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//===- LoopFusion.cpp - Code to perform loop fusion -----------------------===//
//
// 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 loop fusion.
//
//===----------------------------------------------------------------------===//
#include "mlir/Analysis/AffineAnalysis.h"
#include "mlir/Analysis/AffineStructures.h"
#include "mlir/Analysis/LoopAnalysis.h"
#include "mlir/Analysis/Utils.h"
#include "mlir/IR/AffineExpr.h"
#include "mlir/IR/AffineMap.h"
#include "mlir/IR/Builders.h"
#include "mlir/IR/BuiltinOps.h"
#include "mlir/IR/InstVisitor.h"
#include "mlir/Pass.h"
#include "mlir/StandardOps/StandardOps.h"
#include "mlir/Transforms/LoopUtils.h"
#include "mlir/Transforms/Passes.h"
#include "mlir/Transforms/Utils.h"
#include "llvm/ADT/DenseMap.h"
#include "llvm/ADT/DenseSet.h"
#include "llvm/ADT/SetVector.h"
#include "llvm/Support/CommandLine.h"
#include "llvm/Support/Debug.h"
#include "llvm/Support/raw_ostream.h"
#include <iomanip>
#define DEBUG_TYPE "loop-fusion"
using llvm::SetVector;
using namespace mlir;
/// Disables fusion profitability check and fuses if valid.
static llvm::cl::opt<bool>
clMaximalLoopFusion("fusion-maximal", llvm::cl::Hidden,
llvm::cl::desc("Enables maximal loop fusion"));
/// A threshold in percent of additional computation allowed when fusing.
static llvm::cl::opt<double> clFusionAddlComputeTolerance(
"fusion-compute-tolerance", llvm::cl::Hidden,
llvm::cl::desc("Fractional increase in additional"
"computation tolerated while fusing"));
namespace {
/// Loop fusion pass. This pass currently supports a greedy fusion policy,
/// which fuses loop nests with single-writer/single-reader memref dependences
/// with the goal of improving locality.
// TODO(andydavis) Support fusion of source loop nests which write to multiple
// memrefs, where each memref can have multiple users (if profitable).
// TODO(andydavis) Extend this pass to check for fusion preventing dependences,
// and add support for more general loop fusion algorithms.
struct LoopFusion : public FunctionPass {
LoopFusion() : FunctionPass(&LoopFusion::passID) {}
PassResult runOnFunction(Function *f) override;
static char passID;
// The amount of additional computation that is tolerated while fusing
// pair-wise as a fraction of the total computation.
constexpr static double kComputeToleranceThreshold = 0.30f;
};
} // end anonymous namespace
char LoopFusion::passID = 0;
FunctionPass *mlir::createLoopFusionPass() { return new LoopFusion; }
namespace {
// LoopNestStateCollector walks loop nests and collects load and store
// operations, and whether or not an IfInst was encountered in the loop nest.
class LoopNestStateCollector : public InstWalker<LoopNestStateCollector> {
public:
SmallVector<ForInst *, 4> forInsts;
SmallVector<OperationInst *, 4> loadOpInsts;
SmallVector<OperationInst *, 4> storeOpInsts;
bool hasIfInst = false;
void visitForInst(ForInst *forInst) { forInsts.push_back(forInst); }
void visitIfInst(IfInst *ifInst) { hasIfInst = true; }
void visitOperationInst(OperationInst *opInst) {
if (opInst->isa<LoadOp>())
loadOpInsts.push_back(opInst);
if (opInst->isa<StoreOp>())
storeOpInsts.push_back(opInst);
}
};
// TODO(b/117228571) Replace when this is modeled through side-effects/op traits
static bool isMemRefDereferencingOp(const OperationInst &op) {
if (op.isa<LoadOp>() || op.isa<StoreOp>() || op.isa<DmaStartOp>() ||
op.isa<DmaWaitOp>())
return true;
return false;
}
// MemRefDependenceGraph is a graph data structure where graph nodes are
// top-level instructions in a Function which contain load/store ops, and edges
// are memref dependences between the nodes.
// TODO(andydavis) Add a more flexible dependece graph representation.
// TODO(andydavis) Add a depth parameter to dependence graph construction.
struct MemRefDependenceGraph {
public:
// Node represents a node in the graph. A Node is either an entire loop nest
// rooted at the top level which contains loads/stores, or a top level
// load/store.
struct Node {
// The unique identifier of this node in the graph.
unsigned id;
// The top-level statment which is (or contains) loads/stores.
Instruction *inst;
// List of load operations.
SmallVector<OperationInst *, 4> loads;
// List of store op insts.
SmallVector<OperationInst *, 4> stores;
Node(unsigned id, Instruction *inst) : id(id), inst(inst) {}
// Returns the load op count for 'memref'.
unsigned getLoadOpCount(Value *memref) {
unsigned loadOpCount = 0;
for (auto *loadOpInst : loads) {
if (memref == loadOpInst->cast<LoadOp>()->getMemRef())
++loadOpCount;
}
return loadOpCount;
}
// Returns the store op count for 'memref'.
unsigned getStoreOpCount(Value *memref) {
unsigned storeOpCount = 0;
for (auto *storeOpInst : stores) {
if (memref == storeOpInst->cast<StoreOp>()->getMemRef())
++storeOpCount;
}
return storeOpCount;
}
};
// Edge represents a memref data dependece between nodes in the graph.
struct Edge {
// The id of the node at the other end of the edge.
unsigned id;
// The memref on which this edge represents a dependence.
Value *memref;
};
// Map from node id to Node.
DenseMap<unsigned, Node> nodes;
// Map from node id to list of input edges.
DenseMap<unsigned, SmallVector<Edge, 2>> inEdges;
// Map from node id to list of output edges.
DenseMap<unsigned, SmallVector<Edge, 2>> outEdges;
// Map from memref to a count on the dependence edges associated with that
// memref.
DenseMap<Value *, unsigned> memrefEdgeCount;
MemRefDependenceGraph() {}
// Initializes the dependence graph based on operations in 'f'.
// Returns true on success, false otherwise.
bool init(Function *f);
// Returns the graph node for 'id'.
