1 //===- Vectorization.cpp - Implementation of linalg Vectorization ---------===//
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 the linalg dialect Vectorization transformations.
10 //
11 //===----------------------------------------------------------------------===//
12 
13 #include "mlir/Dialect/Linalg/Analysis/DependenceAnalysis.h"
14 #include "mlir/Dialect/Linalg/IR/LinalgOps.h"
15 #include "mlir/Dialect/Linalg/Transforms/Transforms.h"
16 #include "mlir/Dialect/Linalg/Utils/Utils.h"
17 #include "mlir/Dialect/StandardOps/EDSC/Intrinsics.h"
18 #include "mlir/Dialect/Utils/StructuredOpsUtils.h"
19 #include "mlir/Dialect/Vector/EDSC/Intrinsics.h"
20 #include "mlir/Dialect/Vector/VectorOps.h"
21 #include "mlir/IR/AffineExpr.h"
22 #include "mlir/IR/Matchers.h"
23 #include "mlir/IR/PatternMatch.h"
24 #include "mlir/Pass/Pass.h"
25 #include "mlir/Support/LLVM.h"
26 #include "llvm/Support/Debug.h"
27 #include "llvm/Support/raw_ostream.h"
28 #include <type_traits>
29 
30 using namespace mlir;
31 using namespace mlir::edsc;
32 using namespace mlir::edsc::intrinsics;
33 using namespace mlir::linalg;
34 
35 using llvm::dbgs;
36 
37 #define DEBUG_TYPE "linalg-vectorization"
38 
39 static bool hasMultiplyAddBody(Region &r) {
40   if (!llvm::hasSingleElement(r))
41     return false;
42   if (!llvm::hasNItems(r.front().begin(), r.front().end(), 3))
43     return false;
44 
45   using mlir::matchers::m_Val;
46   auto a = m_Val(r.getArgument(0));
47   auto b = m_Val(r.getArgument(1));
48   auto c = m_Val(r.getArgument(2));
49   // TODO: Update this detection once we have  matcher support for specifying
50   // that any permutation of operands matches.
51   auto pattern1 = m_Op<linalg::YieldOp>(m_Op<AddFOp>(m_Op<MulFOp>(a, b), c));
52   auto pattern2 = m_Op<linalg::YieldOp>(m_Op<AddFOp>(c, m_Op<MulFOp>(a, b)));
53   auto pattern3 = m_Op<linalg::YieldOp>(m_Op<AddFOp>(m_Op<MulFOp>(b, a), c));
54   auto pattern4 = m_Op<linalg::YieldOp>(m_Op<AddFOp>(c, m_Op<MulFOp>(b, a)));
55   auto pattern5 = m_Op<linalg::YieldOp>(m_Op<AddIOp>(m_Op<MulIOp>(a, b), c));
56   auto pattern6 = m_Op<linalg::YieldOp>(m_Op<AddIOp>(c, m_Op<MulIOp>(a, b)));
57   auto pattern7 = m_Op<linalg::YieldOp>(m_Op<AddIOp>(m_Op<MulIOp>(b, a), c));
58   auto pattern8 = m_Op<linalg::YieldOp>(m_Op<AddIOp>(c, m_Op<MulIOp>(b, a)));
59   return pattern1.match(&r.front().back()) ||
60          pattern2.match(&r.front().back()) ||
61          pattern3.match(&r.front().back()) ||
62          pattern4.match(&r.front().back()) ||
63          pattern5.match(&r.front().back()) ||
64          pattern6.match(&r.front().back()) ||
65          pattern7.match(&r.front().back()) || pattern8.match(&r.front().back());
66 }
67 
68 // TODO: Should be Tablegen'd from a single source that generates the op itself.
69 static LogicalResult isContraction(Operation *op) {
70   // TODO: interface for named ops.
