localoperator.py

from __future__ import absolute_import
from os.path import splitext

import logging

import numpy as np

from dune.perftool.options import (get_form_option,
                                   get_option,
                                   option_switch)
from dune.perftool.generation import (backend,
                                      base_class,
                                      class_basename,
                                      class_member,
                                      constructor_parameter,
                                      domain,
                                      dump_accumulate_timer,
                                      end_of_file,
                                      function_mangler,
                                      generator_factory,
                                      get_backend,
                                      get_global_context_value,
                                      global_context,
                                      iname,
                                      include_file,
                                      initializer_list,
                                      post_include,
                                      retrieve_cache_functions,
                                      retrieve_cache_items,
                                      template_parameter,
                                      )
from dune.perftool.cgen.clazz import (AccessModifier,
                                      BaseClass,
                                      ClassMember,
                                      )
from dune.perftool.ufl.modified_terminals import Restriction

import dune.perftool.loopy.mangler

from pymbolic.primitives import Variable
import pymbolic.primitives as prim
from pytools import Record, ImmutableRecord

import ufl.classes as uc
import loopy as lp
import cgen


@template_parameter(classtag="operator")
def lop_template_ansatz_gfs():
    name = "GFSU"
    constructor_parameter("const {}&".format(name), name_ansatz_gfs_constructor_param(), classtag="operator")
    return name


def name_ansatz_gfs_constructor_param():
    return "gfsu"


@template_parameter(classtag="operator")
def lop_template_test_gfs():
    name = "GFSV"
    constructor_parameter("const {}&".format(name), name_test_gfs_constructor_param(), classtag="operator")
    return name


def name_test_gfs_constructor_param():
    return "gfsv"


@template_parameter(classtag="operator")
def lop_template_range_field():
    return "RF"


@class_member(classtag="operator")
def lop_domain_field(name):
    # TODO: Rethink for not Galerkin Method
    gfs = lop_template_ansatz_gfs()
    return "using {} = typename {}::Traits::GridView::ctype;".format(name, gfs)


def name_domain_field():
    name = "DF"
    lop_domain_field(name)
    return name


def lop_template_gfs(ma):
    from ufl.classes import Argument, Coefficient
    if isinstance(ma.argexpr, Argument):
        if ma.argexpr.number() == 0:
            return lop_template_test_gfs()
        if ma.argexpr.number() == 1:
            return lop_template_ansatz_gfs()
    if isinstance(ma.argexpr, Coefficient):
        # Index 0 is reserved for trialfunction, index 1 is reserved for jacobian apply function
        assert ma.argexpr.count() < 2
        return lop_template_ansatz_gfs()
    assert False


def name_initree_constructor_param():
    return "iniParams"


@class_member(classtag="operator")
def define_initree(name):
    param_name = name_initree_constructor_param()
    include_file('dune/common/parametertree.hh', filetag="operatorfile")
    constructor_parameter("const Dune::ParameterTree&", param_name, classtag="operator")
    initializer_list(name, [param_name], classtag="operator")

    return "const Dune::ParameterTree& {};".format(name)


@class_member(classtag="operator")
def _enum_pattern(which):
    return "enum {{ doPattern{} = true }};".format(which)


def enum_pattern():
    from dune.perftool.generation import get_global_context_value
    integral_type = get_global_context_value("integral_type")
    from dune.perftool.pdelab.signatures import ufl_measure_to_pdelab_measure
    return _enum_pattern(ufl_measure_to_pdelab_measure(integral_type))


def _pattern_baseclass(measure):
    return base_class('Dune::PDELab::Full{}Pattern'.format(measure), classtag="operator")


def pattern_baseclass():
    from dune.perftool.generation import get_global_context_value
    integral_type = get_global_context_value("integral_type")
    from dune.perftool.pdelab.signatures import ufl_measure_to_pdelab_measure
    return _pattern_baseclass(ufl_measure_to_pdelab_measure(integral_type))


@class_member(classtag="operator")
def _enum_alpha(which):
    return "enum {{ doAlpha{} = true }};".format(which)


def enum_alpha():
    from dune.perftool.generation import get_global_context_value
    integral_type = get_global_context_value("integral_type")
    from dune.perftool.pdelab.signatures import ufl_measure_to_pdelab_measure
    return _enum_alpha(ufl_measure_to_pdelab_measure(integral_type))


def name_initree_member():
    define_initree("_iniParams")
    return "_iniParams"


