def example_psyir_nary():
'''Utility function that creates a PSyIR tree containing a nary MIN
intrinsic operator and returns the operator.
:returns: PSyIR MIN operator instance.
:rtype: :py:class:`psyclone.psyGen.NaryOperation`
'''
symbol_table = SymbolTable()
arg1 = symbol_table.new_symbol("arg",
symbol_type=DataSymbol,
datatype=REAL_TYPE,
interface=ArgumentInterface(
ArgumentInterface.Access.READWRITE))
arg2 = symbol_table.new_symbol("arg",
symbol_type=DataSymbol,
datatype=REAL_TYPE,
interface=ArgumentInterface(
ArgumentInterface.Access.READWRITE))
arg3 = symbol_table.new_symbol("arg",
symbol_type=DataSymbol,
datatype=REAL_TYPE,
interface=ArgumentInterface(
ArgumentInterface.Access.READWRITE))
arg4 = symbol_table.new_symbol(symbol_type=DataSymbol, datatype=REAL_TYPE)
symbol_table.specify_argument_list([arg1, arg2, arg3])
var1 = Reference(arg1)
var2 = Reference(arg2)
var3 = Reference(arg3)
var4 = Reference(arg4)
oper = NaryOperation.Operator.MIN
operation = NaryOperation.create(oper, [var1, var2, var3])
assign = Assignment.create(var4, operation)
_ = KernelSchedule.create("min_example", symbol_table, [assign])
return operation
def example_psyir_binary(create_expression):
'''Utility function that creates a PSyIR tree containing a binary MIN
intrinsic operator and returns the operator.
:param function create_expresssion: function used to create the \
content of the first argument of the MIN operator.
:returns: PSyIR MIN operator instance.
:rtype: :py:class:`psyclone.psyGen.BinaryOperation`
'''
symbol_table = SymbolTable()
arg1 = symbol_table.new_symbol("arg",
symbol_type=DataSymbol,
datatype=REAL_TYPE,
interface=ArgumentInterface(
ArgumentInterface.Access.READWRITE))
arg2 = symbol_table.new_symbol("arg",
symbol_type=DataSymbol,
datatype=REAL_TYPE,
interface=ArgumentInterface(
ArgumentInterface.Access.READWRITE))
arg3 = symbol_table.new_symbol(symbol_type=DataSymbol, datatype=REAL_TYPE)
symbol_table.specify_argument_list([arg1, arg2])
var1 = Reference(arg1)
var2 = Reference(arg2)
var3 = Reference(arg3)
oper = BinaryOperation.Operator.MIN
operation = BinaryOperation.create(oper, create_expression(var1), var2)
assign = Assignment.create(var3, operation)
_ = KernelSchedule.create("min_example", symbol_table, [assign])
return operation
def example_psyir(create_expression):
'''Utility function that creates a PSyIR tree containing an ABS
intrinsic operator and returns the operator.
:param function create_expresssion: function used to create the \
content of the ABS operator.
:returns: PSyIR ABS operator instance.
:rtype: :py:class:`psyclone.psyGen.UnaryOperation`
'''
symbol_table = SymbolTable()
arg1 = symbol_table.new_symbol("arg",
symbol_type=DataSymbol,
datatype=REAL_TYPE,
interface=ArgumentInterface(
ArgumentInterface.Access.READWRITE))
local = symbol_table.new_symbol(symbol_type=DataSymbol, datatype=REAL_TYPE)
symbol_table.specify_argument_list([arg1])
var1 = Reference(arg1)
var2 = Reference(local)
oper = UnaryOperation.Operator.ABS
operation = UnaryOperation.create(oper, create_expression(var1))
assign = Assignment.create(var2, operation)
_ = KernelSchedule.create("abs_example", symbol_table, [assign])
return operation
Reference(NQP_XY),
Reference(NQP_Z)],
"w3",
interface=READ_ARG)
DIFF_BASIS_W3 = lfric_psyir.DiffBasisFunctionQrXyozDataSymbol(
"diff_basis_w3",
[3, Reference(NDF_W3),
Reference(NQP_XY),
Reference(NQP_Z)],
"w3",
interface=READ_ARG)
for symbol in [NQP_XY, NQP_Z, WEIGHTS_XY, WEIGHTS_Z, BASIS_W3, DIFF_BASIS_W3]:
SYMBOL_TABLE.add(symbol)
SYMBOL_TABLE.specify_argument_list([
NDF_W3, UNDF_W3, NCELL_3D, FIELD2, OPERATOR, NQP_XY, NQP_Z, WEIGHTS_XY,
WEIGHTS_Z, BASIS_W3, DIFF_BASIS_W3
])
# Routine symbol
ROUTINE_SYMBOL = RoutineSymbol("my_sub")
# Call
CALL = Call.create(ROUTINE_SYMBOL,
[Reference(FIELD1),
Reference(FIELD2),
Reference(OPERATOR)])
# KernelSchedule
KERNEL_SCHEDULE = KernelSchedule.create("work", SYMBOL_TABLE, [CALL])
# Container
FIELD_TYPE_DEF = StructureType.create(
[("data", ArrayType(SCALAR_TYPE, [10]), Symbol.Visibility.PUBLIC),
("grid", GRID_TYPE_SYMBOL, Symbol.Visibility.PUBLIC),
("sub_meshes", ArrayType(GRID_TYPE_SYMBOL, [3]),
Symbol.Visibility.PUBLIC),
("flag", INTEGER4_TYPE, Symbol.Visibility.PUBLIC)])
FIELD_TYPE_SYMBOL = TypeSymbol("field_type", FIELD_TYPE_DEF)
CONTAINER_SYMBOL_TABLE.add(FIELD_TYPE_SYMBOL)