71   if (isa<linalg::BatchMatmulOp, linalg::MatmulOp, linalg::MatvecOp,
72           linalg::VecmatOp, linalg::DotOp>(op))
73     return success();
74 
75   auto genericOp = dyn_cast<linalg::GenericOp>(op);
76   if (!genericOp)
77     return failure();
78 
79   auto mapRange = genericOp.indexing_maps().getAsValueRange<AffineMapAttr>();
80   return success(
81       genericOp.getNumInputs() == 2 && genericOp.getNumOutputs() == 1 &&
82       llvm::all_of(mapRange,
83                    [](AffineMap m) { return m.isProjectedPermutation(); }) &&
84       hasMultiplyAddBody(genericOp.region()));
85 }
86 
87 static bool hasOnlyScalarElementwiseOp(Region &r) {
88   if (!llvm::hasSingleElement(r))
89     return false;
90   for (Operation &op : r.front()) {
91     if (!(isa<ConstantOp, linalg::YieldOp>(op) ||
92           op.hasTrait<OpTrait::ElementwiseMappable>()) ||
93         llvm::any_of(op.getResultTypes(),
94                      [](Type type) { return !type.isIntOrIndexOrFloat(); }))
95       return false;
96   }
97   return true;
98 }
99 
100 // Return true if the op is an element-wise linalg op.
101 static bool isElementwise(Operation *op) {
102   auto genericOp = dyn_cast<linalg::GenericOp>(op);
103   if (!genericOp)
104     return false;
105   if (genericOp.getNumLoops() != genericOp.getNumParallelLoops())
106     return false;
107   // TODO: relax the restrictions on indexing map.
108   for (unsigned i = 0, e = genericOp.getNumOutputs(); i < e; i++) {
109     if (!genericOp.getOutputIndexingMap(i).isIdentity())
110       return false;
111   }
112   // Currently limit the input indexing map to minor identity as other
113   // permutations might require adding transpose ops to convert the vector read
114   // to the right shape.
115   for (unsigned i = 0, e = genericOp.getNumInputs(); i < e; i++) {
116     if (!genericOp.getInputIndexingMap(i).isMinorIdentity())
117       return false;
118   }
119   return hasOnlyScalarElementwiseOp(genericOp.getRegion());
120 }
121 
122 static VectorType extractVectorTypeFromScalarView(Value v) {
123   MemRefType mt = v.getType().cast<MemRefType>();
124   return mt.getShape().empty()
125              ? VectorType()
126              : VectorType::get(mt.getShape(), mt.getElementType());
127 }
128 
129 static Value transferReadVector(OpBuilder &builder, Value memref) {
130   edsc::ScopedContext scope(builder);
131   auto memrefType = memref.getType().cast<MemRefType>();
132   if (VectorType vectorType = extractVectorTypeFromScalarView(memref)) {
133     SmallVector<Value, 4> indices(memrefType.getRank(), std_constant_index(0));
134     return vector_transfer_read(vectorType, memref, indices);
135   }
136   return std_load(memref);
137 }
138 
139 static void transferWriteVector(OpBuilder &builder, Value value, Value memref) {
140   edsc::ScopedContext scope(builder);
141   auto memrefType = memref.getType().cast<MemRefType>();
142   if (VectorType vectorType = extractVectorTypeFromScalarView(memref)) {
143     SmallVector<Value, 4> indices(memrefType.getRank(), std_constant_index(0));
144     if (vectorType != value.getType())
145       value = vector_broadcast(vectorType, value);
146     vector_transfer_write(value, memref, indices);
147   } else {
148     std_store(value, memref);
149   }
150 }
151 
152 namespace {
153 // Transforms scalar operations into their vectorized counterparts,
154 // while using the provided generic op to map:
155 //   * Its arguments to transfer reads from the views of the generic op.
156 //   * linalg.yield ops to transfer writes to the views of the generic op.
157 class GenericVectorizer {
158 public:
159   GenericVectorizer(OpBuilder &builder, linalg::GenericOp generic)
160       : builder(builder), generic(generic) {}
161 
162   // Takes a scalar operation and builds its vectorized counterpart or
163   // counterparts using the underlying builder.
