Simplify dataloader code

egs/libriheavy/LM/zipformer1/lm_datamodule.py

@@ -115,23 +115,14 @@ class LmDataset(torch.utils.data.IterableDataset):
 
 
 
-def LmDataloader(dataset: LmDataset,
-                 batch_size: int,
-                 num_workers: int):
-
-    return torch.utils.data.DataLoader(
-        dataset=dataset,
-        batch_size=batch_size,
-        num_workers=num_workers,
-        drop_last=False)
-
 
 
 
 def _test():
     l = LmDataset('files.txt')
 
-    d = LmDataloader(l, batch_size=5, num_workers=4)
+    d = torch.utils.data.DataLoader(
+        dataset=l, batch_size=5, num_workers=4, drop_last=True)
 
     for batch in d:
         logging.info("batch shape: ", batch.shape)

egs/libriheavy/LM/zipformer1/train.py

@@ -59,7 +59,7 @@ import optim
 import torch
 import torch.multiprocessing as mp
 import torch.nn as nn
-from lm_datamodule import LmDataset, LmDataloader
+from lm_datamodule import LmDataset
 from subformer import Subformer
 from scaling import ScheduledFloat
 from lhotse.utils import fix_random_seed
@@ -982,14 +982,19 @@ def run(rank, world_size, args):
 
     train = LmDataset(params.train_file_list,
                       bytes_per_segment=params.bytes_per_segment,)
-    train_dl = LmDataloader(train, batch_size=params.batch_size,
-                            num_workers=params.num_workers)
+    train_dl = torch.utils.data.DataLoader(
+        dataset=train,
+        batch_size=params.batch_size,
+        num_workers=params.num_workers,
+        drop_last=True)
 
     valid = LmDataset(params.valid_file_list,
                       bytes_per_segment=params.bytes_per_segment)
-    valid_dl = LmDataloader(valid, batch_size=params.batch_size,
-                            num_workers=params.num_workers)
-
+    valid_dl = torch.utils.data.DataLoader(
+        dataset=valid,
+        batch_size=params.batch_size,
+        num_workers=params.num_workers,
+        drop_last=False)
 
     scaler = GradScaler(enabled=params.use_fp16,
                         init_scale=1.0)