//===- LoopVersioning.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
//
//===----------------------------------------------------------------------===//
//===----------------------------------------------------------------------===//
/// \file
/// This pass looks for loops iterating over assumed-shape arrays, that can
/// be optimized by "guessing" that the stride is element-sized.
///
/// This is done by creating two versions of the same loop: one which assumes
/// that the elements are contiguous (stride == size of element), and one that
/// is the original generic loop.
///
/// As a side-effect of the assumed element size stride, the array is also
/// flattened to make it a 1D array - this is because the internal array
/// structure must be either 1D or have known sizes in all dimensions - and at
/// least one of the dimensions here is already unknown.
///
/// There are two distinct benefits here:
/// 1. The loop that iterates over the elements is somewhat simplified by the
/// constant stride calculation.
/// 2. Since the compiler can understand the size of the stride, it can use
/// vector instructions, where an unknown (at compile time) stride does often
/// prevent vector operations from being used.
///
/// A known drawback is that the code-size is increased, in some cases that can
/// be quite substantial - 3-4x is quite plausible (this includes that the loop
/// gets vectorized, which in itself often more than doubles the size of the
/// code, because unless the loop size is known, there will be a modulo
/// vector-size remainder to deal with.
///
/// TODO: Do we need some size limit where loops no longer get duplicated?
// Maybe some sort of cost analysis.
/// TODO: Should some loop content - for example calls to functions and
/// subroutines inhibit the versioning of the loops. Plausibly, this
/// could be part of the cost analysis above.
//===----------------------------------------------------------------------===//
#include "flang/ISO_Fortran_binding_wrapper.h"
#include "flang/Optimizer/Builder/BoxValue.h"
#include "flang/Optimizer/Builder/FIRBuilder.h"
#include "flang/Optimizer/Builder/Runtime/Inquiry.h"
#include "flang/Optimizer/Dialect/FIRDialect.h"
#include "flang/Optimizer/Dialect/FIROps.h"
#include "flang/Optimizer/Dialect/FIRType.h"
#include "flang/Optimizer/Dialect/Support/FIRContext.h"
#include "flang/Optimizer/Dialect/Support/KindMapping.h"
#include "flang/Optimizer/Support/DataLayout.h"
#include "flang/Optimizer/Transforms/Passes.h"
#include "mlir/Dialect/LLVMIR/LLVMDialect.h"
#include "mlir/IR/Dominance.h"
#include "mlir/IR/Matchers.h"
#include "mlir/IR/TypeUtilities.h"
#include "mlir/Pass/Pass.h"
#include "mlir/Transforms/DialectConversion.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
#include "mlir/Transforms/RegionUtils.h"
#include "llvm/Support/Debug.h"
#include "llvm/Support/raw_ostream.h"
#include <algorithm>
namespace fir {
#define GEN_PASS_DEF_LOOPVERSIONING
#include "flang/Optimizer/Transforms/Passes.h.inc"
} // namespace fir
#define DEBUG_TYPE "flang-loop-versioning"
namespace {
class LoopVersioningPass
: public fir::impl::LoopVersioningBase<LoopVersioningPass> {
public:
void runOnOperation() override;
};
/// @struct ArgInfo
/// A structure to hold an argument, the size of the argument and dimension
/// information.
struct ArgInfo {
mlir::Value arg;
size_t size;
unsigned rank;
fir::BoxDimsOp dims[CFI_MAX_RANK];
};
/// @struct ArgsUsageInLoop
/// A structure providing information about the function arguments
/// usage by the instructions immediately nested in a loop.
struct ArgsUsageInLoop {
/// Mapping between the memref operand of an array indexing
/// operation (e.g. fir.coordinate_of) and the argument information.
llvm::DenseMap<mlir::Value, ArgInfo> usageInfo;
/// Some array indexing operations inside a loop cannot be transformed.
/// This vector holds the memref operands of such operations.
