1 //===- LinalgTransforms.cpp - Linalg transformations as patterns ----------===//
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 file implements logic and helpers to expose Linalg transforms as rewrite
10 // patterns.
11 //
12 //===----------------------------------------------------------------------===//
13 
14 #include "mlir/Dialect/Linalg/Transforms/Transforms.h"
15 #include "mlir/Dialect/Linalg/Analysis/DependenceAnalysis.h"
16 #include "mlir/Dialect/Linalg/IR/LinalgOps.h"
17 #include "mlir/Dialect/Linalg/Utils/Utils.h"
18 #include "mlir/Dialect/StandardOps/EDSC/Intrinsics.h"
19 #include "mlir/Dialect/Utils/StructuredOpsUtils.h"
20 #include "mlir/Dialect/Vector/EDSC/Intrinsics.h"
21 #include "mlir/Dialect/Vector/VectorOps.h"
22 #include "mlir/IR/AffineExpr.h"
23 #include "mlir/IR/Matchers.h"
24 #include "mlir/IR/PatternMatch.h"
25 #include "mlir/Pass/Pass.h"
26 #include "mlir/Support/LLVM.h"
27 #include "llvm/Support/Debug.h"
28 #include "llvm/Support/raw_ostream.h"
29 #include <type_traits>
30 
31 #define DEBUG_TYPE "linalg-transforms"
32 
33 using namespace mlir;
34 using namespace mlir::edsc;
35 using namespace mlir::edsc::intrinsics;
36 using namespace mlir::linalg;
37 
38 #define DBGS() (llvm::dbgs() << "[" DEBUG_TYPE << "]: ")
39 
40 //===----------------------------------------------------------------------===//
41 // Transformations exposed as rewrite patterns.
42 //===----------------------------------------------------------------------===//
43 // Marker used as attribute name in generated Linalg rewriting transformations.
44 const StringLiteral mlir::linalg::LinalgTransforms::kLinalgTransformMarker =
45     "__internal_linalg_transform__";
46 
47 mlir::linalg::LinalgMarker::LinalgMarker(ArrayRef<Identifier> matchDisjunction,
48                                          Optional<Identifier> replacement)
49     : matchDisjunction(matchDisjunction.begin(), matchDisjunction.end()),
50       replacement(replacement) {}
51 
52 LogicalResult
53 mlir::linalg::LinalgMarker::checkAndNotify(PatternRewriter &rewriter,
54                                            Operation *op) const {
55   auto attr = op->template getAttrOfType<StringAttr>(
56       LinalgTransforms::kLinalgTransformMarker);
57 
58   if (!attr) {
59     // 1. Has no marker case and matchDisjunction is empty.
60     if (matchDisjunction.empty())
61       return success();
62 
63     // 2. Has no marker but was expecting a marker.
64     return rewriter.notifyMatchFailure(op, [&](Diagnostic &diag) {
65       diag << " does not have any marker from list: ";
66       interleaveComma(matchDisjunction, diag);
67     });
68   }
69 
70   // 4. Match explicit marker.
71   for (auto marker : matchDisjunction)
72     if (attr.getValue() == marker)
73       return success();
74 
75   // 5. Fail to match.
76   return rewriter.notifyMatchFailure(op, [&](Diagnostic &diag) {
77     diag << " does not have any marker from list: ";
78     interleaveComma(matchDisjunction, diag);
79   });
80 }
81 
82 void mlir::linalg::LinalgMarker::replaceLinalgMarker(PatternRewriter &rewriter,
83                                                      Operation *op) const {
84   if (replacement.hasValue())
85     op->setAttr(LinalgTransforms::kLinalgTransformMarker,
86                 rewriter.getStringAttr(replacement.getValue()));
87   else
88     op->removeAttr(Identifier::get(LinalgTransforms::kLinalgTransformMarker,
89                                    rewriter.getContext()));
90 }
91 
92 LinalgTilingOptions &
93 mlir::linalg::LinalgTilingOptions::setTileSizes(ArrayRef<int64_t> ts) {
94   SmallVector<int64_t, 4> tileSizes(ts.begin(), ts.end());
95   tileSizeComputationFunction = [tileSizes](OpBuilder &b, Operation *op) {
96     OpBuilder::InsertionGuard guard(b);
97     b.setInsertionPointToStart(
98         &op->getParentOfType<FuncOp>().getBody().front());
99     return llvm::to_vector<4>(map_range(tileSizes, [&](int64_t s) {
100       Value v = b.create<ConstantIndexOp>(op->getLoc(), s);
101       return v;
102     }));
103   };
104   return *this;
105 }
106 
107 /// Linalg base tiling pattern.
