FIX consolidated backward working for 2D now

pytorch_finufft/functional.py

@@ -1691,7 +1691,6 @@ def backward(
         _mode_ordering = ctx.mode_ordering
         finufftkwargs = ctx.finufftkwargs
 
-
         points, values = ctx.saved_tensors
 
         start_points = -(np.array(grad_output.shape) // 2)
@@ -1712,7 +1711,7 @@ def backward(
             # wrt points
 
             if _mode_ordering != 0:
-                coord_ramps = torch.fft.ifftshift(coord_ramps)
+                coord_ramps = torch.fft.ifftshift(coord_ramps, dim=tuple(range(1, ndim+1)))
 
             ramped_grad_output = coord_ramps * grad_output[np.newaxis] * 1j * _i_sign
 

tests/test_2d/test_backward_2d.py

@@ -122,6 +122,28 @@ def func(points, values):
     assert gradcheck(func, inputs)
 
 
+@pytest.mark.parametrize("N", Ns)
+@pytest.mark.parametrize("modifier", length_modifiers)
+@pytest.mark.parametrize("fftshift", [False, True])
+@pytest.mark.parametrize("isign", [-1, 1])
+def test_t1_consolidated_backward_CPU_points(N: int, modifier: int, fftshift: bool, isign: int) -> None:
+
+    points = torch.rand((2, N), dtype=torch.float64) * 2 * np.pi
+    values = torch.randn(N, dtype=torch.complex128)
+
+    points.requires_grad = True
+    values.requires_grad = False
+
+    inputs = (points, values)
+
+    def func(points, values):
+        return pytorch_finufft.functional.finufft_type1.apply(
+            points, values, (N,N + modifier), None, fftshift, dict(isign=isign)
+        )
+
+    assert gradcheck(func, inputs, atol=1e-5 * N)
+
+
 @pytest.mark.parametrize("N", Ns)
 @pytest.mark.parametrize("modifier", length_modifiers)
 @pytest.mark.parametrize("fftshift", [True, False])