//====----- Bufferize.cpp - Bufferization of shape ops  ---------*- C++-*--===//
//
// 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/Transforms/Bufferize.h"
#include "PassDetail.h"
#include "mlir/Dialect/Shape/IR/Shape.h"
#include "mlir/Dialect/Shape/Transforms/Passes.h"
#include "mlir/Dialect/StandardOps/IR/Ops.h"
#include "mlir/IR/Operation.h"
#include "mlir/IR/StandardTypes.h"
#include "mlir/Pass/Pass.h"

using namespace mlir;
using namespace mlir::shape;

namespace {
// Propagate tensor to memref conversions through shape.assuming ops.
class TypeConversionAssumingOpConverter
    : public BufferizeOpConversionPattern<shape::AssumingOp> {
public:
  using BufferizeOpConversionPattern<
      shape::AssumingOp>::BufferizeOpConversionPattern;

  LogicalResult
  matchAndRewrite(shape::AssumingOp assumingOp, ArrayRef<Value> operands,
                  ConversionPatternRewriter &rewriter) const final {
    SmallVector<Type, 2> newResultTypes;
    newResultTypes.reserve(assumingOp.getNumResults());
    for (auto result : assumingOp.getResults()) {
      auto originalType = result.getType();
      Type convertedType = converter.convertType(originalType);
      newResultTypes.push_back(convertedType);
    }

    auto newAssumingOp = rewriter.create<shape::AssumingOp>(
        assumingOp.getLoc(), newResultTypes, assumingOp.witness());

    rewriter.replaceOp(assumingOp, newAssumingOp.getResults());
    rewriter.inlineRegionBefore(assumingOp.doRegion(), newAssumingOp.doRegion(),
                                newAssumingOp.doRegion().end());

    return success();
  }
};

struct ShapeBufferizePass : public ShapeBufferizeBase<ShapeBufferizePass> {
  void runOnFunction() override {
    MLIRContext &ctx = getContext();

    OwningRewritePatternList patterns;
    BufferizeTypeConverter converter;
    populateShapeTypeConversionPatterns(&ctx, converter, patterns);

    ConversionTarget target(getContext());
    auto isMemRefType = [](Type type) { return type.isa<BaseMemRefType>(); };

    target.addDynamicallyLegalOp<AssumingOp>([&](shape::AssumingOp op) {
      return std::all_of(op.result_type_begin(), op.result_type_end(),
                         isMemRefType);
    });

    if (failed(mlir::applyPartialConversion(getFunction(), target, patterns)))
      signalPassFailure();
  }
};

} // namespace

/// Populates `patterns` with the conversion patterns of tensor->memref.
//
// TODO: Change this to work generally with any type conversions.
void mlir::populateShapeTypeConversionPatterns(
    MLIRContext *context, BufferizeTypeConverter &converter,
    OwningRewritePatternList &patterns) {
  patterns.insert<TypeConversionAssumingOpConverter>(context, converter);
}

//===----------------------------------------------------------------------===//
// ShapeBufferizePass construction
//===----------------------------------------------------------------------===//

std::unique_ptr<FunctionPass> mlir::createShapeBufferizePass() {
  return std::make_unique<ShapeBufferizePass>();
}