1 //===-- AffinePromotion.cpp -----------------------------------------------===//
2 //
3 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
4 // See https://llvm.org/LICENSE.txt for license information.
5 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
6 //
7 //===----------------------------------------------------------------------===//
8 //
9 // This transformation is a prototype that promote FIR loops operations
10 // to affine dialect operations.
11 // It is not part of the production pipeline and would need more work in order
12 // to be used in production.
13 // More information can be found in this presentation:
14 // https://slides.com/rajanwalia/deck
15 //
16 //===----------------------------------------------------------------------===//
17 
18 #include "PassDetail.h"
19 #include "flang/Optimizer/Dialect/FIRDialect.h"
20 #include "flang/Optimizer/Dialect/FIROps.h"
21 #include "flang/Optimizer/Dialect/FIRType.h"
22 #include "flang/Optimizer/Transforms/Passes.h"
23 #include "mlir/Dialect/Affine/IR/AffineOps.h"
24 #include "mlir/Dialect/Func/IR/FuncOps.h"
25 #include "mlir/Dialect/SCF/IR/SCF.h"
26 #include "mlir/IR/BuiltinAttributes.h"
27 #include "mlir/IR/IntegerSet.h"
28 #include "mlir/IR/Visitors.h"
29 #include "mlir/Transforms/DialectConversion.h"
30 #include "llvm/ADT/DenseMap.h"
31 #include "llvm/ADT/Optional.h"
32 #include "llvm/Support/Debug.h"
33 
34 #define DEBUG_TYPE "flang-affine-promotion"
35 
36 using namespace fir;
37 using namespace mlir;
38 
39 namespace {
40 struct AffineLoopAnalysis;
41 struct AffineIfAnalysis;
42 
43 /// Stores analysis objects for all loops and if operations inside a function
44 /// these analysis are used twice, first for marking operations for rewrite and
45 /// second when doing rewrite.
46 struct AffineFunctionAnalysis {
47   explicit AffineFunctionAnalysis(mlir::func::FuncOp funcOp) {
48     for (fir::DoLoopOp op : funcOp.getOps<fir::DoLoopOp>())
49       loopAnalysisMap.try_emplace(op, op, *this);
50   }
51 
52   AffineLoopAnalysis getChildLoopAnalysis(fir::DoLoopOp op) const;
53 
54   AffineIfAnalysis getChildIfAnalysis(fir::IfOp op) const;
55 
56   llvm::DenseMap<mlir::Operation *, AffineLoopAnalysis> loopAnalysisMap;
57   llvm::DenseMap<mlir::Operation *, AffineIfAnalysis> ifAnalysisMap;
58 };
59 } // namespace
60 
61 static bool analyzeCoordinate(mlir::Value coordinate, mlir::Operation *op) {
62   if (auto blockArg = coordinate.dyn_cast<mlir::BlockArgument>()) {
63     if (isa<fir::DoLoopOp>(blockArg.getOwner()->getParentOp()))
64       return true;
65     LLVM_DEBUG(llvm::dbgs() << "AffineLoopAnalysis: array coordinate is not a "
66                                "loop induction variable (owner not loopOp)\n";
67                op->dump());
68     return false;
69   }
70   LLVM_DEBUG(
71       llvm::dbgs() << "AffineLoopAnalysis: array coordinate is not a loop "
72                       "induction variable (not a block argument)\n";
73       op->dump(); coordinate.getDefiningOp()->dump());
74   return false;
75 }
76 
77 namespace {
78 struct AffineLoopAnalysis {
79   AffineLoopAnalysis() = default;
80 
81   explicit AffineLoopAnalysis(fir::DoLoopOp op, AffineFunctionAnalysis &afa)
82       : legality(analyzeLoop(op, afa)) {}
83 
84   bool canPromoteToAffine() { return legality; }
85 
86 private:
