def initialize(self, pc):
# Make a new DM.
# Hook up a (new) coarsen routine on that DM.
# Make a new PC, of type MG.
# Assign the DM to that PC.
odm = pc.getDM()
ctx = get_appctx(odm)
test, trial = ctx.J.arguments()
if test.function_space() != trial.function_space():
raise NotImplementedError("test and trial spaces must be the same")
# Construct a list with the elements we'll be using
V = test.function_space()
ele = V.ufl_element()
elements = [ele]
while True:
try:
ele_ = self.coarsen_element(ele)
assert ele_.value_shape() == ele.value_shape()
ele = ele_
except ValueError:
break
elements.append(ele)
pdm = PETSc.DMShell().create(comm=pc.comm)
sf = odm.getPointSF()
section = odm.getDefaultSection()
attach_hooks(pdm, level=len(elements) - 1, sf=sf, section=section)
# Now overwrite some routines on the DM
pdm.setRefine(None)
pdm.setCoarsen(self.coarsen)
pdm.setCreateInterpolation(self.create_interpolation)
pdm.setOptionsPrefix(pc.getOptionsPrefix() + "pmg_")
set_function_space(pdm, get_function_space(odm))
parent = get_parent(odm)
assert parent is not None
add_hook(parent,
setup=partial(push_parent, pdm, parent),
teardown=partial(pop_parent, pdm, parent),
call_setup=True)
add_hook(parent,
setup=partial(push_appctx, pdm, ctx),
teardown=partial(pop_appctx, pdm, ctx),
call_setup=True)
ppc = PETSc.PC().create(comm=pc.comm)
ppc.setOptionsPrefix(pc.getOptionsPrefix() + "pmg_")
ppc.setType("mg")
ppc.setOperators(*pc.getOperators())
ppc.setDM(pdm)
ppc.incrementTabLevel(1, parent=pc)
# PETSc unfortunately requires us to make an ugly hack.
# We would like to use GMG for the coarse solve, at least
# sometimes. But PETSc will use this p-DM's getRefineLevels()
# instead of the getRefineLevels() of the MeshHierarchy to
# decide how many levels it should use for PCMG applied to
# the p-MG's coarse problem. So we need to set an option
# for the user, if they haven't already; I don't know any
# other way to get PETSc to know this at the right time.
opts = PETSc.Options(pc.getOptionsPrefix() + "pmg_")
if "mg_coarse_pc_mg_levels" not in opts:
opts["mg_coarse_pc_mg_levels"] = odm.getRefineLevel() + 1
ppc.setFromOptions()
ppc.setUp()
self.ppc = ppc
def initialize(self, pc):
test, trial = self.J.arguments()
if test.function_space() != trial.function_space():
raise NotImplementedError("test and trial spaces must be the same")
Pk = test.function_space()
element = Pk.ufl_element()
shape = element.value_shape()
mesh = Pk.ufl_domain()
if len(shape) == 0:
P1 = firedrake.FunctionSpace(mesh, "CG", 1)
elif len(shape) == 2:
P1 = firedrake.VectorFunctionSpace(mesh, "CG", 1, dim=shape[0])
else:
P1 = firedrake.TensorFunctionSpace(mesh, "CG", 1, shape=shape,
symmetry=element.symmetry())
# TODO: A smarter low-order operator would also interpolate
# any coefficients to the coarse space.
mapper = ArgumentReplacer({test: firedrake.TestFunction(P1),
trial: firedrake.TrialFunction(P1)})
self.lo_J = map_integrands.map_integrand_dags(mapper, self.J)
lo_bcs = []
for bc in self.bcs:
# Don't actually need the value, since it's only used for
# killing parts of the restriction matrix.
lo_bcs.append(firedrake.DirichletBC(P1, firedrake.zero(P1.shape),
bc.sub_domain,
method=bc.method))
self.lo_bcs = tuple(lo_bcs)
mat_type = PETSc.Options().getString(pc.getOptionsPrefix() + "lo_mat_type",
firedrake.parameters["default_matrix_type"])
self.lo_op = firedrake.assemble(self.lo_J, bcs=self.lo_bcs,
mat_type=mat_type)
self.lo_op.force_evaluation()
A, P = pc.getOperators()
nearnullsp = P.getNearNullSpace()
if nearnullsp.handle != 0:
# Actually have a near nullspace
tmp = firedrake.Function(Pk)
low = firedrake.Function(P1)
vecs = []
for vec in nearnullsp.getVecs():
with tmp.dat.vec as v:
vec.copy(v)
low.interpolate(tmp)
with low.dat.vec_ro as v:
vecs.append(v.copy())
nullsp = PETSc.NullSpace().create(vectors=vecs, comm=pc.comm)
self.lo_op.petscmat.setNearNullSpace(nullsp)
lo = PETSc.PC().create(comm=pc.comm)
lo.incrementTabLevel(1, parent=pc)
lo.setOperators(self.lo_op.petscmat, self.lo_op.petscmat)
lo.setOptionsPrefix(pc.getOptionsPrefix() + "lo_")
lo.setFromOptions()
self.lo = lo
self.restriction = restriction_matrix(Pk, P1, self.bcs, self.lo_bcs)
self.work = self.lo_op.petscmat.createVecs()
if len(self.bcs) > 0:
bc_nodes = numpy.unique(numpy.concatenate([bc.nodes for bc in self.bcs]))
bc_nodes = bc_nodes[bc_nodes < Pk.dof_dset.size]
bc_iset = PETSc.IS().createBlock(numpy.prod(shape), bc_nodes,
comm=PETSc.COMM_SELF)
self.bc_indices = bc_iset.getIndices()
bc_iset.destroy()
else:
self.bc_indices = numpy.empty(0, dtype=numpy.int32)
def initialize(self, pc):
from firedrake.assemble import allocate_matrix, create_assembly_callable
_, P = pc.getOperators()
if pc.getType() != "python":
raise ValueError("Expecting PC type python")
opc = pc
appctx = self.get_appctx(pc)
fcp = appctx.get("form_compiler_parameters")
V = get_function_space(pc.getDM())
if len(V) == 1:
V = FunctionSpace(V.mesh(), V.ufl_element())
else:
V = MixedFunctionSpace([V_ for V_ in V])
test = TestFunction(V)
trial = TrialFunction(V)
if P.type == "python":
context = P.getPythonContext()
# It only makes sense to preconditioner/invert a diagonal
# block in general. That's all we're going to allow.
if not context.on_diag:
raise ValueError("Only makes sense to invert diagonal block")
prefix = pc.getOptionsPrefix()
options_prefix = prefix + self._prefix
mat_type = PETSc.Options().getString(options_prefix + "mat_type", "aij")
(a, bcs) = self.form(pc, test, trial)
self.P = allocate_matrix(a, bcs=bcs,
form_compiler_parameters=fcp,
mat_type=mat_type,
options_prefix=options_prefix)
self._assemble_P = create_assembly_callable(a, tensor=self.P,
bcs=bcs,
form_compiler_parameters=fcp,
mat_type=mat_type)
self._assemble_P()
# Transfer nullspace over
Pmat = self.P.petscmat
Pmat.setNullSpace(P.getNullSpace())
tnullsp = P.getTransposeNullSpace()
if tnullsp.handle != 0:
Pmat.setTransposeNullSpace(tnullsp)
Pmat.setNearNullSpace(P.getNearNullSpace())
# Internally, we just set up a PC object that the user can configure
# however from the PETSc command line. Since PC allows the user to specify
# a KSP, we can do iterative by -assembled_pc_type ksp.
pc = PETSc.PC().create(comm=opc.comm)
pc.incrementTabLevel(1, parent=opc)