/// The vector is used to make sure that we do not try to transform
/// any outer loop, since this will imply the operation rewrite
/// in this loop.
llvm::SetVector<mlir::Value> cannotTransform;
// Debug dump of the structure members assuming that
// the information has been collected for the given loop.
void dump(fir::DoLoopOp loop) const {
LLVM_DEBUG({
mlir::OpPrintingFlags printFlags;
printFlags.skipRegions();
llvm::dbgs() << "Arguments usage info for loop:\n";
loop.print(llvm::dbgs(), printFlags);
llvm::dbgs() << "\nUsed args:\n";
for (auto &use : usageInfo) {
mlir::Value v = use.first;
v.print(llvm::dbgs(), printFlags);
llvm::dbgs() << "\n";
}
llvm::dbgs() << "\nCannot transform args:\n";
for (mlir::Value arg : cannotTransform) {
arg.print(llvm::dbgs(), printFlags);
llvm::dbgs() << "\n";
}
llvm::dbgs() << "====\n";
});
}
// Erase usageInfo and cannotTransform entries for a set
// of given arguments.
void eraseUsage(const llvm::SetVector<mlir::Value> &args) {
for (auto &arg : args)
usageInfo.erase(arg);
cannotTransform.set_subtract(args);
}
// Erase usageInfo and cannotTransform entries for a set
// of given arguments provided in the form of usageInfo map.
void eraseUsage(const llvm::DenseMap<mlir::Value, ArgInfo> &args) {
for (auto &arg : args) {
usageInfo.erase(arg.first);
cannotTransform.remove(arg.first);
}
}
};
} // namespace
static fir::SequenceType getAsSequenceType(mlir::Value *v) {
mlir::Type argTy = fir::unwrapPassByRefType(fir::unwrapRefType(v->getType()));
return mlir::dyn_cast<fir::SequenceType>(argTy);
}
/// if a value comes from a fir.declare, follow it to the original source,
/// otherwise return the value
static mlir::Value unwrapFirDeclare(mlir::Value val) {
// fir.declare is for source code variables. We don't have declares of
// declares
if (fir::DeclareOp declare = val.getDefiningOp<fir::DeclareOp>())
return declare.getMemref();
return val;
}
/// if a value comes from a fir.rebox, follow the rebox to the original source,
/// of the value, otherwise return the value
static mlir::Value unwrapReboxOp(mlir::Value val) {
// don't support reboxes of reboxes
if (fir::ReboxOp rebox = val.getDefiningOp<fir::ReboxOp>())
val = rebox.getBox();
return val;
}
/// normalize a value (removing fir.declare and fir.rebox) so that we can
/// more conveniently spot values which came from function arguments
static mlir::Value normaliseVal(mlir::Value val) {
return unwrapFirDeclare(unwrapReboxOp(val));
}
/// some FIR operations accept a fir.shape, a fir.shift or a fir.shapeshift.
/// fir.shift and fir.shapeshift allow us to extract lower bounds
/// if lowerbounds cannot be found, return nullptr
static mlir::Value tryGetLowerBoundsFromShapeLike(mlir::Value shapeLike,
unsigned dim) {
mlir::Value lowerBound{nullptr};
if (auto shift = shapeLike.getDefiningOp<fir::ShiftOp>())
lowerBound = shift.getOrigins()[dim];
if (auto shapeShift = shapeLike.getDefiningOp<fir::ShapeShiftOp>())
lowerBound = shapeShift.getOrigins()[dim];
return lowerBound;
}
/// attempt to get the array lower bounds of dimension dim of the memref
/// argument to a fir.array_coor op
/// 0 <= dim < rank
/// May return nullptr if no lower bounds can be determined
static mlir::Value getLowerBound(fir::ArrayCoorOp coop, unsigned dim) {
// 1) try to get from the shape argument to fir.array_coor
if (mlir::Value shapeLike = coop.getShape())
if (mlir::Value lb = tryGetLowerBoundsFromShapeLike(shapeLike, dim))
return lb;
// It is important not to try to read the lower bound from the box, because
// in the FIR lowering, boxes will sometimes contain incorrect lower bound
// information
// out of ideas
return {};
}
/// gets the i'th index from array coordinate operation op
/// dim should range between 0 and rank - 1
static mlir::Value getIndex(fir::FirOpBuilder &builder, mlir::Operation *op,
unsigned dim) {
if (fir::CoordinateOp coop = mlir::dyn_cast<fir::CoordinateOp>(op))
return coop.getCoor()[dim];
fir::ArrayCoorOp coop = mlir::dyn_cast<fir::ArrayCoorOp>(op);
assert(coop &&
"operation must be either fir.coordiante_of or fir.array_coor");
// fir.coordinate_of indices start at 0: adjust these indices to match by
// subtracting the lower bound
mlir::Value index = coop.getIndices()[dim];
mlir::Value lb = getLowerBound(coop, dim);
if (!lb)
// assume a default lower bound of one
lb = builder.createIntegerConstant(coop.getLoc(), index.getType(), 1);
// index_0 = index - lb;
if (lb.getType() != index.getType())
lb = builder.createConvert(coop.getLoc(), index.getType(), lb);
return builder.create<mlir::arith::SubIOp>(coop.getLoc(), index, lb);
}
void LoopVersioningPass::runOnOperation() {
LLVM_DEBUG(llvm::dbgs() << "=== Begin " DEBUG_TYPE " ===\n");
mlir::func::FuncOp func = getOperation();
// First look for arguments with assumed shape = unknown extent in the lowest
// dimension.