87   bool analyzeBody(fir::DoLoopOp loopOperation,
88                    AffineFunctionAnalysis &functionAnalysis) {
89     for (auto loopOp : loopOperation.getOps<fir::DoLoopOp>()) {
90       auto analysis = functionAnalysis.loopAnalysisMap
91                           .try_emplace(loopOp, loopOp, functionAnalysis)
92                           .first->getSecond();
93       if (!analysis.canPromoteToAffine())
94         return false;
95     }
96     for (auto ifOp : loopOperation.getOps<fir::IfOp>())
97       functionAnalysis.ifAnalysisMap.try_emplace(ifOp, ifOp, functionAnalysis);
98     return true;
99   }
100 
101   bool analyzeLoop(fir::DoLoopOp loopOperation,
102                    AffineFunctionAnalysis &functionAnalysis) {
103     LLVM_DEBUG(llvm::dbgs() << "AffineLoopAnalysis: \n"; loopOperation.dump(););
104     return analyzeMemoryAccess(loopOperation) &&
105            analyzeBody(loopOperation, functionAnalysis);
106   }
107 
108   bool analyzeReference(mlir::Value memref, mlir::Operation *op) {
109     if (auto acoOp = memref.getDefiningOp<ArrayCoorOp>()) {
110       if (acoOp.getMemref().getType().isa<fir::BoxType>()) {
111         // TODO: Look if and how fir.box can be promoted to affine.
112         LLVM_DEBUG(llvm::dbgs() << "AffineLoopAnalysis: cannot promote loop, "
113                                    "array memory operation uses fir.box\n";
114                    op->dump(); acoOp.dump(););
115         return false;
116       }
117       bool canPromote = true;
118       for (auto coordinate : acoOp.getIndices())
119         canPromote = canPromote && analyzeCoordinate(coordinate, op);
120       return canPromote;
121     }
122     if (auto coOp = memref.getDefiningOp<CoordinateOp>()) {
123       LLVM_DEBUG(llvm::dbgs()
124                      << "AffineLoopAnalysis: cannot promote loop, "
125                         "array memory operation uses non ArrayCoorOp\n";
126                  op->dump(); coOp.dump(););
127 
128       return false;
129     }
130     LLVM_DEBUG(llvm::dbgs() << "AffineLoopAnalysis: unknown type of memory "
131                                "reference for array load\n";
132                op->dump(););
133     return false;
134   }
135 
136   bool analyzeMemoryAccess(fir::DoLoopOp loopOperation) {
137     for (auto loadOp : loopOperation.getOps<fir::LoadOp>())
138       if (!analyzeReference(loadOp.getMemref(), loadOp))
139         return false;
140     for (auto storeOp : loopOperation.getOps<fir::StoreOp>())
141       if (!analyzeReference(storeOp.getMemref(), storeOp))
142         return false;
143     return true;
144   }
145 
146   bool legality{};
147 };
148 } // namespace
149 
150 AffineLoopAnalysis
151 AffineFunctionAnalysis::getChildLoopAnalysis(fir::DoLoopOp op) const {
152   auto it = loopAnalysisMap.find_as(op);
153   if (it == loopAnalysisMap.end()) {
154     LLVM_DEBUG(llvm::dbgs() << "AffineFunctionAnalysis: not computed for:\n";
155                op.dump(););
156     op.emitError("error in fetching loop analysis in AffineFunctionAnalysis\n");
157     return {};
158   }
159   return it->getSecond();
160 }
161 
162 namespace {
163 /// Calculates arguments for creating an IntegerSet. symCount, dimCount are the
164 /// final number of symbols and dimensions of the affine map. Integer set if
165 /// possible is in Optional IntegerSet.
166 struct AffineIfCondition {
167   using MaybeAffineExpr = llvm::Optional<mlir::AffineExpr>;
168 
169   explicit AffineIfCondition(mlir::Value fc) : firCondition(fc) {
170     if (auto condDef = firCondition.getDefiningOp<mlir::arith::CmpIOp>())
171       fromCmpIOp(condDef);
172   }
173 
174   bool hasIntegerSet() const { return integerSet.has_value(); }
175 
176   mlir::IntegerSet getIntegerSet() const {
177     assert(hasIntegerSet() && "integer set is missing");
178     return integerSet.getValue();
179   }
180 
181   mlir::ValueRange getAffineArgs() const { return affineArgs; }
182 
183 private:
184   MaybeAffineExpr affineBinaryOp(mlir::AffineExprKind kind, mlir::Value lhs,
185                                  mlir::Value rhs) {
186     return affineBinaryOp(kind, toAffineExpr(lhs), toAffineExpr(rhs));
187   }
188 
189   MaybeAffineExpr affineBinaryOp(mlir::AffineExprKind kind, MaybeAffineExpr lhs,
190                                  MaybeAffineExpr rhs) {
191     if (lhs && rhs)
192       return mlir::getAffineBinaryOpExpr(kind, *lhs, *rhs);
193     return {};
194   }
195 
196   MaybeAffineExpr toAffineExpr(MaybeAffineExpr e) { return e; }
197 
198   MaybeAffineExpr toAffineExpr(int64_t value) {
199     return {mlir::getAffineConstantExpr(value, firCondition.getContext())};
200   }
201 
202   /// Returns an AffineExpr if it is a result of operations that can be done
203   /// in an affine expression, this includes -, +, *, rem, constant.
204   /// block arguments of a loopOp or forOp are used as dimensions
205   MaybeAffineExpr toAffineExpr(mlir::Value value) {
206     if (auto op = value.getDefiningOp<mlir::arith::SubIOp>())
207       return affineBinaryOp(
208           mlir::AffineExprKind::Add, toAffineExpr(op.getLhs()),
209           affineBinaryOp(mlir::AffineExprKind::Mul, toAffineExpr(op.getRhs()),
210                          toAffineExpr(-1)));
211     if (auto op = value.getDefiningOp<mlir::arith::AddIOp>())
212       return affineBinaryOp(mlir::AffineExprKind::Add, op.getLhs(),
213                             op.getRhs());
214     if (auto op = value.getDefiningOp<mlir::arith::MulIOp>())
215       return affineBinaryOp(mlir::AffineExprKind::Mul, op.getLhs(),
216                             op.getRhs());
217     if (auto op = value.getDefiningOp<mlir::arith::RemUIOp>())
218       return affineBinaryOp(mlir::AffineExprKind::Mod, op.getLhs(),
219                             op.getRhs());
220     if (auto op = value.getDefiningOp<mlir::arith::ConstantOp>())
221       if (auto intConstant = op.getValue().dyn_cast<IntegerAttr>())
222         return toAffineExpr(intConstant.getInt());
223     if (auto blockArg = value.dyn_cast<mlir::BlockArgument>()) {
224       affineArgs.push_back(value);
225       if (isa<fir::DoLoopOp>(blockArg.getOwner()->getParentOp()) ||
226           isa<mlir::AffineForOp>(blockArg.getOwner()->getParentOp()))
227         return {mlir::getAffineDimExpr(dimCount++, value.getContext())};
228       return {mlir::getAffineSymbolExpr(symCount++, value.getContext())};
229     }
230     return {};
231   }
232 
233   void fromCmpIOp(mlir::arith::CmpIOp cmpOp) {
234     auto lhsAffine = toAffineExpr(cmpOp.getLhs());
235     auto rhsAffine = toAffineExpr(cmpOp.getRhs());
236     if (!lhsAffine || !rhsAffine)
237       return;
238     auto constraintPair = constraint(