# Create an argument of this derived type. At this point we know only that
# DTYPE_SYMBOL refers to a type defined in the CONT container.
FIELD_SYMBOL = DataSymbol("wind", FIELD_TYPE_SYMBOL,
interface=ArgumentInterface(
ArgumentInterface.Access.READWRITE))
SYMBOL_TABLE.add(FIELD_SYMBOL)
SYMBOL_TABLE.specify_argument_list([FIELD_SYMBOL])
# Create an array of these derived types
FIELD_BUNDLE_SYMBOL = DataSymbol("chi", ArrayType(FIELD_TYPE_SYMBOL, [3]))
SYMBOL_TABLE.add(FIELD_BUNDLE_SYMBOL)
print("Container Symbol Table:")
print(str(CONTAINER_SYMBOL_TABLE))
print("Kernel Symbol Table:")
print(str(SYMBOL_TABLE))
INDEX_SYMBOL = SYMBOL_TABLE.new_symbol(root_name="i", symbol_type=DataSymbol,
datatype=INTEGER4_TYPE)
# Some predefined scalar datatypes
def create_psyir_tree():
''' Create an example PSyIR Tree.
:returns: an example PSyIR tree.
:rtype: :py:class:`psyclone.psyir.nodes.Container`
'''
# Symbol table, symbols and scalar datatypes
symbol_table = SymbolTable()
arg1 = symbol_table.new_symbol(symbol_type=DataSymbol,
datatype=REAL_TYPE,
interface=ArgumentInterface(
ArgumentInterface.Access.READWRITE))
symbol_table.specify_argument_list([arg1])
tmp_symbol = symbol_table.new_symbol(symbol_type=DataSymbol,
datatype=REAL_DOUBLE_TYPE)
index_symbol = symbol_table.new_symbol(root_name="i",
symbol_type=DataSymbol,
datatype=INTEGER4_TYPE)
real_kind = symbol_table.new_symbol(root_name="RKIND",
symbol_type=DataSymbol,
datatype=INTEGER_TYPE,
constant_value=8)
routine_symbol = RoutineSymbol("my_sub")
# Array using precision defined by another symbol
scalar_type = ScalarType(ScalarType.Intrinsic.REAL, real_kind)
array = symbol_table.new_symbol(root_name="a",
symbol_type=DataSymbol,
datatype=ArrayType(scalar_type, [10]))
# Make generators for nodes which do not have other Nodes as children,
# with some predefined scalar datatypes
def zero():
return Literal("0.0", REAL_TYPE)
def one():
return Literal("1.0", REAL4_TYPE)
def two():
return Literal("2.0", scalar_type)
def int_zero():
return Literal("0", INTEGER_SINGLE_TYPE)
def int_one():
return Literal("1", INTEGER8_TYPE)
def tmp1():
return Reference(arg1)
def tmp2():
return Reference(tmp_symbol)
# Unary Operation
oper = UnaryOperation.Operator.SIN
unaryoperation = UnaryOperation.create(oper, tmp2())
# Binary Operation
oper = BinaryOperation.Operator.ADD
binaryoperation = BinaryOperation.create(oper, one(), unaryoperation)
# Nary Operation
oper = NaryOperation.Operator.MAX
naryoperation = NaryOperation.create(oper, [tmp1(), tmp2(), one()])
# Array reference using a range
lbound = BinaryOperation.create(BinaryOperation.Operator.LBOUND,
Reference(array), int_one())
ubound = BinaryOperation.create(BinaryOperation.Operator.UBOUND,
Reference(array), int_one())
my_range = Range.create(lbound, ubound)
tmparray = ArrayReference.create(array, [my_range])
# Assignments
assign1 = Assignment.create(tmp1(), zero())
assign2 = Assignment.create(tmp2(), zero())
assign3 = Assignment.create(tmp2(), binaryoperation)
assign4 = Assignment.create(tmp1(), tmp2())
assign5 = Assignment.create(tmp1(), naryoperation)
assign6 = Assignment.create(tmparray, two())
# Call
call = Call.create(routine_symbol, [tmp1(), binaryoperation.copy()])
# If statement
if_condition = BinaryOperation.create(BinaryOperation.Operator.GT, tmp1(),
zero())
ifblock = IfBlock.create(if_condition, [assign3, assign4])
# Loop
loop = Loop.create(index_symbol, int_zero(), int_one(), int_one(),
[ifblock])
# KernelSchedule
kernel_schedule = KernelSchedule.create(
"work", symbol_table, [assign1, call, assign2, loop, assign5, assign6])
# Container
container_symbol_table = SymbolTable()
container = Container.create("CONTAINER", container_symbol_table,
[kernel_schedule])
# Import data from another container
external_container = ContainerSymbol("some_mod")
container_symbol_table.add(external_container)
external_var = DataSymbol("some_var",
INTEGER_TYPE,
interface=GlobalInterface(external_container))
container_symbol_table.add(external_var)
routine_symbol.interface = GlobalInterface(external_container)
container_symbol_table.add(routine_symbol)
return container
def create_psyir_tree():
''' Create an example PSyIR Tree.
:returns: an example PSyIR tree.
