mv scaler.unscale_ blow grad_clip.

paddlespeech/s2t/exps/u2/model.py

@@ -82,7 +82,6 @@ class U2Trainer(Trainer):
         with context():
             if scaler:
                 scaler.scale(loss).backward()
-                scaler.unscale_(self.optimizer)
             else:
                 loss.backward()
             layer_tools.print_grads(self.model, print_func=None)
@@ -91,6 +90,8 @@ class U2Trainer(Trainer):
         if (batch_index + 1) % train_conf.accum_grad == 0:
             # do global grad clip
             if train_conf.global_grad_clip != 0:
+                if scaler:
+                    scaler.unscale_(self.optimizer)
                 # need paddlepaddle==develop or paddlepaddle>=2.5
                 clip_grad_norm_(self.model.parameters(),
                                 train_conf.global_grad_clip)