239         cmpOp.getPredicate(), rhsAffine.getValue() - lhsAffine.getValue());
240     if (!constraintPair)
241       return;
242     integerSet = mlir::IntegerSet::get(dimCount, symCount,
243                                        {constraintPair.getValue().first},
244                                        {constraintPair.getValue().second});
245   }
246 
247   llvm::Optional<std::pair<AffineExpr, bool>>
248   constraint(mlir::arith::CmpIPredicate predicate, mlir::AffineExpr basic) {
249     switch (predicate) {
250     case mlir::arith::CmpIPredicate::slt:
251       return {std::make_pair(basic - 1, false)};
252     case mlir::arith::CmpIPredicate::sle:
253       return {std::make_pair(basic, false)};
254     case mlir::arith::CmpIPredicate::sgt:
255       return {std::make_pair(1 - basic, false)};
256     case mlir::arith::CmpIPredicate::sge:
257       return {std::make_pair(0 - basic, false)};
258     case mlir::arith::CmpIPredicate::eq:
259       return {std::make_pair(basic, true)};
260     default:
261       return {};
262     }
263   }
264 
265   llvm::SmallVector<mlir::Value> affineArgs;
266   llvm::Optional<mlir::IntegerSet> integerSet;
267   mlir::Value firCondition;
268   unsigned symCount{0u};
269   unsigned dimCount{0u};
270 };
271 } // namespace
272 
273 namespace {
274 /// Analysis for affine promotion of fir.if
275 struct AffineIfAnalysis {
276   AffineIfAnalysis() = default;
277 
278   explicit AffineIfAnalysis(fir::IfOp op, AffineFunctionAnalysis &afa)
279       : legality(analyzeIf(op, afa)) {}
280 
281   bool canPromoteToAffine() { return legality; }
282 
283 private:
284   bool analyzeIf(fir::IfOp op, AffineFunctionAnalysis &afa) {
285     if (op.getNumResults() == 0)
286       return true;
287     LLVM_DEBUG(llvm::dbgs()
288                    << "AffineIfAnalysis: not promoting as op has results\n";);
289     return false;
290   }
291 
292   bool legality{};
293 };
294 } // namespace
295 
296 AffineIfAnalysis
297 AffineFunctionAnalysis::getChildIfAnalysis(fir::IfOp op) const {
298   auto it = ifAnalysisMap.find_as(op);
299   if (it == ifAnalysisMap.end()) {
300     LLVM_DEBUG(llvm::dbgs() << "AffineFunctionAnalysis: not computed for:\n";
301                op.dump(););
302     op.emitError("error in fetching if analysis in AffineFunctionAnalysis\n");
303     return {};
304   }
305   return it->getSecond();
306 }
307 
308 /// AffineMap rewriting fir.array_coor operation to affine apply,
309 /// %dim = fir.gendim %lowerBound, %upperBound, %stride
310 /// %a = fir.array_coor %arr(%dim) %i
311 /// returning affineMap = affine_map<(i)[lb, ub, st] -> (i*st - lb)>
312 static mlir::AffineMap createArrayIndexAffineMap(unsigned dimensions,
313                                                  MLIRContext *context) {
314   auto index = mlir::getAffineConstantExpr(0, context);
315   auto accuExtent = mlir::getAffineConstantExpr(1, context);
316   for (unsigned i = 0; i < dimensions; ++i) {
317     mlir::AffineExpr idx = mlir::getAffineDimExpr(i, context),
318                      lowerBound = mlir::getAffineSymbolExpr(i * 3, context),
319                      currentExtent =
320                          mlir::getAffineSymbolExpr(i * 3 + 1, context),
321                      stride = mlir::getAffineSymbolExpr(i * 3 + 2, context),
322                      currentPart = (idx * stride - lowerBound) * accuExtent;