@class_basename(classtag="operator")
def localoperator_basename(form_ident):
    return get_form_option("classname", form_ident)


def name_gridfunction_member(coeff, restriction, diffOrder=0):
    # We reuse the grid function for volume integrals in skeleton integrals
    if restriction == Restriction.NEGATIVE:
        restriction = Restriction.NONE
    restr = "_n" if restriction == Restriction.POSITIVE else ""
    name = "local_gridfunction_coeff{}_diff{}{}".format(coeff.count(), diffOrder, restr)
    define_gridfunction_member(name, coeff, restriction, diffOrder)
    return name


def name_gridfunction_constructor_argument(coeff):
    _type = type_gridfunction_template_parameter(coeff)
    name = "gridfunction_coeff{}_".format(coeff.count())
    constructor_parameter("const {}&".format(_type), name, classtag="operator")
    return name


@class_member(classtag="operator")
def define_gridfunction_member(name, coeff, restriction, diffOrder):
    _type = type_gridfunction_template_parameter(coeff)
    param = name_gridfunction_constructor_argument(coeff)
    if diffOrder > 0:
        other = name_gridfunction_member(coeff, restriction, diffOrder - 1)
        init = "derivative({})".format(other)
        initializer_list(name, [init], classtag="operator")
        return "mutable decltype({}) {};".format(init, name)
    else:
        init = "localFunction({})".format(param)
        initializer_list(name, [init], classtag="operator")
        return "mutable typename {}::LocalFunction {};".format(_type, name)


@template_parameter(classtag="operator")
def type_gridfunction_template_parameter(coeff):
    return "GRIDFUNCTION_COEFF{}".format(coeff.count())


def class_type_from_cache(classtag):
    from dune.perftool.generation import retrieve_cache_items

    # get the basename
    basename = [i for i in retrieve_cache_items(condition="{} and basename".format(classtag))]
    assert len(basename) == 1
    basename = basename[0]

    # get the template parameters
    tparams = [i for i in retrieve_cache_items(condition="{} and template_param".format(classtag))]
    tparam_str = ''
    if len(tparams) > 0:
        tparam_str = '<{}>'.format(', '.join(t for t in tparams))

    return basename, basename + tparam_str


class AccumulationSpace(Record):
    def __init__(self,
                 lfs=None,
                 index=None,
                 restriction=None,
                 element=None,
                 ):
        Record.__init__(self,
                        lfs=lfs,
                        index=index,
                        restriction=restriction,
                        element=element,
                        )

    def get_args(self):
        if self.lfs is None:
            return ()
        else:
            return (self.lfs, self.index)

    def get_restriction(self):
        if self.restriction is None:
            return ()
        else:
            return (self.restriction,)


# TODO maybe move this onto the visitor as a helper function?
def determine_accumulation_space(info, number):
    if info is None:
        return AccumulationSpace()

    assert info is not None
    element = info.element
    subel = element
    from ufl import MixedElement
    if isinstance(element, MixedElement):
        subel = element.extract_component(info.element_index)[1]

    # And generate a local function space for it!
    from dune.perftool.pdelab.spaces import name_lfs, name_lfs_bound, lfs_iname
    lfs = name_lfs(element, info.restriction, info.element_index)
    from dune.perftool.generation import valuearg
    from loopy.types import NumpyType
    valuearg(lfs, dtype=NumpyType("str"))

    if get_form_option("blockstructured"):
        from dune.perftool.blockstructured.tools import micro_index_to_macro_index
        from dune.perftool.blockstructured.spaces import lfs_inames
        lfsi = micro_index_to_macro_index(subel, lfs_inames(subel, info.restriction, count=number)[0])
    else:
        from dune.perftool.pdelab.spaces import lfs_inames
        lfsi = Variable(lfs_iname(subel, info.restriction, count=number))

    # If the LFS is not yet a pymbolic expression, make it one
    from pymbolic.primitives import Expression
    if not isinstance(lfs, Expression):
        lfs = Variable(lfs)

    return AccumulationSpace(lfs=lfs,
                             index=lfsi,
                             restriction=info.restriction,
                             )


def boundary_predicates(expr, measure, subdomain_id):
    predicates = frozenset([])

    if subdomain_id not in ['everywhere', 'otherwise']:
        # We need to reconstruct the subdomain_data parameter of the measure
        # I am *totally* confused as to why this information is not at hand anyway,
        # but conversation with Martin pointed me to dolfin.fem.assembly where this
        # is done in preprocessing with the limitation of only one possible type of
        # modified measure per integral type.

