def test_jacobi_variable_field_size():
size = (3, 3, 3)
f = Field.create_generic("f", 3)
d = Field.create_generic("d", 3)
jacobi = SympyAssignment(
d[0, 0, 0], (f[1, 0, 0] + f[-1, 0, 0] + f[0, 1, 0] + f[0, -1, 0]) / 4)
body = Block([jacobi])
loop_node, gl_info = make_loop_over_domain(body)
ast_node = KernelFunction(loop_node,
'cpu',
'c',
make_python_function,
ghost_layers=gl_info)
resolve_field_accesses(ast_node)
move_constants_before_loop(ast_node)
src_field_c = np.random.rand(*size)
src_field_py = np.copy(src_field_c)
dst_field_c = np.zeros(size)
dst_field_py = np.zeros(size)
for x in range(1, size[0] - 1):
for y in range(1, size[1] - 1):
for z in range(1, size[2] - 1):
dst_field_py[x, y, z] = 0.25 * (
src_field_py[x - 1, y, z] + src_field_py[x + 1, y, z] +
src_field_py[x, y - 1, z] + src_field_py[x, y + 1, z])
kernel = ast_node.compile()
kernel(f=src_field_c, d=dst_field_c)
error = np.sum(np.abs(dst_field_py - dst_field_c))
np.testing.assert_allclose(error, 0.0, atol=1e-13)
def test_field_basic():
f = Field.create_generic('f', spatial_dimensions=2)
assert FieldType.is_generic(f)
assert f['E'] == f[1, 0]
assert f['N'] == f[0, 1]
assert '_' in f.center._latex('dummy')
f = Field.create_fixed_size('f', (10, 10),
strides=(80, 8),
dtype=np.float64)
assert f.spatial_strides == (10, 1)
assert f.index_strides == ()
assert f.center_vector == sp.Matrix([f.center])
f = Field.create_fixed_size('f', (8, 8, 2, 2), index_dimensions=2)
assert f.center_vector == sp.Matrix([[f(0, 0), f(0, 1)],
[f(1, 0), f(1, 1)]])
field_access = f[1, 1]
assert field_access.nr_of_coordinates == 2
assert field_access.offset_name == 'NE'
neighbor = field_access.neighbor(coord_id=0, offset=-2)
assert neighbor.offsets == (-1, 1)
assert '_' in neighbor._latex('dummy')
f = Field.create_generic('f', spatial_dimensions=5, index_dimensions=2)
field_access = f[1, -1, 2, -3, 0](1, 0)
assert field_access.offsets == (1, -1, 2, -3, 0)
assert field_access.index == (1, 0)
def test_field_access():
field = Field.create_generic('some_field',
spatial_dimensions=2,
index_dimensions=0)
copy(field(0))
field_copy = deepcopy(field(0))
assert field_copy.field.spatial_dimensions == 2
def __init__(self, derivative_coordinates, stencil, dx=1):
self.dim = len(stencil[0])
self.field = Field.create_generic('f', spatial_dimensions=self.dim)
self._derivative = tuple(sorted(derivative_coordinates))
self._stencil = stencil
self._dx = dx
self.weights = {
tuple(d): self.symbolic_weight(*d)
for d in self._stencil
}
def test_staggered_iteration():
dim = 2
f_arr = np.arange(5**dim).reshape([5] * dim).astype(np.float64)
s_arr = np.ones([5] * dim + [dim]) * 1234
s_arr_ref = s_arr.copy()
fields_fixed = (Field.create_from_numpy_array('f', f_arr),
Field.create_from_numpy_array('s',
s_arr,
index_dimensions=1))
fields_var = (Field.create_generic('f', 2),
Field.create_generic('s', 2, index_dimensions=1))
for f, s in [fields_var, fields_fixed]:
# --- Manual
eqs = []
counters = [
LoopOverCoordinate.get_loop_counter_symbol(i) for i in range(dim)
]
conditions = [counters[i] < f.shape[i] - 1 for i in range(dim)]
for d in range(dim):
eq = SympyAssignment(
s(d),
sum(f[o] for o in offsets_in_plane(d, 0, dim)) -
sum(f[o] for o in offsets_in_plane(d, -1, dim)))
cond = sp.And(*[conditions[i] for i in range(dim) if d != i])
eqs.append(Conditional(cond, eq))
func = create_kernel(eqs, ghost_layers=[(1, 0), (1, 0),
(1, 0)]).compile()
# --- Built-in optimized
expressions = []
for d in range(dim):
expressions.append(
sum(f[o] for o in offsets_in_plane(d, 0, dim)) -
sum(f[o] for o in offsets_in_plane(d, -1, dim)))
func_optimized = create_staggered_kernel(s, expressions).compile()
assert not func_optimized.ast.atoms(
Conditional), "Loop cutting optimization did not work"
func(f=f_arr, s=s_arr_ref)