323     index = currentPart + index;
324     accuExtent = accuExtent * currentExtent;
325   }
326   return mlir::AffineMap::get(dimensions, dimensions * 3, index);
327 }
328 
329 static Optional<int64_t> constantIntegerLike(const mlir::Value value) {
330   if (auto definition = value.getDefiningOp<mlir::arith::ConstantOp>())
331     if (auto stepAttr = definition.getValue().dyn_cast<IntegerAttr>())
332       return stepAttr.getInt();
333   return {};
334 }
335 
336 static mlir::Type coordinateArrayElement(fir::ArrayCoorOp op) {
337   if (auto refType =
338           op.getMemref().getType().dyn_cast_or_null<ReferenceType>()) {
339     if (auto seqType = refType.getEleTy().dyn_cast_or_null<SequenceType>()) {
340       return seqType.getEleTy();
341     }
342   }
343   op.emitError(
344       "AffineLoopConversion: array type in coordinate operation not valid\n");
345   return mlir::Type();
346 }
347 
348 static void populateIndexArgs(fir::ArrayCoorOp acoOp, fir::ShapeOp shape,
349                               SmallVectorImpl<mlir::Value> &indexArgs,
350                               mlir::PatternRewriter &rewriter) {
351   auto one = rewriter.create<mlir::arith::ConstantOp>(
352       acoOp.getLoc(), rewriter.getIndexType(), rewriter.getIndexAttr(1));
353   auto extents = shape.getExtents();
354   for (auto i = extents.begin(); i < extents.end(); i++) {
355     indexArgs.push_back(one);
356     indexArgs.push_back(*i);
357     indexArgs.push_back(one);
358   }
359 }
360 
361 static void populateIndexArgs(fir::ArrayCoorOp acoOp, fir::ShapeShiftOp shape,
362                               SmallVectorImpl<mlir::Value> &indexArgs,
363                               mlir::PatternRewriter &rewriter) {
364   auto one = rewriter.create<mlir::arith::ConstantOp>(
365       acoOp.getLoc(), rewriter.getIndexType(), rewriter.getIndexAttr(1));
366   auto extents = shape.getPairs();
367   for (auto i = extents.begin(); i < extents.end();) {
368     indexArgs.push_back(*i++);
369     indexArgs.push_back(*i++);
370     indexArgs.push_back(one);
371   }
372 }
373 
374 static void populateIndexArgs(fir::ArrayCoorOp acoOp, fir::SliceOp slice,
375                               SmallVectorImpl<mlir::Value> &indexArgs,
376                               mlir::PatternRewriter &rewriter) {
377   auto extents = slice.getTriples();
378   for (auto i = extents.begin(); i < extents.end();) {
379     indexArgs.push_back(*i++);
380     indexArgs.push_back(*i++);
381     indexArgs.push_back(*i++);
382   }
383 }
384 
385 static void populateIndexArgs(fir::ArrayCoorOp acoOp,
386                               SmallVectorImpl<mlir::Value> &indexArgs,
387                               mlir::PatternRewriter &rewriter) {
388   if (auto shape = acoOp.getShape().getDefiningOp<ShapeOp>())
389     return populateIndexArgs(acoOp, shape, indexArgs, rewriter);
390   if (auto shapeShift = acoOp.getShape().getDefiningOp<ShapeShiftOp>())
391     return populateIndexArgs(acoOp, shapeShift, indexArgs, rewriter);
392   if (auto slice = acoOp.getShape().getDefiningOp<SliceOp>())
393     return populateIndexArgs(acoOp, slice, indexArgs, rewriter);
394 }
395 
396 /// Returns affine.apply and fir.convert from array_coor and gendims
397 static std::pair<mlir::AffineApplyOp, fir::ConvertOp>
398 createAffineOps(mlir::Value arrayRef, mlir::PatternRewriter &rewriter) {
