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llvm / llvm-project / flang / refs/heads/main / . / lib / Optimizer / HLFIR / IR / HLFIROps.cpp
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//===-- HLFIROps.cpp ------------------------------------------------------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
//
// Coding style: https://mlir.llvm.org/getting_started/DeveloperGuide/
//
//===----------------------------------------------------------------------===//
#include "flang/Optimizer/HLFIR/HLFIROps.h"
#include "flang/Optimizer/Dialect/FIROpsSupport.h"
#include "flang/Optimizer/Dialect/FIRType.h"
#include "flang/Optimizer/Dialect/Support/FIRContext.h"
#include "flang/Optimizer/HLFIR/HLFIRDialect.h"
#include "mlir/IR/Builders.h"
#include "mlir/IR/BuiltinAttributes.h"
#include "mlir/IR/BuiltinTypes.h"
#include "mlir/IR/DialectImplementation.h"
#include "mlir/IR/Matchers.h"
#include "mlir/IR/OpImplementation.h"
#include "llvm/ADT/APInt.h"
#include "llvm/ADT/TypeSwitch.h"
#include "llvm/Support/CommandLine.h"
#include <iterator>
#include <mlir/Interfaces/SideEffectInterfaces.h>
#include <optional>
#include <tuple>
static llvm::cl::opt<bool> useStrictIntrinsicVerifier(
"strict-intrinsic-verifier", llvm::cl::init(false),
llvm::cl::desc("use stricter verifier for HLFIR intrinsic operations"));
/// generic implementation of the memory side effects interface for hlfir
/// transformational intrinsic operations
static void
getIntrinsicEffects(mlir::Operation *self,
llvm::SmallVectorImpl<mlir::SideEffects::EffectInstance<
mlir::MemoryEffects::Effect>> &effects) {
// allocation effect if we return an expr
assert(self->getNumResults() == 1 &&
"hlfir intrinsic ops only produce 1 result");
if (mlir::isa<hlfir::ExprType>(self->getResult(0).getType()))
effects.emplace_back(mlir::MemoryEffects::Allocate::get(),
self->getResult(0),
mlir::SideEffects::DefaultResource::get());
// read effect if we read from a pointer or refference type
// or a box who'se pointer is read from inside of the intrinsic so that
// loop conflicts can be detected in code like
// hlfir.region_assign {
// %2 = hlfir.transpose %0#0 : (!fir.box<!fir.array<?x?xf32>>) ->
// !hlfir.expr<?x?xf32> hlfir.yield %2 : !hlfir.expr<?x?xf32> cleanup {
// hlfir.destroy %2 : !hlfir.expr<?x?xf32>
// }
// } to {
// hlfir.yield %0#0 : !fir.box<!fir.array<?x?xf32>>
// }
for (mlir::Value operand : self->getOperands()) {
mlir::Type opTy = operand.getType();
if (fir::isa_ref_type(opTy) || fir::isa_box_type(opTy))
effects.emplace_back(mlir::MemoryEffects::Read::get(), operand,
mlir::SideEffects::DefaultResource::get());
}
}
//===----------------------------------------------------------------------===//
// DeclareOp
//===----------------------------------------------------------------------===//
/// Is this a fir.[ref/ptr/heap]<fir.[box/class]<fir.heap<T>>> type?
static bool isAllocatableBoxRef(mlir::Type type) {
fir::BaseBoxType boxType =
mlir::dyn_cast_or_null<fir::BaseBoxType>(fir::dyn_cast_ptrEleTy(type));
return boxType && mlir::isa<fir::HeapType>(boxType.getEleTy());
}
mlir::LogicalResult hlfir::AssignOp::verify() {
mlir::Type lhsType = getLhs().getType();
if (isAllocatableAssignment() && !isAllocatableBoxRef(lhsType))
return emitOpError("lhs must be an allocatable when `realloc` is set");
if (mustKeepLhsLengthInAllocatableAssignment() &&
!(isAllocatableAssignment() &&
mlir::isa<fir::CharacterType>(hlfir::getFortranElementType(lhsType))))
return emitOpError("`realloc` must be set and lhs must be a character "
"allocatable when `keep_lhs_length_if_realloc` is set");
return mlir::success();
}
//===----------------------------------------------------------------------===//
// DeclareOp
//===----------------------------------------------------------------------===//
/// Given a FIR memory type, and information about non default lower bounds, get
/// the related HLFIR variable type.
mlir::Type hlfir::DeclareOp::getHLFIRVariableType(mlir::Type inputType,
bool hasExplicitLowerBounds) {
mlir::Type type = fir::unwrapRefType(inputType);
if (mlir::isa<fir::BaseBoxType>(type))
return inputType;
if (auto charType = mlir::dyn_cast<fir::CharacterType>(type))
if (charType.hasDynamicLen())
return fir::BoxCharType::get(charType.getContext(), charType.getFKind());
auto seqType = mlir::dyn_cast<fir::SequenceType>(type);
bool hasDynamicExtents =
seqType && fir::sequenceWithNonConstantShape(seqType);
mlir::Type eleType = seqType ? seqType.getEleTy() : type;
bool hasDynamicLengthParams = fir::characterWithDynamicLen(eleType) ||
fir::isRecordWithTypeParameters(eleType);
if (hasExplicitLowerBounds || hasDynamicExtents || hasDynamicLengthParams)
return fir::BoxType::get(type);
return inputType;
}
static bool hasExplicitLowerBounds(mlir::Value shape) {
return shape &&
mlir::isa<fir::ShapeShiftType, fir::ShiftType>(shape.getType());
}
void hlfir::DeclareOp::build(mlir::OpBuilder &builder,
mlir::OperationState &result, mlir::Value memref,
llvm::StringRef uniq_name, mlir::Value shape,
mlir::ValueRange typeparams,
mlir::Value dummy_scope,
fir::FortranVariableFlagsAttr fortran_attrs,
cuf::DataAttributeAttr data_attr) {
auto nameAttr = builder.getStringAttr(uniq_name);
mlir::Type inputType = memref.getType();
bool hasExplicitLbs = hasExplicitLowerBounds(shape);
mlir::Type hlfirVariableType =
getHLFIRVariableType(inputType, hasExplicitLbs);
build(builder, result, {hlfirVariableType, inputType}, memref, shape,
typeparams, dummy_scope, nameAttr, fortran_attrs, data_attr);
}
mlir::LogicalResult hlfir::DeclareOp::verify() {
if (getMemref().getType() != getResult(1).getType())
return emitOpError("second result type must match input memref type");
mlir::Type hlfirVariableType = getHLFIRVariableType(
getMemref().getType(), hasExplicitLowerBounds(getShape()));
if (hlfirVariableType != getResult(0).getType())
return emitOpError("first result type is inconsistent with variable "
"properties: expected ")
<< hlfirVariableType;
// The rest of the argument verification is done by the
// FortranVariableInterface verifier.
auto fortranVar =
mlir::cast<fir::FortranVariableOpInterface>(this->getOperation());
return fortranVar.verifyDeclareLikeOpImpl(getMemref());
}
//===----------------------------------------------------------------------===//
// DesignateOp
//===----------------------------------------------------------------------===//
void hlfir::DesignateOp::build(
mlir::OpBuilder &builder, mlir::OperationState &result,
mlir::Type result_type, mlir::Value memref, llvm::StringRef component,
mlir::Value component_shape, llvm::ArrayRef<Subscript> subscripts,
mlir::ValueRange substring, std::optional<bool> complex_part,
mlir::Value shape, mlir::ValueRange typeparams,
fir::FortranVariableFlagsAttr fortran_attrs) {
auto componentAttr =
component.empty() ? mlir::StringAttr{} : builder.getStringAttr(component);
llvm::SmallVector<mlir::Value> indices;
llvm::SmallVector<bool> isTriplet;
for (auto subscript : subscripts) {
if (auto *triplet = std::get_if<Triplet>(&subscript)) {
isTriplet.push_back(true);
indices.push_back(std::get<0>(*triplet));
indices.push_back(std::get<1>(*triplet));
indices.push_back(std::get<2>(*triplet));
} else {
isTriplet.push_back(false);
indices.push_back(std::get<mlir::Value>(subscript));
}
}
auto isTripletAttr =
mlir::DenseBoolArrayAttr::get(builder.getContext(), isTriplet);
auto complexPartAttr =
complex_part.has_value()
? mlir::BoolAttr::get(builder.getContext(), *complex_part)
: mlir::BoolAttr{};
build(builder, result, result_type, memref, componentAttr, component_shape,
indices, isTripletAttr, substring, complexPartAttr, shape, typeparams,
fortran_attrs);
}
void hlfir::DesignateOp::build(mlir::OpBuilder &builder,
mlir::OperationState &result,
mlir::Type result_type, mlir::Value memref,
mlir::ValueRange indices,
mlir::ValueRange typeparams,
fir::FortranVariableFlagsAttr fortran_attrs) {
llvm::SmallVector<bool> isTriplet(indices.size(), false);
auto isTripletAttr =
mlir::DenseBoolArrayAttr::get(builder.getContext(), isTriplet);
build(builder, result, result_type, memref,
/*componentAttr=*/mlir::StringAttr{}, /*component_shape=*/mlir::Value{},
indices, isTripletAttr, /*substring*/ mlir::ValueRange{},
/*complexPartAttr=*/mlir::BoolAttr{}, /*shape=*/mlir::Value{},
typeparams, fortran_attrs);
}
static mlir::ParseResult parseDesignatorIndices(
mlir::OpAsmParser &parser,
llvm::SmallVectorImpl<mlir::OpAsmParser::UnresolvedOperand> &indices,
mlir::DenseBoolArrayAttr &isTripletAttr) {
llvm::SmallVector<bool> isTriplet;
if (mlir::succeeded(parser.parseOptionalLParen())) {
do {
mlir::OpAsmParser::UnresolvedOperand i1, i2, i3;
if (parser.parseOperand(i1))
return mlir::failure();
indices.push_back(i1);
if (mlir::succeeded(parser.parseOptionalColon())) {
if (parser.parseOperand(i2) || parser.parseColon() ||
parser.parseOperand(i3))
return mlir::failure();
indices.push_back(i2);
indices.push_back(i3);
isTriplet.push_back(true);
} else {
isTriplet.push_back(false);
}
} while (mlir::succeeded(parser.parseOptionalComma()));
if (parser.parseRParen())
return mlir::failure();
}
isTripletAttr = mlir::DenseBoolArrayAttr::get(parser.getContext(), isTriplet);
return mlir::success();
}
static void
printDesignatorIndices(mlir::OpAsmPrinter &p, hlfir::DesignateOp designateOp,
mlir::OperandRange indices,