:rtype: :py:class:`psyclone.psyir.nodes.Container`
'''
# Symbol table, symbols and scalar datatypes
symbol_table = SymbolTable()
arg1 = symbol_table.new_symbol(symbol_type=DataSymbol,
datatype=REAL_TYPE,
interface=ArgumentInterface(
ArgumentInterface.Access.READWRITE))
symbol_table.specify_argument_list([arg1])
tmp_symbol = symbol_table.new_symbol(symbol_type=DataSymbol,
datatype=REAL_DOUBLE_TYPE)
index_symbol = symbol_table.new_symbol(root_name="i",
symbol_type=DataSymbol,
datatype=INTEGER4_TYPE)
real_kind = symbol_table.new_symbol(root_name="RKIND",
symbol_type=DataSymbol,
datatype=INTEGER_TYPE,
constant_value=8)
routine_symbol = RoutineSymbol("my_sub")
# Array using precision defined by another symbol
scalar_type = ScalarType(ScalarType.Intrinsic.REAL, real_kind)
array = symbol_table.new_symbol(root_name="a",
symbol_type=DataSymbol,
datatype=ArrayType(scalar_type, [10]))
# Nodes which do not have Nodes as children and (some) predefined
# scalar datatypes
# TODO: Issue #1136 looks at how to avoid all of the _x versions
zero_1 = Literal("0.0", REAL_TYPE)
zero_2 = Literal("0.0", REAL_TYPE)
zero_3 = Literal("0.0", REAL_TYPE)
one_1 = Literal("1.0", REAL4_TYPE)
one_2 = Literal("1.0", REAL4_TYPE)
one_3 = Literal("1.0", REAL4_TYPE)
two = Literal("2.0", scalar_type)
int_zero = Literal("0", INTEGER_SINGLE_TYPE)
int_one_1 = Literal("1", INTEGER8_TYPE)
int_one_2 = Literal("1", INTEGER8_TYPE)
int_one_3 = Literal("1", INTEGER8_TYPE)
int_one_4 = Literal("1", INTEGER8_TYPE)
tmp1_1 = Reference(arg1)
tmp1_2 = Reference(arg1)
tmp1_3 = Reference(arg1)
tmp1_4 = Reference(arg1)
tmp1_5 = Reference(arg1)
tmp1_6 = Reference(arg1)
tmp2_1 = Reference(tmp_symbol)
tmp2_2 = Reference(tmp_symbol)
tmp2_3 = Reference(tmp_symbol)
tmp2_4 = Reference(tmp_symbol)
tmp2_5 = Reference(tmp_symbol)
tmp2_6 = Reference(tmp_symbol)
# Unary Operation
oper = UnaryOperation.Operator.SIN
unaryoperation_1 = UnaryOperation.create(oper, tmp2_1)
unaryoperation_2 = UnaryOperation.create(oper, tmp2_2)
# Binary Operation
oper = BinaryOperation.Operator.ADD
binaryoperation_1 = BinaryOperation.create(oper, one_1, unaryoperation_1)
binaryoperation_2 = BinaryOperation.create(oper, one_2, unaryoperation_2)
# Nary Operation
oper = NaryOperation.Operator.MAX
naryoperation = NaryOperation.create(oper, [tmp1_1, tmp2_3, one_3])
# Array reference using a range
lbound = BinaryOperation.create(BinaryOperation.Operator.LBOUND,
Reference(array), int_one_1)
ubound = BinaryOperation.create(BinaryOperation.Operator.UBOUND,
Reference(array), int_one_2)
my_range = Range.create(lbound, ubound)
tmparray = ArrayReference.create(array, [my_range])
# Assignments
assign1 = Assignment.create(tmp1_2, zero_1)
assign2 = Assignment.create(tmp2_4, zero_2)
assign3 = Assignment.create(tmp2_5, binaryoperation_1)
assign4 = Assignment.create(tmp1_3, tmp2_6)
assign5 = Assignment.create(tmp1_4, naryoperation)
assign6 = Assignment.create(tmparray, two)
# Call
call = Call.create(routine_symbol, [tmp1_5, binaryoperation_2])
# If statement
if_condition = BinaryOperation.create(BinaryOperation.Operator.GT, tmp1_6,
zero_3)
ifblock = IfBlock.create(if_condition, [assign3, assign4])
# Loop
loop = Loop.create(index_symbol, int_zero, int_one_3, int_one_4, [ifblock])
# KernelSchedule
kernel_schedule = KernelSchedule.create(
"work", symbol_table, [assign1, call, assign2, loop, assign5, assign6])
# Container
container_symbol_table = SymbolTable()
container = Container.create("CONTAINER", container_symbol_table,
[kernel_schedule])
# Import data from another container
external_container = ContainerSymbol("some_mod")
container_symbol_table.add(external_container)
external_var = DataSymbol("some_var",
INTEGER_TYPE,
interface=GlobalInterface(external_container))
container_symbol_table.add(external_var)
routine_symbol.interface = GlobalInterface(external_container)
container_symbol_table.add(routine_symbol)
return container
class KernelInterface(ArgOrdering):
'''Create the kernel arguments for the supplied kernel as specified by
the associated kernel metadata and the kernel ordering rules
encoded in the ArgOrdering base class as method callbacks.
A PSyIR symbol table is created containing appropriate LFRic PSyIR
symbols to specify the arguments. If an argument is an array with
one or more dimension sizes specified by another argument, then
the associated array symbol is created so that it references the
appropriate symbol.
Related arguments - e.g. a field has an associated dofmap - are
not directly connected, they must be inferred from the function
space names. It is not yet clear whether this would be useful or
not.
TBD: This class should replace the current kernel stub generation
code when all of its methods are implemented, see issue #928.
:param kern: the kernel for which to create arguments.
