
Introduce conditional code to identify stride of "one element", and simplify the array accesses for that case. This allows better loop performance in various benchmarks. Reviewed By: tblah, kiranchandramohan Differential Revision: https://reviews.llvm.org/D141306
314 lines
12 KiB
C++
314 lines
12 KiB
C++
//===- LoopVersioning.cpp -------------------------------------------------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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//===----------------------------------------------------------------------===//
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/// \file
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/// This pass looks for loops iterating over assumed-shape arrays, that can
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/// be optimized by "guessing" that the stride is element-sized.
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///
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/// This is done by createing two versions of the same loop: one which assumes
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/// that the elements are contiguous (stride == size of element), and one that
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/// is the original generic loop.
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///
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/// As a side-effect of the assumed element size stride, the array is also
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/// flattened to make it a 1D array - this is because the internal array
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/// structure must be either 1D or have known sizes in all dimensions - and at
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/// least one of the dimensions here is already unknown.
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///
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/// There are two distinct benefits here:
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/// 1. The loop that iterates over the elements is somewhat simplified by the
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/// constant stride calculation.
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/// 2. Since the compiler can understand the size of the stride, it can use
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/// vector instructions, where an unknown (at compile time) stride does often
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/// prevent vector operations from being used.
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///
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/// A known drawback is that the code-size is increased, in some cases that can
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/// be quite substantial - 3-4x is quite plausible (this includes that the loop
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/// gets vectorized, which in itself often more than doubles the size of the
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/// code, because unless the loop size is known, there will be a modulo
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/// vector-size remainder to deal with.
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///
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/// TODO: Do we need some size limit where loops no longer get duplicated?
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// Maybe some sort of cost analysis.
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/// TODO: Should some loop content - for example calls to functions and
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/// subroutines inhibit the versioning of the loops. Plausibly, this
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/// could be part of the cost analysis above.
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//===----------------------------------------------------------------------===//
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#include "flang/ISO_Fortran_binding.h"
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#include "flang/Optimizer/Builder/BoxValue.h"
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#include "flang/Optimizer/Builder/FIRBuilder.h"
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#include "flang/Optimizer/Builder/Runtime/Inquiry.h"
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#include "flang/Optimizer/Dialect/FIRDialect.h"
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#include "flang/Optimizer/Dialect/FIROps.h"
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#include "flang/Optimizer/Dialect/FIRType.h"
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#include "flang/Optimizer/Dialect/Support/FIRContext.h"
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#include "flang/Optimizer/Dialect/Support/KindMapping.h"
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#include "flang/Optimizer/Transforms/Passes.h"
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#include "mlir/Dialect/LLVMIR/LLVMDialect.h"
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#include "mlir/IR/Matchers.h"
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#include "mlir/IR/TypeUtilities.h"
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#include "mlir/Pass/Pass.h"
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#include "mlir/Transforms/DialectConversion.h"
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#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
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#include "mlir/Transforms/RegionUtils.h"
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#include "llvm/Support/Debug.h"
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#include "llvm/Support/raw_ostream.h"
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#include <algorithm>
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namespace fir {
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#define GEN_PASS_DEF_LOOPVERSIONING
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#include "flang/Optimizer/Transforms/Passes.h.inc"
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} // namespace fir
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#define DEBUG_TYPE "flang-loop-versioning"
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namespace {
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class LoopVersioningPass
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: public fir::impl::LoopVersioningBase<LoopVersioningPass> {
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public:
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void runOnOperation() override;
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};
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} // namespace
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/// @c replaceOuterUses - replace uses outside of @c op with result of @c
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/// outerOp
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static void replaceOuterUses(mlir::Operation *op, mlir::Operation *outerOp) {
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const mlir::Operation *outerParent = outerOp->getParentOp();
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op->replaceUsesWithIf(outerOp, [&](mlir::OpOperand &operand) {
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mlir::Operation *owner = operand.getOwner();
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return outerParent == owner->getParentOp();
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});
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}
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static fir::SequenceType getAsSequenceType(mlir::Value *v) {
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mlir::Type argTy = fir::unwrapPassByRefType(fir::unwrapRefType(v->getType()));
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return argTy.dyn_cast<fir::SequenceType>();
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}
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void LoopVersioningPass::runOnOperation() {
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LLVM_DEBUG(llvm::dbgs() << "=== Begin " DEBUG_TYPE " ===\n");
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mlir::func::FuncOp func = getOperation();
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/// @c ArgInfo
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/// A structure to hold an argument, the size of the argument and dimension
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/// information.