LLVM_DEBUG(llvm::dbgs() << "Func-name:" << func.getSymName() << "\n");
mlir::Block::BlockArgListType args = func.getArguments();
mlir::ModuleOp module = func->getParentOfType<mlir::ModuleOp>();
fir::KindMapping kindMap = fir::getKindMapping(module);
mlir::SmallVector<ArgInfo, 4> argsOfInterest;
std::optional<mlir::DataLayout> dl =
fir::support::getOrSetDataLayout(module, /*allowDefaultLayout=*/false);
if (!dl)
mlir::emitError(module.getLoc(),
"data layout attribute is required to perform " DEBUG_TYPE
"pass");
for (auto &arg : args) {
// Optional arguments must be checked for IsPresent before
// looking for the bounds. They are unsupported for the time being.
if (func.getArgAttrOfType<mlir::UnitAttr>(arg.getArgNumber(),
fir::getOptionalAttrName())) {
LLVM_DEBUG(llvm::dbgs() << "OPTIONAL is not supported\n");
continue;
}
if (auto seqTy = getAsSequenceType(&arg)) {
unsigned rank = seqTy.getDimension();
if (rank > 0 &&
seqTy.getShape()[0] == fir::SequenceType::getUnknownExtent()) {
size_t typeSize = 0;
mlir::Type elementType = fir::unwrapSeqOrBoxedSeqType(arg.getType());
if (mlir::isa<mlir::FloatType>(elementType) ||
mlir::isa<mlir::IntegerType>(elementType) ||
mlir::isa<fir::ComplexType>(elementType)) {
auto [eleSize, eleAlign] = fir::getTypeSizeAndAlignmentOrCrash(
arg.getLoc(), elementType, *dl, kindMap);
typeSize = llvm::alignTo(eleSize, eleAlign);
}
if (typeSize)
argsOfInterest.push_back({arg, typeSize, rank, {}});
else
LLVM_DEBUG(llvm::dbgs() << "Type not supported\n");
}
}
}
if (argsOfInterest.empty()) {
LLVM_DEBUG(llvm::dbgs()
<< "No suitable arguments.\n=== End " DEBUG_TYPE " ===\n");
return;
}
// A list of all loops in the function in post-order.
mlir::SmallVector<fir::DoLoopOp> originalLoops;
// Information about the arguments usage by the instructions
// immediately nested in a loop.
llvm::DenseMap<fir::DoLoopOp, ArgsUsageInLoop> argsInLoops;
auto &domInfo = getAnalysis<mlir::DominanceInfo>();
// Traverse the loops in post-order and see
// if those arguments are used inside any loop.
func.walk([&](fir::DoLoopOp loop) {
mlir::Block &body = *loop.getBody();
auto &argsInLoop = argsInLoops[loop];
originalLoops.push_back(loop);
body.walk([&](mlir::Operation *op) {
// Support either fir.array_coor or fir.coordinate_of.
if (!mlir::isa<fir::ArrayCoorOp, fir::CoordinateOp>(op))
return;
// Process only operations immediately nested in the current loop.
if (op->getParentOfType<fir::DoLoopOp>() != loop)
return;
mlir::Value operand = op->getOperand(0);
for (auto a : argsOfInterest) {
if (a.arg == normaliseVal(operand)) {
// Use the reboxed value, not the block arg when re-creating the loop.
a.arg = operand;
// Check that the operand dominates the loop?
// If this is the case, record such operands in argsInLoop.cannot-
// Transform, so that they disable the transformation for the parent
/// loops as well.
if (!domInfo.dominates(a.arg, loop))
argsInLoop.cannotTransform.insert(a.arg);
// No support currently for sliced arrays.
// This means that we cannot transform properly
// instructions referencing a.arg in the whole loop
// nest this loop is located in.
if (auto arrayCoor = mlir::dyn_cast<fir::ArrayCoorOp>(op))
if (arrayCoor.getSlice())
argsInLoop.cannotTransform.insert(a.arg);
if (argsInLoop.cannotTransform.contains(a.arg)) {
// Remove any previously recorded usage, if any.
argsInLoop.usageInfo.erase(a.arg);
break;
}
// Record the a.arg usage, if not recorded yet.
argsInLoop.usageInfo.try_emplace(a.arg, a);
break;
}
}
});
});
// Dump loops info after initial collection.
