def test_consuming_strides(self):
hostarray = np.arange(10).reshape(2, 5)
face = cuda.to_device(hostarray).__cuda_array_interface__
self.assertIsNone(face['strides'])
got = cuda.from_cuda_array_interface(face).copy_to_host()
np.testing.assert_array_equal(got, hostarray)
self.assertTrue(got.flags['C_CONTIGUOUS'])
# Try non-NULL strides
face['strides'] = hostarray.strides
self.assertIsNotNone(face['strides'])
got = cuda.from_cuda_array_interface(face).copy_to_host()
np.testing.assert_array_equal(got, hostarray)
self.assertTrue(got.flags['C_CONTIGUOUS'])
def test_masked_array(self):
h_arr = np.random.random(10)
h_mask = np.random.randint(2, size=10, dtype='bool')
c_arr = cuda.to_device(h_arr)
c_mask = cuda.to_device(h_mask)
# Manually create a masked CUDA Array Interface dictionary
masked_cuda_array_interface = c_arr.__cuda_array_interface__.copy()
masked_cuda_array_interface['mask'] = c_mask
with self.assertRaises(NotImplementedError) as raises:
cuda.from_cuda_array_interface(masked_cuda_array_interface)
expected_msg = 'Masked arrays are not supported'
self.assertIn(expected_msg, str(raises.exception))
def get_data(self, memtype=MEM_HOST):
"""Get read/write access to a QFunction Context via the specified memory type.
Args:
**memtype: memory type of the array being passed, default CEED_MEM_HOST
Returns:
*data: Numpy or Numba array"""
# Retrieve the length of the array
size_pointer = ffi.new("size_t *")
err_code = lib.CeedQFunctionContextGetContextSize(
self._pointer[0], size_pointer)
self._ceed._check_error(err_code)
# Setup the pointer's pointer
data_pointer = ffi.new("CeedScalar **")
# libCEED call
err_code = lib.CeedQFunctionContextGetData(self._pointer[0], memtype,
data_pointer)
self._ceed._check_error(err_code)
# Return array created from buffer
if memtype == MEM_HOST:
# Create buffer object from returned pointer
buff = ffi.buffer(data_pointer[0], size_pointer[0])
# return Numpy array
return np.frombuffer(buff,
dtype=scalar_types[lib.CEED_SCALAR_TYPE])
else:
# CUDA array interface
# https://numba.pydata.org/numba-doc/latest/cuda/cuda_array_interface.html
import numba.cuda as nbcuda
if lib.CEED_SCALAR_TYPE == lib.CEED_SCALAR_FP32:
scalar_type_str = '>f4'
else:
scalar_type_str = '>f8'
desc = {
'shape': (size_pointer[0] / ffi.sizeof("CeedScalar")),
'typestr': scalar_type_str,
'data': (int(ffi.cast("intptr_t", data_pointer[0])), False),
'version': 2
}
# return Numba array
return nbcuda.from_cuda_array_interface(desc)
def get_array_read(self, memtype=MEM_HOST):
"""Get read-only access to a Vector via the specified memory type.
Args:
**memtype: memory type of the array being passed, default CEED_MEM_HOST
Returns:
*array: Numpy or Numba array"""
# Retrieve the length of the array
length_pointer = ffi.new("CeedInt *")
err_code = lib.CeedVectorGetLength(self._pointer[0], length_pointer)
self._ceed._check_error(err_code)
# Setup the pointer's pointer
array_pointer = ffi.new("CeedScalar **")
# libCEED call
err_code = lib.CeedVectorGetArrayRead(self._pointer[0], memtype,
array_pointer)
self._ceed._check_error(err_code)
# Return array created from buffer
if memtype == MEM_HOST:
# Create buffer object from returned pointer
buff = ffi.buffer(array_pointer[0],
ffi.sizeof("CeedScalar") * length_pointer[0])
# return read only Numpy array
ret = np.frombuffer(buff, dtype="float64")
ret.flags['WRITEABLE'] = False
return ret
else:
# CUDA array interface
# https://numba.pydata.org/numba-doc/latest/cuda/cuda_array_interface.html
import numba.cuda as nbcuda
desc = {
'shape': (length_pointer[0]),
'typestr': '>f8',
'data': (int(ffi.cast("intptr_t", array_pointer[0])), False),
'version': 2
}
# return read only Numba array
return nbcuda.from_cuda_array_interface(desc)
def get_array_write(self, memtype=MEM_HOST):
"""Get write-only access to a Vector via the specified memory type.
All old values should be considered invalid.
Args:
**memtype: memory type of the array being passed, default CEED_MEM_HOST
Returns:
*array: Numpy or Numba array"""
# Retrieve the length of the array
length_pointer = ffi.new("CeedSize *")
err_code = lib.CeedVectorGetLength(self._pointer[0], length_pointer)
self._ceed._check_error(err_code)
# Setup the pointer's pointer
array_pointer = ffi.new("CeedScalar **")
# libCEED call
err_code = lib.CeedVectorGetArrayWrite(self._pointer[0], memtype,
array_pointer)
self._ceed._check_error(err_code)
# Return array created from buffer
if memtype == MEM_HOST:
# Create buffer object from returned pointer
buff = ffi.buffer(array_pointer[0],
ffi.sizeof("CeedScalar") * length_pointer[0])
# return Numpy array
return np.frombuffer(buff,
dtype=scalar_types[lib.CEED_SCALAR_TYPE])
else:
# CUDA array interface
# https://numba.pydata.org/numba-doc/latest/cuda/cuda_array_interface.html
import numba.cuda as nbcuda
desc = {
'shape': (length_pointer[0]),
'typestr': '>f8',
'data': (int(ffi.cast("intptr_t", array_pointer[0])), False),
'version': 2
}
# return Numba array
return nbcuda.from_cuda_array_interface(desc)