Node *getNode(unsigned id) {
auto it = nodes.find(id);
assert(it != nodes.end());
return &it->second;
}
// Remove node 'id' (and its associated edges) from graph.
void removeNode(unsigned id) {
// Remove each edge in 'inEdges[id]'.
if (inEdges.count(id) > 0) {
SmallVector<Edge, 2> oldInEdges = inEdges[id];
for (auto &inEdge : oldInEdges) {
removeEdge(inEdge.id, id, inEdge.memref);
}
}
// Remove each edge in 'outEdges[id]'.
if (outEdges.count(id) > 0) {
SmallVector<Edge, 2> oldOutEdges = outEdges[id];
for (auto &outEdge : oldOutEdges) {
removeEdge(id, outEdge.id, outEdge.memref);
}
}
// Erase remaining node state.
inEdges.erase(id);
outEdges.erase(id);
nodes.erase(id);
}
bool hasOutEdges(unsigned id) {
return outEdges.count(id) > 0 && !outEdges[id].empty();
}
// Returns true if node 'id' writes to any memref which escapes (or is an
// argument to) the function/block. Returns false otherwise.
bool writesToLiveInOrEscapingMemrefs(unsigned id) {
Node *node = getNode(id);
for (auto *storeOpInst : node->stores) {
auto *memref = storeOpInst->cast<StoreOp>()->getMemRef();
auto *inst = memref->getDefiningInst();
auto *opInst = dyn_cast_or_null<OperationInst>(inst);
// Return false if 'memref' is a function argument.
if (opInst == nullptr)
return true;
// Return false if any use of 'memref' escapes the function.
for (auto &use : memref->getUses()) {
auto *user = dyn_cast<OperationInst>(use.getOwner());
if (!user || !isMemRefDereferencingOp(*user))
return true;
}
}
return false;
}
// Returns true iff there is an edge from node 'srcId' to node 'dstId' for
// 'memref'. Returns false otherwise.
bool hasEdge(unsigned srcId, unsigned dstId, Value *memref) {
if (outEdges.count(srcId) == 0 || inEdges.count(dstId) == 0) {
return false;
}
bool hasOutEdge = llvm::any_of(outEdges[srcId], [=](Edge &edge) {
return edge.id == dstId && edge.memref == memref;
});
bool hasInEdge = llvm::any_of(inEdges[dstId], [=](Edge &edge) {
return edge.id == srcId && edge.memref == memref;
});
return hasOutEdge && hasInEdge;
}
// Adds an edge from node 'srcId' to node 'dstId' for 'memref'.
void addEdge(unsigned srcId, unsigned dstId, Value *memref) {
if (!hasEdge(srcId, dstId, memref)) {
outEdges[srcId].push_back({dstId, memref});
inEdges[dstId].push_back({srcId, memref});
memrefEdgeCount[memref]++;
}
}
// Removes an edge from node 'srcId' to node 'dstId' for 'memref'.
void removeEdge(unsigned srcId, unsigned dstId, Value *memref) {
assert(inEdges.count(dstId) > 0);
assert(outEdges.count(srcId) > 0);
assert(memrefEdgeCount.count(memref) > 0);
memrefEdgeCount[memref]--;
// Remove 'srcId' from 'inEdges[dstId]'.
for (auto it = inEdges[dstId].begin(); it != inEdges[dstId].end(); ++it) {
if ((*it).id == srcId && (*it).memref == memref) {
inEdges[dstId].erase(it);
break;
}
}
// Remove 'dstId' from 'outEdges[srcId]'.
for (auto it = outEdges[srcId].begin(); it != outEdges[srcId].end(); ++it) {
if ((*it).id == dstId && (*it).memref == memref) {
outEdges[srcId].erase(it);
break;
}
}
}
// Returns the input edge count for node 'id' and 'memref'.
unsigned getInEdgeCount(unsigned id, Value *memref) {
unsigned inEdgeCount = 0;
if (inEdges.count(id) > 0)
for (auto &inEdge : inEdges[id])
if (inEdge.memref == memref)
++inEdgeCount;
return inEdgeCount;
}
// Returns the output edge count for node 'id' and 'memref'.
unsigned getOutEdgeCount(unsigned id, Value *memref) {
unsigned outEdgeCount = 0;
if (outEdges.count(id) > 0)
for (auto &outEdge : outEdges[id])
if (outEdge.memref == memref)
++outEdgeCount;
return outEdgeCount;
}
// Check for a dependence in Block instruction list range (srcId, dstId) on
// memrefs other than 'memrefToSkip' (which will be privatized for the fused
// loop).
bool hasDependenceTargetInRange(unsigned srcId, unsigned dstId,
Value *memrefToSkip) {
if (outEdges.count(srcId) == 0)
return false;
// Check if any of the outgoing edge targets from srcId lie in
// (srcId, dstId).
SmallPtrSet<Instruction *, 2> depInsts;
for (auto &outEdge : outEdges[srcId]) {
if (outEdge.id != dstId && outEdge.memref != memrefToSkip) {
Node *node = getNode(outEdge.id);
depInsts.insert(node->inst);
}
}
// Do a linear walk from 'srcNode.inst' to 'dstNode.inst' and for each
// instruction 'inst' in range ('srcNode.inst', 'dstNode.inst') test
// if 'depInsts' contains 'inst', and return true if it does.
// TODO(andydavis) If this linear search becomes a compile time issue,
// create a data structure which allows a faster search through ForInsts
// in a Block.
Block::iterator it = std::next(Block::iterator(getNode(srcId)->inst));
Block::iterator itEnd = Block::iterator(getNode(dstId)->inst);
return std::any_of(it, itEnd, [&](Instruction &inst) {
return depInsts.count(&inst) > 0;
});
}
// Updates edge mappings from node 'srcId' to node 'dstId'.
void updateEdges(unsigned srcId, unsigned dstId) {
// For each edge in 'inEdges[srcId]': add new edge remaping to 'dstId'.
if (inEdges.count(srcId) > 0) {
SmallVector<Edge, 2> oldInEdges = inEdges[srcId];
for (auto &inEdge : oldInEdges) {
// Add edge from 'inEdge.id' to 'dstId'.
addEdge(inEdge.id, dstId, inEdge.memref);
}
}
// For each edge in 'outEdges[srcId]': remove edge from 'srcId' to 'dstId'.
if (outEdges.count(srcId) > 0) {
SmallVector<Edge, 2> oldOutEdges = outEdges[srcId];
for (auto &outEdge : oldOutEdges) {
// Remove any out edges from 'srcId' to 'dstId' across memrefs.
if (outEdge.id == dstId)
removeEdge(srcId, outEdge.id, outEdge.memref);
}
}
}
// Adds ops in 'loads' and 'stores' to node at 'id'.
void addToNode(unsigned id, const SmallVectorImpl<OperationInst *> &loads,
const SmallVectorImpl<OperationInst *> &stores) {
Node *node = getNode(id);
for (auto *loadOpInst : loads)
node->loads.push_back(loadOpInst);
for (auto *storeOpInst : stores)
node->stores.push_back(storeOpInst);
}
void clearNodeLoadAndStores(unsigned id) {
Node *node = getNode(id);
node->loads.clear();
node->stores.clear();
}
void print(raw_ostream &os) const {
os << "\nMemRefDependenceGraph\n";
os << "\nNodes:\n";
for (auto &idAndNode : nodes) {
os << "Node: " << idAndNode.first << "\n";
auto it = inEdges.find(idAndNode.first);
if (it != inEdges.end()) {
for (const auto &e : it->second)
os << " InEdge: " << e.id << " " << e.memref << "\n";
}
it = outEdges.find(idAndNode.first);
if (it != outEdges.end()) {
for (const auto &e : it->second)
os << " OutEdge: " << e.id << " " << e.memref << "\n";
}
}
}
void dump() const { print(llvm::errs()); }
};
// Intializes the data dependence graph by walking instructions in 'f'.