LLVM_DEBUG({
llvm::dbgs() << "Initial usage info:\n";
for (fir::DoLoopOp loop : originalLoops) {
auto &argsInLoop = argsInLoops[loop];
argsInLoop.dump(loop);
}
});
// Clear argument usage for parent loops if an inner loop
// contains a non-transformable usage.
for (fir::DoLoopOp loop : originalLoops) {
auto &argsInLoop = argsInLoops[loop];
if (argsInLoop.cannotTransform.empty())
continue;
fir::DoLoopOp parent = loop;
while ((parent = parent->getParentOfType<fir::DoLoopOp>()))
argsInLoops[parent].eraseUsage(argsInLoop.cannotTransform);
}
// If an argument access can be optimized in a loop and
// its descendant loop, then it does not make sense to
// generate the contiguity check for the descendant loop.
// The check will be produced as part of the ancestor
// loop's transformation. So we can clear the argument
// usage for all descendant loops.
for (fir::DoLoopOp loop : originalLoops) {
auto &argsInLoop = argsInLoops[loop];
if (argsInLoop.usageInfo.empty())
continue;
loop.getBody()->walk([&](fir::DoLoopOp dloop) {
argsInLoops[dloop].eraseUsage(argsInLoop.usageInfo);
});
}
LLVM_DEBUG({
llvm::dbgs() << "Final usage info:\n";
for (fir::DoLoopOp loop : originalLoops) {
auto &argsInLoop = argsInLoops[loop];
argsInLoop.dump(loop);
}
});
// Reduce the collected information to a list of loops
// with attached arguments usage information.
// The list must hold the loops in post order, so that
// the inner loops are transformed before the outer loops.
struct OpsWithArgs {
mlir::Operation *op;
mlir::SmallVector<ArgInfo, 4> argsAndDims;
};
mlir::SmallVector<OpsWithArgs, 4> loopsOfInterest;
for (fir::DoLoopOp loop : originalLoops) {
auto &argsInLoop = argsInLoops[loop];
if (argsInLoop.usageInfo.empty())
continue;
OpsWithArgs info;
info.op = loop;
for (auto &arg : argsInLoop.usageInfo)
info.argsAndDims.push_back(arg.second);
loopsOfInterest.emplace_back(std::move(info));
}
if (loopsOfInterest.empty()) {
LLVM_DEBUG(llvm::dbgs()
<< "No loops to transform.\n=== End " DEBUG_TYPE " ===\n");
return;
}
// If we get here, there are loops to process.
fir::FirOpBuilder builder{module, std::move(kindMap)};
mlir::Location loc = builder.getUnknownLoc();
mlir::IndexType idxTy = builder.getIndexType();
LLVM_DEBUG(llvm::dbgs() << "Module Before transformation:");
LLVM_DEBUG(module->dump());
LLVM_DEBUG(llvm::dbgs() << "loopsOfInterest: " << loopsOfInterest.size()
<< "\n");
for (auto op : loopsOfInterest) {
LLVM_DEBUG(op.op->dump());
builder.setInsertionPoint(op.op);
mlir::Value allCompares = nullptr;
// Ensure all of the arrays are unit-stride.
for (auto &arg : op.argsAndDims) {
// Fetch all the dimensions of the array, except the last dimension.
// Always fetch the first dimension, however, so set ndims = 1 if
// we have one dim
unsigned ndims = arg.rank;
for (unsigned i = 0; i < ndims; i++) {
mlir::Value dimIdx = builder.createIntegerConstant(loc, idxTy, i);
arg.dims[i] = builder.create<fir::BoxDimsOp>(loc, idxTy, idxTy, idxTy,
arg.arg, dimIdx);
}
// We only care about lowest order dimension, here.