        # Get the original form and inspect the present measures
        from dune.perftool.generation import get_global_context_value
        data = get_global_context_value("data")
        original_form = data.object_by_name[get_form_option("form")]

        sd = original_form.subdomain_data()
        assert len(sd) == 1
        subdomains, = list(sd.values())
        domain, = list(sd.keys())
        for k in list(subdomains.keys()):
            if subdomains[k] is None:
                    del subdomains[k]

        # Finally extract the original subdomain_data (which needs to be present!)
        assert measure in subdomains
        subdomain_data = subdomains[measure]

        from ufl.classes import Expr
        if isinstance(subdomain_data, Expr):
            visitor = get_visitor(measure, subdomain_id)
            cond = visitor(subdomain_data, do_predicates=True)
        else:
            raise NotImplementedError("Only UFL expressions allowed in subdomain_data right now.")

        predicates = predicates.union([prim.Comparison(cond, '==', subdomain_id)])

    return predicates


class PDELabAccumulationInfo(ImmutableRecord):
    def __init__(self,
                 element=None,
                 element_index=0,
                 restriction=None,
                 inames=(),
                 ):
        ImmutableRecord.__init__(self,
                                 element=element,
                                 element_index=element_index,
                                 restriction=restriction,
                                 inames=inames,
                                 )

    def __eq__(self, other):
        return type(self) == type(other) and self.element_index == other.element_index and self.restriction == other.restriction

    def __hash__(self):
        return (self.element_index, self.restriction)


def _list_infos(expr, number, visitor):
    from dune.perftool.ufl.modified_terminals import extract_modified_arguments
    ma = extract_modified_arguments(expr, argnumber=number)
    if len(ma) == 0:
        if number == 1:
            yield None
        return
    element = ma[0].argexpr.ufl_element()

    from dune.perftool.ufl.modified_terminals import Restriction
    if visitor.measure == "cell":
        restrictions = (Restriction.NONE,)
    elif visitor.measure == "exterior_facet":
        restrictions = (Restriction.NEGATIVE,)
    elif visitor.measure == "interior_facet":
        restrictions = (Restriction.NEGATIVE, Restriction.POSITIVE)
    for res in restrictions:
        for ei in range(element.value_size()):
            yield PDELabAccumulationInfo(element_index=ei, restriction=res)


def list_accumulation_infos(expr, visitor):
    testgen = _list_infos(expr, 0, visitor)
    trialgen = _list_infos(expr, 1, visitor)

    import itertools
    return itertools.product(testgen, trialgen)


def get_accumulation_info(expr, visitor):
    element = expr.ufl_element()
    leaf_element = element
    element_index = 0
    from ufl import MixedElement
    if isinstance(expr.ufl_element(), MixedElement):
        element_index = visitor.indices[0]
        leaf_element = element.extract_component(element_index)[1]

    restriction = visitor.restriction
    if visitor.measure == 'exterior_facet':
        from dune.perftool.pdelab.restriction import Restriction
        restriction = Restriction.NEGATIVE

    inames = visitor.interface.lfs_inames(leaf_element,
                                          restriction,
                                          expr.number()
                                          )

    return PDELabAccumulationInfo(element=expr.ufl_element(),
                                  element_index=element_index,
                                  restriction=restriction,
                                  inames=inames,
                                  )


def generate_accumulation_instruction(expr, visitor):
    # Collect the lfs and lfs indices for the accumulate call
    test_lfs = determine_accumulation_space(visitor.test_info, 0)

    # In the jacobian case, also determine the space for the ansatz space
    ansatz_lfs = determine_accumulation_space(visitor.trial_info, 1)

    # Collect the lfs and lfs indices for the accumulate call
    from dune.perftool.pdelab.argument import name_accumulation_variable
    accumvar = name_accumulation_variable(test_lfs.get_restriction() + ansatz_lfs.get_restriction())

    predicates = boundary_predicates(expr, visitor.measure, visitor.subdomain_id)

    rank = 1 if ansatz_lfs.lfs is None else 2

    from dune.perftool.pdelab.argument import PDELabAccumulationFunction
    from pymbolic.primitives import Call
    accexpr = Call(PDELabAccumulationFunction(accumvar, rank),
                   (test_lfs.get_args() + ansatz_lfs.get_args() + (expr,))
                   )

    from dune.perftool.generation import instruction
    from dune.perftool.options import option_switch
    quad_inames = visitor.interface.quadrature_inames()
    lfs_inames = frozenset(visitor.test_info.inames)
    if visitor.trial_info:
        lfs_inames = lfs_inames.union(visitor.trial_info.inames)

    instruction(assignees=(),
                expression=accexpr,
                forced_iname_deps=lfs_inames.union(frozenset(quad_inames)),
                forced_iname_deps_is_final=True,
                predicates=predicates
                )


def get_visitor(measure, subdomain_id):
    # Get a transformer instance for this kernel
    if get_form_option('sumfact'):
        from dune.perftool.sumfact import SumFactInterface
        interface = SumFactInterface()
    elif get_form_option('blockstructured'):
        from dune.perftool.blockstructured import BlockStructuredInterface
        interface = BlockStructuredInterface()
    else:
        from dune.perftool.pdelab import PDELabInterface
        interface = PDELabInterface()

    from dune.perftool.ufl.visitor import UFL2LoopyVisitor
    return UFL2LoopyVisitor(interface, measure, subdomain_id)


def visit_integral(integral):
    integrand = integral.integrand()
    measure = integral.integral_type()
    subdomain_id = integral.subdomain_id()