# We set a DM and an appropriate SNESContext on the constructed PC so one
# can do e.g. multigrid or patch solves.
from firedrake.variational_solver import NonlinearVariationalProblem
from firedrake.solving_utils import _SNESContext
dm = opc.getDM()
octx = get_appctx(dm)
oproblem = octx._problem
nproblem = NonlinearVariationalProblem(oproblem.F, oproblem.u, bcs, J=a, form_compiler_parameters=fcp)
self._ctx_ref = _SNESContext(nproblem, mat_type, mat_type, octx.appctx, options_prefix=options_prefix)
pc.setDM(dm)
pc.setOptionsPrefix(options_prefix)
pc.setOperators(Pmat, Pmat)
self.pc = pc
with dmhooks.add_hooks(dm, self, appctx=self._ctx_ref, save=False):
pc.setFromOptions()
def initialize(self, pc):
from firedrake import TestFunction, parameters
from firedrake.assemble import allocate_matrix, create_assembly_callable
from firedrake.interpolation import Interpolator
from firedrake.solving_utils import _SNESContext
from firedrake.matrix_free.operators import ImplicitMatrixContext
_, P = pc.getOperators()
appctx = self.get_appctx(pc)
fcp = appctx.get("form_compiler_parameters")
if pc.getType() != "python":
raise ValueError("Expecting PC type python")
ctx = dmhooks.get_appctx(pc.getDM())
if ctx is None:
raise ValueError("No context found.")
if not isinstance(ctx, _SNESContext):
raise ValueError("Don't know how to get form from %r", ctx)
prefix = pc.getOptionsPrefix()
options_prefix = prefix + self._prefix
opts = PETSc.Options()
# Handle the fine operator if type is python
if P.getType() == "python":
ictx = P.getPythonContext()
if ictx is None:
raise ValueError("No context found on matrix")
if not isinstance(ictx, ImplicitMatrixContext):
raise ValueError("Don't know how to get form from %r", ictx)
fine_operator = ictx.a
fine_bcs = ictx.row_bcs
if fine_bcs != ictx.col_bcs:
raise NotImplementedError("Row and column bcs must match")
fine_mat_type = opts.getString(options_prefix + "mat_type",
parameters["default_matrix_type"])
self.fine_op = allocate_matrix(fine_operator,
bcs=fine_bcs,
form_compiler_parameters=fcp,
mat_type=fine_mat_type,
options_prefix=options_prefix)
self._assemble_fine_op = create_assembly_callable(
fine_operator,
tensor=self.fine_op,
bcs=fine_bcs,
form_compiler_parameters=fcp,
mat_type=fine_mat_type)
self._assemble_fine_op()
fine_petscmat = self.fine_op.petscmat
else:
fine_petscmat = P
# Transfer fine operator null space
fine_petscmat.setNullSpace(P.getNullSpace())
fine_transpose_nullspace = P.getTransposeNullSpace()
if fine_transpose_nullspace.handle != 0:
fine_petscmat.setTransposeNullSpace(fine_transpose_nullspace)
# Handle the coarse operator
coarse_options_prefix = options_prefix + "mg_coarse"
coarse_mat_type = opts.getString(coarse_options_prefix + "mat_type",
parameters["default_matrix_type"])
get_coarse_space = appctx.get("get_coarse_space", None)
if not get_coarse_space:
raise ValueError(
"Need to provide a callback which provides the coarse space.")
coarse_space = get_coarse_space()
get_coarse_operator = appctx.get("get_coarse_operator", None)
if not get_coarse_operator:
raise ValueError(
"Need to provide a callback which provides the coarse operator."