:type kern: :py:class:`psyclone.dynamo0p3.DynKern`
'''
#: Mapping from a generic PSyIR datatype to the equivalent
#: LFRic-specific field datasymbol.
field_mapping = {
"integer": lfric_psyir.IntegerFieldDataDataSymbol,
"real": lfric_psyir.RealFieldDataDataSymbol,
"logical": lfric_psyir.LogicalFieldDataDataSymbol
}
#: Mapping from a generic PSyIR datatype to the equivalent
#: LFRic-specific vector field datasymbol.
vector_field_mapping = {
"integer": lfric_psyir.IntegerVectorFieldDataDataSymbol,
"real": lfric_psyir.RealVectorFieldDataDataSymbol,
"logical": lfric_psyir.LogicalVectorFieldDataDataSymbol
}
#: Mapping from the LFRic metadata description of quadrature to the
#: associated LFRic-specific basis function datasymbol.
basis_mapping = {
"gh_quadrature_xyoz": lfric_psyir.BasisFunctionQrXyozDataSymbol,
"gh_quadrature_face": lfric_psyir.BasisFunctionQrFaceDataSymbol,
"gh_quadrature_edge": lfric_psyir.BasisFunctionQrEdgeDataSymbol
}
#: Mapping from the LFRic metadata description of quadrature to the
#: associated LFRic-specific differential basis function datasymbol.
diff_basis_mapping = {
"gh_quadrature_xyoz": lfric_psyir.DiffBasisFunctionQrXyozDataSymbol,
"gh_quadrature_face": lfric_psyir.DiffBasisFunctionQrFaceDataSymbol,
"gh_quadrature_edge": lfric_psyir.DiffBasisFunctionQrEdgeDataSymbol
}
_read_access = ArgumentInterface(ArgumentInterface.Access.READ)
def __init__(self, kern):
super(KernelInterface, self).__init__(kern)
self._symbol_table = SymbolTable()
self._arglist = []
def generate(self, var_accesses=None):
'''Call the generate base class then add the argument list as it can't
be appended as we go along.
:param var_accesses: an unused optional argument that stores \
information about variable accesses.
:type var_accesses: :\
py:class:`psyclone.core.access_info.VariablesAccessInfo`
'''
super(KernelInterface, self).generate(var_accesses=var_accesses)
# Set the argument list for the symbol table. This is done at
# the end after incrementally adding symbols to the _args
# list, as it is not possible to incrementally add symbols to
# the symbol table argument list.
self._symbol_table.specify_argument_list(self._arglist)
# While data dependence analysis does not use the symbol
# table, see #845, we have to provide variable information
# separately. This is done by using the base class append()
# method. However, this method also adds the variable names to the
# internal _arglist list which we do not want as we have
# already added our symbols there. Therefore we need to remove
# them afterwards.
# Map from symbol table accesses to dependence analysis accesses.
mapping = {
ArgumentInterface.Access.READ: AccessType.READ,
ArgumentInterface.Access.READWRITE: AccessType.READWRITE,
ArgumentInterface.Access.WRITE: AccessType.WRITE
}
len_arglist = len(self._arglist)
for symbol in self._symbol_table.symbols:
self.append(symbol.name,
var_accesses,
mode=mapping[symbol.interface.access])
self._arglist = self._arglist[:len_arglist]
def cell_position(self, var_accesses=None):
'''Create an LFRic cell-position object and add it to the symbol table
and argument list.
:param var_accesses: an unused optional argument that stores \
information about variable accesses.
:type var_accesses: :\
py:class:`psyclone.core.access_info.VariablesAccessInfo`
'''
symbol = self._symbol_table.symbol_from_tag(
"cell",
symbol_type=lfric_psyir.CellPositionDataSymbol,
interface=self._read_access)
self._arglist.append(symbol)
def mesh_height(self, var_accesses=None):
'''Create an LFRic mesh height object and add it to the symbol table
and argument list.
:param var_accesses: an unused optional argument that stores \
information about variable accesses.
:type var_accesses: :\
py:class:`psyclone.core.access_info.VariablesAccessInfo`
'''
symbol = self._symbol_table.symbol_from_tag(
"nlayers",
symbol_type=lfric_psyir.MeshHeightDataSymbol,
interface=self._read_access)
self._arglist.append(symbol)
def mesh_ncell2d(self, var_accesses=None):
'''Not implemented.
:param var_accesses: an unused optional argument that stores \
information about variable accesses.
:type var_accesses: :\
py:class:`psyclone.core.access_info.VariablesAccessInfo`
:raises NotImplementedError: as this method is not implemented.
'''
raise NotImplementedError("mesh_ncell2d not implemented")
def cell_map(self, var_accesses=None):
'''Not implemented.
:param var_accesses: an unused optional argument that stores \
information about variable accesses.
:type var_accesses: :\
py:class:`psyclone.core.access_info.VariablesAccessInfo`
:raises NotImplementedError: as this method is not implemented.
'''
raise NotImplementedError("cell_map not implemented")
def field_vector(self, argvect, var_accesses=None):
'''Create LFRic field vector arguments and add them to the symbol
table and argument list. Also declare the associated "undf"
symbol if it has not already been declared so that it can be
used to dimension the field vector arguments.
:param argvect: the field vector to add.
:type argvect: :py:class:`psyclone.dynamo0p3.DynKernelArgument`
:param var_accesses: an unused optional argument that stores \
information about variable accesses.
:type var_accesses: :\
py:class:`psyclone.core.access_info.VariablesAccessInfo`
:raises NotImplementedError: if the datatype of the vector \
field is not supported.
'''
fs_name = argvect.function_space.orig_name
undf_symbol = self._symbol_table.symbol_from_tag(
"undf_{0}".format(fs_name),
fs=fs_name,
symbol_type=lfric_psyir.NumberOfUniqueDofsDataSymbol,
interface=self._read_access)
interface = ArgumentInterface(INTENT_MAPPING[argvect.intent])
try:
field_class = self.vector_field_mapping[argvect.intrinsic_type]
except KeyError as info:
message = ("kernel interface does not support a vector field of "
"type '{0}'.".format(argvect.intrinsic_type))
six.raise_from(NotImplementedError(message), info)
for idx in range(argvect.vector_size):
tag = "{0}_v{1}".format(argvect.name, idx)
field_data_symbol = self._symbol_table.symbol_from_tag(
tag,
symbol_type=field_class,
dims=[Reference(undf_symbol)],
fs=fs_name,
interface=interface)
self._arglist.append(field_data_symbol)
def field(self, arg, var_accesses=None):
'''Create an LFRic field argument and add it to the symbol table and
argument list. Also declare the associated "undf" symbol if it
has not already been declared so that it can be used to
dimension the field argument.