func_optimized(f=f_arr, s=s_arr)
np.testing.assert_almost_equal(s_arr_ref, s_arr)
def create_lb_update_rule(collision_rule=None, lbm_config=None, lbm_optimisation=None, config=None,
optimization=None, **kwargs):
"""Creates an update rule (list of Assignments) for a LB method that describe a full sweep"""
lbm_config, lbm_optimisation, config = update_with_default_parameters(kwargs, optimization,
lbm_config, lbm_optimisation, config)
if lbm_config.collision_rule is not None:
collision_rule = lbm_config.collision_rule
if collision_rule is None:
collision_rule = create_lb_collision_rule(lbm_config.lb_method, lbm_config=lbm_config,
lbm_optimisation=lbm_optimisation,
config=config)
lb_method = collision_rule.method
field_data_type = config.data_type
q = collision_rule.method.stencil.Q
if lbm_optimisation.symbolic_field is not None:
src_field = lbm_optimisation.symbolic_field
elif lbm_optimisation.field_size:
field_size = tuple([s + 2 for s in lbm_optimisation.field_size] + [q])
src_field = Field.create_fixed_size(lbm_config.field_name, field_size, index_dimensions=1,
layout=lbm_optimisation.field_layout, dtype=field_data_type)
else:
src_field = Field.create_generic(lbm_config.field_name, spatial_dimensions=collision_rule.method.dim,
index_shape=(q,), layout=lbm_optimisation.field_layout, dtype=field_data_type)
if lbm_optimisation.symbolic_temporary_field is not None:
dst_field = lbm_optimisation.symbolic_temporary_field
else:
dst_field = src_field.new_field_with_different_name(lbm_config.temporary_field_name)
kernel_type = lbm_config.kernel_type
if kernel_type == 'stream_pull_only':
return create_stream_pull_with_output_kernel(lb_method, src_field, dst_field, lbm_config.output)
else:
if kernel_type == 'default_stream_collide':
if lbm_config.streaming_pattern == 'pull' and any(lbm_optimisation.builtin_periodicity):
accessor = PeriodicTwoFieldsAccessor(lbm_optimisation.builtin_periodicity, ghost_layers=1)
else:
accessor = get_accessor(lbm_config.streaming_pattern, lbm_config.timestep)
elif kernel_type == 'collide_only':
accessor = CollideOnlyInplaceAccessor
elif isinstance(kernel_type, PdfFieldAccessor):
accessor = kernel_type
else:
raise ValueError("Invalid value of parameter 'kernel_type'", lbm_config.kernel_type)
return create_lbm_kernel(collision_rule, src_field, dst_field, accessor)
def _add_boundary(self, boundary_obj, flag=None):
if boundary_obj not in self._boundary_object_to_boundary_info:
sym_index_field = Field.create_generic(
'indexField',
spatial_dimensions=1,
dtype=numpy_data_type_for_boundary_object(
boundary_obj, self.dim))
ast = self._create_boundary_kernel(
self._data_handling.fields[self._field_name], sym_index_field,
boundary_obj)
if flag is None:
flag = self.flag_interface.reserve_next_flag()
boundary_info = self.BoundaryInfo(boundary_obj,
flag=flag,
kernel=ast.compile())
self._boundary_object_to_boundary_info[
boundary_obj] = boundary_info
return self._boundary_object_to_boundary_info[boundary_obj].flag
def compile_macroscopic_values_getter(lb_method,
output_quantities,
pdf_arr=None,
ghost_layers=1,
iteration_slice=None,
field_layout='numpy',
target=Target.CPU,
streaming_pattern='pull',
previous_timestep=Timestep.BOTH):
"""
Create kernel to compute macroscopic value(s) from a pdf field (e.g. density or velocity)
Args:
lb_method: instance of :class:`lbmpy.methods.AbstractLbMethod`
output_quantities: sequence of quantities to compute e.g. ['density', 'velocity']
pdf_arr: optional numpy array for pdf field - used to get optimal loop structure for kernel
ghost_layers: a sequence of pairs for each coordinate with lower and upper nr of ghost layers
that should be excluded from the iteration. If None, the number of ghost layers
is determined automatically and assumed to be equal for all dimensions.