)
coarse_operator = get_coarse_operator()
coarse_space_bcs = appctx.get("coarse_space_bcs", None)
# These should be callbacks which return the relevant nullspaces
get_coarse_nullspace = appctx.get("get_coarse_op_nullspace", None)
get_coarse_transpose_nullspace = appctx.get(
"get_coarse_op_transpose_nullspace", None)
self.coarse_op = allocate_matrix(coarse_operator,
bcs=coarse_space_bcs,
form_compiler_parameters=fcp,
mat_type=coarse_mat_type,
options_prefix=coarse_options_prefix)
self._assemble_coarse_op = create_assembly_callable(
coarse_operator,
tensor=self.coarse_op,
bcs=coarse_space_bcs,
form_compiler_parameters=fcp)
self._assemble_coarse_op()
coarse_opmat = self.coarse_op.petscmat
# Set nullspace if provided
if get_coarse_nullspace:
nsp = get_coarse_nullspace()
coarse_opmat.setNullSpace(nsp.nullspace(comm=pc.comm))
if get_coarse_transpose_nullspace:
tnsp = get_coarse_transpose_nullspace()
coarse_opmat.setTransposeNullSpace(tnsp.nullspace(comm=pc.comm))
interp_petscmat = appctx.get("interpolation_matrix", None)
if interp_petscmat is None:
# Create interpolation matrix from coarse space to fine space
fine_space = ctx.J.arguments()[0].function_space()
interpolator = Interpolator(TestFunction(coarse_space), fine_space)
interpolation_matrix = interpolator.callable()
interp_petscmat = interpolation_matrix.handle
# We set up a PCMG object that uses the constructed interpolation
# matrix to generate the restriction/prolongation operators.
# This is a two-level multigrid preconditioner.
pcmg = PETSc.PC().create(comm=pc.comm)
pcmg.incrementTabLevel(1, parent=pc)
pcmg.setType(pc.Type.MG)
pcmg.setOptionsPrefix(options_prefix)
pcmg.setMGLevels(2)
pcmg.setMGCycleType(pc.MGCycleType.V)
pcmg.setMGInterpolation(1, interp_petscmat)
pcmg.setOperators(A=fine_petscmat, P=fine_petscmat)
coarse_solver = pcmg.getMGCoarseSolve()
coarse_solver.setOperators(A=coarse_opmat, P=coarse_opmat)
# coarse space dm
coarse_dm = coarse_space.dm
coarse_solver.setDM(coarse_dm)
coarse_solver.setDMActive(False)
pcmg.setDM(pc.getDM())
pcmg.setFromOptions()
self.pc = pcmg
self._dm = coarse_dm
prefix = coarse_solver.getOptionsPrefix()
# Create new appctx
self._ctx_ref = self.new_snes_ctx(pc,
coarse_operator,
coarse_space_bcs,
coarse_mat_type,
fcp,
options_prefix=prefix)
with dmhooks.add_hooks(coarse_dm,
self,
appctx=self._ctx_ref,
save=False):
coarse_solver.setFromOptions()
def initialize(self, pc):
A, P = pc.getOperators()
if P.getType() == "python":
from firedrake.matrix_free.operators import ImplicitMatrixContext
ctx = P.getPythonContext()
if ctx is None:
raise ValueError("No context found on matrix")
if not isinstance(ctx, ImplicitMatrixContext):
raise ValueError("Don't know how to get form from %r", ctx)
J = ctx.a
bcs = ctx.row_bcs
if bcs != ctx.col_bcs:
raise NotImplementedError("Row and column bcs must match")
else:
from firedrake.dmhooks import get_appctx
from firedrake.solving_utils import _SNESContext
ctx = get_appctx(pc.getDM())
if ctx is None:
raise ValueError("No context found on form")
if not isinstance(ctx, _SNESContext):
raise ValueError("Don't know how to get form from %r", ctx)
J = ctx.Jp or ctx.J
bcs = ctx._problem.bcs
mesh = J.ufl_domain()
if mesh.cell_set._extruded:
raise NotImplementedError("Not implemented on extruded meshes")
patch = PETSc.PC().create(comm=pc.comm)
patch.setOptionsPrefix(pc.getOptionsPrefix() + "patch_")
