1 //===- ShapeToStandard.cpp - conversion from Shape to Standard dialect ----===// 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/Conversion/ShapeToStandard/ShapeToStandard.h" 10 11 #include "../PassDetail.h" 12 #include "mlir/Dialect/SCF/SCF.h" 13 #include "mlir/Dialect/Shape/IR/Shape.h" 14 #include "mlir/Dialect/StandardOps/IR/Ops.h" 15 #include "mlir/Transforms/DialectConversion.h" 16 17 using namespace mlir; 18 using namespace mlir::shape; 19 20 namespace { 21 22 /// Generated conversion patterns. 23 #include "ShapeToStandardPatterns.inc" 24 25 /// Conversion patterns. 26 template <typename SrcOpTy, typename DstOpTy> 27 class BinaryOpConversion : public OpConversionPattern<SrcOpTy> { 28 public: 29 using OpConversionPattern<SrcOpTy>::OpConversionPattern; 30 31 LogicalResult 32 matchAndRewrite(SrcOpTy op, ArrayRef<Value> operands, 33 ConversionPatternRewriter &rewriter) const override { 34 typename SrcOpTy::Adaptor adaptor(operands); 35 rewriter.replaceOpWithNewOp<DstOpTy>(op.getOperation(), adaptor.lhs(), 36 adaptor.rhs()); 37 return success(); 38 } 39 }; 40 41 class ShapeOfOpConversion : public OpConversionPattern<ShapeOfOp> { 42 public: 43 using OpConversionPattern<ShapeOfOp>::OpConversionPattern; 44 45 LogicalResult 46 matchAndRewrite(ShapeOfOp op, ArrayRef<Value> operands, 47 ConversionPatternRewriter &rewriter) const override { 48 ShapeOfOp::Adaptor transformed(operands); 49 auto loc = op.getLoc(); 50 auto tensorVal = transformed.arg(); 51 auto tensorTy = tensorVal.getType(); 52 53 // For unranked tensors `shape_of` lowers to `scf` and the pattern can be 54 // found in the corresponding pass. 55 if (tensorTy.isa<UnrankedTensorType>()) 56 return failure(); 57 58 // Build values for individual dimensions. 59 SmallVector<Value, 8> dimValues; 60 auto rankedTensorTy = tensorTy.cast<RankedTensorType>(); 61 int64_t rank = rankedTensorTy.getRank(); 62 for (int64_t i = 0; i < rank; i++) { 63 if (rankedTensorTy.isDynamicDim(i)) { 64 auto dimVal = rewriter.create<DimOp>(loc, tensorVal, i); 65 dimValues.push_back(dimVal); 66 } else { 67 int64_t dim = rankedTensorTy.getDimSize(i); 68 auto dimVal = rewriter.create<ConstantIndexOp>(loc, dim); 69 dimValues.push_back(dimVal); 70 } 71 } 72 73 // Materialize shape as ranked tensor. 74 rewriter.replaceOpWithNewOp<TensorFromElementsOp>(op.getOperation(), 75 dimValues); 76 return success(); 77 } 78 }; 79 80 class ConstSizeOpConverter : public OpConversionPattern<ConstSizeOp> { 81 public: 82 using OpConversionPattern<ConstSizeOp>::OpConversionPattern; 83 84 LogicalResult 85 matchAndRewrite(ConstSizeOp op, ArrayRef<Value> operands, 86 ConversionPatternRewriter &rewriter) const override { 87 rewriter.replaceOpWithNewOp<ConstantIndexOp>(op.getOperation(), 88 op.value().getSExtValue()); 89 return success(); 90 } 91 }; 92 93 class RankOpConverter : public OpConversionPattern<shape::RankOp> { 94 public: 95 using OpConversionPattern<shape::RankOp>::OpConversionPattern; 96 97 LogicalResult 98 matchAndRewrite(shape::RankOp op, ArrayRef<Value> operands, 99 ConversionPatternRewriter &rewriter) const override { 100 shape::RankOp::Adaptor transformed(operands); 101 rewriter.replaceOpWithNewOp<DimOp>(op.getOperation(), transformed.shape(), 102 0); 103 return success(); 104 } 105 }; 106 107 /// Type conversions. 108 class ShapeTypeConverter : public TypeConverter { 109 public: 110 using TypeConverter::convertType; 111 112 ShapeTypeConverter(MLIRContext *ctx) { 113 // Add default pass-through conversion. 114 addConversion([&](Type type) { return type; }); 115 116 addConversion([ctx](SizeType type) { return IndexType::get(ctx); }); 117 addConversion([ctx](ShapeType type) { 118 return RankedTensorType::get({ShapedType::kDynamicSize}, 119 IndexType::get(ctx)); 120 }); 121 } 122 }; 123 124 /// Conversion pass. 125 class ConvertShapeToStandardPass 126 : public ConvertShapeToStandardBase<ConvertShapeToStandardPass> { 127 128 void runOnOperation() override { 129 // Setup type conversion. 130 MLIRContext &ctx = getContext(); 131 ShapeTypeConverter typeConverter(&ctx); 132 133 // Setup target legality. 134 ConversionTarget target(ctx); 135 target.addLegalDialect<scf::SCFDialect, StandardOpsDialect>(); 136 target.addLegalOp<ModuleOp, ModuleTerminatorOp, ReturnOp>(); 137 target.addDynamicallyLegalOp<FuncOp>([&](FuncOp op) { 138 return typeConverter.isSignatureLegal(op.getType()) && 139 typeConverter.isLegal(&op.getBody()); 140 }); 141 142 // Setup conversion patterns. 143 OwningRewritePatternList patterns; 144 populateShapeToStandardConversionPatterns(patterns, &ctx); 145 populateFuncOpTypeConversionPattern(patterns, &ctx, typeConverter); 146 147 // Apply conversion. 148 auto module = getOperation(); 149 if (failed(applyFullConversion(module, target, patterns))) 150 signalPassFailure(); 151 } 152 }; 153 154 } // namespace 155 156 void mlir::populateShapeToStandardConversionPatterns( 157 OwningRewritePatternList &patterns, MLIRContext *ctx) { 158 populateWithGenerated(ctx, &patterns); 159 // clang-format off 160 patterns.insert< 161 BinaryOpConversion<AddOp, AddIOp>, 162 BinaryOpConversion<MulOp, MulIOp>, 163 ConstSizeOpConverter, 164 RankOpConverter, 165 ShapeOfOpConversion>(ctx); 166 // clang-format on 167 } 168 169 std::unique_ptr<OperationPass<ModuleOp>> 170 mlir::createConvertShapeToStandardPass() { 171 return std::make_unique<ConvertShapeToStandardPass>(); 172 } 173