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from pystencils import Assignment, Field, CreateKernelConfig, create_kernel, Target
def test_indexed_kernel():
arr = np.zeros((3, 4))
dtype = np.dtype([('x', int), ('y', int), ('value', arr.dtype)])
index_arr = np.zeros((3,), dtype=dtype)
index_arr[0] = (0, 2, 3.0)
index_arr[1] = (1, 3, 42.0)
index_arr[2] = (2, 1, 5.0)
indexed_field = Field.create_from_numpy_array('index', index_arr)
normal_field = Field.create_from_numpy_array('f', arr)
update_rule = Assignment(normal_field[0, 0], indexed_field('value'))
config = CreateKernelConfig(index_fields=[indexed_field])
ast = create_kernel([update_rule], config=config)
kernel = ast.compile()
kernel(f=arr, index=index_arr)
for i in range(index_arr.shape[0]):
np.testing.assert_allclose(arr[index_arr[i]['x'], index_arr[i]['y']], index_arr[i]['value'], atol=1e-13)
def test_indexed_cuda_kernel():
try:
import pycuda
except ImportError:
pycuda = None
if pycuda:
import pycuda.gpuarray as gpuarray
arr = np.zeros((3, 4))
dtype = np.dtype([('x', int), ('y', int), ('value', arr.dtype)])
index_arr = np.zeros((3,), dtype=dtype)
index_arr[0] = (0, 2, 3.0)
index_arr[1] = (1, 3, 42.0)
index_arr[2] = (2, 1, 5.0)
indexed_field = Field.create_from_numpy_array('index', index_arr)
normal_field = Field.create_from_numpy_array('f', arr)
update_rule = Assignment(normal_field[0, 0], indexed_field('value'))
config = CreateKernelConfig(target=Target.GPU, index_fields=[indexed_field])
ast = create_kernel([update_rule], config=config)
kernel = ast.compile()
gpu_arr = gpuarray.to_gpu(arr)
gpu_index_arr = gpuarray.to_gpu(index_arr)
kernel(f=gpu_arr, index=gpu_index_arr)
gpu_arr.get(arr)
for i in range(index_arr.shape[0]):
np.testing.assert_allclose(arr[index_arr[i]['x'], index_arr[i]['y']], index_arr[i]['value'], atol=1e-13)
else:
print("Did not run test on GPU since no pycuda is available")