iteration_slice: if not None, iteration is done only over this slice of the field
field_layout: layout for output field, also used for pdf field if pdf_arr is not given
target: `Target.CPU` or `Target.GPU`
previous_step_accessor: The accessor used by the streaming pattern of the previous timestep
Returns:
a function to compute macroscopic values:
- pdf_array
- keyword arguments from name of conserved quantity (as in output_quantities) to numpy field
"""
if not (isinstance(output_quantities, list)
or isinstance(output_quantities, tuple)):
output_quantities = [output_quantities]
cqc = lb_method.conserved_quantity_computation
unknown_quantities = [
oq for oq in output_quantities if oq not in cqc.conserved_quantities
]
if unknown_quantities:
raise ValueError(
"No such conserved quantity: %s, conserved quantities are %s" %
(str(unknown_quantities), str(cqc.conserved_quantities.keys())))
if pdf_arr is None:
pdf_field = Field.create_generic('pdfs',
lb_method.dim,
index_dimensions=1,
layout=field_layout)
else:
pdf_field = Field.create_from_numpy_array('pdfs',
pdf_arr,
index_dimensions=1)
output_mapping = {}
for output_quantity in output_quantities:
number_of_elements = cqc.conserved_quantities[output_quantity]
assert number_of_elements >= 1
ind_dims = 0 if number_of_elements <= 1 else 1
if pdf_arr is None:
output_field = Field.create_generic(output_quantity,
lb_method.dim,
layout=field_layout,
index_dimensions=ind_dims)
else:
output_field_shape = pdf_arr.shape[:-1]
if ind_dims > 0:
output_field_shape += (number_of_elements, )
field_layout = get_layout_of_array(pdf_arr)
else:
field_layout = get_layout_of_array(
pdf_arr, index_dimension_ids=[len(pdf_field.shape) - 1])
output_field = Field.create_fixed_size(output_quantity,
output_field_shape,
ind_dims, pdf_arr.dtype,
field_layout)
output_mapping[output_quantity] = [
output_field(i) for i in range(number_of_elements)
]
if len(output_mapping[output_quantity]) == 1:
output_mapping[output_quantity] = output_mapping[output_quantity][
0]
stencil = lb_method.stencil
previous_step_accessor = get_accessor(streaming_pattern, previous_timestep)
pdf_symbols = previous_step_accessor.write(pdf_field, stencil)
eqs = cqc.output_equations_from_pdfs(pdf_symbols,
output_mapping).all_assignments
if target == Target.CPU:
import pystencils.cpu as cpu
kernel = cpu.make_python_function(
cpu.create_kernel(eqs,
ghost_layers=ghost_layers,
iteration_slice=iteration_slice))
elif target == Target.GPU:
import pystencils.gpucuda as gpu
kernel = gpu.make_python_function(
gpu.create_cuda_kernel(eqs,
ghost_layers=ghost_layers,
iteration_slice=iteration_slice))
else:
raise ValueError(
"Unknown target '%s'. Possible targets are `Target.CPU` and `Target.GPU`"
% (target, ))
def getter(pdfs, **kwargs):
if pdf_arr is not None:
assert pdfs.shape == pdf_arr.shape and pdfs.strides == pdf_arr.strides, \
"Pdf array not matching blueprint which was used to compile" + str(pdfs.shape) + str(pdf_arr.shape)
if not set(output_quantities).issubset(kwargs.keys()):
raise ValueError(
"You have to specify the output field for each of the following quantities: %s"
% (str(output_quantities), ))
kernel(pdfs=pdfs, **kwargs)
return getter
def compile_macroscopic_values_setter(lb_method,
quantities_to_set,
pdf_arr=None,
ghost_layers=1,
iteration_slice=None,
field_layout='numpy',
target=Target.CPU,
streaming_pattern='pull',
previous_timestep=Timestep.BOTH):
"""
Creates a function that sets a pdf field to specified macroscopic quantities
The returned function can be called with the pdf field to set as single argument
Args:
lb_method: instance of :class:`lbmpy.methods.AbstractLbMethod`
quantities_to_set: map from conserved quantity name to fixed value or numpy array
pdf_arr: optional numpy array for pdf field - used to get optimal loop structure for kernel
ghost_layers: a sequence of pairs for each coordinate with lower and upper nr of ghost layers
that should be excluded from the iteration. If None, the number of ghost layers
is determined automatically and assumed to be equal for all dimensions.
