# -----------------------------------------------------------------------------
# SPDX-FileCopyrightText: Copyright (c) 2023-2026 Science and Technology
# Facilities Council
# SPDX-License-Identifier: BSD-3-Clause
# See the full LICENSE file in the project root for details.
# -----------------------------------------------------------------------------
'''Module providing a transformation from a PSyIR PRODUCT intrinsic to
an equivalent PSyIR loop structure. This could be useful if the PRODUCT
operator is not supported by the back-end, the required
parallelisation approach, or if the performance in the inline code is
better than the intrinsic.
'''
from psyclone.psyir.nodes import IntrinsicCall, BinaryOperation, Literal
from psyclone.psyir.symbols import ScalarType
from psyclone.psyir.transformations.intrinsics.array_reduction_base_trans \
import ArrayReductionBaseTrans
from psyclone.utils import transformation_documentation_wrapper
[docs]
@transformation_documentation_wrapper
class Product2LoopTrans(ArrayReductionBaseTrans):
'''Provides a transformation from a PSyIR PRODUCT IntrinsicCall node to
an equivalent PSyIR loop structure that is suitable for running in
parallel on CPUs and GPUs. Validity checks are also performed.
If PRODUCT contains a single positional argument which is an array,
the maximum value of all of the elements in the array is returned
in the the scalar R.
.. code-block:: fortran
R = PRODUCT(ARRAY)
For example, if the array is two dimensional, the equivalent code
for real data is:
.. code-block:: fortran
R = 1.0
DO J=LBOUND(ARRAY,2),UBOUND(ARRAY,2)
DO I=LBOUND(ARRAY,1),UBOUND(ARRAY,1)
R = R * ARRAY(I,J)
If the mask argument is provided then the mask is used to
determine whether the product is applied:
.. code-block:: fortran
R = PRODUCT(ARRAY, mask=MOD(ARRAY, 2.0)==1)
If the array is two dimensional, the equivalent code
for real data is:
.. code-block:: fortran
R = 1.0
DO J=LBOUND(ARRAY,2),UBOUND(ARRAY,2)
DO I=LBOUND(ARRAY,1),UBOUND(ARRAY,1)
IF (MOD(ARRAY(I,J), 2.0)==1) THEN
R = R * ARRAY(I,J)
The dimension argument is currently not supported and will result
in a TransformationError exception being raised.
.. code-block:: fortran
R = PRODUCT(ARRAY, dimension=2)
The array passed to PRODUCT may use any combination of array
syntax, array notation, array sections and scalar bounds:
.. code-block:: fortran
R = PRODUCT(ARRAY) ! array syntax
R = PRODUCT(ARRAY(:,:)) ! array notation
R = PRODUCT(ARRAY(1:10,lo:hi)) ! array sections
R = PRODUCT(ARRAY(1:10,:)) ! mix of array section and array notation
R = PRODUCT(ARRAY(1:10,2)) ! mix of array section and scalar bound
An example use of this transformation is given below:
>>> from psyclone.psyir.backend.fortran import FortranWriter
>>> from psyclone.psyir.frontend.fortran import FortranReader
>>> from psyclone.psyir.transformations import Product2LoopTrans
>>> code = ("subroutine product_test(array)\\n"
... " real :: array(10,10)\\n"
... " real :: result\\n"
... " result = product(array)\\n"
... "end subroutine\\n")
>>> psyir = FortranReader().psyir_from_source(code)
>>> product_node = psyir.children[0].children[0].children[1]
>>> Product2LoopTrans().apply(product_node)
>>> print(FortranWriter()(psyir))
subroutine product_test(array)
real, dimension(10,10) :: array
real :: result
integer :: idx
integer :: idx_1
real :: reduction_var
<BLANKLINE>
reduction_var = 1.0
do idx = 1, 10, 1
do idx_1 = 1, 10, 1
reduction_var = reduction_var * array(idx_1,idx)
enddo
enddo
result = reduction_var
<BLANKLINE>
end subroutine product_test
<BLANKLINE>
'''
_INTRINSIC_NAME = "PRODUCT"
_INTRINSIC_TYPE = IntrinsicCall.Intrinsic.PRODUCT
def _loop_body(self, lhs, rhs):
'''Provide the body of the nested loop that computes the maximum value
of the lhs and rhs.
:param lhs: the lhs value for the product operation.
:type lhs: :py:class:`psyclone.psyir.nodes.Node`
:param rhs: the rhs value for the product operation.
:type rhs: :py:class:`psyclone.psyir.nodes.Node`
:returns: the product of the lhs and rhs.
:rtype: :py:class:`psyclone.psyir.nodes.BinaryOperation`
'''
# return lhs * rhs
return BinaryOperation.create(BinaryOperation.Operator.MUL, lhs, rhs)
def _init_var(self, reference):
'''The initial value for the variable that computes the product
of an array.
:param reference: the reference used to store the final result.
:type reference: :py:class:`psyclone.psyir.node.Reference`
:returns: PSyIR for the value to initialise the variable that
computes the product.
:rtype: :py:class:`psyclone.psyir.nodes.IntrinsicCall`
'''
intrinsic = reference.datatype.intrinsic
precision = reference.datatype.precision
scalar_type = ScalarType(intrinsic, precision)
if intrinsic == ScalarType.Intrinsic.REAL:
value_str = "1.0"
elif intrinsic == ScalarType.Intrinsic.INTEGER:
value_str = "1"
# Note, the validate method guarantees that an else branch is
# not required.
return Literal(value_str, scalar_type)
[docs]
def apply(self, node, options=None, **kwargs):
'''
Apply the Product2LoopTrans to the input node.
:param node: the PRODUCT intrinsic to transform.
:type node: :py:class:`psyclone.psyir.nodes.IntrinsicCall`
:param options: options for the transformation.
:type options: Optional[Dict[str, Any]]
'''
super().apply(node, options=options, **kwargs)
# For AutoAPI auto-documentation generation.
__all__ = ["Product2LoopTrans"]