:param arg: the field to add.
:type arg: :py:class:`psyclone.dynamo0p3.DynKernelArgument`
:param var_accesses: an unused optional argument that stores \
information about variable accesses.
:type var_accesses: :\
py:class:`psyclone.core.access_info.VariablesAccessInfo`
:raises NotImplementedError: if the datatype of the field is \
not supported.
'''
fs_name = arg.function_space.orig_name
undf_symbol = self._symbol_table.symbol_from_tag(
"undf_{0}".format(fs_name),
symbol_type=lfric_psyir.NumberOfUniqueDofsDataSymbol,
fs=fs_name,
interface=self._read_access)
try:
field_class = self.field_mapping[arg.intrinsic_type]
except KeyError as info:
message = ("kernel interface does not support a field of type "
"'{0}'.".format(arg.intrinsic_type))
six.raise_from(NotImplementedError(message), info)
field_data_symbol = self._symbol_table.symbol_from_tag(
arg.name,
interface=ArgumentInterface(INTENT_MAPPING[arg.intent]),
symbol_type=field_class,
dims=[Reference(undf_symbol)],
fs=fs_name)
self._arglist.append(field_data_symbol)
def stencil_unknown_extent(self, arg, var_accesses=None):
'''Not implemented.
:param arg: the kernel argument with which the stencil is associated.
:type arg: :py:class:`psyclone.dynamo0p3.DynKernelArgument`
:param var_accesses: an unused optional argument that stores \
information about variable accesses.
:type var_accesses: :\
py:class:`psyclone.core.access_info.VariablesAccessInfo`
:raises NotImplementedError: as this method is not implemented.
'''
raise NotImplementedError("stencil_unknown_extent not implemented")
def stencil_unknown_direction(self, arg, var_accesses=None):
'''Not implemented.
:param arg: the kernel argument with which the stencil is associated.
:type arg: :py:class:`psyclone.dynamo0p3.DynKernelArgument`
:param var_accesses: an unused optional argument that stores \
information about variable accesses.
:type var_accesses: :\
py:class:`psyclone.core.access_info.VariablesAccessInfo`
:raises NotImplementedError: as this method is not implemented.
'''
raise NotImplementedError("stencil_unknown_direction not implemented")
def stencil(self, arg, var_accesses=None):
'''Not implemented.
:param arg: the kernel argument with which the stencil is associated.
:type arg: :py:class:`psyclone.dynamo0p3.DynKernelArgument`
:param var_accesses: an unused optional argument that stores \
information about variable accesses.
:type var_accesses: :\
py:class:`psyclone.core.access_info.VariablesAccessInfo`
:raises NotImplementedError: as this method is not implemented.
'''
raise NotImplementedError("stencil not implemented")
def operator(self, arg, var_accesses=None):
'''Create an LFRic operator argument and an ncells argument and add
them to the symbol table and argument list. Also declare the
associated 'fs_from', 'fs_to' symbols if they have not already
been declared so that they can be used to dimension the
operator symbol (as well as ncells).
:param arg: the operator to add.
:type arg: :py:class:`psyclone.dynamo0p3.DynKernelArgument`
:param var_accesses: an unused optional argument that stores \
information about variable accesses.
:type var_accesses: :\
py:class:`psyclone.core.access_info.VariablesAccessInfo`
:raises NotImplementedError: if the datatype of the field is \
not supported.
'''
fs_from_name = arg.function_space_from.orig_name
ndf_symbol_from = self._symbol_table.symbol_from_tag(
"ndf_{0}".format(fs_from_name),
fs=fs_from_name,
symbol_type=lfric_psyir.NumberOfDofsDataSymbol,
interface=self._read_access)
fs_to_name = arg.function_space_to.orig_name
ndf_symbol_to = self._symbol_table.symbol_from_tag(
"ndf_{0}".format(fs_to_name),
fs=fs_to_name,
symbol_type=lfric_psyir.NumberOfDofsDataSymbol,
interface=self._read_access)
ncells = lfric_psyir.NumberOfCellsDataSymbol(
"ncell_3d", interface=self._read_access)
self._symbol_table.add(ncells)
self._arglist.append(ncells)
op_arg_symbol = self._symbol_table.symbol_from_tag(
arg.name,
symbol_type=lfric_psyir.OperatorDataSymbol,
dims=[
Reference(ndf_symbol_from),
Reference(ndf_symbol_to),
Reference(ncells)
],
fs_from=fs_from_name,
fs_to=fs_to_name,
interface=ArgumentInterface(INTENT_MAPPING[arg.intent]))
self._arglist.append(op_arg_symbol)
def cma_operator(self, arg, var_accesses=None):
'''Not implemented.
:param arg: the CMA operator argument.
:type arg: :py:class:`psyclone.dynamo0p3.DynKernelArgument`
:param var_accesses: an unused optional argument that stores \
information about variable accesses.
:type var_accesses: :\
py:class:`psyclone.core.access_info.VariablesAccessInfo`
:raises NotImplementedError: as this method is not implemented.
'''
raise NotImplementedError("cma_operator not implemented")
def scalar(self, scalar_arg, var_accesses=None):
'''Create an LFRic scalar argument and add it to the symbol table and
argument list.
:param scalar_arg: the scalar to add.
:type scalar_arg: :py:class:`psyclone.dynamo0p3.DynKernelArgument`
:param var_accesses: an unused optional argument that stores \
information about variable accesses.
:type var_accesses: :\
py:class:`psyclone.core.access_info.VariablesAccessInfo`
:raises NotImplementedError: if the datatype of the scalar is \
not supported.