    # Start the visiting process!
    visitor = get_visitor(measure, subdomain_id)
    visitor.accumulate(integrand)


def generate_kernel(integrals):
    logger = logging.getLogger(__name__)

    # Visit all integrals once to collect information (dry-run)!
    logger.debug('generate_kernel: visit_integrals (dry run)')
    with global_context(dry_run=True):
        for integral in integrals:
            visit_integral(integral)

    # Now perform some checks on what should be done
    from dune.perftool.sumfact.vectorization import decide_vectorization_strategy
    logger.debug('generate_kernel: decide_vectorization_strategy')
    decide_vectorization_strategy()

    # Delete the cache contents and do the real thing!
    logger.debug('generate_kernel: visit_integrals (no dry run)')
    from dune.perftool.generation import delete_cache_items
    delete_cache_items("kernel_default")
    for integral in integrals:
        visit_integral(integral)
    knl = extract_kernel_from_cache("kernel_default")
    delete_cache_items("kernel_default")

    # Reset the quadrature degree
    from dune.perftool.sumfact.tabulation import set_quadrature_points
    set_quadrature_points(None)

    # Clean the cache from any data collected after the dry run
    delete_cache_items("dryrundata")

    return knl


@backend(interface="generate_kernels_per_integral")
def generate_kernels_per_integral(integrals):
    yield generate_kernel(integrals)


def extract_kernel_from_cache(tag, wrap_in_cgen=True):
    # Now extract regular loopy kernel components
    from dune.perftool.loopy.target import DuneTarget
    domains = [i for i in retrieve_cache_items("{} and domain".format(tag))]

    if not domains:
        domains = ["{[stupid] : 0<=stupid<1}"]

    instructions = [i for i in retrieve_cache_items("{} and instruction".format(tag))]
    substrules = [i for i in retrieve_cache_items("{} and substrule".format(tag)) if i is not None]
    temporaries = {i.name: i for i in retrieve_cache_items("{} and temporary".format(tag))}
    arguments = [i for i in retrieve_cache_items("{} and argument".format(tag))]
    silenced = [l for l in retrieve_cache_items("{} and silenced_warning".format(tag))]
    transformations = [t for t in retrieve_cache_items("{} and transformation".format(tag))]

    # Construct an options object
    from loopy import Options
    opt = Options(ignore_boostable_into=True,
                  check_dep_resolution=False,
                  )

    # Find a name for the kernel
    if wrap_in_cgen:
        from dune.perftool.pdelab.signatures import kernel_name
        name = kernel_name()
    else:
        name = "constructor_kernel"

    # Create the kernel
    from loopy import make_kernel, preprocess_kernel
    kernel = make_kernel(domains,
                         instructions + substrules,
                         arguments,
                         temporary_variables=temporaries,
                         target=DuneTarget(),
                         options=opt,
                         silenced_warnings=silenced,
                         name=name,
                         )

    from loopy import make_reduction_inames_unique
    kernel = make_reduction_inames_unique(kernel)

    from dune.perftool.loopy.transformations.disjointgroups import make_groups_conflicting
    kernel = make_groups_conflicting(kernel)

    # Apply the transformations that were gathered during tree traversals
    for trafo in transformations:
        kernel = trafo[0](kernel, *trafo[1])

    # Precompute all the substrules
    for sr in kernel.substitutions:
        tmpname = "precompute_{}".format(sr)
        kernel = lp.precompute(kernel,
                               sr,
                               temporary_name=tmpname,
                               )
        # Vectorization strategies are actually very likely to eliminate the
        # precomputation temporary. To avoid the temporary elimination warning
        # we need to explicitly disable it.
        kernel = kernel.copy(silenced_warnings=kernel.silenced_warnings + ["temp_to_write({})".format(tmpname)])

    from dune.perftool.loopy import heuristic_duplication
    kernel = heuristic_duplication(kernel)

    # Maybe apply vectorization strategies
    if get_form_option("vectorization_quadloop"):
        if get_form_option("sumfact"):
            from dune.perftool.loopy.transformations.vectorize_quad import vectorize_quadrature_loop
            kernel = vectorize_quadrature_loop(kernel)
        else:
            raise NotImplementedError("Only vectorizing sumfactorized code right now!")