399   auto acoOp = arrayRef.getDefiningOp<ArrayCoorOp>();
400   auto affineMap =
401       createArrayIndexAffineMap(acoOp.getIndices().size(), acoOp.getContext());
402   SmallVector<mlir::Value> indexArgs;
403   indexArgs.append(acoOp.getIndices().begin(), acoOp.getIndices().end());
404 
405   populateIndexArgs(acoOp, indexArgs, rewriter);
406 
407   auto affineApply = rewriter.create<mlir::AffineApplyOp>(acoOp.getLoc(),
408                                                           affineMap, indexArgs);
409   auto arrayElementType = coordinateArrayElement(acoOp);
410   auto newType = mlir::MemRefType::get({-1}, arrayElementType);
411   auto arrayConvert = rewriter.create<fir::ConvertOp>(acoOp.getLoc(), newType,
412                                                       acoOp.getMemref());
413   return std::make_pair(affineApply, arrayConvert);
414 }
415 
416 static void rewriteLoad(fir::LoadOp loadOp, mlir::PatternRewriter &rewriter) {
417   rewriter.setInsertionPoint(loadOp);
418   auto affineOps = createAffineOps(loadOp.getMemref(), rewriter);
419   rewriter.replaceOpWithNewOp<mlir::AffineLoadOp>(
420       loadOp, affineOps.second.getResult(), affineOps.first.getResult());
421 }
422 
423 static void rewriteStore(fir::StoreOp storeOp,
424                          mlir::PatternRewriter &rewriter) {
425   rewriter.setInsertionPoint(storeOp);
426   auto affineOps = createAffineOps(storeOp.getMemref(), rewriter);
427   rewriter.replaceOpWithNewOp<mlir::AffineStoreOp>(storeOp, storeOp.getValue(),
428                                                    affineOps.second.getResult(),
429                                                    affineOps.first.getResult());
430 }
431 
432 static void rewriteMemoryOps(Block *block, mlir::PatternRewriter &rewriter) {
433   for (auto &bodyOp : block->getOperations()) {
434     if (isa<fir::LoadOp>(bodyOp))
435       rewriteLoad(cast<fir::LoadOp>(bodyOp), rewriter);
436     if (isa<fir::StoreOp>(bodyOp))
437       rewriteStore(cast<fir::StoreOp>(bodyOp), rewriter);
438   }
439 }
440 
441 namespace {
442 /// Convert `fir.do_loop` to `affine.for`, creates fir.convert for arrays to
443 /// memref, rewrites array_coor to affine.apply with affine_map. Rewrites fir
444 /// loads and stores to affine.
445 class AffineLoopConversion : public mlir::OpRewritePattern<fir::DoLoopOp> {
446 public:
447   using OpRewritePattern::OpRewritePattern;
448   AffineLoopConversion(mlir::MLIRContext *context, AffineFunctionAnalysis &afa)
449       : OpRewritePattern(context), functionAnalysis(afa) {}
450 
451   mlir::LogicalResult
452   matchAndRewrite(fir::DoLoopOp loop,
453                   mlir::PatternRewriter &rewriter) const override {
454     LLVM_DEBUG(llvm::dbgs() << "AffineLoopConversion: rewriting loop:\n";
455                loop.dump(););
456     LLVM_ATTRIBUTE_UNUSED auto loopAnalysis =
457         functionAnalysis.getChildLoopAnalysis(loop);
458     auto &loopOps = loop.getBody()->getOperations();
459     auto loopAndIndex = createAffineFor(loop, rewriter);
460     auto affineFor = loopAndIndex.first;
461     auto inductionVar = loopAndIndex.second;
462 
463     rewriter.startRootUpdate(affineFor.getOperation());
464     affineFor.getBody()->getOperations().splice(
465         std::prev(affineFor.getBody()->end()), loopOps, loopOps.begin(),
466         std::prev(loopOps.end()));
