def test_inc_reused_loop(self, set):
from pyop2.base import collecting_loops
g = op2.Global(1, 0, dtype=numpy.uint32)
k = """void k(unsigned int* g) { *g += 1; }"""
with collecting_loops(True):
loop = op2.par_loop(op2.Kernel(k, "k"), set, g(op2.INC))
loop()
assert_allclose(g.data, set.size)
loop()
assert_allclose(g.data, 2 * set.size)
g.zero()
loop()
assert_allclose(g.data, set.size)
def test_inc_reused_loop(self, set):
from pyop2.base import collecting_loops
g = op2.Global(1, 0, dtype=numpy.uint32)
k = """static void k(unsigned int* g) { *g += 1; }"""
with collecting_loops(True):
loop = op2.par_loop(op2.Kernel(k, "k"),
set,
g(op2.INC))
loop()
assert_allclose(g.data, set.size)
loop()
assert_allclose(g.data, 2*set.size)
g.zero()
loop()
assert_allclose(g.data, set.size)
def thunk(bcs):
if collect_loops:
loops.append(zero_tensor)
else:
zero_tensor()
for indices, kinfo in kernels:
kernel = kinfo.kernel
integral_type = kinfo.integral_type
domain_number = kinfo.domain_number
subdomain_id = kinfo.subdomain_id
coeff_map = kinfo.coefficient_map
pass_layer_arg = kinfo.pass_layer_arg
needs_orientations = kinfo.oriented
needs_cell_facets = kinfo.needs_cell_facets
needs_cell_sizes = kinfo.needs_cell_sizes
m = domains[domain_number]
subdomain_data = f.subdomain_data()[m]
# Find argument space indices
if is_mat:
i, j = indices
elif is_vec:
i, = indices
else:
assert len(indices) == 0
sdata = subdomain_data.get(integral_type, None)
if integral_type != 'cell' and sdata is not None:
raise NotImplementedError(
"subdomain_data only supported with cell integrals.")
# Extract block from tensor and test/trial spaces
# FIXME Ugly variable renaming required because functions are not
# lexical closures in Python and we're writing to these variables
if is_mat and result_matrix.block_shape > (1, 1):
tsbc = []
trbc = []
# Unwind ComponentFunctionSpace to check for matching BCs
for bc in bcs:
fs = bc.function_space()
if fs.component is not None:
fs = fs.parent
if fs.index == i:
tsbc.append(bc)
if fs.index == j:
trbc.append(bc)
elif is_mat:
tsbc, trbc = bcs, bcs
# Now build arguments for the par_loop
kwargs = {}
# Some integrals require non-coefficient arguments at the
# end (facet number information).
extra_args = []
# Decoration for applying to matrix maps in extruded case
decoration = None
itspace = m.measure_set(integral_type, subdomain_id,
all_integer_subdomain_ids)
if integral_type == "cell":
itspace = sdata or itspace
if subdomain_id not in ["otherwise", "everywhere"] and \
sdata is not None:
raise ValueError(
"Cannot use subdomain data and subdomain_id")
def get_map(x, bcs=None, decoration=None):
return x.cell_node_map(bcs)
elif integral_type in ("exterior_facet", "exterior_facet_vert"):
extra_args.append(m.exterior_facets.local_facet_dat(op2.READ))
def get_map(x, bcs=None, decoration=None):
return x.exterior_facet_node_map(bcs)
elif integral_type in ("exterior_facet_top",
"exterior_facet_bottom"):
# In the case of extruded meshes with horizontal facet integrals, two
# parallel loops will (potentially) get created and called based on the
# domain id: interior horizontal, bottom or top.
decoration = {
"exterior_facet_top": op2.ON_TOP,
"exterior_facet_bottom": op2.ON_BOTTOM
}[integral_type]
kwargs["iterate"] = decoration
def get_map(x, bcs=None, decoration=None):
map_ = x.cell_node_map(bcs)
if decoration is not None:
return op2.DecoratedMap(map_, decoration)
return map_
elif integral_type in ("interior_facet", "interior_facet_vert"):
extra_args.append(m.interior_facets.local_facet_dat(op2.READ))
def get_map(x, bcs=None, decoration=None):
return x.interior_facet_node_map(bcs)
elif integral_type == "interior_facet_horiz":
decoration = op2.ON_INTERIOR_FACETS
kwargs["iterate"] = decoration
def get_map(x, bcs=None, decoration=None):
map_ = x.cell_node_map(bcs)
if decoration is not None:
return op2.DecoratedMap(map_, decoration)
return map_
else:
raise ValueError("Unknown integral type '%s'" % integral_type)
# Output argument
if is_mat:
tensor_arg = mat(lambda s: get_map(s, tsbc, decoration),
lambda s: get_map(s, trbc, decoration), i, j)
elif is_vec:
tensor_arg = vec(lambda s: get_map(s), i)
else:
tensor_arg = tensor(op2.INC)
coords = m.coordinates
args = [
kernel, itspace, tensor_arg,
coords.dat(op2.READ,
get_map(coords)[op2.i[0]])
]
if needs_orientations:
o = m.cell_orientations()
args.append(o.dat(op2.READ, get_map(o)[op2.i[0]]))
if needs_cell_sizes:
o = m.cell_sizes
args.append(o.dat(op2.READ, get_map(o)[op2.i[0]]))
for n in coeff_map:
c = coefficients[n]
for c_ in c.split():
m_ = get_map(c_)
args.append(c_.dat(op2.READ, m_ and m_[op2.i[0]]))
if needs_cell_facets:
assert integral_type == "cell"
extra_args.append(m.cell_to_facets(op2.READ))
args.extend(extra_args)
kwargs["pass_layer_arg"] = pass_layer_arg
try:
with collecting_loops(collect_loops):
loops.append(op2.par_loop(*args, **kwargs))
except MapValueError:
raise RuntimeError(
"Integral measure does not match measure of all coefficients/arguments"
)
# Must apply bcs outside loop over kernels because we may wish
# to apply bcs to a block which is otherwise zero, and
# therefore does not have an associated kernel.
if bcs is not None and is_mat:
for bc in bcs:
fs = bc.function_space()
# Evaluate this outwith a "collecting_loops" block,
# since creation of the bc nodes actually can create a
# par_loop.
nodes = bc.nodes
if len(fs) > 1:
raise RuntimeError(
"""Cannot apply boundary conditions to full mixed space. Did you forget to index it?"""
