//===----------------------------------------------------------------------===//
//
// 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
// Also available under a BSD-style license. See LICENSE.
//
//===----------------------------------------------------------------------===//

#include "torch-mlir/Conversion/TorchToTosa/TorchToTosa.h"
#include "torch-mlir/Conversion/TorchToTosa/TosaLegalizeCommon.h"
#include "torch-mlir/Conversion/TorchToTosa/TosaLegalizeUtils.h"

#include "../PassDetail.h"
#include "mlir/Dialect/Arithmetic/IR/Arithmetic.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Dialect/Tosa/IR/TosaOps.h"
#include "mlir/Dialect/Traits.h"
#include "mlir/IR/Matchers.h"
#include "mlir/Transforms/DialectConversion.h"
#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
#include "torch-mlir/Dialect/TorchConversion/IR/TorchConversionDialect.h"
#include "torch-mlir/Dialect/TorchConversion/Transforms/BackendTypeConversion.h"

using namespace mlir;
using namespace mlir::torch;
using namespace mlir::torch::Torch;

namespace {

// These legalizations are for unary ops with only for floating point datatypes.
// There is no supported quantized integer mode for these.
template <typename AtenOpT, typename TosaOpT>
class ConvertAtenUnaryFPOnlyOp : public OpConversionPattern<AtenOpT> {
public:
  using OpConversionPattern<AtenOpT>::OpConversionPattern;
  using OpAdaptor = typename AtenOpT::Adaptor;
  LogicalResult
  matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    Value self = adaptor.self();
    auto selfTy = self.getType().cast<TensorType>();

    if (!selfTy)
      return op.emitError("Only Tensor types supported in TOSA");

    if (selfTy.getElementType().isa<mlir::FloatType>()) {
      rewriter.replaceOpWithNewOp<TosaOpT>(
          op,
          OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
              op.getType()),
          self);
      return success();
    } else {
      return op.emitError(
          "Only floating-point datatype legalization supported");
    }
  }
};

// These unary op legalizations are identical for floating-point
// or quantized types
template <typename AtenOpT, typename TosaOpT>
class ConvertAtenUnaryOp : public OpConversionPattern<AtenOpT> {
public:
  using OpConversionPattern<AtenOpT>::OpConversionPattern;
  using OpAdaptor = typename AtenOpT::Adaptor;
  LogicalResult
  matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    rewriter.replaceOpWithNewOp<TosaOpT>(
        op,
        OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
            op.getType()),
        adaptor.self());
    return success();
  }
};

// These binary op legalizations are identical for floating-point
// or quantized types
template <typename AtenOpT, typename TosaOpT>
class ConvertAtenBinaryOp : public OpConversionPattern<AtenOpT> {
public:
  using OpConversionPattern<AtenOpT>::OpConversionPattern;
  using OpAdaptor = typename AtenOpT::Adaptor;
  LogicalResult
  matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    Value lhs = adaptor.self();
    auto lhsTy = lhs.getType().cast<TensorType>();
    Value rhs = adaptor.other();
    auto rhsTy = rhs.getType().cast<TensorType>();

    if (!lhsTy || !rhsTy)
      return op.emitError("Only Tensor types supported in TOSA");

    auto lhsElemTy = lhsTy.getElementType();
    auto rhsElemTy = rhsTy.getElementType();

    if (lhsElemTy != rhsElemTy)
      return op.emitError("Add: input datatypes mismatched");

    rewriter.replaceOpWithNewOp<TosaOpT>(
        op,
        OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
            op.getType()),
        lhs, rhs);
    return success();
  }
};

// These binary op legalizations are specific to add/sub which have an
// alpha multiplier.
template <typename AtenOpT, typename TosaOpT>
class ConvertAtenAddSubOp : public OpConversionPattern<AtenOpT> {
public:
  using OpConversionPattern<AtenOpT>::OpConversionPattern;
  using OpAdaptor = typename AtenOpT::Adaptor;
  LogicalResult
  matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    Value lhs = adaptor.self();
    auto lhsTy = lhs.getType().cast<TensorType>();
    Value rhs = adaptor.other();
    auto rhsTy = rhs.getType().cast<TensorType>();

    if (!lhsTy || !rhsTy)
      return op.emitError("Only Tensor types supported in TOSA");

    auto lhsElemTy = lhsTy.getElementType();
    auto rhsElemTy = rhsTy.getElementType();

    if (lhsElemTy != rhsElemTy)
      return op.emitError("Add: input datatypes mismatched");

    // FIXME: Handle alpha.
    // Needs extraction of floating point constant.

    if (lhsElemTy.isa<mlir::FloatType>()) {
      rewriter.replaceOpWithNewOp<TosaOpT>(
          op,
          OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
              op.getType()),
          lhs, rhs);
      return success();
    } else {
      return op.emitError(
          "Only floating-point datatype legalization supported");
    }
  }
};

// This defines a template to construct ops whose legalizations are
// specialized.
template <typename AtenOpT>
class ConvertAtenOp : public OpConversionPattern<AtenOpT> {
public:
  using OpConversionPattern<AtenOpT>::OpConversionPattern;
  using OpAdaptor = typename AtenOpT::Adaptor;
  LogicalResult
  matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override;
};

template <>
LogicalResult ConvertAtenOp<AtenTanhOp>::matchAndRewrite(
    AtenTanhOp op, OpAdaptor adaptor,
    ConversionPatternRewriter &rewriter) const {
  Value self = adaptor.self();
  auto selfTy = self.getType().cast<TensorType>();
  if (selfTy && selfTy.getElementType().isa<mlir::FloatType>()) {
    rewriter.replaceOpWithNewOp<tosa::TanhOp>(
        op, getTypeConverter()->convertType(op.getType()), self);
    return success();
  } else {
    // Sigmoid legalization in TOSA for quantized element-type uses
    // specialized tosa.table construct.
    return op.emitError(
        "Only floating-point datatype legalization currently supported");
  }
}