108 mlir::linalg::LinalgBaseTilingPattern::LinalgBaseTilingPattern(
109     StringRef opName, MLIRContext *context, LinalgTilingOptions options,
110     LinalgMarker marker, PatternBenefit benefit)
111     : RewritePattern(opName, {}, benefit, context), marker(marker),
112       options(options) {}
113 
114 LogicalResult mlir::linalg::LinalgBaseTilingPattern::matchAndRewrite(
115     Operation *op, PatternRewriter &rewriter) const {
116   LinalgOp linalgOp = dyn_cast<LinalgOp>(op);
117   if (!linalgOp)
118     return failure();
119   if (failed(marker.checkAndNotify(rewriter, linalgOp)))
120     return failure();
121 
122   Optional<TiledLinalgOp> res = tileLinalgOp(rewriter, linalgOp, options);
123 
124   if (!res)
125     return failure();
126 
127   // New marker if specified.
128   marker.replaceLinalgMarker(rewriter, res->op.getOperation());
129   return success();
130 }
131 
132 /// Linalg base interchange pattern.
133 mlir::linalg::LinalgBaseInterchangePattern::LinalgBaseInterchangePattern(
134     StringRef opName, MLIRContext *context,
135     ArrayRef<unsigned> interchangeVector, LinalgMarker marker,
136     PatternBenefit benefit)
137     : RewritePattern(opName, {}, benefit, context), marker(marker),
138       interchangeVector(interchangeVector.begin(), interchangeVector.end()) {}
139 
140 LogicalResult mlir::linalg::LinalgBaseInterchangePattern::matchAndRewrite(
141     Operation *op, PatternRewriter &rewriter) const {
142   LinalgOp linalgOp = dyn_cast<LinalgOp>(op);
143   if (!linalgOp)
144     return failure();
145   if (failed(marker.checkAndNotify(rewriter, linalgOp)))
146     return failure();
147   if (failed(interchangeGenericLinalgOpPrecondition(op, interchangeVector)))
148     return failure();
149 
150   // TODO: figure out how this interplays with named ops. In particular this
151   // should break the named op property.
152   rewriter.updateRootInPlace(op, [&]() {
153     interchange(linalgOp, interchangeVector);
154     // New marker if specified.
155     marker.replaceLinalgMarker(rewriter, op);
156   });
157   return success();
158 }
159 
160 mlir::linalg::LinalgBasePromotionPattern::LinalgBasePromotionPattern(
161     StringRef opName, MLIRContext *context, LinalgPromotionOptions options,
162     LinalgMarker marker, PatternBenefit benefit)
163     : RewritePattern(opName, {}, benefit, context), marker(marker),
164       options(options) {}
165 
166 LogicalResult mlir::linalg::LinalgBasePromotionPattern::matchAndRewrite(
167     Operation *op, PatternRewriter &rewriter) const {
168   if (failed(marker.checkAndNotify(rewriter, op)))
169     return failure();
170   if (failed(promoteSubviewsPrecondition(op, options)))
171     return failure();
172 
173   // TODO: We cannot use root update here. This pattern is creating other ops,
174   // so if the promotion fails, those need to be cleaned up, which doesnt seem
175   // to be happening here. So to fail properly, we should be cloning the op and
176   // deleting the previous op. This needs more investigation.