)
shape = result_matrix.block_shape
with collecting_loops(collect_loops):
for i in range(shape[0]):
for j in range(shape[1]):
# Set diagonal entries on bc nodes to 1 if the current
# block is on the matrix diagonal and its index matches the
# index of the function space the bc is defined on.
if i != j:
continue
if fs.component is None and fs.index is not None:
# Mixed, index (no ComponentFunctionSpace)
if fs.index == i:
loops.append(tensor[
i,
j].set_local_diagonal_entries(nodes))
elif fs.component is not None:
# ComponentFunctionSpace, check parent index
if fs.parent.index is not None:
# Mixed, index doesn't match
if fs.parent.index != i:
continue
# Index matches
loops.append(
tensor[i, j].set_local_diagonal_entries(
nodes, idx=fs.component))
elif fs.index is None:
loops.append(tensor[
i, j].set_local_diagonal_entries(nodes))
else:
raise RuntimeError("Unhandled BC case")
if bcs is not None and is_vec:
if len(bcs) > 0 and collect_loops:
raise NotImplementedError(
"Loop collection not handled in this case")
for bc in bcs:
bc.apply(result_function)
if is_mat:
# Queue up matrix assembly (after we've done all the other operations)
loops.append(tensor.assemble())
return result()
def _assemble(f, tensor=None, bcs=None, form_compiler_parameters=None,
inverse=False, mat_type=None, sub_mat_type=None,
appctx={},
options_prefix=None,
assemble_now=False,
allocate_only=False,
zero_tensor=True):
r"""Assemble the form or Slate expression f and return a Firedrake object
representing the result. This will be a :class:`float` for 0-forms/rank-0
Slate tensors, a :class:`.Function` for 1-forms/rank-1 Slate tensors and
a :class:`.Matrix` for 2-forms/rank-2 Slate tensors.
:arg bcs: A tuple of :class`.DirichletBC`\s and/or :class`.EquationBCSplit`\s to be applied.
:arg tensor: An existing tensor object into which the form should be
assembled. If this is not supplied, a new tensor will be created for
the purpose.
:arg form_compiler_parameters: (optional) dict of parameters to pass to
the form compiler.
:arg inverse: (optional) if f is a 2-form, then assemble the inverse
of the local matrices.
:arg mat_type: (optional) type for assembled matrices, one of
"nest", "aij", "baij", or "matfree".
:arg sub_mat_type: (optional) type for assembled sub matrices
inside a "nest" matrix. One of "aij" or "baij".
:arg appctx: Additional information to hang on the assembled
matrix if an implicit matrix is requested (mat_type "matfree").
:arg options_prefix: An options prefix for the PETSc matrix
(ignored if not assembling a bilinear form).
"""
if mat_type is None:
mat_type = parameters.parameters["default_matrix_type"]
if mat_type not in ["matfree", "aij", "baij", "nest"]:
raise ValueError("Unrecognised matrix type, '%s'" % mat_type)
if sub_mat_type is None:
sub_mat_type = parameters.parameters["default_sub_matrix_type"]
if sub_mat_type not in ["aij", "baij"]:
raise ValueError("Invalid submatrix type, '%s' (not 'aij' or 'baij')", sub_mat_type)
if form_compiler_parameters:
form_compiler_parameters = form_compiler_parameters.copy()
else:
form_compiler_parameters = {}
form_compiler_parameters["assemble_inverse"] = inverse
topology = f.ufl_domains()[0].topology
for m in f.ufl_domains():
# Ensure mesh is "initialised" (could have got here without
# building a functionspace (e.g. if integrating a constant)).
m.init()
if m.topology != topology:
raise NotImplementedError("All integration domains must share a mesh topology.")
for o in chain(f.arguments(), f.coefficients()):
domain = o.ufl_domain()
if domain is not None and domain.topology != topology:
raise NotImplementedError("Assembly with multiple meshes not supported.")
if isinstance(f, slate.TensorBase):
kernels = slac.compile_expression(f, tsfc_parameters=form_compiler_parameters)
integral_types = [kernel.kinfo.integral_type for kernel in kernels]
else:
kernels = tsfc_interface.compile_form(f, "form", parameters=form_compiler_parameters, inverse=inverse)
integral_types = [integral.integral_type() for integral in f.integrals()]
if bcs is not None:
for bc in bcs:
integral_types += [integral.integral_type() for integral in bc.integrals()]
rank = len(f.arguments())
is_mat = rank == 2
is_vec = rank == 1
if any((coeff.function_space() and coeff.function_space().component is not None)
for coeff in f.coefficients()):
raise NotImplementedError("Integration of subscripted VFS not yet implemented")
if inverse and rank != 2:
raise ValueError("Can only assemble the inverse of a 2-form")
zero_tensor_parloop = lambda: None
if is_mat:
matfree = mat_type == "matfree"
nest = mat_type == "nest"
if nest:
baij = sub_mat_type == "baij"
else:
baij = mat_type == "baij"
# intercept matrix-free matrices here
if matfree:
if inverse:
raise NotImplementedError("Inverse not implemented with matfree")
if tensor is None:
result_matrix = matrix.ImplicitMatrix(f, bcs,
fc_params=form_compiler_parameters,
appctx=appctx,
options_prefix=options_prefix)
yield lambda: result_matrix
raise StopIteration()
if not isinstance(tensor, matrix.ImplicitMatrix):
raise ValueError("Expecting implicit matrix with matfree")
tensor.assemble()
yield lambda: tensor
raise StopIteration()
test, trial = f.arguments()
map_pairs = []
cell_domains = []
exterior_facet_domains = []
interior_facet_domains = []
if tensor is None:
# For horizontal facets of extruded meshes, the corresponding domain
# in the base mesh is the cell domain. Hence all the maps used for top
# bottom and interior horizontal facets will use the cell to dofs map
# coming from the base mesh as a starting point for the actual dynamic map
# computation.
for integral_type in integral_types:
if integral_type == "cell":
cell_domains.append(op2.ALL)
elif integral_type == "exterior_facet":
exterior_facet_domains.append(op2.ALL)
elif integral_type == "interior_facet":
interior_facet_domains.append(op2.ALL)
elif integral_type == "exterior_facet_bottom":
cell_domains.append(op2.ON_BOTTOM)
elif integral_type == "exterior_facet_top":
cell_domains.append(op2.ON_TOP)
elif integral_type == "exterior_facet_vert":
exterior_facet_domains.append(op2.ALL)
elif integral_type == "interior_facet_horiz":
cell_domains.append(op2.ON_INTERIOR_FACETS)
elif integral_type == "interior_facet_vert":
interior_facet_domains.append(op2.ALL)
else:
raise ValueError('Unknown integral type "%s"' % integral_type)
# Used for the sparsity construction
iteration_regions = []
if cell_domains:
map_pairs.append((test.cell_node_map(), trial.cell_node_map()))
iteration_regions.append(tuple(cell_domains))
if exterior_facet_domains:
map_pairs.append((test.exterior_facet_node_map(), trial.exterior_facet_node_map()))
iteration_regions.append(tuple(exterior_facet_domains))
if interior_facet_domains:
map_pairs.append((test.interior_facet_node_map(), trial.interior_facet_node_map()))
iteration_regions.append(tuple(interior_facet_domains))
map_pairs = tuple(map_pairs)
# Construct OP2 Mat to assemble into
fs_names = (test.function_space().name, trial.function_space().name)
try:
sparsity = op2.Sparsity((test.function_space().dof_dset,
trial.function_space().dof_dset),
map_pairs,
iteration_regions=iteration_regions,
name="%s_%s_sparsity" % fs_names,
nest=nest,
block_sparse=baij)
except SparsityFormatError:
raise ValueError("Monolithic matrix assembly is not supported for systems with R-space blocks.")
result_matrix = matrix.Matrix(f, bcs, mat_type, sparsity, numpy.float64,
"%s_%s_matrix" % fs_names,
options_prefix=options_prefix)
tensor = result_matrix._M
else:
if isinstance(tensor, matrix.ImplicitMatrix):
raise ValueError("Expecting matfree with implicit matrix")
result_matrix = tensor
tensor = tensor._M
zero_tensor_parloop = tensor.zero
if result_matrix.block_shape != (1, 1) and mat_type == "baij":
raise ValueError("BAIJ matrix type makes no sense for mixed spaces, use 'aij'")
def mat(testmap, trialmap, rowbc, colbc, i, j):
m = testmap(test.function_space()[i])
n = trialmap(trial.function_space()[j])
maps = (m if m else None, n if n else None)
rlgmap, clgmap = tensor[i, j].local_to_global_maps
V = test.function_space()[i]
rlgmap = V.local_to_global_map(rowbc, lgmap=rlgmap)
V = trial.function_space()[j]
clgmap = V.local_to_global_map(colbc, lgmap=clgmap)
if rowbc is None:
rowbc = []
if colbc is None:
colbc = []
unroll = any(bc.function_space().component is not None
for bc in chain(rowbc, colbc) if bc is not None)
return tensor[i, j](op2.INC, maps, lgmaps=(rlgmap, clgmap), unroll_map=unroll)
result = lambda: result_matrix
if allocate_only:
yield result
raise StopIteration()
elif is_vec:
test = f.arguments()[0]
if tensor is None:
result_function = function.Function(test.function_space())
tensor = result_function.dat
else:
result_function = tensor
tensor = result_function.dat
zero_tensor_parloop = tensor.zero
def vec(testmap, i):
_testmap = testmap(test.function_space()[i])
return tensor[i](op2.INC, _testmap if _testmap else None)
result = lambda: result_function
else:
# 0-forms are always scalar
if tensor is None:
tensor = op2.Global(1, [0.0])
else:
raise ValueError("Can't assemble 0-form into existing tensor")
result = lambda: tensor.data[0]
coefficients = f.coefficients()
domains = f.ufl_domains()
# These will be used to correctly interpret the "otherwise"
# subdomain
all_integer_subdomain_ids = defaultdict(list)
for k in kernels:
if k.kinfo.subdomain_id != "otherwise":
all_integer_subdomain_ids[k.kinfo.integral_type].append(k.kinfo.subdomain_id)
for k, v in all_integer_subdomain_ids.items():
all_integer_subdomain_ids[k] = tuple(sorted(v))
# In collecting loops mode, we collect the loops, and assume the
# boundary conditions provided are the ones we want. It therefore
# is only used inside residual and jacobian assembly.
if zero_tensor:
yield zero_tensor_parloop
for indices, kinfo in kernels:
kernel = kinfo.kernel
integral_type = kinfo.integral_type
domain_number = kinfo.domain_number
subdomain_id = kinfo.subdomain_id
coeff_map = kinfo.coefficient_map
pass_layer_arg = kinfo.pass_layer_arg
needs_orientations = kinfo.oriented
needs_cell_facets = kinfo.needs_cell_facets
needs_cell_sizes = kinfo.needs_cell_sizes
m = domains[domain_number]
subdomain_data = f.subdomain_data()[m]
# Find argument space indices
if is_mat:
i, j = indices
elif is_vec:
i, = indices
else:
assert len(indices) == 0
sdata = subdomain_data.get(integral_type, None)
if integral_type != 'cell' and sdata is not None:
raise NotImplementedError("subdomain_data only supported with cell integrals.")