467     rewriter.finalizeRootUpdate(affineFor.getOperation());
468 
469     rewriter.startRootUpdate(loop.getOperation());
470     loop.getInductionVar().replaceAllUsesWith(inductionVar);
471     rewriter.finalizeRootUpdate(loop.getOperation());
472 
473     rewriteMemoryOps(affineFor.getBody(), rewriter);
474 
475     LLVM_DEBUG(llvm::dbgs() << "AffineLoopConversion: loop rewriten to:\n";
476                affineFor.dump(););
477     rewriter.replaceOp(loop, affineFor.getOperation()->getResults());
478     return success();
479   }
480 
481 private:
482   std::pair<mlir::AffineForOp, mlir::Value>
483   createAffineFor(fir::DoLoopOp op, mlir::PatternRewriter &rewriter) const {
484     if (auto constantStep = constantIntegerLike(op.getStep()))
485       if (constantStep.getValue() > 0)
486         return positiveConstantStep(op, constantStep.getValue(), rewriter);
487     return genericBounds(op, rewriter);
488   }
489 
490   // when step for the loop is positive compile time constant
491   std::pair<mlir::AffineForOp, mlir::Value>
492   positiveConstantStep(fir::DoLoopOp op, int64_t step,
493                        mlir::PatternRewriter &rewriter) const {
494     auto affineFor = rewriter.create<mlir::AffineForOp>(
495         op.getLoc(), ValueRange(op.getLowerBound()),
496         mlir::AffineMap::get(0, 1,
497                              mlir::getAffineSymbolExpr(0, op.getContext())),
498         ValueRange(op.getUpperBound()),
499         mlir::AffineMap::get(0, 1,
500                              1 + mlir::getAffineSymbolExpr(0, op.getContext())),
501         step);
502     return std::make_pair(affineFor, affineFor.getInductionVar());
503   }
504 
505   std::pair<mlir::AffineForOp, mlir::Value>
506   genericBounds(fir::DoLoopOp op, mlir::PatternRewriter &rewriter) const {
507     auto lowerBound = mlir::getAffineSymbolExpr(0, op.getContext());
508     auto upperBound = mlir::getAffineSymbolExpr(1, op.getContext());
509     auto step = mlir::getAffineSymbolExpr(2, op.getContext());
510     mlir::AffineMap upperBoundMap = mlir::AffineMap::get(
511         0, 3, (upperBound - lowerBound + step).floorDiv(step));
512     auto genericUpperBound = rewriter.create<mlir::AffineApplyOp>(
513         op.getLoc(), upperBoundMap,
514         ValueRange({op.getLowerBound(), op.getUpperBound(), op.getStep()}));
515     auto actualIndexMap = mlir::AffineMap::get(
516         1, 2,
517         (lowerBound + mlir::getAffineDimExpr(0, op.getContext())) *
518             mlir::getAffineSymbolExpr(1, op.getContext()));
519 
520     auto affineFor = rewriter.create<mlir::AffineForOp>(
521         op.getLoc(), ValueRange(),
522         AffineMap::getConstantMap(0, op.getContext()),
523         genericUpperBound.getResult(),
524         mlir::AffineMap::get(0, 1,
525                              1 + mlir::getAffineSymbolExpr(0, op.getContext())),
526         1);
527     rewriter.setInsertionPointToStart(affineFor.getBody());
528     auto actualIndex = rewriter.create<mlir::AffineApplyOp>(
529         op.getLoc(), actualIndexMap,
530         ValueRange(
531             {affineFor.getInductionVar(), op.getLowerBound(), op.getStep()}));
532     return std::make_pair(affineFor, actualIndex.getResult());
533   }
534 
535   AffineFunctionAnalysis &functionAnalysis;
536 };
537 
538 /// Convert `fir.if` to `affine.if`.
