import numpy
import string
from fractions import Fraction
from pyop2 import op2
from firedrake.utils import IntType, as_cstr, complex_mode, ScalarType
from firedrake.functionspacedata import entity_dofs_key
from firedrake.functionspaceimpl import FiredrakeDualSpace
import firedrake
from firedrake.mg import utils
from ufl.algorithms.analysis import extract_arguments, extract_coefficients
from ufl.algorithms import estimate_total_polynomial_degree
from ufl.corealg.map_dag import map_expr_dags
from ufl.domain import extract_unique_domain
import loopy as lp
import pymbolic as pym
import gem
import gem.impero_utils as impero_utils
import ufl
import finat.ufl
import tsfc
import tsfc.kernel_interface.firedrake_loopy as firedrake_interface
from tsfc.loopy import generate as generate_loopy
from tsfc import fem, ufl_utils, spectral
from tsfc.driver import TSFCIntegralDataInfo
from tsfc.kernel_interface.common import lower_integral_type
from tsfc.parameters import default_parameters
from finat.element_factory import create_element
from finat.quadrature import make_quadrature
from firedrake.pointquery_utils import dX_norm_square, X_isub_dX, init_X, inside_check, is_affine, celldist_l1_c_expr
from firedrake.pointquery_utils import to_reference_coords_newton_step as to_reference_coords_newton_step_body
[docs]
def to_reference_coordinates(ufl_coordinate_element, parameters=None):
if parameters is None:
parameters = tsfc.default_parameters()
else:
_ = tsfc.default_parameters()
_.update(parameters)
parameters = _
# Create FInAT element
element = finat.element_factory.create_element(ufl_coordinate_element)
gdim, = ufl_coordinate_element.reference_value_shape
cell = ufl_coordinate_element.cell
code = {
"geometric_dimension": gdim,
"topological_dimension": cell.topological_dimension(),
"to_reference_coords_newton_step": to_reference_coords_newton_step_body(ufl_coordinate_element, parameters, x0_dtype=ScalarType, dX_dtype="double"),
"init_X": init_X(element.cell, parameters),
"max_iteration_count": 1 if is_affine(ufl_coordinate_element) else 16,
"convergence_epsilon": 1e-12,
"dX_norm_square": dX_norm_square(cell.topological_dimension()),
"X_isub_dX": X_isub_dX(cell.topological_dimension()),
"IntType": as_cstr(IntType),
}
evaluate_template_c = """#include <math.h>
#include <stdio.h>
#include <petsc.h>
%(to_reference_coords_newton_step)s
static inline void to_reference_coords_kernel(PetscScalar *X, const PetscScalar *x0, const PetscScalar *C)
{
const int space_dim = %(geometric_dimension)d;
/*
* Mapping coordinates from physical to reference space
*/
%(init_X)s
int converged = 0;
for (int it = 0; !converged && it < %(max_iteration_count)d; it++) {
double dX[%(topological_dimension)d] = { 0.0 };
to_reference_coords_newton_step(C, x0, X, dX);
if (%(dX_norm_square)s < %(convergence_epsilon)g * %(convergence_epsilon)g) {
converged = 1;
}
%(X_isub_dX)s
}
}"""
return evaluate_template_c % code
[docs]
def compile_element(expression, dual_space=None, parameters=None,
name="evaluate"):
"""Generate code for point evaluations.
:arg expression: A UFL expression (may contain up to one coefficient, or one argument)
:arg dual_space: if the expression has an argument, should we also distribute residual data?