iteration_slice: if not None, iteration is done only over this slice of the field
field_layout: layout of the pdf field if pdf_arr was not given
target: `Target.CPU` or `Target.GPU`
previous_step_accessor: The accessor used by the streaming pattern of the previous timestep
Returns:
function taking pdf array as single argument and which sets the field to the given values
"""
if pdf_arr is not None:
pdf_field = Field.create_from_numpy_array('pdfs',
pdf_arr,
index_dimensions=1)
else:
pdf_field = Field.create_generic('pdfs',
lb_method.dim,
index_dimensions=1,
layout=field_layout)
fixed_kernel_parameters = {}
cqc = lb_method.conserved_quantity_computation
value_map = {}
at_least_one_field_input = False
for quantity_name, value in quantities_to_set.items():
if hasattr(value, 'shape'):
fixed_kernel_parameters[quantity_name] = value
at_least_one_field_input = True
num_components = cqc.conserved_quantities[quantity_name]
field = Field.create_from_numpy_array(
quantity_name,
value,
index_dimensions=0 if num_components <= 1 else 1)
if num_components == 1:
value = field(0)
else:
value = [field(i) for i in range(num_components)]
value_map[quantity_name] = value
cq_equations = cqc.equilibrium_input_equations_from_init_values(
**value_map, force_substitution=False)
eq = lb_method.get_equilibrium(conserved_quantity_equations=cq_equations)
if at_least_one_field_input:
simplification = create_simplification_strategy(lb_method)
eq = simplification(eq)
else:
eq = eq.new_without_subexpressions()
previous_step_accessor = get_accessor(streaming_pattern, previous_timestep)
write_accesses = previous_step_accessor.write(pdf_field, lb_method.stencil)
substitutions = {
sym: write_accesses[i]
for i, sym in enumerate(lb_method.post_collision_pdf_symbols)
}
eq = eq.new_with_substitutions(substitutions).all_assignments
if target == Target.CPU:
import pystencils.cpu as cpu
kernel = cpu.make_python_function(cpu.create_kernel(eq))
kernel = functools.partial(kernel, **fixed_kernel_parameters)
elif target == Target.GPU:
import pystencils.gpucuda as gpu
kernel = gpu.make_python_function(gpu.create_cuda_kernel(eq))
kernel = functools.partial(kernel, **fixed_kernel_parameters)
else:
raise ValueError(
"Unknown target '%s'. Possible targets are `Target.CPU` and `Target.GPU`"
% (target, ))
def setter(pdfs, **kwargs):
if pdf_arr is not None:
assert pdfs.shape == pdf_arr.shape and pdfs.strides == pdf_arr.strides, \
"Pdf array not matching blueprint which was used to compile" + str(pdfs.shape) + str(pdf_arr.shape)
kernel(pdfs=pdfs, **kwargs)
return setter
def add_array(self,
name,
values_per_cell=1,
dtype=np.float64,
latex_name=None,
ghost_layers=None,
layout=None,
cpu=True,
gpu=None,
alignment=False):
if ghost_layers is None:
ghost_layers = self.default_ghost_layers
if gpu is None:
gpu = self.default_target == 'gpu'
if layout is None:
layout = self.default_layout
if len(self.blocks) == 0:
raise ValueError(
"Data handling expects that each process has at least one block"
)
if hasattr(dtype, 'type'):
dtype = dtype.type
if name in self.blocks[
0] or self.GPU_DATA_PREFIX + name in self.blocks[0]:
raise ValueError("Data with this name has already been added")
if alignment is False or alignment is None:
alignment = 0
if hasattr(values_per_cell, '__len__'):
raise NotImplementedError(
"Parallel data handling does not support multiple index dimensions"
)
self._fieldInformation[name] = {
'ghost_layers': ghost_layers,
'values_per_cell': values_per_cell,
'layout': layout,
'dtype': dtype,