template <>
LogicalResult ConvertAtenOp<AtenSigmoidOp>::matchAndRewrite(
    AtenSigmoidOp op, OpAdaptor adaptor,
    ConversionPatternRewriter &rewriter) const {
  Value self = adaptor.self();
  auto selfTy = self.getType().cast<TensorType>();
  if (selfTy && selfTy.getElementType().isa<mlir::FloatType>()) {
    rewriter.replaceOpWithNewOp<tosa::SigmoidOp>(
        op, getTypeConverter()->convertType(op.getType()), self);
    return success();
  } else {
    // Sigmoid legalization in TOSA for quantized element-type uses
    // specialized tosa.table construct.
    return op.emitError(
        "Only floating-point datatype legalization currently supported");
  }
}

template <>
LogicalResult ConvertAtenOp<AtenReluOp>::matchAndRewrite(
    AtenReluOp op, OpAdaptor adaptor,
    ConversionPatternRewriter &rewriter) const {
  Value self = adaptor.self();
  auto selfTy = self.getType().cast<TensorType>();

  // Maps to tosa.clamp which has both int and fp limits.
  int64_t clampMin = 0;
  Value clampIn = self;
  if (selfTy) {
    // Rescale the clampIn for quantized types. TBD
    if (!selfTy.getElementType().isa<mlir::FloatType>()) {
      return op.emitError(
          "Only floating-point datatype legalization currently supported");
    }
    rewriter.replaceOpWithNewOp<tosa::ClampOp>(
        op, getTypeConverter()->convertType(op.getType()), clampIn,
        rewriter.getI64IntegerAttr(clampMin),
        rewriter.getI64IntegerAttr(std::numeric_limits<int32_t>::max()),
        rewriter.getF32FloatAttr(0.0f),
        rewriter.getF32FloatAttr(std::numeric_limits<float>::max()));
    return success();
  } else {
    return op.emitError("Only Tensor types supported in TOSA");
  }
}

template <>
LogicalResult ConvertAtenOp<AtenMulTensorOp>::matchAndRewrite(
    AtenMulTensorOp op, OpAdaptor adaptor,
    ConversionPatternRewriter &rewriter) const {

  Value lhs = adaptor.self();
  auto lhsTy = lhs.getType().cast<TensorType>();
  Value rhs = adaptor.other();
  auto rhsTy = rhs.getType().cast<TensorType>();

  if (!lhsTy || !rhsTy)
    return op.emitError("Only Tensor types supported in TOSA");

  auto lhsElemTy = lhsTy.getElementType();
  auto rhsElemTy = rhsTy.getElementType();

  if (lhsElemTy != rhsElemTy)
    return op.emitError("Add: input datatypes mismatched");

  if (lhsElemTy.isa<mlir::FloatType>()) {
    rewriter.replaceOpWithNewOp<tosa::MulOp>(
        op, getTypeConverter()->convertType(op.getType()), lhs, rhs,
        /*shift=*/0);
    return success();
  } else {
    // Quantized multiplication may need to rescale inputs.
    return op.emitError(
        "Only floating-point datatype legalization currently supported");
  }
}

template <>
LogicalResult ConvertAtenOp<AtenDivTensorOp>::matchAndRewrite(
    AtenDivTensorOp op, OpAdaptor adaptor,
    ConversionPatternRewriter &rewriter) const {

  Value lhs = adaptor.self();
  auto lhsTy = lhs.getType().cast<TensorType>();
  Value rhs = adaptor.other();
  auto rhsTy = rhs.getType().cast<TensorType>();

  if (!lhsTy || !rhsTy)
    return op.emitError("Only Tensor types supported in TOSA");

  auto lhsElemTy = lhsTy.getElementType();
  auto rhsElemTy = rhsTy.getElementType();

  if (lhsElemTy != rhsElemTy)
    return op.emitError("Add: input datatypes mismatched");

  if (lhsElemTy.isa<mlir::FloatType>()) {
    auto rcpOp = rewriter.create<tosa::ReciprocalOp>(
        op->getLoc(), getTypeConverter()->convertType(op.getType()), rhs);
    rewriter.replaceOpWithNewOp<tosa::MulOp>(
        op, getTypeConverter()->convertType(op.getType()), lhs,
        rcpOp.getResult(), /*shift=*/0);
  } else {
    rewriter.replaceOpWithNewOp<tosa::DivOp>(
        op, getTypeConverter()->convertType(op.getType()), lhs, rhs);
  }
  return success();
}

using ReductionConvFunc = llvm::Optional<Value> (*)(PatternRewriter &,
                                                    Operation *,
                                                    RankedTensorType, Value,
                                                    ElementsAttr, bool);

// They all constitute a common form invoking the appropriate
// converion function in TosaLegalizeCommon.cpp
template <typename AtenOpT, ReductionConvFunc ConversionFuncT>
class ConvertAtenReductionOp : public OpConversionPattern<AtenOpT> {
public:
  using OpConversionPattern<AtenOpT>::OpConversionPattern;
  using OpAdaptor = typename AtenOpT::Adaptor;

  // Each variant must implement corresponding parameter parsing options
  virtual LogicalResult readReduceDimsAndKeepDims(
      AtenOpT op, OpAdaptor adaptor, ConversionPatternRewriter &rewriter,
      ElementsAttr &reduceDimsAttr, bool &keepDims) const {
    return rewriter.notifyMatchFailure(
        op, "Unimplemented reduce_dims and keep_dims parsing function");
  }

  // Common rewriter for all reduction ops, calls the specific implementation of
  // readReduceDimsAndKeepDims() needed for the op variant.
  LogicalResult
  matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    Value self = adaptor.self();
    auto selfTy = self.getType().cast<TensorType>();

    if (!selfTy)
      return op.emitError("Only Tensor types supported in TOSA");

    auto outputTy = OpConversionPattern<AtenOpT>::getTypeConverter()
                        ->convertType(op.getType())
                        .template cast<RankedTensorType>();
    if (!outputTy)
      return op.emitError(
          "Only ranked tensor type outputs permitted for reduce_mean");

