diff --git a/vdecoder/nsf_hifigan/models.py b/vdecoder/nsf_hifigan/models.py
index c2c889ec..c8ff50b9 100644
--- a/vdecoder/nsf_hifigan/models.py
+++ b/vdecoder/nsf_hifigan/models.py
@@ -146,7 +146,10 @@ def forward(self, f0, upp):
         rand_ini[:, 0] = 0
         rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
         is_half = rad_values.dtype is not torch.float32
-        tmp_over_one = torch.cumsum(rad_values.double(), 1)  # % 1  #####%1意味着后面的cumsum无法再优化
+        if f0.device.type == 'mps':
+            tmp_over_one = torch.cumsum(rad_values, 1)
+        else:
+            tmp_over_one = torch.cumsum(rad_values.double(), 1)  # % 1  #####%1意味着后面的cumsum无法再优化
         if is_half:
             tmp_over_one = tmp_over_one.half()
         else:
@@ -161,8 +164,9 @@ def forward(self, f0, upp):
         tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
         cumsum_shift = torch.zeros_like(rad_values)
         cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-        rad_values = rad_values.double()
-        cumsum_shift = cumsum_shift.double()
+        if f0.device.type != 'mps':
+            rad_values = rad_values.double()
+            cumsum_shift = cumsum_shift.double()
         sine_waves = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi)
         if is_half:
             sine_waves = sine_waves.half()