:returns: The generated code (:class:`loopy.TranslationUnit`)
"""
if parameters is None:
parameters = default_parameters()
else:
_ = default_parameters()
_.update(parameters)
parameters = _
expression = tsfc.ufl_utils.preprocess_expression(expression, complex_mode=complex_mode)
# # Collect required coefficients
try:
arg, = extract_coefficients(expression)
argument_multiindices = ()
coefficient = True
if expression.ufl_shape:
tensor_indices = tuple(gem.Index() for s in expression.ufl_shape)
else:
tensor_indices = ()
except ValueError:
arg, = extract_arguments(expression)
finat_elem = create_element(arg.ufl_element())
argument_multiindices = (finat_elem.get_indices(), )
argument_multiindex, = argument_multiindices
value_shape = finat_elem.value_shape
if value_shape:
tensor_indices = argument_multiindex[-len(value_shape):]
else:
tensor_indices = ()
coefficient = False
# Replace coordinates (if any)
builder = firedrake_interface.KernelBuilderBase(scalar_type=ScalarType)
domain = extract_unique_domain(expression)
# Translate to GEM
cell = domain.ufl_cell()
dim = cell.topological_dimension()
point = gem.Variable('X', (dim,))
point_arg = lp.GlobalArg("X", dtype=ScalarType, shape=(dim,))
config = dict(interface=builder,
ufl_cell=cell,
integral_type="cell",
point_indices=(),
point_expr=point,
argument_multiindices=argument_multiindices,
scalar_type=parameters["scalar_type"])
context = tsfc.fem.GemPointContext(**config)
# Abs-simplification
expression = tsfc.ufl_utils.simplify_abs(expression, complex_mode)
# Translate UFL -> GEM
if coefficient:
assert dual_space is None
builder._coefficient(arg, "f")
f_arg = [builder.generate_arg_from_expression(builder.coefficient_map[arg])]
else:
f_arg = []
translator = tsfc.fem.Translator(context)
result, = map_expr_dags(translator, [expression])
b_arg = []
if coefficient:
if expression.ufl_shape:
return_variable = gem.Indexed(gem.Variable('R', expression.ufl_shape), tensor_indices)
result_arg = lp.GlobalArg("R", dtype=ScalarType, shape=expression.ufl_shape)
result = gem.Indexed(result, tensor_indices)
else:
return_variable = gem.Indexed(gem.Variable('R', (1,)), (0,))
result_arg = lp.GlobalArg("R", dtype=ScalarType, shape=(1,))
else:
return_variable = gem.Indexed(gem.Variable('R', finat_elem.index_shape), argument_multiindex)
result = gem.Indexed(result, tensor_indices)
if dual_space:
value_shape = dual_space.value_shape
if value_shape:
var = gem.Indexed(gem.Variable("b", value_shape), tensor_indices)
b_arg = [lp.GlobalArg("b", dtype=ScalarType, shape=value_shape)]
else:
var = gem.Indexed(gem.Variable("b", (1, )), (0, ))
b_arg = [lp.GlobalArg("b", dtype=ScalarType, shape=(1,))]
result = gem.Product(result, var)
result_arg = lp.GlobalArg("R", dtype=ScalarType, shape=finat_elem.index_shape)
# Unroll
max_extent = parameters["unroll_indexsum"]
if max_extent:
def predicate(index):
return index.extent <= max_extent
result, = gem.optimise.unroll_indexsum([result], predicate=predicate)
# Translate GEM -> loopy
result, = gem.impero_utils.preprocess_gem([result])
impero_c = gem.impero_utils.compile_gem([(return_variable, result)], tensor_indices)
loopy_args = [result_arg] + b_arg + f_arg + [point_arg]
kernel_code, _ = generate_loopy(
impero_c, loopy_args, ScalarType,
