def _compile_macroscopic_setter_and_getter(self):
lb_method = self.method
cqc = lb_method.conserved_quantity_computation
pdf_field = self._data_handling.fields[self._pdf_arr_name]
rho_field = self._data_handling.fields[self.density_data_name]
rho_field = rho_field.center if self.density_data_index is None else rho_field(
self.density_data_index)
vel_field = self._data_handling.fields[self.velocity_data_name]
getter_eqs = cqc.output_equations_from_pdfs(pdf_field.center_vector, {
'density': rho_field,
'velocity': vel_field
})
getter_kernel = create_kernel(
getter_eqs, target=Target.CPU,
cpu_openmp=self._config.cpu_openmp).compile()
setter_eqs = pdf_initialization_assignments(lb_method, rho_field,
vel_field.center_vector,
pdf_field.center_vector)
setter_eqs = create_simplification_strategy(lb_method)(setter_eqs)
setter_kernel = create_kernel(
setter_eqs, target=Target.CPU,
cpu_openmp=self._config.cpu_openmp).compile()
return getter_kernel, setter_kernel
def test_rng_vectorized(target, rng, precision, dtype, t=130, offsets=(1, 3), keys=(0, 0), offset_values=None):
if (target in ['neon', 'vsx', 'rvv'] or target.startswith('sve')) and rng == 'aesni':
pytest.xfail('AES not yet implemented for this architecture')
cpu_vectorize_info = {'assume_inner_stride_one': True, 'assume_aligned': True, 'instruction_set': target}
dh = ps.create_data_handling((131, 131), default_ghost_layers=0, default_target=Target.CPU)
f = dh.add_array("f", values_per_cell=4 if precision == 'float' else 2,
dtype=np.float32 if dtype == 'float' else np.float64, alignment=True)
dh.fill(f.name, 42.0)
ref = dh.add_array("ref", values_per_cell=4 if precision == 'float' else 2)
rng_node = RNGs[(rng, precision)](dh.dim, offsets=offsets)
assignments = [rng_node] + [ps.Assignment(ref(i), s) for i, s in enumerate(rng_node.result_symbols)]
kernel = ps.create_kernel(assignments, target=dh.default_target).compile()
kwargs = {'time_step': t}
if offset_values is not None:
kwargs.update({k.name: v for k, v in zip(offsets, offset_values)})
dh.run_kernel(kernel, **kwargs)
rng_node = RNGs[(rng, precision)](dh.dim, offsets=offsets)
assignments = [rng_node] + [ps.Assignment(f(i), s) for i, s in enumerate(rng_node.result_symbols)]
kernel = ps.create_kernel(assignments, target=dh.default_target, cpu_vectorize_info=cpu_vectorize_info).compile()
dh.run_kernel(kernel, **kwargs)
ref_data = dh.gather_array(ref.name)
data = dh.gather_array(f.name)
assert np.allclose(ref_data, data)
def test_tensorflow_jit_cpu():
pytest.importorskip('tensorflow')
module_name = "Ololol"
target = 'cpu'
z, y, x = pystencils.fields("z, y, x: [20,40]")
a = sympy.Symbol('a')
forward_assignments = pystencils.AssignmentCollection(
{z[0, 0]: x[0, 0] * sympy.log(a * x[0, 0] * y[0, 0])})
backward_assignments = create_backward_assignments(forward_assignments)
forward_ast = pystencils.create_kernel(forward_assignments, target)
forward_ast.function_name = 'forward_jit'
backward_ast = pystencils.create_kernel(backward_assignments, target)
backward_ast.function_name = 'backward_jit'
module = TensorflowModule(module_name, [forward_ast, backward_ast])
lib = pystencils_autodiff.tensorflow_jit.compile_sources_and_load(
[str(module)])
assert 'call_forward_jit' in dir(lib)
assert 'call_backward_jit' in dir(lib)
lib = module.compile()
assert 'call_forward_jit' in dir(lib)
assert 'call_backward_jit' in dir(lib)
def test_full_scalar_field():
"""Tests fully (un)packing a scalar field (from)to a buffer."""