// Assigns each node in the graph a node id based on program order in 'f'.
// TODO(andydavis) Add support for taking a Block arg to construct the
// dependence graph at a different depth.
bool MemRefDependenceGraph::init(Function *f) {
unsigned id = 0;
DenseMap<Value *, SetVector<unsigned>> memrefAccesses;
// TODO: support multi-block functions.
if (f->getBlocks().size() != 1)
return false;
for (auto &inst : f->front()) {
if (auto *forInst = dyn_cast<ForInst>(&inst)) {
// Create graph node 'id' to represent top-level 'forInst' and record
// all loads and store accesses it contains.
LoopNestStateCollector collector;
collector.walkForInst(forInst);
// Return false if IfInsts are found (not currently supported).
if (collector.hasIfInst)
return false;
Node node(id++, &inst);
for (auto *opInst : collector.loadOpInsts) {
node.loads.push_back(opInst);
auto *memref = opInst->cast<LoadOp>()->getMemRef();
memrefAccesses[memref].insert(node.id);
}
for (auto *opInst : collector.storeOpInsts) {
node.stores.push_back(opInst);
auto *memref = opInst->cast<StoreOp>()->getMemRef();
memrefAccesses[memref].insert(node.id);
}
nodes.insert({node.id, node});
}
if (auto *opInst = dyn_cast<OperationInst>(&inst)) {
if (auto loadOp = opInst->dyn_cast<LoadOp>()) {
// Create graph node for top-level load op.
Node node(id++, &inst);
node.loads.push_back(opInst);
auto *memref = opInst->cast<LoadOp>()->getMemRef();
memrefAccesses[memref].insert(node.id);
nodes.insert({node.id, node});
}
if (auto storeOp = opInst->dyn_cast<StoreOp>()) {
// Create graph node for top-level store op.
Node node(id++, &inst);
node.stores.push_back(opInst);
auto *memref = opInst->cast<StoreOp>()->getMemRef();
memrefAccesses[memref].insert(node.id);
nodes.insert({node.id, node});
}
}
// Return false if IfInsts are found (not currently supported).
if (isa<IfInst>(&inst))
return false;
}
// Walk memref access lists and add graph edges between dependent nodes.
for (auto &memrefAndList : memrefAccesses) {
unsigned n = memrefAndList.second.size();
for (unsigned i = 0; i < n; ++i) {
unsigned srcId = memrefAndList.second[i];
bool srcHasStore =
getNode(srcId)->getStoreOpCount(memrefAndList.first) > 0;
for (unsigned j = i + 1; j < n; ++j) {
unsigned dstId = memrefAndList.second[j];
bool dstHasStore =
getNode(dstId)->getStoreOpCount(memrefAndList.first) > 0;
if (srcHasStore || dstHasStore)
addEdge(srcId, dstId, memrefAndList.first);
}
}
}
return true;
}
namespace {
// LoopNestStats aggregates various per-loop statistics (eg. loop trip count
// and operation count) for a loop nest up until the innermost loop body.
struct LoopNestStats {
// Map from ForInst to immediate child ForInsts in its loop body.
DenseMap<ForInst *, SmallVector<ForInst *, 2>> loopMap;
// Map from ForInst to count of operations in its loop body.
DenseMap<ForInst *, uint64_t> opCountMap;
// Map from ForInst to its constant trip count.
DenseMap<ForInst *, uint64_t> tripCountMap;
};
// LoopNestStatsCollector walks a single loop nest and gathers per-loop
// trip count and operation count statistics and records them in 'stats'.
class LoopNestStatsCollector : public InstWalker<LoopNestStatsCollector> {
public:
LoopNestStats *stats;
bool hasLoopWithNonConstTripCount = false;
LoopNestStatsCollector(LoopNestStats *stats) : stats(stats) {}
void visitForInst(ForInst *forInst) {
auto *parentInst = forInst->getParentInst();
if (parentInst != nullptr) {
assert(isa<ForInst>(parentInst) && "Expected parent ForInst");
// Add mapping to 'forInst' from its parent ForInst.
stats->loopMap[cast<ForInst>(parentInst)].push_back(forInst);
}
// Record the number of op instructions in the body of 'forInst'.
unsigned count = 0;
stats->opCountMap[forInst] = 0;
for (auto &inst : *forInst->getBody()) {
if (isa<OperationInst>(&inst))
++count;
}
stats->opCountMap[forInst] = count;
// Record trip count for 'forInst'. Set flag if trip count is not constant.
Optional<uint64_t> maybeConstTripCount = getConstantTripCount(*forInst);
if (!maybeConstTripCount.hasValue()) {
hasLoopWithNonConstTripCount = true;
return;
}
stats->tripCountMap[forInst] = maybeConstTripCount.getValue();
}
};
// Computes the total cost of the loop nest rooted at 'forInst'.
// Currently, the total cost is computed by counting the total operation
// instance count (i.e. total number of operations in the loop bodyloop
// operation count * loop trip count) for the entire loop nest.
// If 'tripCountOverrideMap' is non-null, overrides the trip count for loops
// specified in the map when computing the total op instance count.
// NOTE: this is used to compute the cost of computation slices, which are
// sliced along the iteration dimension, and thus reduce the trip count.
// If 'computeCostMap' is non-null, the total op count for forInsts specified
// in the map is increased (not overridden) by adding the op count from the
// map to the existing op count for the for loop. This is done before
// multiplying by the loop's trip count, and is used to model the cost of
// inserting a sliced loop nest of known cost into the loop's body.
// NOTE: this is used to compute the cost of fusing a slice of some loop nest
// within another loop.
static int64_t getComputeCost(
ForInst *forInst, LoopNestStats *stats,
llvm::SmallDenseMap<ForInst *, uint64_t, 8> *tripCountOverrideMap,
DenseMap<ForInst *, int64_t> *computeCostMap) {
// 'opCount' is the total number operations in one iteration of 'forInst' body
int64_t opCount = stats->opCountMap[forInst];
if (stats->loopMap.count(forInst) > 0) {
for (auto *childForInst : stats->loopMap[forInst]) {
opCount += getComputeCost(childForInst, stats, tripCountOverrideMap,
computeCostMap);
}
}
// Add in additional op instances from slice (if specified in map).
if (computeCostMap != nullptr) {
auto it = computeCostMap->find(forInst);
if (it != computeCostMap->end()) {
opCount += it->second;
}
}
// Override trip count (if specified in map).
int64_t tripCount = stats->tripCountMap[forInst];
if (tripCountOverrideMap != nullptr) {
auto it = tripCountOverrideMap->find(forInst);
if (it != tripCountOverrideMap->end()) {
tripCount = it->second;
}
}
// Returns the total number of dynamic instances of operations in loop body.
return tripCount * opCount;
}
} // end anonymous namespace
static Optional<uint64_t> getConstDifference(AffineMap lbMap, AffineMap ubMap) {
assert(lbMap.getNumResults() == 1 && "expected single result bound map");
assert(ubMap.getNumResults() == 1 && "expected single result bound map");
assert(lbMap.getNumDims() == ubMap.getNumDims());
assert(lbMap.getNumSymbols() == ubMap.getNumSymbols());
// TODO(andydavis) Merge this code with 'mlir::getTripCountExpr'.
// ub_expr - lb_expr
AffineExpr lbExpr(lbMap.getResult(0));
AffineExpr ubExpr(ubMap.getResult(0));
auto loopSpanExpr = simplifyAffineExpr(ubExpr - lbExpr, lbMap.getNumDims(),
lbMap.getNumSymbols());
auto cExpr = loopSpanExpr.dyn_cast<AffineConstantExpr>();
if (!cExpr)
return None;
return cExpr.getValue();
}
// Builds a map 'tripCountMap' from ForInst to constant trip count for loop
// nest surrounding 'srcAccess' utilizing slice loop bounds in 'sliceState'.
// Returns true on success, false otherwise (if a non-constant trip count
// was encountered).
// TODO(andydavis) Make this work with non-unit step loops.
static bool buildSliceTripCountMap(
OperationInst *srcOpInst, ComputationSliceState *sliceState,
llvm::SmallDenseMap<ForInst *, uint64_t, 8> *tripCountMap) {
SmallVector<ForInst *, 4> srcLoopIVs;
getLoopIVs(*srcOpInst, &srcLoopIVs);
unsigned numSrcLoopIVs = srcLoopIVs.size();
// Populate map from ForInst -> trip count
for (unsigned i = 0; i < numSrcLoopIVs; ++i) {
AffineMap lbMap = sliceState->lbs[i];
AffineMap ubMap = sliceState->ubs[i];
if (lbMap == AffineMap::Null() || ubMap == AffineMap::Null()) {
// The iteration of src loop IV 'i' was not sliced. Use full loop bounds.
if (srcLoopIVs[i]->hasConstantLowerBound() &&
srcLoopIVs[i]->hasConstantUpperBound()) {
(*tripCountMap)[srcLoopIVs[i]] =
srcLoopIVs[i]->getConstantUpperBound() -
srcLoopIVs[i]->getConstantLowerBound();
continue;
}
return false;
}
Optional<uint64_t> tripCount = getConstDifference(lbMap, ubMap);
if (!tripCount.hasValue())
return false;
(*tripCountMap)[srcLoopIVs[i]] = tripCount.getValue();
}
return true;
}
// Removes load operations from 'srcLoads' which operate on 'memref', and
// adds them to 'dstLoads'.
static void
moveLoadsAccessingMemrefTo(Value *memref,
SmallVectorImpl<OperationInst *> *srcLoads,
SmallVectorImpl<OperationInst *> *dstLoads) {
dstLoads->clear();
SmallVector<OperationInst *, 4> srcLoadsToKeep;
for (auto *load : *srcLoads) {
if (load->cast<LoadOp>()->getMemRef() == memref)
dstLoads->push_back(load);
else
srcLoadsToKeep.push_back(load);
}
srcLoads->swap(srcLoadsToKeep);
}
// Returns the innermost common loop depth for the set of operations in 'ops'.
static unsigned getInnermostCommonLoopDepth(ArrayRef<OperationInst *> ops) {
unsigned numOps = ops.size();
assert(numOps > 0);
std::vector<SmallVector<ForInst *, 4>> loops(numOps);
unsigned loopDepthLimit = std::numeric_limits<unsigned>::max();
for (unsigned i = 0; i < numOps; ++i) {
getLoopIVs(*ops[i], &loops[i]);
loopDepthLimit =
std::min(loopDepthLimit, static_cast<unsigned>(loops[i].size()));
}
unsigned loopDepth = 0;
for (unsigned d = 0; d < loopDepthLimit; ++d) {
unsigned i;
for (i = 1; i < numOps; ++i) {
if (loops[i - 1][d] != loops[i][d]) {
break;
}
}
if (i != numOps)
break;
++loopDepth;
}
return loopDepth;
}
// Returns the slice union of 'sliceStateA' and 'sliceStateB' in 'sliceStateB'
// using a rectangular bounding box.
// TODO(andydavis) This function assumes that lower bounds for 'sliceStateA'
// and 'sliceStateB' are aligned.
// Specifically, when taking the union of overlapping intervals, it assumes
// that both intervals start at zero. Support needs to be added to take into
// account interval start offset when computing the union.
// TODO(andydavis) Move this function to an analysis library.
static bool getSliceUnion(const ComputationSliceState &sliceStateA,
ComputationSliceState *sliceStateB) {
assert(sliceStateA.lbs.size() == sliceStateB->lbs.size());
assert(sliceStateA.ubs.size() == sliceStateB->ubs.size());
for (unsigned i = 0, e = sliceStateA.lbs.size(); i < e; ++i) {
AffineMap lbMapA = sliceStateA.lbs[i];
AffineMap ubMapA = sliceStateA.ubs[i];
if (lbMapA == AffineMap::Null()) {
assert(ubMapA == AffineMap::Null());
continue;
}
assert(ubMapA && "expected non-null ub map");
AffineMap lbMapB = sliceStateB->lbs[i];
AffineMap ubMapB = sliceStateB->ubs[i];
if (lbMapB == AffineMap::Null()) {
assert(ubMapB == AffineMap::Null());
// Union 'sliceStateB' does not have a bound for 'i' so copy from A.
sliceStateB->lbs[i] = lbMapA;
sliceStateB->ubs[i] = ubMapA;
continue;
}
// TODO(andydavis) Change this code to take the min across all lower bounds
// and max across all upper bounds for each dimension. This code can for
// cases where a unique min or max could not be statically determined.
// Assumption: both lower bounds are the same.
if (lbMapA != lbMapB)
return false;
// Add bound with the largest trip count to union.
Optional<uint64_t> tripCountA = getConstDifference(lbMapA, ubMapA);
Optional<uint64_t> tripCountB = getConstDifference(lbMapB, ubMapB);
if (!tripCountA.hasValue() || !tripCountB.hasValue())
return false;
if (tripCountA.getValue() > tripCountB.getValue()) {
sliceStateB->lbs[i] = lbMapA;
sliceStateB->ubs[i] = ubMapA;
}
}
return true;
}
// Creates and returns a private (single-user) memref for fused loop rooted
// at 'forInst', with (potentially reduced) memref size based on the
// MemRefRegion written to by 'srcStoreOpInst' at depth 'dstLoopDepth'.
// TODO(bondhugula): consider refactoring the common code from generateDma and
// this one.
static Value *createPrivateMemRef(ForInst *forInst,
OperationInst *srcStoreOpInst,
unsigned dstLoopDepth) {
// Create builder to insert alloc op just before 'forInst'.
FuncBuilder b(forInst);
// Builder to create constants at the top level.
FuncBuilder top(forInst->getFunction());
// Create new memref type based on slice bounds.
auto *oldMemRef = srcStoreOpInst->cast<StoreOp>()->getMemRef();
auto oldMemRefType = oldMemRef->getType().cast<MemRefType>();
unsigned rank = oldMemRefType.getRank();
// Compute MemRefRegion for 'srcStoreOpInst' at depth 'dstLoopDepth'.
MemRefRegion region;
getMemRefRegion(srcStoreOpInst, dstLoopDepth, &region);
SmallVector<int64_t, 4> newShape;
std::vector<SmallVector<int64_t, 4>> lbs;
SmallVector<int64_t, 8> lbDivisors;
lbs.reserve(rank);
// Query 'region' for 'newShape' and lower bounds of MemRefRegion accessed
// by 'srcStoreOpInst' at depth 'dstLoopDepth'.
Optional<int64_t> numElements =
region.getConstantBoundingSizeAndShape(&newShape, &lbs, &lbDivisors);
assert(numElements.hasValue());
const FlatAffineConstraints *cst = region.getConstraints();
// 'outerIVs' holds the values that this memory region is symbolic/paramteric
// on; this would correspond to loop IVs surrounding the level at which the
// slice is being materialized.
SmallVector<Value *, 8> outerIVs;
cst->getIdValues(rank, cst->getNumIds(), &outerIVs);
// Build 'rank' AffineExprs from MemRefRegion 'lbs'
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]);
offsets.push_back(offset);
}
// Create 'newMemRefType' using 'newShape' from MemRefRegion accessed
// by 'srcStoreOpInst'.
auto newMemRefType = b.getMemRefType(newShape, oldMemRefType.getElementType(),
{}, oldMemRefType.getMemorySpace());
// Gather alloc operands for the dynamic dimensions of the memref.
SmallVector<Value *, 4> allocOperands;
unsigned dynamicDimCount = 0;
for (auto dimSize : oldMemRefType.getShape()) {
if (dimSize == -1)
allocOperands.push_back(
b.create<DimOp>(forInst->getLoc(), oldMemRef, dynamicDimCount++));
}
// Create new private memref for fused loop 'forInst'.
Value *newMemRef =
b.create<AllocOp>(forInst->getLoc(), newMemRefType, allocOperands);
// Build an AffineMap to remap access functions based on lower bound offsets.
SmallVector<AffineExpr, 4> remapExprs;
remapExprs.reserve(rank);
unsigned zeroOffsetCount = 0;
for (unsigned i = 0; i < rank; i++) {
if (auto constExpr = offsets[i].dyn_cast<AffineConstantExpr>())
if (constExpr.getValue() == 0)
++zeroOffsetCount;
auto dimExpr = b.getAffineDimExpr(outerIVs.size() + i);
auto remapExpr =
simplifyAffineExpr(dimExpr - offsets[i], outerIVs.size() + rank, 0);
remapExprs.push_back(remapExpr);
}
auto indexRemap =
zeroOffsetCount == rank
? AffineMap::Null()
: b.getAffineMap(outerIVs.size() + rank, 0, remapExprs, {});
// Replace all users of 'oldMemRef' with 'newMemRef'.
bool ret =
replaceAllMemRefUsesWith(oldMemRef, newMemRef, {}, indexRemap,
/*extraOperands=*/outerIVs,
/*domInstFilter=*/&*forInst->getBody()->begin());
assert(ret && "replaceAllMemrefUsesWith should always succeed here");
(void)ret;
return newMemRef;
}
// Does the slice have a single iteration?
static uint64_t getSliceIterationCount(
const llvm::SmallDenseMap<ForInst *, uint64_t, 8> &sliceTripCountMap) {
uint64_t iterCount = 1;
for (const auto &count : sliceTripCountMap) {
iterCount *= count.second;
}
return iterCount;
}
// Checks the profitability of fusing a backwards slice of the loop nest
// surrounding 'srcOpInst' into the loop nest surrounding 'dstOpInsts'.
// Returns true if it is profitable to fuse the candidate loop nests. Returns
// false otherwise. `dstLoopDepth` is set to the most profitable depth at which
// to materialize the source loop nest slice.
// The profitability model executes the following steps:
// *) Computes the backward computation slice at 'srcOpInst'. This
// computation slice of the loop nest surrounding 'srcOpInst' is
// represented by modified src loop bounds in 'sliceState', which are
// functions of loop IVs in the loop nest surrounding 'srcOpInst'.
// *) Computes the cost of unfused src/dst loop nests (currently the cost of a
// loop nest is the total number of dynamic operation instances in the loop
// nest).
// *) Computes the cost of fusing a slice of the src loop nest into the dst
// loop nest at various values of dst loop depth, attempting to fuse
// the largest compution slice at the maximal dst loop depth (closest to the
// load) to minimize reuse distance and potentially enable subsequent
// load/store forwarding.
// NOTE: If the dst loop nest includes multiple loads in 'dstOpInsts' for
// the same memref as is written by 'srcOpInst', then the union of slice
// loop bounds is used to compute the slice and associated slice cost.
// NOTE: 'dstLoopDepth' refers to the loop depth within the destination loop
// nest, at which the src computation slice is inserted/fused.
// NOTE: We attempt to maximize the dst loop depth, but there are cases
// where a particular setting for 'dstLoopNest' might fuse an unsliced
// loop (within the src computation slice) at a depth which results in
// execessive recomputation (see unit tests for examples).
