fix callback log multigpu #3559

src/llmtuner/extras/callbacks.py

@@ -70,11 +70,9 @@ class LogCallback(TrainerCallback):
         r"""
         Event called at the beginning of training.
         """
-        if args.should_log:
+        if args.should_save:
             self.do_train = True
             self._reset(max_steps=state.max_steps)
-
-        if args.should_save:
             os.makedirs(args.output_dir, exist_ok=True)
             self.thread_pool = ThreadPoolExecutor(max_workers=1)
 
@@ -98,7 +96,7 @@ class LogCallback(TrainerCallback):
         r"""
         Event called at the end of a training step.
         """
-        if args.should_log:
+        if args.should_save:
             self._timing(cur_steps=state.global_step)
 
         if self.aborted:
@@ -119,7 +117,7 @@ class LogCallback(TrainerCallback):
         Event called after a prediction step.
         """
         eval_dataloader = kwargs.pop("eval_dataloader", None)
-        if args.should_log and has_length(eval_dataloader) and not self.do_train:
+        if args.should_save and has_length(eval_dataloader) and not self.do_train:
             if self.max_steps == 0:
                 self.max_steps = len(eval_dataloader)
 
@@ -131,8 +129,11 @@ class LogCallback(TrainerCallback):
 
     def on_log(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs) -> None:
         r"""
-        Event called after logging the last logs, `args.should_log` has been applied.
+        Event called after logging the last logs.
         """
+        if not args.should_save:
+            return
+
         logs = dict(
             current_steps=self.cur_steps,
             total_steps=self.max_steps,
@@ -148,12 +149,12 @@ class LogCallback(TrainerCallback):
             remaining_time=self.remaining_time,
         )
         logs = {k: v for k, v in logs.items() if v is not None}
-        if self.webui_mode and "loss" in logs and "learning_rate" in logs and "epoch" in logs:
+        if self.webui_mode and all(key in logs for key in ["loss", "learning_rate", "epoch"]):
             logger.info(
                 "{{'loss': {:.4f}, 'learning_rate': {:2.4e}, 'epoch': {:.2f}}}".format(
                     logs["loss"], logs["learning_rate"], logs["epoch"]
                 )
             )
 
-        if args.should_save and self.thread_pool is not None:
+        if self.thread_pool is not None:
             self.thread_pool.submit(self._write_log, args.output_dir, logs)