fields = _generate_fields()
for (src_arr, dst_arr, buffer_arr) in fields:
src_field = Field.create_from_numpy_array("src_field", src_arr)
dst_field = Field.create_from_numpy_array("dst_field", dst_arr)
buffer = Field.create_generic("buffer",
spatial_dimensions=1,
field_type=FieldType.BUFFER,
dtype=src_arr.dtype)
pack_eqs = [Assignment(buffer.center(), src_field.center())]
pack_code = create_kernel(pack_eqs,
data_type={
'src_field': src_arr.dtype,
'buffer': buffer.dtype
})
pack_kernel = pack_code.compile()
pack_kernel(buffer=buffer_arr, src_field=src_arr)
unpack_eqs = [Assignment(dst_field.center(), buffer.center())]
unpack_code = create_kernel(unpack_eqs,
data_type={
'dst_field': dst_arr.dtype,
'buffer': buffer.dtype
})
unpack_kernel = unpack_code.compile()
unpack_kernel(dst_field=dst_arr, buffer=buffer_arr)
np.testing.assert_equal(src_arr, dst_arr)
def test_strided(instruction_set, dtype):
f, g = ps.fields(f"f, g : float{64 if dtype == 'double' else 32}[2D]")
update_rule = [
ps.Assignment(g[0, 0],
f[0, 0] + f[-1, 0] + f[1, 0] + f[0, 1] + f[0, -1] + 42.0)
]
if 'storeS' not in get_vector_instruction_set(
dtype, instruction_set) and not instruction_set in [
'avx512', 'rvv'
] and not instruction_set.startswith('sve'):
with pytest.warns(UserWarning) as warn:
config = ps.CreateKernelConfig(
cpu_vectorize_info={'instruction_set': instruction_set})
ast = ps.create_kernel(update_rule, config=config)
assert 'Could not vectorize loop' in warn[0].message.args[0]
else:
with pytest.warns(None) as warn:
config = ps.CreateKernelConfig(
cpu_vectorize_info={'instruction_set': instruction_set})
ast = ps.create_kernel(update_rule, config=config)
assert len(warn) == 0
func = ast.compile()
ref_func = ps.create_kernel(update_rule).compile()
arr = np.random.random(
(23 + 2,
17 + 2)).astype(np.float64 if dtype == 'double' else np.float32)
dst = np.zeros_like(arr,
dtype=np.float64 if dtype == 'double' else np.float32)
ref = np.zeros_like(arr,
dtype=np.float64 if dtype == 'double' else np.float32)
func(g=dst, f=arr)
ref_func(g=ref, f=arr)
np.testing.assert_almost_equal(dst, ref, 13 if dtype == 'double' else 5)
def test_dynamic_matrix_location_dependent():
try:
from pystencils.data_types import TypedMatrixSymbol
except ImportError:
import pytest
pytest.skip()
x, y = pystencils.fields('x, y: float32[3d]')
A = TypedMatrixSymbol('A', 3, 1, create_type('double'),
CustomCppType('Vector3<double>'))
my_fun_call = DynamicFunction(
TypedSymbol('my_fun', 'std::function<Vector3<double>(int, int, int)>'),
A.dtype, *pystencils.x_vector(3))
assignments = pystencils.AssignmentCollection({
A: my_fun_call,
y.center: A[0] + A[1] + A[2]
})
ast = pystencils.create_kernel(assignments)
pystencils.show_code(ast, custom_backend=FrameworkIntegrationPrinter())
my_fun_call = DynamicFunction(
TypedSymbol('my_fun', TemplateType('Functor_T')), A.dtype,
*pystencils.x_vector(3))
assignments = pystencils.AssignmentCollection({
A: my_fun_call,
y.center: A[0] + A[1] + A[2]
})
ast = pystencils.create_kernel(assignments)
pystencils.show_code(ast, custom_backend=FrameworkIntegrationPrinter())
def test_logical_operators(instruction_set=instruction_set):
arr = np.zeros((22, 22))
@ps.kernel
def kernel_and(s):
f, g = ps.fields(f=arr, g=arr)
s.c @= sp.And(f[0, 1] < 0.0, f[1, 0] < 0.0)
g[0, 0] @= sp.Piecewise([1.0 / f[1, 0], s.c], [1.0, True])
ast = ps.create_kernel(kernel_and)
vectorize(ast, instruction_set=instruction_set)
ast.compile()
@ps.kernel
def kernel_or(s):
f, g = ps.fields(f=arr, g=arr)
s.c @= sp.Or(f[0, 1] < 0.0, f[1, 0] < 0.0)
g[0, 0] @= sp.Piecewise([1.0 / f[1, 0], s.c], [1.0, True])
ast = ps.create_kernel(kernel_or)
vectorize(ast, instruction_set=instruction_set)
ast.compile()
