diff --git a/egs/librispeech/ASR/conformer_ctc/subsampling.py b/egs/librispeech/ASR/conformer_ctc/subsampling.py
index 3a1eda3f1..1e31c0a20 100644
--- a/egs/librispeech/ASR/conformer_ctc/subsampling.py
+++ b/egs/librispeech/ASR/conformer_ctc/subsampling.py
@@ -267,23 +267,6 @@ def _exp_scale_relu(x: Tensor, scale: Tensor, speed: float) -> Tensor:
     return (x * (scale * speed).exp()).relu()
 
 
-class ExpScaleReluFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, x: Tensor, scale: Tensor, speed: float) -> Tensor:
-        ctx.save_for_backward(x.detach(), scale.detach())
-        ctx.speed = speed
-        return _exp_scale_swish(x, scale, speed)
-
-    @staticmethod
-    def backward(ctx, y_grad: Tensor) -> Tensor:
-        x, scale = ctx.saved_tensors
-        x.requires_grad = True
-        scale.requires_grad = True
-        with torch.enable_grad():
-            y = _exp_scale_swish(x, scale, ctx.speed)
-            y.backward(gradient=y_grad)
-            return x.grad, scale.grad, None
-
 
 
 class ExpScaleReluFunction(torch.autograd.Function):
@@ -563,16 +546,32 @@ class DerivBalancer(torch.nn.Module):
                                            self.max_abs)
 
 
+def _double_swish(x: Tensor) -> Tensor:
+    # double-swish, implemented/approximated as offset-swish
+    return x * torch.sigmoid(x - 1.0)
+
+class DoubleSwishFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x: Tensor) -> Tensor:
+        ctx.save_for_backward(x.detach())
+        return _double_swish(x)
+
+    @staticmethod
+    def backward(ctx, y_grad: Tensor) -> Tensor:
+        # TODO: can make this more efficient.
+        x, = ctx.saved_tensors
+        x.requires_grad = True
+        with torch.enable_grad():
+            y = _double_swish(x)
+            y.backward(gradient=y_grad)
+            return x.grad
+
 class DoubleSwish(torch.nn.Module):
     def forward(self, x: Tensor) -> Tensor:
         """Return double-swish activation function which is an approximation to Swish(Swish(x)),
-           that we approximate closely with x * sigmoid(x-1), expressed for more memory-efficient
-           backprop as (x-1) * torch.sigmoid(x - 1) + torch.sigmoid(x - 1)
+           that we approximate closely with x * sigmoid(x-1).
         """
-        x1 = x - 1.0
-        s = torch.sigmoid(x1)
-        return (x1 * s) + s  # (x-1) * s + s == x * s
-
+        return DoubleSwishFunction.apply(x)
 
 def _test_exp_scale_swish():