385 lines
15 KiB
C++
385 lines
15 KiB
C++
//===- Loops.cpp - conversion from Linalg named and generic ops to loops --===//
|
|
//
|
|
// 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
|
|
//
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
#include "mlir/Dialect/Linalg/Passes.h"
|
|
|
|
#include "mlir/Dialect/Affine/IR/AffineOps.h"
|
|
#include "mlir/Dialect/Arith/IR/Arith.h"
|
|
#include "mlir/Dialect/Arith/Utils/Utils.h"
|
|
#include "mlir/Dialect/Func/IR/FuncOps.h"
|
|
#include "mlir/Dialect/Linalg/IR/Linalg.h"
|
|
#include "mlir/Dialect/Linalg/Transforms/Transforms.h"
|
|
#include "mlir/Dialect/Linalg/Utils/Utils.h"
|
|
#include "mlir/Dialect/SCF/Transforms/Transforms.h"
|
|
#include "mlir/Dialect/SCF/Utils/AffineCanonicalizationUtils.h"
|
|
#include "mlir/IR/AffineExpr.h"
|
|
#include "mlir/IR/AffineMap.h"
|
|
#include "mlir/IR/BlockAndValueMapping.h"
|
|
#include "mlir/Support/LLVM.h"
|
|
#include "mlir/Transforms/DialectConversion.h"
|
|
#include "mlir/Transforms/FoldUtils.h"
|
|
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
|
|
#include "llvm/ADT/TypeSwitch.h"
|
|
|
|
namespace mlir {
|
|
#define GEN_PASS_DEF_LINALGLOWERTOAFFINELOOPS
|
|
#define GEN_PASS_DEF_LINALGLOWERTOLOOPS
|
|
#define GEN_PASS_DEF_LINALGLOWERTOPARALLELLOOPS
|
|
#include "mlir/Dialect/Linalg/Passes.h.inc"
|
|
} // namespace mlir
|
|
|
|
using namespace mlir;
|
|
using namespace mlir::linalg;
|
|
|
|
static SmallVector<Value> makeCanonicalAffineApplies(OpBuilder &b, Location loc,
|
|
AffineMap map,
|
|
ArrayRef<Value> vals) {
|
|
if (map.isEmpty())
|
|
return {};
|
|
|
|
assert(map.getNumInputs() == vals.size());
|
|
SmallVector<Value> res;
|
|
res.reserve(map.getNumResults());
|
|
auto dims = map.getNumDims();
|
|
for (auto e : map.getResults()) {
|
|
auto exprMap = AffineMap::get(dims, map.getNumSymbols(), e);
|
|
SmallVector<Value> operands(vals.begin(), vals.end());
|
|
canonicalizeMapAndOperands(&exprMap, &operands);
|
|
res.push_back(b.create<AffineApplyOp>(loc, exprMap, operands));
|
|
}
|
|
return res;
|
|
}
|
|
|
|
template <typename LoadOpTy, typename StoreOpTy, typename OpType>
|
|
static void inlineRegionAndEmitStore(OpBuilder &b, Location loc, OpType op,
|
|
ArrayRef<Value> indexedValues,
|
|
ArrayRef<SmallVector<Value>> indexing,
|
|
ArrayRef<Value> outputBuffers) {
|
|
auto &block = op->getRegion(0).front();
|
|
BlockAndValueMapping map;
|
|
map.map(block.getArguments(), indexedValues);
|
|
for (auto &op : block.without_terminator()) {
|
|
auto *newOp = b.clone(op, map);
|
|
map.map(op.getResults(), newOp->getResults());
|
|
}
|
|
|
|
Operation *terminator = block.getTerminator();
|
|
for (OpOperand &operand : terminator->getOpOperands()) {
|
|
Value toStore = map.lookupOrDefault(operand.get());
|
|
b.create<StoreOpTy>(loc, toStore, outputBuffers[operand.getOperandNumber()],
|
|
indexing[operand.getOperandNumber()]);
|
|
}
|
|
}
|
|
|
|
// Returns a pair that contains input indices and output indices of a
|
|
// SingleInputPoolingOp `op`.
|
|
struct InputAndOutputIndices {
|
|
SmallVector<Value> inputs;
|
|
SmallVector<Value> outputs;
|
|
};
|
|
template <typename SingleInputPoolingOp>
|
|
static InputAndOutputIndices
|
|
getInputAndOutputIndices(OpBuilder &b, Location loc, ArrayRef<Value> allIvs,
|
|
SingleInputPoolingOp op) {
|
|
auto mapsRange = op.getIndexingMapsArray();
|
|
auto maps = llvm::to_vector<8>(
|
|
llvm::map_range(mapsRange, [](AffineMapAttr a) { return a.getValue(); }));
|
|
return InputAndOutputIndices{
|
|
makeCanonicalAffineApplies(b, loc, maps[0], allIvs),
|
|
makeCanonicalAffineApplies(b, loc, maps[2], allIvs)};
|
|
}
|
|
|
|
/// Emits the MLIR for the scalar part of the generic op by:
|
|
/// 1. Emitting load ops for each input and output view in order. This is
|
|
/// achieved by applying the appropriate input or output map to the
|
|
/// enclosing induction variables.
|
|
/// 2. Emitting a call to `op.fun()` that takes as arguments the scalars
|
|
/// from point 1. above.
|
|
/// 3. Emitting store ops to store the results of 2. to the output
|
|
/// views.
|
|
///
|
|
/// An example output may resemble:
|
|
///
|
|
/// ```
|
|
/// scf.for %i = %c0 to %0 step %c1 {
|
|
/// scf.for %j = %c0 to %1 step %c1 {
|
|
/// scf.for %k = %c0 to %4 step %c1 {
|
|
/// %11 = load %arg0[%i, %j] :
|
|
/// memref<?x?xf32, stride_specification>
|
|
/// %12 = load %arg1[%i, %j, %k] :
|
|
/// memref<?x?x?xf32, stride_specification>
|
|
/// %13 = load %arg2[%i, %k, %j] :
|
|
/// memref<?x?x?xf32, stride_specification>
|
|
/// %14:2 = call @foo(%11, %12, %13) : (f32, f32, f32) -> (f32, f32)
|
|
/// store %14#0, %arg1[%i, %j, %k] :
|
|
/// memref<?x?x?Xf32, stride_specification>
|
|
/// store %14#1, %arg2[%i, %k, %j] :
|
|
/// memref<?x?x?Xf32, stride_specification>
|
|
/// }
|
|
/// }
|
|
/// }
|
|
/// ```
|
|
template <typename LoadOpTy, typename StoreOpTy>
|
|
static void emitScalarImplementation(OpBuilder &b, Location loc,
|
|
ArrayRef<Value> allIvs,
|
|
LinalgOp linalgOp) {
|
|
assert(linalgOp.hasBufferSemantics() &&
|
|
"expected linalg op with buffer semantics");
|
|
SmallVector<Value> indexedValues;
|
|
indexedValues.reserve(linalgOp->getNumOperands());
|
|
|
|
auto allIvsPlusDims = SmallVector<Value>(allIvs.begin(), allIvs.end());
|
|
|
|
// TODO: Avoid the loads if the corresponding argument of the
|
|
// region has no uses.
|
|
// 1.a. Emit load from input operand or for scalars access the operand itself.
|
|
for (OpOperand *inputOperand : linalgOp.getDpsInputOperands()) {
|
|
if (linalgOp.isScalar(inputOperand)) {
|
|
indexedValues.push_back(inputOperand->get());
|
|
continue;
|
|
}
|
|
auto indexing = makeCanonicalAffineApplies(
|
|
b, loc, linalgOp.getMatchingIndexingMap(inputOperand), allIvsPlusDims);
|
|
indexedValues.push_back(
|
|
b.create<LoadOpTy>(loc, inputOperand->get(), indexing));
|
|
}
|
|
// 1.b. Emit load from output views.
|
|
for (OpOperand *outputOperand : linalgOp.getDpsInitOperands()) {
|
|
SmallVector<Value> indexing = makeCanonicalAffineApplies(
|
|
b, loc, linalgOp.getMatchingIndexingMap(outputOperand), allIvsPlusDims);
|
|
indexedValues.push_back(
|
|
b.create<LoadOpTy>(loc, outputOperand->get(), indexing));
|
|
}
|
|
|
|
// TODO: When a region inliner exists, use it.
|
|
// 2. Inline region, currently only works for a single basic block.
|
|
// 3. Emit store.
|
|
SmallVector<SmallVector<Value>, 8> indexing;
|
|
SmallVector<Value> outputBuffers;
|
|
for (OpOperand *outputOperand : linalgOp.getDpsInitOperands()) {
|
|
if (!outputOperand->get().getType().isa<MemRefType>())
|
|
continue;
|
|
indexing.push_back(makeCanonicalAffineApplies(
|
|
b, loc, linalgOp.getMatchingIndexingMap(outputOperand),
|
|
allIvsPlusDims));
|
|
outputBuffers.push_back(outputOperand->get());
|
|
}
|
|
inlineRegionAndEmitStore<LoadOpTy, StoreOpTy>(b, loc, linalgOp, indexedValues,
|
|
indexing, outputBuffers);
|
|
}
|
|
|
|
/// Replace the index operations in the body of the loop nest by the matching
|
|
/// induction variables.
|
|
static void replaceIndexOpsByInductionVariables(LinalgOp linalgOp,
|
|
PatternRewriter &rewriter,
|
|
ArrayRef<Operation *> loopOps) {
|
|
// Extract the induction variables of the loop nest from outer to inner.
|
|
SmallVector<Value> allIvs;
|
|
for (Operation *loopOp : loopOps) {
|
|
llvm::TypeSwitch<Operation *>(loopOp)
|
|
.Case([&](scf::ParallelOp parallelOp) {
|
|
allIvs.append(parallelOp.getInductionVars().begin(),
|
|
parallelOp.getInductionVars().end());
|
|
})
|
|
.Case([&](scf::ForOp forOp) {
|
|
allIvs.push_back(forOp.getInductionVar());
|
|
})
|
|
.Case([&](AffineForOp affineForOp) {
|
|
allIvs.push_back(affineForOp.getInductionVar());
|
|
})
|
|
.Default([&](Operation *op) { assert(false && "unexpected op"); });
|
|
}
|
|
assert(linalgOp.getNumLoops() == allIvs.size() &&
|
|
"expected the number of loops and induction variables to match");
|
|
// Replace the index operations in the body of the innermost loop op.
|
|
if (!loopOps.empty()) {
|
|
LoopLikeOpInterface loopOp = loopOps.back();
|
|
for (IndexOp indexOp :
|
|
llvm::make_early_inc_range(loopOp.getLoopBody().getOps<IndexOp>()))
|
|
rewriter.replaceOp(indexOp, allIvs[indexOp.getDim()]);
|
|
}
|
|
}
|
|
|
|
template <typename LoopTy>
|
|
static FailureOr<LinalgLoops> linalgOpToLoopsImpl(PatternRewriter &rewriter,
|
|
LinalgOp linalgOp) {
|
|
using LoadOpTy = std::conditional_t<std::is_same<LoopTy, AffineForOp>::value,
|
|
AffineLoadOp, memref::LoadOp>;
|
|
using StoreOpTy = std::conditional_t<std::is_same<LoopTy, AffineForOp>::value,
|
|
AffineStoreOp, memref::StoreOp>;
|
|
|
|
// The flattened loopToOperandRangesMaps is expected to be an invertible
|
|
// permutation map (which is asserted in the inverse calculation).