@ps.kernel
def kernel_equal(s):
f, g = ps.fields(f=arr, g=arr)
s.c @= sp.Eq(f[0, 1], 2.0)
g[0, 0] @= sp.Piecewise([1.0 / f[1, 0], s.c], [1.0, True])
ast = ps.create_kernel(kernel_equal)
vectorize(ast, instruction_set=instruction_set)
ast.compile()
def test_native_tensorflow_compilation_cpu():
tf = pytest.importorskip('tensorflow')
module_name = "Ololol"
target = 'cpu'
z, y, x = pystencils.fields("z, y, x: [20,40]")
a = sympy.Symbol('a')
forward_assignments = pystencils.AssignmentCollection({
z[0, 0]: x[0, 0] * sympy.log(a * x[0, 0] * y[0, 0])
})
backward_assignments = create_backward_assignments(forward_assignments)
forward_ast = pystencils.create_kernel(forward_assignments, target)
forward_ast.function_name = 'forward'
backward_ast = pystencils.create_kernel(backward_assignments, target)
backward_ast.function_name = 'backward'
module = TensorflowModule(module_name, [forward_ast, backward_ast])
print(module)
# temp_file = write_cached_content(str(module), '.cpp')
# command = ['c++', '-fPIC', temp_file, '-O2', '-shared',
# '-o', 'foo.so'] + compile_flags + link_flags + extra_flags
# print(command)
# subprocess.check_call(command, env=_compile_env)
lib = module.compile()
assert 'call_forward' in dir(lib)
assert 'call_backward' in dir(lib)
def test_pybind11_compilation_cpu(with_python_bindings):
pytest.importorskip('pybind11')
pytest.importorskip('cppimport')
module_name = "Olololsada"
target = 'cpu'
z, y, x = pystencils.fields("z, y, x: [20,40]")
a = sympy.Symbol('a')
forward_assignments = pystencils.AssignmentCollection(
{z[0, 0]: x[0, 0] * sympy.log(a * x[0, 0] * y[0, 0])})
backward_assignments = create_backward_assignments(forward_assignments)
forward_ast = pystencils.create_kernel(forward_assignments, target)
forward_ast.function_name = 'forward'
backward_ast = pystencils.create_kernel(backward_assignments, target)
backward_ast.function_name = 'backward'
module = PybindModule(module_name, [forward_ast, backward_ast],
with_python_bindings=with_python_bindings)
print(module)
if with_python_bindings:
pybind_extension = module.compile()
assert pybind_extension is not None
assert 'call_forward' in dir(pybind_extension)
assert 'call_backward' in dir(pybind_extension)
def test_advection(dim):
L = (8, ) * dim
dh = ps.create_data_handling(L,
periodicity=True,
default_target=ps.Target.CPU)
c = dh.add_array('c', values_per_cell=1)
j = dh.add_array('j',
values_per_cell=3**dh.dim // 2,
field_type=ps.FieldType.STAGGERED_FLUX)
u = dh.add_array('u', values_per_cell=dh.dim)
dh.cpu_arrays[c.name][:] = (np.random.random([l + 2 for l in L]))
dh.cpu_arrays[u.name][:] = (np.random.random([l + 2 for l in L] + [dim]) -
0.5) / 5
vof1 = ps.create_kernel(ps.fd.VOF(j, u, c)).compile()
dh.fill(j.name, np.nan, ghost_layers=True)
dh.run_kernel(vof1)
j1 = dh.gather_array(j.name).copy()
vof2 = ps.create_kernel(VOF2(j, u, c, simplify=False)).compile()
dh.fill(j.name, np.nan, ghost_layers=True)
dh.run_kernel(vof2)
j2 = dh.gather_array(j.name)
assert np.allclose(j1, j2)
def test_sqrt_of_integer():
"""Regression test for bug where sqrt(3) was classified as integer"""
f = ps.fields("f: [1D]")
tmp = sp.symbols("tmp")
assignments = [ps.Assignment(tmp, sp.sqrt(3)),
ps.Assignment(f[0], tmp)]
arr_double = np.array([1], dtype=np.float64)
kernel = ps.create_kernel(assignments).compile()
kernel(f=arr_double)
assert 1.7 < arr_double[0] < 1.8
f = ps.fields("f: float32[1D]")
tmp = sp.symbols("tmp")
assignments = [ps.Assignment(tmp, sp.sqrt(3)),
ps.Assignment(f[0], tmp)]
arr_single = np.array([1], dtype=np.float32)
config = ps.CreateKernelConfig(data_type="float32")
kernel = ps.create_kernel(assignments, config=config).compile()
kernel(f=arr_single)