kernel_name="pyop2_kernel_"+name, index_names={})
return lp.generate_code_v2(kernel_code).device_code()
[docs]
def prolong_kernel(expression):
meshc = extract_unique_domain(expression)
hierarchy, level = utils.get_level(extract_unique_domain(expression))
levelf = level + Fraction(1, hierarchy.refinements_per_level)
cache = hierarchy._shared_data_cache["transfer_kernels"]
coordinates = extract_unique_domain(expression).coordinates
if meshc.cell_set._extruded:
idx = levelf * hierarchy.refinements_per_level
assert idx == int(idx)
assert hierarchy._meshes[int(idx)].cell_set._extruded
V = expression.function_space()
key = (("prolong",)
+ V.value_shape
+ entity_dofs_key(V.finat_element.complex.get_topology())
+ entity_dofs_key(V.finat_element.entity_dofs())
+ entity_dofs_key(coordinates.function_space().finat_element.entity_dofs()))
try:
return cache[key]
except KeyError:
mesh = extract_unique_domain(coordinates)
eval_code = compile_element(expression)
to_reference_kernel = to_reference_coordinates(coordinates.ufl_element())
element = create_element(expression.ufl_element())
coords_element = create_element(coordinates.ufl_element())
my_kernel = """#include <petsc.h>
%(to_reference)s
%(evaluate)s
__attribute__((noinline)) /* Clang bug */
static void pyop2_kernel_prolong(PetscScalar *R, PetscScalar *f, const PetscScalar *X, const PetscScalar *Xc)
{
PetscScalar Xref[%(tdim)d];
int cell = -1;
int bestcell = -1;
double bestdist = 1e10;
for (int i = 0; i < %(ncandidate)d; i++) {
const PetscScalar *Xci = Xc + i*%(Xc_cell_inc)d;
double celldist = 2*bestdist;
to_reference_coords_kernel(Xref, X, Xci);
if (%(inside_cell)s) {
cell = i;
break;
}
celldist = %(celldist_l1_c_expr)s;
if (celldist < bestdist) {
bestdist = celldist;
bestcell = i;
}
}
if (cell == -1) {
/* We didn't find a cell that contained this point exactly.
Did we find one that was close enough? */
if (bestdist < 10) {
cell = bestcell;
} else {
fprintf(stderr, "Could not identify cell in transfer operator. Point: ");
for (int coord = 0; coord < %(spacedim)s; coord++) {
fprintf(stderr, "%%.14e ", X[coord]);
}
fprintf(stderr, "\\n");
fprintf(stderr, "Number of candidates: %%d. Best distance located: %%14e", %(ncandidate)d, bestdist);
abort();
}
}
const PetscScalar *coarsei = f + cell*%(coarse_cell_inc)d;
for ( int i = 0; i < %(Rdim)d; i++ ) {
R[i] = 0;
}
pyop2_kernel_evaluate(R, coarsei, Xref);
}
""" % {"to_reference": str(to_reference_kernel),
"evaluate": eval_code,
"spacedim": element.cell.get_spatial_dimension(),
"ncandidate": hierarchy.fine_to_coarse_cells[levelf].shape[1],
"Rdim": V.value_size,
"inside_cell": inside_check(element.cell, eps=1e-8, X="Xref"),
"celldist_l1_c_expr": celldist_l1_c_expr(element.cell, X="Xref"),
"Xc_cell_inc": coords_element.space_dimension(),
"coarse_cell_inc": element.space_dimension(),
"tdim": mesh.topological_dimension()}
return cache.setdefault(key, op2.Kernel(my_kernel, name="pyop2_kernel_prolong"))
[docs]
def restrict_kernel(Vf, Vc):
hierarchy, level = utils.get_level(Vc.mesh())
levelf = level + Fraction(1, hierarchy.refinements_per_level)
cache = hierarchy._shared_data_cache["transfer_kernels"]
coordinates = Vc.mesh().coordinates
if Vf.extruded:
assert Vc.extruded
key = (("restrict",)
+ Vf.value_shape