'''
mapping = {
"integer": lfric_psyir.LfricIntegerScalarDataSymbol,
"real": lfric_psyir.LfricRealScalarDataSymbol,
"logical": lfric_psyir.LfricLogicalScalarDataSymbol
}
try:
symbol = self._symbol_table.symbol_from_tag(
scalar_arg.name,
symbol_type=mapping[scalar_arg.intrinsic_type],
interface=ArgumentInterface(INTENT_MAPPING[scalar_arg.intent]))
except KeyError as info:
message = (
"scalar of type '{0}' not implemented in KernelInterface "
"class.".format(scalar_arg.intrinsic_type))
six.raise_from(NotImplementedError(message), info)
self._arglist.append(symbol)
def fs_common(self, function_space, var_accesses=None):
'''Create any arguments that are common to a particular function
space. At this time the only common argument is the number of
degrees of freedom. Create the associated LFRic symbol, and
add it to the symbol table and argument list.
:param function_space: the function space for any common arguments.
:type function_space: :py:class:`psyclone.domain.lfric.FunctionSpace`
:param var_accesses: an unused optional argument that stores \
information about variable accesses.
:type var_accesses: :\
py:class:`psyclone.core.access_info.VariablesAccessInfo`
'''
fs_name = function_space.orig_name
ndf_symbol = self._symbol_table.symbol_from_tag(
"ndf_{0}".format(fs_name),
fs=fs_name,
symbol_type=lfric_psyir.NumberOfDofsDataSymbol,
interface=self._read_access)
self._arglist.append(ndf_symbol)
def fs_intergrid(self, function_space, var_accesses=None):
'''Not implemented.
:param arg: the CMA operator argument.
:type arg: :py:class:`psyclone.dynamo0p3.DynKernelArgument`
:param var_accesses: an unused optional argument that stores \
information about variable accesses.
:type var_accesses: :\
py:class:`psyclone.core.access_info.VariablesAccessInfo`
:raises NotImplementedError: as this method is not implemented.
'''
raise NotImplementedError("fs_intergrid not implemented")
def fs_compulsory_field(self, function_space, var_accesses=None):
'''Create any arguments that are compulsory for a field on a
particular function space. At this time the compulsory
arguments are the unique number of degrees of freedom and the
dofmap. Create the associated LFRic symbol, and add it to the
symbol table and argument list. Also declare the number of
degrees of freedom and add to the symbol table if one has not
yet been added.
:param function_space: the function space for any compulsory arguments.
:type function_space: :py:class:`psyclone.domain.lfric.FunctionSpace`
:param var_accesses: an unused optional argument that stores \
information about variable accesses.
:type var_accesses: :\
py:class:`psyclone.core.access_info.VariablesAccessInfo`
'''
fs_name = function_space.orig_name
undf_symbol = self._symbol_table.symbol_from_tag(
"undf_{0}".format(fs_name),
fs=fs_name,
symbol_type=lfric_psyir.NumberOfUniqueDofsDataSymbol,
interface=self._read_access)
self._arglist.append(undf_symbol)
fs_name = function_space.orig_name
ndf_symbol = self._symbol_table.symbol_from_tag(
"ndf_{0}".format(fs_name),
fs=fs_name,
symbol_type=lfric_psyir.NumberOfDofsDataSymbol,
interface=self._read_access)
dofmap_symbol = self._symbol_table.symbol_from_tag(
"dofmap_{0}".format(fs_name),
fs=fs_name,
symbol_type=lfric_psyir.DofMapDataSymbol,
dims=[Reference(ndf_symbol)],
interface=self._read_access)
self._arglist.append(dofmap_symbol)
def banded_dofmap(self, function_space, var_accesses=None):
'''Not implemented.
:param function_space: the function space for this dofmap.
:type function_space: :py:class:`psyclone.domain.lfric.FunctionSpace`
:param var_accesses: an unused optional argument that stores \
information about variable accesses.
:type var_accesses: :\
py:class:`psyclone.core.access_info.VariablesAccessInfo`
:raises NotImplementedError: as this method is not implemented.
'''
raise NotImplementedError("banded_dofmap not implemented")
def indirection_dofmap(self,
function_space,
operator=None,
var_accesses=None):
'''Not implemented.
:param function_space: the function space for this dofmap.
:type function_space: :py:class:`psyclone.domain.lfric.FunctionSpace`
:param operator: the CMA operator.
:type operator: :py:class:`psyclone.dynamo0p3.DynKernelArgument`
:param var_accesses: an unused optional argument that stores \
information about variable accesses.
:type var_accesses: :\
py:class:`psyclone.core.access_info.VariablesAccessInfo`
:raises NotImplementedError: as this method is not implemented.
'''
raise NotImplementedError("indirection_dofmap not implemented")
def basis(self, function_space, var_accesses=None):
'''Create an LFRic basis function argument and add it to the symbol
table and argument list.
:param function_space: the function space for this basis function.
:type function_space: :py:class:`psyclone.domain.lfric.FunctionSpace`
:param var_accesses: an unused optional argument that stores \
information about variable accesses.
:type var_accesses: :\
py:class:`psyclone.core.access_info.VariablesAccessInfo`
'''
basis_name_func = function_space.get_basis_name
# This import must be placed here to avoid circular dependencies
# pylint: disable=import-outside-toplevel
from psyclone.dynamo0p3 import DynBasisFunctions
first_dim_value_func = DynBasisFunctions.basis_first_dim_value
self._create_basis(function_space, self.basis_mapping, basis_name_func,
first_dim_value_func)
def diff_basis(self, function_space, var_accesses=None):
'''Create an LFRic differential basis function argument and add it to
the symbol table and argument list.
:param function_space: the function space for this \
differential basis function.