// *) Compares the total cost of the unfused loop nests to the min cost fused
// loop nest computed in the previous step, and returns true if the latter
// is lower.
static bool isFusionProfitable(OperationInst *srcOpInst,
ArrayRef<OperationInst *> dstOpInsts,
ComputationSliceState *sliceState,
unsigned *dstLoopDepth) {
LLVM_DEBUG({
llvm::dbgs() << "Checking whether fusion is profitable between:\n";
llvm::dbgs() << " ";
srcOpInst->dump();
llvm::dbgs() << " and \n";
for (auto dstOpInst : dstOpInsts) {
llvm::dbgs() << " ";
dstOpInst->dump();
};
});
// Compute cost of sliced and unsliced src loop nest.
SmallVector<ForInst *, 4> srcLoopIVs;
getLoopIVs(*srcOpInst, &srcLoopIVs);
unsigned numSrcLoopIVs = srcLoopIVs.size();
// Walk src loop nest and collect stats.
LoopNestStats srcLoopNestStats;
LoopNestStatsCollector srcStatsCollector(&srcLoopNestStats);
srcStatsCollector.walk(srcLoopIVs[0]);
// Currently only constant trip count loop nests are supported.
if (srcStatsCollector.hasLoopWithNonConstTripCount)
return false;
// Compute cost of dst loop nest.
SmallVector<ForInst *, 4> dstLoopIVs;
getLoopIVs(*dstOpInsts[0], &dstLoopIVs);
LoopNestStats dstLoopNestStats;
LoopNestStatsCollector dstStatsCollector(&dstLoopNestStats);
dstStatsCollector.walk(dstLoopIVs[0]);
// Currently only constant trip count loop nests are supported.
if (dstStatsCollector.hasLoopWithNonConstTripCount)
return false;
// Compute the innermost common loop for ops in 'dstOpInst'.
unsigned maxDstLoopDepth = getInnermostCommonLoopDepth(dstOpInsts);
if (maxDstLoopDepth == 0)
return false;
// Search for min cost value for 'dstLoopDepth'. At each value of
// 'dstLoopDepth' from 'maxDstLoopDepth' to '1', compute computation slice
// bounds between 'srcOpInst' and each op in 'dstOpinsts' (taking the union
// of these bounds). Next the union slice bounds are used to calculate
// the cost of the slice and the cost of the slice inserted into the dst
// loop nest at 'dstLoopDepth'.
uint64_t minFusedLoopNestComputeCost = std::numeric_limits<uint64_t>::max();
uint64_t maxStorageReduction = 0;
Optional<uint64_t> sliceMemEstimate = None;
SmallVector<ComputationSliceState, 4> sliceStates;
sliceStates.resize(maxDstLoopDepth);
// The best loop depth at which to materialize the slice.
Optional<unsigned> bestDstLoopDepth = None;
// Compute op instance count for the src loop nest without iteration slicing.
uint64_t srcLoopNestCost = getComputeCost(srcLoopIVs[0], &srcLoopNestStats,
/*tripCountOverrideMap=*/nullptr,
/*computeCostMap=*/nullptr);
// Compute op instance count for the src loop nest.
uint64_t dstLoopNestCost = getComputeCost(dstLoopIVs[0], &dstLoopNestStats,
/*tripCountOverrideMap=*/nullptr,
/*computeCostMap=*/nullptr);
llvm::SmallDenseMap<ForInst *, uint64_t, 8> sliceTripCountMap;
DenseMap<ForInst *, int64_t> computeCostMap;
for (unsigned i = maxDstLoopDepth; i >= 1; --i) {
MemRefAccess srcAccess(srcOpInst);
// Handle the common case of one dst load without a copy.
if (!mlir::getBackwardComputationSliceState(
srcAccess, MemRefAccess(dstOpInsts[0]), i, &sliceStates[i - 1]))
return false;
// Compute the union of slice bound of all ops in 'dstOpInsts'.
for (int j = 1, e = dstOpInsts.size(); j < e; ++j) {
MemRefAccess dstAccess(dstOpInsts[j]);
ComputationSliceState tmpSliceState;
if (!mlir::getBackwardComputationSliceState(srcAccess, dstAccess, i,
&tmpSliceState))
return false;
// Compute slice boun dunion of 'tmpSliceState' and 'sliceStates[i - 1]'.
getSliceUnion(tmpSliceState, &sliceStates[i - 1]);
}
// Build trip count map for computation slice. We'll skip cases where the
// trip count was non-constant.
sliceTripCountMap.clear();
if (!buildSliceTripCountMap(srcOpInst, &sliceStates[i - 1],
&sliceTripCountMap))
continue;
// Checks whether a store to load forwarding will happen.
int64_t sliceIterationCount = getSliceIterationCount(sliceTripCountMap);
assert(sliceIterationCount > 0);
bool storeLoadFwdGuaranteed = (sliceIterationCount == 1);
// Compute cost of fusion for this dest loop depth.
computeCostMap.clear();
// The store and loads to this memref will disappear.
if (storeLoadFwdGuaranteed) {
// A single store disappears: -1 for that.
computeCostMap[srcLoopIVs[numSrcLoopIVs - 1]] = -1;
for (auto *loadOp : dstOpInsts) {
if (auto *loadLoop = dyn_cast_or_null<ForInst>(loadOp->getParentInst()))
computeCostMap[loadLoop] = -1;
}
}
// Compute op instance count for the src loop nest with iteration slicing.
int64_t sliceComputeCost =
getComputeCost(srcLoopIVs[0], &srcLoopNestStats,
/*tripCountOverrideMap=*/&sliceTripCountMap,
/*computeCostMap=*/&computeCostMap);
// Compute cost of fusion for this depth.
computeCostMap[dstLoopIVs[i - 1]] = sliceComputeCost;
int64_t fusedLoopNestComputeCost =
getComputeCost(dstLoopIVs[0], &dstLoopNestStats,
/*tripCountOverrideMap=*/nullptr, &computeCostMap);
double additionalComputeFraction =
fusedLoopNestComputeCost /
(static_cast<double>(srcLoopNestCost) + dstLoopNestCost) -
1;
// TODO(bondhugula): This is an ugly approximation. Fix this by finding a
// good way to calculate the footprint of the memref in the slice and
// divide it by the total memory footprint of the fused computation.
double storageReduction =
static_cast<double>(srcLoopNestCost) / sliceIterationCount;
LLVM_DEBUG({
std::stringstream msg;
msg << " evaluating fusion profitability at depth : " << i << "\n"
<< std::setprecision(2) << " additional compute fraction: "
<< 100.0 * additionalComputeFraction << "%\n"
<< " storage reduction factor: " << storageReduction << "x\n"
<< " fused nest cost: " << fusedLoopNestComputeCost << "\n"
<< " slice iteration count: " << sliceIterationCount << "\n";
llvm::dbgs() << msg.str();
});
double computeToleranceThreshold =
clFusionAddlComputeTolerance.getNumOccurrences() > 0
? clFusionAddlComputeTolerance
: LoopFusion::kComputeToleranceThreshold;
// TODO(b/123247369): This is a placeholder cost model.