const mlir::DenseBoolArrayAttr &isTripletAttr) {
if (!indices.empty()) {
p << '(';
unsigned i = 0;
for (auto isTriplet : isTripletAttr.asArrayRef()) {
if (isTriplet) {
assert(i + 2 < indices.size() && "ill-formed indices");
p << indices[i] << ":" << indices[i + 1] << ":" << indices[i + 2];
i += 3;
} else {
p << indices[i++];
}
if (i != indices.size())
p << ", ";
}
p << ')';
}
}
static mlir::ParseResult
parseDesignatorComplexPart(mlir::OpAsmParser &parser,
mlir::BoolAttr &complexPart) {
if (mlir::succeeded(parser.parseOptionalKeyword("imag")))
complexPart = mlir::BoolAttr::get(parser.getContext(), true);
else if (mlir::succeeded(parser.parseOptionalKeyword("real")))
complexPart = mlir::BoolAttr::get(parser.getContext(), false);
return mlir::success();
}
static void printDesignatorComplexPart(mlir::OpAsmPrinter &p,
hlfir::DesignateOp designateOp,
mlir::BoolAttr complexPartAttr) {
if (complexPartAttr) {
if (complexPartAttr.getValue())
p << "imag";
else
p << "real";
}
}
mlir::LogicalResult hlfir::DesignateOp::verify() {
mlir::Type memrefType = getMemref().getType();
mlir::Type baseType = getFortranElementOrSequenceType(memrefType);
mlir::Type baseElementType = fir::unwrapSequenceType(baseType);
unsigned numSubscripts = getIsTriplet().size();
unsigned subscriptsRank =
llvm::count_if(getIsTriplet(), [](bool isTriplet) { return isTriplet; });
unsigned outputRank = 0;
mlir::Type outputElementType;
bool hasBoxComponent;
if (getComponent()) {
auto component = getComponent().value();
auto recType = mlir::dyn_cast<fir::RecordType>(baseElementType);
if (!recType)
return emitOpError(
"component must be provided only when the memref is a derived type");
unsigned fieldIdx = recType.getFieldIndex(component);
if (fieldIdx > recType.getNumFields()) {
return emitOpError("component ")
<< component << " is not a component of memref element type "
<< recType;
}
mlir::Type fieldType = recType.getType(fieldIdx);
mlir::Type componentBaseType = getFortranElementOrSequenceType(fieldType);
hasBoxComponent = mlir::isa<fir::BaseBoxType>(fieldType);
if (mlir::isa<fir::SequenceType>(componentBaseType) &&
mlir::isa<fir::SequenceType>(baseType) &&
(numSubscripts == 0 || subscriptsRank > 0))
return emitOpError("indices must be provided and must not contain "
"triplets when both memref and component are arrays");
if (numSubscripts != 0) {
if (!mlir::isa<fir::SequenceType>(componentBaseType))
return emitOpError("indices must not be provided if component appears "
"and is not an array component");
if (!getComponentShape())
return emitOpError(
"component_shape must be provided when indexing a component");
mlir::Type compShapeType = getComponentShape().getType();
unsigned componentRank =
mlir::cast<fir::SequenceType>(componentBaseType).getDimension();
auto shapeType = mlir::dyn_cast<fir::ShapeType>(compShapeType);
auto shapeShiftType = mlir::dyn_cast<fir::ShapeShiftType>(compShapeType);
if (!((shapeType && shapeType.getRank() == componentRank) ||
(shapeShiftType && shapeShiftType.getRank() == componentRank)))
return emitOpError("component_shape must be a fir.shape or "
"fir.shapeshift with the rank of the component");
if (numSubscripts > componentRank)
return emitOpError("indices number must match array component rank");
}
if (auto baseSeqType = mlir::dyn_cast<fir::SequenceType>(baseType))
// This case must come first to cover "array%array_comp(i, j)" that has
// subscripts for the component but whose rank come from the base.
outputRank = baseSeqType.getDimension();
else if (numSubscripts != 0)
outputRank = subscriptsRank;
else if (auto componentSeqType =
mlir::dyn_cast<fir::SequenceType>(componentBaseType))
outputRank = componentSeqType.getDimension();
outputElementType = fir::unwrapSequenceType(componentBaseType);
} else {
outputElementType = baseElementType;
unsigned baseTypeRank =
mlir::isa<fir::SequenceType>(baseType)
? mlir::cast<fir::SequenceType>(baseType).getDimension()
: 0;
if (numSubscripts != 0) {
if (baseTypeRank != numSubscripts)
return emitOpError("indices number must match memref rank");
outputRank = subscriptsRank;
} else if (auto baseSeqType = mlir::dyn_cast<fir::SequenceType>(baseType)) {
outputRank = baseSeqType.getDimension();
}
}
if (!getSubstring().empty()) {
if (!mlir::isa<fir::CharacterType>(outputElementType))
return emitOpError("memref or component must have character type if "
"substring indices are provided");
if (getSubstring().size() != 2)
return emitOpError("substring must contain 2 indices when provided");
}
if (getComplexPart()) {
if (!fir::isa_complex(outputElementType))
return emitOpError("memref or component must have complex type if "
"complex_part is provided");
if (auto firCplx = mlir::dyn_cast<fir::ComplexType>(outputElementType))
outputElementType = firCplx.getElementType();
else
outputElementType =
mlir::cast<mlir::ComplexType>(outputElementType).getElementType();
}
mlir::Type resultBaseType =
getFortranElementOrSequenceType(getResult().getType());
unsigned resultRank = 0;
if (auto resultSeqType = mlir::dyn_cast<fir::SequenceType>(resultBaseType))
resultRank = resultSeqType.getDimension();
if (resultRank != outputRank)
return emitOpError("result type rank is not consistent with operands, "
"expected rank ")
<< outputRank;
mlir::Type resultElementType = fir::unwrapSequenceType(resultBaseType);
// result type must match the one that was inferred here, except the character
// length may differ because of substrings.
if (resultElementType != outputElementType &&
!(mlir::isa<fir::CharacterType>(resultElementType) &&
mlir::isa<fir::CharacterType>(outputElementType)) &&
!(mlir::isa<mlir::FloatType>(resultElementType) &&
mlir::isa<fir::RealType>(outputElementType)))
return emitOpError(
"result element type is not consistent with operands, expected ")
<< outputElementType;
if (isBoxAddressType(getResult().getType())) {
if (!hasBoxComponent || numSubscripts != 0 || !getSubstring().empty() ||
getComplexPart())
return emitOpError(
"result type must only be a box address type if it designates a "
"component that is a fir.box or fir.class and if there are no "
"indices, substrings, and complex part");
} else {
if ((resultRank == 0) != !getShape())
return emitOpError("shape must be provided if and only if the result is "
"an array that is not a box address");
if (resultRank != 0) {
auto shapeType = mlir::dyn_cast<fir::ShapeType>(getShape().getType());
auto shapeShiftType =
mlir::dyn_cast<fir::ShapeShiftType>(getShape().getType());
if (!((shapeType && shapeType.getRank() == resultRank) ||
(shapeShiftType && shapeShiftType.getRank() == resultRank)))
return emitOpError("shape must be a fir.shape or fir.shapeshift with "
"the rank of the result");
}
auto numLenParam = getTypeparams().size();
if (mlir::isa<fir::CharacterType>(outputElementType)) {
if (numLenParam != 1)
return emitOpError("must be provided one length parameter when the "
"result is a character");
} else if (fir::isRecordWithTypeParameters(outputElementType)) {
if (numLenParam !=
mlir::cast<fir::RecordType>(outputElementType).getNumLenParams())
return emitOpError("must be provided the same number of length "
"parameters as in the result derived type");
} else if (numLenParam != 0) {
return emitOpError("must not be provided length parameters if the result "
"type does not have length parameters");
}
}
return mlir::success();
}
//===----------------------------------------------------------------------===//
// ParentComponentOp
//===----------------------------------------------------------------------===//
mlir::LogicalResult hlfir::ParentComponentOp::verify() {
mlir::Type baseType =
hlfir::getFortranElementOrSequenceType(getMemref().getType());
auto maybeInputSeqType = mlir::dyn_cast<fir::SequenceType>(baseType);
unsigned inputTypeRank =
maybeInputSeqType ? maybeInputSeqType.getDimension() : 0;
unsigned shapeRank = 0;
if (mlir::Value shape = getShape())
if (auto shapeType = mlir::dyn_cast<fir::ShapeType>(shape.getType()))
shapeRank = shapeType.getRank();
if (inputTypeRank != shapeRank)
return emitOpError(
"must be provided a shape if and only if the base is an array");
mlir::Type outputBaseType = hlfir::getFortranElementOrSequenceType(getType());
auto maybeOutputSeqType = mlir::dyn_cast<fir::SequenceType>(outputBaseType);
unsigned outputTypeRank =
maybeOutputSeqType ? maybeOutputSeqType.getDimension() : 0;
if (inputTypeRank != outputTypeRank)
return emitOpError("result type rank must match input type rank");
if (maybeOutputSeqType && maybeInputSeqType)
for (auto [inputDim, outputDim] :
llvm::zip(maybeInputSeqType.getShape(), maybeOutputSeqType.getShape()))
if (inputDim != fir::SequenceType::getUnknownExtent() &&
outputDim != fir::SequenceType::getUnknownExtent())
if (inputDim != outputDim)
return emitOpError(
"result type extents are inconsistent with memref type");
fir::RecordType baseRecType =
mlir::dyn_cast<fir::RecordType>(hlfir::getFortranElementType(baseType));
fir::RecordType outRecType = mlir::dyn_cast<fir::RecordType>(
hlfir::getFortranElementType(outputBaseType));
if (!baseRecType || !outRecType)
return emitOpError("result type and input type must be derived types");
// Note: result should not be a fir.class: its dynamic type is being set to
// the parent type and allowing fir.class would break the operation codegen:
// it would keep the input dynamic type.
