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llvm-project/mlir/lib/Dialect/Linalg/Transforms/Tiling.cpp

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//===- Tiling.cpp - Implementation of linalg Tiling -----------------------===//
//
// 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 file implements the linalg dialect Tiling pass.
//
//===----------------------------------------------------------------------===//
#include "PassDetail.h"
#include "mlir/Dialect/Linalg/IR/Linalg.h"
#include "mlir/Dialect/Linalg/Passes.h"
#include "mlir/Dialect/Linalg/Transforms/Transforms.h"
#include "mlir/Dialect/Linalg/Utils/Utils.h"
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/Dialect/SCF/Transforms.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/IR/AffineExpr.h"
#include "mlir/IR/AffineMap.h"
#include "mlir/Transforms/FoldUtils.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
#include "llvm/Support/CommandLine.h"
using namespace mlir;
using namespace mlir::linalg;
using namespace mlir::scf;
#define DEBUG_TYPE "linalg-tiling"
static bool isZero(Value v) {
if (auto cst = v.getDefiningOp<arith::ConstantIndexOp>())
return cst.value() == 0;
return false;
}
using LoopIndexToRangeIndexMap = DenseMap<int, int>;
// Creates a number of ranges equal to the number of non-zero in `tileSizes`.
// One for each loop of the LinalgOp that is tiled. The `tileSizes` argument has
// one entry per surrounding loop. It uses zero as the convention that a
// particular loop is not tiled. This convention simplifies implementations by
// avoiding affine map manipulations.
// The returned ranges correspond to the loop ranges, in the proper order, that
// are tiled and for which new loops will be created. Also the function returns
// a map from loop indices of the LinalgOp to the corresponding non-empty range
// indices of newly created loops.
static std::tuple<SmallVector<Range, 4>, LoopIndexToRangeIndexMap>
makeTiledLoopRanges(OpBuilder &b, Location loc, AffineMap map,
ValueRange allShapeSizes, ValueRange allTileSizes) {
assert(allTileSizes.size() == map.getNumResults());
// Apply `map` to get shape sizes in loop order.
auto shapeSizes = applyMapToValues(b, loc, map, allShapeSizes);
SmallVector<Value, 4> tileSizes(allTileSizes.begin(), allTileSizes.end());
// Traverse the tile sizes, which are in loop order, erase zeros everywhere.
LoopIndexToRangeIndexMap loopIndexToRangeIndex;
for (int idx = 0, e = tileSizes.size(), zerosCount = 0; idx < e; ++idx) {
if (isZero(tileSizes[idx - zerosCount])) {
shapeSizes.erase(shapeSizes.begin() + idx - zerosCount);
tileSizes.erase(tileSizes.begin() + idx - zerosCount);
++zerosCount;
continue;
}
loopIndexToRangeIndex[idx] = idx - zerosCount;
}
// Create a new range with the applied tile sizes.
SmallVector<Range, 4> res;
for (unsigned idx = 0, e = tileSizes.size(); idx < e; ++idx)
res.push_back(Range{b.create<arith::ConstantIndexOp>(loc, 0),
shapeSizes[idx], tileSizes[idx]});
return std::make_tuple(res, loopIndexToRangeIndex);
}
// All indices returned by IndexOp should be invariant with respect to tiling.
// Therefore, if an operation is tiled, we have to transform the indices
// accordingly, i.e. offset them by the values of the corresponding induction
// variables that are captured implicitly in the body of the op.
//
// Example. `linalg.generic` before tiling:
//
// #id_2d = (i, j) -> (i, j)
// #pointwise_2d_trait = {
// indexing_maps = [#id_2d, #id_2d],
// iterator_types = ["parallel", "parallel"]
// }
// linalg.generic #pointwise_2d_trait %operand, %result {
// ^bb0(%operand_in: f32, %result_in: f32):
// %i = linalg.index 0 : index
// %j = linalg.index 1 : index
// <some operations that use %i, %j>
// }: memref<50x100xf32>, memref<50x100xf32>
//
// After tiling pass with tiles sizes 10 and 25:
//
// #strided = (i, j)[s0, s1, s2] -> (i * s1 + s0 + j * s2)
//
// %c1 = arith.constant 1 : index
// %c0 = arith.constant 0 : index
// %c25 = arith.constant 25 : index
// %c10 = arith.constant 10 : index
// operand_dim_0 = dim %operand, 0 : memref<50x100xf32>
// operand_dim_1 = dim %operand, 1 : memref<50x100xf32>
// scf.for %k = %c0 to operand_dim_0 step %c10 {
// scf.for %l = %c0 to operand_dim_1 step %c25 {
// %4 = std.subview %operand[%k, %l][%c10, %c25][%c1, %c1]
// : memref<50x100xf32> to memref<?x?xf32, #strided>
// %5 = std.subview %result[%k, %l][%c10, %c25][%c1, %c1]
// : memref<50x100xf32> to memref<?x?xf32, #strided>
// linalg.generic pointwise_2d_trait %4, %5 {
// ^bb0(%operand_in: f32, %result_in: f32):
// %i = linalg.index 0 : index
// %j = linalg.index 1 : index
// // Indices `k` and `l` are implicitly captured in the body.
// %transformed_i = arith.addi %i, %k : index // index `i` is offset by %k
// %transformed_j = arith.addi %j, %l : index // index `j` is offset by %l
// // Every use of %i, %j is replaced with %transformed_i, %transformed_j
// <some operations that use %transformed_i, %transformed_j>
// }: memref<?x?xf32, #strided>, memref<?x?xf32, #strided>
// }
// }
//
// TODO: Investigate whether mixing implicit and explicit indices
// does not lead to losing information.
static void
transformIndexOps(OpBuilder &b, LinalgOp op, SmallVectorImpl<Value> &ivs,
const LoopIndexToRangeIndexMap &loopIndexToRangeIndex) {
SmallVector<Value> allIvs(op.getNumLoops(), nullptr);
for (auto &en : enumerate(allIvs)) {
auto rangeIndex = loopIndexToRangeIndex.find(en.index());
if (rangeIndex == loopIndexToRangeIndex.end())
continue;
en.value() = ivs[rangeIndex->second];
}
addTileLoopIvsToIndexOpResults(b, op, allIvs);
}
// Insert a tile `source` into the destination tensor `dest`. The position at
// which the tile is inserted (as well as size of tile) is taken from a given
// ExtractSliceOp `sliceOp`.
static Value insertSliceIntoTensor(OpBuilder &b, Location loc,
tensor::ExtractSliceOp sliceOp, Value source,
Value dest) {
return b.create<tensor::InsertSliceOp>(
loc, sliceOp.source().getType(), source, dest, sliceOp.offsets(),
sliceOp.sizes(), sliceOp.strides(), sliceOp.static_offsets(),
sliceOp.static_sizes(), sliceOp.static_strides());
}
template <typename LoopTy>
static FailureOr<TiledLinalgOp>
tileLinalgOpImpl(OpBuilder &b, LinalgOp op, ValueRange tileSizes,
const LinalgTilingOptions &options) {
auto nLoops = op.getNumLoops();
// Initial tile sizes may be too big, only take the first nLoops.
tileSizes = tileSizes.take_front(nLoops);
if (llvm::all_of(tileSizes, isZero)) {
TiledLinalgOp tiledOp;
tiledOp.op = cast<LinalgOp>(b.clone(*op.getOperation()));
tiledOp.tensorResults.assign(tiledOp.op->result_begin(),
tiledOp.op->result_end());
return tiledOp;
}
// 1. Build the tiled loop ranges.
auto allShapeSizes = op.createFlatListOfOperandDims(b, op.getLoc());
AffineMap shapeSizesToLoopsMap = op.getShapesToLoopsMap();
if (!shapeSizesToLoopsMap)
return failure();
SmallVector<Range, 4> loopRanges;
LoopIndexToRangeIndexMap loopIndexToRangeIndex;
std::tie(loopRanges, loopIndexToRangeIndex) = makeTiledLoopRanges(
b, op.getLoc(), shapeSizesToLoopsMap, allShapeSizes, tileSizes);
SmallVector<Attribute, 4> iteratorTypes;
for (auto attr :
enumerate(op.iterator_types().cast<ArrayAttr>().getValue())) {
if (loopIndexToRangeIndex.count(attr.index()))
iteratorTypes.push_back(attr.value());
}
// If interchangeVector is empty, use the identity. Build the permutation map
// otherwise.
auto invPermutationMap =
AffineMap::getMultiDimIdentityMap(tileSizes.size(), b.getContext());
if (!options.interchangeVector.empty()) {
// Based on the pruned iterations (due to zero tile size), recompute the
// interchange vector.
SmallVector<unsigned, 4> interchangeVector;
interchangeVector.reserve(options.interchangeVector.size());
for (auto pos : options.interchangeVector) {
auto it = loopIndexToRangeIndex.find(pos);
if (it == loopIndexToRangeIndex.end())
continue;
interchangeVector.push_back(it->second);
}
// Interchange vector is guaranteed to be a permutation,
// `inversePermutation` must succeed.
invPermutationMap = inversePermutation(
AffineMap::getPermutationMap(interchangeVector, b.getContext()));
assert(invPermutationMap);
SmallVector<int64_t> permutation(interchangeVector.begin(),
interchangeVector.end());
applyPermutationToVector(loopRanges, permutation);
applyPermutationToVector(iteratorTypes, permutation);
}
// 2. Create the tiled loops.
LinalgOp res = op;
SmallVector<Value, 4> ivs, tensorResults;
auto tiledLoopBodyBuilder =
[&](OpBuilder &b, Location loc, ValueRange localIvs,
ValueRange operandValuesToUse) -> scf::ValueVector {
ivs.assign(localIvs.begin(), localIvs.end());
// When an `interchangeVector` is present, it has been applied to the
// loop ranges and the iterator types. Apply its inverse to the
// resulting loop `ivs` to match the op definition.
SmallVector<Value, 4> interchangedIvs;
if (!options.interchangeVector.empty())
interchangedIvs = applyMapToValues(b, loc, invPermutationMap, ivs);
else
interchangedIvs.assign(ivs.begin(), ivs.end());
// Tile the `operandValuesToUse` that either match the `op` operands
// themselves or the tile loop arguments forwarding them.
assert(operandValuesToUse.size() ==
static_cast<size_t>(op.getNumInputsAndOutputs()) &&
"expect the number of operands and inputs and outputs to match");
SmallVector<Value> valuesToTile = operandValuesToUse;
auto sizeBounds =
applyMapToValues(b, loc, shapeSizesToLoopsMap, allShapeSizes);
SmallVector<Value, 4> tiledOperands = makeTiledShapes(
b, loc, op, valuesToTile, interchangedIvs, tileSizes, sizeBounds);
// TODO: use an interface/adaptor to avoid leaking position in
// `tiledOperands`.
SmallVector<Type, 4> resultTensorTypes;
for (OpOperand *opOperand : op.getOutputTensorOperands())
resultTensorTypes.push_back(
tiledOperands[opOperand->getOperandNumber()].getType());
res = op.clone(b, loc, resultTensorTypes, tiledOperands);
// Insert a insert_slice for each output tensor.
unsigned resultIdx = 0;
for (OpOperand *opOperand : op.getOutputTensorOperands()) {
// TODO: use an interface/adaptor to avoid leaking position in
// `tiledOperands`.
Value outputTensor = tiledOperands[opOperand->getOperandNumber()];
if (auto sliceOp = outputTensor.getDefiningOp<tensor::ExtractSliceOp>()) {
tensorResults.push_back(insertSliceIntoTensor(
b, loc, sliceOp, res->getResult(resultIdx), sliceOp.source()));
} else {
tensorResults.push_back(res->getResult(resultIdx));
}
++resultIdx;
}
return scf::ValueVector(tensorResults.begin(), tensorResults.end());
};
GenerateLoopNest<LoopTy>::doit(b, op.getLoc(), loopRanges, op, iteratorTypes,
tiledLoopBodyBuilder, options.distribution,
options.distributionTypes);
// 3. Transform IndexOp results w.r.t. the tiling.
transformIndexOps(b, res, ivs, loopIndexToRangeIndex);
// 4. Gather the newly created loops and return them with the new op.
SmallVector<Operation *, 8> loops;
loops.reserve(ivs.size());
for (auto iv : ivs) {
if (iv.isa<BlockArgument>()) {
loops.push_back(iv.cast<BlockArgument>().getOwner()->getParentOp());
assert(loops.back() && "no owner found for induction variable!");
} else {
// TODO: Instead of doing this, try to recover the ops used instead of the
// loop.
loops.push_back(nullptr);
}
}
// 5. Get the tensor results from the outermost loop if available. Otherwise
// use the previously captured `tensorResults`.
Operation *outermostLoop = nullptr;
for (Operation *loop : loops)
if ((outermostLoop = loop))
break;
return TiledLinalgOp{
res, loops, outermostLoop ? outermostLoop->getResults() : tensorResults};
}
template <typename LoopTy>
FailureOr<TiledLinalgOp> static tileLinalgOpImpl(
OpBuilder &b, LinalgOp op, const LinalgTilingOptions &options) {
OpBuilder::InsertionGuard g(b);
b.setInsertionPoint(op);
if (!options.tileSizeComputationFunction)
return failure();
// Enforce the convention that "tiling by zero" skips tiling a particular
// dimension. This convention is significantly simpler to handle instead of
// adjusting affine maps to account for missing dimensions.
auto nLoops = op.getNumLoops();
SmallVector<Value, 4> tileSizeVector =
options.tileSizeComputationFunction(b, op);
if (tileSizeVector.size() < nLoops) {
auto zero = b.create<arith::ConstantIndexOp>(op.getLoc(), 0);
tileSizeVector.append(nLoops - tileSizeVector.size(), zero);
}
return tileLinalgOpImpl<LoopTy>(b, op, tileSizeVector, options);
}
FailureOr<TiledLinalgOp>
mlir::linalg::tileLinalgOp(OpBuilder &b, LinalgOp op,
const LinalgTilingOptions &options) {
switch (options.loopType) {
case LinalgTilingLoopType::Loops:
return tileLinalgOpImpl<scf::ForOp>(b, op, options);
case LinalgTilingLoopType::ParallelLoops:
return tileLinalgOpImpl<scf::ParallelOp>(b, op, options);
case LinalgTilingLoopType::TiledLoops:
return tileLinalgOpImpl<linalg::TiledLoopOp>(b, op, options);
default:;
}
return failure();
}
/// Generate a loop nest around a given PadTensorOp (for tiling). `newPadOp`
/// and `loopNest` are output parameters that return the new (tiled) PadTensorOp
/// and the loop nest.
static LogicalResult tilePadTensorOp(OpBuilder &builder, PadTensorOp op,
PadTensorOp &newPadOp, LoopNest &loopNest,
const LinalgTilingOptions &options) {
Location loc = op.getLoc();
OpBuilder::InsertionGuard g(builder);
builder.setInsertionPoint(op);
// Clone PadTensorOp so that the existing op can be replaced more easily.
newPadOp = cast<PadTensorOp>(builder.clone(*op.getOperation()));
// Get rank and tile sizes.
int64_t rank = op.getResultType().getRank();
SmallVector<Value> tileSizes =
options.tileSizeComputationFunction(builder, op);
assert(static_cast<int64_t>(tileSizes.size()) == rank);
// Compute lower and upper bounds of the loop nest.
SmallVector<Range> ranges = op.getIterationDomain(builder);
SmallVector<Value> lbs, dims, allDims, steps;
for (int64_t i = 0; i < rank; ++i) {
allDims.push_back(ranges[i].size);
if (!isZero(tileSizes[i])) {
lbs.push_back(ranges[i].offset);
dims.push_back(ranges[i].size);
steps.push_back(tileSizes[i]);
}
}
// Generate loop nest: One loop per dimension.
SmallVector<Value> destOperand = op.getDestinationOperands(builder);
loopNest = mlir::scf::buildLoopNest(
builder, loc, lbs, /*ubs=*/dims, steps, ValueRange(destOperand),
[&](OpBuilder &b, Location loc, ValueRange localIvs,
ValueRange iterArgs) -> scf::ValueVector {
// Compute offsets and sizes of ExtractSliceOp.
SmallVector<Value> offsets =
computeTileOffsets(b, loc, localIvs, tileSizes);
SmallVector<Value> sizes =
computeTileSizes(b, loc, localIvs, tileSizes, allDims);
// Create ExtractSliceOp: Extract a tile from the PadTensorOp.