177   rewriter.startRootUpdate(op);
178   Optional<LinalgOp> promotedOp = promoteSubViews(rewriter, op, options);
179   if (!promotedOp) {
180     rewriter.cancelRootUpdate(op);
181     return op->emitError("subview promotion failed");
182   }
183   rewriter.finalizeRootUpdate(op);
184   marker.replaceLinalgMarker(rewriter, op);
185   return success();
186 }
187 
188 mlir::linalg::LinalgBaseVectorizationPattern::LinalgBaseVectorizationPattern(
189     StringRef opName, MLIRContext *context, LinalgMarker marker,
190     PatternBenefit benefit)
191     : RewritePattern(opName, {}, benefit, context), marker(marker) {}
192 
193 LogicalResult mlir::linalg::LinalgBaseVectorizationPattern::matchAndRewrite(
194     Operation *op, PatternRewriter &rewriter) const {
195   LinalgOp linalgOp = dyn_cast<LinalgOp>(op);
196   if (!linalgOp)
197     return failure();
198   if (failed(marker.checkAndNotify(rewriter, linalgOp)))
199     return failure();
200   if (failed(vectorizeLinalgOpPrecondition(op)))
201     return failure();
202   vectorizeLinalgOp(rewriter, op);
203   rewriter.eraseOp(op);
204   return success();
205 }
206 
207 LogicalResult mlir::linalg::applyStagedPatterns(
208     Operation *op, ArrayRef<OwningRewritePatternList> stage1Patterns,
209     const OwningRewritePatternList &stage2Patterns,
210     function_ref<LogicalResult(Operation *)> stage3Lambda) {
211   unsigned iteration = 0;
212   (void)iteration;
213   for (const auto &patterns : stage1Patterns) {
214     LLVM_DEBUG(DBGS() << "Before 1st stage, iter: " << ++iteration << "\n"
215                       << *op);
216     if (failed(applyPatternsAndFoldGreedily(op, patterns))) {
217       LLVM_DEBUG(DBGS() << "Underlying first stage rewrite did not converge");
218       return failure();
219     }
220     LLVM_DEBUG(DBGS() << "After 1st stage, iter: " << ++iteration << "\n"
221                       << *op);
222     if (failed(applyPatternsAndFoldGreedily(op, stage2Patterns))) {
223       LLVM_DEBUG(DBGS() << "Underlying 2nd stage rewrite did not converge");
224       return failure();
225     }
226     LLVM_DEBUG(DBGS() << "After 2nd stage, iter : " << iteration << "\n"
227                       << *op);
228     if (stage3Lambda) {
229       if (failed(stage3Lambda(op)))
230         return failure();
231       LLVM_DEBUG(DBGS() << "After 3rd stage, iter : " << iteration << "\n"
232                         << *op);
233     }
234   }
235   return success();
236 }
237 
238 /// Traverse `e` and return an AffineExpr where all occurrences of `dim` have
239 /// been replaced by either:
240 ///  - `min` if `positivePath` is true when we reach an occurrence of `dim`
241 ///  - `max` if `positivePath` is true when we reach an occurrence of `dim`
242 /// `positivePath` is negated each time we hit a multiplicative or divisive
243 /// binary op with a constant negative coefficient.