code = ps.get_code_str(kernel.ast)
# ps.show_code(kernel.ast)
# 1.7320508075688772935 --> it is actually correct to round to ...773. This was wrong before !282
assert "1.7320508075688773f" in code
assert 1.7 < arr_single[0] < 1.8
def test_torch_native_compilation_cpu():
from torch.utils.cpp_extension import load
module_name = "Ololol"
target = 'cpu'
z, y, x = pystencils.fields("z, y, x: [20,40]")
a = sympy.Symbol('a')
forward_assignments = pystencils.AssignmentCollection(
{z[0, 0]: x[0, 0] * sympy.log(a * x[0, 0] * y[0, 0])})
backward_assignments = create_backward_assignments(forward_assignments)
forward_ast = pystencils.create_kernel(forward_assignments, target)
forward_ast.function_name = 'forward'
backward_ast = pystencils.create_kernel(backward_assignments, target)
backward_ast.function_name = 'backward'
module = TorchModule(module_name, [forward_ast, backward_ast])
print(module)
temp_file = write_cached_content(str(module), '.cpp')
torch_extension = load(module_name, [temp_file])
assert torch_extension is not None
assert 'call_forward' in dir(torch_extension)
assert 'call_backward' in dir(torch_extension)
torch_extension = module.compile()
assert torch_extension is not None
assert 'call_forward' in dir(torch_extension)
assert 'call_backward' in dir(torch_extension)
def test_reproducability():
from sympy.core.cache import clear_cache
output_0 = None
for i in range(10):
module_name = "Ololol"
target = 'cpu'
z, y, x = pystencils.fields("z, y, x: [20,40]")
a = sympy.Symbol('a')
forward_assignments = pystencils.AssignmentCollection(
{z[0, 0]: x[0, 0] * sympy.log(a * x[0, 0] * y[0, 0])})
backward_assignments = create_backward_assignments(forward_assignments)
forward_ast = pystencils.create_kernel(forward_assignments, target)
forward_ast.function_name = 'forward'
backward_ast = pystencils.create_kernel(backward_assignments, target)
backward_ast.function_name = 'backward'
new_output = str(TorchModule(module_name, [forward_ast, backward_ast]))
TorchModule(module_name, [forward_ast, backward_ast]).compile()
clear_cache()
if not output_0:
output_0 = new_output
assert output_0 == new_output
def test_fixed_constant_bh(num_ghost_layers):
ndim = 2
offsets = list(itertools.product(range(num_ghost_layers + 1), repeat=ndim))
x, y = pystencils.fields(f'x, y: float64[{ndim}d]')
assignments = pystencils.AssignmentCollection({
y.center:
sp.Add(*[x.__getitem__(o) for o in offsets]) / len(offsets)
})
kernel = pystencils.create_kernel(assignments).compile()
print(kernel.code)
bh_assignments = add_fixed_constant_boundary_handling(
assignments, num_ghost_layers)
bh_kernel = pystencils.create_kernel(bh_assignments,
ghost_layers=0).compile()
print(bh_kernel.code)
noise = np.random.rand(*[20, 30, 40][:ndim])
out1 = np.zeros_like(noise)
out2 = np.zeros_like(noise)
kernel(x=noise, y=out1)
bh_kernel(x=noise, y=out2)
def test_module_printing_parameter():
module_name = "Ololol"
for target in ('cpu', 'gpu'):
z, y, x = pystencils.fields("z, y, x: [20,40]")
a = sympy.Symbol('a')
forward_assignments = pystencils.AssignmentCollection(
{z[0, 0]: x[0, 0] * sympy.log(a * x[0, 0] * y[0, 0])})
backward_assignments = create_backward_assignments(forward_assignments)
forward_ast = pystencils.create_kernel(forward_assignments, target)
forward_ast.function_name = 'forward'
backward_ast = pystencils.create_kernel(backward_assignments, target)
backward_ast.function_name = 'backward'
module = TorchModule(module_name, [forward_ast, backward_ast])
print(module)
module = TensorflowModule(module_name, {forward_ast: backward_ast})
print(module)
if target == 'cpu':
module = PybindModule(module_name, [forward_ast, backward_ast])
print(module)
module = PybindModule(module_name, forward_ast)