mlir::Value elemSize =
builder.createIntegerConstant(loc, idxTy, arg.size);
mlir::Value cmp = builder.create<mlir::arith::CmpIOp>(
loc, mlir::arith::CmpIPredicate::eq, arg.dims[0].getResult(2),
elemSize);
if (!allCompares) {
allCompares = cmp;
} else {
allCompares =
builder.create<mlir::arith::AndIOp>(loc, cmp, allCompares);
}
}
auto ifOp =
builder.create<fir::IfOp>(loc, op.op->getResultTypes(), allCompares,
/*withElse=*/true);
builder.setInsertionPointToStart(&ifOp.getThenRegion().front());
LLVM_DEBUG(llvm::dbgs() << "Creating cloned loop\n");
mlir::Operation *clonedLoop = op.op->clone();
bool changed = false;
for (auto &arg : op.argsAndDims) {
fir::SequenceType::Shape newShape;
newShape.push_back(fir::SequenceType::getUnknownExtent());
auto elementType = fir::unwrapSeqOrBoxedSeqType(arg.arg.getType());
mlir::Type arrTy = fir::SequenceType::get(newShape, elementType);
mlir::Type boxArrTy = fir::BoxType::get(arrTy);
mlir::Type refArrTy = builder.getRefType(arrTy);
auto carg = builder.create<fir::ConvertOp>(loc, boxArrTy, arg.arg);
auto caddr = builder.create<fir::BoxAddrOp>(loc, refArrTy, carg);
auto insPt = builder.saveInsertionPoint();
// Use caddr instead of arg.
clonedLoop->walk([&](mlir::Operation *coop) {
if (!mlir::isa<fir::CoordinateOp, fir::ArrayCoorOp>(coop))
return;
// Reduce the multi-dimensioned index to a single index.
// This is required becase fir arrays do not support multiple dimensions
// with unknown dimensions at compile time.
// We then calculate the multidimensional array like this:
// arr(x, y, z) bedcomes arr(z * stride(2) + y * stride(1) + x)
// where stride is the distance between elements in the dimensions
// 0, 1 and 2 or x, y and z.
if (coop->getOperand(0) == arg.arg && coop->getOperands().size() >= 2) {
builder.setInsertionPoint(coop);
mlir::Value totalIndex;
for (unsigned i = arg.rank - 1; i > 0; i--) {
mlir::Value curIndex =
builder.createConvert(loc, idxTy, getIndex(builder, coop, i));
// Multiply by the stride of this array. Later we'll divide by the
// element size.
mlir::Value scale =
builder.createConvert(loc, idxTy, arg.dims[i].getResult(2));
curIndex =
builder.create<mlir::arith::MulIOp>(loc, scale, curIndex);
totalIndex = (totalIndex) ? builder.create<mlir::arith::AddIOp>(
loc, curIndex, totalIndex)
: curIndex;
}
// This is the lowest dimension - which doesn't need scaling
mlir::Value finalIndex =
builder.createConvert(loc, idxTy, getIndex(builder, coop, 0));
if (totalIndex) {
assert(llvm::isPowerOf2_32(arg.size) &&
"Expected power of two here");
unsigned bits = llvm::Log2_32(arg.size);
mlir::Value elemShift =
builder.createIntegerConstant(loc, idxTy, bits);
totalIndex = builder.create<mlir::arith::AddIOp>(
loc,
builder.create<mlir::arith::ShRSIOp>(loc, totalIndex,
elemShift),
finalIndex);
} else {
totalIndex = finalIndex;
}
auto newOp = builder.create<fir::CoordinateOp>(
loc, builder.getRefType(elementType), caddr,
mlir::ValueRange{totalIndex});
LLVM_DEBUG(newOp->dump());
coop->getResult(0).replaceAllUsesWith(newOp->getResult(0));
coop->erase();
changed = true;
}
});
builder.restoreInsertionPoint(insPt);
}
assert(changed && "Expected operations to have changed");
builder.insert(clonedLoop);
// Forward the result(s), if any, from the loop operation to the
//
mlir::ResultRange results = clonedLoop->getResults();
bool hasResults = (results.size() > 0);
if (hasResults)
builder.create<fir::ResultOp>(loc, results);
// Add the original loop in the else-side of the if operation.
builder.setInsertionPointToStart(&ifOp.getElseRegion().front());
op.op->replaceAllUsesWith(ifOp);
op.op->remove();
builder.insert(op.op);
// Rely on "cloned loop has results, so original loop also has results".
if (hasResults) {
builder.create<fir::ResultOp>(loc, op.op->getResults());
} else {
// Use an assert to check this.
assert(op.op->getResults().size() == 0 &&
"Weird, the cloned loop doesn't have results, but the original "
"does?");
}
}
LLVM_DEBUG(llvm::dbgs() << "After transform:\n");
LLVM_DEBUG(module->dump());
LLVM_DEBUG(llvm::dbgs() << "=== End " DEBUG_TYPE " ===\n");
}
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