diff --git a/wenet/paraformer/convert_paraformer_to_wenet_config.py b/wenet/paraformer/convert_paraformer_to_wenet_config.py
index cce25f783..1c9045603 100644
--- a/wenet/paraformer/convert_paraformer_to_wenet_config.py
+++ b/wenet/paraformer/convert_paraformer_to_wenet_config.py
@@ -99,6 +99,11 @@ def convert_to_wenet_yaml(configs, wenet_yaml_path: str, fields_to_keep: List):
     configs['dataset_conf']['shuffle'] = False
     configs['dataset_conf']['sort'] = False
 
+    configs['grad_clip'] = 5
+    configs['accum_grad'] = 1
+    configs['max_epoch'] = 100
+    configs['log_interval'] = 100
+
     with open(wenet_yaml_path, '+w') as f:
         f.write(yaml.dump(configs))
         f.flush()
diff --git a/wenet/paraformer/paraformer.py b/wenet/paraformer/paraformer.py
index 47e148f80..ebca77c32 100644
--- a/wenet/paraformer/paraformer.py
+++ b/wenet/paraformer/paraformer.py
@@ -183,7 +183,8 @@ def _sampler(self, encoder_out, encoder_out_mask, ys_pad, ys_pad_lens,
                 if target_num > 0:
                     input_mask[li].scatter_(
                         dim=0,
-                        index=torch.randperm(ys_pad_lens[li])[:target_num],
+                        index=torch.randperm(ys_pad_lens[li],
+                                             device=device)[:target_num],
                         value=0,
                     )
             input_mask = torch.where(input_mask > 0, 1, 0)
diff --git a/wenet/utils/init_ali_paraformer.py b/wenet/utils/init_ali_paraformer.py
index 13af51757..c0db11357 100644
--- a/wenet/utils/init_ali_paraformer.py
+++ b/wenet/utils/init_ali_paraformer.py
@@ -17,9 +17,9 @@ def init_model(configs, checkpoint_path=None):
     encoder = SanmEncoder(global_cmvn=global_cmvn,
                           input_size=configs['lfr_conf']['lfr_m'] * input_dim,
                           **configs['encoder_conf'])
-    decoder = decoder = SanmDecoder(vocab_size=vocab_size,
-                                    encoder_output_size=encoder.output_size(),
-                                    **configs['decoder_conf'])
+    decoder = SanmDecoder(vocab_size=vocab_size,
+                          encoder_output_size=encoder.output_size(),
+                          **configs['decoder_conf'])
     predictor = Cif(**configs['cif_predictor_conf'])
     model = Paraformer(
         vocab_size=vocab_size,