+ entity_dofs_key(Vf.finat_element.complex.get_topology())
+ entity_dofs_key(Vc.finat_element.complex.get_topology())
+ entity_dofs_key(Vf.finat_element.entity_dofs())
+ entity_dofs_key(Vc.finat_element.entity_dofs())
+ entity_dofs_key(coordinates.function_space().finat_element.entity_dofs()))
try:
return cache[key]
except KeyError:
assert isinstance(Vc, FiredrakeDualSpace) and isinstance(Vf, FiredrakeDualSpace)
mesh = extract_unique_domain(coordinates)
evaluate_code = compile_element(firedrake.TestFunction(Vc.dual()), Vf.dual())
to_reference_kernel = to_reference_coordinates(coordinates.ufl_element())
coords_element = create_element(coordinates.ufl_element())
element = create_element(Vc.ufl_element())
my_kernel = """#include <petsc.h>
%(to_reference)s
%(evaluate)s
__attribute__((noinline)) /* Clang bug */
static void pyop2_kernel_restrict(PetscScalar *R, PetscScalar *b, const PetscScalar *X, const PetscScalar *Xc)
{
PetscScalar Xref[%(tdim)d];
int cell = -1;
int bestcell = -1;
double bestdist = 1e10;
for (int i = 0; i < %(ncandidate)d; i++) {
const PetscScalar *Xci = Xc + i*%(Xc_cell_inc)d;
double celldist = 2*bestdist;
to_reference_coords_kernel(Xref, X, Xci);
if (%(inside_cell)s) {
cell = i;
break;
}
celldist = %(celldist_l1_c_expr)s;
/* fprintf(stderr, "cell %%d celldist: %%.14e\\n", i, celldist);
fprintf(stderr, "Xref: %%.14e %%.14e %%.14e\\n", Xref[0], Xref[1], Xref[2]); */
if (celldist < bestdist) {
bestdist = celldist;
bestcell = i;
}
}
if (cell == -1) {
/* We didn't find a cell that contained this point exactly.
Did we find one that was close enough? */
if (bestdist < 10) {
cell = bestcell;
} else {
fprintf(stderr, "Could not identify cell in transfer operator. Point: ");
for (int coord = 0; coord < %(spacedim)s; coord++) {
fprintf(stderr, "%%.14e ", X[coord]);
}
fprintf(stderr, "\\n");
fprintf(stderr, "Number of candidates: %%d. Best distance located: %%14e", %(ncandidate)d, bestdist);
abort();
}
}
{
const PetscScalar *Ri = R + cell*%(coarse_cell_inc)d;
pyop2_kernel_evaluate(Ri, b, Xref);
}
}
""" % {"to_reference": str(to_reference_kernel),
"evaluate": evaluate_code,
"ncandidate": hierarchy.fine_to_coarse_cells[levelf].shape[1],
"inside_cell": inside_check(element.cell, eps=1e-8, X="Xref"),
"celldist_l1_c_expr": celldist_l1_c_expr(element.cell, X="Xref"),
"Xc_cell_inc": coords_element.space_dimension(),
"coarse_cell_inc": element.space_dimension(),
"spacedim": element.cell.get_spatial_dimension(),
"tdim": mesh.topological_dimension()}
return cache.setdefault(key, op2.Kernel(my_kernel, name="pyop2_kernel_restrict"))
[docs]
def inject_kernel(Vf, Vc):
hierarchy, level = utils.get_level(Vc.mesh())
cache = hierarchy._shared_data_cache["transfer_kernels"]
coordinates = Vf.mesh().coordinates
if Vf.extruded:
assert Vc.extruded
level_ratio = (Vf.mesh().layers - 1) // (Vc.mesh().layers - 1)
else:
level_ratio = 1
key = (("inject", level_ratio)
+ Vf.value_shape
+ entity_dofs_key(Vc.finat_element.complex.get_topology())
+ entity_dofs_key(Vf.finat_element.complex.get_topology())
+ entity_dofs_key(Vc.finat_element.entity_dofs())
+ entity_dofs_key(Vf.finat_element.entity_dofs())
+ entity_dofs_key(Vc.mesh().coordinates.function_space().finat_element.entity_dofs())
+ entity_dofs_key(coordinates.function_space().finat_element.entity_dofs()))
