//===- SparseTensorCodegen.cpp - Sparse tensor primitives conversion ------===//
//
// 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
//
//===----------------------------------------------------------------------===//
//
// A pass that converts sparse tensor types and primitives to actual compiler
// visible buffers and actual compiler IR that implements these primitives on
// the selected sparse tensor storage schemes. This pass provides an alternative
// to the SparseTensorConversion pass, eliminating the dependence on a runtime
// support library, and providing much more opportunities for subsequent
// compiler optimization of the generated code.
//
//===----------------------------------------------------------------------===//

#include "CodegenUtils.h"

#include "mlir/Dialect/Func/IR/FuncOps.h"
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/Dialect/SparseTensor/IR/SparseTensor.h"
#include "mlir/Dialect/SparseTensor/Transforms/Passes.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Transforms/DialectConversion.h"

using namespace mlir;
using namespace mlir::sparse_tensor;

namespace {

//===----------------------------------------------------------------------===//
// Helper methods.
//===----------------------------------------------------------------------===//

/// Maps each sparse tensor type to the appropriate buffer.
static Optional<Type> convertSparseTensorTypes(Type type) {
  if (getSparseTensorEncoding(type) != nullptr) {
    // TODO: this is just a dummy rule to get the ball rolling....
    RankedTensorType rTp = type.cast<RankedTensorType>();
    return MemRefType::get({ShapedType::kDynamicSize}, rTp.getElementType());
  }
  return llvm::None;
}

//===----------------------------------------------------------------------===//
// Conversion rules.
//===----------------------------------------------------------------------===//

/// Sparse conversion rule for returns.
class SparseReturnConverter : public OpConversionPattern<func::ReturnOp> {
public:
  using OpConversionPattern::OpConversionPattern;
  LogicalResult
  matchAndRewrite(func::ReturnOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    rewriter.replaceOpWithNewOp<func::ReturnOp>(op, adaptor.getOperands());
    return success();
  }
};

} // namespace

//===----------------------------------------------------------------------===//
// Sparse tensor type conversion into an actual buffer.
//===----------------------------------------------------------------------===//

mlir::SparseTensorTypeToBufferConverter::SparseTensorTypeToBufferConverter() {
  addConversion([](Type type) { return type; });
  addConversion(convertSparseTensorTypes);
}

//===----------------------------------------------------------------------===//
// Public method for populating conversion rules.
//===----------------------------------------------------------------------===//

/// Populates the given patterns list with conversion rules required for
/// the sparsification of linear algebra operations.
void mlir::populateSparseTensorCodegenPatterns(TypeConverter &typeConverter,
                                               RewritePatternSet &patterns) {
  patterns.add<SparseReturnConverter>(typeConverter, patterns.getContext());
}