add a callback hook right before the optimizer step

src/transformers/trainer.py

@@ -2417,6 +2417,8 @@ def _inner_training_loop(
                         else:
                             grad_norm = _grad_norm
 
+                    self.control = self.callback_handler.on_pre_optimizer_step(args, self.state, self.control)
+
                     self.optimizer.step()
 
                     self.control = self.callback_handler.on_optimizer_step(args, self.state, self.control)

src/transformers/trainer_callback.py

@@ -344,6 +344,12 @@ def on_step_begin(self, args: TrainingArguments, state: TrainerState, control: T
         """
         pass
 
+    def on_pre_optimizer_step(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs):
+        """
+        Event called before the optimizer step but after gradient clipping. Useful for monitoring gradients.
+        """
+        pass
+
     def on_optimizer_step(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs):
         """
         Event called after the optimizer step but before gradients are zeroed out. Useful for monitoring gradients.
@@ -475,6 +481,9 @@ def on_step_begin(self, args: TrainingArguments, state: TrainerState, control: T
         control.should_save = False
         return self.call_event("on_step_begin", args, state, control)
 
+    def on_pre_optimizer_step(self, args: TrainingArguments, state: TrainerState, control: TrainerControl):
+        return self.call_event("on_pre_optimizer_step", args, state, control)
+
     def on_optimizer_step(self, args: TrainingArguments, state: TrainerState, control: TrainerControl):
         return self.call_event("on_optimizer_step", args, state, control)
 

tests/trainer/test_trainer_callback.py

@@ -78,6 +78,9 @@ def on_epoch_end(self, args, state, control, **kwargs):
     def on_step_begin(self, args, state, control, **kwargs):
         self.events.append("on_step_begin")
 
+    def on_pre_optimizer_step(self, args, state, control, **kwargs):
+        self.events.append("on_pre_optimizer_step")
+
     def on_optimizer_step(self, args, state, control, **kwargs):
         self.events.append("on_optimizer_step")
 
@@ -151,7 +154,7 @@ def get_expected_events(self, trainer):
             expected_events.append("on_epoch_begin")
             for _ in range(train_dl_len):
                 step += 1
-                expected_events += ["on_step_begin", "on_optimizer_step", "on_step_end"]
+                expected_events += ["on_step_begin", "on_pre_optimizer_step", "on_optimizer_step", "on_step_end"]
                 if step % trainer.args.logging_steps == 0:
                     expected_events.append("on_log")
                 if trainer.args.eval_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0: