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