// Among all choices that add an acceptable amount of redundant computation
// (as per computeToleranceThreshold), we will simply pick the one that
// reduces the intermediary size the most.
if ((storageReduction > maxStorageReduction) &&
(clMaximalLoopFusion ||
(additionalComputeFraction < computeToleranceThreshold))) {
maxStorageReduction = storageReduction;
bestDstLoopDepth = i;
minFusedLoopNestComputeCost = fusedLoopNestComputeCost;
// TODO(bondhugula,andydavis): find a good way to compute the memory
// footprint of the materialized slice.
// Approximating this to the compute cost of the slice. This could be an
// under-approximation or an overapproximation, but in many cases
// accurate.
sliceMemEstimate = sliceIterationCount;
}
}
// A simple cost model: fuse if it reduces the memory footprint. If
// -maximal-fusion is set, fuse nevertheless.
if (!clMaximalLoopFusion && !bestDstLoopDepth.hasValue()) {
LLVM_DEBUG(llvm::dbgs()
<< "All fusion choices involve more than the threshold amount of"
"redundant computation; NOT fusing.\n");
return false;
}
assert(bestDstLoopDepth.hasValue() &&
"expected to have a value per logic above");
// Set dstLoopDepth based on best values from search.
*dstLoopDepth = bestDstLoopDepth.getValue();
LLVM_DEBUG(
llvm::dbgs() << " LoopFusion fusion stats:"
<< "\n best loop depth: " << bestDstLoopDepth
<< "\n src loop nest compute cost: " << srcLoopNestCost
<< "\n dst loop nest compute cost: " << dstLoopNestCost
<< "\n fused loop nest compute cost: "
<< minFusedLoopNestComputeCost << "\n");
auto dstMemSize = getMemoryFootprintBytes(*dstLoopIVs[0]);
auto srcMemSize = getMemoryFootprintBytes(*srcLoopIVs[0]);
Optional<double> storageReduction = None;
if (!clMaximalLoopFusion) {
if (!dstMemSize.hasValue() || !srcMemSize.hasValue()) {
LLVM_DEBUG(
llvm::dbgs()
<< " fusion memory benefit cannot be evaluated; NOT fusing.\n");
return false;
}
auto srcMemSizeVal = srcMemSize.getValue();
auto dstMemSizeVal = dstMemSize.getValue();
assert(sliceMemEstimate.hasValue() && "expected value");
// This is an inaccurate estimate since sliceMemEstimate is isaccurate.
auto fusedMem = dstMemSizeVal + sliceMemEstimate.getValue();
LLVM_DEBUG(llvm::dbgs() << " src mem: " << srcMemSizeVal << "\n"
<< " dst mem: " << dstMemSizeVal << "\n"
<< " fused mem: " << fusedMem << "\n"
<< " slice mem: " << sliceMemEstimate << "\n");
if (fusedMem > srcMemSizeVal + dstMemSizeVal) {
LLVM_DEBUG(llvm::dbgs() << "Fusion is not profitable; NOT fusing.\n");
return false;
}
storageReduction =
100.0 *
(1.0 - fusedMem / (static_cast<double>(srcMemSizeVal) + dstMemSizeVal));
}
double additionalComputeFraction =
100.0 * (minFusedLoopNestComputeCost /
(static_cast<double>(srcLoopNestCost) + dstLoopNestCost) -
1);
(void)additionalComputeFraction;
LLVM_DEBUG({
std::stringstream msg;
msg << " fusion is most profitable at depth " << *dstLoopDepth << " with "
<< setprecision(2) << additionalComputeFraction
<< "% redundant computation and a ";
msg << (storageReduction.hasValue()
? std::to_string(storageReduction.getValue())
: "<unknown>");
msg << "% storage reduction.\n";
llvm::dbgs() << msg.str();
});
// Update return parameter 'sliceState' with 'bestSliceState'.
ComputationSliceState *bestSliceState = &sliceStates[*dstLoopDepth - 1];
sliceState->lbs = bestSliceState->lbs;
sliceState->ubs = bestSliceState->ubs;
sliceState->lbOperands = bestSliceState->lbOperands;
sliceState->ubOperands = bestSliceState->ubOperands;
// Canonicalize slice bound affine maps.
for (unsigned i = 0; i < numSrcLoopIVs; ++i) {
if (sliceState->lbs[i] != AffineMap::Null()) {
canonicalizeMapAndOperands(&sliceState->lbs[i],
&sliceState->lbOperands[i]);
}
if (sliceState->ubs[i] != AffineMap::Null()) {
canonicalizeMapAndOperands(&sliceState->ubs[i],
&sliceState->ubOperands[i]);
}
}
return true;
}
// GreedyFusion greedily fuses loop nests which have a producer/consumer
// relationship on a memref, with the goal of improving locality. Currently,
// this the producer/consumer relationship is required to be unique in the
// Function (there are TODOs to relax this constraint in the future).
//
// The steps of the algorithm are as follows:
//
// *) A worklist is initialized with node ids from the dependence graph.
// *) For each node id in the worklist:
// *) Pop a ForInst of the worklist. This 'dstForInst' will be a candidate
// destination ForInst into which fusion will be attempted.
// *) Add each LoadOp currently in 'dstForInst' into list 'dstLoadOps'.
// *) For each LoadOp in 'dstLoadOps' do:
// *) Lookup dependent loop nests at earlier positions in the Function
// which have a single store op to the same memref.
// *) Check if dependences would be violated by the fusion. For example,
// the src loop nest may load from memrefs which are different than
// the producer-consumer memref between src and dest loop nests.
// *) Get a computation slice of 'srcLoopNest', which adjusts its loop
// bounds to be functions of 'dstLoopNest' IVs and symbols.
// *) Fuse the 'srcLoopNest' computation slice into the 'dstLoopNest',
// just before the dst load op user.
// *) Add the newly fused load/store operation instructions to the state,
// and also add newly fuse load ops to 'dstLoopOps' to be considered
// as fusion dst load ops in another iteration.
// *) Remove old src loop nest and its associated state.
