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

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//===- HoistPadding.cpp - Hoisting transformation for PadTensorOp ---------===//
//
// 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 functions concerned with hoisting padding operations.
//
//===----------------------------------------------------------------------===//
#include "mlir/Dialect/Linalg/Transforms/HoistPadding.h"
#include "mlir/Analysis/AffineStructures.h"
#include "mlir/Analysis/SliceAnalysis.h"
#include "mlir/Dialect/Affine/IR/AffineValueMap.h"
#include "mlir/Dialect/Affine/Utils.h"
#include "mlir/Dialect/Linalg/IR/LinalgOps.h"
#include "mlir/Dialect/Linalg/Transforms/Transforms.h"
#include "mlir/Dialect/SCF/SCF.h"
#include "mlir/Dialect/SCF/Utils.h"
#include "mlir/Dialect/StandardOps/IR/Ops.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Dialect/Vector/VectorOps.h"
#include "mlir/Dialect/Vector/VectorUtils.h"
#include "mlir/IR/AsmState.h"
#include "mlir/IR/BuiltinOps.h"
#include "mlir/IR/Dominance.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
#include "mlir/Transforms/LoopUtils.h"
#include "llvm/ADT/StringRef.h"
#include "llvm/Support/Debug.h"
using llvm::dbgs;
#define DEBUG_TYPE "hoist-padding"
#define DBGS() (dbgs() << '[' << DEBUG_TYPE << "] ")
using namespace mlir;
using namespace mlir::linalg;
/// Analysis class to support PadTensorOp hoisting across multiple enclosing
/// loops. The failure conditions are:
/// 1. Pad op has a use that is not an input of a LinalgOp.
/// 2. There is no immediately enclosing scf::ForOp.
/// 3. The backward slice from the pad op to the scf::ForOp to hoist above
/// contains an unknown op with a region.
/// 4. The backward slice from the pad op to the scf::ForOp to hoist above is
/// empty.
/// Other cases succeed and will trigger hoisting of the pad op.
struct HoistingAnalysis {
HoistingAnalysis(PadTensorOp padTensorOp, int nLevels);
bool isValid() { return valid; }
/// Footprint of the packedTensor, computed from the packingLoops and
/// `backwardSlice`.
FailureOr<SmallVector<Value>> getPackedTensorSizes(ImplicitLocOpBuilder &b);
/// The padTensorOp that needs to be hoisted.
PadTensorOp padTensorOp;
/// The maximum number of immediately enclosing scf::ForOp to hoist over.
int nLevels;
/// The outermost loop, determined by `nLevels` above which `padTensorOp` will
/// be hoisted.
scf::ForOp outermostEnclosingForOp;
/// Backward slice rooted at `padTensorOp` and nested under
/// `outermostEnclosingForOp`.
SetVector<Operation *> backwardSlice;
/// The scf::ForOp immediately enclosing `padTensorOp` such that:
/// 1. they are nested under `outermostEnclosingForOp` (inclusive)
/// 2. whose induction variable is used, directly or indirectly, in the
/// computation of `padTensorOp`.
/// The span of these loops determines the footprint of the packed tensor.
/// SmallSetVector<scf::ForOp> packingLoops;
SetVector<scf::ForOp, SmallVector<scf::ForOp>, DenseSet<Operation *>>
packingLoops;
private:
/// Encodes whether the analysis is valid and hoisting can proceed.
bool valid;
};
/// Return true if all uses of `padTensorOp` are an input tensor of some
/// LinalgOp.
static bool isOnlyUsedAsInputOfLinalgOp(PadTensorOp padTensorOp) {
for (OpOperand &use : padTensorOp.result().getUses()) {
auto linalgUser = dyn_cast<linalg::LinalgOp>(use.getOwner());
if (!linalgUser || !linalgUser.isInputTensor(&use)) {
LLVM_DEBUG(DBGS() << "Found a use of " << *(padTensorOp)
<< "\nthat is not an input tensor of a LinalgOp, "
<< "cannot hoist\n"
<< *(use.getOwner()) << "\n");
return false;
}
}
return true;
}
/// Return at most nLevels of immediately enclosing scf::ForOp loops.
/// Stops at the first parent that is not an scf::ForOp.
/// Multi-loops such as scf.parallel or linalg.tiled_loop are not modeled atm.
/// Control-flow and other containing ops with regions are not modeled atm.
static void
getAtMostNEnclosingLoops(PadTensorOp padTensorOp, int nLevels,
SmallVector<scf::ForOp> &reverseEnclosingLoops) {
AsmState state(padTensorOp->getParentOfType<mlir::FuncOp>());
(void)state;
scf::ForOp outermostEnclosingForOp = nullptr;
Operation *nextEnclosingOp = padTensorOp->getParentOp();
while (nLevels-- > 0 &&
(outermostEnclosingForOp = dyn_cast<scf::ForOp>(nextEnclosingOp))) {
LLVM_DEBUG(
DBGS() << "loops: ";
outermostEnclosingForOp.getInductionVar().printAsOperand(dbgs(), state);
dbgs() << "\n");
reverseEnclosingLoops.push_back(outermostEnclosingForOp);
nextEnclosingOp = outermostEnclosingForOp->getParentOp();
}
}
HoistingAnalysis::HoistingAnalysis(PadTensorOp padTensorOp, int nLevels)
: padTensorOp(padTensorOp), nLevels(nLevels), valid(false) {
AsmState state(padTensorOp->getParentOfType<mlir::FuncOp>());
(void)state;
// Bail on any use that isn't an input of a Linalg op.
// Hoisting of inplace updates happens after vectorization.
if (!isOnlyUsedAsInputOfLinalgOp(padTensorOp))
return;
// Get at most nLevels of immediately enclosing loops.
SmallVector<scf::ForOp> reverseEnclosingLoops;
getAtMostNEnclosingLoops(padTensorOp, nLevels, reverseEnclosingLoops);
if (reverseEnclosingLoops.empty()) {
LLVM_DEBUG(DBGS() << "No immediately enclosing loop -> skip\n");
return;
}
outermostEnclosingForOp = reverseEnclosingLoops.back();
// Get all the ops in the backwards slice starting from `padTensorOp` and that
// are dominated by the outermost enclosing loop.
// Bail on any op with a region that is not either a scf::ForOp or a LinalgOp.
bool analysisFailure = false;
DominanceInfo domInfo(outermostEnclosingForOp);
getBackwardSlice(
padTensorOp.getOperation(), &backwardSlice, [&](Operation *op) {
if (!domInfo.dominates(outermostEnclosingForOp, op))
return false;
if (op != padTensorOp && op->getNumRegions() > 0 &&
!isa<scf::ForOp, LinalgOp>(op)) {
analysisFailure = true;
LLVM_DEBUG(DBGS()
<< "Unsupported op with region: " << *op << " -> skip\n");
return false;
}
return true;
});
if (analysisFailure || backwardSlice.empty())
return;
// Backward slice is a topologically sorted list of ops starting at
// `outermostEnclosingForOp`.
assert(outermostEnclosingForOp == backwardSlice.front());
// Filter out the loops whose induction variable is not used to compute the
// padded result. As a first approximation, just look for IVs that have no use
// in the backwardSlice.
// These are the dimensions of reuse that we can exploit to reduce the amount
// of copy / memory.
for (scf::ForOp forOp : llvm::reverse(reverseEnclosingLoops)) {
for (Operation *user : forOp.getInductionVar().getUsers()) {
if (backwardSlice.contains(user)) {
packingLoops.insert(forOp);
break;
}
}
}
// The analysis is valid and hoisting can occur.
valid = true;
}
static bool isDefinedOutsideOrConstant(scf::ForOp outer, Value v) {
return outer.isDefinedOutsideOfLoop(v) || v.getDefiningOp<ConstantOp>();
}
/// For each loop in `loops`, determine the ops involved in the construction of
/// its upper bound---up to the outerLimit loop--- and fold them as new
/// inequalities in the constraint set.
/// This is achieved by computing the backwardSlice of the loop's upper bound
/// and iteratively folding each op in reverse topological order to guarantee
/// use-def ordering.
/// As operations are folded in, their result is projected out of the
/// constraints set.
/// The following operations are supported:
/// - scf::ForOp are simply skipped.
/// - AffineApplyOp are composed to replace the result by an equality.
/// - AffineMinOp are composed by adding each entry as an upper bound.
/// If any other operation is met, return failure.
// TODO: extend on a per-need basis.
static LogicalResult
foldUpperBoundsIntoConstraintsSet(FlatAffineValueConstraints &constraints,
scf::ForOp outerLimit,
ArrayRef<scf::ForOp> loops) {
SetVector<Value> toProjectOut;
for (scf::ForOp loop : loops) {
auto ub = loop.upperBound();
if (isDefinedOutsideOrConstant(outerLimit, ub))
continue;
// Compute a backward slice up to, but not including, `outerLimit`.
SetVector<Operation *> backwardSlice;
getBackwardSlice(ub, &backwardSlice, [&](Operation *op) {
return outerLimit->isProperAncestor(op);
});
backwardSlice.insert(ub.getDefiningOp());
// Iterate over all ops in the slice and compose them in the constraints.
for (Operation *op : llvm::reverse(backwardSlice)) {
if (!isa<scf::ForOp, AffineApplyOp, AffineMinOp>(op))
return failure();
if (isa<scf::ForOp>(op))
continue;
// Ensure there is a
auto ensureIdFailed = [&](Value v) {
if (constraints.containsId(v)) {
unsigned pos;
constraints.findId(v, &pos);
return pos >= constraints.getNumDimIds();
}
constraints.appendDimId(v);
return false;
};
// Ensure all ids exist and add results for later projection.
if (llvm::any_of(op->getResults(), ensureIdFailed) ||
llvm::any_of(op->getOperands(), ensureIdFailed))
return failure();
// All supported ops have 1 result.
// TODO: extend when needed.
toProjectOut.insert(op->getResult(0));
// Compose supported ops.
if (auto affineApplyOp = dyn_cast<AffineApplyOp>(op)) {
AffineValueMap avm(affineApplyOp.getAffineMap(),
affineApplyOp.getOperands(),
affineApplyOp.getResult());
if (failed(constraints.composeMap(&avm)))
return failure();
continue;
}
auto affineMinOp = cast<AffineMinOp>(op);
unsigned pos;
bool foundMinOp = constraints.findId(affineMinOp.getResult(), &pos);
(void)foundMinOp;
assert(foundMinOp);
AffineMap alignedMap = constraints.computeAlignedMap(
affineMinOp.getAffineMap(), affineMinOp.getOperands());
if (failed(
constraints.addBound(FlatAffineConstraints::UB, pos, alignedMap)))
return failure();
}
}
for (Value v : toProjectOut)
constraints.projectOut(v);
return success();
}
// Footprint of the packedTensor, computed from the packingLoops and
// `backwardSlice`.
FailureOr<SmallVector<Value>>
HoistingAnalysis::getPackedTensorSizes(ImplicitLocOpBuilder &b) {
// Create the base affine constaints for the packedLoops.
auto constraints = FlatAffineValueConstraints::getHyperrectangular(
llvm::to_vector<8>(llvm::map_range(
packingLoops, [](scf::ForOp op) { return op.getInductionVar(); })),
llvm::to_vector<8>(llvm::map_range(
packingLoops, [](scf::ForOp op) { return op.lowerBound(); })),
llvm::to_vector<8>(llvm::map_range(
packingLoops, [](scf::ForOp op) { return op.upperBound(); })));
// Iteratively try to fold the upper bounds into the constraints set.
if (failed(foldUpperBoundsIntoConstraintsSet(
constraints, outermostEnclosingForOp, packingLoops.getArrayRef())))
return failure();
int nPackedLoops = packingLoops.size();
SmallVector<AffineMap> lbs(nPackedLoops), ubs(nPackedLoops);
// Compute the bounds of the first positions, assuming the others are fixed.
constraints.getSliceBounds(/*pos=*/0, /*num=*/nPackedLoops,
outermostEnclosingForOp->getContext(), &lbs, &ubs);
SmallVector<Value> allValues;
constraints.getAllValues(&allValues);
SmallVector<Value> allNonLoopValues(allValues.begin() + nPackedLoops,
allValues.end());
// For each packingLoop, create the extent by (ub - lb).ceilDiv(step).
// IP just before the outermost loop considered that we hoist above.
assert(nPackedLoops == static_cast<int64_t>(lbs.size()) &&
"expected matching lb sizes");
assert(nPackedLoops == static_cast<int64_t>(ubs.size()) &&
"expected matching ub sizes");
SmallVector<Value> dynamicTensorSizes;
for (auto it : llvm::zip(packingLoops, lbs, ubs)) {
scf::ForOp loop = std::get<0>(it);
AffineMap lbMap = std::get<1>(it);
AffineMap ubMap = std::get<2>(it);
SmallVector<Value> lbOperands(allNonLoopValues);
canonicalizeMapAndOperands(&lbMap, &lbOperands);
Value lbVal = b.createOrFold<AffineMaxOp>(lbMap, lbOperands);
SmallVector<Value> ubOperands(allNonLoopValues);
canonicalizeMapAndOperands(&ubMap, &ubOperands);
Value ubVal = b.createOrFold<AffineMinOp>(ubMap, ubOperands);
AffineExpr lb, ub, step;
bindDims(b.getContext(), lb, ub);
bindSymbols(b.getContext(), step);
Value res = b.createOrFold<AffineApplyOp>(
(ub - lb).ceilDiv(step),
ValueRange{lbVal, ubVal, cast<scf::ForOp>(loop).step()});
dynamicTensorSizes.push_back(res);
}
return dynamicTensorSizes;
}
/// Return the current iteration number in the loop (iv - lb).ceilDiv(step).
/// The returned Value is guaranteed not to depend on any loop comprised in
/// [`outer`, `forOp`].
/// Return null if such a loop-independent quantity cannot be computed.
static Value buildLoopIterationCount(OpBuilder &b, scf::ForOp outer,
scf::ForOp forOp) {
MLIRContext *ctx = forOp->getContext();
AffineExpr iv, lb, step;
bindDims(ctx, iv, lb);
bindSymbols(ctx, step);
if (!isDefinedOutsideOrConstant(outer, forOp.lowerBound()) ||
!isDefinedOutsideOrConstant(outer, forOp.step()))
return Value();
Value ivVal = forOp.getInductionVar(), lbVal = forOp.lowerBound(),
stepVal = forOp.step();
auto loc = forOp->getLoc();
return b.createOrFold<AffineApplyOp>(loc, (iv - lb).ceilDiv(step),
ValueRange{ivVal, lbVal, stepVal});
}
FailureOr<Value> mlir::linalg::hoistPaddingOnTensors(PadTensorOp opToHoist,
int numLoops,
PadTensorOp &hoistedOp) {
LLVM_DEBUG(DBGS() << "Try to hoist " << *(opToHoist) << " by " << numLoops
<< " loops\n");
HoistingAnalysis analysis(opToHoist, numLoops);
if (!analysis.isValid()) {
LLVM_DEBUG(DBGS() << "Analysis failed -> Skip\n");
return failure();
}
scf::ForOp outer = analysis.outermostEnclosingForOp;
ImplicitLocOpBuilder b(outer->getLoc(), outer);
auto maybeDynamicTensorSizes = analysis.getPackedTensorSizes(b);
if (failed(maybeDynamicTensorSizes))
return failure();
SmallVector<Value> dynamicTensorSizes = *maybeDynamicTensorSizes;
// Update actual number of loops, which may be smaller.
int nPackedLoops = analysis.packingLoops.size();
Location loc = opToHoist->getLoc();
RankedTensorType paddedTensorType = opToHoist.getResultType();
int paddedRank = paddedTensorType.getRank();
// Create the packed tensor<?x?x..?xpadded_shape> into which we amortize
// padding.
SmallVector<int64_t> packedShape(nPackedLoops, ShapedType::kDynamicSize);
// TODO: go grab dims when necessary, for now PadTensorOp returns a static
// tensor.
llvm::append_range(packedShape, paddedTensorType.getShape());
auto packedTensorType =
RankedTensorType::get(packedShape, paddedTensorType.getElementType());
Value packedTensor = b.create<linalg::InitTensorOp>(
loc, dynamicTensorSizes, packedTensorType.getShape(),
packedTensorType.getElementType());
// Clone the operations involved in the backward slice, iteratively stepping
// into the loops that we encounter.
// The implementation proceeds in a stack-like fashion:
// 1. Iteratively clone and step into the loops, pushing the `packedTensor`
// deeper in the stack.
// 2. Create a InsertSliceOp at the top of the stack.
// 3. Iteratively pop and yield the result of the InsertSliceOp across
// the cloned loops.
SmallVector<Value> clonedLoopIvs, leadingPackedTensorIndexings;
clonedLoopIvs.reserve(nPackedLoops);
leadingPackedTensorIndexings.reserve(nPackedLoops);
BlockAndValueMapping bvm;
// Insert `opToHoist` into the backwardSlice so we clone it too.
analysis.backwardSlice.insert(opToHoist);
// Stack step 1. iteratively clone loops and push `packedTensor`.
for (Operation *op : analysis.backwardSlice) {
// Specifically sit out in the extract_slice(packedTensor) case: this is the
// piece we seek to replace.
if (auto sliceOp = dyn_cast<tensor::ExtractSliceOp>(op))
if (bvm.lookupOrDefault(sliceOp.source()) == packedTensor)
continue;
auto effects = dyn_cast<MemoryEffectOpInterface>(op);
bool hasNoEffects = !effects || effects.hasNoEffect();
if (hasNoEffects &&
(op->getNumRegions() == 0 || isa<linalg::PadTensorOp>(op))) {
b.clone(*op, bvm);
continue;
}
// TODO: support more cases as they appear.
auto forOp = dyn_cast<scf::ForOp>(op);
assert(forOp && "Expected scf::ForOp when hoisting pad ops");
// Unused loop, just skip it.
if (!analysis.packingLoops.contains(forOp))
continue;
auto clonedForOp =
b.create<scf::ForOp>(loc, bvm.lookupOrDefault(forOp.lowerBound()),
bvm.lookupOrDefault(forOp.upperBound()),
bvm.lookupOrDefault(forOp.step()), packedTensor);
// Map the induction var, region args and results to the `clonedForOp`.
bvm.map(forOp.getInductionVar(), clonedForOp.getInductionVar());
bvm.map(forOp.getRegionIterArgs(), clonedForOp.getRegionIterArgs());
bvm.map(forOp.getResults(), clonedForOp.getResults());
assert(clonedForOp->getNumRegions() == 1);
clonedLoopIvs.push_back(clonedForOp.getInductionVar());
b.setInsertionPointToStart(&clonedForOp->getRegion(0).front());
Value loopIndependentIterationCount =
buildLoopIterationCount(b, outer, clonedForOp);
// Assert the loop-independent iteration count can be computed.
if (!loopIndependentIterationCount)
llvm_unreachable("loop independence prerequisite not met");
leadingPackedTensorIndexings.push_back(loopIndependentIterationCount);
packedTensor = clonedForOp.getRegionIterArgs().front();
}
// Stack step 2. create InsertSliceOp at the top of the stack.
// offsets = [clonedLoopIvs, 0 .. 0].
SmallVector<OpFoldResult> offsets(leadingPackedTensorIndexings.begin(),
leadingPackedTensorIndexings.end());
offsets.append(paddedRank, b.getIndexAttr(0));
// sizes = [1 .. 1, paddedShape].
SmallVector<OpFoldResult> sizes(nPackedLoops, b.getIndexAttr(1));
for (int64_t sz : paddedTensorType.getShape()) {
// TODO: go grab dims when necessary, for now PadTensorOp returns a static
// tensor.
assert(!ShapedType::isDynamic(sz) && "padded tensor needs static sizes");
sizes.push_back(b.getIndexAttr(sz));
}
// strides = [1 .. 1].
SmallVector<OpFoldResult> strides(nPackedLoops + paddedRank,
b.getIndexAttr(1));
Value inserted =
b.create<tensor::InsertSliceOp>(loc, bvm.lookup(opToHoist.result()),
packedTensor, offsets, sizes, strides);
// Stack step 3. iteratively pop the stack and propagate the yield.
Value valueToYield = inserted;
for (Value iv : llvm::reverse(clonedLoopIvs)) {
auto forOp = scf::getForInductionVarOwner(iv);
b.setInsertionPointToEnd(&forOp.getRegion().front());
b.create<scf::YieldOp>(loc, valueToYield);
valueToYield = forOp.getResult(0);
}
// Now the packed tensor is ready, replace the original padding op by a
// 1x..x1 slice [originalLoopIvs, 0 .. 0][1 .. 1, paddedShape][1 .. 1].
b.setInsertionPoint(opToHoist);
SmallVector<Value> loopIterationCounts = llvm::to_vector<4>(
llvm::map_range(analysis.packingLoops, [&](Operation *loop) {
return buildLoopIterationCount(b, outer, cast<scf::ForOp>(loop));
}));
// Assert all loop iteration counts can be computed.
if (llvm::any_of(loopIterationCounts, [](Value v) { return !v; }))
llvm_unreachable("loop independence prerequisite not met");
// offsets = [originalLoopIvs, 0 .. 0].
offsets.assign(loopIterationCounts.begin(), loopIterationCounts.end());
offsets.append(paddedRank, b.getIndexAttr(0));
// sizes = [1 .. 1, paddedShape] (definedabove).
// strides = [1 .. 1] (defined above)
packedTensor =
scf::getForInductionVarOwner(clonedLoopIvs.front())->getResult(0);
Value newResult = b.create<tensor::ExtractSliceOp>(
loc, opToHoist.getResultType(), packedTensor, offsets, sizes, strides);
// Make the newly cloned `opToHoist` available to the caller.
hoistedOp = cast<PadTensorOp>(bvm.lookup(opToHoist.result()).getDefiningOp());
return newResult;
}
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