244 static AffineExpr substWithMin(AffineExpr e, AffineExpr dim, AffineExpr min,
245                                AffineExpr max, bool positivePath = true) {
246   if (e == dim)
247     return positivePath ? min : max;
248   if (auto bin = e.dyn_cast<AffineBinaryOpExpr>()) {
249     AffineExpr lhs = bin.getLHS();
250     AffineExpr rhs = bin.getRHS();
251     if (bin.getKind() == mlir::AffineExprKind::Add)
252       return substWithMin(lhs, dim, min, max, positivePath) +
253              substWithMin(rhs, dim, min, max, positivePath);
254 
255     auto c1 = bin.getLHS().dyn_cast<AffineConstantExpr>();
256     auto c2 = bin.getRHS().dyn_cast<AffineConstantExpr>();
257     if (c1 && c1.getValue() < 0)
258       return getAffineBinaryOpExpr(
259           bin.getKind(), c1, substWithMin(rhs, dim, min, max, !positivePath));
260     if (c2 && c2.getValue() < 0)
261       return getAffineBinaryOpExpr(
262           bin.getKind(), substWithMin(lhs, dim, min, max, !positivePath), c2);
263     return getAffineBinaryOpExpr(
264         bin.getKind(), substWithMin(lhs, dim, min, max, positivePath),
265         substWithMin(rhs, dim, min, max, positivePath));
266   }
267   return e;
268 }
269 
270 /// Given the `lbVal`, `ubVal` and `stepVal` of a loop, append `lbVal` and
271 /// `ubVal` to `dims` and `stepVal` to `symbols`.
272 /// Create new AffineDimExpr (`%lb` and `%ub`) and AffineSymbolExpr (`%step`)
273 /// with positions matching the newly appended values. Substitute occurrences of
274 /// `dimExpr` by either the min expression (i.e. `%lb`) or the max expression
275 /// (i.e. `%lb + %step * floordiv(%ub -1 - %lb, %step)`), depending on whether
276 /// the induction variable is used with a positive or negative  coefficient.
277 static AffineExpr substituteLoopInExpr(AffineExpr expr, AffineExpr dimExpr,
278                                        Value lbVal, Value ubVal, Value stepVal,
279                                        SmallVectorImpl<Value> &dims,
280                                        SmallVectorImpl<Value> &symbols) {
281   MLIRContext *ctx = lbVal.getContext();
282   AffineExpr lb = getAffineDimExpr(dims.size(), ctx);
283   dims.push_back(lbVal);
284   AffineExpr ub = getAffineDimExpr(dims.size(), ctx);
285   dims.push_back(ubVal);
286   AffineExpr step = getAffineSymbolExpr(symbols.size(), ctx);
287   symbols.push_back(stepVal);
288   LLVM_DEBUG(DBGS() << "Before: " << expr << "\n");
289   AffineExpr ee = substWithMin(expr, dimExpr, lb,
290                                lb + step * ((ub - 1) - lb).floorDiv(step));
291   LLVM_DEBUG(DBGS() << "After: " << expr << "\n");
292   return ee;
293 }
294 
295 /// Traverse the `dims` and substitute known min or max expressions in place of
296 /// induction variables in `exprs`.
297 static AffineMap substitute(AffineMap map, SmallVectorImpl<Value> &dims,
298                             SmallVectorImpl<Value> &symbols) {
299   auto exprs = llvm::to_vector<4>(map.getResults());
300   for (AffineExpr &expr : exprs) {
301     bool substituted = true;
302     while (substituted) {
303       substituted = false;
304       for (unsigned dimIdx = 0; dimIdx < dims.size(); ++dimIdx) {
305         Value dim = dims[dimIdx];
306         AffineExpr dimExpr = getAffineDimExpr(dimIdx, expr.getContext());
307         LLVM_DEBUG(DBGS() << "Subst: " << dim << " @ " << dimExpr << "\n");
308         AffineExpr substitutedExpr;
309         if (auto forOp = scf::getForInductionVarOwner(dim))
310           substitutedExpr = substituteLoopInExpr(
311               expr, dimExpr, forOp.lowerBound(), forOp.upperBound(),
312               forOp.step(), dims, symbols);
313 
314         if (auto parallelForOp = scf::getParallelForInductionVarOwner(dim))
315           for (unsigned idx = 0, e = parallelForOp.getNumLoops(); idx < e;
316                ++idx)
317             substitutedExpr = substituteLoopInExpr(
318                 expr, dimExpr, parallelForOp.lowerBound()[idx],
319                 parallelForOp.upperBound()[idx], parallelForOp.step()[idx],
320                 dims, symbols);
321 
322         if (!substitutedExpr)
323           continue;
324 
325         substituted = (substitutedExpr != expr);
326         expr = substitutedExpr;
327       }
328     }
329 
330     // Cleanup and simplify the results.
