def setUp(self):
if self.memory == 'managed':
if cuda.runtime.is_hip:
pytest.skip('HIP does not support managed memory')
self.old_pool = cupy.get_default_memory_pool()
self.new_pool = cuda.MemoryPool(cuda.malloc_managed)
cuda.set_allocator(self.new_pool.malloc)
pass
def __eq__(self, other):
return isinstance(other, DummyDevice)
def __ne__(self, other):
return not (self == other)
DummyDevice = DummyDeviceType()
# ------------------------------------------------------------------------------
# Global states
# ------------------------------------------------------------------------------
if available:
memory_pool = cuda.MemoryPool()
cuda.set_allocator(memory_pool.malloc)
# ------------------------------------------------------------------------------
# Global states
# ------------------------------------------------------------------------------
def get_device(*args):
"""Gets the device from an ID integer or an array object.
This is a convenient utility to select a correct device if the type of
``arg`` is unknown (i.e., one can use this function on arrays that may be
on CPU or GPU). The returned device object supports the context management
protocol of Python for the *with* statement.
Args:
"""
for arg in args:
if isinstance(
arg,
(ndarray, sparse.spmatrix, cupy.core.fusion.FusionVarPython)):
return _cupy
return numpy
fuse = cupy.core.fusion.fuse
disable_experimental_feature_warning = False
# set default allocator
_default_memory_pool = cuda.MemoryPool()
_default_pinned_memory_pool = cuda.PinnedMemoryPool()
cuda.set_allocator(_default_memory_pool.malloc)
cuda.set_pinned_memory_allocator(_default_pinned_memory_pool.malloc)
def get_default_memory_pool():
"""Returns CuPy default memory pool for GPU memory.
Returns:
cupy.cuda.MemoryPool: The memory pool object.
.. note::
If you want to disable memory pool, please use the following code.
"""
for arg in args:
if isinstance(
arg,
(ndarray, sparse.spmatrix, cupy.core.fusion._FusionVarScalar,
cupy.core.fusion._FusionVarArray)):
return _cupy
return numpy
fuse = cupy.core.fusion.fuse
disable_experimental_feature_warning = False
# set default allocator
_default_memory_pool = cuda.MemoryPool(cupy.cuda.memory.malloc_managed)
_default_pinned_memory_pool = cuda.PinnedMemoryPool(
) #cupy.cuda.memory.malloc_managed)
_default_device_memory_pool = cuda.MemoryPool()
#print("it is OC-cupy")
cuda.set_allocator(_default_memory_pool.malloc)
cuda.set_pinned_memory_allocator(_default_pinned_memory_pool.malloc)
def get_default_memory_pool():
"""Returns CuPy default memory pool for GPU memory.
Returns:
cupy.cuda.MemoryPool: The memory pool object.
.. note::
pass
def __eq__(self, other):
return isinstance(other, DummyDevice)
def __ne__(self, other):
return not (self == other)
DummyDevice = DummyDeviceType()
# ------------------------------------------------------------------------------
# Global states
# ------------------------------------------------------------------------------
if available:
cuda.set_allocator(cuda.MemoryPool().malloc)
# ------------------------------------------------------------------------------
# Global states
# ------------------------------------------------------------------------------
def get_device(*args):
"""Gets the device from an ID integer or an array object.
This is a convenient utility to select a correct device if the type of
``arg`` is unknown (i.e., one can use this function on arrays that may be
on CPU or GPU). The returned device object supports the context management
protocol of Python for the *with* statement.
Args:
args: Values to specify a GPU device. :class:`numpy.ndarray` objects