|
|
assert(linalgOp.hasBufferSemantics() &&
|
|
"expected linalg op with buffer semantics");
|
|
|
|
auto loopRanges = linalgOp.createLoopRanges(rewriter, linalgOp.getLoc());
|
|
auto iteratorTypes = linalgOp.getIteratorTypesArray();
|
|
|
|
SmallVector<Value> allIvs;
|
|
GenerateLoopNest<LoopTy>::doit(
|
|
rewriter, linalgOp.getLoc(), loopRanges, linalgOp, iteratorTypes,
|
|
[&](OpBuilder &b, Location loc, ValueRange ivs,
|
|
ValueRange operandValuesToUse) -> scf::ValueVector {
|
|
assert(operandValuesToUse == linalgOp->getOperands() &&
|
|
"expect operands are captured and not passed by loop argument");
|
|
allIvs.append(ivs.begin(), ivs.end());
|
|
emitScalarImplementation<LoadOpTy, StoreOpTy>(b, loc, allIvs, linalgOp);
|
|
return scf::ValueVector{};
|
|
});
|
|
// Number of loop ops might be different from the number of ivs since some
|
|
// loops like affine.parallel and scf.parallel have multiple ivs.
|
|
SetVector<Operation *> loopSet;
|
|
for (Value iv : allIvs) {
|
|
if (!iv)
|
|
return failure();
|
|
// The induction variable is a block argument of the entry block of the
|
|
// loop operation.
|
|
BlockArgument ivVal = iv.dyn_cast<BlockArgument>();
|
|
if (!ivVal)
|
|
return failure();
|
|
loopSet.insert(ivVal.getOwner()->getParentOp());
|
|
}
|
|
LinalgLoops loops(loopSet.begin(), loopSet.end());
|
|
// Replace all index operations in the loop body.
|
|
replaceIndexOpsByInductionVariables(linalgOp, rewriter, loops);
|
|
return loops;
|
|
}
|
|
|
|
namespace {
|
|
template <typename LoopType>
|
|
class LinalgRewritePattern : public RewritePattern {
|
|
public:
|
|
LinalgRewritePattern(MLIRContext *context)
|
|
: RewritePattern(MatchAnyOpTypeTag(), /*benefit=*/1, context) {}
|
|
|
|
LogicalResult matchAndRewrite(Operation *op,
|
|
PatternRewriter &rewriter) const override {
|
|
auto linalgOp = dyn_cast<LinalgOp>(op);
|
|
if (!isa<LinalgOp>(op))
|
|
return failure();
|
|
if (failed(linalgOpToLoopsImpl<LoopType>(rewriter, linalgOp)))
|
|
return failure();
|
|
rewriter.eraseOp(op);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Local folding pattern for AffineApplyOp that we can apply greedily.
|
|
/// This replaces AffineApplyOp by the proper value in cases where the
|
|
/// associated map is trivial.
|
|
/// A trivial map here is defined as a map with a single result and either:
|
|
/// 1. Zero operand + returns a single AffineConstantExpr
|
|
/// 2. One operand + returns a single AffineDimExpr
|
|
/// 3. One operand + returns a single AffineSymbolExpr
|
|
//
|
|
/// In the first case, the AffineApplyOp is replaced by a new constant. In the
|
|
/// other cases, it is replaced by its unique operand.
|
|
struct FoldAffineOp : public RewritePattern {
|
|
FoldAffineOp(MLIRContext *context)
|
|
: RewritePattern(AffineApplyOp::getOperationName(), 0, context) {}
|
|
|
|
LogicalResult matchAndRewrite(Operation *op,
|
|
PatternRewriter &rewriter) const override {
|
|
AffineApplyOp affineApplyOp = cast<AffineApplyOp>(op);
|
|
auto map = affineApplyOp.getAffineMap();
|
|
if (map.getNumResults() != 1 || map.getNumInputs() > 1)
|
|
return failure();
|
|
|
|
AffineExpr expr = map.getResult(0);
|
|
if (map.getNumInputs() == 0) {
|
|
if (auto val = expr.dyn_cast<AffineConstantExpr>()) {
|
|
rewriter.replaceOpWithNewOp<arith::ConstantIndexOp>(op, val.getValue());
|
|
return success();
|
|
}
|
|
return failure();
|
|
}
|
|
if (expr.dyn_cast<AffineDimExpr>() || expr.dyn_cast<AffineSymbolExpr>()) {
|
|
rewriter.replaceOp(op, op->getOperand(0));
|
|
return success();
|
|
}
|
|
return failure();
|
|
}
|
|
};
|
|
|
|
template <typename LoopType>
|
|
static void lowerLinalgToLoopsImpl(func::FuncOp funcOp) {
|
|
MLIRContext *context = funcOp.getContext();
|
|
RewritePatternSet patterns(context);
|
|
patterns.add<LinalgRewritePattern<LoopType>>(context);
|
|
memref::DimOp::getCanonicalizationPatterns(patterns, context);
|
|
tensor::DimOp::getCanonicalizationPatterns(patterns, context);
|
|
AffineApplyOp::getCanonicalizationPatterns(patterns, context);
|
|
patterns.add<FoldAffineOp>(context);
|
|
// Just apply the patterns greedily.