    ElementsAttr reduceDimsAttr;
    bool keepDims;

    if (failed(readReduceDimsAndKeepDims(op, adaptor, rewriter, reduceDimsAttr,
                                         keepDims)))
      return failure();

    llvm::Optional<Value> result =
        ConversionFuncT(rewriter, op, outputTy, self, reduceDimsAttr, keepDims);

    if (!result)
      return failure();

    // TBD - support dtype casting.

    rewriter.replaceOp(op, {result.getValue()});

    return success();
  }
};

// This reduction op legalization template handles op variants that have
// explicit reduce_dims dimensions (provided as a list) and keep_dims
// parameters.
template <typename AtenOpT, ReductionConvFunc ConversionFuncT>
class ConvertAtenMultipleDimsReductionOp
    : public ConvertAtenReductionOp<AtenOpT, ConversionFuncT> {
  using ConvertAtenReductionOp<AtenOpT,
                               ConversionFuncT>::ConvertAtenReductionOp;
  using OpAdaptor = typename AtenOpT::Adaptor;
  LogicalResult readReduceDimsAndKeepDims(AtenOpT op, OpAdaptor adaptor,
                                          ConversionPatternRewriter &rewriter,
                                          ElementsAttr &reduceDimsAttr,
                                          bool &keepDims) const override {
    SmallVector<int64_t, 4> reduceDims;
    if (!matchPattern(op.dim(), m_TorchConstantIntList(reduceDims)))
      return rewriter.notifyMatchFailure(op,
                                         "non-const dim parameter unsupported");
    int64_t N = reduceDims.size();
    auto reduceDimsType = RankedTensorType::get({N}, rewriter.getI64Type());
    reduceDimsAttr = DenseIntElementsAttr::get(reduceDimsType,
                                               llvm::makeArrayRef(reduceDims));

    keepDims = false;
    if (!matchPattern(op.keepdim(), m_TorchConstantBool(&keepDims)))
      return rewriter.notifyMatchFailure(
          op, "non-const keepdim parameter unsupported");

    return success();
  }
};

// This reduction op legalization template handles op variants that reduce in
// only one explicit dim which is provided as a number (rather than a list), and
// a keep_dims parameter.
template <typename AtenOpT, ReductionConvFunc ConversionFuncT>
class ConvertAtenOneDimReductionOp
    : public ConvertAtenReductionOp<AtenOpT, ConversionFuncT> {
  using ConvertAtenReductionOp<AtenOpT,
                               ConversionFuncT>::ConvertAtenReductionOp;
  using OpAdaptor = typename AtenOpT::Adaptor;
  LogicalResult readReduceDimsAndKeepDims(AtenOpT op, OpAdaptor adaptor,
                                          ConversionPatternRewriter &rewriter,
                                          ElementsAttr &reduceDimsAttr,
                                          bool &keepDims) const override {
    int64_t reduceDim;
    if (!matchPattern(op.dim(), m_TorchConstantInt(&reduceDim)))
      return rewriter.notifyMatchFailure(op,
                                         "non-const dim parameter unsupported");
    auto reduceDimsType = RankedTensorType::get({1}, rewriter.getI64Type());
    reduceDimsAttr = DenseIntElementsAttr::get(reduceDimsType,
                                               llvm::makeArrayRef({reduceDim}));

    keepDims = false;
    if (!matchPattern(op.keepdim(), m_TorchConstantBool(&keepDims)))
      return rewriter.notifyMatchFailure(
          op, "non-const keepdim parameter unsupported");

    return success();
  }
};

// This reduction op legalization template handles op variants that reduce all
// dims does not keep dims.
template <typename AtenOpT, ReductionConvFunc ConversionFuncT>
class ConvertAtenAllDimsReductionOp
    : public ConvertAtenReductionOp<AtenOpT, ConversionFuncT> {
public:
  using ConvertAtenReductionOp<AtenOpT,
                               ConversionFuncT>::ConvertAtenReductionOp;
  using OpAdaptor = typename AtenOpT::Adaptor;
  LogicalResult readReduceDimsAndKeepDims(AtenOpT op, OpAdaptor adaptor,
                                          ConversionPatternRewriter &rewriter,
                                          ElementsAttr &reduceDimsAttr,
                                          bool &keepDims) const override {
    auto self = adaptor.self();
    auto selfTy = self.getType().template cast<RankedTensorType>();

    // Select all dims to reduce
    SmallVector<int64_t, 4> reduceDims;
    for (int64_t i = 0; i < selfTy.getRank(); i++)
      reduceDims.push_back(i);
    int64_t N = selfTy.getRank();
    auto reduceDimsType = RankedTensorType::get({N}, rewriter.getI64Type());
    reduceDimsAttr = DenseIntElementsAttr::get(reduceDimsType,
                                               llvm::makeArrayRef(reduceDims));
    keepDims = false;

    return success();
  }
};

template <>
LogicalResult ConvertAtenOp<AtenArgmaxOp>::matchAndRewrite(
    AtenArgmaxOp op, OpAdaptor adaptor,
    ConversionPatternRewriter &rewriter) const {

  Value self = adaptor.self();
  auto selfTy = self.getType().template cast<RankedTensorType>();

  if (!selfTy)
    return op.emitError("Only ranked tensor types supported in TOSA argmax");

  int64_t reduceDim;
  if (!matchPattern(op.dim(), m_TorchConstantInt(&reduceDim))) {
    // NoneType indicates reduce on all dims
    reduceDim = -1;
  }

  bool keepDim = false;
  if (!matchPattern(op.keepdim(), m_TorchConstantBool(&keepDim)))
    return rewriter.notifyMatchFailure(
        op, "non-const keepdim parameter unsupported");

  auto resultTy = getTypeConverter()
                      ->convertType(op.getResult().getType())
                      .cast<RankedTensorType>();
  auto outputETy = resultTy.getElementType();