if (mlir::isa<fir::ClassType>(getType()))
return emitOpError("result type must not be polymorphic");
// The array results are known to not be dis-contiguous in most cases (the
// exception being if the parent type was extended by a type without any
// components): require a fir.box to be used for the result to carry the
// strides.
if (!mlir::isa<fir::BoxType>(getType()) &&
(outputTypeRank != 0 || fir::isRecordWithTypeParameters(outRecType)))
return emitOpError("result type must be a fir.box if the result is an "
"array or has length parameters");
return mlir::success();
}
//===----------------------------------------------------------------------===//
// LogicalReductionOp
//===----------------------------------------------------------------------===//
template <typename LogicalReductionOp>
static mlir::LogicalResult
verifyLogicalReductionOp(LogicalReductionOp reductionOp) {
mlir::Operation *op = reductionOp->getOperation();
auto results = op->getResultTypes();
assert(results.size() == 1);
mlir::Value mask = reductionOp->getMask();
mlir::Value dim = reductionOp->getDim();
fir::SequenceType maskTy = mlir::cast<fir::SequenceType>(
hlfir::getFortranElementOrSequenceType(mask.getType()));
mlir::Type logicalTy = maskTy.getEleTy();
llvm::ArrayRef<int64_t> maskShape = maskTy.getShape();
mlir::Type resultType = results[0];
if (mlir::isa<fir::LogicalType>(resultType)) {
// Result is of the same type as MASK
if ((resultType != logicalTy) && useStrictIntrinsicVerifier)
return reductionOp->emitOpError(
"result must have the same element type as MASK argument");
} else if (auto resultExpr =
mlir::dyn_cast_or_null<hlfir::ExprType>(resultType)) {
// Result should only be in hlfir.expr form if it is an array
if (maskShape.size() > 1 && dim != nullptr) {
if (!resultExpr.isArray())
return reductionOp->emitOpError("result must be an array");
if ((resultExpr.getEleTy() != logicalTy) && useStrictIntrinsicVerifier)
return reductionOp->emitOpError(
"result must have the same element type as MASK argument");
llvm::ArrayRef<int64_t> resultShape = resultExpr.getShape();
// Result has rank n-1
if (resultShape.size() != (maskShape.size() - 1))
return reductionOp->emitOpError(
"result rank must be one less than MASK");
} else {
return reductionOp->emitOpError("result must be of logical type");
}
} else {
return reductionOp->emitOpError("result must be of logical type");
}
return mlir::success();
}
//===----------------------------------------------------------------------===//
// AllOp
//===----------------------------------------------------------------------===//
mlir::LogicalResult hlfir::AllOp::verify() {
return verifyLogicalReductionOp<hlfir::AllOp *>(this);
}
void hlfir::AllOp::getEffects(
llvm::SmallVectorImpl<
mlir::SideEffects::EffectInstance<mlir::MemoryEffects::Effect>>
&effects) {
getIntrinsicEffects(getOperation(), effects);
}
//===----------------------------------------------------------------------===//
// AnyOp
//===----------------------------------------------------------------------===//
mlir::LogicalResult hlfir::AnyOp::verify() {
return verifyLogicalReductionOp<hlfir::AnyOp *>(this);
}
void hlfir::AnyOp::getEffects(
llvm::SmallVectorImpl<
mlir::SideEffects::EffectInstance<mlir::MemoryEffects::Effect>>
&effects) {
getIntrinsicEffects(getOperation(), effects);
}
//===----------------------------------------------------------------------===//
// CountOp
//===----------------------------------------------------------------------===//
mlir::LogicalResult hlfir::CountOp::verify() {
mlir::Operation *op = getOperation();
auto results = op->getResultTypes();
assert(results.size() == 1);
mlir::Value mask = getMask();
mlir::Value dim = getDim();
fir::SequenceType maskTy = mlir::cast<fir::SequenceType>(
hlfir::getFortranElementOrSequenceType(mask.getType()));
llvm::ArrayRef<int64_t> maskShape = maskTy.getShape();
mlir::Type resultType = results[0];
if (auto resultExpr = mlir::dyn_cast_or_null<hlfir::ExprType>(resultType)) {
if (maskShape.size() > 1 && dim != nullptr) {
if (!resultExpr.isArray())
return emitOpError("result must be an array");
llvm::ArrayRef<int64_t> resultShape = resultExpr.getShape();
// Result has rank n-1
if (resultShape.size() != (maskShape.size() - 1))
return emitOpError("result rank must be one less than MASK");
} else {
return emitOpError("result must be of numerical array type");
}
} else if (!hlfir::isFortranScalarNumericalType(resultType)) {
return emitOpError("result must be of numerical scalar type");
}
return mlir::success();
}
void hlfir::CountOp::getEffects(
llvm::SmallVectorImpl<
mlir::SideEffects::EffectInstance<mlir::MemoryEffects::Effect>>
&effects) {
getIntrinsicEffects(getOperation(), effects);
}
//===----------------------------------------------------------------------===//
// ConcatOp
//===----------------------------------------------------------------------===//
static unsigned getCharacterKind(mlir::Type t) {
return mlir::cast<fir::CharacterType>(hlfir::getFortranElementType(t))
.getFKind();
}
static std::optional<fir::CharacterType::LenType>
getCharacterLengthIfStatic(mlir::Type t) {
if (auto charType =
mlir::dyn_cast<fir::CharacterType>(hlfir::getFortranElementType(t)))
if (charType.hasConstantLen())
return charType.getLen();
return std::nullopt;
}
mlir::LogicalResult hlfir::ConcatOp::verify() {
if (getStrings().size() < 2)
return emitOpError("must be provided at least two string operands");
unsigned kind = getCharacterKind(getResult().getType());
for (auto string : getStrings())
if (kind != getCharacterKind(string.getType()))
return emitOpError("strings must have the same KIND as the result type");
return mlir::success();
}
void hlfir::ConcatOp::build(mlir::OpBuilder &builder,
mlir::OperationState &result,
mlir::ValueRange strings, mlir::Value len) {
fir::CharacterType::LenType resultTypeLen = 0;
assert(!strings.empty() && "must contain operands");
unsigned kind = getCharacterKind(strings[0].getType());
for (auto string : strings)
if (auto cstLen = getCharacterLengthIfStatic(string.getType())) {
resultTypeLen += *cstLen;
} else {
resultTypeLen = fir::CharacterType::unknownLen();
break;
}
auto resultType = hlfir::ExprType::get(
builder.getContext(), hlfir::ExprType::Shape{},
fir::CharacterType::get(builder.getContext(), kind, resultTypeLen),
false);
build(builder, result, resultType, strings, len);
}
void hlfir::ConcatOp::getEffects(
llvm::SmallVectorImpl<
mlir::SideEffects::EffectInstance<mlir::MemoryEffects::Effect>>
&effects) {
getIntrinsicEffects(getOperation(), effects);
}
//===----------------------------------------------------------------------===//
// NumericalReductionOp
//===----------------------------------------------------------------------===//
template <typename NumericalReductionOp>
static mlir::LogicalResult
verifyArrayAndMaskForReductionOp(NumericalReductionOp reductionOp) {
mlir::Value array = reductionOp->getArray();
mlir::Value mask = reductionOp->getMask();
fir::SequenceType arrayTy = mlir::cast<fir::SequenceType>(
hlfir::getFortranElementOrSequenceType(array.getType()));
llvm::ArrayRef<int64_t> arrayShape = arrayTy.getShape();
if (mask) {
fir::SequenceType maskSeq = mlir::dyn_cast<fir::SequenceType>(
hlfir::getFortranElementOrSequenceType(mask.getType()));
llvm::ArrayRef<int64_t> maskShape;
if (maskSeq)
maskShape = maskSeq.getShape();
if (!maskShape.empty()) {
if (maskShape.size() != arrayShape.size())
return reductionOp->emitWarning("MASK must be conformable to ARRAY");
if (useStrictIntrinsicVerifier) {
static_assert(fir::SequenceType::getUnknownExtent() ==
hlfir::ExprType::getUnknownExtent());
constexpr int64_t unknownExtent = fir::SequenceType::getUnknownExtent();
for (std::size_t i = 0; i < arrayShape.size(); ++i) {
int64_t arrayExtent = arrayShape[i];
int64_t maskExtent = maskShape[i];
if ((arrayExtent != maskExtent) && (arrayExtent != unknownExtent) &&
(maskExtent != unknownExtent))
return reductionOp->emitWarning(
"MASK must be conformable to ARRAY");
}
}
}
}
return mlir::success();
}
template <typename NumericalReductionOp>
static mlir::LogicalResult
verifyNumericalReductionOp(NumericalReductionOp reductionOp) {