164   // If operands of the scalar operation are referring to previously vectorized
165   // operations, then in their vectorized form these operands will be referring
166   // to previous vectorization results.
167   void vectorize(Operation &scalarOp) {
168     auto yieldOp = dyn_cast<linalg::YieldOp>(scalarOp);
169     if (yieldOp) {
170       for (auto outputAndMemref :
171            llvm::zip(yieldOp.values(), generic.getOutputBuffers())) {
172         Value vectorValue = vectorize(std::get<0>(outputAndMemref));
173         transferWriteVector(builder, vectorValue, std::get<1>(outputAndMemref));
174       }
175       return;
176     }
177     Operation *vectorOp = uncachedVectorize(scalarOp);
178     assert(scalarOp.getNumResults() == vectorOp->getNumResults());
179     for (auto result :
180          llvm::zip(scalarOp.getResults(), vectorOp->getResults())) {
181       valueCache[std::get<0>(result)] = std::get<1>(result);
182     }
183   }
184 
185 private:
186   // Transforms a scalar value into its vectorized counterpart, recursively
187   // vectorizing operations as necessary using the underlying builder.
188   // Keeps track of previously vectorized values and reuses vectorization
189   // results if these values come up again.
190   Value vectorize(Value scalarValue) {
191     // Don't vectorize values coming from outside the region.
192     if (scalarValue.getParentRegion() != &generic.region())
193       return scalarValue;
194     auto vectorValueIt = valueCache.find(scalarValue);
195     if (vectorValueIt != valueCache.end())
196       return vectorValueIt->second;
197 
198     // If the value is from the region but not in the cache it means it is a
199     // block argument.
200     auto scalarArg = scalarValue.cast<BlockArgument>();
201     assert(scalarArg.getOwner() == &generic.region().front());
202     Value vectorArg = generic.getShapedOperand(scalarArg.getArgNumber());
203     Value vectorResult = transferReadVector(builder, vectorArg);
204     valueCache[scalarArg] = vectorResult;
205     return vectorResult;
206   }
207 
208   // Return the largest shape of all the given values. Return an empty
209   // SmallVector if there are no vector value.
210   static SmallVector<int64_t, 4> getLargestShape(ArrayRef<Value> values) {
211     SmallVector<int64_t, 4> largestShape;
212     int64_t maxSize = 1;
213     for (Value value : values) {
214       auto vecType = value.getType().dyn_cast<VectorType>();
215       if (!vecType)
216         continue;
217       if (maxSize < vecType.getNumElements()) {
218         maxSize = vecType.getNumElements();
219         largestShape.assign(vecType.getShape().begin(),
220                             vecType.getShape().end());
221       }
222     }
223     return largestShape;
224   }
225 
226   // If the value's type doesn't have the given shape broadcast it.
227   Value broadcastIfNeeded(Value value, ArrayRef<int64_t> shape) {
228     auto vecType = value.getType().dyn_cast<VectorType>();
229     if (shape.empty() || (vecType != nullptr && vecType.getShape() == shape))
230       return value;
231     auto newVecType = VectorType::get(shape, vecType ? vecType.getElementType()
232                                                      : value.getType());
233     return builder.create<vector::BroadcastOp>(
234         builder.getInsertionPoint()->getLoc(), newVecType, value);
235   }
236 
237   // Takes a scalar operation and builds its vectorized counterpart or
238   // counterparts using underlying builder without involving any caches.
239   Operation *uncachedVectorize(Operation &base_scalarOp) {
240     SmallVector<Value, 4> vectorizedOperands;
241     for (Value operand : base_scalarOp.getOperands()) {
242       vectorizedOperands.push_back(vectorize(operand));
243     }
244     SmallVector<int64_t, 4> shape = getLargestShape(vectorizedOperands);
245     for (Value &operand : vectorizedOperands)
246       operand = broadcastIfNeeded(operand, shape);
247     OperationState state(base_scalarOp.getLoc(), base_scalarOp.getName());
248     state.addAttributes(base_scalarOp.getAttrs());
249     state.addOperands(vectorizedOperands);
250     if (shape.empty()) {
251       state.addTypes(base_scalarOp.getResultTypes());
252     } else {
253       SmallVector<VectorType, 4> vectorizedTypes;
254       for (auto Type : base_scalarOp.getResultTypes())
255         vectorizedTypes.push_back(VectorType::get(shape, Type));
256       state.addTypes(vectorizedTypes);
257     }
258     return builder.createOperation(state);
259   }
260 
261   OpBuilder &builder;
262   linalg::GenericOp generic;
263   llvm::DenseMap<Value, Value> valueCache;
264 };
265 } // namespace
266 
267 // Replaces elementwise linalg.generic ops with their bodies with scalar
268 // operations from these bodies promoted to vector operations.
269 static void vectorizeElementwise(linalg::GenericOp op, OpBuilder &builder) {
270   GenericVectorizer vectorizer(builder, op);
271   for (Operation &scalarOp : op.region().front()) {
272     vectorizer.vectorize(scalarOp);
273   }
274 }
275 
276 LogicalResult mlir::linalg::vectorizeLinalgOpPrecondition(Operation *op) {
277   auto linalgOp = cast<linalg::LinalgOp>(op);
278   // All types must be static shape to go to vector.
279   for (Value operand : linalgOp.getShapedOperands())
280     if (!operand.getType().cast<ShapedType>().hasStaticShape())
281       return failure();
282   for (Type outputTensorType : linalgOp.getOutputTensorTypes())
283     if (!outputTensorType.cast<ShapedType>().hasStaticShape())
284       return failure();
285 
286   if (isa<linalg::FillOp, linalg::CopyOp>(op))
287     return success();
288   if (isElementwise(op))
289     return success();
290   return isContraction(op);
291 }
292 
293 void mlir::linalg::vectorizeLinalgOp(OpBuilder &builder, Operation *op) {
294   assert(succeeded(vectorizeLinalgOpPrecondition(op)));
295 
296   StringRef dbgPref = "\n[" DEBUG_TYPE "]: ";
297   (void)dbgPref;
298   edsc::ScopedContext scope(builder, op->getLoc());
299   // In the case of 0-D memrefs, return null and special case to scalar load or
300   // store later.
301   if (auto fillOp = dyn_cast<linalg::FillOp>(op)) {
302     // Vectorize fill as a vector.broadcast.
303     LLVM_DEBUG(dbgs() << dbgPref
304                       << "Rewrite linalg.fill as vector.broadcast: " << *op);
305     transferWriteVector(builder, fillOp.value(), fillOp.output());
306     return;
307   }
308   if (auto copyOp = dyn_cast<linalg::CopyOp>(op)) {
309     // Vectorize copy as a vector.transfer_read+vector.transfer_write.
310     LLVM_DEBUG(dbgs() << dbgPref
311                       << "Rewrite linalg.copy as vector.transfer_read + "
312                          "vector.transfer_write: "
313                       << *op);
314     Value vector = transferReadVector(builder, copyOp.input());
315     transferWriteVector(builder, vector, copyOp.output());
316     return;
317   }
318 
319   if (isElementwise(op)) {
320     LLVM_DEBUG(dbgs() << dbgPref
321                       << "Rewrite linalg op as vector.transfer_read + "
322                          "vector_op + vector.transfer_write: "
323                       << *op);
324     return vectorizeElementwise(cast<linalg::GenericOp>(op), builder);
325   }
326 
327   assert(succeeded(isContraction(op)) && "Expected contraction");
328 
329   // Vectorize other ops as vector contraction.
330   // TODO: interface.