331     // This needs to happen outside of the loop iterating on dims.size() since
332     // it modifies dims.
333     SmallVector<Value, 4> operands(dims.begin(), dims.end());
334     operands.append(symbols.begin(), symbols.end());
335     auto map = AffineMap::get(dims.size(), symbols.size(), exprs,
336                               exprs.front().getContext());
337 
338     LLVM_DEBUG(DBGS() << "Map to simplify: " << map << "\n");
339 
340     // Pull in affine.apply operations and compose them fully into the
341     // result.
342     fullyComposeAffineMapAndOperands(&map, &operands);
343     canonicalizeMapAndOperands(&map, &operands);
344     map = simplifyAffineMap(map);
345     // Assign the results.
346     exprs.assign(map.getResults().begin(), map.getResults().end());
347     dims.assign(operands.begin(), operands.begin() + map.getNumDims());
348     symbols.assign(operands.begin() + map.getNumDims(), operands.end());
349 
350     LLVM_DEBUG(DBGS() << "Map simplified: " << map << "\n");
351   }
352 
353   assert(!exprs.empty() && "Unexpected empty exprs");
354   return AffineMap::get(dims.size(), symbols.size(), exprs, map.getContext());
355 }
356 
357 LogicalResult AffineMinSCFCanonicalizationPattern::matchAndRewrite(
358     AffineMinOp minOp, PatternRewriter &rewriter) const {
359   LLVM_DEBUG(DBGS() << "Canonicalize AffineMinSCF: " << *minOp.getOperation()
360                     << "\n");
361 
362   SmallVector<Value, 4> dims(minOp.getDimOperands()),
363       symbols(minOp.getSymbolOperands());
364   AffineMap map = substitute(minOp.getAffineMap(), dims, symbols);
365 
366   LLVM_DEBUG(DBGS() << "Resulting map: " << map << "\n");
367 
368   // Check whether any of the expressions, when subtracted from all other
369   // expressions, produces only >= 0 constants. If so, it is the min.
370   for (auto e : minOp.getAffineMap().getResults()) {
371     LLVM_DEBUG(DBGS() << "Candidate min: " << e << "\n");
372     if (!e.isSymbolicOrConstant())
373       continue;
374 
375     auto isNonPositive = [](AffineExpr e) {
376       if (auto cst = e.dyn_cast<AffineConstantExpr>())
377         return cst.getValue() < 0;
378       return true;
379     };
380 
381     // Build the subMap and check everything is statically known to be
382     // positive.
383     SmallVector<AffineExpr, 4> subExprs;
384     subExprs.reserve(map.getNumResults());
385     for (auto ee : map.getResults())
386       subExprs.push_back(ee - e);
387     MLIRContext *ctx = minOp.getContext();
388     AffineMap subMap = simplifyAffineMap(
389         AffineMap::get(map.getNumDims(), map.getNumSymbols(), subExprs, ctx));
390     LLVM_DEBUG(DBGS() << "simplified subMap: " << subMap << "\n");
391     if (llvm::any_of(subMap.getResults(), isNonPositive))
392       continue;
393 
394     // Static min found.
395     if (auto cst = e.dyn_cast<AffineConstantExpr>()) {
396       rewriter.replaceOpWithNewOp<ConstantIndexOp>(minOp, cst.getValue());
397     } else {
398       auto resultMap = AffineMap::get(0, map.getNumSymbols(), {e}, ctx);
399       SmallVector<Value, 4> resultOperands = dims;
400       resultOperands.append(symbols.begin(), symbols.end());
401       canonicalizeMapAndOperands(&resultMap, &resultOperands);
402       resultMap = simplifyAffineMap(resultMap);
403       rewriter.replaceOpWithNewOp<AffineApplyOp>(minOp, resultMap,
404                                                  resultOperands);
405     }
406     return success();
407   }
408 
409   return failure();
410 }
411