print(module)
def test_kernel_decorator_config():
config = ps.CreateKernelConfig()
a, b, c = ps.fields(a=np.ones(100), b=np.ones(100), c=np.ones(100))
@ps.kernel_config(config)
def test():
a[0] @= b[0] + c[0]
ps.create_kernel(**test)
def test_native_tensorflow_compilation_gpu():
tf = pytest.importorskip('tensorflow')
module_name = "Ololol"
target = 'gpu'
z, y, x = pystencils.fields("z, y, x: [20,40]")
a = sympy.Symbol('a')
forward_assignments = pystencils.AssignmentCollection({
z[0, 0]: x[0, 0] * sympy.log(a * x[0, 0] * y[0, 0])
})
backward_assignments = create_backward_assignments(forward_assignments)
forward_ast = pystencils.create_kernel(forward_assignments, target)
forward_ast.function_name = 'forward2'
backward_ast = pystencils.create_kernel(backward_assignments, target)
backward_ast.function_name = 'backward2'
module = TensorflowModule(module_name, [forward_ast, backward_ast])
print(str(module))
# temp_file = write_cached_content(str(module), '.cu')
# if 'tensorflow_host_compiler' not in get_compiler_config():
# get_compiler_config()['tensorflow_host_compiler'] = get_compiler_config()['command']
# # on my machine g++-6 and clang-7 are working
# # '-ccbin',
# # 'g++-6',
# command = ['nvcc',
# temp_file.name,
# '--expt-relaxed-constexpr',
# '-ccbin',
# get_compiler_config()['tensorflow_host_compiler'],
# '-std=c++14',
# '-x',
# 'cu',
# '-Xcompiler',
# '-fPIC',
# '-c',
# '-o',
# 'foo_gpu.o'] + compile_flags + extra_flags
# subprocess.check_call(command)
# command = ['c++', '-fPIC', 'foo_gpu.o',
# '-shared', '-o', 'foo_gpu.so'] + link_flags
# subprocess.check_call(command)
lib = module.compile()
assert 'call_forward2' in dir(lib)
#
assert 'call_backward2' in dir(lib)
def generate_lattice_model(generation_context,
class_name,
collision_rule,
refinement_scaling=None,
**create_kernel_params):
# usually a numpy layout is chosen by default i.e. xyzf - which is bad for waLBerla where at least the spatial
# coordinates should be ordered in reverse direction i.e. zyx
is_float = not generation_context.double_accuracy
dtype = np.float32 if is_float else np.float64
lb_method = collision_rule.method
q = len(lb_method.stencil)
dim = lb_method.dim
create_kernel_params = default_create_kernel_parameters(
generation_context, create_kernel_params)
if create_kernel_params['target'] == 'gpu':
raise ValueError(
"Lattice Models can only be generated for CPUs. To generate LBM on GPUs use sweeps directly"
)
src_field = ps.Field.create_generic('pdfs',
dim,
dtype,
index_dimensions=1,
layout='fzyx',
index_shape=(q, ))
dst_field = ps.Field.create_generic('pdfs_tmp',
dim,
dtype,
index_dimensions=1,
layout='fzyx',
index_shape=(q, ))
stream_collide_update_rule = create_lbm_kernel(
collision_rule, src_field, dst_field, StreamPullTwoFieldsAccessor())
stream_collide_ast = create_kernel(stream_collide_update_rule,
**create_kernel_params)
stream_collide_ast.function_name = 'kernel_streamCollide'
collide_update_rule = create_lbm_kernel(collision_rule, src_field,
dst_field,
CollideOnlyInplaceAccessor())
collide_ast = create_kernel(collide_update_rule, **create_kernel_params)
collide_ast.function_name = 'kernel_collide'
stream_update_rule = create_stream_pull_only_kernel(
lb_method.stencil, None, 'pdfs', 'pdfs_tmp', 'fzyx', dtype)
stream_ast = create_kernel(stream_update_rule, **create_kernel_params)
stream_ast.function_name = 'kernel_stream'
__lattice_model(generation_context, class_name, lb_method,
stream_collide_ast, collide_ast, stream_ast,
refinement_scaling)
def test_fixed_size_mismatch_check():
"""Create kernel with two differently sized but constant fields """
src = np.zeros((20, 21, 9))
dst = np.zeros((21, 21, 9))