try:
return cache[key]
except KeyError:
ncandidate = hierarchy.coarse_to_fine_cells[level].shape[1] * level_ratio
if Vc.finat_element.entity_dofs() == Vc.finat_element.entity_closure_dofs():
return cache.setdefault(key, (dg_injection_kernel(Vf, Vc, ncandidate), True))
coordinates = Vf.mesh().coordinates
evaluate_code = compile_element(ufl.Coefficient(Vf))
to_reference_kernel = to_reference_coordinates(coordinates.ufl_element())
coords_element = create_element(coordinates.ufl_element())
Vf_element = create_element(Vf.ufl_element())
kernel = """
%(to_reference)s
%(evaluate)s
__attribute__((noinline)) /* Clang bug */
static void pyop2_kernel_inject(PetscScalar *R, const PetscScalar *X, const PetscScalar *f, const PetscScalar *Xf)
{
PetscScalar Xref[%(tdim)d];
int cell = -1;
int bestcell = -1;
double bestdist = 1e10;
for (int i = 0; i < %(ncandidate)d; i++) {
const PetscScalar *Xfi = Xf + i*%(Xf_cell_inc)d;
double celldist = 2*bestdist;
to_reference_coords_kernel(Xref, X, Xfi);
if (%(inside_cell)s) {
cell = i;
break;
}
celldist = %(celldist_l1_c_expr)s;
if (celldist < bestdist) {
bestdist = celldist;
bestcell = i;
}
}
if (cell == -1) {
/* We didn't find a cell that contained this point exactly.
Did we find one that was close enough? */
if (bestdist < 10) {
cell = bestcell;
} else {
fprintf(stderr, "Could not identify cell in transfer operator. Point: ");
for (int coord = 0; coord < %(spacedim)s; coord++) {
fprintf(stderr, "%%.14e ", X[coord]);
}
fprintf(stderr, "\\n");
fprintf(stderr, "Number of candidates: %%d. Best distance located: %%14e", %(ncandidate)d, bestdist);
abort();
}
}
const PetscScalar *fi = f + cell*%(f_cell_inc)d;
for ( int i = 0; i < %(Rdim)d; i++ ) {
R[i] = 0;
}
pyop2_kernel_evaluate(R, fi, Xref);
}
""" % {
"to_reference": str(to_reference_kernel),
"evaluate": evaluate_code,
"inside_cell": inside_check(Vc.finat_element.cell, eps=1e-8, X="Xref"),
"spacedim": Vc.finat_element.cell.get_spatial_dimension(),
"celldist_l1_c_expr": celldist_l1_c_expr(Vc.finat_element.cell, X="Xref"),
"tdim": Vc.mesh().topological_dimension(),
"ncandidate": ncandidate,
"Rdim": numpy.prod(Vf.value_shape),
"Xf_cell_inc": coords_element.space_dimension(),
"f_cell_inc": Vf_element.space_dimension()
}
return cache.setdefault(key, (op2.Kernel(kernel, name="pyop2_kernel_inject"), False))
[docs]
class MacroKernelBuilder(firedrake_interface.KernelBuilderBase):
"""Kernel builder for integration on a macro-cell."""
oriented = False
def __init__(self, scalar_type, num_entities):
""":arg num_entities: the number of micro-entities to integrate over."""
super().__init__(scalar_type)
self.indices = (gem.Index("entity", extent=num_entities), )
self.shape = tuple(i.extent for i in self.indices)
self.unsummed_coefficient_indices = frozenset(self.indices)
[docs]
def set_coefficients(self, coefficients):
self.coefficients = []
self.coefficient_split = {}
self.kernel_args = []
for i, coefficient in enumerate(coefficients):
if type(coefficient.ufl_element()) == finat.ufl.MixedElement:
raise NotImplementedError("Sorry, not for mixed.")
self.coefficients.append(coefficient)
self.kernel_args.append(self._coefficient(coefficient, "macro_w_%d" % (i, )))
[docs]
def set_coordinates(self, domain):
"""Prepare the coordinate field.