539 class AffineIfConversion : public mlir::OpRewritePattern<fir::IfOp> {
540 public:
541   using OpRewritePattern::OpRewritePattern;
542   AffineIfConversion(mlir::MLIRContext *context, AffineFunctionAnalysis &afa)
543       : OpRewritePattern(context) {}
544   mlir::LogicalResult
545   matchAndRewrite(fir::IfOp op,
546                   mlir::PatternRewriter &rewriter) const override {
547     LLVM_DEBUG(llvm::dbgs() << "AffineIfConversion: rewriting if:\n";
548                op.dump(););
549     auto &ifOps = op.getThenRegion().front().getOperations();
550     auto affineCondition = AffineIfCondition(op.getCondition());
551     if (!affineCondition.hasIntegerSet()) {
552       LLVM_DEBUG(
553           llvm::dbgs()
554               << "AffineIfConversion: couldn't calculate affine condition\n";);
555       return failure();
556     }
557     auto affineIf = rewriter.create<mlir::AffineIfOp>(
558         op.getLoc(), affineCondition.getIntegerSet(),
559         affineCondition.getAffineArgs(), !op.getElseRegion().empty());
560     rewriter.startRootUpdate(affineIf);
561     affineIf.getThenBlock()->getOperations().splice(
562         std::prev(affineIf.getThenBlock()->end()), ifOps, ifOps.begin(),
563         std::prev(ifOps.end()));
564     if (!op.getElseRegion().empty()) {
565       auto &otherOps = op.getElseRegion().front().getOperations();
566       affineIf.getElseBlock()->getOperations().splice(
567           std::prev(affineIf.getElseBlock()->end()), otherOps, otherOps.begin(),
568           std::prev(otherOps.end()));
569     }
570     rewriter.finalizeRootUpdate(affineIf);
571     rewriteMemoryOps(affineIf.getBody(), rewriter);
572 
573     LLVM_DEBUG(llvm::dbgs() << "AffineIfConversion: if converted to:\n";
574                affineIf.dump(););
575     rewriter.replaceOp(op, affineIf.getOperation()->getResults());
576     return success();
577   }
578 };
579 
580 /// Promote fir.do_loop and fir.if to affine.for and affine.if, in the cases
581 /// where such a promotion is possible.
582 class AffineDialectPromotion
583     : public AffineDialectPromotionBase<AffineDialectPromotion> {
584 public:
585   void runOnOperation() override {
586 
587     auto *context = &getContext();
588     auto function = getOperation();
589     markAllAnalysesPreserved();
590     auto functionAnalysis = AffineFunctionAnalysis(function);
591     mlir::RewritePatternSet patterns(context);
592     patterns.insert<AffineIfConversion>(context, functionAnalysis);
593     patterns.insert<AffineLoopConversion>(context, functionAnalysis);
594     mlir::ConversionTarget target = *context;
595     target.addLegalDialect<
596         mlir::AffineDialect, FIROpsDialect, mlir::scf::SCFDialect,
597         mlir::arith::ArithmeticDialect, mlir::func::FuncDialect>();
598     target.addDynamicallyLegalOp<IfOp>([&functionAnalysis](fir::IfOp op) {
599       return !(functionAnalysis.getChildIfAnalysis(op).canPromoteToAffine());
600     });
601     target.addDynamicallyLegalOp<DoLoopOp>([&functionAnalysis](
602                                                fir::DoLoopOp op) {
603       return !(functionAnalysis.getChildLoopAnalysis(op).canPromoteToAffine());
604     });
605 
606     LLVM_DEBUG(llvm::dbgs()
607                    << "AffineDialectPromotion: running promotion on: \n";
608                function.print(llvm::dbgs()););
609     // apply the patterns
610     if (mlir::failed(mlir::applyPartialConversion(function, target,
611                                                   std::move(patterns)))) {
612       mlir::emitError(mlir::UnknownLoc::get(context),
613                       "error in converting to affine dialect\n");
614       signalPassFailure();
615     }
616   }
617 };
618 } // namespace
619 
620 /// Convert FIR loop constructs to the Affine dialect
621 std::unique_ptr<mlir::Pass> fir::createPromoteToAffinePass() {
622   return std::make_unique<AffineDialectPromotion>();
623 }
624