sym_src = Field.create_from_numpy_array("src", src, index_dimensions=1)
sym_dst = Field.create_from_numpy_array("dst", dst, index_dimensions=1)
update_rule = Assignment(sym_dst(0), sym_src[-1, 1](1) + sym_src[1, -1](2))
with pytest.raises(ValueError) as e:
create_kernel([update_rule])
assert 'Differently sized field accesses' in str(e.value)
def test_timeloop():
dh = create_data_handling(domain_size=(2, 2), periodicity=True)
pre = dh.add_array('pre_run_field', values_per_cell=1)
dh.fill("pre_run_field", 0.0, ghost_layers=True)
f = dh.add_array('field', values_per_cell=1)
dh.fill("field", 0.0, ghost_layers=True)
post = dh.add_array('post_run_field', values_per_cell=1)
dh.fill("post_run_field", 0.0, ghost_layers=True)
single_step = dh.add_array('single_step_field', values_per_cell=1)
dh.fill("single_step_field", 0.0, ghost_layers=True)
pre_assignments = Assignment(pre.center, pre.center + 1)
pre_kernel = create_kernel(pre_assignments).compile()
assignments = Assignment(f.center, f.center + 1)
kernel = create_kernel(assignments).compile()
post_assignments = Assignment(post.center, post.center + 1)
post_kernel = create_kernel(post_assignments).compile()
single_step_assignments = Assignment(single_step.center, single_step.center + 1)
single_step_kernel = create_kernel(single_step_assignments).compile()
fixed_steps = 2
timeloop = TimeLoop(steps=fixed_steps)
assert timeloop.fixed_steps == fixed_steps
def pre_run():
dh.run_kernel(pre_kernel)
def post_run():
dh.run_kernel(post_kernel)
def single_step_run():
dh.run_kernel(single_step_kernel)
timeloop.add_pre_run_function(pre_run)
timeloop.add_post_run_function(post_run)
timeloop.add_single_step_function(single_step_run)
timeloop.add_call(kernel, {'field': dh.cpu_arrays["field"]})
# the timeloop is initialised with 2 steps. This means a single time step consists of two steps.
# Therefore, we have 2 main iterations and one single step iteration in this configuration
timeloop.run(time_steps=5)
assert np.all(dh.cpu_arrays["pre_run_field"] == 1.0)
assert np.all(dh.cpu_arrays["field"] == 2.0)
assert np.all(dh.cpu_arrays["single_step_field"] == 1.0)
assert np.all(dh.cpu_arrays["post_run_field"] == 1.0)
seconds = 2
start = time.perf_counter()
timeloop.run_time_span(seconds=seconds)
end = time.perf_counter()
np.testing.assert_almost_equal(seconds, end - start, decimal=2)
def test_fixed_and_variable_field_check():
"""Create kernel with two variable sized fields - calling them with different sizes"""
src = np.zeros((20, 21, 9))
sym_src = Field.create_from_numpy_array("src", src, index_dimensions=1)
sym_dst = Field.create_generic("dst",
spatial_dimensions=2,
index_dimensions=1)
update_rule = Assignment(sym_dst(0), sym_src[-1, 1](1) + sym_src[1, -1](2))
with pytest.raises(ValueError) as e:
create_kernel(update_rule)
assert 'Mixing fixed-shaped and variable-shape fields' in str(e.value)
def test_loop_independence_checks():
f, g = fields("f, g : double[2D]")
v = fields("v(2) : double[2D]")
with pytest.raises(ValueError) as e:
create_kernel(
[Assignment(g[0, 1], f[0, 1]),
Assignment(g[0, 0], f[1, 0])])
assert 'Field g is written at two different locations' in str(e.value)
# This is allowed - because only one element of g is accessed
create_kernel(
[Assignment(g[0, 2], f[0, 1]),
Assignment(g[0, 2], 2 * g[0, 2])])
create_kernel([
Assignment(v[0, 2](1), f[0, 1]),
Assignment(v[0, 1](0), 4),
Assignment(v[0, 2](1), 2 * v[0, 2](1))
])
with pytest.raises(ValueError) as e:
create_kernel(
[Assignment(g[0, 1], 3),
Assignment(f[0, 1], 2 * g[0, 2])])
assert 'Field g is read at (0, 2) and written at (0, 1)' in str(e.value)
def test_subset_cell_values():
"""Tests (un)packing a subset of cell values of the a field (from)to a buffer."""