:arg domain: :class:`ufl.AbstractDomain`
"""
# Create a fake coordinate coefficient for a domain.
f = ufl.Coefficient(ufl.FunctionSpace(domain, domain.ufl_coordinate_element()))
self.domain_coordinate[domain] = f
self._coefficient(f, "macro_coords")
def _coefficient(self, coefficient, name):
element = create_element(coefficient.ufl_element())
shape = self.shape + element.index_shape
size = numpy.prod(shape, dtype=int)
funarg = lp.GlobalArg(name, dtype=ScalarType, shape=(size,))
expression = gem.reshape(gem.Variable(name, (size, )), shape)
expression = gem.partial_indexed(expression, self.indices)
self.coefficient_map[coefficient] = expression
return funarg
[docs]
def dg_injection_kernel(Vf, Vc, ncell):
from firedrake import Tensor, AssembledVector, TestFunction, TrialFunction
from firedrake.slate.slac import compile_expression
if complex_mode:
raise NotImplementedError("In complex mode we are waiting for Slate")
macro_builder = MacroKernelBuilder(ScalarType, ncell)
f = ufl.Coefficient(Vf)
macro_builder.set_coefficients([f])
macro_builder.set_coordinates(Vf.mesh())
Vfe = create_element(Vf.ufl_element())
ref_complex = Vfe.complex
variant = Vf.ufl_element().variant() or "default"
if "alfeld" in variant.lower():
from FIAT import macro
ref_complex = macro.PowellSabinSplit(Vfe.cell)
macro_quadrature_rule = make_quadrature(ref_complex, estimate_total_polynomial_degree(ufl.inner(f, f)))
index_cache = {}
parameters = default_parameters()
integration_dim, entity_ids = lower_integral_type(Vfe.cell, "cell")
macro_cfg = dict(interface=macro_builder,
ufl_cell=Vf.ufl_cell(),
integral_type="cell",
integration_dim=integration_dim,
entity_ids=entity_ids,
index_cache=index_cache,
quadrature_rule=macro_quadrature_rule,
scalar_type=parameters["scalar_type"])
macro_context = fem.PointSetContext(**macro_cfg)
fexpr, = fem.compile_ufl(f, macro_context)
X = ufl.SpatialCoordinate(Vf.mesh())
C_a, = fem.compile_ufl(X, macro_context)
detJ = ufl_utils.preprocess_expression(abs(ufl.JacobianDeterminant(extract_unique_domain(f))),
complex_mode=complex_mode)
macro_detJ, = fem.compile_ufl(detJ, macro_context)
Vce = create_element(Vc.ufl_element())
info = TSFCIntegralDataInfo(domain=Vc.mesh(),
integral_type="cell",
subdomain_id=("otherwise",),
domain_number=0,
arguments=(ufl.TestFunction(Vc), ),
coefficients=(),
coefficient_numbers=())
coarse_builder = firedrake_interface.KernelBuilder(info, parameters["scalar_type"])
coarse_builder.set_coordinates(Vc.mesh())
argument_multiindices = coarse_builder.argument_multiindices
argument_multiindex, = argument_multiindices
return_variable, = coarse_builder.return_variables
integration_dim, entity_ids = lower_integral_type(Vce.cell, "cell")
# Midpoint quadrature for jacobian on coarse cell.
quadrature_rule = make_quadrature(Vce.cell, 0)
coarse_cfg = dict(interface=coarse_builder,
ufl_cell=Vc.ufl_cell(),
integral_type="cell",
integration_dim=integration_dim,
entity_ids=entity_ids,
index_cache=index_cache,
quadrature_rule=quadrature_rule,
scalar_type=parameters["scalar_type"])
X = ufl.SpatialCoordinate(Vc.mesh())
K = ufl_utils.preprocess_expression(ufl.JacobianInverse(Vc.mesh()),
complex_mode=complex_mode)
coarse_context = fem.PointSetContext(**coarse_cfg)
C_0, = fem.compile_ufl(X, coarse_context)
K, = fem.compile_ufl(K, coarse_context)
i = gem.Index()
j = gem.Index()
C_0 = gem.Indexed(C_0, (j, ))
C_0 = gem.index_sum(C_0, quadrature_rule.point_set.indices)
C_a = gem.Indexed(C_a, (j, ))
X_a = gem.Sum(C_0, gem.Product(gem.Literal(-1), C_a))
K_ij = gem.Indexed(K, (i, j))
K_ij = gem.index_sum(K_ij, quadrature_rule.point_set.indices)
X_a = gem.index_sum(gem.Product(K_ij, X_a), (j, ))
C_0, = quadrature_rule.point_set.points
C_0 = gem.Indexed(gem.Literal(C_0), (i, ))
