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llvm-project/mlir/lib/Transforms/Utils/LoopUtils.cpp

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//===- LoopUtils.cpp ---- Misc utilities for loop transformation ----------===//
//
// 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 miscellaneous loop transformation routines.
//
//===----------------------------------------------------------------------===//
#include "mlir/Transforms/LoopUtils.h"
#include "mlir/Analysis/AffineAnalysis.h"
#include "mlir/Analysis/LoopAnalysis.h"
#include "mlir/Analysis/SliceAnalysis.h"
#include "mlir/Analysis/Utils.h"
#include "mlir/Dialect/AffineOps/AffineOps.h"
#include "mlir/Dialect/LoopOps/LoopOps.h"
#include "mlir/IR/AffineMap.h"
#include "mlir/IR/BlockAndValueMapping.h"
#include "mlir/IR/Function.h"
#include "mlir/Transforms/RegionUtils.h"
#include "mlir/Transforms/Utils.h"
#include "llvm/ADT/DenseMap.h"
#include "llvm/ADT/MapVector.h"
#include "llvm/ADT/SetVector.h"
#include "llvm/ADT/SmallPtrSet.h"
#include "llvm/Support/Debug.h"
#include "llvm/Support/raw_ostream.h"
#define DEBUG_TYPE "LoopUtils"
using namespace mlir;
using llvm::SetVector;
using llvm::SmallMapVector;
/// Computes the cleanup loop lower bound of the loop being unrolled with
/// the specified unroll factor; this bound will also be upper bound of the main
/// part of the unrolled loop. Computes the bound as an AffineMap with its
/// operands or a null map when the trip count can't be expressed as an affine
/// expression.
void mlir::getCleanupLoopLowerBound(AffineForOp forOp, unsigned unrollFactor,
AffineMap *map,
SmallVectorImpl<Value> *operands,
OpBuilder &b) {
auto lbMap = forOp.getLowerBoundMap();
// Single result lower bound map only.
if (lbMap.getNumResults() != 1) {
*map = AffineMap();
return;
}
AffineMap tripCountMap;
SmallVector<Value, 4> tripCountOperands;
buildTripCountMapAndOperands(forOp, &tripCountMap, &tripCountOperands);
// Sometimes the trip count cannot be expressed as an affine expression.
if (!tripCountMap) {
*map = AffineMap();
return;
}
unsigned step = forOp.getStep();
auto lb = b.create<AffineApplyOp>(forOp.getLoc(), lbMap,
forOp.getLowerBoundOperands());
// For each upper bound expr, get the range.
// Eg: affine.for %i = lb to min (ub1, ub2),
// where tripCountExprs yield (tr1, tr2), we create affine.apply's:
// lb + tr1 - tr1 % ufactor, lb + tr2 - tr2 % ufactor; the results of all
// these affine.apply's make up the cleanup loop lower bound.
SmallVector<AffineExpr, 4> bumpExprs(tripCountMap.getNumResults());
SmallVector<Value, 4> bumpValues(tripCountMap.getNumResults());
for (unsigned i = 0, e = tripCountMap.getNumResults(); i < e; i++) {
auto tripCountExpr = tripCountMap.getResult(i);
bumpExprs[i] = (tripCountExpr - tripCountExpr % unrollFactor) * step;
auto bumpMap = AffineMap::get(tripCountMap.getNumDims(),
tripCountMap.getNumSymbols(), bumpExprs[i]);
bumpValues[i] =
b.create<AffineApplyOp>(forOp.getLoc(), bumpMap, tripCountOperands);
}
SmallVector<AffineExpr, 4> newUbExprs(tripCountMap.getNumResults());
for (unsigned i = 0, e = bumpExprs.size(); i < e; i++)
newUbExprs[i] = b.getAffineDimExpr(0) + b.getAffineDimExpr(i + 1);
operands->clear();
operands->push_back(lb);
operands->append(bumpValues.begin(), bumpValues.end());
*map = AffineMap::get(1 + tripCountMap.getNumResults(), 0, newUbExprs);
// Simplify the map + operands.
fullyComposeAffineMapAndOperands(map, operands);
*map = simplifyAffineMap(*map);
canonicalizeMapAndOperands(map, operands);
// Remove any affine.apply's that became dead from the simplification above.
for (auto v : bumpValues) {
if (v.use_empty())
v.getDefiningOp()->erase();
}
if (lb.use_empty())
lb.erase();
}
/// Promotes the loop body of a forOp to its containing block if the forOp
/// was known to have a single iteration.
// TODO(bondhugula): extend this for arbitrary affine bounds.
LogicalResult mlir::promoteIfSingleIteration(AffineForOp forOp) {
Optional<uint64_t> tripCount = getConstantTripCount(forOp);
if (!tripCount.hasValue() || tripCount.getValue() != 1)
return failure();
// TODO(mlir-team): there is no builder for a max.
if (forOp.getLowerBoundMap().getNumResults() != 1)
return failure();
// Replaces all IV uses to its single iteration value.
auto iv = forOp.getInductionVar();
Operation *op = forOp.getOperation();
if (!iv.use_empty()) {
if (forOp.hasConstantLowerBound()) {
OpBuilder topBuilder(op->getParentOfType<FuncOp>().getBody());
auto constOp = topBuilder.create<ConstantIndexOp>(
forOp.getLoc(), forOp.getConstantLowerBound());
iv.replaceAllUsesWith(constOp);
} else {
AffineBound lb = forOp.getLowerBound();
SmallVector<Value, 4> lbOperands(lb.operand_begin(), lb.operand_end());
OpBuilder builder(op->getBlock(), Block::iterator(op));
if (lb.getMap() == builder.getDimIdentityMap()) {
// No need of generating an affine.apply.
iv.replaceAllUsesWith(lbOperands[0]);
} else {
auto affineApplyOp = builder.create<AffineApplyOp>(
op->getLoc(), lb.getMap(), lbOperands);
iv.replaceAllUsesWith(affineApplyOp);
}
}
}
// Move the loop body operations, except for terminator, to the loop's
// containing block.
auto *block = op->getBlock();
forOp.getBody()->getOperations().back().erase();
block->getOperations().splice(Block::iterator(op),
forOp.getBody()->getOperations());
forOp.erase();
return success();
}
/// Promotes all single iteration for op's in the FuncOp, i.e., moves
/// their body into the containing Block.
void mlir::promoteSingleIterationLoops(FuncOp f) {
// Gathers all innermost loops through a post order pruned walk.
f.walk([](AffineForOp forOp) { promoteIfSingleIteration(forOp); });
}
/// Generates a 'affine.for' op with the specified lower and upper bounds
/// while generating the right IV remappings for the shifted operations. The
/// operation blocks that go into the loop are specified in instGroupQueue
/// starting from the specified offset, and in that order; the first element of
/// the pair specifies the shift applied to that group of operations; note
/// that the shift is multiplied by the loop step before being applied. Returns
/// nullptr if the generated loop simplifies to a single iteration one.
static AffineForOp
generateLoop(AffineMap lbMap, AffineMap ubMap,
const std::vector<std::pair<uint64_t, ArrayRef<Operation *>>>
&instGroupQueue,
unsigned offset, AffineForOp srcForInst, OpBuilder b) {
SmallVector<Value, 4> lbOperands(srcForInst.getLowerBoundOperands());
SmallVector<Value, 4> ubOperands(srcForInst.getUpperBoundOperands());
assert(lbMap.getNumInputs() == lbOperands.size());
assert(ubMap.getNumInputs() == ubOperands.size());
auto loopChunk =
b.create<AffineForOp>(srcForInst.getLoc(), lbOperands, lbMap, ubOperands,
ubMap, srcForInst.getStep());
auto loopChunkIV = loopChunk.getInductionVar();
auto srcIV = srcForInst.getInductionVar();
BlockAndValueMapping operandMap;
OpBuilder bodyBuilder = loopChunk.getBodyBuilder();
for (auto it = instGroupQueue.begin() + offset, e = instGroupQueue.end();
it != e; ++it) {
uint64_t shift = it->first;
auto insts = it->second;
// All 'same shift' operations get added with their operands being
// remapped to results of cloned operations, and their IV used remapped.
// Generate the remapping if the shift is not zero: remappedIV = newIV -
// shift.
if (!srcIV.use_empty() && shift != 0) {
auto ivRemap = bodyBuilder.create<AffineApplyOp>(
srcForInst.getLoc(),
bodyBuilder.getSingleDimShiftAffineMap(
-static_cast<int64_t>(srcForInst.getStep() * shift)),
loopChunkIV);
operandMap.map(srcIV, ivRemap);
} else {
operandMap.map(srcIV, loopChunkIV);
}
for (auto *op : insts) {
if (!isa<AffineTerminatorOp>(op))
bodyBuilder.clone(*op, operandMap);
}
};
if (succeeded(promoteIfSingleIteration(loopChunk)))
return AffineForOp();
return loopChunk;
}
/// Skew the operations in the body of a 'affine.for' operation with the
/// specified operation-wise shifts. The shifts are with respect to the
/// original execution order, and are multiplied by the loop 'step' before being
/// applied. A shift of zero for each operation will lead to no change.
// The skewing of operations with respect to one another can be used for
// example to allow overlap of asynchronous operations (such as DMA
// communication) with computation, or just relative shifting of operations
// for better register reuse, locality or parallelism. As such, the shifts are
// typically expected to be at most of the order of the number of operations.
// This method should not be used as a substitute for loop distribution/fission.
// This method uses an algorithm// in time linear in the number of operations
// in the body of the for loop - (using the 'sweep line' paradigm). This method
// asserts preservation of SSA dominance. A check for that as well as that for
// memory-based dependence preservation check rests with the users of this
// method.
