Source code for psyclone.psyir.transformations.intrinsics.product2loop_trans

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# Author: R. W. Ford, STFC Daresbury Lab

'''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


[docs]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 <BLANKLINE> result = 1.0 do idx = 1, 10, 1 do idx_1 = 1, 10, 1 result = result * array(idx_1,idx) enddo enddo <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)