:type function_space: :py:class:`psyclone.domain.lfric.FunctionSpace`
:param var_accesses: an unused optional argument that stores \
information about variable accesses.
:type var_accesses: :\
py:class:`psyclone.core.access_info.VariablesAccessInfo`
'''
basis_name_func = function_space.get_diff_basis_name
# This import must be placed here to avoid circular dependencies
# pylint: disable=import-outside-toplevel
from psyclone.dynamo0p3 import DynBasisFunctions
first_dim_value_func = DynBasisFunctions.diff_basis_first_dim_value
self._create_basis(function_space, self.diff_basis_mapping,
basis_name_func, first_dim_value_func)
def field_bcs_kernel(self, function_space, var_accesses=None):
'''Not implemented.
:param function_space: the function space for this boundary condition.
:type function_space: :py:class:`psyclone.domain.lfric.FunctionSpace`
:param var_accesses: an unused optional argument that stores \
information about variable accesses.
:type var_accesses: :\
py:class:`psyclone.core.access_info.VariablesAccessInfo`
:raises NotImplementedError: as this method is not implemented.
'''
raise NotImplementedError("field_bcs_kernel not implemented")
def operator_bcs_kernel(self, function_space, var_accesses=None):
'''Not implemented.
:param function_space: the function space for this bcs kernel
:type function_space: :py:class:`psyclone.domain.lfric.FunctionSpace`
:param var_accesses: an unused optional argument that stores \
information about variable accesses.
:type var_accesses: :\
py:class:`psyclone.core.access_info.VariablesAccessInfo`
:raises NotImplementedError: as this method is not implemented.
'''
raise NotImplementedError("operator_bcs_kernel not implemented")
def ref_element_properties(self, var_accesses=None):
''' Properties associated with the reference element
:param var_accesses: an unused optional argument that stores \
information about variable accesses.
:type var_accesses: :\
py:class:`psyclone.core.access_info.VariablesAccessInfo`
'''
# This callback does not contribute any kernel arguments
def mesh_properties(self, var_accesses=None):
''' Properties associated with the mesh
:param var_accesses: an unused optional argument that stores \
information about variable accesses.
:type var_accesses: :\
py:class:`psyclone.core.access_info.VariablesAccessInfo`
'''
# This callback does not contribute any kernel arguments
def quad_rule(self, var_accesses=None):
'''Create LFRic arguments associated with the required quadrature, if
they do not already exist, and add them to the symbol table
and argument list. The arguments depend on the type of
quadrature requested.
:param var_accesses: an unused optional argument that stores \
information about variable accesses.
:type var_accesses: :\
py:class:`psyclone.core.access_info.VariablesAccessInfo`
:raises InternalError: if an unsupported quadrature shape is \
found.
'''
# The kernel captures all the required quadrature shapes
for shape in self._kern.qr_rules:
if shape == "gh_quadrature_xyoz":
nqp_xy = self._symbol_table.symbol_from_tag(
"nqp_xy",
symbol_type=lfric_psyir.NumberOfQrPointsInXyDataSymbol,
interface=self._read_access)
nqp_z = self._symbol_table.symbol_from_tag(
"nqp_z",
symbol_type=lfric_psyir.NumberOfQrPointsInZDataSymbol,
interface=self._read_access)
weights_xy = self._symbol_table.symbol_from_tag(
"weights_xy",
symbol_type=lfric_psyir.QrWeightsInXyDataSymbol,
dims=[Reference(nqp_xy)],
interface=self._read_access)
weights_z = self._symbol_table.symbol_from_tag(
"weights_z",
symbol_type=lfric_psyir.QrWeightsInZDataSymbol,
dims=[Reference(nqp_z)],
interface=self._read_access)
self._arglist.extend([nqp_xy, nqp_z, weights_xy, weights_z])
elif shape == "gh_quadrature_face":
nfaces = self._symbol_table.symbol_from_tag(
"nfaces",
symbol_type=lfric_psyir.NumberOfFacesDataSymbol,
interface=self._read_access)
nqp = self._symbol_table.symbol_from_tag(
"nqp_faces",
symbol_type=lfric_psyir.NumberOfQrPointsInFacesDataSymbol,
interface=self._read_access)
weights = self._symbol_table.symbol_from_tag(
"weights_faces",
symbol_type=lfric_psyir.QrWeightsInFacesDataSymbol,
dims=[Reference(nqp)],
interface=self._read_access)
self._arglist.extend([nfaces, nqp, weights])
elif shape == "gh_quadrature_edge":
nedges = self._symbol_table.symbol_from_tag(
"nedges",
symbol_type=lfric_psyir.NumberOfEdgesDataSymbol,
interface=self._read_access)
nqp = self._symbol_table.symbol_from_tag(
"nqp_edges",
symbol_type=lfric_psyir.NumberOfQrPointsInEdgesDataSymbol,
interface=self._read_access)
weights = self._symbol_table.symbol_from_tag(
"weights_edges",
symbol_type=lfric_psyir.QrWeightsInEdgesDataSymbol,
dims=[Reference(nqp)],
interface=self._read_access)
self._arglist.extend([nedges, nqp, weights])
else:
raise InternalError("Unsupported quadrature shape '{0}' "
"found in kernel_interface.".format(shape))
def _create_basis(self, function_space, mapping, basis_name_func,
first_dim_value_func):
'''Internal utility to create an LFRic basis or differential basis
function argument specific to the particular quadrature that
is being used and add it to the symbol table and argument
list. Also declare the associated "ndf" symbol and any
quadrature-specific symbols if they have not already been
declared so that they can be used to dimension the basis or
differential basis symbol.
This utility function is used to avoid code replication as the
structure of a basis function is very similar to the structure
of a differential basis function.
:param function_space: the function space that this basis or \
differential basis function is on.