  // Create a single instance of tosa.argmax.
  // Multiple dims require chained construct.
  auto buildArgmax = [&](int64_t reduceDim, Value input) -> Value {
    auto inputTy = input.getType().cast<RankedTensorType>();
    auto inputShape = inputTy.getShape();
    SmallVector<int64_t> outputShapeArr = {};
    int32_t i = 0;

    for (auto &dim : inputShape) {
      if (i++ != reduceDim) {
        outputShapeArr.push_back(dim);
      } else {
        if (keepDim)
          outputShapeArr.push_back(1);
      }
    }

    // Tosa argmax output is i32, while Torch backend mandates i64.
    auto outputReduceTy = RankedTensorType::get(
        ArrayRef<int64_t>(outputShapeArr), rewriter.getI32Type());
    auto reduceDimAttr =
        rewriter.getIntegerAttr(rewriter.getI64Type(), reduceDim);
    return rewriter
        .create<tosa::ArgMaxOp>(op->getLoc(),
                                getTypeConverter()->convertType(outputReduceTy),
                                input, reduceDimAttr)
        .getResult();
  };

  // Convert the final index to i64 for backend finalization, However, i64
  // is not a defined type for tosa.cast, so using arith.extsi instead.
  auto castToInt64 = [&](Value result) -> LogicalResult {
    auto resTy = result.getType().cast<ShapedType>();
    if (!resTy)
      return op.emitError("Argmax: Result is not a shaped type");

    auto resShape = resTy.getShape();
    auto outTy =
        RankedTensorType::get(resShape, outputETy); // rewriter.getI64Type());

    rewriter.replaceOpWithNewOp<arith::ExtSIOp>(
        op, getTypeConverter()->convertType(outTy), result);

    return success();
  };

  if (reduceDim == -1) { // reducing on all dims
    Value input = self;
    for (int dim = 0; dim < selfTy.getRank(); dim++) {
      // progressively reduce each 0-th dim
      input = buildArgmax(0, input);
    }
    return castToInt64(input);
  } else {
    return castToInt64(buildArgmax(reduceDim, self));
  }

  return success();
}

template <typename AtenOpT>
class ConvertAtenSqueezeOp : public OpConversionPattern<AtenOpT> {
public:
  using OpConversionPattern<AtenOpT>::OpConversionPattern;
  using OpAdaptor = typename AtenOpT::Adaptor;

  // Each variant must implement corresponding parameter parsing options
  virtual LogicalResult
  generateSqueezedShape(AtenOpT op, RankedTensorType selfTy,
                        ConversionPatternRewriter &rewriter,
                        SmallVector<int64_t> &squeezedShape) const {
    return rewriter.notifyMatchFailure(
        op, "Unimplemented dim/dim-list parsing function");
  }

  // Common rewriter for all squeeze ops, calls the specific implementation of
  // generateSqueezedShape() needed for the op variant.
  LogicalResult
  matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    Value self = adaptor.self();
    auto selfTy = self.getType().template cast<RankedTensorType>();

    if (!selfTy)
      return op.emitError("Only ranked tensor types supported in TOSA argmax");

    SmallVector<int64_t> newOutputShape;
    if (failed(generateSqueezedShape(op, selfTy, rewriter, newOutputShape)))
      return op.emitError("Squeeze could not compute new shape");

    auto resultTy = OpConversionPattern<AtenOpT>::getTypeConverter()
                        ->convertType(op.getResult().getType())
                        .template cast<RankedTensorType>();
    auto resultElemTy = resultTy.getElementType();

    auto newOutputTy = RankedTensorType::get(newOutputShape, resultElemTy);

    auto reshapeOp = rewriter.create<tosa::ReshapeOp>(
        op->getLoc(),
        OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
            newOutputTy),
        self, rewriter.getI64ArrayAttr(newOutputShape));
    rewriter.replaceOpWithNewOp<tensor::CastOp>(
        op,
        OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
            newOutputTy),
        reshapeOp);

    return success();
  }
};

template <typename AtenOpT>
class ConvertAtenSqueezeOneDimOp : public ConvertAtenSqueezeOp<AtenOpT> {
  using ConvertAtenSqueezeOp<AtenOpT>::ConvertAtenSqueezeOp;
  using OpAdaptor = typename AtenOpT::Adaptor;

  LogicalResult
  generateSqueezedShape(AtenOpT op, RankedTensorType selfTy,
                        ConversionPatternRewriter &rewriter,
                        SmallVector<int64_t> &squeezedShape) const override {
    int64_t squeezeDim;
    if (!matchPattern(op.dim(), m_TorchConstantInt(&squeezeDim)))
      return rewriter.notifyMatchFailure(op,
                                         "non-const dim parameter unsupported");

    // Handle negative dim
    if (squeezeDim < 0)
      squeezeDim = squeezeDim + selfTy.getRank();

    auto selfShape = selfTy.getShape();

    // Only dims statically known to have size=1 are reduced.
    // Dynamic dims are treated as unknowns and will not be squeezed
    // even if dim parameter says it should be.
    uint32_t dimNum = 0;
    for (auto &dim : selfShape) {
      if (dim != 1 || squeezeDim != dimNum)
        squeezedShape.push_back(dim);
      dimNum++;
    }

    return success();
  }
};

template <typename AtenOpT>
class ConvertAtenSqueezeAllDimsOp : public ConvertAtenSqueezeOp<AtenOpT> {
  using ConvertAtenSqueezeOp<AtenOpT>::ConvertAtenSqueezeOp;
  using OpAdaptor = typename AtenOpT::Adaptor;

  LogicalResult
  generateSqueezedShape(AtenOpT op, RankedTensorType selfTy,
                        ConversionPatternRewriter &rewriter,
                        SmallVector<int64_t> &squeezedShape) const override {
    auto selfShape = selfTy.getShape();

    // Dims that may dynamically resolve to 1 are not reduced here. Only
    // compile-time resolvable dims are handled here.
    for (auto &dim : selfShape) {
      if (dim != 1)
        squeezedShape.push_back(dim);
    }
    return success();
  }
};

