clang-p2996/mlir/lib/Conversion/ShapeToStandard/ShapeToStandard.cpp

//===- ShapeToStandard.cpp - conversion from Shape to Standard dialect ----===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//

#include "mlir/Conversion/ShapeToStandard/ShapeToStandard.h"

#include "../PassDetail.h"
#include "mlir/Dialect/SCF/SCF.h"
#include "mlir/Dialect/Shape/IR/Shape.h"
#include "mlir/Dialect/StandardOps/IR/Ops.h"
#include "mlir/Transforms/DialectConversion.h"

using namespace mlir;
using namespace mlir::shape;

namespace {

/// Generated conversion patterns.
#include "ShapeToStandardPatterns.inc"

/// Conversion patterns.
template <typename SrcOpTy, typename DstOpTy>
class BinaryOpConversion : public OpConversionPattern<SrcOpTy> {
public:
  using OpConversionPattern<SrcOpTy>::OpConversionPattern;

  LogicalResult
  matchAndRewrite(SrcOpTy op, ArrayRef<Value> operands,
                  ConversionPatternRewriter &rewriter) const override {
    typename SrcOpTy::Adaptor adaptor(operands);
    rewriter.replaceOpWithNewOp<DstOpTy>(op.getOperation(), adaptor.lhs(),
                                         adaptor.rhs());
    return success();
  }
};

class ShapeOfOpConversion : public OpConversionPattern<ShapeOfOp> {
public:
  using OpConversionPattern<ShapeOfOp>::OpConversionPattern;

  LogicalResult
  matchAndRewrite(ShapeOfOp op, ArrayRef<Value> operands,
                  ConversionPatternRewriter &rewriter) const override {
    ShapeOfOp::Adaptor transformed(operands);
    auto loc = op.getLoc();
    auto tensorVal = transformed.arg();
    auto tensorTy = tensorVal.getType();

    // For unranked tensors `shape_of` lowers to `scf` and the pattern can be
    // found in the corresponding pass.
    if (tensorTy.isa<UnrankedTensorType>())
      return failure();

    // Build values for individual dimensions.
    SmallVector<Value, 8> dimValues;
    auto rankedTensorTy = tensorTy.cast<RankedTensorType>();
    int64_t rank = rankedTensorTy.getRank();
    for (int64_t i = 0; i < rank; i++) {
      if (rankedTensorTy.isDynamicDim(i)) {
        auto dimVal = rewriter.create<DimOp>(loc, tensorVal, i);
        dimValues.push_back(dimVal);
      } else {
        int64_t dim = rankedTensorTy.getDimSize(i);
        auto dimVal = rewriter.create<ConstantIndexOp>(loc, dim);
        dimValues.push_back(dimVal);
      }
    }

    // Materialize shape as ranked tensor.
    rewriter.replaceOpWithNewOp<TensorFromElementsOp>(op.getOperation(),
                                                      dimValues);
    return success();
  }
};

class ConstSizeOpConverter : public OpConversionPattern<ConstSizeOp> {
public:
  using OpConversionPattern<ConstSizeOp>::OpConversionPattern;

  LogicalResult
  matchAndRewrite(ConstSizeOp op, ArrayRef<Value> operands,
                  ConversionPatternRewriter &rewriter) const override {
    rewriter.replaceOpWithNewOp<ConstantIndexOp>(op.getOperation(),
                                                 op.value().getSExtValue());
    return success();
  }
};

class GetExtentOpConverter : public OpConversionPattern<GetExtentOp> {
  using OpConversionPattern<GetExtentOp>::OpConversionPattern;

  LogicalResult
  matchAndRewrite(GetExtentOp op, ArrayRef<Value> operands,
                  ConversionPatternRewriter &rewriter) const override {
    GetExtentOp::Adaptor transformed(operands);

    // Derive shape extent directly from shape origin if possible.
    // This circumvents the necessity to materialize the shape in memory.
    if (auto shapeOfOp = op.shape().getDefiningOp<ShapeOfOp>()) {
      rewriter.replaceOpWithNewOp<DimOp>(op, shapeOfOp.arg(),
                                         transformed.dim());
      return success();
    }

    rewriter.replaceOpWithNewOp<ExtractElementOp>(
        op, rewriter.getIndexType(), transformed.shape(),
        ValueRange{transformed.dim()});
    return success();
  }
};

class RankOpConverter : public OpConversionPattern<shape::RankOp> {
public:
  using OpConversionPattern<shape::RankOp>::OpConversionPattern;

  LogicalResult
  matchAndRewrite(shape::RankOp op, ArrayRef<Value> operands,
                  ConversionPatternRewriter &rewriter) const override {
    shape::RankOp::Adaptor transformed(operands);
    rewriter.replaceOpWithNewOp<DimOp>(op.getOperation(), transformed.shape(),
                                       0);
    return success();
  }
};

/// Type conversions.
class ShapeTypeConverter : public TypeConverter {
public:
  using TypeConverter::convertType;

  ShapeTypeConverter(MLIRContext *ctx) {
    // Add default pass-through conversion.
    addConversion([&](Type type) { return type; });

    addConversion([ctx](SizeType type) { return IndexType::get(ctx); });
    addConversion([ctx](ShapeType type) {
      return RankedTensorType::get({ShapedType::kDynamicSize},
                                   IndexType::get(ctx));
    });
  }
};

/// Conversion pass.
class ConvertShapeToStandardPass
    : public ConvertShapeToStandardBase<ConvertShapeToStandardPass> {

  void runOnOperation() override {
    // Setup type conversion.
    MLIRContext &ctx = getContext();
    ShapeTypeConverter typeConverter(&ctx);

    // Setup target legality.
    ConversionTarget target(ctx);
    target.addLegalDialect<scf::SCFDialect, StandardOpsDialect>();
    target.addLegalOp<ModuleOp, ModuleTerminatorOp, ReturnOp>();
    target.addDynamicallyLegalOp<FuncOp>([&](FuncOp op) {
      return typeConverter.isSignatureLegal(op.getType()) &&
             typeConverter.isLegal(&op.getBody());
    });

    // Setup conversion patterns.
    OwningRewritePatternList patterns;
    populateShapeToStandardConversionPatterns(patterns, &ctx);
    populateFuncOpTypeConversionPattern(patterns, &ctx, typeConverter);

    // Apply conversion.
    auto module = getOperation();
    if (failed(applyFullConversion(module, target, patterns)))
      signalPassFailure();
  }
};

} // namespace

void mlir::populateShapeToStandardConversionPatterns(
    OwningRewritePatternList &patterns, MLIRContext *ctx) {
  populateWithGenerated(ctx, &patterns);
  // clang-format off
  patterns.insert<
      BinaryOpConversion<AddOp, AddIOp>,
      BinaryOpConversion<MulOp, MulIOp>,
      ConstSizeOpConverter,
      GetExtentOpConverter,
      RankOpConverter,
      ShapeOfOpConversion>(ctx);
  // clang-format on
}

std::unique_ptr<OperationPass<ModuleOp>>
mlir::createConvertShapeToStandardPass() {
  return std::make_unique<ConvertShapeToStandardPass>();
}