Cosmetic changes

Author: Thomas

mpi4py_fft/distarray.py

@@ -246,7 +246,7 @@ def local_slice(self):
         (slice(0, 16, None), slice(7, 14, None), slice(6, 12, None))
         """
         v = [slice(start, start+shape) for start, shape in zip(self._p0.substart,
-                                                                 self._p0.subshape)]
+                                                               self._p0.subshape)]
         return tuple([slice(0, s) for s in self.shape[:self.rank]] + v)
 
     def redistribute(self, axis=None, out=None):
@@ -298,10 +298,10 @@ def redistribute(self, axis=None, out=None):
         p1, transfer = self.get_pencil_and_transfer(axis)
         if out is None:
             out = type(self)(self.global_shape,
-                subcomm=p1.subcomm,
-                dtype=self.dtype,
-                alignment=axis,
-                rank=self.rank)
+                             subcomm=p1.subcomm,
+                             dtype=self.dtype,
+                             alignment=axis,
+                             rank=self.rank)
 
         if self.rank == 0:
             transfer.forward(self, out)

mpi4py_fft/libfft.py

@@ -154,7 +154,7 @@ def _Xfftn_plan_mkl(shape, axes, dtype, transforms, options): #pragma: no cover
 
 def _Xfftn_plan_cupyx_scipy(shape, axes, dtype, transforms, options):
     import cupy as cp
-    import cupyx.scipy.fftpack as cufft
+    import cupyx.scipy.fft as cufft
 
     transforms = {} if transforms is None else transforms
     if tuple(axes) in transforms:
@@ -168,8 +168,8 @@ def _Xfftn_plan_cupyx_scipy(shape, axes, dtype, transforms, options):
     V = plan_fwd(U, s=s, axes=axes)
     V = cp.array(V)
     M = np.prod(s)
-    return (_Yfftn_wrap(plan_fwd, U, V, 1, {'shape': s, 'axes': axes, 'overwrite_x': True}),
-            _Yfftn_wrap(plan_bck, V, U, M, {'shape': s, 'axes': axes, 'overwrite_x': True}))
+    return (_Yfftn_wrap(plan_fwd, U, V, 1, {'s': s, 'axes': axes, 'overwrite_x': True}),
+            _Yfftn_wrap(plan_bck, V, U, M, {'s': s, 'axes': axes, 'overwrite_x': True}))
 
 def _Xfftn_plan_scipy(shape, axes, dtype, transforms, options):
 

mpi4py_fft/pencil.py

@@ -471,8 +471,6 @@ def transfer(self, pencil, dtype):
             transfer_class = Transfer
         elif self.backend == 'NCCL':
             transfer_class = NCCLTransfer
-        elif self.backend == 'CUDAMemCpy':
-            transfer_class = CUDAMemCpy
         elif self.backend == 'customMPI':
             transfer_class = CustomMPITransfer
         else:

tests/test_transfer_classes.py

@@ -2,6 +2,17 @@
 from mpi4py_fft.pencil import Transfer, CustomMPITransfer, Pencil, Subcomm
 import numpy as np
 
+transfer_classes = [CustomMPITransfer]
+xps = {CustomMPITransfer: np}
+
+try:
+    import cupy as cp
+    from mpi4py_fft.pencil import NCCLTransfer
+    transfer_classes += [NCCLTransfer]
+    xps[NCCLTransfer] = cp
+except ModuleNotFoundError:
+    pass
+
 
 def get_args(comm, shape, dtype):
     subcomm = Subcomm(comm=comm, dims=None)
@@ -20,43 +31,43 @@ def get_args(comm, shape, dtype):
     return kwargs
 
 
-def get_arrays(kwargs):
-    arrayA = np.zeros(shape=kwargs['subshapeA'], dtype=kwargs['dtype'])
-    arrayB = np.zeros(shape=kwargs['subshapeB'], dtype=kwargs['dtype'])
+def get_arrays(kwargs, xp):
+    arrayA = xp.zeros(shape=kwargs['subshapeA'], dtype=kwargs['dtype'])
+    arrayB = xp.zeros(shape=kwargs['subshapeB'], dtype=kwargs['dtype'])
 
-    arrayA[:] = np.random.random(arrayA.shape).astype(arrayA.dtype)
+    arrayA[:] = xp.random.random(arrayA.shape).astype(arrayA.dtype)
     return arrayA, arrayB
 
 
-def single_test_all_to_allw(transfer_class, shape, dtype, comm=None):
+def single_test_all_to_allw(transfer_class, shape, dtype, comm=None, xp=None):
     comm = comm if comm else MPI.COMM_WORLD
     kwargs = get_args(comm, shape, dtype)
-    arrayA, arrayB = get_arrays(kwargs)
+    arrayA, arrayB = get_arrays(kwargs, xp)
     arrayB_ref = arrayB.copy()
 
     transfer = transfer_class(**kwargs)
     reference_transfer = Transfer(**kwargs)
 
     transfer.Alltoallw(arrayA, transfer._subtypesA, arrayB, transfer._subtypesB)
     reference_transfer.Alltoallw(arrayA, transfer._subtypesA, arrayB_ref, transfer._subtypesB)
-    assert np.allclose(arrayB, arrayB_ref), f'Did not get the same result from `alltoallw` with {transfer_class.__name__} transfer class as MPI implementation on rank {comm.rank}!'
+    assert xp.allclose(arrayB, arrayB_ref), f'Did not get the same result from `alltoallw` with {transfer_class.__name__} transfer class as MPI implementation on rank {comm.rank}!'
 
     comm.Barrier()
     if comm.rank == 0:
         print(f'{transfer_class.__name__} passed alltoallw test with shape {shape} and dtype {dtype}')
 
 
-def single_test_forward_backward(transfer_class, shape, dtype, comm=None):
+def single_test_forward_backward(transfer_class, shape, dtype, comm=None, xp=None):
     comm = comm if comm else MPI.COMM_WORLD
     kwargs = get_args(comm, shape, dtype)
-    arrayA, arrayB = get_arrays(kwargs)
+    arrayA, arrayB = get_arrays(kwargs, xp)
     arrayA_ref = arrayA.copy()
 
     transfer = transfer_class(**kwargs)
 
     transfer.forward(arrayA, arrayB)
     transfer.backward(arrayB, arrayA)
-    assert np.allclose(arrayA, arrayA_ref), f'Did not get the same result when transferring back and forth with {transfer_class.__name__} transfer class on rank {comm.rank}!'
+    assert xp.allclose(arrayA, arrayA_ref), f'Did not get the same result when transferring back and forth with {transfer_class.__name__} transfer class on rank {comm.rank}!'
 
     comm.Barrier()
     if comm.rank == 0:
@@ -67,14 +78,14 @@ def test_transfer_class():
     dims = (2, 3)
     sizes = (7, 8, 9, 128)
     dtypes = 'fFdD'
-    transfer_class = CustomMPITransfer
 
     shapes = [[size] * dim for size in sizes for dim in dims] + [[32, 256, 129]]
 
-    for shape in shapes:
-        for dtype in dtypes:
-            single_test_all_to_allw(transfer_class, shape, dtype)
-            single_test_forward_backward(transfer_class, shape, dtype)
+    for transfer_class in transfer_classes:
+        for shape in shapes:
+            for dtype in dtypes:
+                single_test_all_to_allw(transfer_class, shape, dtype, xp=xps[transfer_class])
+                single_test_forward_backward(transfer_class, shape, dtype, xp=xps[transfer_class])
 
 
 if __name__ == '__main__':