mlir::Operation *op = reductionOp->getOperation();
auto results = op->getResultTypes();
assert(results.size() == 1);
auto res = verifyArrayAndMaskForReductionOp(reductionOp);
if (failed(res))
return res;
mlir::Value array = reductionOp->getArray();
mlir::Value dim = reductionOp->getDim();
fir::SequenceType arrayTy = mlir::cast<fir::SequenceType>(
hlfir::getFortranElementOrSequenceType(array.getType()));
mlir::Type numTy = arrayTy.getEleTy();
llvm::ArrayRef<int64_t> arrayShape = arrayTy.getShape();
mlir::Type resultType = results[0];
if (hlfir::isFortranScalarNumericalType(resultType)) {
// Result is of the same type as ARRAY
if ((resultType != numTy) && useStrictIntrinsicVerifier)
return reductionOp->emitOpError(
"result must have the same element type as ARRAY argument");
} else if (auto resultExpr =
mlir::dyn_cast_or_null<hlfir::ExprType>(resultType)) {
if (arrayShape.size() > 1 && dim != nullptr) {
if (!resultExpr.isArray())
return reductionOp->emitOpError("result must be an array");
if ((resultExpr.getEleTy() != numTy) && useStrictIntrinsicVerifier)
return reductionOp->emitOpError(
"result must have the same element type as ARRAY argument");
llvm::ArrayRef<int64_t> resultShape = resultExpr.getShape();
// Result has rank n-1
if (resultShape.size() != (arrayShape.size() - 1))
return reductionOp->emitOpError(
"result rank must be one less than ARRAY");
} else {
return reductionOp->emitOpError(
"result must be of numerical scalar type");
}
} else {
return reductionOp->emitOpError("result must be of numerical scalar type");
}
return mlir::success();
}
//===----------------------------------------------------------------------===//
// ProductOp
//===----------------------------------------------------------------------===//
mlir::LogicalResult hlfir::ProductOp::verify() {
return verifyNumericalReductionOp<hlfir::ProductOp *>(this);
}
void hlfir::ProductOp::getEffects(
llvm::SmallVectorImpl<
mlir::SideEffects::EffectInstance<mlir::MemoryEffects::Effect>>
&effects) {
getIntrinsicEffects(getOperation(), effects);
}
//===----------------------------------------------------------------------===//
// CharacterReductionOp
//===----------------------------------------------------------------------===//
template <typename CharacterReductionOp>
static mlir::LogicalResult
verifyCharacterReductionOp(CharacterReductionOp reductionOp) {
mlir::Operation *op = reductionOp->getOperation();
auto results = op->getResultTypes();
assert(results.size() == 1);
auto res = verifyArrayAndMaskForReductionOp(reductionOp);
if (failed(res))
return res;
mlir::Value array = reductionOp->getArray();
mlir::Value dim = reductionOp->getDim();
fir::SequenceType arrayTy = mlir::cast<fir::SequenceType>(
hlfir::getFortranElementOrSequenceType(array.getType()));
mlir::Type numTy = arrayTy.getEleTy();
llvm::ArrayRef<int64_t> arrayShape = arrayTy.getShape();
auto resultExpr = mlir::cast<hlfir::ExprType>(results[0]);
mlir::Type resultType = resultExpr.getEleTy();
assert(mlir::isa<fir::CharacterType>(resultType) &&
"result must be character");
// Result is of the same type as ARRAY
if ((resultType != numTy) && useStrictIntrinsicVerifier)
return reductionOp->emitOpError(
"result must have the same element type as ARRAY argument");
if (arrayShape.size() > 1 && dim != nullptr) {
if (!resultExpr.isArray())
return reductionOp->emitOpError("result must be an array");
llvm::ArrayRef<int64_t> resultShape = resultExpr.getShape();
// Result has rank n-1
if (resultShape.size() != (arrayShape.size() - 1))
return reductionOp->emitOpError(
"result rank must be one less than ARRAY");
} else if (!resultExpr.isScalar()) {
return reductionOp->emitOpError("result must be scalar character");
}
return mlir::success();
}
//===----------------------------------------------------------------------===//
// MaxvalOp
//===----------------------------------------------------------------------===//
mlir::LogicalResult hlfir::MaxvalOp::verify() {
mlir::Operation *op = getOperation();
auto results = op->getResultTypes();
assert(results.size() == 1);
auto resultExpr = mlir::dyn_cast<hlfir::ExprType>(results[0]);
if (resultExpr && mlir::isa<fir::CharacterType>(resultExpr.getEleTy())) {
return verifyCharacterReductionOp<hlfir::MaxvalOp *>(this);
}
return verifyNumericalReductionOp<hlfir::MaxvalOp *>(this);
}
void hlfir::MaxvalOp::getEffects(
llvm::SmallVectorImpl<
mlir::SideEffects::EffectInstance<mlir::MemoryEffects::Effect>>
&effects) {
getIntrinsicEffects(getOperation(), effects);
}
//===----------------------------------------------------------------------===//
// MinvalOp
//===----------------------------------------------------------------------===//
mlir::LogicalResult hlfir::MinvalOp::verify() {
mlir::Operation *op = getOperation();
auto results = op->getResultTypes();
assert(results.size() == 1);
auto resultExpr = mlir::dyn_cast<hlfir::ExprType>(results[0]);
if (resultExpr && mlir::isa<fir::CharacterType>(resultExpr.getEleTy())) {
return verifyCharacterReductionOp<hlfir::MinvalOp *>(this);
}
return verifyNumericalReductionOp<hlfir::MinvalOp *>(this);
}
void hlfir::MinvalOp::getEffects(
llvm::SmallVectorImpl<
mlir::SideEffects::EffectInstance<mlir::MemoryEffects::Effect>>
&effects) {
getIntrinsicEffects(getOperation(), effects);
}
//===----------------------------------------------------------------------===//
// MinlocOp
//===----------------------------------------------------------------------===//
template <typename NumericalReductionOp>
static mlir::LogicalResult
verifyResultForMinMaxLoc(NumericalReductionOp reductionOp) {
mlir::Operation *op = reductionOp->getOperation();
auto results = op->getResultTypes();
assert(results.size() == 1);
mlir::Value array = reductionOp->getArray();
mlir::Value dim = reductionOp->getDim();
fir::SequenceType arrayTy = mlir::cast<fir::SequenceType>(
hlfir::getFortranElementOrSequenceType(array.getType()));
llvm::ArrayRef<int64_t> arrayShape = arrayTy.getShape();
mlir::Type resultType = results[0];
if (dim && arrayShape.size() == 1) {
if (!fir::isa_integer(resultType))
return reductionOp->emitOpError("result must be scalar integer");
} else if (auto resultExpr =
mlir::dyn_cast_or_null<hlfir::ExprType>(resultType)) {
if (!resultExpr.isArray())
return reductionOp->emitOpError("result must be an array");
if (!fir::isa_integer(resultExpr.getEleTy()))
return reductionOp->emitOpError("result must have integer elements");
llvm::ArrayRef<int64_t> resultShape = resultExpr.getShape();
// With dim the result has rank n-1
if (dim && resultShape.size() != (arrayShape.size() - 1))
return reductionOp->emitOpError(
"result rank must be one less than ARRAY");
// With dim the result has rank n
if (!dim && resultShape.size() != 1)
return reductionOp->emitOpError("result rank must be 1");
} else {
return reductionOp->emitOpError("result must be of numerical expr type");
}
return mlir::success();
}
mlir::LogicalResult hlfir::MinlocOp::verify() {
auto res = verifyArrayAndMaskForReductionOp(this);
if (failed(res))
return res;
return verifyResultForMinMaxLoc(this);
}
void hlfir::MinlocOp::getEffects(
llvm::SmallVectorImpl<
mlir::SideEffects::EffectInstance<mlir::MemoryEffects::Effect>>
&effects) {
getIntrinsicEffects(getOperation(), effects);
}
//===----------------------------------------------------------------------===//
// MaxlocOp
//===----------------------------------------------------------------------===//
mlir::LogicalResult hlfir::MaxlocOp::verify() {
auto res = verifyArrayAndMaskForReductionOp(this);
if (failed(res))
return res;
return verifyResultForMinMaxLoc(this);
}
void hlfir::MaxlocOp::getEffects(
llvm::SmallVectorImpl<
mlir::SideEffects::EffectInstance<mlir::MemoryEffects::Effect>>
&effects) {
getIntrinsicEffects(getOperation(), effects);
}
//===----------------------------------------------------------------------===//
// SetLengthOp
//===----------------------------------------------------------------------===//
void hlfir::SetLengthOp::build(mlir::OpBuilder &builder,
mlir::OperationState &result, mlir::Value string,
mlir::Value len) {
fir::CharacterType::LenType resultTypeLen = fir::CharacterType::unknownLen();
if (auto cstLen = fir::getIntIfConstant(len))