331   LLVM_DEBUG(dbgs() << dbgPref
332                     << "Rewrite linalg op as vector.contract: " << *op);
333   auto linalgOp = cast<linalg::LinalgOp>(op);
334   Value viewA = linalgOp.getInput(0);
335   Value viewB = linalgOp.getInput(1);
336   Value viewC = linalgOp.getOutputBuffer(0);
337   VectorType vtA = extractVectorTypeFromScalarView(viewA);
338   VectorType vtB = extractVectorTypeFromScalarView(viewB);
339   VectorType vtC = extractVectorTypeFromScalarView(viewC);
340   Value zero = std_constant_index(0);
341   SmallVector<Value, 4> indicesA, indicesB, indicesC;
342   if (vtA)
343     indicesA = SmallVector<Value, 4>(vtA.getRank(), zero);
344   if (vtB)
345     indicesB = SmallVector<Value, 4>(vtB.getRank(), zero);
346   if (vtC)
347     indicesC = SmallVector<Value, 4>(vtC.getRank(), zero);
348   Value a = vtA ? vector_transfer_read(vtA, viewA, indicesA).value
349                 : std_load(viewA, indicesA).value;
350   Value b = vtB ? vector_transfer_read(vtB, viewB, indicesB).value
351                 : std_load(viewB, indicesB).value;
352   Value c = vtC ? vector_transfer_read(vtC, viewC, indicesC).value
353                 : std_load(viewC, indicesC).value;
354   Value res = vector_contract(a, b, c, linalgOp.indexing_maps(),
355                               linalgOp.iterator_types());
356   if (vtC)
357     vector_transfer_write(res, viewC, indicesC);
358   else
359     std_store(res, viewC, indicesC);
360 }
361 
362 /// Check whether there is any interleaved use of any `values` between `firstOp`
363 /// and `secondOp`. Conservatively return `true` if any op or value is in a
364 /// different block.
365 static bool mayExistInterleavedUses(Operation *firstOp, Operation *secondOp,
366                                     ValueRange values) {
367   StringRef dbgPref = "\n[" DEBUG_TYPE "]: ";
368   (void)dbgPref;
369   if (firstOp->getBlock() != secondOp->getBlock() ||
370       !firstOp->isBeforeInBlock(secondOp)) {
371     LLVM_DEBUG(llvm::dbgs()
372                << dbgPref << "interleavedUses precondition failed, firstOp: "
373                << *firstOp << ", second op: " << *secondOp);
374     return true;
375   }
376   for (auto v : values) {
377     for (auto &u : v.getUses()) {
378       Operation *owner = u.getOwner();
379       if (owner == firstOp || owner == secondOp)
380         continue;
381       // TODO: this is too conservative, use dominance info in the future.
382       if (owner->getBlock() == firstOp->getBlock() &&
383           (owner->isBeforeInBlock(firstOp) || secondOp->isBeforeInBlock(owner)))
384         continue;
385       LLVM_DEBUG(llvm::dbgs()
386                  << dbgPref << " found interleaved op " << *owner
387                  << ", firstOp: " << *firstOp << ", second op: " << *secondOp);
388       return true;
389     }
390   }
391   return false;
392 }
393 
394 /// Return the unique subview use of `v` if it is indeed unique, null otherwise.
395 static SubViewOp getSubViewUseIfUnique(Value v) {
396   SubViewOp subViewOp;
397   for (auto &u : v.getUses()) {
398     if (auto newSubViewOp = dyn_cast<SubViewOp>(u.getOwner())) {
399       if (subViewOp)
400         return SubViewOp();
401       subViewOp = newSubViewOp;
402     }
403   }
404   return subViewOp;
405 }
406 
407 /// TODO: use interfaces, side-effects and aliasing analysis as appropriate,
408 /// when available.
409 LogicalResult LinalgCopyVTRForwardingPattern::matchAndRewrite(
410     vector::TransferReadOp xferOp, PatternRewriter &rewriter) const {
411 
412   // Transfer into `view`.