    # Now add the preambles to the kernel
    preambles = [(i, p) for i, p in enumerate(retrieve_cache_items("{} and preamble".format(tag)))]
    kernel = kernel.copy(preambles=preambles)

    # Remove inames that have become obsolete
    kernel = lp.remove_unused_inames(kernel)

    # Do the loopy preprocessing!
    kernel = preprocess_kernel(kernel)

    # *REALLY* ignore boostability. This is - so far - necessary due to a mystery bug.
    kernel = kernel.copy(instructions=[i.copy(boostable=False, boostable_into=frozenset()) for i in kernel.instructions])

    from dune.perftool.loopy.transformations.matchfma import match_fused_multiply_add
    kernel = match_fused_multiply_add(kernel)

    if wrap_in_cgen:
        # Wrap the kernel in something which can generate code
        from dune.perftool.pdelab.signatures import assembly_routine_signature
        signature = assembly_routine_signature()
        kernel = LoopyKernelMethod(signature, kernel)

    return kernel


def name_time_dumper_os():
    return "os"


def name_time_dumper_reset():
    return "reset"


def name_time_dumper_ident():
    return "ident"


@generator_factory(item_tags=("cached",), cache_key_generator=lambda **kw: None)
def name_example_kernel(name=None):
    return name


class TimerMethod(ClassMember):
    def __init__(self):
        os = name_time_dumper_os()
        reset = name_time_dumper_reset()
        ident = name_time_dumper_ident()
        knl = name_example_kernel()
        assert(knl is not None)

        content = ["template <typename Stream>",
                   "void dump_timers(Stream& {}, std::string {}, bool {})".format(os, ident, reset),
                   "{"]
        dump_timers = [i for i in retrieve_cache_items(condition='dump_timers')]
        content.extend(map(lambda x: '  ' + x, dump_timers))
        content.append("}")
        ClassMember.__init__(self, content)


class LoopyKernelMethod(ClassMember):
    def __init__(self, signature, kernel, add_timings=True, initializer_list=[]):
        from loopy import generate_body
        from cgen import LiteralLines, Block
        content = signature

        # Add initializer list if this is a constructor
        if initializer_list:
            content[-1] = content[-1] + " :"
            for init in initializer_list[:-1]:
                content.append(" " * 4 + init + ",")
            content.append(" " * 4 + initializer_list[-1])

        content.append('{')
        if kernel is not None:
            # Start timer
            if add_timings and get_option('instrumentation_level') >= 3:
                from dune.perftool.pdelab.signatures import assembler_routine_name
                timer_name = assembler_routine_name() + '_kernel'
                name_example_kernel(name=timer_name)
                post_include('HP_DECLARE_TIMER({});'.format(timer_name), filetag='operatorfile')
                content.append('  ' + 'HP_TIMER_START({});'.format(timer_name))
                dump_accumulate_timer(timer_name)

            if add_timings and get_option("instrumentation_level") >= 4:
                setuptimer = '{}_kernel_setup'.format(assembler_routine_name())
                post_include('HP_DECLARE_TIMER({});'.format(setuptimer), filetag='operatorfile')
                content.append('  HP_TIMER_START({});'.format(setuptimer))
                dump_accumulate_timer(setuptimer)

            # Add kernel preamble
            for i, p in kernel.preambles:
                content.append('  ' + p)

            # Add kernel body
            content.extend(l for l in generate_body(kernel).split('\n')[1:-1])

            # Stop timer
            if add_timings and get_option('instrumentation_level') >= 3:
                content.append('  ' + 'HP_TIMER_STOP({});'.format(timer_name))

        content.append('}')
        ClassMember.__init__(self, content)


def cgen_class_from_cache(tag, members=[]):
    from dune.perftool.generation import retrieve_cache_items

    # Generate the name by concatenating basename and template parameters
    basename, fullname = class_type_from_cache(tag)

    base_classes = [bc for bc in retrieve_cache_items('{} and baseclass'.format(tag))]
    constructor_params = [bc for bc in retrieve_cache_items('{} and constructor_param'.format(tag))]
    il = [i for i in retrieve_cache_items('{} and initializer'.format(tag))]
    pm = [m for m in retrieve_cache_items('{} and member'.format(tag))]
    tparams = [i for i in retrieve_cache_items('{} and template_param'.format(tag))]