resultTypeLen = *cstLen;
unsigned kind = getCharacterKind(string.getType());
auto resultType = hlfir::ExprType::get(
builder.getContext(), hlfir::ExprType::Shape{},
fir::CharacterType::get(builder.getContext(), kind, resultTypeLen),
false);
build(builder, result, resultType, string, len);
}
void hlfir::SetLengthOp::getEffects(
llvm::SmallVectorImpl<
mlir::SideEffects::EffectInstance<mlir::MemoryEffects::Effect>>
&effects) {
getIntrinsicEffects(getOperation(), effects);
}
//===----------------------------------------------------------------------===//
// SumOp
//===----------------------------------------------------------------------===//
mlir::LogicalResult hlfir::SumOp::verify() {
return verifyNumericalReductionOp<hlfir::SumOp *>(this);
}
void hlfir::SumOp::getEffects(
llvm::SmallVectorImpl<
mlir::SideEffects::EffectInstance<mlir::MemoryEffects::Effect>>
&effects) {
getIntrinsicEffects(getOperation(), effects);
}
//===----------------------------------------------------------------------===//
// DotProductOp
//===----------------------------------------------------------------------===//
mlir::LogicalResult hlfir::DotProductOp::verify() {
mlir::Value lhs = getLhs();
mlir::Value rhs = getRhs();
fir::SequenceType lhsTy = mlir::cast<fir::SequenceType>(
hlfir::getFortranElementOrSequenceType(lhs.getType()));
fir::SequenceType rhsTy = mlir::cast<fir::SequenceType>(
hlfir::getFortranElementOrSequenceType(rhs.getType()));
llvm::ArrayRef<int64_t> lhsShape = lhsTy.getShape();
llvm::ArrayRef<int64_t> rhsShape = rhsTy.getShape();
std::size_t lhsRank = lhsShape.size();
std::size_t rhsRank = rhsShape.size();
mlir::Type lhsEleTy = lhsTy.getEleTy();
mlir::Type rhsEleTy = rhsTy.getEleTy();
mlir::Type resultTy = getResult().getType();
if ((lhsRank != 1) || (rhsRank != 1))
return emitOpError("both arrays must have rank 1");
int64_t lhsSize = lhsShape[0];
int64_t rhsSize = rhsShape[0];
constexpr int64_t unknownExtent = fir::SequenceType::getUnknownExtent();
if ((lhsSize != unknownExtent) && (rhsSize != unknownExtent) &&
(lhsSize != rhsSize) && useStrictIntrinsicVerifier)
return emitOpError("both arrays must have the same size");
if (useStrictIntrinsicVerifier) {
if (mlir::isa<fir::LogicalType>(lhsEleTy) !=
mlir::isa<fir::LogicalType>(rhsEleTy))
return emitOpError("if one array is logical, so should the other be");
if (mlir::isa<fir::LogicalType>(lhsEleTy) !=
mlir::isa<fir::LogicalType>(resultTy))
return emitOpError("the result type should be a logical only if the "
"argument types are logical");
}
if (!hlfir::isFortranScalarNumericalType(resultTy) &&
!mlir::isa<fir::LogicalType>(resultTy))
return emitOpError(
"the result must be of scalar numerical or logical type");
return mlir::success();
}
void hlfir::DotProductOp::getEffects(
llvm::SmallVectorImpl<
mlir::SideEffects::EffectInstance<mlir::MemoryEffects::Effect>>
&effects) {
getIntrinsicEffects(getOperation(), effects);
}
//===----------------------------------------------------------------------===//
// MatmulOp
//===----------------------------------------------------------------------===//
mlir::LogicalResult hlfir::MatmulOp::verify() {
mlir::Value lhs = getLhs();
mlir::Value rhs = getRhs();
fir::SequenceType lhsTy = mlir::cast<fir::SequenceType>(
hlfir::getFortranElementOrSequenceType(lhs.getType()));
fir::SequenceType rhsTy = mlir::cast<fir::SequenceType>(
hlfir::getFortranElementOrSequenceType(rhs.getType()));
llvm::ArrayRef<int64_t> lhsShape = lhsTy.getShape();
llvm::ArrayRef<int64_t> rhsShape = rhsTy.getShape();
std::size_t lhsRank = lhsShape.size();
std::size_t rhsRank = rhsShape.size();
mlir::Type lhsEleTy = lhsTy.getEleTy();
mlir::Type rhsEleTy = rhsTy.getEleTy();
hlfir::ExprType resultTy = mlir::cast<hlfir::ExprType>(getResult().getType());
llvm::ArrayRef<int64_t> resultShape = resultTy.getShape();
mlir::Type resultEleTy = resultTy.getEleTy();
if (((lhsRank != 1) && (lhsRank != 2)) || ((rhsRank != 1) && (rhsRank != 2)))
return emitOpError("array must have either rank 1 or rank 2");
if ((lhsRank == 1) && (rhsRank == 1))
return emitOpError("at least one array must have rank 2");
if (mlir::isa<fir::LogicalType>(lhsEleTy) !=
mlir::isa<fir::LogicalType>(rhsEleTy))
return emitOpError("if one array is logical, so should the other be");
if (!useStrictIntrinsicVerifier)
return mlir::success();
int64_t lastLhsDim = lhsShape[lhsRank - 1];
int64_t firstRhsDim = rhsShape[0];
constexpr int64_t unknownExtent = fir::SequenceType::getUnknownExtent();
if (lastLhsDim != firstRhsDim)
if ((lastLhsDim != unknownExtent) && (firstRhsDim != unknownExtent))
return emitOpError(
"the last dimension of LHS should match the first dimension of RHS");
if (mlir::isa<fir::LogicalType>(lhsEleTy) !=
mlir::isa<fir::LogicalType>(resultEleTy))
return emitOpError("the result type should be a logical only if the "
"argument types are logical");
llvm::SmallVector<int64_t, 2> expectedResultShape;
if (lhsRank == 2) {
if (rhsRank == 2) {
expectedResultShape.push_back(lhsShape[0]);
expectedResultShape.push_back(rhsShape[1]);
} else {
// rhsRank == 1
expectedResultShape.push_back(lhsShape[0]);
}
} else {
// lhsRank == 1
// rhsRank == 2
expectedResultShape.push_back(rhsShape[1]);
}
if (resultShape.size() != expectedResultShape.size())
return emitOpError("incorrect result shape");
if (resultShape[0] != expectedResultShape[0] &&
expectedResultShape[0] != unknownExtent)
return emitOpError("incorrect result shape");
if (resultShape.size() == 2 && resultShape[1] != expectedResultShape[1] &&
expectedResultShape[1] != unknownExtent)
return emitOpError("incorrect result shape");
return mlir::success();
}
mlir::LogicalResult
hlfir::MatmulOp::canonicalize(MatmulOp matmulOp,
mlir::PatternRewriter &rewriter) {
// the only two uses of the transposed matrix should be for the hlfir.matmul
// and hlfir.destroy
auto isOtherwiseUnused = [&](hlfir::TransposeOp transposeOp) -> bool {
std::size_t numUses = 0;
for (mlir::Operation *user : transposeOp.getResult().getUsers()) {
++numUses;
if (user == matmulOp)
continue;
if (mlir::dyn_cast_or_null<hlfir::DestroyOp>(user))
continue;
// some other use!
return false;
}
return numUses <= 2;
};
mlir::Value lhs = matmulOp.getLhs();
// Rewrite MATMUL(TRANSPOSE(lhs), rhs) => hlfir.matmul_transpose lhs, rhs
if (auto transposeOp = lhs.getDefiningOp<hlfir::TransposeOp>()) {
if (isOtherwiseUnused(transposeOp)) {
mlir::Location loc = matmulOp.getLoc();
mlir::Type resultTy = matmulOp.getResult().getType();
auto matmulTransposeOp = rewriter.create<hlfir::MatmulTransposeOp>(
loc, resultTy, transposeOp.getArray(), matmulOp.getRhs());
// we don't need to remove any hlfir.destroy because it will be needed for
// the new intrinsic result anyway
rewriter.replaceOp(matmulOp, matmulTransposeOp.getResult());
// but we do need to get rid of the hlfir.destroy for the hlfir.transpose
// result (which is entirely removed)
llvm::SmallVector<mlir::Operation *> users(
transposeOp->getResult(0).getUsers());
for (mlir::Operation *user : users)
if (auto destroyOp = mlir::dyn_cast_or_null<hlfir::DestroyOp>(user))
rewriter.eraseOp(destroyOp);
rewriter.eraseOp(transposeOp);
return mlir::success();
}
}
return mlir::failure();
}
void hlfir::MatmulOp::getEffects(
llvm::SmallVectorImpl<
mlir::SideEffects::EffectInstance<mlir::MemoryEffects::Effect>>
&effects) {
getIntrinsicEffects(getOperation(), effects);
}
//===----------------------------------------------------------------------===//
// TransposeOp
//===----------------------------------------------------------------------===//
mlir::LogicalResult hlfir::TransposeOp::verify() {
mlir::Value array = getArray();
fir::SequenceType arrayTy = mlir::cast<fir::SequenceType>(
hlfir::getFortranElementOrSequenceType(array.getType()));
llvm::ArrayRef<int64_t> inShape = arrayTy.getShape();
std::size_t rank = inShape.size();
mlir::Type eleTy = arrayTy.getEleTy();
hlfir::ExprType resultTy = mlir::cast<hlfir::ExprType>(getResult().getType());
llvm::ArrayRef<int64_t> resultShape = resultTy.getShape();
std::size_t resultRank = resultShape.size();
mlir::Type resultEleTy = resultTy.getEleTy();
if (rank != 2 || resultRank != 2)
return emitOpError("input and output arrays should have rank 2");