413   Value viewOrAlloc = xferOp.source();
414   if (!viewOrAlloc.getDefiningOp<ViewOp>() &&
415       !viewOrAlloc.getDefiningOp<AllocOp>())
416     return failure();
417 
418   StringRef dbgPref = "\n[" DEBUG_TYPE "]: VTRForwarding: ";
419   (void)dbgPref;
420   LLVM_DEBUG(llvm::dbgs() << dbgPref << viewOrAlloc);
421 
422   // Ensure there is exactly one subview of `viewOrAlloc` defining `subView`.
423   SubViewOp subViewOp = getSubViewUseIfUnique(viewOrAlloc);
424   if (!subViewOp)
425     return failure();
426   Value subView = subViewOp.getResult();
427   LLVM_DEBUG(llvm::dbgs() << dbgPref << "with subView " << subView);
428 
429   // Find the copy into `subView` without interleaved uses.
430   CopyOp copyOp;
431   for (auto &u : subView.getUses()) {
432     if (auto newCopyOp = dyn_cast<CopyOp>(u.getOwner())) {
433       if (newCopyOp.getOutputBuffer(0) != subView)
434         continue;
435       LLVM_DEBUG(llvm::dbgs() << dbgPref << "copy candidate " << *newCopyOp);
436       if (mayExistInterleavedUses(newCopyOp, xferOp, {viewOrAlloc, subView}))
437         continue;
438       copyOp = newCopyOp;
439       break;
440     }
441   }
442   if (!copyOp)
443     return failure();
444   LLVM_DEBUG(llvm::dbgs() << dbgPref << "with copy " << *copyOp);
445 
446   // Find the fill into `viewOrAlloc` without interleaved uses before the copy.
447   FillOp maybeFillOp;
448   for (auto &u : viewOrAlloc.getUses()) {
449     if (auto newFillOp = dyn_cast<FillOp>(u.getOwner())) {
450       if (newFillOp.getOutputBuffer(0) != viewOrAlloc)
451         continue;
452       LLVM_DEBUG(llvm::dbgs() << dbgPref << "fill candidate " << *newFillOp);
453       if (mayExistInterleavedUses(newFillOp, copyOp, {viewOrAlloc, subView}))
454         continue;
455       maybeFillOp = newFillOp;
456       break;
457     }
458   }
459   // Ensure padding matches.
460   if (maybeFillOp && xferOp.padding() != maybeFillOp.value())
461     return failure();
462   if (maybeFillOp)
463     LLVM_DEBUG(llvm::dbgs() << dbgPref << "with maybeFillOp " << *maybeFillOp);
464 
465   // `in` is the subview that linalg.copy reads. Replace it.
466   Value in = copyOp.getInput(0);
467 
468   // linalg.copy + linalg.fill can be used to create a padded local buffer.
469   // The `masked` attribute is only valid on this padded buffer.
470   // When forwarding to vector.transfer_read, the attribute must be reset
471   // conservatively.
472   Value res = rewriter.create<vector::TransferReadOp>(
473       xferOp.getLoc(), xferOp.getVectorType(), in, xferOp.indices(),
474       xferOp.permutation_map(), xferOp.padding(), ArrayAttr());
475 
476   if (maybeFillOp)
477     rewriter.eraseOp(maybeFillOp);
478   rewriter.eraseOp(copyOp);
479   rewriter.replaceOp(xferOp, res);
480 
481   return success();
482 }
483 
484 /// TODO: use interfaces, side-effects and aliasing analysis as appropriate,
485 /// when available.
486 LogicalResult LinalgCopyVTWForwardingPattern::matchAndRewrite(
487     vector::TransferWriteOp xferOp, PatternRewriter &rewriter) const {
488   // Transfer into `viewOrAlloc`.
489   Value viewOrAlloc = xferOp.source();
490   if (!viewOrAlloc.getDefiningOp<ViewOp>() &&
491       !viewOrAlloc.getDefiningOp<AllocOp>())
492     return failure();
493 
494   // Ensure there is exactly one subview of `viewOrAlloc` defining `subView`.
