diff --git a/doc/array.rst b/doc/array.rst
index dc9f3974729707d2768c2f2d49f6a86c416e36e3..0e1f05bc2d1a3ec129c1b5e9bb8711b9ca077309 100644
--- a/doc/array.rst
+++ b/doc/array.rst
@@ -170,6 +170,8 @@ Reductions
^^^^^^^^^^
.. autofunction:: sum
+.. autofunction:: all
+.. autofunction:: any
.. autofunction:: dot
.. autofunction:: vdot
.. autofunction:: subset_dot
diff --git a/pyopencl/array.py b/pyopencl/array.py
index fa96692f03b2d07a26ce0d7372741a7421698d03..075ccfee05bab7f20bd91963c7f1266ec4e6bf0a 100644
--- a/pyopencl/array.py
+++ b/pyopencl/array.py
@@ -1754,7 +1754,7 @@ class Array:
shape[idx] = 0
else:
shape[idx] = self.size // size
- if any(s < 0 for s in shape):
+ if _builtin_any(s < 0 for s in shape):
raise ValueError("can only specify one unknown dimension")
shape = tuple(shape)
@@ -2744,19 +2744,19 @@ def stack(arrays, axis=0, queue=None):
queue = ary.queue
break
- if not all(ary.shape == input_shape for ary in arrays[1:]):
+ if not _builtin_all(ary.shape == input_shape for ary in arrays[1:]):
raise ValueError("arrays must have the same shape")
if not (0 <= axis <= input_ndim):
raise ValueError("invalid axis")
- if (axis == 0 and not all(ary.flags.c_contiguous
- for ary in arrays)):
+ if (axis == 0 and not _builtin_all(
+ ary.flags.c_contiguous for ary in arrays)):
# pyopencl.Array.__setitem__ does not support non-contiguous assignments
raise NotImplementedError
- if (axis == input_ndim and not all(ary.flags.f_contiguous
- for ary in arrays)):
+ if (axis == input_ndim and not _builtin_all(
+ ary.flags.f_contiguous for ary in arrays)):
# pyopencl.Array.__setitem__ does not support non-contiguous assignments
raise NotImplementedError
@@ -2928,23 +2928,48 @@ def minimum(a, b, out=None, queue=None):
# {{{ reductions
+
_builtin_sum = sum
_builtin_min = min
_builtin_max = max
+_builtin_any = any
+_builtin_all = all
-def sum(a, dtype=None, queue=None, slice=None):
+def sum(a, dtype=None, queue=None, slice=None, initial=np._NoValue):
"""
.. versionadded:: 2011.1
"""
+ if initial is not np._NoValue and not isinstance(initial, SCALAR_CLASSES):
+ raise ValueError("'initial' is not a scalar")
+
from pyopencl.reduction import get_sum_kernel
krnl = get_sum_kernel(a.context, dtype, a.dtype)
result, event1 = krnl(a, queue=queue, slice=slice, wait_for=a.events,
return_event=True)
result.add_event(event1)
+
+ # NOTE: neutral element in `get_sum_kernel` is 0 by default
+ if initial is not np._NoValue:
+ result += a.dtype.type(initial)
+
return result
+def any(a, queue=None, wait_for=None):
+ if len(a) == 0:
+ return _BOOL_DTYPE.type(False)
+
+ return a.any(queue=queue, wait_for=wait_for)
+
+
+def all(a, queue=None, wait_for=None):
+ if len(a) == 0:
+ return _BOOL_DTYPE.type(True)
+
+ return a.all(queue=queue, wait_for=wait_for)
+
+
def dot(a, b, dtype=None, queue=None, slice=None):
"""
.. versionadded:: 2011.1
@@ -2985,23 +3010,46 @@ def subset_dot(subset, a, b, dtype=None, queue=None, slice=None):
def _make_minmax_kernel(what):
- def f(a, queue=None):
+ def f(a, queue=None, initial=np._NoValue):
+ if len(a) == 0:
+ if initial is np._NoValue:
+ raise ValueError(
+ f"zero-size array to reduction '{what}' "
+ "which has no identity")
+ else:
+ return initial
+
+ if initial is not np._NoValue and not isinstance(initial, SCALAR_CLASSES):
+ raise ValueError("'initial' is not a scalar")
+
from pyopencl.reduction import get_minmax_kernel
krnl = get_minmax_kernel(a.context, what, a.dtype)
result, event1 = krnl(a, queue=queue, wait_for=a.events,
return_event=True)
result.add_event(event1)
+
+ if initial is not np._NoValue:
+ initial = a.dtype.type(initial)
+ if what == "min":
+ result = minimum(result, initial, queue=queue)
+ elif what == "max":
+ result = maximum(result, initial, queue=queue)
+ else:
+ raise ValueError(f"unknown minmax reduction type: '{what}'")
+
return result
return f
min = _make_minmax_kernel("min")
+min.__name__ = "min"
min.__doc__ = """
.. versionadded:: 2011.1
"""
max = _make_minmax_kernel("max")
+max.__name__ = "max"
max.__doc__ = """
.. versionadded:: 2011.1
"""
diff --git a/test/test_array.py b/test/test_array.py
index a94e36ac903cc70db6a4c9e84ebed230954605c1..e283417996d118ec950cfff2d06f311530f57a66 100644
--- a/test/test_array.py
+++ b/test/test_array.py
@@ -1705,6 +1705,68 @@ def test_maximum_minimum_with_scalars(ctx_factory):
np.testing.assert_allclose(result.get(), b_np)
+@pytest.mark.parametrize(("reduction", "supports_initial"), [
+ (cl_array.any, False),
+ (cl_array.all, False),
+ (cl_array.sum, True),
+ (cl_array.max, True),
+ (cl_array.min, True),
+ ])
+def test_empty_reductions_vs_numpy(ctx_factory, reduction, supports_initial):
+ ctx = ctx_factory()
+ cq = cl.CommandQueue(ctx)
+
+ # {{{ empty
+
+ x_np = np.array([], dtype=np.float64)
+ x_cl = cl_array.to_device(cq, x_np)
+
+ try:
+ ref = getattr(np, reduction.__name__)(x_np)
+ except ValueError:
+ ref = None
+
+ if ref is None:
+ with pytest.raises(ValueError):
+ reduction(x_cl)
+ else:
+ result = reduction(x_cl)
+ if isinstance(result, cl_array.Array):
+ result = result.get()
+
+ np.testing.assert_allclose(result, ref)
+
+ # }}}
+
+ # {{{ empty with initial
+
+ if supports_initial:
+ ref = getattr(np, reduction.__name__)(x_np, initial=5.0)
+ result = reduction(x_cl, initial=5.0)
+ if isinstance(result, cl_array.Array):
+ result = result.get()
+
+ np.testing.assert_allclose(result, ref)
+
+ # }}}
+
+ # {{{ non-empty with initial
+
+ if supports_initial:
+ x_np = np.linspace(-1, 1, 10)
+ x_cl = cl_array.to_device(cq, x_np)
+
+ ref = getattr(np, reduction.__name__)(x_np, initial=5.0)
+ result = reduction(x_cl, initial=5.0).get()
+ np.testing.assert_allclose(result, ref)
+
+ ref = getattr(np, reduction.__name__)(x_np, initial=-5.0)
+ result = reduction(x_cl, initial=-5.0).get()
+ np.testing.assert_allclose(result, ref)
+
+ # }}}
+
+
if __name__ == "__main__":
if len(sys.argv) > 1:
exec(sys.argv[1])