// FIXME(AG): This will eventually go into a Tosa*Utils file
// Convert an fp32 scalar into tosa fp32 tensor.
static LogicalResult
tosaF32TensorFromTorchFloat(ConversionPatternRewriter &rewriter, Operation *op,
                            Value torchScalarValue, Value &tosaTensor) {
  double scalarValue;

  if (!matchPattern(torchScalarValue, m_TorchConstantFloat(&scalarValue)))
    return failure();

  // Construct a tosa.const
  tosaTensor =
      mlir::tosa::getTosaConstTensorSingleF32(rewriter, op, scalarValue);

  return success();
}

template <>
LogicalResult ConvertAtenOp<AtenPowTensorScalarOp>::matchAndRewrite(
    AtenPowTensorScalarOp op, OpAdaptor adaptor,
    ConversionPatternRewriter &rewriter) const {

  Value self = adaptor.self();
  auto selfTy = self.getType().template cast<RankedTensorType>();

  if (!selfTy)
    return op.emitError("Only ranked tensor types supported in TOSA Pow");

  if (!selfTy.getElementType().isa<mlir::FloatType>())
    return op.emitError("Only floating-point datatype legalization supported");

  Value expTensor;
  Value expScalar = op.exponent();
  if (failed(tosaF32TensorFromTorchFloat(rewriter, op.getOperation(), expScalar,
                                         expTensor)))
    return op.emitError("Currently only scalar constants are supported for "
                        "conversion in TOSA Pow operation");

  rewriter.replaceOpWithNewOp<tosa::PowOp>(
      op, getTypeConverter()->convertType(op.getType()), self, expTensor);

  return success();
}

// Perform torch matmul, mm and bmm
template <typename AtenOpT>
class ConvertAtenMatMulOp : public OpConversionPattern<AtenOpT> {
public:
  using OpConversionPattern<AtenOpT>::OpConversionPattern;
  using OpAdaptor = typename AtenOpT::Adaptor;
  LogicalResult
  matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    Value lhs = adaptor.self();
    auto lhsTy = lhs.getType().cast<RankedTensorType>();

    // Aten matmul, mm and bmm call operand2 by different names.
    Value rhs = adaptor.getOperands()[1];
    auto rhsTy = rhs.getType().cast<RankedTensorType>();

    if (!lhsTy || !rhsTy)
      return op.emitError("Only ranked tensor types supported in TOSA matmul");

    auto lhsRank = lhsTy.getRank();
    auto rhsRank = rhsTy.getRank();

    // Mm takes two 2D tensors
    if (isa<AtenMmOp>(op)) {
      assert(lhsRank == 2 && rhsRank == 2 &&
             "aten.mm called but matrix rank != 2");
    }

    // Bmm takes two 2D tensors
    if (isa<AtenBmmOp>(op)) {
      assert(lhsRank == 3 && rhsRank == 3 &&
             "aten.bmm called but matrix rank != 2");
    }

    auto lhsShape = lhsTy.getShape();
    auto rhsShape = rhsTy.getShape();

    auto lhsElemTy = lhsTy.getElementType();
    auto rhsElemTy = rhsTy.getElementType();

    if (lhsElemTy != rhsElemTy)
      return op.emitError("Matmul: input datatypes mismatched");

    // Legalization constructs may offer input shapes but expect output shapes
    // to be inferred, e.g.
    // func @forward(%arg0: !torch.vtensor<[14,19],f32>,
    //               %arg1: !torch.vtensor<[19,28],f32>) ->
    //               !torch.vtensor<[?,?],f32>
    // This is tricky with matmul, since TOSA matmul is on 3D inputs.
    // This means the need to reshape potentially both inputs and outputs,
    // and reshape to unknown shape is undefined.

    auto maxInputRank = lhsRank > rhsRank ? lhsRank : rhsRank;
    // If performing dot product on vectors, the RHS is synthetically transposed
    if (maxInputRank == 1)
      maxInputRank++;

    // Obtaining the rank broadcasted shapes of tensors makes it easier to
    // construct the input and output reshaping logic.
    auto getRankBroadcastedShape = [&](Value tensor,
                                       bool isRHS) -> SmallVector<int64_t> {
      auto tensorTy = tensor.getType().cast<TensorType>();
      auto tensorShape = tensorTy.getShape();
      auto tensorRank = tensorTy.getRank();

      SmallVector<int64_t> bcastedShape;

      auto bcastDims = maxInputRank - tensorRank;

      if (isRHS && (tensorRank == 1) && bcastDims) {
        // RHS with rank1 is special. It be synthetically transposed to dim[:-2]
        for (int32_t i = 0; i < bcastDims - 1; i++)
          bcastedShape.push_back(1);
        bcastedShape.push_back(tensorShape[0]);
        bcastedShape.push_back(1);
      } else {
        if (bcastDims > 0) { // rank broadcast
          for (uint32_t i = 0; i < bcastDims; i++)
            bcastedShape.push_back(1);
        }
        for (auto &dim : tensorShape)
          bcastedShape.push_back(dim);
      }
      return bcastedShape;
    };

    // Step: Rank broadcast the two inputs.
    auto lhsBroadcastedShape = getRankBroadcastedShape(lhs, false);
    auto lhsBroadcastedTy =
        RankedTensorType::get(lhsBroadcastedShape, lhsElemTy);
    auto rhsBroadcastedShape = getRankBroadcastedShape(rhs, true);
    auto rhsBroadcastedTy =
        RankedTensorType::get(rhsBroadcastedShape, rhsElemTy);

    auto rankBroadcastedLhs =
        lhsRank == maxInputRank
            ? lhs
            : rewriter.create<tosa::ReshapeOp>(
                  op->getLoc(),
                  OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
                      lhsBroadcastedTy),
                  lhs, rewriter.getI64ArrayAttr(lhsBroadcastedShape));

    auto rankBroadcastedRhs =
        rhsRank == maxInputRank
            ? rhs
            : rewriter.create<tosa::ReshapeOp>(
                  op->getLoc(),
                  OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
                      rhsBroadcastedTy),
                  rhs, rewriter.getI64ArrayAttr(rhsBroadcastedShape));