// Note: The PadTensorOp is located outside of the loop nest. It is
// later moved inside by ExtractSliceOfPadTensorSwapPattern.
auto map = AffineMap::getMultiDimIdentityMap(rank, b.getContext());
Value tiledOutput =
makeTiledShape(b, loc, newPadOp->getResult(0), tileSizes, map,
offsets, allDims, sizes);
auto sliceOp = tiledOutput.getDefiningOp<tensor::ExtractSliceOp>();
assert(sliceOp && "expected ExtractSliceOp");
// Insert the tile into the output tensor.
Value yieldValue =
insertSliceIntoTensor(b, loc, sliceOp, sliceOp, iterArgs[0]);
return scf::ValueVector({yieldValue});
});
return success();
}
namespace {
struct PadTensorOpTilingPattern : public OpRewritePattern<PadTensorOp> {
PadTensorOpTilingPattern(MLIRContext *ctx, LinalgTilingOptions opt)
: OpRewritePattern<PadTensorOp>(ctx), options(opt) {}
LogicalResult matchAndRewrite(PadTensorOp op,
PatternRewriter &rewriter) const override {
if (op->hasAttr(LinalgTransforms::kLinalgTransformMarker))
return failure();
PadTensorOp newPadOp;
LoopNest loopNest;
if (failed(tilePadTensorOp(rewriter, op, newPadOp, loopNest, options)))
return failure();
newPadOp->setAttr(LinalgTransforms::kLinalgTransformMarker,
rewriter.getUnitAttr());
// Replace all uses of the original PadTensorOp.
rewriter.replaceOp(op, loopNest.getResults()[0]);
return success();
}
LinalgTilingOptions options;
};
} // namespace
namespace {
/// Helper classes for type list expansion.
template <typename... OpTypes>
class CanonicalizationPatternList;
template <>
class CanonicalizationPatternList<> {
public:
static void insert(RewritePatternSet &patterns) {}
};
template <typename OpTy, typename... OpTypes>
class CanonicalizationPatternList<OpTy, OpTypes...> {
public:
static void insert(RewritePatternSet &patterns) {
OpTy::getCanonicalizationPatterns(patterns, patterns.getContext());
CanonicalizationPatternList<OpTypes...>::insert(patterns);
}
};
/// Helper classes for type list expansion.
template <typename... OpTypes>
class RewritePatternList;
template <>
class RewritePatternList<> {
public:
static void insert(RewritePatternSet &patterns,
const LinalgTilingOptions &options) {}
};
template <typename OpTy, typename... OpTypes>
class RewritePatternList<OpTy, OpTypes...> {
public:
static void insert(RewritePatternSet &patterns,
const LinalgTilingOptions &options) {
auto *ctx = patterns.getContext();
patterns.add<LinalgTilingPattern<OpTy>>(
ctx, options,
LinalgTransformationFilter(ArrayRef<StringAttr>{},
StringAttr::get(ctx, "tiled")));
RewritePatternList<OpTypes...>::insert(patterns, options);
}
};
} // namespace
RewritePatternSet
mlir::linalg::getLinalgTilingCanonicalizationPatterns(MLIRContext *ctx) {
RewritePatternSet patterns(ctx);
populateLinalgTilingCanonicalizationPatterns(patterns);
return patterns;
}
void mlir::linalg::populateLinalgTilingCanonicalizationPatterns(
RewritePatternSet &patterns) {
auto *ctx = patterns.getContext();
AffineApplyOp::getCanonicalizationPatterns(patterns, ctx);
AffineForOp::getCanonicalizationPatterns(patterns, ctx);
AffineMinOp::getCanonicalizationPatterns(patterns, ctx);
AffineMaxOp::getCanonicalizationPatterns(patterns, ctx);
arith::ConstantIndexOp::getCanonicalizationPatterns(patterns, ctx);
memref::SubViewOp::getCanonicalizationPatterns(patterns, ctx);
memref::ViewOp::getCanonicalizationPatterns(patterns, ctx);
scf::ForOp::getCanonicalizationPatterns(patterns, ctx);
scf::ParallelOp::getCanonicalizationPatterns(patterns, ctx);
tensor::CastOp::getCanonicalizationPatterns(patterns, ctx);
tensor::ExtractSliceOp::getCanonicalizationPatterns(patterns, ctx);
tensor::InsertSliceOp::getCanonicalizationPatterns(patterns, ctx);
InitTensorOp::getCanonicalizationPatterns(patterns, ctx);
PadTensorOp::getCanonicalizationPatterns(patterns, ctx);
ctx->getLoadedDialect<LinalgDialect>()->getCanonicalizationPatterns(patterns);
CanonicalizationPatternList<
#define GET_OP_LIST
#include "mlir/Dialect/Linalg/IR/LinalgStructuredOps.cpp.inc"
>::insert(patterns);
}
/// Populate the given list with patterns that apply Linalg tiling.
