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Merged
mloubout merged 3 commits into from
Jul 24, 2025
Merged

data: prevent mpi comm with single rank #2685

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ad3c629
mpi: make data behave as serial with a single rank
mloubout Jul 24, 2025
ba00b18
mpi: better naming
mloubout Jul 24, 2025
03ff042
mpi: fix typo in comm size
mloubout Jul 24, 2025
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26 changes: 13 additions & 13 deletions devito/data/data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -154,7 +154,7 @@ def _local(self):

def _global(self, glb_idx, decomposition):
"""A "global" view of ``self`` over a given Decomposition."""
if self._is_distributed:
if self._is_decomposed:
raise ValueError("Cannot derive a decomposed view from a decomposed Data")
if len(decomposition) != self.ndim:
raise ValueError("`decomposition` should have ndim=%d entries" % self.ndim)
Expand All @@ -176,13 +176,12 @@ def _check_idx(func):
@wraps(func)
def wrapper(data, *args, **kwargs):
glb_idx = args[0]
is_gather = isinstance(kwargs.get('gather_rank', None), int)
is_gather = isinstance(kwargs.get('gather_rank'), int)
if is_gather and all(i == slice(None, None, 1) for i in glb_idx):
comm_type = gather
elif len(args) > 1 and isinstance(args[1], Data) \
and args[1]._is_mpi_distributed:
elif len(args) > 1 and isinstance(args[1], Data) and args[1]._is_decomposed:
comm_type = index_by_index
elif data._is_mpi_distributed:
elif data._is_decomposed:
for i in as_tuple(glb_idx):
if isinstance(i, slice) and i.step is not None and i.step < 0:
comm_type = index_by_index
Expand All @@ -196,8 +195,9 @@ def wrapper(data, *args, **kwargs):
return wrapper

@property
def _is_mpi_distributed(self):
return self._is_distributed and configuration['mpi']
def _is_decomposed(self):
return self._is_distributed and configuration['mpi'] and \
self._distributor.comm.size > 1

def __repr__(self):
return super(Data, self._local).__repr__()
Expand Down Expand Up @@ -341,7 +341,7 @@ def __setitem__(self, glb_idx, val, comm_type):
super().__setitem__(loc_idx, val)
else:
super().__setitem__(glb_idx, val)
elif isinstance(val, Data) and val._is_distributed:
elif isinstance(val, Data) and val._is_decomposed:
if comm_type is index_by_index:
glb_idx, val = self._process_args(glb_idx, val)
val_idx = as_tuple([slice(i.glb_min, i.glb_max+1, 1) for
Expand All @@ -361,14 +361,14 @@ def __setitem__(self, glb_idx, val, comm_type):
or data_global[j].size == 0
if not skip:
self.__setitem__(idx_global[j], data_global[j])
elif self._is_distributed:
elif self._is_decomposed:
# `val` is decomposed, `self` is decomposed -> local set
super().__setitem__(glb_idx, val)
else:
# `val` is decomposed, `self` is replicated -> gatherall-like
raise NotImplementedError
elif isinstance(val, np.ndarray):
if self._is_distributed:
if self._is_decomposed:
# `val` is replicated, `self` is decomposed -> `val` gets decomposed
glb_idx = self._normalize_index(glb_idx)
glb_idx, val = self._process_args(glb_idx, val)
Expand Down Expand Up @@ -401,7 +401,7 @@ def __setitem__(self, glb_idx, val, comm_type):
pass
super().__setitem__(glb_idx, val)
elif isinstance(val, Iterable):
if self._is_mpi_distributed:
if self._is_decomposed:
raise NotImplementedError("With MPI, data can only be set "
"via scalars, numpy arrays or "
"other data ")
Expand Down Expand Up @@ -478,7 +478,7 @@ def _index_glb_to_loc(self, glb_idx):
if len(glb_idx) > self.ndim:
# Maybe user code is trying to add a new axis (see np.newaxis),
# so the resulting array will be higher dimensional than `self`
if self._is_mpi_distributed:
if self._is_decomposed:
raise ValueError("Cannot increase dimensionality of MPI-distributed Data")
else:
# As by specification, we are forced to ignore modulo indexing
Expand All @@ -494,7 +494,7 @@ def _index_glb_to_loc(self, glb_idx):
try:
v = convert_index(i, dec, mode='glb_to_loc')
except TypeError:
if self._is_mpi_distributed:
if self._is_decomposed:
raise NotImplementedError("Unsupported advanced indexing with "
"MPI-distributed Data")
v = i
Expand Down
99 changes: 49 additions & 50 deletions examples/seismic/self_adjoint/sa_03_iso_correctness.ipynb
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16 changes: 8 additions & 8 deletions examples/seismic/self_adjoint/test_wavesolver_iso.py
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Original file line number Diff line number Diff line change
Expand Up @@ -128,12 +128,12 @@ def test_linearization_F(self, shape, dtype, so):
ns = 2 * size + 1
if len(shape) == 2:
nx2, nz2 = shape[0]//2, shape[1]//2
dm.data[nx2-size:nx2+size, nz2-size:nz2+size] = \
dm.data[nx2-size:nx2+size+1, nz2-size:nz2+size+1] = \
-1 + 2 * np.random.rand(ns, ns)
else:
nx2, ny2, nz2 = shape[0]//2, shape[1]//2, shape[2]//2
nx, ny, nz = shape
dm.data[nx2-size:nx2+size, ny2-size:ny2+size, nz2-size:nz2+size] = \
dm.data[nx2-size:nx2+size+1, ny2-size:ny2+size+1, nz2-size:nz2+size+1] = \
-1 + 2 * np.random.rand(ns, ns, ns)

