1 //===- ElementwiseToLinalg.cpp - conversion of elementwise to linalg ------===//
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 #include "mlir/Dialect/Linalg/Passes.h"
10 
11 #include "PassDetail.h"
12 #include "mlir/Dialect/Arithmetic/Utils/Utils.h"
13 #include "mlir/Dialect/Linalg/IR/Linalg.h"
14 #include "mlir/Dialect/Linalg/Transforms/Transforms.h"
15 #include "mlir/Dialect/Linalg/Utils/Utils.h"
16 #include "mlir/Transforms/DialectConversion.h"
17 
18 using namespace mlir;
19 
20 static bool isElementwiseMappableOpOnRankedTensors(Operation *op) {
21   if (!OpTrait::hasElementwiseMappableTraits(op))
22     return false;
23 
24   // TODO: The conversion pattern can be made to work for `any_of` here, but
25   // it's more complex as it requires tracking which operands are scalars.
26   return llvm::all_of(op->getOperandTypes(),
27                       [](Type type) { return type.isa<RankedTensorType>(); });
28 }
29 
30 /// Given `op` assumed `isElementwiseMappableOpOnRankedTensors`, iterate over
31 /// the result types and return a list of values such that, for each result type
32 /// `t` and value `v` at the same index `idx`:
33 ///   1. `v.getType() == t`
34 ///   2. If an operand of `op` has type `t`, let `operand_first` be the first
35 ///      such operand. Then`v == operand_first`.
36 ///   3. Otherwise, v is a newly created `linalg::InitTensorOp` with:
37 ///        a. Static and dynamic dims extracted from the first operand of `op`.
38 ///        b. Elemental type equal to the elemental type of `t`.
39 ///
40 /// This is sufficient because ElementwiseMappable guarantees that "The static
41 /// types of all vector (resp. tensor) operands and results must have the same
42 /// shape".
43 static SmallVector<Value, 4>
44 getOrCreateOperandsMatchingResultTypes(OpBuilder &b, Operation *op) {
45   assert(isElementwiseMappableOpOnRankedTensors(op));
46   Location loc = op->getLoc();
47   ValueRange operands = op->getOperands();
48   TypeRange rankedTensorTypes = op->getResultTypes();
49   SmallVector<Value, 4> res;
50   res.reserve(rankedTensorTypes.size());
51   for (Type t : rankedTensorTypes) {
52     // Try to find an operand with type matching the result tensor.
53     bool found = false;
54     for (Value v : operands) {
55       if (v.getType() == t) {
56         found = true;
57         res.push_back(v);
58         break;
59       }
60     }
61     if (found)
62       continue;
63 
64     // Extract static / dynamic shape mix from the first operand.
65     Value firstOperand = operands.front();
66     auto rankedTensorType = t.cast<RankedTensorType>();
67     auto staticShape = llvm::to_vector<4>(rankedTensorType.getShape());
68     auto dynamicShape = linalg::getDynOperands(loc, firstOperand, b);
69 
70     res.push_back(b.create<linalg::InitTensorOp>(
71         loc, dynamicShape, staticShape, rankedTensorType.getElementType()));
72   }
73   return res;
74 }
75 
76 namespace {
77 struct ConvertAnyElementwiseMappableOpOnRankedTensors : public RewritePattern {
78   ConvertAnyElementwiseMappableOpOnRankedTensors(MLIRContext *context)
79       : RewritePattern(MatchAnyOpTypeTag(), /*benefit=*/1, context) {}
80   LogicalResult matchAndRewrite(Operation *op,
81                                 PatternRewriter &rewriter) const final {
82     if (!isElementwiseMappableOpOnRankedTensors(op))
83       return rewriter.notifyMatchFailure(
84           op, "requires elementwise op on ranked tensors");
85 
86     auto rank = op->getResult(0).getType().cast<RankedTensorType>().getRank();
87     SmallVector<AffineMap, 3> indexingMaps(
88         op->getNumResults() + op->getNumOperands(),
89         rewriter.getMultiDimIdentityMap(rank));
90     SmallVector<StringRef, 6> iteratorTypes(rank,
91                                             getParallelIteratorTypeName());
92     auto outputs = getOrCreateOperandsMatchingResultTypes(rewriter, op);
93     rewriter.replaceOpWithNewOp<linalg::GenericOp>(
94         op, /*resultTensorTypes=*/op->getResultTypes(),
95         /*inputs=*/op->getOperands(),
96         /*outputs=*/outputs,
97         /*indexingMaps=*/indexingMaps,
98         /*iteratorTypes=*/iteratorTypes,
99         /*bodyBuilder=*/
100         [&](OpBuilder &builder, Location loc, ValueRange regionArgs) {
101           auto resultTypes = llvm::to_vector<6>(
102               llvm::map_range(op->getResultTypes(), [](Type type) {
103                 return type.cast<TensorType>().getElementType();
104               }));
105           auto *scalarOp =
106               builder.create(loc, op->getName().getIdentifier(),
107                              regionArgs.take_front(op->getNumOperands()),
108                              resultTypes, op->getAttrs());
109           builder.create<linalg::YieldOp>(loc, scalarOp->getResults());
110         });
111     return success();
112   }
113 };
114 } // namespace
115 
116 void mlir::linalg::populateElementwiseToLinalgConversionPatterns(
117     RewritePatternSet &patterns) {
118   patterns.add<ConvertAnyElementwiseMappableOpOnRankedTensors>(
119       patterns.getContext());
120 }
121 
122 namespace {
123 class ConvertElementwiseToLinalgPass
124     : public ConvertElementwiseToLinalgBase<ConvertElementwiseToLinalgPass> {
125 
126   void runOnOperation() final {
127     auto *func = getOperation();
128     auto *context = &getContext();
129     ConversionTarget target(*context);
130     RewritePatternSet patterns(context);
131 
132     mlir::linalg::populateElementwiseToLinalgConversionPatterns(patterns);
133     target.markUnknownOpDynamicallyLegal([](Operation *op) {
134       return !isElementwiseMappableOpOnRankedTensors(op);
135     });
136 
137     if (failed(applyPartialConversion(func, target, std::move(patterns))))
138       signalPassFailure();
139   }
140 };
141 } // namespace
142 
143 std::unique_ptr<Pass> mlir::createConvertElementwiseToLinalgPass() {
144   return std::make_unique<ConvertElementwiseToLinalgPass>();
145 }
146