if (!useStrictIntrinsicVerifier)
return mlir::success();
constexpr int64_t unknownExtent = fir::SequenceType::getUnknownExtent();
if ((inShape[0] != resultShape[1]) && (inShape[0] != unknownExtent))
return emitOpError("output shape does not match input array");
if ((inShape[1] != resultShape[0]) && (inShape[1] != unknownExtent))
return emitOpError("output shape does not match input array");
if (eleTy != resultEleTy)
return emitOpError(
"input and output arrays should have the same element type");
return mlir::success();
}
void hlfir::TransposeOp::getEffects(
llvm::SmallVectorImpl<
mlir::SideEffects::EffectInstance<mlir::MemoryEffects::Effect>>
&effects) {
getIntrinsicEffects(getOperation(), effects);
}
//===----------------------------------------------------------------------===//
// MatmulTransposeOp
//===----------------------------------------------------------------------===//
mlir::LogicalResult hlfir::MatmulTransposeOp::verify() {
mlir::Value lhs = getLhs();
mlir::Value rhs = getRhs();
fir::SequenceType lhsTy = mlir::cast<fir::SequenceType>(
hlfir::getFortranElementOrSequenceType(lhs.getType()));
fir::SequenceType rhsTy = mlir::cast<fir::SequenceType>(
hlfir::getFortranElementOrSequenceType(rhs.getType()));
llvm::ArrayRef<int64_t> lhsShape = lhsTy.getShape();
llvm::ArrayRef<int64_t> rhsShape = rhsTy.getShape();
std::size_t lhsRank = lhsShape.size();
std::size_t rhsRank = rhsShape.size();
mlir::Type lhsEleTy = lhsTy.getEleTy();
mlir::Type rhsEleTy = rhsTy.getEleTy();
hlfir::ExprType resultTy = mlir::cast<hlfir::ExprType>(getResult().getType());
llvm::ArrayRef<int64_t> resultShape = resultTy.getShape();
mlir::Type resultEleTy = resultTy.getEleTy();
// lhs must have rank 2 for the transpose to be valid
if ((lhsRank != 2) || ((rhsRank != 1) && (rhsRank != 2)))
return emitOpError("array must have either rank 1 or rank 2");
if (!useStrictIntrinsicVerifier)
return mlir::success();
if (mlir::isa<fir::LogicalType>(lhsEleTy) !=
mlir::isa<fir::LogicalType>(rhsEleTy))
return emitOpError("if one array is logical, so should the other be");
// for matmul we compare the last dimension of lhs with the first dimension of
// rhs, but for MatmulTranspose, dimensions of lhs are inverted by the
// transpose
int64_t firstLhsDim = lhsShape[0];
int64_t firstRhsDim = rhsShape[0];
constexpr int64_t unknownExtent = fir::SequenceType::getUnknownExtent();
if (firstLhsDim != firstRhsDim)
if ((firstLhsDim != unknownExtent) && (firstRhsDim != unknownExtent))
return emitOpError(
"the first dimension of LHS should match the first dimension of RHS");
if (mlir::isa<fir::LogicalType>(lhsEleTy) !=
mlir::isa<fir::LogicalType>(resultEleTy))
return emitOpError("the result type should be a logical only if the "
"argument types are logical");
llvm::SmallVector<int64_t, 2> expectedResultShape;
if (rhsRank == 2) {
expectedResultShape.push_back(lhsShape[1]);
expectedResultShape.push_back(rhsShape[1]);
} else {
// rhsRank == 1
expectedResultShape.push_back(lhsShape[1]);
}
if (resultShape.size() != expectedResultShape.size())
return emitOpError("incorrect result shape");
if (resultShape[0] != expectedResultShape[0])
return emitOpError("incorrect result shape");
if (resultShape.size() == 2 && resultShape[1] != expectedResultShape[1])
return emitOpError("incorrect result shape");
return mlir::success();
}
void hlfir::MatmulTransposeOp::getEffects(
llvm::SmallVectorImpl<
mlir::SideEffects::EffectInstance<mlir::MemoryEffects::Effect>>
&effects) {
getIntrinsicEffects(getOperation(), effects);
}
//===----------------------------------------------------------------------===//
// AssociateOp
//===----------------------------------------------------------------------===//
void hlfir::AssociateOp::build(mlir::OpBuilder &builder,
mlir::OperationState &result, mlir::Value source,
llvm::StringRef uniq_name, mlir::Value shape,
mlir::ValueRange typeparams,
fir::FortranVariableFlagsAttr fortran_attrs) {
auto nameAttr = builder.getStringAttr(uniq_name);
mlir::Type dataType = getFortranElementOrSequenceType(source.getType());
// Preserve polymorphism of polymorphic expr.
mlir::Type firVarType;
auto sourceExprType = mlir::dyn_cast<hlfir::ExprType>(source.getType());
if (sourceExprType && sourceExprType.isPolymorphic())
firVarType = fir::ClassType::get(fir::HeapType::get(dataType));
else
firVarType = fir::ReferenceType::get(dataType);
mlir::Type hlfirVariableType =
DeclareOp::getHLFIRVariableType(firVarType, /*hasExplicitLbs=*/false);
mlir::Type i1Type = builder.getI1Type();
build(builder, result, {hlfirVariableType, firVarType, i1Type}, source, shape,
typeparams, nameAttr, fortran_attrs);
}
void hlfir::AssociateOp::build(
mlir::OpBuilder &builder, mlir::OperationState &result, mlir::Value source,
mlir::Value shape, mlir::ValueRange typeparams,
fir::FortranVariableFlagsAttr fortran_attrs,
llvm::ArrayRef<mlir::NamedAttribute> attributes) {
mlir::Type dataType = getFortranElementOrSequenceType(source.getType());
// Preserve polymorphism of polymorphic expr.
mlir::Type firVarType;
auto sourceExprType = mlir::dyn_cast<hlfir::ExprType>(source.getType());
if (sourceExprType && sourceExprType.isPolymorphic())
firVarType = fir::ClassType::get(fir::HeapType::get(dataType));
else
firVarType = fir::ReferenceType::get(dataType);
mlir::Type hlfirVariableType =
DeclareOp::getHLFIRVariableType(firVarType, /*hasExplicitLbs=*/false);
mlir::Type i1Type = builder.getI1Type();
build(builder, result, {hlfirVariableType, firVarType, i1Type}, source, shape,
typeparams, {}, fortran_attrs);
result.addAttributes(attributes);
}
//===----------------------------------------------------------------------===//
// EndAssociateOp
//===----------------------------------------------------------------------===//
void hlfir::EndAssociateOp::build(mlir::OpBuilder &builder,
mlir::OperationState &result,
hlfir::AssociateOp associate) {
mlir::Value hlfirBase = associate.getBase();
mlir::Value firBase = associate.getFirBase();
// If EndAssociateOp may need to initiate the deallocation
// of allocatable components, it has to have access to the variable
// definition, so we cannot use the FIR base as the operand.
return build(builder, result,
hlfir::mayHaveAllocatableComponent(hlfirBase.getType())
? hlfirBase
: firBase,
associate.getMustFreeStrorageFlag());
}
mlir::LogicalResult hlfir::EndAssociateOp::verify() {
mlir::Value var = getVar();
if (hlfir::mayHaveAllocatableComponent(var.getType()) &&
!hlfir::isFortranEntity(var))
return emitOpError("that requires components deallocation must have var "
"operand that is a Fortran entity");
return mlir::success();
}
//===----------------------------------------------------------------------===//
// AsExprOp
//===----------------------------------------------------------------------===//
void hlfir::AsExprOp::build(mlir::OpBuilder &builder,
mlir::OperationState &result, mlir::Value var,
mlir::Value mustFree) {
hlfir::ExprType::Shape typeShape;
bool isPolymorphic = fir::isPolymorphicType(var.getType());
mlir::Type type = getFortranElementOrSequenceType(var.getType());
if (auto seqType = mlir::dyn_cast<fir::SequenceType>(type)) {
typeShape.append(seqType.getShape().begin(), seqType.getShape().end());
type = seqType.getEleTy();
}
auto resultType = hlfir::ExprType::get(builder.getContext(), typeShape, type,
isPolymorphic);
return build(builder, result, resultType, var, mustFree);
}
void hlfir::AsExprOp::getEffects(
llvm::SmallVectorImpl<
mlir::SideEffects::EffectInstance<mlir::MemoryEffects::Effect>>
&effects) {
// this isn't a transformational intrinsic but follows the same pattern: it
// creates a hlfir.expr and so needs to have an allocation effect, plus it
// might have a pointer-like argument, in which case it has a read effect
// upon those
getIntrinsicEffects(getOperation(), effects);
}
//===----------------------------------------------------------------------===//
// ElementalOp
//===----------------------------------------------------------------------===//
/// Common builder for ElementalOp and ElementalAddrOp to add the arguments and
/// create the elemental body. Result and clean-up body must be handled in
/// specific builders.