    # Construct the constructor
    constructor_knl = extract_kernel_from_cache(tag, wrap_in_cgen=False)
    from dune.perftool.loopy.target import DuneTarget
    constructor_knl = constructor_knl.copy(target=DuneTarget(declare_temporaries=False))
    signature = "{}({})".format(basename, ", ".join(next(iter(p.generate(with_semicolon=False))) for p in constructor_params))
    constructor = LoopyKernelMethod([signature], constructor_knl, add_timings=False, initializer_list=il)

    # Take any temporary declarations from the kernel and make them class members
    target = DuneTarget()
    from loopy.codegen import CodeGenerationState
    codegen_state = CodeGenerationState(kernel=constructor_knl,
                                        implemented_data_info=None,
                                        implemented_domain=None,
                                        implemented_predicates=frozenset(),
                                        seen_dtypes=frozenset(),
                                        seen_functions=frozenset(),
                                        seen_atomic_dtypes=frozenset(),
                                        var_subst_map={},
                                        allow_complex=False,
                                        is_generating_device_code=True,
                                        )
    decls = [cgen.Line("\n  " + next(iter(d.generate()))) for d in target.get_device_ast_builder().get_temporary_decls(codegen_state, 0)]

    from dune.perftool.cgen import Class
    return Class(basename, base_classes=base_classes, members=[constructor] + members + pm + decls, tparam_decls=tparams)


def local_operator_default_settings(operator, form):
    # Manage includes and base classes that we always need
    include_file('dune/pdelab/gridfunctionspace/gridfunctionspace.hh', filetag="operatorfile")
    include_file('dune/pdelab/localoperator/idefault.hh', filetag="operatorfile")
    include_file('dune/pdelab/localoperator/flags.hh', filetag="operatorfile")
    include_file('dune/pdelab/localoperator/pattern.hh', filetag="operatorfile")

    post_include("#pragma GCC diagnostic push", filetag="operatorfile")
    post_include("#pragma GCC diagnostic ignored \"-Wsign-compare\"", filetag="operatorfile")
    post_include("#pragma GCC diagnostic ignored \"-Wunused-variable\"", filetag="operatorfile")
    post_include("#pragma GCC diagnostic ignored \"-Wunused-but-set-variable\"", filetag="operatorfile")
    end_of_file("#pragma GCC diagnostic pop", filetag="operatorfile")

    # Trigger this one once early on to assure that template
    # parameters are set in the right order
    localoperator_basename(operator)
    lop_template_ansatz_gfs()
    lop_template_test_gfs()
    lop_template_range_field()

    # Make sure there is always the same constructor arguments, even if some of them are
    # not strictly needed. Also ensure the order.
    name_initree_member()

    # Iterate over the needed grid functions in correct order
    for c in sorted(filter(lambda c: c.count() > 2, form.coefficients()), key=lambda c: c.count()):
        name_gridfunction_constructor_argument(c)

    # Set some options!
    from dune.perftool.pdelab.driver import isQuadrilateral
    if isQuadrilateral(form.arguments()[0].ufl_element().cell()):
        from dune.perftool.options import set_form_option
        # For Yasp Grids the jacobian of the transformation is diagonal and constant on each cell
        set_form_option('diagonal_transformation_matrix', True)
        set_form_option('constant_transformation_matrix', True)

    # Add right base classes for stationary/instationary operators
    base_class('Dune::PDELab::LocalOperatorDefaultFlags', classtag="operator")
    from dune.perftool.pdelab.driver import is_stationary
    if not is_stationary():
        rf = lop_template_range_field()
        base_class('Dune::PDELab::InstationaryLocalOperatorDefaultMethods<{}>'
                   .format(rf), classtag="operator")


def generate_residual_kernels(form):
    logger = logging.getLogger(__name__)
    with global_context(form_type='residual'):
        operator_kernels = {}

        # Generate the necessary residual methods
        for measure in set(i.integral_type() for i in form.integrals()):
            logger.info("generate_residual_kernels: measure {}".format(measure))
            with global_context(integral_type=measure):
                enum_pattern()
                pattern_baseclass()
                enum_alpha()

                from dune.perftool.pdelab.signatures import assembler_routine_name
                with global_context(kernel=assembler_routine_name()):
                    kernel = [k for k in get_backend(interface="generate_kernels_per_integral")(form.integrals_by_type(measure))]

                # Maybe add numerical differentiation
                if get_form_option("numerical_jacobian"):
                    # Include headers for numerical methods
                    include_file("dune/pdelab/localoperator/defaultimp.hh", filetag="operatorfile")

                    # Numerical jacobian base class
                    _, loptype = class_type_from_cache("operator")
                    from dune.perftool.pdelab.signatures import ufl_measure_to_pdelab_measure
                    which = ufl_measure_to_pdelab_measure(measure)
                    base_class("Dune::PDELab::NumericalJacobian{}<{}>".format(which, loptype), classtag="operator")

