments, state: TrainerState, control: Tra
         # 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 @@ def on_step_end(self, args: TrainingArguments, state: TrainerState, control: Tra
         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:

src/transformers/trainer_utils.py

@@ -107,6 +107,12 @@ class EvaluationStrategy(ExplicitEnum):
     EPOCH = "epoch"
 
 
+class LoggingStrategy(ExplicitEnum):
+    NO = "no"
+    STEPS = "steps"
+    EPOCH = "epoch"
+
+
 class BestRun(NamedTuple):
     """
     The best run found by an hyperparameter search (see :class:`~transformers.Trainer.hyperparameter_search`).

src/transformers/training_args.py

@@ -25,7 +25,7 @@
     is_torch_tpu_available,
     torch_required,
 )
-from .trainer_utils import EvaluationStrategy, SchedulerType
+from .trainer_utils import EvaluationStrategy, LoggingStrategy, SchedulerType
 from .utils import logging
 
 
@@ -139,10 +139,17 @@ class TrainingArguments:
         logging_dir (:obj:`str`, `optional`):
             `TensorBoard <https://www.tensorflow.org/tensorboard>`__ log directory. Will default to
             `runs/**CURRENT_DATETIME_HOSTNAME**`.
+        logging_strategy (:obj:`str` or :class:`~transformers.trainer_utils.LoggingStrategy`, `optional`, defaults to :obj:`"steps"`):
+            The logging strategy to adopt during training. Possible values are:
+
+                * :obj:`"no"`: No logging is done during training.
+                * :obj:`"epoch"`: Logging is done at the end of each epoch.
+                * :obj:`"steps"`: Logging is done every :obj:`logging_steps`.
+
         logging_first_step (:obj:`bool`, `optional`, defaults to :obj:`False`):
             Whether to log and evaluate the first :obj:`global_step` or not.
         logging_steps (:obj:`int`, `optional`, defaults to 500):
-            Number of update steps between two logs.
+            Number of update steps between two logs if :obj:`logging_strategy="steps"`.
         save_steps (:obj:`int`, `optional`, defaults to 500):
             Number of updates steps before two checkpoint saves.
         save_total_limit (:obj:`int`, `optional`):
@@ -336,6 +343,10 @@ class TrainingArguments:
     warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."})
 
     logging_dir: Optional[str] = field(default_factory=default_logdir, metadata={"help": "Tensorboard log dir."})
+    logging_strategy: LoggingStrategy = field(
+        default="steps",
+        metadata={"help": "The logging strategy to use."},
+    )
     logging_first_step: bool = field(default=False, metadata={"help": "Log the first global_step"})
     logging_steps: int = field(default=500, metadata={"help": "Log every X updates steps."})
     save_steps: int = field(default=500, metadata={"help": "Save checkpoint every X updates steps."})
@@ -475,6 +486,7 @@ def __post_init__(self):
         if self.disable_tqdm is None:
             self.disable_tqdm = logger.getEffectiveLevel() > logging.WARN
         self.evaluation_strategy = EvaluationStrategy(self.evaluation_strategy)
+        self.logging_strategy = LoggingStrategy(self.logging_strategy)
         self.lr_scheduler_type = SchedulerType(self.lr_scheduler_type)
         if self.do_eval is False and self.evaluation_strategy != EvaluationStrategy.NO:
             self.do_eval = True

src/transformers/training_args_tf.py

@@ -102,10 +102,17 @@ class TFTrainingArguments(TrainingArguments):
         logging_dir (:obj:`str`, `optional`):
             `TensorBoard <https://www.tensorflow.org/tensorboard>`__ log directory. Will default to
             `runs/**CURRENT_DATETIME_HOSTNAME**`.
+        logging_strategy (:obj:`str` or :class:`~transformers.trainer_utils.LoggingStrategy`, `optional`, defaults to :obj:`"steps"`):
+            The logging strategy to adopt during training. Possible values are:
+
+                * :obj:`"no"`: No logging is done during training.
+                * :obj:`"epoch"`: Logging is done at the end of each epoch.
+                * :obj:`"steps"`: Logging is done every :obj:`logging_steps`.
+
         logging_first_step (:obj:`bool`, `optional`, defaults to :obj:`False`):
             Whether to log and evaluate the first :obj:`global_step` or not.
         logging_steps (:obj:`int`, `optional`, defaults to 500):
-            Number of update steps between two logs.
+            Number of update steps between two logs if :obj:`logging_strategy="steps"`.
         save_steps (:obj:`int`, `optional`, defaults to 500):
             Number of updates steps before two checkpoint saves.
         save_total_limit (:obj:`int`, `optional`):