LogicalResult mlir::instBodySkew(AffineForOp forOp, ArrayRef<uint64_t> shifts,
bool unrollPrologueEpilogue) {
if (forOp.getBody()->begin() == std::prev(forOp.getBody()->end()))
return success();
// If the trip counts aren't constant, we would need versioning and
// conditional guards (or context information to prevent such versioning). The
// better way to pipeline for such loops is to first tile them and extract
// constant trip count "full tiles" before applying this.
auto mayBeConstTripCount = getConstantTripCount(forOp);
if (!mayBeConstTripCount.hasValue()) {
LLVM_DEBUG(forOp.emitRemark("non-constant trip count loop not handled"));
return success();
}
uint64_t tripCount = mayBeConstTripCount.getValue();
assert(isInstwiseShiftValid(forOp, shifts) &&
"shifts will lead to an invalid transformation\n");
int64_t step = forOp.getStep();
unsigned numChildInsts = forOp.getBody()->getOperations().size();
// Do a linear time (counting) sort for the shifts.
uint64_t maxShift = 0;
for (unsigned i = 0; i < numChildInsts; i++) {
maxShift = std::max(maxShift, shifts[i]);
}
// Such large shifts are not the typical use case.
if (maxShift >= numChildInsts) {
forOp.emitWarning("not shifting because shifts are unrealistically large");
return success();
}
// An array of operation groups sorted by shift amount; each group has all
// operations with the same shift in the order in which they appear in the
// body of the 'affine.for' op.
std::vector<std::vector<Operation *>> sortedInstGroups(maxShift + 1);
unsigned pos = 0;
for (auto &op : *forOp.getBody()) {
auto shift = shifts[pos++];
sortedInstGroups[shift].push_back(&op);
}
// Unless the shifts have a specific pattern (which actually would be the
// common use case), prologue and epilogue are not meaningfully defined.
// Nevertheless, if 'unrollPrologueEpilogue' is set, we will treat the first
// loop generated as the prologue and the last as epilogue and unroll these
// fully.
AffineForOp prologue;
AffineForOp epilogue;
// Do a sweep over the sorted shifts while storing open groups in a
// vector, and generating loop portions as necessary during the sweep. A block
// of operations is paired with its shift.
std::vector<std::pair<uint64_t, ArrayRef<Operation *>>> instGroupQueue;
auto origLbMap = forOp.getLowerBoundMap();
uint64_t lbShift = 0;
OpBuilder b(forOp.getOperation());
for (uint64_t d = 0, e = sortedInstGroups.size(); d < e; ++d) {
// If nothing is shifted by d, continue.
if (sortedInstGroups[d].empty())
continue;
if (!instGroupQueue.empty()) {
assert(d >= 1 &&
"Queue expected to be empty when the first block is found");
// The interval for which the loop needs to be generated here is:
// [lbShift, min(lbShift + tripCount, d)) and the body of the
// loop needs to have all operations in instQueue in that order.
AffineForOp res;
if (lbShift + tripCount * step < d * step) {
res = generateLoop(
b.getShiftedAffineMap(origLbMap, lbShift),
b.getShiftedAffineMap(origLbMap, lbShift + tripCount * step),
instGroupQueue, 0, forOp, b);
// Entire loop for the queued op groups generated, empty it.
instGroupQueue.clear();
lbShift += tripCount * step;
} else {
res = generateLoop(b.getShiftedAffineMap(origLbMap, lbShift),
b.getShiftedAffineMap(origLbMap, d), instGroupQueue,
0, forOp, b);
lbShift = d * step;
}
if (!prologue && res)
prologue = res;
epilogue = res;
} else {
// Start of first interval.
lbShift = d * step;
}
// Augment the list of operations that get into the current open interval.
instGroupQueue.push_back({d, sortedInstGroups[d]});
}
// Those operations groups left in the queue now need to be processed (FIFO)
// and their loops completed.
for (unsigned i = 0, e = instGroupQueue.size(); i < e; ++i) {
uint64_t ubShift = (instGroupQueue[i].first + tripCount) * step;
epilogue = generateLoop(b.getShiftedAffineMap(origLbMap, lbShift),
b.getShiftedAffineMap(origLbMap, ubShift),
instGroupQueue, i, forOp, b);
lbShift = ubShift;
if (!prologue)
prologue = epilogue;
}
// Erase the original for op.
forOp.erase();
if (unrollPrologueEpilogue && prologue)
loopUnrollFull(prologue);
if (unrollPrologueEpilogue && !epilogue &&
epilogue.getOperation() != prologue.getOperation())
loopUnrollFull(epilogue);
return success();
}
// Collect perfectly nested loops starting from `rootForOps`. Loops are
// perfectly nested if each loop is the first and only non-terminator operation
// in the parent loop. Collect at most `maxLoops` loops and append them to
// `forOps`.
template <typename T>
static void getPerfectlyNestedLoopsImpl(
SmallVectorImpl<T> &forOps, T rootForOp,
unsigned maxLoops = std::numeric_limits<unsigned>::max()) {
for (unsigned i = 0; i < maxLoops; ++i) {
forOps.push_back(rootForOp);
Block &body = rootForOp.region().front();
if (body.begin() != std::prev(body.end(), 2))
return;
rootForOp = dyn_cast<T>(&body.front());
if (!rootForOp)
return;
}
}
/// Get perfectly nested sequence of loops starting at root of loop nest
/// (the first op being another AffineFor, and the second op - a terminator).
/// A loop is perfectly nested iff: the first op in the loop's body is another
/// AffineForOp, and the second op is a terminator).
void mlir::getPerfectlyNestedLoops(SmallVectorImpl<AffineForOp> &nestedLoops,
AffineForOp root) {
getPerfectlyNestedLoopsImpl(nestedLoops, root);
}
void mlir::getPerfectlyNestedLoops(SmallVectorImpl<loop::ForOp> &nestedLoops,
loop::ForOp root) {
getPerfectlyNestedLoopsImpl(nestedLoops, root);
}
/// Unrolls this loop completely.
LogicalResult mlir::loopUnrollFull(AffineForOp forOp) {
Optional<uint64_t> mayBeConstantTripCount = getConstantTripCount(forOp);
if (mayBeConstantTripCount.hasValue()) {
uint64_t tripCount = mayBeConstantTripCount.getValue();
if (tripCount == 1) {
return promoteIfSingleIteration(forOp);
}
return loopUnrollByFactor(forOp, tripCount);
}
return failure();
}
/// Unrolls and jams this loop by the specified factor or by the trip count (if
/// constant) whichever is lower.
LogicalResult mlir::loopUnrollUpToFactor(AffineForOp forOp,
uint64_t unrollFactor) {
Optional<uint64_t> mayBeConstantTripCount = getConstantTripCount(forOp);
if (mayBeConstantTripCount.hasValue() &&
mayBeConstantTripCount.getValue() < unrollFactor)
return loopUnrollByFactor(forOp, mayBeConstantTripCount.getValue());
return loopUnrollByFactor(forOp, unrollFactor);
}
/// Unrolls this loop by the specified factor. Returns success if the loop
/// is successfully unrolled.
LogicalResult mlir::loopUnrollByFactor(AffineForOp forOp,
uint64_t unrollFactor) {
assert(unrollFactor >= 1 && "unroll factor should be >= 1");
if (unrollFactor == 1)
return promoteIfSingleIteration(forOp);
if (forOp.getBody()->empty() ||
forOp.getBody()->begin() == std::prev(forOp.getBody()->end()))
return failure();
// Loops where the lower bound is a max expression isn't supported for
// unrolling since the trip count can be expressed as an affine function when
// both the lower bound and the upper bound are multi-result maps. However,
// one meaningful way to do such unrolling would be to specialize the loop for
// the 'hotspot' case and unroll that hotspot.
if (forOp.getLowerBoundMap().getNumResults() != 1)
return failure();
// If the trip count is lower than the unroll factor, no unrolled body.
// TODO(bondhugula): option to specify cleanup loop unrolling.
Optional<uint64_t> mayBeConstantTripCount = getConstantTripCount(forOp);
if (mayBeConstantTripCount.hasValue() &&
mayBeConstantTripCount.getValue() < unrollFactor)
return failure();
// Generate the cleanup loop if trip count isn't a multiple of unrollFactor.
Operation *op = forOp.getOperation();
if (getLargestDivisorOfTripCount(forOp) % unrollFactor != 0) {
OpBuilder builder(op->getBlock(), ++Block::iterator(op));
auto cleanupForInst = cast<AffineForOp>(builder.clone(*op));
AffineMap cleanupMap;
SmallVector<Value, 4> cleanupOperands;
getCleanupLoopLowerBound(forOp, unrollFactor, &cleanupMap, &cleanupOperands,
builder);
assert(cleanupMap &&
"cleanup loop lower bound map for single result lower bound maps "
"can always be determined");
cleanupForInst.setLowerBound(cleanupOperands, cleanupMap);
// Promote the loop body up if this has turned into a single iteration loop.
promoteIfSingleIteration(cleanupForInst);
// Adjust upper bound of the original loop; this is the same as the lower
// bound of the cleanup loop.
forOp.setUpperBound(cleanupOperands, cleanupMap);
}
// Scale the step of loop being unrolled by unroll factor.
int64_t step = forOp.getStep();
forOp.setStep(step * unrollFactor);
// Builder to insert unrolled bodies just before the terminator of the body of
// 'forOp'.
OpBuilder builder = forOp.getBodyBuilder();
// Keep a pointer to the last non-terminator operation in the original block
// so that we know what to clone (since we are doing this in-place).
Block::iterator srcBlockEnd = std::prev(forOp.getBody()->end(), 2);
// Unroll the contents of 'forOp' (append unrollFactor-1 additional copies).
auto forOpIV = forOp.getInductionVar();
for (unsigned i = 1; i < unrollFactor; i++) {
BlockAndValueMapping operandMap;
// If the induction variable is used, create a remapping to the value for
// this unrolled instance.
if (!forOpIV.use_empty()) {
// iv' = iv + 1/2/3...unrollFactor-1;
auto d0 = builder.getAffineDimExpr(0);
auto bumpMap = AffineMap::get(1, 0, {d0 + i * step});
auto ivUnroll =
builder.create<AffineApplyOp>(forOp.getLoc(), bumpMap, forOpIV);
operandMap.map(forOpIV, ivUnroll);
}
// Clone the original body of 'forOp'.
for (auto it = forOp.getBody()->begin(); it != std::next(srcBlockEnd);
it++) {
builder.clone(*it, operandMap);
}
}
// Promote the loop body up if this has turned into a single iteration loop.
promoteIfSingleIteration(forOp);
return success();
}
/// Performs loop interchange on 'forOpA' and 'forOpB', where 'forOpB' is
/// nested within 'forOpA' as the only non-terminator operation in its block.
void mlir::interchangeLoops(AffineForOp forOpA, AffineForOp forOpB) {
auto *forOpAInst = forOpA.getOperation();
assert(&*forOpA.getBody()->begin() == forOpB.getOperation());
auto &forOpABody = forOpA.getBody()->getOperations();
auto &forOpBBody = forOpB.getBody()->getOperations();
// 1) Splice forOpA's non-terminator operations (which is just forOpB) just
// before forOpA (in ForOpA's parent's block) this should leave 'forOpA's
// body containing only the terminator.
forOpAInst->getBlock()->getOperations().splice(Block::iterator(forOpAInst),
forOpABody, forOpABody.begin(),
std::prev(forOpABody.end()));
// 2) Splice forOpB's non-terminator operations into the beginning of forOpA's
// body (this leaves forOpB's body containing only the terminator).
forOpABody.splice(forOpABody.begin(), forOpBBody, forOpBBody.begin(),
std::prev(forOpBBody.end()));
// 3) Splice forOpA into the beginning of forOpB's body.
forOpBBody.splice(forOpBBody.begin(), forOpAInst->getBlock()->getOperations(),
Block::iterator(forOpAInst));
}
// Checks each dependence component against the permutation to see if the
// desired loop interchange would violate dependences by making the
// dependence component lexicographically negative.
static bool checkLoopInterchangeDependences(
const std::vector<SmallVector<DependenceComponent, 2>> &depCompsVec,
ArrayRef<AffineForOp> loops, ArrayRef<unsigned> loopPermMap) {
// Invert permutation map.
unsigned maxLoopDepth = loops.size();
SmallVector<unsigned, 4> loopPermMapInv;
loopPermMapInv.resize(maxLoopDepth);
for (unsigned i = 0; i < maxLoopDepth; ++i)
loopPermMapInv[loopPermMap[i]] = i;
// Check each dependence component against the permutation to see if the
// desired loop interchange permutation would make the dependence vectors
// lexicographically negative.
// Example 1: [-1, 1][0, 0]
// Example 2: [0, 0][-1, 1]
for (unsigned i = 0, e = depCompsVec.size(); i < e; ++i) {
const SmallVector<DependenceComponent, 2> &depComps = depCompsVec[i];
assert(depComps.size() >= maxLoopDepth);
// Check if the first non-zero dependence component is positive.
// This iterates through loops in the desired order.
for (unsigned j = 0; j < maxLoopDepth; ++j) {
unsigned permIndex = loopPermMapInv[j];
assert(depComps[permIndex].lb.hasValue());
int64_t depCompLb = depComps[permIndex].lb.getValue();
if (depCompLb > 0)
break;
if (depCompLb < 0)
return false;
}
}
return true;
}
/// Checks if the loop interchange permutation 'loopPermMap' of the perfectly
/// nested sequence of loops in 'loops' would violate dependences.
bool mlir::isValidLoopInterchangePermutation(ArrayRef<AffineForOp> loops,
ArrayRef<unsigned> loopPermMap) {
// Gather dependence components for dependences between all ops in loop nest
// rooted at 'loops[0]', at loop depths in range [1, maxLoopDepth].
assert(loopPermMap.size() == loops.size());
unsigned maxLoopDepth = loops.size();
std::vector<SmallVector<DependenceComponent, 2>> depCompsVec;
getDependenceComponents(loops[0], maxLoopDepth, &depCompsVec);
return checkLoopInterchangeDependences(depCompsVec, loops, loopPermMap);
}
/// Performs a sequence of loop interchanges of loops in perfectly nested
/// sequence of loops in 'loops', as specified by permutation in 'loopPermMap'.
unsigned mlir::interchangeLoops(ArrayRef<AffineForOp> loops,
ArrayRef<unsigned> loopPermMap) {
Optional<unsigned> loopNestRootIndex;
for (int i = loops.size() - 1; i >= 0; --i) {
int permIndex = static_cast<int>(loopPermMap[i]);
// Store the index of the for loop which will be the new loop nest root.
if (permIndex == 0)
loopNestRootIndex = i;
if (permIndex > i) {
// Sink loop 'i' by 'permIndex - i' levels deeper into the loop nest.
sinkLoop(loops[i], permIndex - i);
}
}
assert(loopNestRootIndex.hasValue());
return loopNestRootIndex.getValue();
}
// Sinks all sequential loops to the innermost levels (while preserving
// relative order among them) and moves all parallel loops to the
// outermost (while again preserving relative order among them).
AffineForOp mlir::sinkSequentialLoops(AffineForOp forOp) {
SmallVector<AffineForOp, 4> loops;
getPerfectlyNestedLoops(loops, forOp);
if (loops.size() < 2)
return forOp;
// Gather dependence components for dependences between all ops in loop nest
// rooted at 'loops[0]', at loop depths in range [1, maxLoopDepth].
unsigned maxLoopDepth = loops.size();
std::vector<SmallVector<DependenceComponent, 2>> depCompsVec;
getDependenceComponents(loops[0], maxLoopDepth, &depCompsVec);
// Mark loops as either parallel or sequential.
SmallVector<bool, 8> isParallelLoop(maxLoopDepth, true);
for (unsigned i = 0, e = depCompsVec.size(); i < e; ++i) {
SmallVector<DependenceComponent, 2> &depComps = depCompsVec[i];
assert(depComps.size() >= maxLoopDepth);
for (unsigned j = 0; j < maxLoopDepth; ++j) {
DependenceComponent &depComp = depComps[j];
assert(depComp.lb.hasValue() && depComp.ub.hasValue());
if (depComp.lb.getValue() != 0 || depComp.ub.getValue() != 0)
isParallelLoop[j] = false;
}
}
// Count the number of parallel loops.
unsigned numParallelLoops = 0;
for (unsigned i = 0, e = isParallelLoop.size(); i < e; ++i)
if (isParallelLoop[i])
++numParallelLoops;
// Compute permutation of loops that sinks sequential loops (and thus raises
// parallel loops) while preserving relative order.
SmallVector<unsigned, 4> loopPermMap(maxLoopDepth);
unsigned nextSequentialLoop = numParallelLoops;
unsigned nextParallelLoop = 0;
for (unsigned i = 0; i < maxLoopDepth; ++i) {
if (isParallelLoop[i]) {
loopPermMap[i] = nextParallelLoop++;
} else {
loopPermMap[i] = nextSequentialLoop++;
}
}
// Check if permutation 'loopPermMap' would violate dependences.
if (!checkLoopInterchangeDependences(depCompsVec, loops, loopPermMap))
return forOp;
// Perform loop interchange according to permutation 'loopPermMap'.
unsigned loopNestRootIndex = interchangeLoops(loops, loopPermMap);
return loops[loopNestRootIndex];
}
/// Performs a series of loop interchanges to sink 'forOp' 'loopDepth' levels
/// deeper in the loop nest.
void mlir::sinkLoop(AffineForOp forOp, unsigned loopDepth) {
for (unsigned i = 0; i < loopDepth; ++i) {
AffineForOp nextForOp = cast<AffineForOp>(forOp.getBody()->front());
interchangeLoops(forOp, nextForOp);
}
}
// Factors out common behavior to add a new `iv` (resp. `iv` + `offset`) to the
// lower (resp. upper) loop bound. When called for both the lower and upper
// bounds, the resulting IR resembles:
//
// ```mlir
// affine.for %i = max (`iv, ...) to min (`iv` + `offset`) {
// ...
// }
// ```
static void augmentMapAndBounds(OpBuilder &b, Value iv, AffineMap *map,
SmallVector<Value, 4> *operands,
int64_t offset = 0) {
auto bounds = llvm::to_vector<4>(map->getResults());
bounds.push_back(b.getAffineDimExpr(map->getNumDims()) + offset);
operands->insert(operands->begin() + map->getNumDims(), iv);
*map = AffineMap::get(map->getNumDims() + 1, map->getNumSymbols(), bounds);
canonicalizeMapAndOperands(map, operands);
}
// Stripmines `forOp` by `factor` and sinks it under each of the `targets`.
// Stripmine-sink is a primitive building block for generalized tiling of
// imperfectly nested loops.
// This transformation is purely mechanical and does not check legality,
// profitability or even structural correctness. It is the user's
// responsibility to specify `targets` that are dominated by `forOp`.
// Returns the new AffineForOps, one per `targets`, nested immediately under
// each of the `targets`.
static SmallVector<AffineForOp, 8>
stripmineSink(AffineForOp forOp, uint64_t factor,
ArrayRef<AffineForOp> targets) {
auto originalStep = forOp.getStep();
auto scaledStep = originalStep * factor;
forOp.setStep(scaledStep);
auto *op = forOp.getOperation();
OpBuilder b(op->getBlock(), ++Block::iterator(op));
// Lower-bound map creation.
auto lbMap = forOp.getLowerBoundMap();
SmallVector<Value, 4> lbOperands(forOp.getLowerBoundOperands());
augmentMapAndBounds(b, forOp.getInductionVar(), &lbMap, &lbOperands);
// Upper-bound map creation.
auto ubMap = forOp.getUpperBoundMap();
SmallVector<Value, 4> ubOperands(forOp.getUpperBoundOperands());
augmentMapAndBounds(b, forOp.getInductionVar(), &ubMap, &ubOperands,
/*offset=*/scaledStep);
auto iv = forOp.getInductionVar();
SmallVector<AffineForOp, 8> innerLoops;
for (auto t : targets) {
// Insert newForOp before the terminator of `t`.
OpBuilder b = t.getBodyBuilder();
auto newForOp = b.create<AffineForOp>(t.getLoc(), lbOperands, lbMap,
ubOperands, ubMap, originalStep);
auto begin = t.getBody()->begin();
// Skip terminator and `newForOp` which is just before the terminator.
auto nOps = t.getBody()->getOperations().size() - 2;
newForOp.getBody()->getOperations().splice(
newForOp.getBody()->getOperations().begin(),
t.getBody()->getOperations(), begin, std::next(begin, nOps));
replaceAllUsesInRegionWith(iv, newForOp.getInductionVar(),
newForOp.region());
innerLoops.push_back(newForOp);
}
return innerLoops;
}
static Loops stripmineSink(loop::ForOp forOp, Value factor,
ArrayRef<loop::ForOp> targets) {
auto originalStep = forOp.step();
auto iv = forOp.getInductionVar();
OpBuilder b(forOp);
forOp.setStep(b.create<MulIOp>(forOp.getLoc(), originalStep, factor));
Loops innerLoops;
for (auto t : targets) {
// Save information for splicing ops out of t when done
auto begin = t.getBody()->begin();
auto nOps = t.getBody()->getOperations().size();
// Insert newForOp before the terminator of `t`.
OpBuilder b(t.getBodyBuilder());
Value stepped = b.create<AddIOp>(t.getLoc(), iv, forOp.step());
Value less = b.create<CmpIOp>(t.getLoc(), CmpIPredicate::slt,
forOp.upperBound(), stepped);
Value ub =
b.create<SelectOp>(t.getLoc(), less, forOp.upperBound(), stepped);
// Splice [begin, begin + nOps - 1) into `newForOp` and replace uses.
auto newForOp = b.create<loop::ForOp>(t.getLoc(), iv, ub, originalStep);
newForOp.getBody()->getOperations().splice(
newForOp.getBody()->getOperations().begin(),
t.getBody()->getOperations(), begin, std::next(begin, nOps - 1));
replaceAllUsesInRegionWith(iv, newForOp.getInductionVar(),
newForOp.region());
innerLoops.push_back(newForOp);
}
return innerLoops;
}
// Stripmines a `forOp` by `factor` and sinks it under a single `target`.
// Returns the new AffineForOps, nested immediately under `target`.
template <typename ForType, typename SizeType>
static ForType stripmineSink(ForType forOp, SizeType factor, ForType target) {
// TODO(ntv): Use cheap structural assertions that targets are nested under
// forOp and that targets are not nested under each other when DominanceInfo
// exposes the capability. It seems overkill to construct a whole function
// dominance tree at this point.
auto res = stripmineSink(forOp, factor, ArrayRef<ForType>{target});
assert(res.size() == 1 && "Expected 1 inner forOp");
return res[0];
}
template <typename ForType, typename SizeType>
static SmallVector<SmallVector<ForType, 8>, 8>
tileImpl(ArrayRef<ForType> forOps, ArrayRef<SizeType> sizes,
ArrayRef<ForType> targets) {
SmallVector<SmallVector<ForType, 8>, 8> res;
SmallVector<ForType, 8> currentTargets(targets.begin(), targets.end());
for (auto it : llvm::zip(forOps, sizes)) {
auto step = stripmineSink(std::get<0>(it), std::get<1>(it), currentTargets);
res.push_back(step);
currentTargets = step;
}
return res;
}
SmallVector<SmallVector<AffineForOp, 8>, 8>
mlir::tile(ArrayRef<AffineForOp> forOps, ArrayRef<uint64_t> sizes,
ArrayRef<AffineForOp> targets) {
return tileImpl(forOps, sizes, targets);
}
SmallVector<Loops, 8> mlir::tile(ArrayRef<loop::ForOp> forOps,
ArrayRef<Value> sizes,
ArrayRef<loop::ForOp> targets) {
return tileImpl(forOps, sizes, targets);
}
template <typename ForType, typename SizeType>
static SmallVector<ForType, 8>
tileImpl(ArrayRef<ForType> forOps, ArrayRef<SizeType> sizes, ForType target) {
SmallVector<ForType, 8> res;
for (auto loops : tile(forOps, sizes, ArrayRef<ForType>{target})) {
assert(loops.size() == 1);
res.push_back(loops[0]);
}
return res;
}
SmallVector<AffineForOp, 8> mlir::tile(ArrayRef<AffineForOp> forOps,
ArrayRef<uint64_t> sizes,
AffineForOp target) {
return tileImpl(forOps, sizes, target);
}
Loops mlir::tile(ArrayRef<loop::ForOp> forOps, ArrayRef<Value> sizes,
loop::ForOp target) {
return tileImpl(forOps, sizes, target);
}
Loops mlir::tilePerfectlyNested(loop::ForOp rootForOp, ArrayRef<Value> sizes) {
// Collect perfectly nested loops. If more size values provided than nested
// loops available, truncate `sizes`.
SmallVector<loop::ForOp, 4> forOps;
forOps.reserve(sizes.size());
getPerfectlyNestedLoopsImpl(forOps, rootForOp, sizes.size());
if (forOps.size() < sizes.size())
sizes = sizes.take_front(forOps.size());
return ::tile(forOps, sizes, forOps.back());
}
// Build the IR that performs ceil division of a positive value by a constant:
// ceildiv(a, B) = divis(a + (B-1), B)
// where divis is rounding-to-zero division.
static Value ceilDivPositive(OpBuilder &builder, Location loc, Value dividend,
int64_t divisor) {
assert(divisor > 0 && "expected positive divisor");
assert(dividend.getType().isIndex() && "expected index-typed value");
Value divisorMinusOneCst = builder.create<ConstantIndexOp>(loc, divisor - 1);
Value divisorCst = builder.create<ConstantIndexOp>(loc, divisor);
Value sum = builder.create<AddIOp>(loc, dividend, divisorMinusOneCst);
return builder.create<SignedDivIOp>(loc, sum, divisorCst);
}
// Build the IR that performs ceil division of a positive value by another
// positive value:
// ceildiv(a, b) = divis(a + (b - 1), b)
// where divis is rounding-to-zero division.
static Value ceilDivPositive(OpBuilder &builder, Location loc, Value dividend,
Value divisor) {
assert(dividend.getType().isIndex() && "expected index-typed value");
Value cstOne = builder.create<ConstantIndexOp>(loc, 1);
Value divisorMinusOne = builder.create<SubIOp>(loc, divisor, cstOne);
Value sum = builder.create<AddIOp>(loc, dividend, divisorMinusOne);
return builder.create<SignedDivIOp>(loc, sum, divisor);
}
// Hoist the ops within `outer` that appear before `inner`.
// Such ops include the ops that have been introduced by parametric tiling.
// Ops that come from triangular loops (i.e. that belong to the program slice
// rooted at `outer`) and ops that have side effects cannot be hoisted.
// Return failure when any op fails to hoist.
static LogicalResult hoistOpsBetween(loop::ForOp outer, loop::ForOp inner) {
SetVector<Operation *> forwardSlice;
getForwardSlice(outer.getOperation(), &forwardSlice, [&inner](Operation *op) {
return op != inner.getOperation();
});
LogicalResult status = success();
SmallVector<Operation *, 8> toHoist;
for (auto &op : outer.getBody()->without_terminator()) {
// Stop when encountering the inner loop.
if (&op == inner.getOperation())
break;
// Skip over non-hoistable ops.
if (forwardSlice.count(&op) > 0) {
status = failure();
continue;
}
// Skip loop::ForOp, these are not considered a failure.
if (op.getNumRegions() > 0)
continue;
// Skip other ops with regions.
if (op.getNumRegions() > 0) {
status = failure();
continue;
}
// Skip if op has side effects.
// TODO(ntv): loads to immutable memory regions are ok.
if (!MemoryEffectOpInterface::hasNoEffect(&op)) {
status = failure();
continue;
}
toHoist.push_back(&op);
}
auto *outerForOp = outer.getOperation();
for (auto *op : toHoist)
op->moveBefore(outerForOp);
return status;
}
// Traverse the interTile and intraTile loops and try to hoist ops such that
// bands of perfectly nested loops are isolated.
// Return failure if either perfect interTile or perfect intraTile bands cannot
// be formed.
static LogicalResult tryIsolateBands(const TileLoops &tileLoops) {
LogicalResult status = success();
auto &interTile = tileLoops.first;
auto &intraTile = tileLoops.second;
auto size = interTile.size();
assert(size == intraTile.size());
if (size <= 1)
return success();
for (unsigned s = 1; s < size; ++s)
status = succeeded(status) ? hoistOpsBetween(intraTile[0], intraTile[s])
: failure();
for (unsigned s = 1; s < size; ++s)
status = succeeded(status) ? hoistOpsBetween(interTile[0], interTile[s])
: failure();
return status;
}
TileLoops mlir::extractFixedOuterLoops(loop::ForOp rootForOp,
ArrayRef<int64_t> sizes) {
// Collect perfectly nested loops. If more size values provided than nested
// loops available, truncate `sizes`.
SmallVector<loop::ForOp, 4> forOps;
forOps.reserve(sizes.size());
getPerfectlyNestedLoopsImpl(forOps, rootForOp, sizes.size());
if (forOps.size() < sizes.size())
sizes = sizes.take_front(forOps.size());
// Compute the tile sizes such that i-th outer loop executes size[i]
// iterations. Given that the loop current executes
// numIterations = ceildiv((upperBound - lowerBound), step)
// iterations, we need to tile with size ceildiv(numIterations, size[i]).
SmallVector<Value, 4> tileSizes;
tileSizes.reserve(sizes.size());
for (unsigned i = 0, e = sizes.size(); i < e; ++i) {
assert(sizes[i] > 0 && "expected strictly positive size for strip-mining");
auto forOp = forOps[i];
OpBuilder builder(forOp);
auto loc = forOp.getLoc();
Value diff =
builder.create<SubIOp>(loc, forOp.upperBound(), forOp.lowerBound());
Value numIterations = ceilDivPositive(builder, loc, diff, forOp.step());
Value iterationsPerBlock =
ceilDivPositive(builder, loc, numIterations, sizes[i]);
tileSizes.push_back(iterationsPerBlock);
}
// Call parametric tiling with the given sizes.
auto intraTile = tile(forOps, tileSizes, forOps.back());
TileLoops tileLoops = std::make_pair(forOps, intraTile);
// TODO(ntv, zinenko) for now we just ignore the result of band isolation.
// In the future, mapping decisions may be impacted by the ability to
// isolate perfectly nested bands.
tryIsolateBands(tileLoops);
return tileLoops;
}
// Replaces all uses of `orig` with `replacement` except if the user is listed
// in `exceptions`.
static void
replaceAllUsesExcept(Value orig, Value replacement,
const SmallPtrSetImpl<Operation *> &exceptions) {
for (auto &use : llvm::make_early_inc_range(orig.getUses())) {
if (exceptions.count(use.getOwner()) == 0)
use.set(replacement);
}
}
// Transform a loop with a strictly positive step
// for %i = %lb to %ub step %s
// into a 0-based loop with step 1
// for %ii = 0 to ceildiv(%ub - %lb, %s) step 1 {
// %i = %ii * %s + %lb
// Insert the induction variable remapping in the body of `inner`, which is
// expected to be either `loop` or another loop perfectly nested under `loop`.
// Insert the definition of new bounds immediate before `outer`, which is
// expected to be either `loop` or its parent in the loop nest.
static void normalizeLoop(loop::ForOp loop, loop::ForOp outer,
loop::ForOp inner) {
OpBuilder builder(outer);
Location loc = loop.getLoc();
// Check if the loop is already known to have a constant zero lower bound or
// a constant one step.
bool isZeroBased = false;
if (auto ubCst =
dyn_cast_or_null<ConstantIndexOp>(loop.lowerBound().getDefiningOp()))
isZeroBased = ubCst.getValue() == 0;
bool isStepOne = false;
if (auto stepCst =
dyn_cast_or_null<ConstantIndexOp>(loop.step().getDefiningOp()))
isStepOne = stepCst.getValue() == 1;
if (isZeroBased && isStepOne)
return;
// Compute the number of iterations the loop executes: ceildiv(ub - lb, step)
// assuming the step is strictly positive. Update the bounds and the step
// of the loop to go from 0 to the number of iterations, if necessary.
// TODO(zinenko): introduce support for negative steps or emit dynamic asserts
// on step positivity, whatever gets implemented first.
Value diff =
builder.create<SubIOp>(loc, loop.upperBound(), loop.lowerBound());
Value numIterations = ceilDivPositive(builder, loc, diff, loop.step());
loop.setUpperBound(numIterations);
Value lb = loop.lowerBound();
if (!isZeroBased) {
Value cst0 = builder.create<ConstantIndexOp>(loc, 0);
loop.setLowerBound(cst0);
}
Value step = loop.step();
if (!isStepOne) {
Value cst1 = builder.create<ConstantIndexOp>(loc, 1);
loop.setStep(cst1);
}
// Insert code computing the value of the original loop induction variable
// from the "normalized" one.
builder.setInsertionPointToStart(inner.getBody());
Value scaled =
isStepOne ? loop.getInductionVar()
: builder.create<MulIOp>(loc, loop.getInductionVar(), step);
Value shifted =
isZeroBased ? scaled : builder.create<AddIOp>(loc, scaled, lb);
SmallPtrSet<Operation *, 2> preserve{scaled.getDefiningOp(),
shifted.getDefiningOp()};
replaceAllUsesExcept(loop.getInductionVar(), shifted, preserve);
}
void mlir::coalesceLoops(MutableArrayRef<loop::ForOp> loops) {
if (loops.size() < 2)
return;
loop::ForOp innermost = loops.back();
loop::ForOp outermost = loops.front();
// 1. Make sure all loops iterate from 0 to upperBound with step 1. This
// allows the following code to assume upperBound is the number of iterations.
for (auto loop : loops)
normalizeLoop(loop, outermost, innermost);
// 2. Emit code computing the upper bound of the coalesced loop as product
// of the number of iterations of all loops.
OpBuilder builder(outermost);
Location loc = outermost.getLoc();
Value upperBound = outermost.upperBound();
for (auto loop : loops.drop_front())
upperBound = builder.create<MulIOp>(loc, upperBound, loop.upperBound());
outermost.setUpperBound(upperBound);
builder.setInsertionPointToStart(outermost.getBody());
// 3. Remap induction variables. For each original loop, the value of the
// induction variable can be obtained by dividing the induction variable of
// the linearized loop by the total number of iterations of the loops nested
// in it modulo the number of iterations in this loop (remove the values
// related to the outer loops):
// iv_i = floordiv(iv_linear, product-of-loop-ranges-until-i) mod range_i.
// Compute these iteratively from the innermost loop by creating a "running
// quotient" of division by the range.
Value previous = outermost.getInductionVar();
for (unsigned i = 0, e = loops.size(); i < e; ++i) {
unsigned idx = loops.size() - i - 1;
if (i != 0)
previous = builder.create<SignedDivIOp>(loc, previous,
loops[idx + 1].upperBound());
Value iv = (i == e - 1) ? previous
: builder.create<SignedRemIOp>(
loc, previous, loops[idx].upperBound());
replaceAllUsesInRegionWith(loops[idx].getInductionVar(), iv,
loops.back().region());
}
// 4. Move the operations from the innermost just above the second-outermost
// loop, delete the extra terminator and the second-outermost loop.
loop::ForOp second = loops[1];
innermost.getBody()->back().erase();
outermost.getBody()->getOperations().splice(
Block::iterator(second.getOperation()),
innermost.getBody()->getOperations());
second.erase();
}
void mlir::mapLoopToProcessorIds(loop::ForOp forOp, ArrayRef<Value> processorId,
ArrayRef<Value> numProcessors) {
assert(processorId.size() == numProcessors.size());
if (processorId.empty())
return;
OpBuilder b(forOp);
Location loc(forOp.getLoc());
Value mul = processorId.front();
for (unsigned i = 1, e = processorId.size(); i < e; ++i)
mul = b.create<AddIOp>(loc, b.create<MulIOp>(loc, mul, numProcessors[i]),
processorId[i]);
Value lb = b.create<AddIOp>(loc, forOp.lowerBound(),
b.create<MulIOp>(loc, forOp.step(), mul));
forOp.setLowerBound(lb);
Value step = forOp.step();
for (auto numProcs : numProcessors)
step = b.create<MulIOp>(loc, step, numProcs);
forOp.setStep(step);
}
/// Given a memref region, determine the lowest depth at which transfers can be
/// placed for it, and return the corresponding block, start and end positions
/// in the block for placing incoming (read) and outgoing (write) copies
/// respectively. The lowest depth depends on whether the region being accessed
/// is hoistable with respect to one or more immediately surrounding loops.
static void
findHighestBlockForPlacement(const MemRefRegion &region, Block &block,
Block::iterator &begin, Block::iterator &end,
Block **copyPlacementBlock,
Block::iterator *copyInPlacementStart,
Block::iterator *copyOutPlacementStart) {
const auto *cst = region.getConstraints();
SmallVector<Value, 4> symbols;
cst->getIdValues(cst->getNumDimIds(), cst->getNumDimAndSymbolIds(), &symbols);
SmallVector<AffineForOp, 4> enclosingFors;
getLoopIVs(*block.begin(), &enclosingFors);
// Walk up loop parents till we find an IV on which this region is
// symbolic/variant.
auto it = enclosingFors.rbegin();
for (auto e = enclosingFors.rend(); it != e; ++it) {
// TODO(bondhugula): also need to be checking this for regions symbols that
// aren't loop IVs, whether we are within their resp. defs' dominance scope.
if (llvm::is_contained(symbols, it->getInductionVar()))
break;
}
if (it != enclosingFors.rbegin()) {
auto lastInvariantIV = *std::prev(it);
*copyInPlacementStart = Block::iterator(lastInvariantIV.getOperation());
*copyOutPlacementStart = std::next(*copyInPlacementStart);
*copyPlacementBlock = lastInvariantIV.getOperation()->getBlock();
} else {
*copyInPlacementStart = begin;
*copyOutPlacementStart = end;
*copyPlacementBlock = &block;
}
}
// Info comprising stride and number of elements transferred every stride.
struct StrideInfo {
int64_t stride;
int64_t numEltPerStride;
};
/// Returns striding information for a copy/transfer of this region with
/// potentially multiple striding levels from outermost to innermost. For an
/// n-dimensional region, there can be at most n-1 levels of striding
/// successively nested.
// TODO(bondhugula): make this work with non-identity layout maps.
static void getMultiLevelStrides(const MemRefRegion &region,
ArrayRef<int64_t> bufferShape,
SmallVectorImpl<StrideInfo> *strideInfos) {
if (bufferShape.size() <= 1)
return;
int64_t numEltPerStride = 1;
int64_t stride = 1;
for (int d = bufferShape.size() - 1; d >= 1; d--) {
int64_t dimSize = region.memref.getType().cast<MemRefType>().getDimSize(d);
stride *= dimSize;
numEltPerStride *= bufferShape[d];
// A stride is needed only if the region has a shorter extent than the
// memref along the dimension *and* has an extent greater than one along the
// next major dimension.
if (bufferShape[d] < dimSize && bufferShape[d - 1] > 1) {
strideInfos->push_back({stride, numEltPerStride});
}
}
}
/// Generates a point-wise copy from/to `memref' to/from `fastMemRef' and
/// returns the outermost AffineForOp of the copy loop nest. `memIndicesStart'
/// holds the lower coordinates of the region in the original memref to copy
/// in/out. If `copyOut' is true, generates a copy-out; otherwise a copy-in.
static AffineForOp generatePointWiseCopy(Location loc, Value memref,
Value fastMemRef,
AffineMap memAffineMap,
ArrayRef<Value> memIndicesStart,
ArrayRef<int64_t> fastBufferShape,
bool isCopyOut, OpBuilder b) {
assert(!memIndicesStart.empty() && "only 1-d or more memrefs");
// The copy-in nest is generated as follows as an example for a 2-d region:
// for x = ...
// for y = ...
// fast_buf[x][y] = buf[mem_x + x][mem_y + y]
SmallVector<Value, 4> fastBufIndices, memIndices;
AffineForOp copyNestRoot;
for (unsigned d = 0, e = fastBufferShape.size(); d < e; ++d) {
auto forOp = b.create<AffineForOp>(loc, 0, fastBufferShape[d]);
if (d == 0)
copyNestRoot = forOp;
b = forOp.getBodyBuilder();
fastBufIndices.push_back(forOp.getInductionVar());
Value memBase =
(memAffineMap == b.getMultiDimIdentityMap(memAffineMap.getNumDims()))
? memIndicesStart[d]
: b.create<AffineApplyOp>(
loc,
AffineMap::get(memAffineMap.getNumDims(),
memAffineMap.getNumSymbols(),
memAffineMap.getResult(d)),
memIndicesStart);
// Construct the subscript for the slow memref being copied.
auto memIndex = b.create<AffineApplyOp>(
loc,
AffineMap::get(2, 0, b.getAffineDimExpr(0) + b.getAffineDimExpr(1)),
ValueRange({memBase, forOp.getInductionVar()}));
memIndices.push_back(memIndex);
}
if (!isCopyOut) {
// Copy in.
auto load = b.create<AffineLoadOp>(loc, memref, memIndices);
b.create<AffineStoreOp>(loc, load, fastMemRef, fastBufIndices);
return copyNestRoot;
}
// Copy out.
auto load = b.create<AffineLoadOp>(loc, fastMemRef, fastBufIndices);
b.create<AffineStoreOp>(loc, load, memref, memIndices);
return copyNestRoot;
}
static InFlightDiagnostic LLVM_ATTRIBUTE_UNUSED
emitRemarkForBlock(Block &block) {
return block.getParentOp()->emitRemark();
}
/// Creates a buffer in the faster memory space for the specified memref region;
/// generates a copy from the lower memory space to this one, and replaces all
/// loads/stores in the block range [`begin', `end') of `block' to load/store
/// from that buffer. Returns failure if copies could not be generated due to
/// yet unimplemented cases. `copyInPlacementStart` and `copyOutPlacementStart`
/// in copyPlacementBlock specify the insertion points where the incoming copies
/// and outgoing copies, respectively, should be inserted (the insertion happens
/// right before the insertion point). Since `begin` can itself be invalidated
/// due to the memref rewriting done from this method, the output argument
/// `nBegin` is set to its replacement (set to `begin` if no invalidation
/// happens). Since outgoing copies could have been inserted at `end`, the
/// output argument `nEnd` is set to the new end. `sizeInBytes` is set to the
/// size of the fast buffer allocated.
static LogicalResult generateCopy(
const MemRefRegion &region, Block *block, Block::iterator begin,
Block::iterator end, Block *copyPlacementBlock,
Block::iterator copyInPlacementStart, Block::iterator copyOutPlacementStart,
AffineCopyOptions copyOptions, DenseMap<Value, Value> &fastBufferMap,
DenseSet<Operation *> &copyNests, uint64_t *sizeInBytes,
Block::iterator *nBegin, Block::iterator *nEnd) {
*nBegin = begin;
*nEnd = end;
FuncOp f = begin->getParentOfType<FuncOp>();
OpBuilder topBuilder(f.getBody());
Value zeroIndex = topBuilder.create<ConstantIndexOp>(f.getLoc(), 0);
if (begin == end)
return success();
// Is the copy out point at the end of the block where we are doing
// explicit copying.
bool isCopyOutAtEndOfBlock = (end == copyOutPlacementStart);
// Copies for read regions are going to be inserted at 'begin'.
OpBuilder prologue(copyPlacementBlock, copyInPlacementStart);
// Copies for write regions are going to be inserted at 'end'.
OpBuilder epilogue(copyPlacementBlock, copyOutPlacementStart);
OpBuilder &b = region.isWrite() ? epilogue : prologue;
// Builder to create constants at the top level.
auto func = copyPlacementBlock->getParent()->getParentOfType<FuncOp>();
OpBuilder top(func.getBody());
auto loc = region.loc;
auto memref = region.memref;
auto memRefType = memref.getType().cast<MemRefType>();
auto layoutMaps = memRefType.getAffineMaps();
if (layoutMaps.size() > 1 ||
(layoutMaps.size() == 1 && !layoutMaps[0].isIdentity())) {
LLVM_DEBUG(llvm::dbgs() << "Non-identity layout map not yet supported\n");
return failure();
}
// Indices to use for the copying.
// Indices for the original memref being copied from/to.
SmallVector<Value, 4> memIndices;
// Indices for the faster buffer being copied into/from.
SmallVector<Value, 4> bufIndices;
unsigned rank = memRefType.getRank();
SmallVector<int64_t, 4> fastBufferShape;
// Compute the extents of the buffer.
std::vector<SmallVector<int64_t, 4>> lbs;
SmallVector<int64_t, 8> lbDivisors;
lbs.reserve(rank);
Optional<int64_t> numElements = region.getConstantBoundingSizeAndShape(
&fastBufferShape, &lbs, &lbDivisors);
if (!numElements.hasValue()) {
LLVM_DEBUG(llvm::dbgs() << "Non-constant region size not supported\n");
return failure();
}
if (numElements.getValue() == 0) {
LLVM_DEBUG(llvm::dbgs() << "Nothing to copy\n");
*sizeInBytes = 0;
return success();
}
const FlatAffineConstraints *cst = region.getConstraints();
// 'regionSymbols' hold values that this memory region is symbolic/parametric
// on; these typically include loop IVs surrounding the level at which the
// copy generation is being done or other valid symbols in MLIR.
SmallVector<Value, 8> regionSymbols;
cst->getIdValues(rank, cst->getNumIds(), &regionSymbols);
// Construct the index expressions for the fast memory buffer. The index
// expression for a particular dimension of the fast buffer is obtained by
// subtracting out the lower bound on the original memref's data region
// along the corresponding dimension.
// Index start offsets for faster memory buffer relative to the original.
SmallVector<AffineExpr, 4> offsets;
offsets.reserve(rank);
for (unsigned d = 0; d < rank; d++) {
assert(lbs[d].size() == cst->getNumCols() - rank && "incorrect bound size");
AffineExpr offset = top.getAffineConstantExpr(0);
for (unsigned j = 0, e = cst->getNumCols() - rank - 1; j < e; j++) {
offset = offset + lbs[d][j] * top.getAffineDimExpr(j);
}
assert(lbDivisors[d] > 0);
offset =
(offset + lbs[d][cst->getNumCols() - 1 - rank]).floorDiv(lbDivisors[d]);
// Set copy start location for this dimension in the lower memory space
// memref.
if (auto caf = offset.dyn_cast<AffineConstantExpr>()) {
auto indexVal = caf.getValue();
if (indexVal == 0) {
memIndices.push_back(zeroIndex);
} else {
memIndices.push_back(
top.create<ConstantIndexOp>(loc, indexVal).getResult());
}
} else {
// The coordinate for the start location is just the lower bound along the
// corresponding dimension on the memory region (stored in 'offset').
auto map = AffineMap::get(
cst->getNumDimIds() + cst->getNumSymbolIds() - rank, 0, offset);
memIndices.push_back(b.create<AffineApplyOp>(loc, map, regionSymbols));
}
// The fast buffer is copied into at location zero; addressing is relative.
bufIndices.push_back(zeroIndex);
// Record the offsets since they are needed to remap the memory accesses of
// the original memref further below.
offsets.push_back(offset);
}
// The faster memory space buffer.
Value fastMemRef;
// Check if a buffer was already created.
bool existingBuf = fastBufferMap.count(memref) > 0;
if (!existingBuf) {
AffineMap fastBufferLayout = b.getMultiDimIdentityMap(rank);
auto fastMemRefType =
MemRefType::get(fastBufferShape, memRefType.getElementType(),
fastBufferLayout, copyOptions.fastMemorySpace);
// Create the fast memory space buffer just before the 'affine.for'
// operation.
fastMemRef = prologue.create<AllocOp>(loc, fastMemRefType).getResult();
// Record it.
fastBufferMap[memref] = fastMemRef;
// fastMemRefType is a constant shaped memref.
*sizeInBytes = getMemRefSizeInBytes(fastMemRefType).getValue();
LLVM_DEBUG(emitRemarkForBlock(*block)
<< "Creating fast buffer of type " << fastMemRefType
<< " and size " << llvm::divideCeil(*sizeInBytes, 1024)
<< " KiB\n");
} else {
// Reuse the one already created.
fastMemRef = fastBufferMap[memref];
*sizeInBytes = 0;
}
auto numElementsSSA =
top.create<ConstantIndexOp>(loc, numElements.getValue());
Value dmaStride = nullptr;
Value numEltPerDmaStride = nullptr;
if (copyOptions.generateDma) {
SmallVector<StrideInfo, 4> dmaStrideInfos;
getMultiLevelStrides(region, fastBufferShape, &dmaStrideInfos);
// TODO(bondhugula): use all stride levels once DmaStartOp is extended for
// multi-level strides.
if (dmaStrideInfos.size() > 1) {
LLVM_DEBUG(llvm::dbgs() << "Only up to one level of stride supported\n");
return failure();
}
if (!dmaStrideInfos.empty()) {
dmaStride = top.create<ConstantIndexOp>(loc, dmaStrideInfos[0].stride);
numEltPerDmaStride =
top.create<ConstantIndexOp>(loc, dmaStrideInfos[0].numEltPerStride);
}
}
// Record the last operation where we want the memref replacement to end. We
// later do the memref replacement only in [begin, postDomFilter] so
// that the original memref's used in the data movement code themselves don't
// get replaced.
auto postDomFilter = std::prev(end);
// Create fully composed affine maps for each memref.
auto memAffineMap = b.getMultiDimIdentityMap(memIndices.size());
fullyComposeAffineMapAndOperands(&memAffineMap, &memIndices);
auto bufAffineMap = b.getMultiDimIdentityMap(bufIndices.size());
fullyComposeAffineMapAndOperands(&bufAffineMap, &bufIndices);
if (!copyOptions.generateDma) {
// Point-wise copy generation.
auto copyNest = generatePointWiseCopy(loc, memref, fastMemRef, memAffineMap,
memIndices, fastBufferShape,
/*isCopyOut=*/region.isWrite(), b);
// Record this so that we can skip it from yet another copy.
copyNests.insert(copyNest);
// Since new ops are being appended (for copy out's), adjust the end to
// mark end of block range being processed if necessary.
if (region.isWrite() && isCopyOutAtEndOfBlock)
*nEnd = Block::iterator(copyNest.getOperation());
} else {
// DMA generation.
// Create a tag (single element 1-d memref) for the DMA.
auto tagMemRefType = MemRefType::get({1}, top.getIntegerType(32), {},
copyOptions.tagMemorySpace);
auto tagMemRef = prologue.create<AllocOp>(loc, tagMemRefType);
SmallVector<Value, 4> tagIndices({zeroIndex});
auto tagAffineMap = b.getMultiDimIdentityMap(tagIndices.size());
fullyComposeAffineMapAndOperands(&tagAffineMap, &tagIndices);
if (!region.isWrite()) {
// DMA non-blocking read from original buffer to fast buffer.
b.create<AffineDmaStartOp>(loc, memref, memAffineMap, memIndices,
fastMemRef, bufAffineMap, bufIndices,
tagMemRef, tagAffineMap, tagIndices,
numElementsSSA, dmaStride, numEltPerDmaStride);
} else {
// DMA non-blocking write from fast buffer to the original memref.
auto op = b.create<AffineDmaStartOp>(
loc, fastMemRef, bufAffineMap, bufIndices, memref, memAffineMap,
memIndices, tagMemRef, tagAffineMap, tagIndices, numElementsSSA,
dmaStride, numEltPerDmaStride);
// Since new ops may be appended at 'end' (for outgoing DMAs), adjust the
// end to mark end of block range being processed.
if (isCopyOutAtEndOfBlock)
*nEnd = Block::iterator(op.getOperation());
}
// Matching DMA wait to block on completion; tag always has a 0 index.
b.create<AffineDmaWaitOp>(loc, tagMemRef, tagAffineMap, zeroIndex,
numElementsSSA);
// Generate dealloc for the tag.
auto tagDeallocOp = epilogue.create<DeallocOp>(loc, tagMemRef);
if (*nEnd == end && isCopyOutAtEndOfBlock)
// Since new ops are being appended (for outgoing DMAs), adjust the end to
// mark end of range of the original.
*nEnd = Block::iterator(tagDeallocOp.getOperation());
}
// Generate dealloc for the buffer.
if (!existingBuf) {
auto bufDeallocOp = epilogue.create<DeallocOp>(loc, fastMemRef);
// When generating pointwise copies, `nEnd' has to be set to deallocOp on
// the fast buffer (since it marks the new end insertion point).
if (!copyOptions.generateDma && *nEnd == end && isCopyOutAtEndOfBlock)
*nEnd = Block::iterator(bufDeallocOp.getOperation());
}
// Replace all uses of the old memref with the faster one while remapping
// access indices (subtracting out lower bound offsets for each dimension).
// Ex: to replace load %A[%i, %j] with load %Abuf[%i - %iT, %j - %jT],
// index remap will be (%i, %j) -> (%i - %iT, %j - %jT),
// i.e., affine.apply (d0, d1, d2, d3) -> (d2-d0, d3-d1) (%iT, %jT, %i, %j),
// and (%iT, %jT) will be the 'extraOperands' for 'rep all memref uses with'.
// d2, d3 correspond to the original indices (%i, %j).
SmallVector<AffineExpr, 4> remapExprs;
remapExprs.reserve(rank);
for (unsigned i = 0; i < rank; i++) {
// The starting operands of indexRemap will be regionSymbols (the symbols on
// which the memref region is parametric); then those corresponding to
// the memref's original indices follow.
auto dimExpr = b.getAffineDimExpr(regionSymbols.size() + i);
remapExprs.push_back(dimExpr - offsets[i]);
}
auto indexRemap = AffineMap::get(regionSymbols.size() + rank, 0, remapExprs);
// Record the begin since it may be invalidated by memref replacement.
Block::iterator prevOfBegin;
bool isBeginAtStartOfBlock = (begin == block->begin());
if (!isBeginAtStartOfBlock)
prevOfBegin = std::prev(begin);
// *Only* those uses within the range [begin, end) of 'block' are replaced.
replaceAllMemRefUsesWith(memref, fastMemRef,
/*extraIndices=*/{}, indexRemap,
/*extraOperands=*/regionSymbols,
/*symbolOperands=*/{},
/*domInstFilter=*/&*begin,
/*postDomInstFilter=*/&*postDomFilter);
*nBegin = isBeginAtStartOfBlock ? block->begin() : std::next(prevOfBegin);
return success();
}
/// Construct the memref region to just include the entire memref. Returns false
/// dynamic shaped memref's for now. `numParamLoopIVs` is the number of
/// enclosing loop IVs of opInst (starting from the outermost) that the region
/// is parametric on.
static bool getFullMemRefAsRegion(Operation *opInst, unsigned numParamLoopIVs,
MemRefRegion *region) {
unsigned rank;
if (auto loadOp = dyn_cast<AffineLoadOp>(opInst)) {
rank = loadOp.getMemRefType().getRank();
region->memref = loadOp.getMemRef();
region->setWrite(false);
} else if (auto storeOp = dyn_cast<AffineStoreOp>(opInst)) {
rank = storeOp.getMemRefType().getRank();
region->memref = storeOp.getMemRef();
region->setWrite(true);
} else {
assert(false && "expected load or store op");
return false;
}
auto memRefType = region->memref.getType().cast<MemRefType>();
if (!memRefType.hasStaticShape())
return false;
auto *regionCst = region->getConstraints();
// Just get the first numSymbols IVs, which the memref region is parametric
// on.
SmallVector<AffineForOp, 4> ivs;
getLoopIVs(*opInst, &ivs);
ivs.resize(numParamLoopIVs);
SmallVector<Value, 4> symbols;
extractForInductionVars(ivs, &symbols);
regionCst->reset(rank, numParamLoopIVs, 0);
regionCst->setIdValues(rank, rank + numParamLoopIVs, symbols);
// Memref dim sizes provide the bounds.
for (unsigned d = 0; d < rank; d++) {
auto dimSize = memRefType.getDimSize(d);
assert(dimSize > 0 && "filtered dynamic shapes above");
regionCst->addConstantLowerBound(d, 0);
regionCst->addConstantUpperBound(d, dimSize - 1);
}
return true;
}
/// Performs explicit copying for the contiguous sequence of operations in the
/// block iterator range [`begin', `end'), where `end' can't be past the
/// terminator of the block (since additional operations are potentially
/// inserted right before `end`. Returns the total size of fast memory space
/// buffers used. `copyOptions` provides various parameters, and the output
/// argument `copyNests` is the set of all copy nests inserted, each represented
/// by its root affine.for. Since we generate alloc's and dealloc's for all fast
/// buffers (before and after the range of operations resp. or at a hoisted
/// position), all of the fast memory capacity is assumed to be available for
/// processing this block range. When 'filterMemRef' is specified, copies are
/// only generated for the provided MemRef.
uint64_t mlir::affineDataCopyGenerate(Block::iterator begin,
Block::iterator end,
const AffineCopyOptions &copyOptions,
Optional<Value> filterMemRef,
DenseSet<Operation *> &copyNests) {
if (begin == end)
return 0;
assert(begin->getBlock() == std::prev(end)->getBlock() &&
"Inconsistent block begin/end args");
assert(end != end->getBlock()->end() && "end can't be the block terminator");
Block *block = begin->getBlock();
// Copies will be generated for this depth, i.e., symbolic in all loops
// surrounding the this block range.
unsigned copyDepth = getNestingDepth(*begin);
LLVM_DEBUG(llvm::dbgs() << "Generating copies at depth " << copyDepth
<< "\n");
LLVM_DEBUG(llvm::dbgs() << "from begin: " << *begin << "\n");
LLVM_DEBUG(llvm::dbgs() << "to inclusive end: " << *std::prev(end) << "\n");
// List of memory regions to copy for. We need a map vector to have a
// guaranteed iteration order to write test cases. CHECK-DAG doesn't help here
// since the alloc's for example are identical except for the SSA id.