                    # Numerical jacobian initializer list
                    ini = name_initree_member()
                    ini_constructor = name_initree_constructor_param()
                    initializer_list("Dune::PDELab::NumericalJacobian{}<{}>".format(which, loptype),
                                     ["{}.get<double>(\"numerical_epsilon.{}\", 1e-9)".format(ini_constructor, ini, which.lower())],
                                     classtag="operator",
                                     )

                    # In the case of matrix free operator evaluation we need jacobian apply methods
                    if get_form_option("matrix_free"):
                        from dune.perftool.pdelab.driver import is_linear
                        if is_linear(original_form):
                            # Numeical jacobian apply base class
                            base_class("Dune::PDELab::NumericalJacobianApply{}<{}>".format(which, loptype), classtag="operator")

                            # Numerical jacobian apply initializer list
                            initializer_list("Dune::PDELab::NumericalJacobianApply{}<{}>".format(which, loptype),
                                             ["{}.get<double>(\"numerical_epsilon.{}\", 1e-9)".format(ini_constructor, ini, which.lower())],
                                             classtag="operator",
                                             )
                        else:
                            # Numerical nonlinear jacobian apply base class
                            base_class("Dune::PDELab::NumericalNonlinearJacobianApply{}<{}>".format(which, loptype), classtag="operator")

                            # Numerical nonlinear jacobian apply initializer list
                            initializer_list("Dune::PDELab::NumericalNonlinearJacobianApply{}<{}>".format(which, loptype),
                                             ["{}.get<double>(\"numerical_epsilon.{}\", 1e-9)".format(ini_constructor, ini, which.lower())],
                                             classtag="operator",
                                             )

            operator_kernels[(measure, 'residual')] =  kernel

        return operator_kernels


def generate_jacobian_kernels(form, original_form):
    logger = logging.getLogger(__name__)

    from ufl import derivative
    jacform = derivative(original_form, original_form.coefficients()[0])

    from dune.perftool.ufl.preprocess import preprocess_form
    jacform = preprocess_form(jacform).preprocessed_form

    operator_kernels = {}
    with global_context(form_type="jacobian"):
        if get_form_option("generate_jacobians"):
            for measure in set(i.integral_type() for i in jacform.integrals()):
                logger.info("generate_jacobian_kernels: measure {}".format(measure))
                with global_context(integral_type=measure):
                    from dune.perftool.pdelab.signatures import assembler_routine_name
                    with global_context(kernel=assembler_routine_name()):
                        kernel = [k for k in get_backend(interface="generate_kernels_per_integral")(jacform.integrals_by_type(measure))]
                operator_kernels[(measure, 'jacobian')] = kernel

            # Generate dummy functions for those kernels, that vanished in the differentiation process
            # We *could* solve this problem by using lambda_* terms but we do not really want that, so
            # we use empty jacobian assembly methods instead
            alpha_measures = set(i.integral_type() for i in form.integrals())
            jacobian_measures = set(i.integral_type() for i in jacform.integrals())
            for it in alpha_measures - jacobian_measures:
                with global_context(integral_type=it):
                    from dune.perftool.pdelab.signatures import assembly_routine_signature
                    operator_kernels[(it, 'jacobian')] = [LoopyKernelMethod(assembly_routine_signature(), kernel=None)]

    # Jacobian apply methods for matrix-free computations
    if get_form_option("matrix_free"):
        # The apply vector has reserved index 1 so we directly use Coefficient class from ufl
        from ufl import Coefficient
        apply_coefficient = Coefficient(form.coefficients()[0].ufl_element(), 1)

        # Create application of jacobian on vector
        from ufl import action
        jac_apply_form = action(jacform, apply_coefficient)

        # Create kernel for jacobian application
        with global_context(form_type="jacobian_apply"):
            for measure in set(i.integral_type() for i in jac_apply_form.integrals()):
                with global_context(integral_type=measure):
                    with global_context(kernel=assembler_routine_name()):
                        kernel = [k for k in get_backend(interface="generate_kernels_per_integral")(jac_apply_form.integrals_by_type(measure))]
                operator_kernels[(measure, 'jacobian_apply')] = kernel

