//===- BufferizableOpInterfaceImpl.cpp - Impl. of BufferizableOpInterface -===//
//
// 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/Dialect/Linalg/Transforms/BufferizableOpInterfaceImpl.h"
#include "mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h"
#include "mlir/Dialect/Bufferization/IR/Bufferization.h"
#include "mlir/Dialect/Linalg/IR/Linalg.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/IR/Dialect.h"
#include "mlir/IR/Operation.h"

using namespace mlir;
using namespace linalg;
using namespace mlir::bufferization;

namespace {

/// Generic conversion for any LinalgOp on tensors.
static LogicalResult bufferizeLinalgOp(RewriterBase &rewriter, LinalgOp op,
                                       const BufferizationOptions &options) {
  // Take a guard before anything else.
  OpBuilder::InsertionGuard g(rewriter);
  rewriter.setInsertionPoint(op);

  // Nothing to do. This op is already bufferized.
  if (op.hasBufferSemantics())
    return success();

  // Ensure op has only tensors. Allow mixed tensor-buffer mode on a per-need
  // basis.
  if (!op.hasTensorSemantics())
    return op->emitError() << "op does not have tensor semantics";

  // New input operands for the cloned op.
  SmallVector<Value> newInputBuffers;
  newInputBuffers.reserve(op.getNumInputs());
  for (OpOperand *opOperand : op.getInputOperands()) {
    if (op.isScalar(opOperand)) {
      newInputBuffers.push_back(opOperand->get());
      continue;
    }
    newInputBuffers.push_back(getBuffer(rewriter, opOperand->get(), options));
  }

  // New output operands for the cloned op.
  SmallVector<Value> newOutputBuffers;
  for (OpResult opResult : op->getOpResults()) {
    OpOperand *opOperand = op.getOutputOperand(opResult.getResultNumber());
    Value resultBuffer = getBuffer(rewriter, opOperand->get(), options);
    newOutputBuffers.push_back(resultBuffer);
  }

  // Merge input/output operands.
  SmallVector<Value> newOperands = newInputBuffers;
  newOperands.append(newOutputBuffers.begin(), newOutputBuffers.end());

  // Set insertion point now that potential alloc/dealloc are introduced.
  rewriter.setInsertionPoint(op);
  // Clone the op, but use the new operands. Move the existing block into the
  // new op. Since the new op does not have any tensor results, it does not
  // return anything.
  assert(op->getNumRegions() == 1 && "expected that op has 1 region");
  auto newOp = cast<LinalgOp>(op.cloneWithoutRegions(
      rewriter, op.getLoc(), /*resultTypes=*/TypeRange{}, newOperands));
  rewriter.inlineRegionBefore(op->getRegion(0), newOp->getRegion(0),
                              newOp->getRegion(0).begin());

  // Replace the results of the old op with the new output buffers.
  replaceOpWithBufferizedValues(rewriter, op, newOutputBuffers);

  return success();
}

/// Bufferization of linalg.generic. Replace with a new linalg.generic that
/// operates entirely on memrefs.
template <typename OpTy>
struct LinalgOpInterface
    : public BufferizableOpInterface::ExternalModel<LinalgOpInterface<OpTy>,
                                                    OpTy> {
  bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
                              const AnalysisState &state) const {
    // Operand is read if it is used in the computation.
    auto genericOp = cast<linalg::LinalgOp>(op);
    return genericOp.payloadUsesValueFromOperand(&opOperand);
  }

  bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
                               const AnalysisState &state) const {
    // Operand is written to if it has an aliasing OpResult.
    auto bufferizableOp = cast<BufferizableOpInterface>(op);
    return !bufferizableOp.getAliasingOpResult(opOperand, state).empty();
  }

  SmallVector<OpOperand *>
  getAliasingOpOperand(Operation *op, OpResult opResult,
                       const AnalysisState &state) const {
    auto genericOp = cast<linalg::LinalgOp>(op);

    // The i-th OpResult may alias with the i-th "out" tensor.
    return {genericOp.getOutputOperand(opResult.getResultNumber())};
  }

  SmallVector<OpResult> getAliasingOpResult(Operation *op, OpOperand &opOperand,
                                            const AnalysisState &state) const {
    auto genericOp = cast<linalg::LinalgOp>(op);

    // The i-th "out" tensor may alias with the i-th OpResult.
    if (genericOp.isOutputTensor(&opOperand))
      return {genericOp.getTiedOpResult(&opOperand)};
    return {};
  }

  BufferRelation bufferRelation(Operation *op, OpResult opResult,
                                const AnalysisState &state) const {
    return BufferRelation::Equivalent;
  }

  LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
                          const BufferizationOptions &options) const {
    return bufferizeLinalgOp(rewriter, cast<LinalgOp>(op), options);
  }
};

/// Helper structure that iterates over all LinalgOps in `OpTys` and registers
/// the `BufferizableOpInterface` with each of them.
template <typename... Ops>
struct LinalgOpInterfaceHelper {
  static void registerOpInterface(MLIRContext *ctx) {
    (void)std::initializer_list<int>{
        0, (Ops::template attachInterface<LinalgOpInterface<Ops>>(*ctx), 0)...};
  }
};
} // namespace

void mlir::linalg::registerBufferizableOpInterfaceExternalModels(
    DialectRegistry &registry) {
  registry.addExtension(+[](MLIRContext *ctx, linalg::LinalgDialect *dialect) {
    // Register all Linalg structured ops. `LinalgOp` is an interface and it is
    // not possible to attach an external interface to an existing interface.
    // Therefore, attach the `BufferizableOpInterface` to all ops one-by-one.
    LinalgOpInterfaceHelper<
#define GET_OP_LIST
#include "mlir/Dialect/Linalg/IR/LinalgStructuredOps.cpp.inc"
        >::registerOpInterface(ctx);
  });
}