:type function_space: :py:class:`psyclone.domain.lfric.FunctionSpace`
:param dict mapping: a mapping from quadrature type to basis \
or differential basis class name.
:param method basis_name_func: a method that returns the name \
of the basis or differential basis function for the \
current function space.
:param function first_dim_value_func: a function that returns \
the size of the first dimension of the basis or \
differential basis function for the current function \
space.
:raises NotImplementedError: if an evaluator shape is found \
that is not a quadrature shape (currently just \
'gh_evaluator').
:raises InternalError: if the supplied evaluator shape is not \
recognised.
'''
# pylint: disable=too-many-locals
# This import must be placed here to avoid circular dependencies
# pylint: disable=import-outside-toplevel
from psyclone.dynamo0p3 import VALID_EVALUATOR_SHAPES
for shape in self._kern.eval_shapes:
fs_name = function_space.orig_name
ndf_symbol = self._symbol_table.symbol_from_tag(
"ndf_{0}".format(fs_name),
symbol_type=lfric_psyir.NumberOfDofsDataSymbol,
fs=fs_name,
interface=self._read_access)
# Create the qr tag by appending the last part of the shape
# name to "qr_".
quad_name = shape.split("_")[-1]
basis_tag = basis_name_func(qr_var="qr_" + quad_name)
if shape == "gh_quadrature_xyoz":
nqp_xy = self._symbol_table.symbol_from_tag(
"nqp_xy",
symbol_type=lfric_psyir.NumberOfQrPointsInXyDataSymbol,
interface=self._read_access)
nqp_z = self._symbol_table.symbol_from_tag(
"nqp_z",
symbol_type=lfric_psyir.NumberOfQrPointsInZDataSymbol,
interface=self._read_access)
arg = mapping["gh_quadrature_xyoz"](
basis_tag, [
int(first_dim_value_func(function_space)),
Reference(ndf_symbol),
Reference(nqp_xy),
Reference(nqp_z)
],
fs_name,
interface=self._read_access)
elif shape == "gh_quadrature_face":
nfaces = self._symbol_table.symbol_from_tag(
"nfaces",
symbol_type=lfric_psyir.NumberOfFacesDataSymbol,
interface=self._read_access)
nqp = self._symbol_table.symbol_from_tag(
"nqp_faces",
symbol_type=lfric_psyir.NumberOfQrPointsInFacesDataSymbol,
interface=self._read_access)
arg = mapping["gh_quadrature_face"](
basis_tag, [
int(first_dim_value_func(function_space)),
Reference(ndf_symbol),
Reference(nqp),
Reference(nfaces)
],
fs_name,
interface=self._read_access)
elif shape == "gh_quadrature_edge":
nedges = self._symbol_table.symbol_from_tag(
"nedges",
symbol_type=lfric_psyir.NumberOfEdgesDataSymbol,
interface=self._read_access)
nqp = self._symbol_table.symbol_from_tag(
"nqp_edges",
symbol_type=lfric_psyir.NumberOfQrPointsInEdgesDataSymbol,
interface=self._read_access)
arg = mapping["gh_quadrature_edge"](
basis_tag, [
int(first_dim_value_func(function_space)),
Reference(ndf_symbol),
Reference(nqp),
Reference(nedges)
],
fs_name,
interface=self._read_access)
elif shape in VALID_EVALUATOR_SHAPES:
# Need a (diff) basis array for each target space upon
# which the basis functions have been
# evaluated. _kern.eval_targets is a dict where the
# values are 2-tuples of (FunctionSpace, argument).
for _, _ in self._kern.eval_targets.items():
raise NotImplementedError(
"Evaluator shapes not implemented in kernel_interface "
"class.")
else:
raise InternalError(
"Unrecognised quadrature or evaluator shape '{0}'. "
"Expected one of: {1}.".format(shape,
VALID_EVALUATOR_SHAPES))
self._symbol_table.add(arg)
self._arglist.append(arg)
# Symbol table, symbols and scalar datatypes
SYMBOL_TABLE = SymbolTable()
TMP_NAME1 = SYMBOL_TABLE.new_symbol_name()
ARG1 = DataSymbol(TMP_NAME1,
REAL_TYPE,
interface=ArgumentInterface(
ArgumentInterface.Access.READWRITE))
SYMBOL_TABLE.add(ARG1)
TMP_NAME2 = SYMBOL_TABLE.new_symbol_name()
TMP_SYMBOL = DataSymbol(TMP_NAME2, REAL_DOUBLE_TYPE)
SYMBOL_TABLE.add(TMP_SYMBOL)
INDEX_NAME = SYMBOL_TABLE.new_symbol_name(root_name="i")
INDEX_SYMBOL = DataSymbol(INDEX_NAME, INTEGER4_TYPE)
SYMBOL_TABLE.add(INDEX_SYMBOL)
SYMBOL_TABLE.specify_argument_list([ARG1])
REAL_KIND_NAME = SYMBOL_TABLE.new_symbol_name(root_name="RKIND")
REAL_KIND = DataSymbol(REAL_KIND_NAME, INTEGER_TYPE, constant_value=8)
SYMBOL_TABLE.add(REAL_KIND)
ROUTINE_SYMBOL = RoutineSymbol("my_sub")
# Array using precision defined by another symbol
ARRAY_NAME = SYMBOL_TABLE.new_symbol_name(root_name="a")
SCALAR_TYPE = ScalarType(ScalarType.Intrinsic.REAL, REAL_KIND)
ARRAY = DataSymbol(ARRAY_NAME, ArrayType(SCALAR_TYPE, [10]))
SYMBOL_TABLE.add(ARRAY)
# Nodes which do not have Nodes as children and (some) predefined
# scalar datatypes
ZERO = Literal("0.0", REAL_TYPE)
ONE = Literal("1.0", REAL4_TYPE)