# Extract block from tensor and test/trial spaces
# FIXME Ugly variable renaming required because functions are not
# lexical closures in Python and we're writing to these variables
if is_mat:
if bcs is not None:
tsbc = list(bc for bc in chain(*bcs))
if result_matrix.block_shape > (1, 1):
trbc = [bc for bc in tsbc if bc.function_space_index() == j and isinstance(bc, DirichletBC)]
tsbc = [bc for bc in tsbc if bc.function_space_index() == i]
else:
trbc = [bc for bc in tsbc if isinstance(bc, DirichletBC)]
else:
tsbc = []
trbc = []
# Now build arguments for the par_loop
kwargs = {}
# Some integrals require non-coefficient arguments at the
# end (facet number information).
extra_args = []
# Decoration for applying to matrix maps in extruded case
decoration = None
itspace = m.measure_set(integral_type, subdomain_id,
all_integer_subdomain_ids)
if integral_type == "cell":
itspace = sdata or itspace
if subdomain_id not in ["otherwise", "everywhere"] and sdata is not None:
raise ValueError("Cannot use subdomain data and subdomain_id")
def get_map(x):
return x.cell_node_map()
elif integral_type in ("exterior_facet", "exterior_facet_vert"):
extra_args.append(m.exterior_facets.local_facet_dat(op2.READ))
def get_map(x):
return x.exterior_facet_node_map()
elif integral_type in ("exterior_facet_top", "exterior_facet_bottom"):
# In the case of extruded meshes with horizontal facet integrals, two
# parallel loops will (potentially) get created and called based on the
# domain id: interior horizontal, bottom or top.
decoration = {"exterior_facet_top": op2.ON_TOP,
"exterior_facet_bottom": op2.ON_BOTTOM}[integral_type]
kwargs["iterate"] = decoration
def get_map(x):
return x.cell_node_map()
elif integral_type in ("interior_facet", "interior_facet_vert"):
extra_args.append(m.interior_facets.local_facet_dat(op2.READ))
def get_map(x):
return x.interior_facet_node_map()
elif integral_type == "interior_facet_horiz":
decoration = op2.ON_INTERIOR_FACETS
kwargs["iterate"] = decoration
def get_map(x):
return x.cell_node_map()
else:
raise ValueError("Unknown integral type '%s'" % integral_type)
# Output argument
if is_mat:
tensor_arg = mat(lambda s: get_map(s),
lambda s: get_map(s),
tsbc, trbc,
i, j)
elif is_vec:
tensor_arg = vec(lambda s: get_map(s), i)
else:
tensor_arg = tensor(op2.INC)
coords = m.coordinates
args = [kernel, itspace, tensor_arg,
coords.dat(op2.READ, get_map(coords))]
if needs_orientations:
o = m.cell_orientations()
args.append(o.dat(op2.READ, get_map(o)))
if needs_cell_sizes:
o = m.cell_sizes
args.append(o.dat(op2.READ, get_map(o)))
for n in coeff_map:
c = coefficients[n]
for c_ in c.split():
m_ = get_map(c_)
args.append(c_.dat(op2.READ, m_))
if needs_cell_facets:
assert integral_type == "cell"
extra_args.append(m.cell_to_facets(op2.READ))
args.extend(extra_args)
kwargs["pass_layer_arg"] = pass_layer_arg
try:
with collecting_loops(True):
yield op2.par_loop(*args, **kwargs)
except MapValueError:
raise RuntimeError("Integral measure does not match measure of all coefficients/arguments")
# Must apply bcs outside loop over kernels because we may wish
# to apply bcs to a block which is otherwise zero, and
# therefore does not have an associated kernel.
if bcs is not None and is_mat:
for bc in bcs:
if isinstance(bc, DirichletBC):
fs = bc.function_space()
# Evaluate this outwith a "collecting_loops" block,
# since creation of the bc nodes actually can create a
# par_loop.
nodes = bc.nodes
if len(fs) > 1:
raise RuntimeError(r"""Cannot apply boundary conditions to full mixed space. Did you forget to index it?""")
shape = result_matrix.block_shape
with collecting_loops(True):
for i in range(shape[0]):
for j in range(shape[1]):
# Set diagonal entries on bc nodes to 1 if the current
# block is on the matrix diagonal and its index matches the
# index of the function space the bc is defined on.
if i != j:
continue
if fs.component is None and fs.index is not None:
# Mixed, index (no ComponentFunctionSpace)
if fs.index == i:
yield tensor[i, j].set_local_diagonal_entries(nodes)
elif fs.component is not None:
# ComponentFunctionSpace, check parent index
if fs.parent.index is not None:
# Mixed, index doesn't match
if fs.parent.index != i:
continue
# Index matches
yield tensor[i, j].set_local_diagonal_entries(nodes, idx=fs.component)
elif fs.index is None:
yield tensor[i, j].set_local_diagonal_entries(nodes)
else:
raise RuntimeError("Unhandled BC case")
elif isinstance(bc, EquationBCSplit):
yield from _assemble(bc.f, tensor=result_matrix, bcs=bc.bcs,
form_compiler_parameters=form_compiler_parameters,
inverse=inverse, mat_type=mat_type,
sub_mat_type=sub_mat_type,
appctx=appctx,
assemble_now=assemble_now,
allocate_only=False,
zero_tensor=False)
else:
raise NotImplementedError("Undefined type of bcs class provided.")