                # Generate dummy functions for those kernels, that vanished in the differentiation process
                # We *could* solve this problem by using lambda_* terms but we do not really want that, so
                # we use empty jacobian assembly methods instead
                alpha_measures = set(i.integral_type() for i in form.integrals())
                jacobian_apply_measures = set(i.integral_type() for i in jac_apply_form.integrals())
                for it in alpha_measures - jacobian_apply_measures:
                    with global_context(integral_type=it):
                        from dune.perftool.pdelab.signatures import assembly_routine_signature
                        operator_kernels[(it, 'jacobian_apply')] = [LoopyKernelMethod(assembly_routine_signature(), kernel=None)]

    return operator_kernels


def generate_control_kernels(forms):
    pass


def generate_localoperator_kernels(operator):
    logger = logging.getLogger(__name__)

    data = get_global_context_value("data")
    original_form = data.object_by_name[get_form_option("form")]
    from dune.perftool.ufl.preprocess import preprocess_form

    if get_form_option("adjoint"):
        # Generate adjoint operator
        #
        # The jacobian of the adjoint form is just the jacobian of the
        # original form with test and ansazt function swapped. A a
        # linear form you have to subtract the derivative of the
        # objective function w.r.t the ansatz function to get the
        # final residual formulation of the adjoint.
        #
        # Might not be true in all cases but works for the simple ones.
        assert get_form_option("objective_function") is not None
        assert get_form_option("control") is False

        from ufl import derivative, adjoint, action, replace
        from ufl.classes import Coefficient

        # Jacobian of the adjoint form
        jacform = derivative(original_form, original_form.coefficients()[0])
        adjoint_jacform = adjoint(jacform)

        # Derivative of objective function w.r.t. state
        objective = data.object_by_name[get_form_option("objective_function")]
        objective_jacobian = derivative(objective, objective.coefficients()[0])

        # Replace coefficient belonging to ansatz function with new coefficient
        element = objective.coefficients()[0].ufl_element()
        coeff = Coefficient(element, count=3)
        objective_jacobian = replace(objective_jacobian, {objective.coefficients()[0]: coeff})
        if len(adjoint_jacform.coefficients()) > 0:
            adjoint_jacform = replace(adjoint_jacform, {adjoint_jacform.coefficients()[0]: coeff})

        # Residual of the adjoint form
        adjoint_form = action(adjoint_jacform, original_form.coefficients()[0])
        adjoint_form = adjoint_form + objective_jacobian

        # Update form and original_form
        original_form = adjoint_form
        form = preprocess_form(adjoint_form).preprocessed_form

    elif get_form_option("control"):
        # Generate control operator
        #
        # This is the normal form derived w.r.t. the control
        # variable.
        assert get_form_option("control_variable") is not None
        controls = [data.object_by_name[ctrl.strip()] for ctrl in get_form_option("control_variable").split(",")]
        assert len(controls) == 1

        # We need to transform numpy ints to python native ints
        def _unravel(flat_index, shape):
            multi_index = np.unravel_index(flat_index, shape)
            multi_index = tuple(int(i) for i in multi_index)
            return multi_index

        forms = []
        element = original_form.coefficients()[0].ufl_element()
        coeff = Coefficient(element, count=3)
        for control in controls:
            shape = control.ufl_shape
            flat_length = np.prod(shape)
            for i in range(flat_length):
                c = control[_unravel(i, shape)]
                control_form = diff(original_form, control)
                control_form = action(control_form, coeff)
                objective = data.object_by_name[get_form_option("objective_function")]
                objective_gradient = diff(objective, control)
                control_form = control_form + objective_gradient
                forms.append(preprocess_form(control_form).preprocessed_form)

        # Used to create local operator default settings
        form = preprocess_form(original_form).preprocessed_form

        # control = data.object_by_name[get_form_option("control_variable")]
        # assert control.ufl_shape is ()

        # from ufl import diff, replace
        # from ufl.classes import Coefficient
        # control_form = diff(original_form, control)
        # # element = control_form.coefficients()[0].ufl_element()
        # # coeff = Coefficient(element, count=3)
        # # control_form = replace(control_form, {control_form.coefficients()[0]: coeff})
        # original_form = control_form
        # form = preprocess_form(control_form).preprocessed_form

    else:
        form = preprocess_form(original_form).preprocessed_form


    # Reset the generation cache
    from dune.perftool.generation import delete_cache_items
    delete_cache_items()

    # Have a data structure collect the generated kernels
    operator_kernels = {}

    # Generate things needed for all local operator files
    local_operator_default_settings(operator, form)

    if get_form_option("control"):
        logger.info("generate_localoperator_kernels: create methods for control operator")
        operator_kernels.update(generate_control_kernels(forms))
    else:
        logger.info("generate_localoperator_kernels: create residual methods")
        operator_kernels.update(generate_residual_kernels(form))

        # Generate the necessary jacobian methods
        if not get_form_option("numerical_jacobian"):
            logger.info("generate_localoperator_kernels: create jacobian methods")
            operator_kernels.update(generate_jacobian_kernels(form, original_form))