diff --git a/pytorch_finufft/functional.py b/pytorch_finufft/functional.py
index cce4635..e523758 100644
--- a/pytorch_finufft/functional.py
+++ b/pytorch_finufft/functional.py
@@ -774,9 +774,8 @@ def forward(
             finufft_out = torch.fft.ifftshift(finufft_out)
 
         return finufft_out
-
     @staticmethod
-    def backward(
+    def backward(  # type: ignore[override]
         ctx: Any, grad_output: torch.Tensor
     ) -> Tuple[Union[torch.Tensor, None], ...]:
         """
@@ -800,15 +799,6 @@ def backward(
 
         points, values = ctx.saved_tensors
 
-        start_points = -(np.array(grad_output.shape) // 2)
-        end_points = start_points + grad_output.shape
-        slices = tuple(
-            slice(start, end) for start, end in zip(start_points, end_points)
-        )
-
-        # CPU idiosyncracy that needs to be done differently
-        coord_ramps = torch.from_numpy(np.mgrid[slices]).to(points.device)
-
         grads_points = None
         grad_values = None
 
@@ -816,20 +806,23 @@ def backward(
 
         nufft_func = get_nufft_func(ndim, 2, points.device.type)
 
+        if any(ctx.needs_input_grad) and _mode_ordering:
+            grad_output = torch.fft.fftshift(grad_output)
+
         if ctx.needs_input_grad[0]:
             # wrt points
-
-            if _mode_ordering:
-                coord_ramps = torch.fft.ifftshift(
-                    coord_ramps, dim=tuple(range(1, ndim + 1))
-                )
+            # CPU idiosyncracy that needs to be done differently
+            start_points = -(np.array(grad_output.shape) // 2)
+            end_points = start_points + grad_output.shape
+            slices = tuple(
+                slice(start, end) for start, end in zip(start_points, end_points)
+            )
+            coord_ramps = torch.from_numpy(np.mgrid[slices]).to(points.device)
 
             ramped_grad_output = coord_ramps * grad_output[np.newaxis] * 1j * _i_sign
 
-            grads_points = []
+            grads_points_ = []
             for ramp in ramped_grad_output:  # we can batch this into finufft
-                if _mode_ordering:
-                    ramp = torch.fft.fftshift(ramp)
 
                 backprop_ramp = nufft_func(
                     *points,
@@ -840,14 +833,11 @@ def backward(
 
                 grad_points = (backprop_ramp.conj() * values).real
 
-                grads_points.append(grad_points)
+                grads_points_.append(grad_points)
 
-            grads_points = torch.stack(grads_points)
+            grads_points = torch.stack(grads_points_)
 
         if ctx.needs_input_grad[1]:
-            if _mode_ordering:
-                grad_output = torch.fft.fftshift(grad_output)
-
             grad_values = nufft_func(
                 *points,
                 grad_output,