if bcs is not None and is_vec:
for bc in bcs:
if isinstance(bc, DirichletBC):
if assemble_now:
yield functools.partial(bc.apply, result_function)
else:
yield functools.partial(bc.zero, result_function)
elif isinstance(bc, EquationBCSplit):
yield functools.partial(bc.zero, result_function)
yield from _assemble(bc.f, tensor=result_function, bcs=bc.bcs,
form_compiler_parameters=form_compiler_parameters,
inverse=inverse, mat_type=mat_type,
sub_mat_type=sub_mat_type,
appctx=appctx,
assemble_now=assemble_now,
allocate_only=False,
zero_tensor=False)
if zero_tensor:
if is_mat:
# Queue up matrix assembly (after we've done all the other operations)
yield tensor.assemble()
if assemble_now:
yield result
def _assemble(f,
tensor=None,
bcs=None,
form_compiler_parameters=None,
inverse=False,
mat_type=None,
sub_mat_type=None,
appctx={},
options_prefix=None,
assemble_now=False,
allocate_only=False,
zero_tensor=True):
r"""Assemble the form or Slate expression f and return a Firedrake object
representing the result. This will be a :class:`float` for 0-forms/rank-0
Slate tensors, a :class:`.Function` for 1-forms/rank-1 Slate tensors and
a :class:`.Matrix` for 2-forms/rank-2 Slate tensors.
:arg bcs: A tuple of :class`.DirichletBC`\s and/or :class`.EquationBCSplit`\s to be applied.
:arg tensor: An existing tensor object into which the form should be
assembled. If this is not supplied, a new tensor will be created for
the purpose.
:arg form_compiler_parameters: (optional) dict of parameters to pass to
the form compiler.
:arg inverse: (optional) if f is a 2-form, then assemble the inverse
of the local matrices.
:arg mat_type: (optional) type for assembled matrices, one of
"nest", "aij", "baij", or "matfree".
:arg sub_mat_type: (optional) type for assembled sub matrices
inside a "nest" matrix. One of "aij" or "baij".
:arg appctx: Additional information to hang on the assembled
matrix if an implicit matrix is requested (mat_type "matfree").
:arg options_prefix: An options prefix for the PETSc matrix
(ignored if not assembling a bilinear form).
"""
if mat_type is None:
mat_type = parameters.parameters["default_matrix_type"]
if mat_type not in ["matfree", "aij", "baij", "nest"]:
raise ValueError("Unrecognised matrix type, '%s'" % mat_type)
if sub_mat_type is None:
sub_mat_type = parameters.parameters["default_sub_matrix_type"]
if sub_mat_type not in ["aij", "baij"]:
raise ValueError("Invalid submatrix type, '%s' (not 'aij' or 'baij')",
sub_mat_type)
if form_compiler_parameters:
form_compiler_parameters = form_compiler_parameters.copy()
else:
form_compiler_parameters = {}
form_compiler_parameters["assemble_inverse"] = inverse
topology = f.ufl_domains()[0].topology
for m in f.ufl_domains():
# Ensure mesh is "initialised" (could have got here without
# building a functionspace (e.g. if integrating a constant)).
m.init()
if m.topology != topology:
raise NotImplementedError(
"All integration domains must share a mesh topology.")
for o in chain(f.arguments(), f.coefficients()):
domain = o.ufl_domain()
if domain is not None and domain.topology != topology:
raise NotImplementedError(
"Assembly with multiple meshes not supported.")
if isinstance(f, slate.TensorBase):
kernels = slac.compile_expression(
f, tsfc_parameters=form_compiler_parameters)
integral_types = [kernel.kinfo.integral_type for kernel in kernels]
else:
kernels = tsfc_interface.compile_form(
f, "form", parameters=form_compiler_parameters, inverse=inverse)
integral_types = [
integral.integral_type() for integral in f.integrals()
]
if bcs is not None:
for bc in bcs:
integral_types += [
integral.integral_type() for integral in bc.integrals()
]
rank = len(f.arguments())
is_mat = rank == 2
is_vec = rank == 1
if any((coeff.function_space()
and coeff.function_space().component is not None)
for coeff in f.coefficients()):
raise NotImplementedError(
"Integration of subscripted VFS not yet implemented")
if inverse and rank != 2:
raise ValueError("Can only assemble the inverse of a 2-form")
zero_tensor_parloop = lambda: None
if is_mat:
matfree = mat_type == "matfree"
nest = mat_type == "nest"
if nest:
baij = sub_mat_type == "baij"
else:
baij = mat_type == "baij"
# intercept matrix-free matrices here
if matfree:
if inverse:
raise NotImplementedError(
"Inverse not implemented with matfree")
if tensor is None:
result_matrix = matrix.ImplicitMatrix(
f,
bcs,
fc_params=form_compiler_parameters,
appctx=appctx,
options_prefix=options_prefix)
yield lambda: result_matrix
raise StopIteration()
if not isinstance(tensor, matrix.ImplicitMatrix):
raise ValueError("Expecting implicit matrix with matfree")
tensor.assemble()
yield lambda: tensor
raise StopIteration()
test, trial = f.arguments()
map_pairs = []
cell_domains = []
exterior_facet_domains = []
interior_facet_domains = []
if tensor is None:
# For horizontal facets of extruded meshes, the corresponding domain
# in the base mesh is the cell domain. Hence all the maps used for top
# bottom and interior horizontal facets will use the cell to dofs map
# coming from the base mesh as a starting point for the actual dynamic map
# computation.
for integral_type in integral_types:
if integral_type == "cell":
cell_domains.append(op2.ALL)
elif integral_type == "exterior_facet":
exterior_facet_domains.append(op2.ALL)
elif integral_type == "interior_facet":
interior_facet_domains.append(op2.ALL)
elif integral_type == "exterior_facet_bottom":
cell_domains.append(op2.ON_BOTTOM)
elif integral_type == "exterior_facet_top":
cell_domains.append(op2.ON_TOP)
elif integral_type == "exterior_facet_vert":
exterior_facet_domains.append(op2.ALL)
elif integral_type == "interior_facet_horiz":
cell_domains.append(op2.ON_INTERIOR_FACETS)
elif integral_type == "interior_facet_vert":
interior_facet_domains.append(op2.ALL)
else:
raise ValueError('Unknown integral type "%s"' %
integral_type)
# Used for the sparsity construction
iteration_regions = []
if cell_domains:
map_pairs.append((test.cell_node_map(), trial.cell_node_map()))
iteration_regions.append(tuple(cell_domains))
if exterior_facet_domains:
map_pairs.append((test.exterior_facet_node_map(),
trial.exterior_facet_node_map()))
iteration_regions.append(tuple(exterior_facet_domains))
if interior_facet_domains:
map_pairs.append((test.interior_facet_node_map(),
trial.interior_facet_node_map()))
iteration_regions.append(tuple(interior_facet_domains))
map_pairs = tuple(map_pairs)
# Construct OP2 Mat to assemble into
fs_names = (test.function_space().name,
trial.function_space().name)
try:
sparsity = op2.Sparsity((test.function_space().dof_dset,
trial.function_space().dof_dset),
map_pairs,
iteration_regions=iteration_regions,
name="%s_%s_sparsity" % fs_names,
nest=nest,
block_sparse=baij)
except SparsityFormatError:
raise ValueError(
"Monolithic matrix assembly is not supported for systems with R-space blocks."
)
result_matrix = matrix.Matrix(f,
bcs,
mat_type,
sparsity,
numpy.float64,
"%s_%s_matrix" % fs_names,
options_prefix=options_prefix)
tensor = result_matrix._M
else:
if isinstance(tensor, matrix.ImplicitMatrix):
raise ValueError("Expecting matfree with implicit matrix")
result_matrix = tensor
tensor = tensor._M
zero_tensor_parloop = tensor.zero
if result_matrix.block_shape != (1, 1) and mat_type == "baij":
raise ValueError(
"BAIJ matrix type makes no sense for mixed spaces, use 'aij'")
def mat(testmap, trialmap, rowbc, colbc, i, j):
m = testmap(test.function_space()[i])
n = trialmap(trial.function_space()[j])
maps = (m if m else None, n if n else None)
rlgmap, clgmap = tensor[i, j].local_to_global_maps
V = test.function_space()[i]
rlgmap = V.local_to_global_map(rowbc, lgmap=rlgmap)
V = trial.function_space()[j]
clgmap = V.local_to_global_map(colbc, lgmap=clgmap)
if rowbc is None:
rowbc = []
if colbc is None:
colbc = []
unroll = any(bc.function_space().component is not None
for bc in chain(rowbc, colbc) if bc is not None)
return tensor[i, j](op2.INC,
maps,
lgmaps=(rlgmap, clgmap),
unroll_map=unroll)
result = lambda: result_matrix
if allocate_only:
yield result
raise StopIteration()
elif is_vec:
test = f.arguments()[0]
if tensor is None:
result_function = function.Function(test.function_space())
tensor = result_function.dat
else:
result_function = tensor
tensor = result_function.dat
zero_tensor_parloop = tensor.zero
def vec(testmap, i):
_testmap = testmap(test.function_space()[i])
return tensor[i](op2.INC, _testmap if _testmap else None)
result = lambda: result_function
else:
# 0-forms are always scalar
if tensor is None:
tensor = op2.Global(1, [0.0])
else:
raise ValueError("Can't assemble 0-form into existing tensor")
result = lambda: tensor.data[0]
coefficients = f.coefficients()
domains = f.ufl_domains()
# These will be used to correctly interpret the "otherwise"
# subdomain
all_integer_subdomain_ids = defaultdict(list)
for k in kernels:
if k.kinfo.subdomain_id != "otherwise":
all_integer_subdomain_ids[k.kinfo.integral_type].append(
k.kinfo.subdomain_id)
for k, v in all_integer_subdomain_ids.items():
all_integer_subdomain_ids[k] = tuple(sorted(v))
# In collecting loops mode, we collect the loops, and assume the
# boundary conditions provided are the ones we want. It therefore
# is only used inside residual and jacobian assembly.
if zero_tensor:
yield zero_tensor_parloop
for indices, kinfo in kernels:
kernel = kinfo.kernel
integral_type = kinfo.integral_type
domain_number = kinfo.domain_number
subdomain_id = kinfo.subdomain_id
coeff_map = kinfo.coefficient_map
pass_layer_arg = kinfo.pass_layer_arg
needs_orientations = kinfo.oriented
needs_cell_facets = kinfo.needs_cell_facets
needs_cell_sizes = kinfo.needs_cell_sizes
m = domains[domain_number]
subdomain_data = f.subdomain_data()[m]
# Find argument space indices
if is_mat:
i, j = indices
elif is_vec:
i, = indices
else:
assert len(indices) == 0
sdata = subdomain_data.get(integral_type, None)
if integral_type != 'cell' and sdata is not None:
raise NotImplementedError(
"subdomain_data only supported with cell integrals.")
