llvm-project/flang/lib/Optimizer/Transforms/AffineDemotion.cpp

178 lines
6.6 KiB
C++

//===-- AffineDemotion.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
//
//===----------------------------------------------------------------------===//
//
// This transformation is a prototype that demote affine dialects operations
// after optimizations to FIR loops operations.
// It is used after the AffinePromotion pass.
// It is not part of the production pipeline and would need more work in order
// to be used in production.
// More information can be found in this presentation:
// https://slides.com/rajanwalia/deck
//
//===----------------------------------------------------------------------===//
#include "PassDetail.h"
#include "flang/Optimizer/Dialect/FIRDialect.h"
#include "flang/Optimizer/Dialect/FIROps.h"
#include "flang/Optimizer/Dialect/FIRType.h"
#include "flang/Optimizer/Transforms/Passes.h"
#include "mlir/Dialect/Affine/IR/AffineOps.h"
#include "mlir/Dialect/Affine/Utils.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/Dialect/SCF/SCF.h"
#include "mlir/IR/BuiltinAttributes.h"
#include "mlir/IR/IntegerSet.h"
#include "mlir/IR/Visitors.h"
#include "mlir/Pass/Pass.h"
#include "mlir/Transforms/DialectConversion.h"
#include "llvm/ADT/DenseMap.h"
#include "llvm/ADT/Optional.h"
#include "llvm/Support/CommandLine.h"
#include "llvm/Support/Debug.h"
#define DEBUG_TYPE "flang-affine-demotion"
using namespace fir;
using namespace mlir;
namespace {
class AffineLoadConversion : public OpConversionPattern<mlir::AffineLoadOp> {
public:
using OpConversionPattern<mlir::AffineLoadOp>::OpConversionPattern;
LogicalResult
matchAndRewrite(mlir::AffineLoadOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
SmallVector<Value> indices(adaptor.indices());
auto maybeExpandedMap =
expandAffineMap(rewriter, op.getLoc(), op.getAffineMap(), indices);
if (!maybeExpandedMap)
return failure();
auto coorOp = rewriter.create<fir::CoordinateOp>(
op.getLoc(), fir::ReferenceType::get(op.getResult().getType()),
adaptor.memref(), *maybeExpandedMap);
rewriter.replaceOpWithNewOp<fir::LoadOp>(op, coorOp.getResult());
return success();
}
};
class AffineStoreConversion : public OpConversionPattern<mlir::AffineStoreOp> {
public:
using OpConversionPattern<mlir::AffineStoreOp>::OpConversionPattern;
LogicalResult
matchAndRewrite(mlir::AffineStoreOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
SmallVector<Value> indices(op.indices());
auto maybeExpandedMap =
expandAffineMap(rewriter, op.getLoc(), op.getAffineMap(), indices);
if (!maybeExpandedMap)
return failure();
auto coorOp = rewriter.create<fir::CoordinateOp>(
op.getLoc(), fir::ReferenceType::get(op.getValueToStore().getType()),
adaptor.memref(), *maybeExpandedMap);
rewriter.replaceOpWithNewOp<fir::StoreOp>(op, adaptor.value(),
coorOp.getResult());
return success();
}
};
class ConvertConversion : public mlir::OpRewritePattern<fir::ConvertOp> {
public:
using OpRewritePattern::OpRewritePattern;
mlir::LogicalResult
matchAndRewrite(fir::ConvertOp op,
mlir::PatternRewriter &rewriter) const override {
if (op.getRes().getType().isa<mlir::MemRefType>()) {
// due to index calculation moving to affine maps we still need to
// add converts for sequence types this has a side effect of losing
// some information about arrays with known dimensions by creating:
// fir.convert %arg0 : (!fir.ref<!fir.array<5xi32>>) ->
// !fir.ref<!fir.array<?xi32>>
if (auto refTy = op.getValue().getType().dyn_cast<fir::ReferenceType>())
if (auto arrTy = refTy.getEleTy().dyn_cast<fir::SequenceType>()) {
fir::SequenceType::Shape flatShape = {
fir::SequenceType::getUnknownExtent()};
auto flatArrTy = fir::SequenceType::get(flatShape, arrTy.getEleTy());
auto flatTy = fir::ReferenceType::get(flatArrTy);
rewriter.replaceOpWithNewOp<fir::ConvertOp>(op, flatTy,
op.getValue());
return success();
}
rewriter.startRootUpdate(op->getParentOp());
op.getResult().replaceAllUsesWith(op.getValue());
rewriter.finalizeRootUpdate(op->getParentOp());
rewriter.eraseOp(op);
}
return success();
}
};
mlir::Type convertMemRef(mlir::MemRefType type) {
return fir::SequenceType::get(
SmallVector<int64_t>(type.getShape().begin(), type.getShape().end()),
type.getElementType());
}
class StdAllocConversion : public mlir::OpRewritePattern<memref::AllocOp> {
public:
using OpRewritePattern::OpRewritePattern;
mlir::LogicalResult
matchAndRewrite(memref::AllocOp op,
mlir::PatternRewriter &rewriter) const override {
rewriter.replaceOpWithNewOp<fir::AllocaOp>(op, convertMemRef(op.getType()),
op.memref());
return success();
}
};
class AffineDialectDemotion
: public AffineDialectDemotionBase<AffineDialectDemotion> {
public:
void runOnOperation() override {
auto *context = &getContext();
auto function = getOperation();
LLVM_DEBUG(llvm::dbgs() << "AffineDemotion: running on function:\n";
function.print(llvm::dbgs()););
mlir::RewritePatternSet patterns(context);
patterns.insert<ConvertConversion>(context);
patterns.insert<AffineLoadConversion>(context);
patterns.insert<AffineStoreConversion>(context);
patterns.insert<StdAllocConversion>(context);
mlir::ConversionTarget target(*context);
target.addIllegalOp<memref::AllocOp>();
target.addDynamicallyLegalOp<fir::ConvertOp>([](fir::ConvertOp op) {
if (op.getRes().getType().isa<mlir::MemRefType>())
return false;
return true;
});
target.addLegalDialect<FIROpsDialect, mlir::scf::SCFDialect,
mlir::arith::ArithmeticDialect,
mlir::func::FuncDialect>();
if (mlir::failed(mlir::applyPartialConversion(function, target,
std::move(patterns)))) {
mlir::emitError(mlir::UnknownLoc::get(context),
"error in converting affine dialect\n");
signalPassFailure();
}
}
};
} // namespace
std::unique_ptr<mlir::Pass> fir::createAffineDemotionPass() {
return std::make_unique<AffineDialectDemotion>();
}