num_cell_values = 19
# Cell indices of the field to be (un)packed (from)to the buffer
cell_indices = [1, 5, 7, 8, 10, 12, 13]
fields = _generate_fields(num_directions=num_cell_values)
for (src_arr, dst_arr, bufferArr) in fields:
src_field = Field.create_from_numpy_array("src_field",
src_arr,
index_dimensions=1)
dst_field = Field.create_from_numpy_array("dst_field",
dst_arr,
index_dimensions=1)
buffer = Field.create_generic("buffer",
spatial_dimensions=1,
index_dimensions=1,
field_type=FieldType.BUFFER,
dtype=src_arr.dtype)
pack_eqs = []
# Since we are packing all cell values for all cells, then
# the buffer index is equivalent to the field index
for buffer_idx, cell_idx in enumerate(cell_indices):
eq = Assignment(buffer(buffer_idx), src_field(cell_idx))
pack_eqs.append(eq)
pack_code = create_kernel(pack_eqs,
data_type={
'src_field': src_arr.dtype,
'buffer': buffer.dtype
})
pack_kernel = pack_code.compile()
pack_kernel(buffer=bufferArr, src_field=src_arr)
unpack_eqs = []
for buffer_idx, cell_idx in enumerate(cell_indices):
eq = Assignment(dst_field(cell_idx), buffer(buffer_idx))
unpack_eqs.append(eq)
unpack_code = create_kernel(unpack_eqs,
data_type={
'dst_field': dst_arr.dtype,
'buffer': buffer.dtype
})
unpack_kernel = unpack_code.compile()
unpack_kernel(buffer=bufferArr, dst_field=dst_arr)
mask_arr = np.ma.masked_where((src_arr - dst_arr) != 0, src_arr)
np.testing.assert_equal(dst_arr, mask_arr.filled(int(0)))
def test_address_of_with_cse():
x, y = pystencils.fields('x,y: int64[2d]')
s = pystencils.TypedSymbol('s', PointerType(create_type('int64')))
assignments = pystencils.AssignmentCollection({
y[0, 0]: cast_func(address_of(x[0, 0]), create_type('int64')) + s,
x[0, 0]: cast_func(address_of(x[0, 0]), create_type('int64')) + 1
}, {})
ast = pystencils.create_kernel(assignments)
pystencils.show_code(ast)
assignments_cse = sympy_cse(assignments)
ast = pystencils.create_kernel(assignments_cse)
pystencils.show_code(ast)
def pystencils_2d_cpu_impl(x, y, coef, N, I=1):
if x.dtype == np.dtype('f4'):
src, dst = ps.fields(
'src, dst: float32[2D]',
src=x, dst=y
)
elif x.dtype == np.dtype('f8'):
src, dst = ps.fields(
'src, dst: double[2D]',
src=x, dst=y
)
else:
raise TypeError
if N == 1:
update_rule = make_2d_update_rule_1(src, dst, coef)
elif N == 6:
update_rule = make_2d_update_rule_6(src, dst, coef)
else:
raise ValueError
kernel = ps.create_kernel(update_rule, cpu_openmp=True).compile()
s = time.time()
for i in range(I):
if i % 2 == 0:
kernel(src=x, dst=y)
else:
kernel(src=y, dst=x)
e = time.time()
if (I - 1) % 2 == 0:
res = y
else:
res = x
return e - s, res
def test_vectorization_fixed_size():
configurations = []
# Fixed size - multiple of four
arr = np.ones((20 + 2, 24 + 2)) * 5.0
f, g = ps.fields(f=arr, g=arr)
configurations.append((arr, f, g))
# Fixed size - no multiple of four
arr = np.ones((21 + 2, 25 + 2)) * 5.0
f, g = ps.fields(f=arr, g=arr)
configurations.append((arr, f, g))
# Fixed size - different remainder
arr = np.ones((23 + 2, 17 + 2)) * 5.0
f, g = ps.fields(f=arr, g=arr)
configurations.append((arr, f, g))
for arr, f, g in configurations:
update_rule = [ps.Assignment(g[0, 0], f[0, 0] + f[-1, 0] + f[1, 0] + f[0, 1] + f[0, -1] + 42.0)]
ast = ps.create_kernel(update_rule)
vectorize(ast)
func = ast.compile()
dst = np.zeros_like(arr)