patch.setOperators(A, P)
patch.setType("patch")
funptr, kinfo = matrix_funptr(J)
V, _ = map(operator.methodcaller("function_space"), J.arguments())
mesh = V.ufl_domain()
if len(bcs) > 0:
ghost_bc_nodes = numpy.unique(
numpy.concatenate([bcdofs(bc, ghost=True) for bc in bcs]))
global_bc_nodes = numpy.unique(
numpy.concatenate([bcdofs(bc, ghost=False) for bc in bcs]))
else:
ghost_bc_nodes = numpy.empty(0, dtype=PETSc.IntType)
global_bc_nodes = numpy.empty(0, dtype=PETSc.IntType)
op_coeffs = [mesh.coordinates]
for n in kinfo.coefficient_map:
op_coeffs.append(J.coefficients()[n])
op_args = []
for c in op_coeffs:
for c_ in c.split():
op_args.append(c_.dat._data.ctypes.data)
c_map = c_.cell_node_map()
if c_map is not None:
op_args.append(c_map._values.ctypes.data)
def op(pc, point, mat, cellIS, cell_dofmap):
cells = cellIS.indices
ncell = len(cells)
dofs = cell_dofmap.ctypes.data
funptr(0, ncell, cells.ctypes.data, mat.handle, dofs, dofs,
*op_args)
mat.assemble()
patch.setDM(mesh._plex)
patch.setPatchCellNumbering(mesh._cell_numbering)
offsets = numpy.append([0], numpy.cumsum([W.dof_count for W in V
])).astype(PETSc.IntType)
patch.setPatchDiscretisationInfo([W.dm for W in V],
numpy.array([W.value_size for W in V],
dtype=PETSc.IntType),
[W.cell_node_list
for W in V], offsets, ghost_bc_nodes,
global_bc_nodes)
patch.setPatchComputeOperator(op)
patch.setPatchConstructType(patch.PatchConstructType.PYTHON,
operator=self.user_construction_op)
patch.setAttr("ctx", ctx)
patch.incrementTabLevel(1, parent=pc)
patch.setFromOptions()
patch.setUp()
self.patch = patch
def initialize(self, pc):
from firedrake.assemble import allocate_matrix, create_assembly_callable
_, P = pc.getOperators()
if pc.getType() != "python":
raise ValueError("Expecting PC type python")
opc = pc
appctx = self.get_appctx(pc)
fcp = appctx.get("form_compiler_parameters")
V = get_function_space(pc.getDM())
if len(V) == 1:
V = FunctionSpace(V.mesh(), V.ufl_element())
else:
V = MixedFunctionSpace([V_ for V_ in V])
test = TestFunction(V)
trial = TrialFunction(V)
if P.type == "python":
context = P.getPythonContext()
# It only makes sense to preconditioner/invert a diagonal
# block in general. That's all we're going to allow.
if not context.on_diag:
raise ValueError("Only makes sense to invert diagonal block")
prefix = pc.getOptionsPrefix()
options_prefix = prefix + self._prefix
mat_type = PETSc.Options().getString(options_prefix + "mat_type", "aij")
(a, bcs) = self.form(pc, test, trial)
self.P = allocate_matrix(a, bcs=bcs,
form_compiler_parameters=fcp,
mat_type=mat_type,
options_prefix=options_prefix)
self._assemble_P = create_assembly_callable(a, tensor=self.P,
bcs=bcs,
form_compiler_parameters=fcp,
mat_type=mat_type)
self._assemble_P()
self.P.force_evaluation()
# Transfer nullspace over
Pmat = self.P.petscmat
Pmat.setNullSpace(P.getNullSpace())
tnullsp = P.getTransposeNullSpace()
if tnullsp.handle != 0:
Pmat.setTransposeNullSpace(tnullsp)
# Internally, we just set up a PC object that the user can configure
# however from the PETSc command line. Since PC allows the user to specify
# a KSP, we can do iterative by -assembled_pc_type ksp.
pc = PETSc.PC().create(comm=opc.comm)
pc.incrementTabLevel(1, parent=opc)
pc.setOptionsPrefix(options_prefix)
pc.setOperators(Pmat, Pmat)
pc.setFromOptions()
pc.setUp()
self.pc = pc
