diff --git a/kazane/decimate.py b/kazane/decimate.py
index d764cce..1155938 100644
--- a/kazane/decimate.py
+++ b/kazane/decimate.py
@@ -7,8 +7,6 @@
 from .upsample import _pad_to_block_2
 from .fftconv import _custom_fft_conv1d
 
-BLOCK_RATIO = 4
-
 
 class Decimate(nn.Module):
     r"""Downsampling by an integer amount.
@@ -32,6 +30,8 @@ class Decimate(nn.Module):
         250
 
     """
+    __constants__ = ['BLOCK_RATIO']
+    BLOCK_RATIO: int = 4
 
     def __init__(self,
                  q: int = 2,
@@ -54,7 +54,7 @@ def forward(self, x: torch.Tensor):
         shape = x.shape
         x = x.view(-1, 1, shape[-1])
 
-        block_length = self.kernel.shape[-1] * self.stride * BLOCK_RATIO
+        block_length = self.kernel.shape[-1] * self.stride * self.BLOCK_RATIO
         out_size = shape[-1] // self.stride
         if shape[-1] < block_length:
             x = F.pad(x, [self.padding] * 2, mode='reflect')
@@ -66,4 +66,4 @@ def forward(self, x: torch.Tensor):
             y = _custom_fft_conv1d(x, self.kernel, stride=self.stride)
             y = y.view(-1, num_blocks * y.shape[-1])[..., :out_size]
 
-        return y.view(*shape[:-1], -1)
+        return y.view(shape[:-1] + (-1,))
diff --git a/kazane/fftconv.py b/kazane/fftconv.py
index 6a9501a..5ecdb67 100644
--- a/kazane/fftconv.py
+++ b/kazane/fftconv.py
@@ -22,7 +22,8 @@ def _custom_fft_conv1d(input: Tensor, weight: Tensor,
 
     X = rfft(input, n=s)
     W = rfft(weight, n=s)
-    W.imag.mul_(-1)
+    # W.imag.mul_(-1)
+    torch.view_as_real(W)[..., 1].mul_(-1)
     Y = X * W
 
     # handle stride
@@ -32,8 +33,11 @@ def _custom_fft_conv1d(input: Tensor, weight: Tensor,
         step_size = new_n_fft // 2
         strided_Y_size = step_size + 1
 
-        unfolded_Y_real = Y.real.unfold(-1, strided_Y_size, step_size)
-        unfolded_Y_imag = Y.imag[...,
+        tmpY = torch.view_as_real(Y)
+        Y_real, Y_imag = tmpY[..., 0], tmpY[..., 1]
+
+        unfolded_Y_real = Y_real.unfold(-1, strided_Y_size, step_size)
+        unfolded_Y_imag = Y_imag[...,
                                  1:].unfold(-1, strided_Y_size - 2, step_size)
         Y_pos_real, Y_pos_imag = unfolded_Y_real[..., ::2,
                                                  :].sum(-2), unfolded_Y_imag[..., ::2, :].sum(-2)
diff --git a/kazane/upsample.py b/kazane/upsample.py
index 42515de..0e76034 100644
--- a/kazane/upsample.py
+++ b/kazane/upsample.py
@@ -5,8 +5,6 @@
 from .sinc import sinc_kernel
 from .fftconv import _custom_fft_conv1d
 
-BLOCK_RATIO = 5
-
 
 def _pad_to_block(x, block_size):
     offset = x.shape[-1] % block_size
@@ -15,7 +13,7 @@ def _pad_to_block(x, block_size):
     return x.view(*x.shape[:-1], -1, block_size)
 
 
-def _pad_to_block_2(x, block_size, padding):
+def _pad_to_block_2(x: torch.Tensor, block_size: int, padding: int):
     offset = x.shape[-1] % block_size
     if offset:
         offset = block_size - offset
@@ -45,6 +43,8 @@ class Upsample(nn.Module):
         1500
 
     """
+    __constants__ = ['BLOCK_RATIO']
+    BLOCK_RATIO: int = 5
 
     def __init__(self,
                  q: int = 2,
@@ -67,7 +67,7 @@ def forward(self, x: torch.Tensor):
         shape = x.shape
         x = x.view(-1, 1, shape[-1])
 
-        block_length = self.kernel.shape[-1] * BLOCK_RATIO
+        block_length = self.kernel.shape[-1] * self.BLOCK_RATIO
         if shape[-1] < block_length:
             x = F.pad(x, [self.padding] * 2, mode='reflect')
             y = _custom_fft_conv1d(x, self.kernel)
@@ -81,4 +81,4 @@ def forward(self, x: torch.Tensor):
                                                                   2).reshape(-1, q, num_blocks * block_length)[..., :shape[-1]]
 
         y = y.transpose(1, 2).contiguous()
-        return y.view(*shape[:-1], -1)
+        return y.view(shape[:-1] + (-1,))
diff --git a/tests/test_decimate.py b/tests/test_decimate.py
index cda5780..e84781d 100644
--- a/tests/test_decimate.py
+++ b/tests/test_decimate.py
@@ -29,7 +29,7 @@ def test_quality_sine(q, zeros, rms):
     DURATION = 2
     sr = 44100
     sr_new = sr // q
-    dec = Decimate(q, zeros, roll_off=0.945).to(dtype)
+    dec = torch.jit.script(Decimate(q, zeros, roll_off=0.945)).to(dtype)
 
     x = make_tone(FREQ, sr, DURATION)
     y = make_tone(FREQ, sr_new, DURATION)
@@ -52,7 +52,7 @@ def test_quality_sweep(q, zeros, rms):
     DURATION = 5
     sr = 44100
     sr_new = sr // q
-    dec = Decimate(q, zeros, roll_off=0.945).to(dtype)
+    dec = torch.jit.script(Decimate(q, zeros, roll_off=0.945)).to(dtype)
 
     x = make_sweep(FREQ, sr, DURATION)
     y = make_sweep(FREQ, sr_new, DURATION)
diff --git a/tests/test_upsample.py b/tests/test_upsample.py
index b4296a8..ba062e7 100644
--- a/tests/test_upsample.py
+++ b/tests/test_upsample.py
@@ -19,8 +19,8 @@ def test_quality_sine(q, zeros, rms):
     DURATION = 2
     sr = 22050
     sr_new = sr * q
-    up = Upsample(q, zeros,
-                  roll_off=0.945).to(dtype)
+    up = torch.jit.script(Upsample(q, zeros,
+                                   roll_off=0.945)).to(dtype)
 
     x = make_tone(FREQ, sr, DURATION)
     y = make_tone(FREQ, sr_new, DURATION)
@@ -43,8 +43,8 @@ def test_quality_sweep(q, zeros, rms):
     DURATION = 5
     sr = 22050
     sr_new = sr * q
-    up = Upsample(q, zeros,
-                  roll_off=0.945).to(dtype)
+    up = torch.jit.script(Upsample(q, zeros,
+                                   roll_off=0.945)).to(dtype)
 
     x = make_sweep(FREQ, sr, DURATION)
     y = make_sweep(FREQ, sr_new, DURATION)