func(g=dst, f=arr)
np.testing.assert_equal(dst[1:-1, 1:-1], 5 * 5.0 + 42.0)
def test_sum_use_float():
sum = sympy.Sum(k, (k, 1, 100))
expanded_sum = sum.doit()
print(sum)
print(expanded_sum)
x = pystencils.fields('x: float32[1d]')
assignments = pystencils.AssignmentCollection({x.center(): sum})
ast = pystencils.create_kernel(assignments,
data_type=create_type('float32'))
code = str(pystencils.show_code(ast))
kernel = ast.compile()
print(code)
print(pystencils.show_code(ast))
assert 'float sum' in code
array = np.zeros((10, ), np.float32)
kernel(x=array)
assert np.allclose(array, int(expanded_sum) * np.ones_like(array))
def test_vec_all(instruction_set, dtype):
if instruction_set in ['sve', 'rvv']:
width = 1000 # we don't know the actual value, need something guaranteed larger than vector
else:
width = get_vector_instruction_set(dtype, instruction_set)['width']
data_arr = np.zeros((4 * width, 4 * width),
dtype=np.float64 if dtype == 'double' else np.float32)
data_arr[3:9, 1:3 * width - 1] = 1.0
data = ps.fields(f"data: {dtype}[2D]", data=data_arr)
c = [
Conditional(vec_all(data.center() > 0.0),
Block([ps.Assignment(data.center(), 2.0)]))
]
ast = ps.create_kernel(
c,
target=Target.CPU,
cpu_vectorize_info={'instruction_set': instruction_set})
kernel = ast.compile()
kernel(data=data_arr)
if instruction_set in ['sve', 'rvv']:
# we only know that some values in the middle have been replaced
assert np.all(data_arr[3:9, :2] <= 1.0)
assert np.any(data_arr[3:9, 2:] == 2.0)
else:
np.testing.assert_equal(data_arr[3:9, :1], 0.0)
np.testing.assert_equal(data_arr[3:9, 1:width], 1.0)
np.testing.assert_equal(data_arr[3:9, width:2 * width], 2.0)
np.testing.assert_equal(data_arr[3:9, 2 * width:3 * width - 1], 1.0)
np.testing.assert_equal(data_arr[3:9, 3 * width - 1:], 0.0)
def test_vec_any(instruction_set, dtype):
if instruction_set in ['sve', 'rvv']:
width = 4 # we don't know the actual value
else:
width = get_vector_instruction_set(dtype, instruction_set)['width']
data_arr = np.zeros((4 * width, 4 * width),
dtype=np.float64 if dtype == 'double' else np.float32)
data_arr[3:9, 1:3 * width - 1] = 1.0
data = ps.fields(f"data: {dtype}[2D]", data=data_arr)
c = [
ps.Assignment(sp.Symbol("t1"), vec_any(data.center() > 0.0)),
Conditional(vec_any(data.center() > 0.0),
Block([ps.Assignment(data.center(), 2.0)]))
]
ast = ps.create_kernel(
c,
target=ps.Target.CPU,
cpu_vectorize_info={'instruction_set': instruction_set})
kernel = ast.compile()
kernel(data=data_arr)
if instruction_set in ['sve', 'rvv']:
# we only know that the first value has changed
np.testing.assert_equal(data_arr[3:9, :3 * width - 1], 2.0)
else:
np.testing.assert_equal(data_arr[3:9, :3 * width], 2.0)
def test_prod_var_limit():
k = pystencils.TypedSymbol('k', create_type('int64'))
limit = pystencils.TypedSymbol('limit', create_type('int64'))
sum = sympy.Sum(k, (k, 1, limit))
expanded_sum = sum.replace(limit, 100).doit()
print(sum)
print(expanded_sum)
x = pystencils.fields('x: int64[1d]')
assignments = pystencils.AssignmentCollection({x.center(): sum})
ast = pystencils.create_kernel(assignments)
code = str(pystencils.show_code(ast))
kernel = ast.compile()
print(code)
array = np.zeros((10, ), np.int64)
kernel(x=array, limit=100)
assert np.allclose(array, int(expanded_sum) * np.ones_like(array))