495   SubViewOp subViewOp = getSubViewUseIfUnique(viewOrAlloc);
496   if (!subViewOp)
497     return failure();
498   Value subView = subViewOp.getResult();
499 
500   // Find the copy from `subView` without interleaved uses.
501   CopyOp copyOp;
502   for (auto &u : subViewOp.getResult().getUses()) {
503     if (auto newCopyOp = dyn_cast<CopyOp>(u.getOwner())) {
504       if (newCopyOp.getInput(0) != subView)
505         continue;
506       if (mayExistInterleavedUses(xferOp, newCopyOp, {viewOrAlloc, subView}))
507         continue;
508       copyOp = newCopyOp;
509       break;
510     }
511   }
512   if (!copyOp)
513     return failure();
514 
515   // `out` is the subview copied into that we replace.
516   Value out = copyOp.getOutputBuffer(0);
517 
518   // Forward vector.transfer into copy.
519   // linalg.copy + linalg.fill can be used to create a padded local buffer.
520   // The `masked` attribute is only valid on this padded buffer.
521   // When forwarding to vector.transfer_write, the attribute must be reset
522   // conservatively.
523   rewriter.create<vector::TransferWriteOp>(
524       xferOp.getLoc(), xferOp.vector(), out, xferOp.indices(),
525       xferOp.permutation_map(), ArrayAttr());
526 
527   rewriter.eraseOp(copyOp);
528   rewriter.eraseOp(xferOp);
529 
530   return success();
531 }
532 
533 template <class ConvOp, int N>
534 LogicalResult ConvOpVectorization<ConvOp, N>::matchAndRewrite(
535     ConvOp op, PatternRewriter &rewriter) const {
536   Location loc = op.getLoc();
537   MLIRContext *context = op.getContext();
538   edsc::ScopedContext scope(rewriter, loc);
539 
540   ShapedType inShapeType = op.getInputShapedType(0);
541   ShapedType kShapeType = op.getInputShapedType(1);
542 
543   ArrayRef<int64_t> inShape = inShapeType.getShape();
544   ArrayRef<int64_t> kShape = kShapeType.getShape();
545 
546   if (!inShapeType.hasStaticShape() || !kShapeType.hasStaticShape())
547     return failure();
548 
549   SmallVector<AffineExpr, 4> mapping;
550   SmallVector<int64_t, 4> vectorDims;
551   // Fail to apply when the size of not vectorized dimension is not 1.
552   for (unsigned i = 0; i < N; i++) {
553     if (!mask[i] && (inShape[i] != 1 || kShape[i] != 1))
554       return failure();
555 
556     if (mask[i] && inShape[i] != kShape[i])
557       return failure();
558 
559     if (mask[i]) {
560       mapping.push_back(getAffineDimExpr(i, context));
561       vectorDims.push_back(inShape[i]);
562     }
563   }
564 
565   Value input = op.getInput(0);
566   Value kernel = op.getInput(1);
567   Value output = op.getOutputBuffer(0);
568 
569   unsigned rank = inShapeType.getRank();
570   unsigned numDims = mapping.size();
571   Type elemType = inShapeType.getElementType();
572 
573   auto map = AffineMap::get(rank, 0, mapping, context);
574   SmallVector<Value, 4> zeros(rank, std_constant_index(0));
575   auto vecType = VectorType::get(vectorDims, elemType);
576 
577   auto inputVec = vector_transfer_read(vecType, input, zeros, map);
578   auto kernelVec = vector_transfer_read(vecType, kernel, zeros, map);
579 
580   auto acc = std_constant(elemType, rewriter.getZeroAttr(elemType));
581 
582   std::array<AffineMap, 3> indexingMaps{
583       AffineMap::getMultiDimIdentityMap(numDims, context),
584       AffineMap::getMultiDimIdentityMap(numDims, context),
585       AffineMap::get(numDims, 0, {}, context)};
586 
587   std::vector<StringRef> iteratorTypes(numDims, "reduction");
588 
589   auto result = rewriter.create<vector::ContractionOp>(
590       loc, inputVec, kernelVec, acc,
591       rewriter.getAffineMapArrayAttr(indexingMaps),
592       rewriter.getStrArrayAttr(iteratorTypes));
593 
594   rewriter.create<StoreOp>(loc, result, output, ValueRange(zeros));
595   rewriter.eraseOp(op);
596   return success();
597 }
598 
599 using ConvOpConst = ConvOpVectorization<ConvWOp, 1>;
600 
601 /// Inserts tiling, promotion and vectorization pattern for ConvOp
602 /// conversion into corresponding pattern lists.