# fine quad points in coarse reference space.
X_a = gem.Sum(C_0, gem.Product(gem.Literal(-1), X_a))
X_a = gem.ComponentTensor(X_a, (i, ))
# Coarse basis function evaluated at fine quadrature points
phi_c = fem.fiat_to_ufl(Vce.point_evaluation(0, X_a, (Vce.cell.get_dimension(), 0)), 0)
tensor_indices = tuple(gem.Index(extent=d) for d in f.ufl_shape)
phi_c = gem.Indexed(phi_c, argument_multiindex + tensor_indices)
fexpr = gem.Indexed(fexpr, tensor_indices)
quadrature_weight = macro_quadrature_rule.weight_expression
expr = gem.Product(gem.IndexSum(gem.Product(phi_c, fexpr), tensor_indices),
gem.Product(macro_detJ, quadrature_weight))
quadrature_indices = macro_builder.indices + macro_quadrature_rule.point_set.indices
reps = spectral.Integrals([expr], quadrature_indices, argument_multiindices, parameters)
assignments = spectral.flatten([(return_variable, reps)], index_cache)
return_variables, expressions = zip(*assignments)
expressions = impero_utils.preprocess_gem(expressions, **spectral.finalise_options)
assignments = list(zip(return_variables, expressions))
impero_c = impero_utils.compile_gem(assignments, quadrature_indices + argument_multiindex,
remove_zeros=True)
index_names = []
def name_index(index, name):
index_names.append((index, name))
if index in index_cache:
for multiindex, suffix in zip(index_cache[index],
string.ascii_lowercase):
name_multiindex(multiindex, name + suffix)
def name_multiindex(multiindex, name):
if len(multiindex) == 1:
name_index(multiindex[0], name)
else:
for i, index in enumerate(multiindex):
name_index(index, name + str(i))
name_multiindex(quadrature_indices, 'ip')
for multiindex, name in zip(argument_multiindices, ['j', 'k']):
name_multiindex(multiindex, name)
index_names.extend(zip(macro_builder.indices, ["entity"]))
# now construct the outermost kernel
domains = []
instructions = []
kernel_data = []
subkernels = []
depends_on = frozenset()
local_tensor = coarse_builder.generate_arg_from_expression(coarse_builder.return_variables)
# 1. Zero the local tensor
iname = "i0"
domains.append(f"{{ [{iname}]: 0 <= {iname} < {Vce.space_dimension()} }}")
instructions.append(
lp.Assignment(
pym.subscript(pym.var(local_tensor.name), (pym.var(iname),)), 0,
within_inames=frozenset({iname}), id="zero", depends_on=depends_on))
kernel_data.append(
lp.TemporaryVariable(local_tensor.name, shape=local_tensor.shape, dtype=local_tensor.dtype))
depends_on |= {"zero"}
# 2. Fill the local tensor
macro_coordinates_arg = macro_builder.generate_arg_from_expression(
macro_builder.coefficient_map[macro_builder.domain_coordinate[Vf.mesh()]])
coarse_coordinates_arg = coarse_builder.generate_arg_from_expression(
coarse_builder.coefficient_map[coarse_builder.domain_coordinate[Vc.mesh()]])
eval_args = [
lp.GlobalArg(
local_tensor.name, dtype=local_tensor.dtype, shape=local_tensor.shape,
is_input=True, is_output=True),
*macro_builder.kernel_args,
macro_coordinates_arg,
coarse_coordinates_arg,
]
eval_kernel, _ = generate_loopy(
impero_c, eval_args,
ScalarType, kernel_name="pyop2_kernel_evaluate", index_names=index_names)
subkernels.append(eval_kernel)
fill_insn, extra_domains = _generate_call_insn(
"pyop2_kernel_evaluate", eval_args, iname_prefix="fill", id="fill",
depends_on=depends_on, within_inames_is_final=True)
instructions.append(fill_insn)
domains.extend(extra_domains)
