Add function wrappers around .apply

pytorch_finufft/functional.py

@@ -71,13 +71,13 @@ def coordinate_ramps(shape, device):
     return coord_ramps
 
 
-class finufft_type1(torch.autograd.Function):
+class FinufftType1(torch.autograd.Function):
     @staticmethod
     def forward(  # type: ignore[override]
         ctx: Any,
         points: torch.Tensor,
         values: torch.Tensor,
-        output_shape: Union[int, Tuple[int, int], Tuple[int, int, int]],
+        output_shape: Union[int, Tuple[int], Tuple[int, int], Tuple[int, int, int]],
         finufftkwargs: Optional[Dict[str, Union[int, float]]] = None,
     ) -> torch.Tensor:
         """
@@ -181,7 +181,7 @@ def backward(  # type: ignore[override]
         )
 
 
-class finufft_type2(torch.autograd.Function):
+class FinufftType2(torch.autograd.Function):
     """
     FINUFFT 2D problem type 2
     """
@@ -324,3 +324,80 @@ def backward(  # type: ignore[override]
             None,
             None,
         )
+
+
+def finufft_type1(
+    points: torch.Tensor,
+    values: torch.Tensor,
+    output_shape: Union[int, Tuple[int], Tuple[int, int], Tuple[int, int, int]],
+    **finufftkwargs: Union[int, float],
+) -> torch.Tensor:
+    """
+    Evaluates the Type 1 (nonuniform-to-uniform) NUFFT on the inputs.
+
+    This is a wrapper around :func:`finufft.nufft1d1`, :func:`finufft.nufft2d1`, and
+    :func:`finufft.nufft3d1` on CPU, and :func:`cufinufft.nufft1d1`,
+    :func:`cufinufft.nufft2d1`, and :func:`cufinufft.nufft3d1` on GPU.
+
+    Parameters
+    ----------
+    points : torch.Tensor
+        DxN tensor of locations of the non-uniform points.
+        Points must lie in the range ``[-3pi, 3pi]``.
+    values : torch.Tensor
+        Length N complex-valued tensor of values at the non-uniform points
+    output_shape : int | tuple(int, ...)
+        Requested output shape of Fourier modes. Must be a tuple of length D or
+        an integer (1D only).
+    **finufftkwargs : int | float
+        Additional keyword arguments are forwarded to the underlying
+        FINUFFT functions. A few notable options are
+
+        - ``eps``: precision requested (default: ``1e-6``)
+        - ``modeord``: 0 for FINUFFT default, 1 for Pytorch default (default: ``1``)
+        - ``isign``: Sign of the exponent in the Fourier transform (default: ``-1``)
+
+    Returns
+    -------
+    torch.Tensor
+        Tensor with shape ``output_shape`` containing the Fourier
+        transform of the values.
+    """
+    res: torch.Tensor = FinufftType1.apply(points, values, output_shape, finufftkwargs)
+    return res
+
+
+def finufft_type2(
+    points: torch.Tensor,
+    targets: torch.Tensor,
+    **finufftkwargs: Union[int, float],
+) -> torch.Tensor:
+    """
+    Evaluates the Type 2 (uniform-to-nonuniform) NUFFT on the inputs.
+
+    This is a wrapper around :func:`finufft.nufft1d2`, :func:`finufft.nufft2d2`, and
+    :func:`finufft.nufft3d2` on CPU, and :func:`cufinufft.nufft1d2`,
+    :func:`cufinufft.nufft2d2`, and :func:`cufinufft.nufft3d2` on GPU.
+
+    Parameters
+    ----------
+    points : torch.Tensor
+        DxN tensor of locations of the non-uniform points.
+        Points must lie in the range ``[-3pi, 3pi]``.
+    targets : torch.Tensor
+        D-dimensional complex-valued tensor of Fourier modes to evaluate at the points
+    **finufftkwargs : int | float
+        Additional keyword arguments are forwarded to the underlying
+        FINUFFT functions. A few notable options are
+
+        - ``eps``: precision requested (default: ``1e-6``)
+        - ``modeord``: 0 for FINUFFT default, 1 for Pytorch default (default: ``1``)
+        - ``isign``: Sign of the exponent in the Fourier transform (default: ``-1``)
+
+    Returns
+    -------
+    torch.Tensor
+        A DxN tensor of values at the non-uniform points.
+    """
+    res: torch.Tensor = FinufftType2.apply(points, targets, finufftkwargs)
+    return res

tests/test_1d/test_backward_1d.py

@@ -43,11 +43,12 @@ def check_t1_backward(
     inputs = (points, values)
 
     def func(points, values):
-        return pytorch_finufft.functional.finufft_type1.apply(
+        return pytorch_finufft.functional.finufft_type1(
             points,
             values,
             (N + modifier,),
-            dict(modeord=int(not fftshift), isign=isign),
+            modeord=int(not fftshift),
+            isign=isign,
         )
 
     assert gradcheck(func, inputs, eps=1e-8, atol=1e-5 * N)
@@ -110,10 +111,11 @@ def check_t2_backward(
     inputs = (points, targets)
 
     def func(points, targets):
-        return pytorch_finufft.functional.finufft_type2.apply(
+        return pytorch_finufft.functional.finufft_type2(
             points,
             targets,
-            dict(modeord=int(not fftshift), isign=isign),
+            modeord=int(not fftshift),
+            isign=isign,
         )
 
     assert gradcheck(func, inputs, eps=1e-8, atol=1.5e-3 * N)

tests/test_1d/test_forward_1d.py

@@ -36,7 +36,7 @@ def check_t1_forward(N: int, device: str) -> None:
     print("shape of points is " + str(points.shape))
     print("shape of values is " + str(values.shape))
 