603 template <typename ConvOp, unsigned N>
604 static void
605 populateVectorizationPatterns(OwningRewritePatternList &tilingPatterns,
606                               OwningRewritePatternList &promotionPatterns,
607                               OwningRewritePatternList &vectorizationPatterns,
608                               ArrayRef<int64_t> tileSizes,
609                               MLIRContext *context) {
610   if (tileSizes.size() < N)
611     return;
612 
613   constexpr static StringRef kTiledMarker = "TILED";
614   constexpr static StringRef kPromotedMarker = "PROMOTED";
615   tilingPatterns.insert<LinalgTilingPattern<ConvOp>>(
616       context, LinalgTilingOptions().setTileSizes(tileSizes),
617       LinalgMarker({}, Identifier::get(kTiledMarker, context)));
618 
619   promotionPatterns.insert<LinalgPromotionPattern<ConvOp>>(
620       context, LinalgPromotionOptions().setUseFullTileBuffersByDefault(true),
621       LinalgMarker(Identifier::get(kTiledMarker, context),
622                    Identifier::get(kPromotedMarker, context)));
623 
624   SmallVector<bool, 4> mask(N);
625   int offset = tileSizes.size() - N;
626   std::transform(tileSizes.begin() + offset, tileSizes.end(), mask.begin(),
627                  [](int64_t i) -> bool { return i > 1; });
628 
629   vectorizationPatterns.insert<ConvOpVectorization<ConvOp, N>>(context, mask);
630 }
631 
632 void mlir::linalg::populateConvVectorizationPatterns(
633     MLIRContext *context, SmallVectorImpl<OwningRewritePatternList> &patterns,
634     ArrayRef<int64_t> tileSizes) {
635   OwningRewritePatternList tiling, promotion, vectorization;
636   populateVectorizationPatterns<ConvWOp, 1>(tiling, promotion, vectorization,
637                                             tileSizes, context);
638 
639   populateVectorizationPatterns<ConvNWCOp, 3>(tiling, promotion, vectorization,
640                                               tileSizes, context);
641 
642   populateVectorizationPatterns<ConvNCWOp, 3>(tiling, promotion, vectorization,
643                                               tileSizes, context);
644 
645   populateVectorizationPatterns<ConvHWOp, 2>(tiling, promotion, vectorization,
646                                              tileSizes, context);
647 
648   populateVectorizationPatterns<ConvNHWCOp, 4>(tiling, promotion, vectorization,
649                                                tileSizes, context);
650 
651   populateVectorizationPatterns<ConvNCHWOp, 4>(tiling, promotion, vectorization,
652                                                tileSizes, context);
653 
654   populateVectorizationPatterns<ConvDHWOp, 3>(tiling, promotion, vectorization,
655                                               tileSizes, context);
656 
657   populateVectorizationPatterns<ConvNDHWCOp, 5>(
658       tiling, promotion, vectorization, tileSizes, context);
659 
660   populateVectorizationPatterns<ConvNCDHWOp, 5>(
661       tiling, promotion, vectorization, tileSizes, context);
662 
663   patterns.push_back(std::move(tiling));
664   patterns.push_back(std::move(promotion));
665   patterns.push_back(std::move(vectorization));
666 }
667