depends_on |= {fill_insn.id}
# 3. Now we have the kernel that computes <f, phi_c>dx_c.
# So now we need to hit it with the inverse mass matrix on dx_c
retarg = lp.GlobalArg(
"R", dtype=ScalarType, shape=local_tensor.shape, is_output=True)
kernel_data = [
retarg, *macro_builder.kernel_args, macro_coordinates_arg,
coarse_coordinates_arg, *kernel_data]
u = TrialFunction(Vc)
v = TestFunction(Vc)
expr = Tensor(ufl.inner(u, v)*ufl.dx).inv * AssembledVector(ufl.Coefficient(Vc))
Ainv, = compile_expression(expr)
Ainv = Ainv.kinfo.kernel
subkernels.append(Ainv.code)
eval_args = [retarg, coarse_coordinates_arg, local_tensor]
inv_insn, extra_domains = _generate_call_insn(
Ainv.name, eval_args, iname_prefix="inv", id="inv",
depends_on=depends_on, within_inames_is_final=True)
instructions.append(inv_insn)
domains.extend(extra_domains)
depends_on |= {inv_insn.id}
kernel_name = "pyop2_kernel_injection_dg"
kernel = lp.make_kernel(
domains, instructions, kernel_data, name=kernel_name,
target=tsfc.parameters.target, lang_version=(2018, 2))
kernel = lp.merge([kernel, *subkernels])
return op2.Kernel(
kernel, name=kernel_name, include_dirs=Ainv.include_dirs,
headers=Ainv.headers, events=Ainv.events)
def _generate_call_insn(name, args, *, iname_prefix=None, **kwargs):
"""Create an appropriate loopy call instruction from its arguments.
This function is useful because :class:`loopy.CallInstruction` are a
faff to build since each argument needs to be wrapped in a
:class:`loopy.symbolic.SubArrayRef` with an associated iname and domain.
Parameters
----------
name : str
The name of the kernel to be called.
args : iterable of loopy.ArrayArg
The arguments used to construct the callee kernel. These must have
vector shape.
iname_prefix : str, optional
Prefix to the autogenerated inames, defaults to ``name``.
kwargs
All other keyword arguments are passed to the
:class:`loopy.CallInstruction` constructor.
Returns
-------
insn : loopy.CallInstruction
The generated call instruction.
extra_domains
Iterable of extra loop domains that must be added to the caller kernel.
"""
if not iname_prefix:
iname_prefix = name
domains = []
assignees = []
parameters = []
swept_iname_counter = 0
for arg in args:
try:
shape, = arg.shape
except ValueError:
raise NotImplementedError("Expecting vector-shaped arguments")
swept_iname = f"{iname_prefix}_i{swept_iname_counter}"
swept_iname_counter += 1
domains.append(f"{{ [{swept_iname}]: 0 <= {swept_iname} < {shape} }}")
swept_index = (pym.var(swept_iname),)
param = lp.symbolic.SubArrayRef(
swept_index, pym.subscript(pym.var(arg.name), swept_index))
parameters.append(param)
if arg.is_output:
assignees.append(param)
assignees = tuple(assignees)
parameters = tuple(parameters)
expression = pym.primitives.Call(pym.var(name), parameters)
return lp.CallInstruction(assignees, expression, **kwargs), domains