static void insertTilingPatterns(RewritePatternSet &patterns,
const LinalgTilingOptions &options) {
RewritePatternList<GenericOp,
#define GET_OP_LIST
#include "mlir/Dialect/Linalg/IR/LinalgStructuredOps.cpp.inc"
>::insert(patterns, options);
patterns.add<PadTensorOpTilingPattern>(patterns.getContext(), options);
}
static void applyExtractSliceOfPadTensorSwapPattern(FuncOp funcOp) {
MLIRContext *ctx = funcOp.getContext();
RewritePatternSet patterns(ctx);
patterns.add<ExtractSliceOfPadTensorSwapPattern>(patterns.getContext());
(void)applyPatternsAndFoldGreedily(funcOp, std::move(patterns));
(void)applyPatternsAndFoldGreedily(
funcOp, getLinalgTilingCanonicalizationPatterns(ctx));
}
namespace {
struct LinalgTilingPass : public LinalgTilingBase<LinalgTilingPass> {
LinalgTilingPass() = default;
LinalgTilingPass(ArrayRef<int64_t> tileSizes, LinalgTilingLoopType loopType,
ArrayRef<StringRef> distributionTypes) {
this->tileSizes = tileSizes;
this->loopType = "";
this->loopTypeEnum = loopType;
this->distributionTypes = llvm::to_vector<2>(llvm::map_range(
distributionTypes, [](StringRef ref) { return ref.str(); }));
}
void runOnFunction() override {
FuncOp funcOp = getFunction();
LinalgTilingLoopType type =
llvm::StringSwitch<LinalgTilingLoopType>(loopType)
.Case("for", LinalgTilingLoopType::Loops)
.Case("affine", LinalgTilingLoopType::AffineLoops)
.Case("parallel", LinalgTilingLoopType::ParallelLoops)
.Case("tiled_loop", LinalgTilingLoopType::TiledLoops)
.Default(loopTypeEnum);
auto distTypes = llvm::to_vector<2>(llvm::map_range(
distributionTypes, [](std::string &str) { return StringRef(str); }));
auto options = LinalgTilingOptions()
.setTileSizes(tileSizes)
.setLoopType(type)
.setDistributionTypes(distTypes);
MLIRContext *ctx = funcOp.getContext();
RewritePatternSet patterns(ctx);
insertTilingPatterns(patterns, options);
scf::populateSCFForLoopCanonicalizationPatterns(patterns);
(void)applyPatternsAndFoldGreedily(funcOp, std::move(patterns));
(void)applyPatternsAndFoldGreedily(
funcOp, getLinalgTilingCanonicalizationPatterns(ctx));
// Drop the marker.
funcOp.walk([](LinalgOp op) {
op->removeAttr(LinalgTransforms::kLinalgTransformMarker);
});
// Apply swap pattern after generating loop nest and running
// canonicalizations.
applyExtractSliceOfPadTensorSwapPattern(funcOp);
}
LinalgTilingLoopType loopTypeEnum;
};
} // namespace
std::unique_ptr<OperationPass<FuncOp>>
mlir::createLinalgTilingPass(ArrayRef<int64_t> tileSizes,
linalg::LinalgTilingLoopType loopType,
ArrayRef<StringRef> distributionTypes) {
return std::make_unique<LinalgTilingPass>(tileSizes, loopType,
distributionTypes);
}
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