SmallMapVector<Value, std::unique_ptr<MemRefRegion>, 4> readRegions;
SmallMapVector<Value, std::unique_ptr<MemRefRegion>, 4> writeRegions;
// Map from original memref's to the fast buffers that their accesses are
// replaced with.
DenseMap<Value, Value> fastBufferMap;
// To check for errors when walking the block.
bool error = false;
// Walk this range of operations to gather all memory regions.
block->walk(begin, end, [&](Operation *opInst) {
// Gather regions to allocate to buffers in faster memory space.
if (auto loadOp = dyn_cast<AffineLoadOp>(opInst)) {
if ((filterMemRef.hasValue() && filterMemRef != loadOp.getMemRef()) ||
(loadOp.getMemRefType().getMemorySpace() !=
copyOptions.slowMemorySpace))
return;
} else if (auto storeOp = dyn_cast<AffineStoreOp>(opInst)) {
if ((filterMemRef.hasValue() && filterMemRef != storeOp.getMemRef()) ||
storeOp.getMemRefType().getMemorySpace() !=
copyOptions.slowMemorySpace)
return;
} else {
// Neither load nor a store op.
return;
}
// Compute the MemRefRegion accessed.
auto region = std::make_unique<MemRefRegion>(opInst->getLoc());
if (failed(region->compute(opInst, copyDepth))) {
LLVM_DEBUG(llvm::dbgs()
<< "Error obtaining memory region: semi-affine maps?\n");
LLVM_DEBUG(llvm::dbgs() << "over-approximating to the entire memref\n");
if (!getFullMemRefAsRegion(opInst, copyDepth, region.get())) {
LLVM_DEBUG(
opInst->emitError("non-constant memref sizes not yet supported"));
error = true;
return;
}
}
// Each memref has a single buffer associated with it irrespective of how
// many load's and store's happen on it.
// TODO(bondhugula): in the future, when regions don't intersect and satisfy
// other properties (based on load/store regions), we could consider
// multiple buffers per memref.
// Add to the appropriate region if it's not already in it, or take a
// bounding box union with the existing one if it's already in there.
// Note that a memref may have both read and write regions - so update the
// region in the other list if one exists (write in case of read and vice
// versa) since there is a single bounding box for a memref across all reads
// and writes that happen on it.
// Attempts to update; returns true if 'region' exists in targetRegions.
auto updateRegion =
[&](const SmallMapVector<Value, std::unique_ptr<MemRefRegion>, 4>
&targetRegions) {
auto it = targetRegions.find(region->memref);
if (it == targetRegions.end())
return false;
// Perform a union with the existing region.
if (failed(it->second->unionBoundingBox(*region))) {
LLVM_DEBUG(llvm::dbgs()
<< "Memory region bounding box failed; "
"over-approximating to the entire memref\n");
// If the union fails, we will overapproximate.
if (!getFullMemRefAsRegion(opInst, copyDepth, region.get())) {
LLVM_DEBUG(opInst->emitError(
"non-constant memref sizes not yet supported"));
error = true;
return true;
}
it->second->getConstraints()->clearAndCopyFrom(
*region->getConstraints());
} else {
// Union was computed and stored in 'it->second': copy to 'region'.
region->getConstraints()->clearAndCopyFrom(
*it->second->getConstraints());
}
return true;
};
bool existsInRead = updateRegion(readRegions);
if (error)
return;
bool existsInWrite = updateRegion(writeRegions);
if (error)
return;
// Finally add it to the region list.
if (region->isWrite() && !existsInWrite) {
writeRegions[region->memref] = std::move(region);
} else if (!region->isWrite() && !existsInRead) {
readRegions[region->memref] = std::move(region);
}
});
if (error) {
begin->emitError(
"copy generation failed for one or more memref's in this block\n");
return 0;
}
uint64_t totalCopyBuffersSizeInBytes = 0;
bool ret = true;
auto processRegions =
[&](const SmallMapVector<Value, std::unique_ptr<MemRefRegion>, 4>
&regions) {
for (const auto &regionEntry : regions) {
// For each region, hoist copy in/out past all hoistable
// 'affine.for's.
Block::iterator copyInPlacementStart, copyOutPlacementStart;
Block *copyPlacementBlock;
findHighestBlockForPlacement(
*regionEntry.second, *block, begin, end, &copyPlacementBlock,
&copyInPlacementStart, &copyOutPlacementStart);
uint64_t sizeInBytes;
Block::iterator nBegin, nEnd;
LogicalResult iRet = generateCopy(
*regionEntry.second, block, begin, end, copyPlacementBlock,
copyInPlacementStart, copyOutPlacementStart, copyOptions,
fastBufferMap, copyNests, &sizeInBytes, &nBegin, &nEnd);
if (succeeded(iRet)) {
// begin/end could have been invalidated, and need update.
begin = nBegin;
end = nEnd;
totalCopyBuffersSizeInBytes += sizeInBytes;
}
ret = ret & succeeded(iRet);
}
};
processRegions(readRegions);
processRegions(writeRegions);
if (!ret) {
begin->emitError(
"copy generation failed for one or more memref's in this block\n");
return totalCopyBuffersSizeInBytes;
}
// For a range of operations, a note will be emitted at the caller.
AffineForOp forOp;
uint64_t sizeInKib = llvm::divideCeil(totalCopyBuffersSizeInBytes, 1024);
if (llvm::DebugFlag && (forOp = dyn_cast<AffineForOp>(&*begin))) {
forOp.emitRemark()
<< sizeInKib
<< " KiB of copy buffers in fast memory space for this block\n";
}
if (totalCopyBuffersSizeInBytes > copyOptions.fastMemCapacityBytes) {
StringRef str = "Total size of all copy buffers' for this block "
"exceeds fast memory capacity\n";
block->getParentOp()->emitError(str);
}
return totalCopyBuffersSizeInBytes;
}
// A convenience version of affineDataCopyGenerate for all ops in the body of
// an AffineForOp.
uint64_t mlir::affineDataCopyGenerate(AffineForOp forOp,
const AffineCopyOptions &copyOptions,
Optional<Value> filterMemRef,
DenseSet<Operation *> &copyNests) {
return affineDataCopyGenerate(forOp.getBody()->begin(),
std::prev(forOp.getBody()->end()), copyOptions,
filterMemRef, copyNests);
}
LogicalResult mlir::generateCopyForMemRegion(
const MemRefRegion &memrefRegion, Operation *analyzedOp,
const AffineCopyOptions &copyOptions, CopyGenerateResult &result) {
Block *block = analyzedOp->getBlock();
auto begin = analyzedOp->getIterator();
auto end = std::next(begin);
DenseMap<Value, Value> fastBufferMap;
DenseSet<Operation *> copyNests;
auto err = generateCopy(memrefRegion, block, begin, end, block, begin, end,
copyOptions, fastBufferMap, copyNests,
&result.sizeInBytes, &begin, &end);
if (failed(err))
return err;
result.alloc =
fastBufferMap.find(memrefRegion.memref)->second.getDefiningOp();
assert(copyNests.size() <= 1 && "At most one copy nest is expected.");
result.copyNest = copyNests.empty() ? nullptr : *copyNests.begin();
return success();
}
/// Gathers all AffineForOps in 'block' at 'currLoopDepth' in 'depthToLoops'.
static void
gatherLoopsInBlock(Block *block, unsigned currLoopDepth,
std::vector<SmallVector<AffineForOp, 2>> &depthToLoops) {
// Add a new empty level to output if it doesn't exist level already.
assert(currLoopDepth <= depthToLoops.size() && "Unexpected currLoopDepth");
if (currLoopDepth == depthToLoops.size())
depthToLoops.push_back(SmallVector<AffineForOp, 2>());
for (auto &op : *block) {
if (auto forOp = dyn_cast<AffineForOp>(op)) {
depthToLoops[currLoopDepth].push_back(forOp);
gatherLoopsInBlock(forOp.getBody(), currLoopDepth + 1, depthToLoops);
}
}
}
/// Gathers all AffineForOps in 'func' grouped by loop depth.
void mlir::gatherLoops(FuncOp func,
std::vector<SmallVector<AffineForOp, 2>> &depthToLoops) {
for (auto &block : func)
gatherLoopsInBlock(&block, /*currLoopDepth=*/0, depthToLoops);
// Remove last loop level from output since it's empty.
if (!depthToLoops.empty()) {
assert(depthToLoops.back().empty() && "Last loop level is not empty?");
depthToLoops.pop_back();
}
}
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