diff --git a/fastNLP/core/dataloaders/torch_dataloader/fdl.py b/fastNLP/core/dataloaders/torch_dataloader/fdl.py
index 99faec7e..9818ab39 100644
--- a/fastNLP/core/dataloaders/torch_dataloader/fdl.py
+++ b/fastNLP/core/dataloaders/torch_dataloader/fdl.py
@@ -199,7 +199,7 @@ class TorchDataLoader(DataLoader):
 def prepare_torch_dataloader(ds_or_db,
                              batch_size: int = 16,
                              shuffle: bool = False,
-                             train_sampler: Union["Sampler[int]", ReproducibleSampler, UnrepeatedSampler] = None,
+                             sampler: Union["Sampler[int]", ReproducibleSampler, UnrepeatedSampler] = None,
                              batch_sampler: Union["Sampler[Sequence[int]]", ReproducibleBatchSampler] = None,
                              num_workers: int = 0, collate_fn: Union[Callable, str, None] = 'auto',
                              pin_memory: bool = False, drop_last: bool = False,
@@ -261,7 +261,7 @@ def prepare_torch_dataloader(ds_or_db,
     from fastNLP.io import DataBundle
     if isinstance(ds_or_db, DataSet):
         dl = TorchDataLoader(dataset=ds_or_db, batch_size=batch_size,
-                             shuffle=shuffle, sampler=train_sampler, batch_sampler=batch_sampler,
+                             shuffle=shuffle, sampler=sampler, batch_sampler=batch_sampler,
                              num_workers=num_workers, collate_fn=collate_fn, pin_memory=pin_memory,
                              drop_last=drop_last, timeout=timeout, worker_init_fn=worker_init_fn,
                              multiprocessing_context=multiprocessing_context, generator=generator,
@@ -274,7 +274,7 @@ def prepare_torch_dataloader(ds_or_db,
         for name, ds in ds_or_db.iter_datasets():
             if 'train' in name:
                 dl_bundle[name] = TorchDataLoader(dataset=ds, batch_size=batch_size,
-                                                  shuffle=shuffle, sampler=train_sampler, batch_sampler=batch_sampler,
+                                                  shuffle=shuffle, sampler=sampler, batch_sampler=batch_sampler,
                                                   num_workers=num_workers, collate_fn=collate_fn, pin_memory=pin_memory,
                                                   drop_last=drop_last, timeout=timeout, worker_init_fn=worker_init_fn,
                                                   multiprocessing_context=multiprocessing_context, generator=generator,
@@ -285,7 +285,7 @@ def prepare_torch_dataloader(ds_or_db,
                 dl_bundle[name] = TorchDataLoader(dataset=ds,
                                                   batch_size=non_train_batch_size if non_train_batch_size else batch_size,
                                                   shuffle=shuffle,
-                                                  sampler=non_train_sampler if non_train_sampler else train_sampler,
+                                                  sampler=non_train_sampler if non_train_sampler else sampler,
                                                   batch_sampler=batch_sampler,
                                                   num_workers=num_workers, collate_fn=collate_fn, pin_memory=pin_memory,
                                                   drop_last=drop_last, timeout=timeout, worker_init_fn=worker_init_fn,
@@ -300,10 +300,10 @@ def prepare_torch_dataloader(ds_or_db,
         for idx, ds in enumerate(ds_or_db):
             if idx > 0:
                 batch_size = non_train_batch_size if non_train_batch_size else batch_size
-                train_sampler = non_train_sampler if non_train_sampler else train_sampler
+                sampler = non_train_sampler if non_train_sampler else sampler
             dl_bundle.append(
                 TorchDataLoader(dataset=ds, batch_size=batch_size,
-                                shuffle=shuffle, sampler=train_sampler, batch_sampler=batch_sampler,
+                                shuffle=shuffle, sampler=sampler, batch_sampler=batch_sampler,
                                 num_workers=num_workers, collate_fn=collate_fn, pin_memory=pin_memory,
                                 drop_last=drop_last, timeout=timeout, worker_init_fn=worker_init_fn,
                                 multiprocessing_context=multiprocessing_context, generator=generator,
@@ -317,7 +317,7 @@ def prepare_torch_dataloader(ds_or_db,
         for name, ds in ds_or_db.items():
             if 'train' in name:
                 dl_bundle[name] = TorchDataLoader(dataset=ds, batch_size=batch_size,
-                                                  shuffle=shuffle, sampler=train_sampler, batch_sampler=batch_sampler,
+                                                  shuffle=shuffle, sampler=sampler, batch_sampler=batch_sampler,
                                                   num_workers=num_workers, collate_fn=collate_fn, pin_memory=pin_memory,
                                                   drop_last=drop_last, timeout=timeout, worker_init_fn=worker_init_fn,
                                                   multiprocessing_context=multiprocessing_context, generator=generator,
@@ -328,7 +328,7 @@ def prepare_torch_dataloader(ds_or_db,
                 dl_bundle[name] = TorchDataLoader(dataset=ds,
                                                   batch_size=non_train_batch_size if non_train_batch_size else batch_size,
                                                   shuffle=shuffle,
-                                                  sampler=non_train_sampler if non_train_sampler else train_sampler,
+                                                  sampler=non_train_sampler if non_train_sampler else sampler,
                                                   batch_sampler=batch_sampler,
                                                   num_workers=num_workers, collate_fn=collate_fn, pin_memory=pin_memory,
                                                   drop_last=drop_last, timeout=timeout, worker_init_fn=worker_init_fn,