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# Authors: S. Siso and N. Nobre, STFC Daresbury Lab
'''This module contains the FoldConditionalReturnExpressionsTrans. '''
from psyclone.psyir.nodes import (Routine, IfBlock, Return,
UnaryOperation)
from psyclone.psyGen import Transformation
from psyclone.psyir.transformations.transformation_error import \
TransformationError
[docs]class FoldConditionalReturnExpressionsTrans(Transformation):
''' Provides a transformation that folds conditional expressions with only
a return statement inside so that the Return statement is moved to the end
of the Routine and therefore it can be safely removed. This simplifies the
control flow of the kernel to facilitate other transformations like kernel
fusions. For example, the following code:
.. code-block:: fortran
subroutine test(i)
if (i < 5) then
return
endif
if (i > 10) then
return
endif
! CODE
end subroutine
will be transformed to:
.. code-block:: fortran
subroutine test(i)
if (.not.(i < 5)) then
if (.not.(i > 10)) then
! CODE
endif
endif
end subroutine
'''
def __str__(self):
return ("Re-structure kernel statements to eliminate conditional "
"Return expressions.")
@property
def name(self):
'''Returns the name of this transformation as a string.'''
return "FoldConditionalReturnExpressionsTrans"
[docs] def validate(self, node, options=None):
'''Ensure that it is valid to apply this transformation to the
supplied node.
:param node: the node to validate.
:type node: :py:class:`psyclone.psyir.nodes.Routine`
:param options: a dictionary with options for transformations.
:type options: Optional[Dict[str, Any]]
:raises TransformationError: if the node is not a Routine.
'''
if not isinstance(node, Routine):
raise TransformationError(
f"Error in {self.name} transformation. This transformation "
f"can only be applied to 'Routine' nodes, but found "
f"'{type(node).__name__}'.")
[docs] def apply(self, node, options=None):
'''Apply this transformation to the supplied node.
:param node: the node to transform.
:type node: :py:class:`psyclone.psyir.nodes.Routine`
:param options: a dictionary with options for transformations.
:type options: Optional[Dict[str, Any]]
'''
routine = node
self.validate(routine, options)
def is_conditional_return(node):
'''
:param node: node to evaluate.
:type node: :py:class:`psyclone.psyir.nodes.Node`
:returns: whether the given node represents a conditional return \
expression.
'''
if not isinstance(node, IfBlock):
return False
if node.else_body is not None:
return False
return isinstance(node.if_body[0], Return)
for statement in routine[:]:
if is_conditional_return(statement):
# Reverse condition adding a NOT operator
new_condition = UnaryOperation.create(
UnaryOperation.Operator.NOT,
statement.condition.copy())
statement.children[0] = new_condition
# Remove return statement (and any dead code inside the loop)
statement.if_body.children = []
# Then move any remaining statement after the conditional
# statement inside the loop body
while len(statement.parent.children) > statement.position + 1:
move = statement.parent.children.pop()
statement.if_body.children.insert(0, move)
# For Sphinx AutoAPI documentation generation
__all__ = ['FoldConditionalReturnExpressionsTrans']