//===- Bufferize.cpp - Bufferization for std ops --------------------------===//
//
// 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
//
//===----------------------------------------------------------------------===//
//
// This file implements bufferization of std ops.
//
//===----------------------------------------------------------------------===//

#include "mlir/Transforms/Bufferize.h"
#include "PassDetail.h"
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/Dialect/SCF/SCF.h"
#include "mlir/Dialect/StandardOps/IR/Ops.h"
#include "mlir/Dialect/StandardOps/Transforms/Passes.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/IR/BlockAndValueMapping.h"
#include "mlir/Transforms/DialectConversion.h"

using namespace mlir;

namespace {
class BufferizeIndexCastOp : public OpConversionPattern<IndexCastOp> {
public:
  using OpConversionPattern::OpConversionPattern;
  LogicalResult
  matchAndRewrite(IndexCastOp op, ArrayRef<Value> operands,
                  ConversionPatternRewriter &rewriter) const override {
    IndexCastOp::Adaptor adaptor(operands);
    auto tensorType = op.getType().cast<RankedTensorType>();
    rewriter.replaceOpWithNewOp<IndexCastOp>(
        op, adaptor.in(),
        MemRefType::get(tensorType.getShape(), tensorType.getElementType()));
    return success();
  }
};

class BufferizeSelectOp : public OpConversionPattern<SelectOp> {
public:
  using OpConversionPattern::OpConversionPattern;
  LogicalResult
  matchAndRewrite(SelectOp op, ArrayRef<Value> operands,
                  ConversionPatternRewriter &rewriter) const override {
    if (!op.condition().getType().isa<IntegerType>())
      return rewriter.notifyMatchFailure(op, "requires scalar condition");

    SelectOp::Adaptor adaptor(operands);
    rewriter.replaceOpWithNewOp<SelectOp>(
        op, adaptor.condition(), adaptor.true_value(), adaptor.false_value());
    return success();
  }
};
} // namespace

void mlir::populateStdBufferizePatterns(BufferizeTypeConverter &typeConverter,
                                        RewritePatternSet &patterns) {
  patterns.add<BufferizeSelectOp, BufferizeIndexCastOp>(typeConverter,
                                                        patterns.getContext());
}

namespace {
struct StdBufferizePass : public StdBufferizeBase<StdBufferizePass> {
  void runOnFunction() override {
    auto *context = &getContext();
    BufferizeTypeConverter typeConverter;
    RewritePatternSet patterns(context);
    ConversionTarget target(*context);

    target.addLegalDialect<scf::SCFDialect, StandardOpsDialect,
                           memref::MemRefDialect>();

    populateStdBufferizePatterns(typeConverter, patterns);
    // We only bufferize the case of tensor selected type and scalar condition,
    // as that boils down to a select over memref descriptors (don't need to
    // touch the data).
    target.addDynamicallyLegalOp<IndexCastOp>(
        [&](IndexCastOp op) { return typeConverter.isLegal(op.getType()); });
    target.addDynamicallyLegalOp<SelectOp>([&](SelectOp op) {
      return typeConverter.isLegal(op.getType()) ||
             !op.condition().getType().isa<IntegerType>();
    });
    if (failed(
            applyPartialConversion(getFunction(), target, std::move(patterns))))
      signalPassFailure();
  }
};
} // namespace

std::unique_ptr<Pass> mlir::createStdBufferizePass() {
  return std::make_unique<StdBufferizePass>();
}