diff --git a/egs/aishell/ASR/conformer_mmi/transformer.py b/egs/aishell/ASR/conformer_mmi/transformer.py
index dfd888414..a3e50e385 100644
--- a/egs/aishell/ASR/conformer_mmi/transformer.py
+++ b/egs/aishell/ASR/conformer_mmi/transformer.py
@@ -545,7 +545,6 @@ class TransformerDecoderLayer(nn.Module):
         memory_mask: Optional[torch.Tensor] = None,
         tgt_key_padding_mask: Optional[torch.Tensor] = None,
         memory_key_padding_mask: Optional[torch.Tensor] = None,
-        **kwargs,
     ) -> torch.Tensor:
         """Pass the inputs (and mask) through the decoder layer.
 
diff --git a/egs/tedlium3/ASR/conformer_ctc2/transformer.py b/egs/tedlium3/ASR/conformer_ctc2/transformer.py
index 804c92957..9dbf32e48 100644
--- a/egs/tedlium3/ASR/conformer_ctc2/transformer.py
+++ b/egs/tedlium3/ASR/conformer_ctc2/transformer.py
@@ -612,7 +612,6 @@ class TransformerDecoderLayer(nn.Module):
         tgt_key_padding_mask: Optional[torch.Tensor] = None,
         memory_key_padding_mask: Optional[torch.Tensor] = None,
         warmup: float = 1.0,
-        **kwargs,
     ) -> torch.Tensor:
         """Pass the inputs (and mask) through the decoder layer.
 
diff --git a/icefall/utils.py b/icefall/utils.py
index a04bedffd..427755090 100644
--- a/icefall/utils.py
+++ b/icefall/utils.py
@@ -1391,20 +1391,13 @@ def add_eos(ragged: k2.RaggedTensor, eos_id: int) -> k2.RaggedTensor:
     return concat(ragged, eos_id, direction="right")
 
 
-def make_pad_mask(
-    lengths: torch.Tensor,
-    max_len: int = 0,
-    pad_left: bool = False,
-) -> torch.Tensor:
+def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor:
     """
     Args:
       lengths:
         A 1-D tensor containing sentence lengths.
       max_len:
         The length of masks.
-      pad_left:
-        If ``False`` (default), padding is on the right.
-        If ``True``, padding is on the left.
     Returns:
       Return a 2-D bool tensor, where masked positions
       are filled with `True` and non-masked positions are
@@ -1421,14 +1414,9 @@ def make_pad_mask(
     max_len = max(max_len, lengths.max())
     n = lengths.size(0)
     seq_range = torch.arange(0, max_len, device=lengths.device)
-    expanded_lengths = seq_range.unsqueeze(0).expand(n, max_len)
+    expaned_lengths = seq_range.unsqueeze(0).expand(n, max_len)
 
-    if pad_left:
-        mask = expanded_lengths < (max_len - lengths).unsqueeze(1)
-    else:
-        mask = expanded_lengths >= lengths.unsqueeze(-1)
-
-    return mask
+    return expaned_lengths >= lengths.unsqueeze(-1)
 
 
 # Copied and modified from https://github.com/wenet-e2e/wenet/blob/main/wenet/utils/mask.py