//
// Given a graph where top-level instructions are vertices in the set 'V' and
// edges in the set 'E' are dependences between vertices, this algorithm
// takes O(V) time for initialization, and has runtime O(V + E).
//
// This greedy algorithm is not 'maximal' due to the current restriction of
// fusing along single producer consumer edges, but there is a TODO to fix this.
//
// TODO(andydavis) Experiment with other fusion policies.
// TODO(andydavis) Add support for fusing for input reuse (perhaps by
// constructing a graph with edges which represent loads from the same memref
// in two different loop nests.
struct GreedyFusion {
public:
MemRefDependenceGraph *mdg;
SmallVector<unsigned, 4> worklist;
GreedyFusion(MemRefDependenceGraph *mdg) : mdg(mdg) {
// Initialize worklist with nodes from 'mdg'.
worklist.resize(mdg->nodes.size());
std::iota(worklist.begin(), worklist.end(), 0);
}
void run() {
while (!worklist.empty()) {
unsigned dstId = worklist.back();
worklist.pop_back();
// Skip if this node was removed (fused into another node).
if (mdg->nodes.count(dstId) == 0)
continue;
// Get 'dstNode' into which to attempt fusion.
auto *dstNode = mdg->getNode(dstId);
// Skip if 'dstNode' is not a loop nest.
if (!isa<ForInst>(dstNode->inst))
continue;
SmallVector<OperationInst *, 4> loads = dstNode->loads;
SmallVector<OperationInst *, 4> dstLoadOpInsts;
DenseSet<Value *> visitedMemrefs;
while (!loads.empty()) {
// Get memref of load on top of the stack.
auto *memref = loads.back()->cast<LoadOp>()->getMemRef();
if (visitedMemrefs.count(memref) > 0)
continue;
visitedMemrefs.insert(memref);
// Move all loads in 'loads' accessing 'memref' to 'dstLoadOpInsts'.
moveLoadsAccessingMemrefTo(memref, &loads, &dstLoadOpInsts);
// Skip if no input edges along which to fuse.
if (mdg->inEdges.count(dstId) == 0)
continue;
// Iterate through in edges for 'dstId'.
for (auto &srcEdge : mdg->inEdges[dstId]) {
// Skip 'srcEdge' if not for 'memref'.
if (srcEdge.memref != memref)
continue;
auto *srcNode = mdg->getNode(srcEdge.id);
// Skip if 'srcNode' is not a loop nest.
if (!isa<ForInst>(srcNode->inst))
continue;
// Skip if 'srcNode' has more than one store to any memref.
// TODO(andydavis) Support fusing multi-output src loop nests.
if (srcNode->stores.size() != 1)
continue;
// Skip 'srcNode' if it has in dependence edges. NOTE: This is overly
// TODO(andydavis) Track dependence type with edges, and just check
// for WAW dependence edge here.
if (mdg->getInEdgeCount(srcNode->id, memref) != 0)
continue;
// Skip if 'srcNode' has out edges on memrefs other than 'memref'
// for nodes in instruction list range (srcNode.inst, dstNode.inst).
if (mdg->hasDependenceTargetInRange(srcNode->id, dstNode->id, memref))
continue;
// Check if fusion would be profitable and at what depth.
// Get unique 'srcNode' store op.
auto *srcStoreOpInst = srcNode->stores.front();
unsigned bestDstLoopDepth;
mlir::ComputationSliceState sliceState;
if (!isFusionProfitable(srcStoreOpInst, dstLoadOpInsts, &sliceState,
&bestDstLoopDepth))
continue;
// Fuse computation slice of 'srcLoopNest' into 'dstLoopNest'.
auto *sliceLoopNest = mlir::insertBackwardComputationSlice(
srcStoreOpInst, dstLoadOpInsts[0], bestDstLoopDepth, &sliceState);
if (sliceLoopNest != nullptr) {
// Update edges between 'srcNode' and 'dstNode'.
mdg->updateEdges(srcNode->id, dstNode->id);
// Collect slice loop stats.
LoopNestStateCollector sliceCollector;
sliceCollector.walkForInst(sliceLoopNest);
// Promote single iteration slice loops to single IV value.
for (auto *forInst : sliceCollector.forInsts) {
promoteIfSingleIteration(forInst);
}
// Create private memref for 'memref' in 'dstForInst'.
auto *dstForInst = cast<ForInst>(dstNode->inst);
SmallVector<OperationInst *, 4> storesForMemref;
for (auto *storeOpInst : sliceCollector.storeOpInsts) {
if (storeOpInst->cast<StoreOp>()->getMemRef() == memref)
storesForMemref.push_back(storeOpInst);
}
assert(storesForMemref.size() == 1);
auto *newMemRef = createPrivateMemRef(
dstForInst, storesForMemref[0], bestDstLoopDepth);
visitedMemrefs.insert(newMemRef);
// Collect dst loop stats after memref privatizaton transformation.
LoopNestStateCollector dstLoopCollector;
dstLoopCollector.walkForInst(dstForInst);
// Add new load ops to current Node load op list 'loads' to
// continue fusing based on new operands.
for (auto *loadOpInst : dstLoopCollector.loadOpInsts) {
auto *loadMemRef = loadOpInst->cast<LoadOp>()->getMemRef();
if (visitedMemrefs.count(loadMemRef) == 0)
loads.push_back(loadOpInst);
}
// Clear and add back loads and stores
mdg->clearNodeLoadAndStores(dstNode->id);
mdg->addToNode(dstId, dstLoopCollector.loadOpInsts,
dstLoopCollector.storeOpInsts);
// Remove old src loop nest if it no longer has outgoing dependence
// edges, and it does not write to a memref which escapes the
// function.
if (!mdg->hasOutEdges(srcNode->id) &&
!mdg->writesToLiveInOrEscapingMemrefs(srcNode->id)) {
mdg->removeNode(srcNode->id);
cast<ForInst>(srcNode->inst)->erase();
}
}
}
}
}
// Clean up any allocs with no users.
for (auto &pair : mdg->memrefEdgeCount) {
if (pair.second > 0)
continue;
auto *memref = pair.first;
// Skip if there exist other uses (return instruction or function calls).
if (!memref->use_empty())
continue;
// Use list expected to match the dep graph info.
auto *inst = memref->getDefiningInst();
auto *opInst = dyn_cast_or_null<OperationInst>(inst);
if (opInst && opInst->isa<AllocOp>())
opInst->erase();
}
}
};
} // end anonymous namespace
PassResult LoopFusion::runOnFunction(Function *f) {
MemRefDependenceGraph g;
if (g.init(f))
GreedyFusion(&g).run();
return success();
}
static PassRegistration<LoopFusion> pass("loop-fusion", "Fuse loop nests");