    // TOSA matmul is performed on two 3D inputs and generates a 3D output.
    // Lower ranked tensors are dim-1 reshaped up to 3D
    auto reshapeUpTo3DTensor = [&](Value tensor) -> Value {
      auto tensorTy = tensor.getType().cast<TensorType>();
      auto rank = tensorTy.getRank();

      assert(rank <= 3 && "reshapeUpTo3D tensor must receive rank <= 3");
      if (rank == 3)
        return tensor;

      auto shape = tensorTy.getShape();
      SmallVector<int64_t> newShape({1, 1, 1});

      if (rank == 2) { // batchsize = 1
        newShape[1] = shape[0];
        newShape[2] = shape[1];
      } else { // rank 1
        newShape[2] = shape[0];
      }
      auto newType = RankedTensorType::get(newShape, tensorTy.getElementType());

      return rewriter.create<tosa::ReshapeOp>(
          op->getLoc(),
          OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
              newType),
          tensor, rewriter.getI64ArrayAttr(newShape));
    };

    // Where broadcasting is required in one or more batch dims, the following
    // is done.
    // Where all batch dims are involved in broadcasting:
    // Given A: 3x1x5x6 and B: 1x4x6x7
    // 1. Reshape A to 1x15x6 (squeeze all batchdims into dim1)
    // 2. Transpose B to 6x1x4x7, Reshape to 1x6x28
    // 3. tosa.Matmul 1x15x6 1x6x28 = 1x15x28
    // 4. Reshape out to 3x5x4x7, Transpose to 3x4x5x7
    // Where there are batch dimensions that are broadcast and not, the
    // treatment is to have dim0 correspond to product of all non-broadcast
    // dimsizes:
    // Given A: 4x8x16x32 B: 8x32x17
    // 1. Reshape A to 8x64x32 (squeeze all unbroadcasted dims into dim0,
    // broadcasted dims into dim1)
    // 2. No transpose or reshape of B as its batchdims are not broadcast to.
    // 3. tosa.Matmul 8x64x32 8x32x17 = 8x64x17
    // 4. Reshape to 8x4x16x17, Transpose to 4x8x16x17

    // Check if we need to perform the broadcast on batch dim
    // Not needed if max rank < 3, or if maxrank == 3 and dim[0] matches
    auto needsBatchDimBroadcast = [&]() -> bool {
      if (maxInputRank < 3) {
        return false;
      } else {
        if (maxInputRank == 3 &&
            lhsBroadcastedShape[0] == rhsBroadcastedShape[0]) {
          return false;
        }
        return true;
      }
    };

    auto performBatchDimBroadcast = needsBatchDimBroadcast();

    // Inputs to the tosa.matmul
    Value matmulLhs, matmulRhs;

    using TensorShape_t = struct {
      int64_t dim;
      int64_t shape;
    };

    // Transpose needs to done if transposeDims are not non-monotonically
    // increasing. E.g. [0, 1, 2, 3]: No transpose [1, 0, 2, 3]: Transpose dim0
    // and dim1 The order need not be sequential, since one or more dims may
    // have been removed due to broadcasting.
    auto isTransposeRequired = [](SmallVector<int32_t> transposedDims) -> bool {
      int32_t lastDim = -1;
      for (auto &dim : transposedDims) {
        if (lastDim > dim)
          return true;
        lastDim = dim;
      }
      return false;
    };

    SmallVector<TensorShape_t> commonElems, lhsSqueezedElems, rhsSqueezedElems;

    if (!performBatchDimBroadcast) {
      // Simple with no broadcasting artifacts. Just reshape up to 3D
      matmulLhs = reshapeUpTo3DTensor(rankBroadcastedLhs);
      matmulRhs = reshapeUpTo3DTensor(rankBroadcastedRhs);

    } else {
      // In this case, either or both input matrices involve broadcasting on
      // their batch dimensions. For example:
      // 4x5x6, 1x6x7 -> 4x5x7
      // 4x1x5x6, 1x3x6x7 -> 4x3x5x7
      // Though maxInputRank is necessarily >=3 here, individual matrices may be
      // lower rank.
      // E.g. 3x4x5x6, 6 -> 3x4x5

      // These are the accumulated products of the shape of each dim:
      // 1. common dimensions: upper dimensions (dims other than two rightmost)
      // whose shapes are the same for both LHS and RHS.
      // 2. LHS squeezed dimensions: all dimensions of LHS that involve
      // broadcasting in either direction, plus the LHS[-2] shape
      // 3. RHS squeezed dimensions: all dimensions of RHS that involve
      // broadcasting in either direction, plus the RHS[-1] shape
      int64_t commonValue = 1, lhsSqueezedValue = 1, rhsSqueezedValue = 1;

      // For both LHS and RHS, the dimensions are separated into the common,
      // squeezed and remaining dim. E.g. given
      // LHS = 3x4x5x6
      // RHS = 1x4x6x7
      // common = {{dim=1, shape=4}}
      // lhs squeezed = {{dim=0, shape=3},
      //                 {dim=2, shape=5}}
      // rhs squeezed = {{dim=0, shape=1},
      //                 {dim=2, shape=7}}
      // The matmul dim is LHS[-1] and RHS[-2], i.e. 6.
      // Once this is obtained, LHS and RHS are expressed as:
      // LHS = {common, lhs_squeezed, matmul_dim}
      // RHS = {common, matmul_dim, rhs_squeezed}
      // The matmul is then performed to obtain output:
      // matmul_out = {common, lhs_squeezed, rhs_squeezed}
      // Finally, we reshape to 'unsqueeze' the LHS and RHS parts and transpose
      // them back to their correct positions.

