avoid zero size batch caused by grad_accumulation

steps/trainer.py

@@ -90,6 +90,8 @@ class Trainer:
                     cur_batch = {key: batch[key][cur_ind] for key in batch}
                     with torch.cuda.amp.autocast(dtype=torch.float16 if self.args.precision=="float16" else torch.float32):
                         out = self.model(cur_batch)
+                        if out == None:
+                            continue
 
                     record_loss = out['loss'].detach().to(self.rank) 
                     top10acc = out['top10acc'].to(self.rank)