# Extract block from tensor and test/trial spaces
# FIXME Ugly variable renaming required because functions are not
# lexical closures in Python and we're writing to these variables
if is_mat:
if bcs is not None:
tsbc = list(bc for bc in chain(*bcs))
if result_matrix.block_shape > (1, 1):
trbc = [
bc for bc in tsbc if bc.function_space_index() == j
and isinstance(bc, DirichletBC)
]
tsbc = [
bc for bc in tsbc if bc.function_space_index() == i
]
else:
trbc = [bc for bc in tsbc if isinstance(bc, DirichletBC)]
else:
tsbc = []
trbc = []
# Now build arguments for the par_loop
kwargs = {}
# Some integrals require non-coefficient arguments at the
# end (facet number information).
extra_args = []
# Decoration for applying to matrix maps in extruded case
decoration = None
itspace = m.measure_set(integral_type, subdomain_id,
all_integer_subdomain_ids)
if integral_type == "cell":
itspace = sdata or itspace
if subdomain_id not in ["otherwise", "everywhere"
] and sdata is not None:
raise ValueError("Cannot use subdomain data and subdomain_id")
def get_map(x):
return x.cell_node_map()
elif integral_type in ("exterior_facet", "exterior_facet_vert"):
extra_args.append(m.exterior_facets.local_facet_dat(op2.READ))
def get_map(x):
return x.exterior_facet_node_map()
elif integral_type in ("exterior_facet_top", "exterior_facet_bottom"):
# In the case of extruded meshes with horizontal facet integrals, two
# parallel loops will (potentially) get created and called based on the
# domain id: interior horizontal, bottom or top.
decoration = {
"exterior_facet_top": op2.ON_TOP,
"exterior_facet_bottom": op2.ON_BOTTOM
}[integral_type]
kwargs["iterate"] = decoration
def get_map(x):
return x.cell_node_map()
elif integral_type in ("interior_facet", "interior_facet_vert"):
extra_args.append(m.interior_facets.local_facet_dat(op2.READ))
def get_map(x):
return x.interior_facet_node_map()
elif integral_type == "interior_facet_horiz":
decoration = op2.ON_INTERIOR_FACETS
kwargs["iterate"] = decoration
def get_map(x):
return x.cell_node_map()
else:
raise ValueError("Unknown integral type '%s'" % integral_type)
# Output argument
if is_mat:
tensor_arg = mat(lambda s: get_map(s), lambda s: get_map(s), tsbc,
trbc, i, j)
elif is_vec:
tensor_arg = vec(lambda s: get_map(s), i)
else:
tensor_arg = tensor(op2.INC)
coords = m.coordinates
args = [
kernel, itspace, tensor_arg,
coords.dat(op2.READ, get_map(coords))
]
if needs_orientations:
o = m.cell_orientations()
args.append(o.dat(op2.READ, get_map(o)))
if needs_cell_sizes:
o = m.cell_sizes
args.append(o.dat(op2.READ, get_map(o)))
for n in coeff_map:
c = coefficients[n]
for c_ in c.split():
m_ = get_map(c_)
args.append(c_.dat(op2.READ, m_))
if needs_cell_facets:
assert integral_type == "cell"
extra_args.append(m.cell_to_facets(op2.READ))
args.extend(extra_args)
kwargs["pass_layer_arg"] = pass_layer_arg
try:
with collecting_loops(True):
yield op2.par_loop(*args, **kwargs)
except MapValueError:
raise RuntimeError(
"Integral measure does not match measure of all coefficients/arguments"
)
# Must apply bcs outside loop over kernels because we may wish
# to apply bcs to a block which is otherwise zero, and
# therefore does not have an associated kernel.
if bcs is not None and is_mat:
for bc in bcs:
if isinstance(bc, DirichletBC):
fs = bc.function_space()
# Evaluate this outwith a "collecting_loops" block,
# since creation of the bc nodes actually can create a
# par_loop.
nodes = bc.nodes
if len(fs) > 1:
raise RuntimeError(
r"""Cannot apply boundary conditions to full mixed space. Did you forget to index it?"""
)
shape = result_matrix.block_shape
with collecting_loops(True):
for i in range(shape[0]):
for j in range(shape[1]):
# Set diagonal entries on bc nodes to 1 if the current
# block is on the matrix diagonal and its index matches the
# index of the function space the bc is defined on.
if i != j:
continue
if fs.component is None and fs.index is not None:
# Mixed, index (no ComponentFunctionSpace)
if fs.index == i:
yield tensor[
i, j].set_local_diagonal_entries(nodes)
elif fs.component is not None:
# ComponentFunctionSpace, check parent index
if fs.parent.index is not None:
# Mixed, index doesn't match
if fs.parent.index != i:
continue
# Index matches
yield tensor[i, j].set_local_diagonal_entries(
nodes, idx=fs.component)
elif fs.index is None:
yield tensor[i, j].set_local_diagonal_entries(
nodes)
else:
raise RuntimeError("Unhandled BC case")
elif isinstance(bc, EquationBCSplit):
yield from _assemble(
bc.f,
tensor=result_matrix,
bcs=bc.bcs,
form_compiler_parameters=form_compiler_parameters,
inverse=inverse,
mat_type=mat_type,
sub_mat_type=sub_mat_type,
appctx=appctx,
assemble_now=assemble_now,
allocate_only=False,
zero_tensor=False)
else:
raise NotImplementedError(
"Undefined type of bcs class provided.")
if bcs is not None and is_vec:
for bc in bcs:
if isinstance(bc, DirichletBC):
if assemble_now:
yield functools.partial(bc.apply, result_function)
else:
yield functools.partial(bc.zero, result_function)
elif isinstance(bc, EquationBCSplit):
yield functools.partial(bc.zero, result_function)
yield from _assemble(
bc.f,
tensor=result_function,
bcs=bc.bcs,
form_compiler_parameters=form_compiler_parameters,
inverse=inverse,
mat_type=mat_type,
sub_mat_type=sub_mat_type,
appctx=appctx,
assemble_now=assemble_now,
allocate_only=False,
zero_tensor=False)
if zero_tensor:
if is_mat:
# Queue up matrix assembly (after we've done all the other operations)
yield tensor.assemble()
if assemble_now:
yield result
def thunk(bcs):
if collect_loops:
loops.append(zero_tensor)
else:
zero_tensor()
for indices, (kernel, integral_type, needs_orientations, subdomain_id, domain_number, coeff_map, needs_cell_facets) in kernels:
m = domains[domain_number]
subdomain_data = f.subdomain_data()[m]
# Find argument space indices
if is_mat:
i, j = indices
elif is_vec:
i, = indices
else:
assert len(indices) == 0
sdata = subdomain_data.get(integral_type, None)
if integral_type != 'cell' and sdata is not None:
raise NotImplementedError("subdomain_data only supported with cell integrals.")
