def test_user_extension(self):
# User can use MemoryPointer to wrap externally defined pointers.
# This test checks if the finalizer is invokded at correct time
fake_ptr = ctypes.c_void_p(0xdeadbeef)
dtor_invoked = [0]
def dtor():
dtor_invoked[0] += 1
# Ensure finalizer is called when pointer is deleted
ptr = driver.MemoryPointer(context=self.context,
pointer=fake_ptr,
size=40,
finalizer=dtor)
self.assertEqual(dtor_invoked[0], 0)
del ptr
self.assertEqual(dtor_invoked[0], 1)
# Ensure removing derived pointer doesn't call finalizer
ptr = driver.MemoryPointer(context=self.context,
pointer=fake_ptr,
size=40,
finalizer=dtor)
owned = ptr.own()
del owned
self.assertEqual(dtor_invoked[0], 1)
del ptr
self.assertEqual(dtor_invoked[0], 2)
def __init__(self,
shape,
strides,
dtype,
stream=0,
writeback=None,
gpu_data=None):
"""
Args
----
shape
array shape.
strides
array strides.
dtype
data type as np.dtype coercible object.
stream
cuda stream.
writeback
Deprecated.
gpu_data
user provided device memory for the ndarray data buffer
"""
if isinstance(shape, int):
shape = (shape, )
if isinstance(strides, int):
strides = (strides, )
dtype = np.dtype(dtype)
self.ndim = len(shape)
if len(strides) != self.ndim:
raise ValueError('strides not match ndim')
self._dummy = dummyarray.Array.from_desc(0, shape, strides,
dtype.itemsize)
self.shape = tuple(shape)
self.strides = tuple(strides)
self.dtype = dtype
self.size = int(functools.reduce(operator.mul, self.shape, 1))
# prepare gpu memory
if self.size > 0:
if gpu_data is None:
self.alloc_size = _driver.memory_size_from_info(
self.shape, self.strides, self.dtype.itemsize)
gpu_data = devices.get_context().memalloc(self.alloc_size)
else:
self.alloc_size = _driver.device_memory_size(gpu_data)
else:
# Make NULL pointer for empty allocation
gpu_data = _driver.MemoryPointer(context=devices.get_context(),
pointer=c_void_p(0),
size=0)
self.alloc_size = 0
self.gpu_data = gpu_data
self.__writeback = writeback # should deprecate the use of this
self.stream = stream