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struct ArgInfo {
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mlir::Value *arg;
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size_t size;
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fir::BoxDimsOp dims[CFI_MAX_RANK];
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};
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// First look for arguments with assumed shape = unknown extent in the lowest
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// dimension.
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LLVM_DEBUG(llvm::dbgs() << "Func-name:" << func.getSymName() << "\n");
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mlir::Block::BlockArgListType args = func.getArguments();
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mlir::ModuleOp module = func->getParentOfType<mlir::ModuleOp>();
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fir::KindMapping kindMap = fir::getKindMapping(module);
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mlir::SmallVector<ArgInfo> argsOfInterest;
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for (auto &arg : args) {
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if (auto seqTy = getAsSequenceType(&arg)) {
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unsigned rank = seqTy.getDimension();
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// Currently limited to 1D or 2D arrays as that seems to give good
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// improvement without excessive increase in code-size, etc.
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if (rank > 0 && rank < 3 &&
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seqTy.getShape()[0] == fir::SequenceType::getUnknownExtent()) {
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size_t typeSize = 0;
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mlir::Type elementType = fir::unwrapSeqOrBoxedSeqType(arg.getType());
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if (elementType.isa<mlir::FloatType>() ||
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elementType.isa<mlir::IntegerType>())
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typeSize = elementType.getIntOrFloatBitWidth() / 8;
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else if (auto cty = elementType.dyn_cast<fir::ComplexType>())
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typeSize = 2 * cty.getEleType(kindMap).getIntOrFloatBitWidth() / 8;
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if (typeSize)
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argsOfInterest.push_back({&arg, typeSize, {}});
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else
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LLVM_DEBUG(llvm::dbgs() << "Type not supported\n");
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} else {
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LLVM_DEBUG(llvm::dbgs() << "Too many dimensions\n");
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}
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}
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}
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if (argsOfInterest.empty())
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return;
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struct OpsWithArgs {
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mlir::Operation *op;
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mlir::SmallVector<ArgInfo> argsAndDims;
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};
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// Now see if those arguments are used inside any loop.
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mlir::SmallVector<OpsWithArgs, 4> loopsOfInterest;
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func.walk([&](fir::DoLoopOp loop) {
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mlir::Block &body = *loop.getBody();
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mlir::SmallVector<ArgInfo> argsInLoop;
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body.walk([&](fir::CoordinateOp op) {
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// The current operation could be inside another loop than
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// the one we're currently processing. Skip it, we'll get
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// to it later.
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if (op->getParentOfType<fir::DoLoopOp>() != loop)
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return;
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const mlir::Value &operand = op->getOperand(0);
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for (auto a : argsOfInterest) {
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if (*a.arg == operand) {
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// Only add if it's not already in the list.
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if (std::find_if(argsInLoop.begin(), argsInLoop.end(), [&](auto it) {
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return it.arg == a.arg;
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}) == argsInLoop.end()) {
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argsInLoop.push_back(a);
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break;
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}
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}
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}
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});
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if (!argsInLoop.empty()) {
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OpsWithArgs ops = {loop, argsInLoop};
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loopsOfInterest.push_back(ops);
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}
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});
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if (loopsOfInterest.empty())
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return;
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// If we get here, there are loops to process.
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fir::FirOpBuilder builder{module, kindMap};
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mlir::Location loc = builder.getUnknownLoc();
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mlir::IndexType idxTy = builder.getIndexType();
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LLVM_DEBUG(llvm::dbgs() << "Module Before transformation:");
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LLVM_DEBUG(module->dump());
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LLVM_DEBUG(llvm::dbgs() << "loopsOfInterest: " << loopsOfInterest.size()
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<< "\n");
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for (auto op : loopsOfInterest) {
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LLVM_DEBUG(op.op->dump());
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builder.setInsertionPoint(op.op);
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mlir::Value allCompares = nullptr;
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// Ensure all of the arrays are unit-stride.
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for (auto &arg : op.argsAndDims) {
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fir::SequenceType seqTy = getAsSequenceType(arg.arg);
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unsigned rank = seqTy.getDimension();
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// We only care about lowest order dimension.