-    finufft_out = pytorch_finufft.functional.finufft_type1.apply(
+    finufft_out = pytorch_finufft.functional.finufft_type1(
         points,
         values,
         (N,),
@@ -89,7 +89,7 @@ def test_t2_forward_CPU(N: int) -> None:
     print("shape of points is " + str(points.shape))
     print("shape of targets is " + str(targets.shape))
 
-    finufft_out = pytorch_finufft.functional.finufft_type2.apply(
+    finufft_out = pytorch_finufft.functional.finufft_type2(
         points,
         targets,
     )

tests/test_2d/test_backward_2d.py

@@ -54,11 +54,8 @@ def check_t1_backward(
     inputs = (points, values)
 
     def func(points, values):
-        return pytorch_finufft.functional.finufft_type1.apply(
-            points,
-            values,
-            (N, N + modifier),
-            dict(modeord=int(not fftshift), isign=isign),
+        return pytorch_finufft.functional.finufft_type1(
+            points, values, (N, N + modifier), modeord=int(not fftshift), isign=isign
         )
 
     assert gradcheck(func, inputs, atol=1e-5 * N)
@@ -121,10 +118,11 @@ def check_t2_backward(
     inputs = (points, targets)
 
     def func(points, targets):
-        return pytorch_finufft.functional.finufft_type2.apply(
+        return pytorch_finufft.functional.finufft_type2(
             points,
             targets,
-            dict(modeord=int(not fftshift), isign=isign),
+            modeord=int(not fftshift),
+            isign=isign,
         )
 
     assert gradcheck(func, inputs, atol=1e-5 * N)

tests/test_2d/test_forward_2d.py

@@ -35,7 +35,7 @@ def check_t1_forward(N: int, device: str) -> None:
     print("shape of points is " + str(points.shape))
     print("shape of values is " + str(values.shape))
 
-    finufft_out = pytorch_finufft.functional.finufft_type1.apply(
+    finufft_out = pytorch_finufft.functional.finufft_type1(
         points,
         values,
         (N, N),
@@ -79,10 +79,10 @@ def test_t2_forward_CPU(N: int, fftshift: bool) -> None:
     print("shape of points is " + str(points.shape))
     print("shape of targets is " + str(targets.shape))
 
-    finufft_out = pytorch_finufft.functional.finufft_type2.apply(
+    finufft_out = pytorch_finufft.functional.finufft_type2(
         points,
         targets,
-        {"modeord": int(not fftshift)},
+        modeord=int(not fftshift),
     )
 
     if fftshift:

tests/test_3d/test_backward_3d.py

@@ -46,11 +46,12 @@ def check_t1_backward(
     inputs = (points, values)
 
     def func(points, values):
-        return pytorch_finufft.functional.finufft_type1.apply(
+        return pytorch_finufft.functional.finufft_type1(
             points,
             values,
             (N, N + modifier, N + 2 * modifier),
-            dict(modeord=int(not fftshift), isign=isign),
+            modeord=int(not fftshift),
+            isign=isign,
         )
 
     assert gradcheck(func, inputs, eps=1e-8, atol=1e-5 * N)
@@ -118,10 +119,8 @@ def check_t2_backward(
     inputs = (points, targets)
 
     def func(points, targets):
-        return pytorch_finufft.functional.finufft_type2.apply(
-            points,
-            targets,
-            dict(modeord=int(not fftshift), isign=isign),
+        return pytorch_finufft.functional.finufft_type2(
+            points, targets, modeord=int(not fftshift), isign=isign
         )
 
     assert gradcheck(func, inputs, atol=1e-5 * N)

tests/test_3d/test_forward_3d.py

@@ -34,7 +34,7 @@ def check_t1_forward(N: int, device: str) -> None:
     print("shape of points is " + str(points.shape))
     print("shape of values is " + str(values.shape))
 
-    finufft_out = pytorch_finufft.functional.finufft_type1.apply(
+    finufft_out = pytorch_finufft.functional.finufft_type1(
         points,
         values,
         (N, N, N),
@@ -77,7 +77,7 @@ def test_t2_forward_CPU(N: int) -> None:
     print("shape of points is " + str(points.shape))
     print("shape of targets is " + str(targets.shape))
 
-    finufft_out = pytorch_finufft.functional.finufft_type2.apply(
+    finufft_out = pytorch_finufft.functional.finufft_type2(
         points,
         targets,
     )