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//===- UseDefAnalysis.cpp - Analysis for Transitive UseDef chains ---------===//
//
// Copyright 2019 The MLIR Authors.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// =============================================================================
//
// This file implements Analysis functions specific to slicing in Function.
//
//===----------------------------------------------------------------------===//
#include "mlir/Analysis/SliceAnalysis.h"
#include "mlir/AffineOps/AffineOps.h"
#include "mlir/Analysis/VectorAnalysis.h"
#include "mlir/IR/BuiltinOps.h"
#include "mlir/IR/Instruction.h"
#include "mlir/Support/Functional.h"
#include "mlir/Support/STLExtras.h"
#include "llvm/ADT/SetVector.h"
#include <type_traits>
///
/// Implements Analysis functions specific to slicing in Function.
///
using namespace mlir;
using llvm::DenseSet;
using llvm::SetVector;
void mlir::getForwardSlice(Instruction *inst,
SetVector<Instruction *> *forwardSlice,
TransitiveFilter filter, bool topLevel) {
if (!inst) {
return;
}
// Evaluate whether we should keep this use.
// This is useful in particular to implement scoping; i.e. return the
// transitive forwardSlice in the current scope.
if (!filter(inst)) {
return;
}
if (auto forOp = inst->dyn_cast<AffineForOp>()) {
for (auto &u : forOp->getInductionVar()->getUses()) {
auto *ownerInst = u.getOwner();
if (forwardSlice->count(ownerInst) == 0) {
getForwardSlice(ownerInst, forwardSlice, filter,
/*topLevel=*/false);
}
}
} else {
assert(inst->getNumResults() <= 1 && "NYI: multiple results");
if (inst->getNumResults() > 0) {
for (auto &u : inst->getResult(0)->getUses()) {
auto *ownerInst = u.getOwner();
if (forwardSlice->count(ownerInst) == 0) {
getForwardSlice(ownerInst, forwardSlice, filter,
/*topLevel=*/false);
}
}
}
}
// At the top level we reverse to get back the actual topological order.
if (topLevel) {
// std::reverse does not work out of the box on SetVector and I want an
// in-place swap based thing (the real std::reverse, not the LLVM adapter).
// TODO(clattner): Consider adding an extra method?
std::vector<Instruction *> v(forwardSlice->takeVector());
forwardSlice->insert(v.rbegin(), v.rend());
} else {
forwardSlice->insert(inst);
}
}
void mlir::getBackwardSlice(Instruction *inst,
SetVector<Instruction *> *backwardSlice,
TransitiveFilter filter, bool topLevel) {
if (!inst) {
return;
}
// Evaluate whether we should keep this def.
// This is useful in particular to implement scoping; i.e. return the
// transitive forwardSlice in the current scope.
if (!filter(inst)) {
return;
}
for (auto *operand : inst->getOperands()) {
auto *inst = operand->getDefiningInst();
if (backwardSlice->count(inst) == 0) {
getBackwardSlice(inst, backwardSlice, filter,
/*topLevel=*/false);
}
}
// Don't insert the top level instruction, we just queried on it and don't
// want it in the results.
if (!topLevel) {
backwardSlice->insert(inst);
}
}
SetVector<Instruction *> mlir::getSlice(Instruction *inst,
TransitiveFilter backwardFilter,
TransitiveFilter forwardFilter) {
SetVector<Instruction *> slice;
slice.insert(inst);
unsigned currentIndex = 0;
SetVector<Instruction *> backwardSlice;
SetVector<Instruction *> forwardSlice;
while (currentIndex != slice.size()) {
auto *currentInst = (slice)[currentIndex];
// Compute and insert the backwardSlice starting from currentInst.
backwardSlice.clear();
getBackwardSlice(currentInst, &backwardSlice, backwardFilter);
slice.insert(backwardSlice.begin(), backwardSlice.end());
// Compute and insert the forwardSlice starting from currentInst.
forwardSlice.clear();
getForwardSlice(currentInst, &forwardSlice, forwardFilter);
slice.insert(forwardSlice.begin(), forwardSlice.end());
++currentIndex;
}
return topologicalSort(slice);
}
namespace {
/// DFS post-order implementation that maintains a global count to work across
/// multiple invocations, to help implement topological sort on multi-root DAGs.
/// We traverse all instructions but only record the ones that appear in
/// `toSort` for the final result.
struct DFSState {
DFSState(const SetVector<Instruction *> &set)
: toSort(set), topologicalCounts(), seen() {}
const SetVector<Instruction *> &toSort;
SmallVector<Instruction *, 16> topologicalCounts;
DenseSet<Instruction *> seen;
};
} // namespace
static void DFSPostorder(Instruction *current, DFSState *state) {
assert(current->getNumResults() <= 1 && "NYI: multi-result");
if (current->getNumResults() > 0) {
for (auto &u : current->getResult(0)->getUses()) {
auto *inst = u.getOwner();
DFSPostorder(inst, state);
}
}
bool inserted;
using IterTy = decltype(state->seen.begin());
IterTy iter;
std::tie(iter, inserted) = state->seen.insert(current);
if (inserted) {
if (state->toSort.count(current) > 0) {
state->topologicalCounts.push_back(current);
}
}
}
SetVector<Instruction *>
mlir::topologicalSort(const SetVector<Instruction *> &toSort) {
if (toSort.empty()) {
return toSort;
}
// Run from each root with global count and `seen` set.
DFSState state(toSort);
for (auto *s : toSort) {
assert(toSort.count(s) == 1 && "NYI: multi-sets not supported");
DFSPostorder(s, &state);
}
// Reorder and return.
SetVector<Instruction *> res;
for (auto it = state.topologicalCounts.rbegin(),
eit = state.topologicalCounts.rend();
it != eit; ++it) {
res.insert(*it);
}
return res;
}
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