template <typename Op>
static void buildElemental(mlir::OpBuilder &builder,
mlir::OperationState &odsState, mlir::Value shape,
mlir::Value mold, mlir::ValueRange typeparams,
bool isUnordered) {
odsState.addOperands(shape);
if (mold)
odsState.addOperands(mold);
odsState.addOperands(typeparams);
odsState.addAttribute(
Op::getOperandSegmentSizesAttrName(odsState.name),
builder.getDenseI32ArrayAttr({/*shape=*/1, (mold ? 1 : 0),
static_cast<int32_t>(typeparams.size())}));
if (isUnordered)
odsState.addAttribute(Op::getUnorderedAttrName(odsState.name),
isUnordered ? builder.getUnitAttr() : nullptr);
mlir::Region *bodyRegion = odsState.addRegion();
bodyRegion->push_back(new mlir::Block{});
if (auto shapeType = mlir::dyn_cast<fir::ShapeType>(shape.getType())) {
unsigned dim = shapeType.getRank();
mlir::Type indexType = builder.getIndexType();
for (unsigned d = 0; d < dim; ++d)
bodyRegion->front().addArgument(indexType, odsState.location);
}
}
void hlfir::ElementalOp::build(mlir::OpBuilder &builder,
mlir::OperationState &odsState,
mlir::Type resultType, mlir::Value shape,
mlir::Value mold, mlir::ValueRange typeparams,
bool isUnordered) {
odsState.addTypes(resultType);
buildElemental<hlfir::ElementalOp>(builder, odsState, shape, mold, typeparams,
isUnordered);
}
mlir::Value hlfir::ElementalOp::getElementEntity() {
return mlir::cast<hlfir::YieldElementOp>(getBody()->back()).getElementValue();
}
mlir::LogicalResult hlfir::ElementalOp::verify() {
mlir::Value mold = getMold();
hlfir::ExprType resultType = mlir::cast<hlfir::ExprType>(getType());
if (!!mold != resultType.isPolymorphic())
return emitOpError("result must be polymorphic when mold is present "
"and vice versa");
return mlir::success();
}
//===----------------------------------------------------------------------===//
// ApplyOp
//===----------------------------------------------------------------------===//
void hlfir::ApplyOp::build(mlir::OpBuilder &builder,
mlir::OperationState &odsState, mlir::Value expr,
mlir::ValueRange indices,
mlir::ValueRange typeparams) {
mlir::Type resultType = expr.getType();
if (auto exprType = mlir::dyn_cast<hlfir::ExprType>(resultType))
resultType = exprType.getElementExprType();
build(builder, odsState, resultType, expr, indices, typeparams);
}
//===----------------------------------------------------------------------===//
// NullOp
//===----------------------------------------------------------------------===//
void hlfir::NullOp::build(mlir::OpBuilder &builder,
mlir::OperationState &odsState) {
return build(builder, odsState,
fir::ReferenceType::get(builder.getNoneType()));
}
//===----------------------------------------------------------------------===//
// DestroyOp
//===----------------------------------------------------------------------===//
mlir::LogicalResult hlfir::DestroyOp::verify() {
if (mustFinalizeExpr()) {
mlir::Value expr = getExpr();
hlfir::ExprType exprTy = mlir::cast<hlfir::ExprType>(expr.getType());
mlir::Type elemTy = hlfir::getFortranElementType(exprTy);
if (!mlir::isa<fir::RecordType>(elemTy))
return emitOpError(
"the element type must be finalizable, when 'finalize' is set");
}
return mlir::success();
}
//===----------------------------------------------------------------------===//
// CopyInOp
//===----------------------------------------------------------------------===//
void hlfir::CopyInOp::build(mlir::OpBuilder &builder,
mlir::OperationState &odsState, mlir::Value var,
mlir::Value var_is_present) {
return build(builder, odsState, {var.getType(), builder.getI1Type()}, var,
var_is_present);
}
//===----------------------------------------------------------------------===//
// ShapeOfOp
//===----------------------------------------------------------------------===//
void hlfir::ShapeOfOp::build(mlir::OpBuilder &builder,
mlir::OperationState &result, mlir::Value expr) {
hlfir::ExprType exprTy = mlir::cast<hlfir::ExprType>(expr.getType());
mlir::Type type = fir::ShapeType::get(builder.getContext(), exprTy.getRank());
build(builder, result, type, expr);
}
std::size_t hlfir::ShapeOfOp::getRank() {
mlir::Type resTy = getResult().getType();
fir::ShapeType shape = mlir::cast<fir::ShapeType>(resTy);
return shape.getRank();
}
mlir::LogicalResult hlfir::ShapeOfOp::verify() {
mlir::Value expr = getExpr();
hlfir::ExprType exprTy = mlir::cast<hlfir::ExprType>(expr.getType());
std::size_t exprRank = exprTy.getShape().size();
if (exprRank == 0)
return emitOpError("cannot get the shape of a shape-less expression");
std::size_t shapeRank = getRank();
if (shapeRank != exprRank)
return emitOpError("result rank and expr rank do not match");
return mlir::success();
}
mlir::LogicalResult
hlfir::ShapeOfOp::canonicalize(ShapeOfOp shapeOf,
mlir::PatternRewriter &rewriter) {
// if extent information is available at compile time, immediately fold the
// hlfir.shape_of into a fir.shape
mlir::Location loc = shapeOf.getLoc();
hlfir::ExprType expr =
mlir::cast<hlfir::ExprType>(shapeOf.getExpr().getType());
mlir::Value shape = hlfir::genExprShape(rewriter, loc, expr);
if (!shape)
// shape information is not available at compile time
return mlir::LogicalResult::failure();
rewriter.replaceAllUsesWith(shapeOf.getResult(), shape);
rewriter.eraseOp(shapeOf);
return mlir::LogicalResult::success();
}
//===----------------------------------------------------------------------===//
// GetExtent
//===----------------------------------------------------------------------===//
void hlfir::GetExtentOp::build(mlir::OpBuilder &builder,
mlir::OperationState &result, mlir::Value shape,
unsigned dim) {
mlir::Type indexTy = builder.getIndexType();
mlir::IntegerAttr dimAttr = mlir::IntegerAttr::get(indexTy, dim);
build(builder, result, indexTy, shape, dimAttr);
}
mlir::LogicalResult hlfir::GetExtentOp::verify() {
fir::ShapeType shapeTy = mlir::cast<fir::ShapeType>(getShape().getType());
std::uint64_t rank = shapeTy.getRank();
llvm::APInt dim = getDim();
if (dim.sge(rank))
return emitOpError("dimension index out of bounds");
return mlir::success();
}
//===----------------------------------------------------------------------===//
// RegionAssignOp
//===----------------------------------------------------------------------===//
/// Add a fir.end terminator to a parsed region if it does not already has a
/// terminator.
static void ensureTerminator(mlir::Region &region, mlir::Builder &builder,
mlir::Location loc) {
// Borrow YielOp::ensureTerminator MLIR generated implementation to add a
// fir.end if there is no terminator. This has nothing to do with YielOp,
// other than the fact that yieldOp has the
// SingleBlocklicitTerminator<"fir::FirEndOp"> interface that
// cannot be added on other HLFIR operations with several regions which are
// not all terminated the same way.
hlfir::YieldOp::ensureTerminator(region, builder, loc);
}
mlir::ParseResult hlfir::RegionAssignOp::parse(mlir::OpAsmParser &parser,
mlir::OperationState &result) {
mlir::Region &rhsRegion = *result.addRegion();
if (parser.parseRegion(rhsRegion))
return mlir::failure();
mlir::Region &lhsRegion = *result.addRegion();
if (parser.parseKeyword("to") || parser.parseRegion(lhsRegion))
return mlir::failure();
mlir::Region &userDefinedAssignmentRegion = *result.addRegion();
if (succeeded(parser.parseOptionalKeyword("user_defined_assign"))) {
mlir::OpAsmParser::Argument rhsArg, lhsArg;
if (parser.parseLParen() || parser.parseArgument(rhsArg) ||
parser.parseColon() || parser.parseType(rhsArg.type) ||
parser.parseRParen() || parser.parseKeyword("to") ||
parser.parseLParen() || parser.parseArgument(lhsArg) ||
parser.parseColon() || parser.parseType(lhsArg.type) ||
parser.parseRParen())
return mlir::failure();
if (parser.parseRegion(userDefinedAssignmentRegion, {rhsArg, lhsArg}))
return mlir::failure();
ensureTerminator(userDefinedAssignmentRegion, parser.getBuilder(),
result.location);
}
return mlir::success();
}
void hlfir::RegionAssignOp::print(mlir::OpAsmPrinter &p) {
p << " ";
p.printRegion(getRhsRegion(), /*printEntryBlockArgs=*/false,
/*printBlockTerminators=*/true);
p << " to ";
p.printRegion(getLhsRegion(), /*printEntryBlockArgs=*/false,
/*printBlockTerminators=*/true);
if (!getUserDefinedAssignment().empty()) {
p << " user_defined_assign ";
mlir::Value userAssignmentRhs = getUserAssignmentRhs();
mlir::Value userAssignmentLhs = getUserAssignmentLhs();
p << " (" << userAssignmentRhs << ": " << userAssignmentRhs.getType()
<< ") to (";
p << userAssignmentLhs << ": " << userAssignmentLhs.getType() << ") ";
p.printRegion(getUserDefinedAssignment(), /*printEntryBlockArgs=*/false,
/*printBlockTerminators=*/false);
}
}
static mlir::Operation *getTerminator(mlir::Region &region) {
if (region.empty() || region.back().empty())
return nullptr;
return &region.back().back();
}
mlir::LogicalResult hlfir::RegionAssignOp::verify() {
if (!mlir::isa_and_nonnull<hlfir::YieldOp>(getTerminator(getRhsRegion())))
return emitOpError(
"right-hand side region must be terminated by an hlfir.yield");
if (!mlir::isa_and_nonnull<hlfir::YieldOp, hlfir::ElementalAddrOp>(
getTerminator(getLhsRegion())))
return emitOpError("left-hand side region must be terminated by an "
"hlfir.yield or hlfir.elemental_addr");
return mlir::success();
}
//===----------------------------------------------------------------------===//
// YieldOp
//===----------------------------------------------------------------------===//
static mlir::ParseResult parseYieldOpCleanup(mlir::OpAsmParser &parser,
mlir::Region &cleanup) {
if (succeeded(parser.parseOptionalKeyword("cleanup"))) {
if (parser.parseRegion(cleanup, /*arguments=*/{},
/*argTypes=*/{}))
return mlir::failure();
hlfir::YieldOp::ensureTerminator(cleanup, parser.getBuilder(),
parser.getBuilder().getUnknownLoc());
}
return mlir::success();
}
template <typename YieldOp>
static void printYieldOpCleanup(mlir::OpAsmPrinter &p, YieldOp yieldOp,
mlir::Region &cleanup) {
if (!cleanup.empty()) {
p << "cleanup ";
p.printRegion(cleanup, /*printEntryBlockArgs=*/false,
/*printBlockTerminators=*/false);
}
}
//===----------------------------------------------------------------------===//
// ElementalAddrOp
//===----------------------------------------------------------------------===//
void hlfir::ElementalAddrOp::build(mlir::OpBuilder &builder,
mlir::OperationState &odsState,
mlir::Value shape, mlir::Value mold,
mlir::ValueRange typeparams,
bool isUnordered) {
buildElemental<hlfir::ElementalAddrOp>(builder, odsState, shape, mold,
typeparams, isUnordered);
// Push cleanUp region.