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for (unsigned i = 0; i < rank; i++) {
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mlir::Value dimIdx = builder.createIntegerConstant(loc, idxTy, i);
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arg.dims[i] = builder.create<fir::BoxDimsOp>(loc, idxTy, idxTy, idxTy,
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*arg.arg, dimIdx);
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}
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mlir::Value elemSize =
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builder.createIntegerConstant(loc, idxTy, arg.size);
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mlir::Value cmp = builder.create<mlir::arith::CmpIOp>(
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loc, mlir::arith::CmpIPredicate::eq, arg.dims[0].getResult(2),
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elemSize);
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if (!allCompares) {
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allCompares = cmp;
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} else {
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allCompares =
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builder.create<mlir::arith::AndIOp>(loc, cmp, allCompares);
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}
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}
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auto ifOp =
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builder.create<fir::IfOp>(loc, op.op->getResultTypes(), allCompares,
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/*withElse=*/true);
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builder.setInsertionPointToStart(&ifOp.getThenRegion().front());
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LLVM_DEBUG(llvm::dbgs() << "Creating cloned loop\n");
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mlir::Operation *clonedLoop = op.op->clone();
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bool changed = false;
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for (auto &arg : op.argsAndDims) {
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fir::SequenceType::Shape newShape;
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newShape.push_back(fir::SequenceType::getUnknownExtent());
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auto elementType = fir::unwrapSeqOrBoxedSeqType(arg.arg->getType());
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mlir::Type arrTy = fir::SequenceType::get(newShape, elementType);
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mlir::Type boxArrTy = fir::BoxType::get(arrTy);
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mlir::Type refArrTy = builder.getRefType(arrTy);
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auto carg = builder.create<fir::ConvertOp>(loc, boxArrTy, *arg.arg);
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auto caddr = builder.create<fir::BoxAddrOp>(loc, refArrTy, carg);
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auto insPt = builder.saveInsertionPoint();
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// Use caddr instead of arg.
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clonedLoop->walk([&](fir::CoordinateOp coop) {
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// Reduce the multi-dimensioned index to a single index.
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// This is required becase fir arrays do not support multiple dimensions
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// with unknown dimensions at compile time.
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if (coop->getOperand(0) == *arg.arg &&
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coop->getOperands().size() >= 2) {
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builder.setInsertionPoint(coop);
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mlir::Value totalIndex = builder.createIntegerConstant(loc, idxTy, 0);
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// Operand(1) = array; Operand(2) = index1; Operand(3) = index2
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for (unsigned i = coop->getOperands().size() - 1; i > 1; i--) {
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mlir::Value curIndex =
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builder.createConvert(loc, idxTy, coop->getOperand(i));
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// First arg is Operand2, so dims[i-2] is 0-based i-1!
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mlir::Value scale =
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builder.createConvert(loc, idxTy, arg.dims[i - 2].getResult(1));
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totalIndex = builder.create<mlir::arith::AddIOp>(
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loc, totalIndex,
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builder.create<mlir::arith::MulIOp>(loc, scale, curIndex));
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}
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totalIndex = builder.create<mlir::arith::AddIOp>(
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loc, totalIndex,
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builder.createConvert(loc, idxTy, coop->getOperand(1)));
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auto newOp = builder.create<fir::CoordinateOp>(
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loc, builder.getRefType(elementType), caddr,
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mlir::ValueRange{totalIndex});
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LLVM_DEBUG(newOp->dump());
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coop->getResult(0).replaceAllUsesWith(newOp->getResult(0));
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coop->erase();
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changed = true;
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}
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});
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builder.restoreInsertionPoint(insPt);
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}
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assert(changed && "Expected operations to have changed");
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builder.insert(clonedLoop);
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// Forward the result(s), if any, from the loop operation to the
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//
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mlir::ResultRange results = clonedLoop->getResults();
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bool hasResults = (results.size() > 0);
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if (hasResults)
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builder.create<fir::ResultOp>(loc, results);
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// Add the original loop in the else-side of the if operation.
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builder.setInsertionPointToStart(&ifOp.getElseRegion().front());
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replaceOuterUses(op.op, ifOp);
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op.op->remove();
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builder.insert(op.op);
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// Rely on "cloned loop has results, so original loop also has results".
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if (hasResults) {
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builder.create<fir::ResultOp>(loc, op.op->getResults());
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} else {
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// Use an assert to check this.
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assert(op.op->getResults().size() == 0 &&
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"Weird, the cloned loop doesn't have results, but the original "
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"does?");
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}
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}
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LLVM_DEBUG(llvm::dbgs() << "After transform:\n");
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LLVM_DEBUG(module->dump());
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LLVM_DEBUG(llvm::dbgs() << "=== End " DEBUG_TYPE " ===\n");
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}
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std::unique_ptr<mlir::Pass> fir::createLoopVersioningPass() {
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return std::make_unique<LoopVersioningPass>();
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}
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