'alignment': alignment
}
layout_map = {
'fzyx': wlb.field.Layout.fzyx,
'zyxf': wlb.field.Layout.zyxf,
'f': wlb.field.Layout.fzyx,
'SoA': wlb.field.Layout.fzyx,
'AoS': wlb.field.Layout.zyxf
}
if cpu:
wlb.field.addToStorage(self.blocks,
name,
dtype,
fSize=values_per_cell,
layout=layout_map[layout],
ghostLayers=ghost_layers,
alignment=alignment)
if gpu:
if alignment != 0:
raise ValueError(
"Alignment for walberla GPU fields not yet supported")
wlb.cuda.addGpuFieldToStorage(self.blocks,
self.GPU_DATA_PREFIX + name,
dtype,
fSize=values_per_cell,
usePitchedMem=False,
ghostLayers=ghost_layers,
layout=layout_map[layout])
if cpu and gpu:
self._cpu_gpu_pairs.append((name, self.GPU_DATA_PREFIX + name))
block_bb = self.blocks.getBlockCellBB(self.blocks[0])
shape = tuple(s + 2 * ghost_layers for s in block_bb.size[:self.dim])
index_dimensions = 1 if values_per_cell > 1 else 0
if index_dimensions == 1:
shape += (values_per_cell, )
assert all(f.name != name for f in self.fields.values()
), "Symbolic field with this name already exists"
self.fields[name] = Field.create_generic(
name,
self.dim,
dtype,
index_dimensions,
layout,
index_shape=(values_per_cell, ) if index_dimensions > 0 else None)
self.fields[name].latex_name = latex_name
self._field_name_to_cpu_data_name[name] = name
if gpu:
self._field_name_to_gpu_data_name[
name] = self.GPU_DATA_PREFIX + name
return self.fields[name]
def test_error_handling():
struct_dtype = np.dtype([('a', np.int32), ('b', np.float64),
('c', np.uint32)])
Field.create_generic('f',
spatial_dimensions=2,
index_dimensions=0,
dtype=struct_dtype)
with pytest.raises(ValueError) as e:
Field.create_generic('f',
spatial_dimensions=2,
index_dimensions=1,
dtype=struct_dtype)
assert 'index dimension' in str(e.value)
arr = np.array([[1, 2.0, 3], [1, 2.0, 3]], dtype=struct_dtype)
Field.create_from_numpy_array('f', arr, index_dimensions=0)
with pytest.raises(ValueError) as e:
Field.create_from_numpy_array('f', arr, index_dimensions=1)
assert 'Structured arrays' in str(e.value)
arr = np.zeros([3, 3, 3])
Field.create_from_numpy_array('f', arr, index_dimensions=2)
with pytest.raises(ValueError) as e:
Field.create_from_numpy_array('f', arr, index_dimensions=3)
assert 'Too many' in str(e.value)
Field.create_fixed_size('f', (3, 2, 4),
index_dimensions=0,
dtype=struct_dtype,
layout='reverse_numpy')
with pytest.raises(ValueError) as e:
Field.create_fixed_size('f', (3, 2, 4),
index_dimensions=1,
dtype=struct_dtype,
layout='reverse_numpy')
assert 'Structured arrays' in str(e.value)
f = Field.create_fixed_size('f', (10, 10))
with pytest.raises(ValueError) as e:
f[1]
assert 'Wrong number of spatial indices' in str(e.value)
f = Field.create_generic('f', spatial_dimensions=2, index_shape=(3, ))
with pytest.raises(ValueError) as e:
f(3)
assert 'out of bounds' in str(e.value)
f = Field.create_fixed_size('f', (10, 10, 3, 4), index_dimensions=2)
with pytest.raises(ValueError) as e:
f(3, 0)
assert 'out of bounds' in str(e.value)
with pytest.raises(ValueError) as e:
f(1, 0)(1, 0)
assert 'Indexing an already indexed' in str(e.value)
with pytest.raises(ValueError) as e:
f(1)
assert 'Wrong number of indices' in str(e.value)
with pytest.raises(ValueError) as e:
Field.create_generic('f', spatial_dimensions=2, layout='wrong')
assert 'Unknown layout descriptor' in str(e.value)
assert layout_string_to_tuple('fzyx', dim=4) == (3, 2, 1, 0)
with pytest.raises(ValueError) as e:
layout_string_to_tuple('wrong', dim=4)
assert 'Unknown layout descriptor' in str(e.value)