      SmallVector<int64_t> transposedLhsShape;
      SmallVector<int32_t> transposedLhsDims;

      // Step: generate the common dim/shape information
      for (uint32_t dim = 0; dim < maxInputRank - 2; dim++) {
        bool isDynamicDim =
            lhsBroadcastedTy.isDynamic(lhsBroadcastedShape[dim]);
        if (isDynamicDim ||
            lhsBroadcastedShape[dim] == rhsBroadcastedShape[dim]) {
          commonValue *= lhsBroadcastedShape[dim];
          commonElems.push_back({dim, lhsBroadcastedShape[dim]});
        }
      }

      // Step: generate the LHS squeezed dim/shape information.
      bool hasDynamicDims = false;
      for (uint32_t dim = 0; dim < maxInputRank - 2; dim++) {
        bool isDynamicDim =
            lhsBroadcastedTy.isDynamic(lhsBroadcastedShape[dim]);
        hasDynamicDims |= isDynamicDim;
        if (!isDynamicDim &&
            lhsBroadcastedShape[dim] != rhsBroadcastedShape[dim]) {
          lhsSqueezedValue *= lhsBroadcastedShape[dim];
          lhsSqueezedElems.push_back({dim, lhsBroadcastedShape[dim]});
        }
      }
      // including LHS[-2]
      lhsSqueezedElems.push_back(
          {maxInputRank - 2, lhsBroadcastedShape[maxInputRank - 2]});
      lhsSqueezedValue *= lhsBroadcastedShape[maxInputRank - 2];

      // Step: Create the tosa.transpose array. If this array has a
      // non-monotonic series of dims, perform transpose.
      // First the common_elems
      for (uint32_t i = 0; i < commonElems.size(); i++) {
        transposedLhsShape.push_back(commonElems[i].shape);
        transposedLhsDims.push_back(commonElems[i].dim);
      }
      // then the lhs_squeezed elems
      for (uint32_t i = 0; i < lhsSqueezedElems.size(); i++) {
        transposedLhsShape.push_back(lhsSqueezedElems[i].shape);
        transposedLhsDims.push_back(lhsSqueezedElems[i].dim);
      }
      // then the final dim
      transposedLhsDims.push_back(maxInputRank - 1);
      transposedLhsShape.push_back(lhsBroadcastedShape[maxInputRank - 1]);

      bool lhsNeedsTranspose = isTransposeRequired(transposedLhsDims);

      auto lhsReshapeInput = rankBroadcastedLhs;

      if (lhsNeedsTranspose) {
        auto transposedLhsType =
            RankedTensorType::get(transposedLhsShape, rhsElemTy);

        llvm::Optional<Value> transposedLhsDimsConst =
            tosa::getConstTensor<int32_t>(
                rewriter, op,
                /*vec=*/transposedLhsDims,
                /*shape=*/{static_cast<int32_t>(transposedLhsDims.size())});

        lhsReshapeInput =
            rewriter
                .create<tosa::TransposeOp>(
                    op->getLoc(),
                    OpConversionPattern<AtenOpT>::getTypeConverter()
                        ->convertType(transposedLhsType),
                    rankBroadcastedLhs, transposedLhsDimsConst.getValue())
                .getResult();
      }

      // LHS = {common, lhs_squeezed, matmul_dim}
      SmallVector<int64_t> newLhsShape(
          {1, 1, lhsBroadcastedShape[maxInputRank - 1]});
      newLhsShape[0] = commonValue;
      newLhsShape[1] =
          hasDynamicDims ? ShapedType::kDynamicSize : lhsSqueezedValue;

      auto newLhsType = RankedTensorType::get(newLhsShape, lhsElemTy);

      matmulLhs = rewriter.create<tosa::ReshapeOp>(
          op->getLoc(),
          OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
              newLhsType),
          lhsReshapeInput, rewriter.getI64ArrayAttr(newLhsShape));

      SmallVector<int64_t> transposedRhsShape;
      SmallVector<int32_t> transposedRhsDims;

      // Step: Create the RHS transpose sequence
      // RHS = {common, matmul_dim, rhs_squeezed}
      // first the common_dims
      for (uint32_t i = 0; i < commonElems.size(); i++) {
        transposedRhsShape.push_back(commonElems[i].shape);
        transposedRhsDims.push_back(commonElems[i].dim);
      }
      // The matmul_dim of RHS
      transposedRhsDims.push_back(maxInputRank - 2);
      transposedRhsShape.push_back(rhsBroadcastedShape[maxInputRank - 2]);
      // finally all the rhs_squeeze dims
      hasDynamicDims = false;
      for (uint32_t dim = 0; dim < maxInputRank - 2; dim++) {
        bool isDynamicDim =
            rhsBroadcastedTy.isDynamic(rhsBroadcastedShape[dim]);
        hasDynamicDims |= isDynamicDim;
        if (!isDynamicDim &&
            rhsBroadcastedShape[dim] != lhsBroadcastedShape[dim]) {
          rhsSqueezedElems.push_back({dim, rhsBroadcastedShape[dim]});
          rhsSqueezedValue *= rhsBroadcastedShape[dim];
        }
      }
      rhsSqueezedElems.push_back(
          {maxInputRank - 1, rhsBroadcastedShape[maxInputRank - 1]});
      rhsSqueezedValue *= rhsBroadcastedShape[maxInputRank - 1];
      for (uint32_t i = 0; i < rhsSqueezedElems.size(); i++) {
        transposedRhsShape.push_back(rhsSqueezedElems[i].shape);
        transposedRhsDims.push_back(rhsSqueezedElems[i].dim);
      }

      auto transposedRhsType =
          RankedTensorType::get(transposedRhsShape, rhsElemTy);

      if (hasDynamicDims)
        rhsSqueezedValue = ShapedType::kDynamicSize;