# Compute F(m + dm)
Expand Down Expand Up @@ -194,12 +194,12 @@ def test_linearity_forward_J(self, shape, dtype, so):
ns = 2 * size + 1
if len(shape) == 2:
nx2, nz2 = shape[0]//2, shape[1]//2
m1.data[nx2-size:nx2+size, nz2-size:nz2+size] = \
m1.data[nx2-size:nx2+size+1, nz2-size:nz2+size+1] = \
-1 + 2 * np.random.rand(ns, ns)
else:
nx2, ny2, nz2 = shape[0]//2, shape[1]//2, shape[2]//2
nx, ny, nz = shape
m1.data[nx2-size:nx2+size, ny2-size:ny2+size, nz2-size:nz2+size] = \
m1.data[nx2-size:nx2+size+1, ny2-size:ny2+size+1, nz2-size:nz2+size+1] = \
-1 + 2 * np.random.rand(ns, ns, ns)

a = np.random.rand()
Expand Down Expand Up @@ -242,12 +242,12 @@ def test_linearity_adjoint_J(self, shape, dtype, so):
ns = 2 * size + 1
if len(shape) == 2:
nx2, nz2 = shape[0]//2, shape[1]//2
m1.data[nx2-size:nx2+size, nz2-size:nz2+size] = \
m1.data[nx2-size:nx2+size+1, nz2-size:nz2+size+1] = \
-1 + 2 * np.random.rand(ns, ns)
else:
nx2, ny2, nz2 = shape[0]//2, shape[1]//2, shape[2]//2
nx, ny, nz = shape
m1.data[nx2-size:nx2+size, ny2-size:ny2+size, nz2-size:nz2+size] = \
m1.data[nx2-size:nx2+size+1, ny2-size:ny2+size+1, nz2-size:nz2+size+1] = \
-1 + 2 * np.random.rand(ns, ns, ns)

a = np.random.rand()
Expand Down Expand Up @@ -289,12 +289,12 @@ def test_adjoint_J(self, shape, dtype, so):
ns = 2 * size + 1
if len(shape) == 2:
nx2, nz2 = shape[0]//2, shape[1]//2
dm1.data[nx2-size:nx2+size, nz2-size:nz2+size] = \
dm1.data[nx2-size:nx2+size+1, nz2-size:nz2+size+1] = \
-1 + 2 * np.random.rand(ns, ns)
else:
nx2, ny2, nz2 = shape[0]//2, shape[1]//2, shape[2]//2
nx, ny, nz = shape
dm1.data[nx2-size:nx2+size, ny2-size:ny2+size, nz2-size:nz2+size] = \
dm1.data[nx2-size:nx2+size+1, ny2-size:ny2+size+1, nz2-size:nz2+size+1] = \
-1 + 2 * np.random.rand(ns, ns, ns)

# Data perturbation
Expand Down
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