diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/conformer.py b/egs/librispeech/ASR/pruned_transducer_stateless2/conformer.py
index 8db9054a5..625ea10d6 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless2/conformer.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless2/conformer.py
@@ -35,6 +35,12 @@ from torch import Tensor, nn
 from icefall.utils import make_pad_mask, subsequent_chunk_mask
 
 
+class MakePadMask(nn.Module):
+    def forward(self, lengths: Tensor) -> Tensor:
+        """See doc for :func:`make_pad_mask`"""
+        return make_pad_mask(lengths)
+
+
 class Conformer(EncoderInterface):
     """
     Args:
@@ -111,6 +117,7 @@ class Conformer(EncoderInterface):
         self.num_left_chunks = num_left_chunks
 
         self.encoder_pos = RelPositionalEncoding(d_model, dropout)
+        self.make_pad_mask = MakePadMask()
 
         encoder_layer = ConformerEncoderLayer(
             d_model,
@@ -158,7 +165,7 @@ class Conformer(EncoderInterface):
         if not torch.jit.is_tracing():
             assert x.size(0) == lengths.max().item()
 
-        src_key_padding_mask = make_pad_mask(lengths)
+        src_key_padding_mask = self.make_pad_mask(lengths)
 
         if self.dynamic_chunk_training:
             assert (
@@ -835,7 +842,7 @@ class RelPositionalEncoding(torch.nn.Module):
         pe_positive = torch.flip(pe_positive, [0]).unsqueeze(0)
         pe_negative = pe_negative[1:].unsqueeze(0)
         pe = torch.cat([pe_positive, pe_negative], dim=1)
-        self.register_buffer("pe", pe.to(device=x.device, dtype=x.dtype))
+        self.pe = pe.to(device=x.device, dtype=x.dtype)
 
     def forward(
         self,
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/test_generate_make_pad_mask.py b/egs/librispeech/ASR/pruned_transducer_stateless3/test_generate_make_pad_mask.py
new file mode 100755
index 000000000..83ce3dd1d
--- /dev/null
+++ b/egs/librispeech/ASR/pruned_transducer_stateless3/test_generate_make_pad_mask.py
@@ -0,0 +1,42 @@
+#!/usr/bin/env python3
+
+
+import torch
+import torch.nn as nn
+from conformer import MakePadMask
+
+
+class Foo(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.make_pad_mask = MakePadMask()
+
+    def forward(self, x: torch.Tensor):
+        """
+        Args:
+          x:
+            (N,)
+        """
+        src_key_padding_mask = self.make_pad_mask(x)
+        return src_key_padding_mask
+
+
+def generate_pt():
+    f = Foo()
+    f.eval()
+    x = torch.tensor([1, 3, 5])
+    y = f(x)
+    print("y.shape", y.shape)
+    print(y)
+    m = torch.jit.trace(f, x)
+    m.save("foo/make_pad_mask.pt")
+    print(m.graph)
+
+
+def main():
+    generate_pt()
+
+
+if __name__ == "__main__":
+    torch.manual_seed(20220809)
+    main()
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/test_ncnn_make_pad_mask.py b/egs/librispeech/ASR/pruned_transducer_stateless3/test_ncnn_make_pad_mask.py
new file mode 100755
index 000000000..ff321e730
--- /dev/null
+++ b/egs/librispeech/ASR/pruned_transducer_stateless3/test_ncnn_make_pad_mask.py
@@ -0,0 +1,37 @@
+#!/usr/bin/env python3
+
+import ncnn
+import torch
+
+
+@torch.no_grad()
+def main():
+    x = torch.tensor([1, 3, 5, 8])
+    m = torch.jit.load("foo/make_pad_mask.pt")
+    t = m(x)
+    print(t.shape)
+    print(t)
+
+    param = "foo/make_pad_mask.ncnn.param"
+    model = "foo/make_pad_mask.ncnn.bin"
+    with ncnn.Net() as net:
+        net.load_param(param)
+        net.load_model(model)
+        with net.create_extractor() as ex:
+            x = x.to(torch.int32)
+            ex.input("in0", ncnn.Mat(x.numpy()).clone())
+
+            ret, ncnn_out0 = ex.extract("out0")
+            assert ret == 0, ret
+            n = ncnn_out0.numpy("i")
+            print(n.shape)
+            n = torch.from_numpy(n).to(torch.bool)
+            print(n)
+            assert torch.equal(t, n), (t, n)
+
+
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+if __name__ == "__main__":
+    torch.manual_seed(202208010)
+    main()