odsState.addRegion();
}
mlir::LogicalResult hlfir::ElementalAddrOp::verify() {
hlfir::YieldOp yieldOp =
mlir::dyn_cast_or_null<hlfir::YieldOp>(getTerminator(getBody()));
if (!yieldOp)
return emitOpError("body region must be terminated by an hlfir.yield");
mlir::Type elementAddrType = yieldOp.getEntity().getType();
if (!hlfir::isFortranVariableType(elementAddrType) ||
mlir::isa<fir::SequenceType>(
hlfir::getFortranElementOrSequenceType(elementAddrType)))
return emitOpError("body must compute the address of a scalar entity");
unsigned shapeRank =
mlir::cast<fir::ShapeType>(getShape().getType()).getRank();
if (shapeRank != getIndices().size())
return emitOpError("body number of indices must match shape rank");
return mlir::success();
}
hlfir::YieldOp hlfir::ElementalAddrOp::getYieldOp() {
hlfir::YieldOp yieldOp =
mlir::dyn_cast_or_null<hlfir::YieldOp>(getTerminator(getBody()));
assert(yieldOp && "element_addr is ill-formed");
return yieldOp;
}
mlir::Value hlfir::ElementalAddrOp::getElementEntity() {
return getYieldOp().getEntity();
}
mlir::Region *hlfir::ElementalAddrOp::getElementCleanup() {
mlir::Region *cleanup = &getYieldOp().getCleanup();
return cleanup->empty() ? nullptr : cleanup;
}
//===----------------------------------------------------------------------===//
// OrderedAssignmentTreeOpInterface
//===----------------------------------------------------------------------===//
mlir::LogicalResult hlfir::OrderedAssignmentTreeOpInterface::verifyImpl() {
if (mlir::Region *body = getSubTreeRegion())
if (!body->empty())
for (mlir::Operation &op : body->front())
if (!mlir::isa<hlfir::OrderedAssignmentTreeOpInterface, fir::FirEndOp>(
op))
return emitOpError(
"body region must only contain OrderedAssignmentTreeOpInterface "
"operations or fir.end");
return mlir::success();
}
//===----------------------------------------------------------------------===//
// ForallOp
//===----------------------------------------------------------------------===//
static mlir::ParseResult parseForallOpBody(mlir::OpAsmParser &parser,
mlir::Region &body) {
mlir::OpAsmParser::Argument bodyArg;
if (parser.parseLParen() || parser.parseArgument(bodyArg) ||
parser.parseColon() || parser.parseType(bodyArg.type) ||
parser.parseRParen())
return mlir::failure();
if (parser.parseRegion(body, {bodyArg}))
return mlir::failure();
ensureTerminator(body, parser.getBuilder(),
parser.getBuilder().getUnknownLoc());
return mlir::success();
}
static void printForallOpBody(mlir::OpAsmPrinter &p, hlfir::ForallOp forall,
mlir::Region &body) {
mlir::Value forallIndex = forall.getForallIndexValue();
p << " (" << forallIndex << ": " << forallIndex.getType() << ") ";
p.printRegion(body, /*printEntryBlockArgs=*/false,
/*printBlockTerminators=*/false);
}
/// Predicate implementation of YieldIntegerOrEmpty.
static bool yieldsIntegerOrEmpty(mlir::Region &region) {
if (region.empty())
return true;
auto yield = mlir::dyn_cast_or_null<hlfir::YieldOp>(getTerminator(region));
return yield && fir::isa_integer(yield.getEntity().getType());
}
//===----------------------------------------------------------------------===//
// ForallMaskOp
//===----------------------------------------------------------------------===//
static mlir::ParseResult parseAssignmentMaskOpBody(mlir::OpAsmParser &parser,
mlir::Region &body) {
if (parser.parseRegion(body))
return mlir::failure();
ensureTerminator(body, parser.getBuilder(),
parser.getBuilder().getUnknownLoc());
return mlir::success();
}
template <typename ConcreteOp>
static void printAssignmentMaskOpBody(mlir::OpAsmPrinter &p, ConcreteOp,
mlir::Region &body) {
// ElseWhereOp is a WhereOp/ElseWhereOp terminator that should be printed.
bool printBlockTerminators =
!body.empty() &&
mlir::isa_and_nonnull<hlfir::ElseWhereOp>(body.back().getTerminator());
p.printRegion(body, /*printEntryBlockArgs=*/false, printBlockTerminators);
}
static bool yieldsLogical(mlir::Region &region, bool mustBeScalarI1) {
if (region.empty())
return false;
auto yield = mlir::dyn_cast_or_null<hlfir::YieldOp>(getTerminator(region));
if (!yield)
return false;
mlir::Type yieldType = yield.getEntity().getType();
if (mustBeScalarI1)
return hlfir::isI1Type(yieldType);
return hlfir::isMaskArgument(yieldType) &&
mlir::isa<fir::SequenceType>(
hlfir::getFortranElementOrSequenceType(yieldType));
}
mlir::LogicalResult hlfir::ForallMaskOp::verify() {
if (!yieldsLogical(getMaskRegion(), /*mustBeScalarI1=*/true))
return emitOpError("mask region must yield a scalar i1");
mlir::Operation *op = getOperation();
hlfir::ForallOp forallOp =
mlir::dyn_cast_or_null<hlfir::ForallOp>(op->getParentOp());
if (!forallOp || op->getParentRegion() != &forallOp.getBody())
return emitOpError("must be inside the body region of an hlfir.forall");
return mlir::success();
}
//===----------------------------------------------------------------------===//
// WhereOp and ElseWhereOp
//===----------------------------------------------------------------------===//
template <typename ConcreteOp>
static mlir::LogicalResult verifyWhereAndElseWhereBody(ConcreteOp &concreteOp) {
for (mlir::Operation &op : concreteOp.getBody().front())
if (mlir::isa<hlfir::ForallOp>(op))
return concreteOp.emitOpError(
"body region must not contain hlfir.forall");
return mlir::success();
}
mlir::LogicalResult hlfir::WhereOp::verify() {
if (!yieldsLogical(getMaskRegion(), /*mustBeScalarI1=*/false))
return emitOpError("mask region must yield a logical array");
return verifyWhereAndElseWhereBody(*this);
}
mlir::LogicalResult hlfir::ElseWhereOp::verify() {
if (!getMaskRegion().empty())
if (!yieldsLogical(getMaskRegion(), /*mustBeScalarI1=*/false))
return emitOpError(
"mask region must yield a logical array when provided");
return verifyWhereAndElseWhereBody(*this);
}
//===----------------------------------------------------------------------===//
// ForallIndexOp
//===----------------------------------------------------------------------===//
mlir::LogicalResult
hlfir::ForallIndexOp::canonicalize(hlfir::ForallIndexOp indexOp,
mlir::PatternRewriter &rewriter) {
for (mlir::Operation *user : indexOp->getResult(0).getUsers())
if (!mlir::isa<fir::LoadOp>(user))
return mlir::failure();
auto insertPt = rewriter.saveInsertionPoint();
llvm::SmallVector<mlir::Operation *> users(indexOp->getResult(0).getUsers());
for (mlir::Operation *user : users)
if (auto loadOp = mlir::dyn_cast<fir::LoadOp>(user)) {
rewriter.setInsertionPoint(loadOp);
rewriter.replaceOpWithNewOp<fir::ConvertOp>(
user, loadOp.getResult().getType(), indexOp.getIndex());
}
rewriter.restoreInsertionPoint(insertPt);
rewriter.eraseOp(indexOp);
return mlir::success();
}
//===----------------------------------------------------------------------===//
// CharExtremumOp
//===----------------------------------------------------------------------===//
mlir::LogicalResult hlfir::CharExtremumOp::verify() {
if (getStrings().size() < 2)
return emitOpError("must be provided at least two string operands");
unsigned kind = getCharacterKind(getResult().getType());
for (auto string : getStrings())
if (kind != getCharacterKind(string.getType()))
return emitOpError("strings must have the same KIND as the result type");
return mlir::success();
}
void hlfir::CharExtremumOp::build(mlir::OpBuilder &builder,
mlir::OperationState &result,
hlfir::CharExtremumPredicate predicate,
mlir::ValueRange strings) {
fir::CharacterType::LenType resultTypeLen = 0;
assert(!strings.empty() && "must contain operands");
unsigned kind = getCharacterKind(strings[0].getType());
for (auto string : strings)
if (auto cstLen = getCharacterLengthIfStatic(string.getType())) {
resultTypeLen = std::max(resultTypeLen, *cstLen);
} else {
resultTypeLen = fir::CharacterType::unknownLen();
break;
}
auto resultType = hlfir::ExprType::get(
builder.getContext(), hlfir::ExprType::Shape{},
fir::CharacterType::get(builder.getContext(), kind, resultTypeLen),
false);
build(builder, result, resultType, predicate, strings);
}
void hlfir::CharExtremumOp::getEffects(
llvm::SmallVectorImpl<
mlir::SideEffects::EffectInstance<mlir::MemoryEffects::Effect>>
&effects) {
getIntrinsicEffects(getOperation(), effects);
}
//===----------------------------------------------------------------------===//
// GetLength
//===----------------------------------------------------------------------===//
mlir::LogicalResult
hlfir::GetLengthOp::canonicalize(GetLengthOp getLength,
mlir::PatternRewriter &rewriter) {
mlir::Location loc = getLength.getLoc();
auto exprTy = mlir::cast<hlfir::ExprType>(getLength.getExpr().getType());
auto charTy = mlir::cast<fir::CharacterType>(exprTy.getElementType());
if (!charTy.hasConstantLen())
return mlir::failure();
mlir::Type indexTy = rewriter.getIndexType();
auto cstLen = rewriter.create<mlir::arith::ConstantOp>(
loc, indexTy, mlir::IntegerAttr::get(indexTy, charTy.getLen()));
rewriter.replaceOp(getLength, cstLen);
return mlir::success();
}
#include "flang/Optimizer/HLFIR/HLFIROpInterfaces.cpp.inc"
#define GET_OP_CLASSES
#include "flang/Optimizer/HLFIR/HLFIREnums.cpp.inc"
#include "flang/Optimizer/HLFIR/HLFIROps.cpp.inc"