|
|
(void)applyPatternsAndFoldGreedily(funcOp, std::move(patterns));
|
|
}
|
|
|
|
struct LowerToAffineLoops
|
|
: public impl::LinalgLowerToAffineLoopsBase<LowerToAffineLoops> {
|
|
void getDependentDialects(DialectRegistry ®istry) const override {
|
|
registry.insert<memref::MemRefDialect>();
|
|
}
|
|
void runOnOperation() override {
|
|
lowerLinalgToLoopsImpl<AffineForOp>(getOperation());
|
|
}
|
|
};
|
|
|
|
struct LowerToLoops : public impl::LinalgLowerToLoopsBase<LowerToLoops> {
|
|
void getDependentDialects(DialectRegistry ®istry) const override {
|
|
registry.insert<memref::MemRefDialect, scf::SCFDialect>();
|
|
}
|
|
void runOnOperation() override {
|
|
lowerLinalgToLoopsImpl<scf::ForOp>(getOperation());
|
|
}
|
|
};
|
|
|
|
struct LowerToParallelLoops
|
|
: public impl::LinalgLowerToParallelLoopsBase<LowerToParallelLoops> {
|
|
void runOnOperation() override {
|
|
lowerLinalgToLoopsImpl<scf::ParallelOp>(getOperation());
|
|
}
|
|
};
|
|
|
|
} // namespace
|
|
|
|
std::unique_ptr<OperationPass<func::FuncOp>>
|
|
mlir::createConvertLinalgToLoopsPass() {
|
|
return std::make_unique<LowerToLoops>();
|
|
}
|
|
|
|
std::unique_ptr<OperationPass<func::FuncOp>>
|
|
mlir::createConvertLinalgToParallelLoopsPass() {
|
|
return std::make_unique<LowerToParallelLoops>();
|
|
}
|
|
|
|
std::unique_ptr<OperationPass<func::FuncOp>>
|
|
mlir::createConvertLinalgToAffineLoopsPass() {
|
|
return std::make_unique<LowerToAffineLoops>();
|
|
}
|
|
|
|
/// Emits a loop nest of `affine.for` with the proper body for `linalgOp`.
|
|
FailureOr<LinalgLoops>
|
|
mlir::linalg::linalgOpToAffineLoops(PatternRewriter &rewriter,
|
|
LinalgOp linalgOp) {
|
|
return linalgOpToLoopsImpl<AffineForOp>(rewriter, linalgOp);
|
|
}
|
|
|
|
/// Emits a loop nest of `scf.for` with the proper body for `linalgOp`.
|
|
FailureOr<LinalgLoops> mlir::linalg::linalgOpToLoops(PatternRewriter &rewriter,
|
|
LinalgOp linalgOp) {
|
|
return linalgOpToLoopsImpl<scf::ForOp>(rewriter, linalgOp);
|
|
}
|
|
|
|
/// Emits a loop nest of `scf.parallel` with the proper body for `linalgOp`.
|
|
FailureOr<LinalgLoops>
|
|
mlir::linalg::linalgOpToParallelLoops(PatternRewriter &rewriter,
|
|
LinalgOp linalgOp) {
|
|
return linalgOpToLoopsImpl<scf::ParallelOp>(rewriter, linalgOp);
|
|
}
|