# Extract block from tensor and test/trial spaces
# FIXME Ugly variable renaming required because functions are not
# lexical closures in Python and we're writing to these variables
if is_mat and result_matrix.block_shape > (1, 1):
tsbc = []
trbc = []
# Unwind ComponentFunctionSpace to check for matching BCs
for bc in bcs:
fs = bc.function_space()
if fs.component is not None:
fs = fs.parent
if fs.index == i:
tsbc.append(bc)
if fs.index == j:
trbc.append(bc)
elif is_mat:
tsbc, trbc = bcs, bcs
# Now build arguments for the par_loop
kwargs = {}
# Some integrals require non-coefficient arguments at the
# end (facet number information).
extra_args = []
# Decoration for applying to matrix maps in extruded case
decoration = None
itspace = m.measure_set(integral_type, subdomain_id,
all_integer_subdomain_ids)
if integral_type == "cell":
itspace = sdata or itspace
if subdomain_id not in ["otherwise", "everywhere"] and \
sdata is not None:
raise ValueError("Cannot use subdomain data and subdomain_id")
def get_map(x, bcs=None, decoration=None):
return x.cell_node_map(bcs)
elif integral_type in ("exterior_facet", "exterior_facet_vert"):
extra_args.append(m.exterior_facets.local_facet_dat(op2.READ))
def get_map(x, bcs=None, decoration=None):
return x.exterior_facet_node_map(bcs)
elif integral_type in ("exterior_facet_top", "exterior_facet_bottom"):
# In the case of extruded meshes with horizontal facet integrals, two
# parallel loops will (potentially) get created and called based on the
# domain id: interior horizontal, bottom or top.
decoration = {"exterior_facet_top": op2.ON_TOP,
"exterior_facet_bottom": op2.ON_BOTTOM}[integral_type]
kwargs["iterate"] = decoration
def get_map(x, bcs=None, decoration=None):
map_ = x.cell_node_map(bcs)
if decoration is not None:
return op2.DecoratedMap(map_, decoration)
return map_
elif integral_type in ("interior_facet", "interior_facet_vert"):
extra_args.append(m.interior_facets.local_facet_dat(op2.READ))
def get_map(x, bcs=None, decoration=None):
return x.interior_facet_node_map(bcs)
elif integral_type == "interior_facet_horiz":
decoration = op2.ON_INTERIOR_FACETS
kwargs["iterate"] = decoration
def get_map(x, bcs=None, decoration=None):
map_ = x.cell_node_map(bcs)
if decoration is not None:
return op2.DecoratedMap(map_, decoration)
return map_
else:
raise ValueError("Unknown integral type '%s'" % integral_type)
# Output argument
if is_mat:
tensor_arg = mat(lambda s: get_map(s, tsbc, decoration),
lambda s: get_map(s, trbc, decoration),
i, j)
elif is_vec:
tensor_arg = vec(lambda s: get_map(s), i)
else:
tensor_arg = tensor(op2.INC)
coords = m.coordinates
args = [kernel, itspace, tensor_arg,
coords.dat(op2.READ, get_map(coords))]
if needs_orientations:
o = m.cell_orientations()
args.append(o.dat(op2.READ, get_map(o)))
for n in coeff_map:
c = coefficients[n]
for c_ in c.split():
args.append(c_.dat(op2.READ, get_map(c_)))
if needs_cell_facets:
assert integral_type == "cell"
extra_args.append(m.cell_to_facet_map(op2.READ))
args.extend(extra_args)
try:
with collecting_loops(collect_loops):
loops.append(op2.par_loop(*args, **kwargs))
except MapValueError:
raise RuntimeError("Integral measure does not match measure of all coefficients/arguments")
# Must apply bcs outside loop over kernels because we may wish
# to apply bcs to a block which is otherwise zero, and
# therefore does not have an associated kernel.
if bcs is not None and is_mat:
for bc in bcs:
fs = bc.function_space()
# Evaluate this outwith a "collecting_loops" block,
# since creation of the bc nodes actually can create a
# par_loop.
nodes = bc.nodes
if len(fs) > 1:
raise RuntimeError("""Cannot apply boundary conditions to full mixed space. Did you forget to index it?""")
shape = result_matrix.block_shape
with collecting_loops(collect_loops):
for i in range(shape[0]):
for j in range(shape[1]):
# Set diagonal entries on bc nodes to 1 if the current
# block is on the matrix diagonal and its index matches the
# index of the function space the bc is defined on.
if i != j:
continue
if fs.component is None and fs.index is not None:
# Mixed, index (no ComponentFunctionSpace)
if fs.index == i:
loops.append(tensor[i, j].set_local_diagonal_entries(nodes))
elif fs.component is not None:
# ComponentFunctionSpace, check parent index
if fs.parent.index is not None:
# Mixed, index doesn't match
if fs.parent.index != i:
continue
# Index matches
loops.append(tensor[i, j].set_local_diagonal_entries(nodes, idx=fs.component))
elif fs.index is None:
loops.append(tensor[i, j].set_local_diagonal_entries(nodes))
else:
raise RuntimeError("Unhandled BC case")
if bcs is not None and is_vec:
if len(bcs) > 0 and collect_loops:
raise NotImplementedError("Loop collection not handled in this case")
for bc in bcs:
bc.apply(result_function)
if is_mat:
# Queue up matrix assembly (after we've done all the other operations)
loops.append(tensor.assemble())
return result()