      SmallVector<int64_t> newRhsShape({commonValue,
                                        rhsBroadcastedShape[maxInputRank - 2],
                                        rhsSqueezedValue});
      auto newRhsType = RankedTensorType::get(newRhsShape, rhsElemTy);

      bool rhsNeedsTranspose = isTransposeRequired(transposedRhsDims);

      auto transposedRhsValue = rankBroadcastedRhs;

      if (rhsNeedsTranspose) {
        llvm::Optional<Value> transposedRhsDimsConst =
            tosa::getConstTensor<int32_t>(
                rewriter, op,
                /*vec=*/transposedRhsDims,
                /*shape=*/{static_cast<int32_t>(transposedRhsDims.size())});

        transposedRhsValue =
            rewriter
                .create<tosa::TransposeOp>(
                    op->getLoc(),
                    OpConversionPattern<AtenOpT>::getTypeConverter()
                        ->convertType(transposedRhsType),
                    rankBroadcastedRhs, transposedRhsDimsConst.getValue())
                .getResult();
      }

      // reshape
      matmulRhs = rewriter.create<tosa::ReshapeOp>(
          op->getLoc(),
          OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
              newRhsType),
          transposedRhsValue, rewriter.getI64ArrayAttr(newRhsShape));
    }

    auto matmulLhsShape =
        matmulLhs.getType().template cast<RankedTensorType>().getShape();
    auto matmulRhsShape =
        matmulRhs.getType().template cast<RankedTensorType>().getShape();

    // The reshape/transpose should ensure the tosa.matmul always has same
    // batch size for either matrix. If if shapes are dynamic, they'll be
    // appropriately handled.
    assert(matmulLhsShape[0] == matmulRhsShape[0] &&
           "tosa.matmul needs same batchsize on LHS and RHS");

    SmallVector<int64_t> matmulOutputShape(
        {matmulLhsShape[0], matmulLhsShape[1], matmulRhsShape[2]});
    Type outputElemTy;
    if (lhsElemTy.isa<mlir::FloatType>()) {
      outputElemTy = lhsElemTy;
    } else { // qint8 emits i32 matmul output
      outputElemTy = rewriter.getIntegerType(32);
    }

    auto mmOutputTy = RankedTensorType::get(matmulOutputShape, outputElemTy);
    auto mmOpResult =
        rewriter
            .create<tosa::MatMulOp>(
                op->getLoc(),
                OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
                    mmOutputTy),
                matmulLhs, matmulRhs)
            .getResult();

    // Perform the reshape to output shape. This is always required unless both
    // inputs are rank=3, in which case the tosa.matmul output itself is
    // correctly shaped.
    bool performOpReshape = !(lhsRank == 3 && rhsRank == 3);

    auto outputTy = OpConversionPattern<AtenOpT>::getTypeConverter()
                        ->convertType(op.getType())
                        .template cast<RankedTensorType>();

    if (performOpReshape) {
      // Since the output shape may be unknown, we construct it
      // independently and reshape. Otherwise reshape may be expressed for
      // an unknown to-be-inferred output shape. The final tensor.cast
      // reshapes the known shape to the desired output shape.
      auto computeOpShape = [&](SmallVector<int64_t> &reshapedOpShape,
                                SmallVector<int32_t> &transposedOpDims,
                                SmallVector<int64_t> &transposedOpShapes) {
        if (maxInputRank == 1)
          return;

        if (maxInputRank == 2) {
          if (lhsRank == 2)
            reshapedOpShape.push_back(lhsShape[0]);
          if (rhsRank == 2)
            reshapedOpShape.push_back(rhsShape[1]);
          return;
        }

        // Step: Construct the output transpose/reshape information
        // First the common_dims
        for (uint32_t i = 0; i < commonElems.size(); i++) {
          reshapedOpShape.push_back(commonElems[i].shape);
          transposedOpDims.push_back(commonElems[i].dim);
        }

        // Then the LHS squeezed dims
        for (uint32_t i = 0; i < lhsSqueezedElems.size() - 1; i++) {
          // Only dims that don't broadcast - broadcasting ones come from the
          // other input.
          if (lhsSqueezedElems[i].shape != 1) {
            reshapedOpShape.push_back(lhsSqueezedElems[i].shape);
            transposedOpDims.push_back(lhsSqueezedElems[i].dim);
          }
        }
        // The last squeezed dim is lhs[-2] which needs to be
        // checked separately for broadcasting
        if (lhsRank > 1) {
          reshapedOpShape.push_back(lhsBroadcastedShape[maxInputRank - 2]);
          transposedOpDims.push_back(maxInputRank - 2);
        }

        // then the RHS squeezed dims except rhs[-1] which is handled like
        // lhs[-2]
        for (uint32_t i = 0; i < rhsSqueezedElems.size() - 1; i++) {
          if (rhsSqueezedElems[i].shape != 1) {
            reshapedOpShape.push_back(rhsSqueezedElems[i].shape);
            transposedOpDims.push_back(rhsSqueezedElems[i].dim);
          }
        }
        // rhs[-1]
        if (rhsRank > 1) {
          reshapedOpShape.push_back(rhsBroadcastedShape[maxInputRank - 1]);
          transposedOpDims.push_back(maxInputRank - 1);
        }

        // Final transposed output shape construction
        for (uint32_t i = 0; i < maxInputRank - 2; i++) {
          if (lhsBroadcastedTy.isDynamicDim(i)) {
            transposedOpShapes.push_back(ShapedType::kDynamicSize);
          } else {
            if (lhsBroadcastedShape[i] == rhsBroadcastedShape[i]) {
              transposedOpShapes.push_back(lhsBroadcastedShape[i]);
            } else {
              transposedOpShapes.push_back(lhsBroadcastedShape[i] == 1
                                               ? rhsBroadcastedShape[i]
                                               : lhsBroadcastedShape[i]);
            }
          }
        }
        if (lhsRank > 1)
          transposedOpShapes.push_back(lhsBroadcastedShape[maxInputRank - 2]);
        if (rhsRank > 1)
          transposedOpShapes.push_back(rhsBroadcastedShape[maxInputRank - 1]);

        return;
      };

      SmallVector<int64_t> reshapedOpShape, transposedOpShape;
      SmallVector<int32_t> transposedOpDims;

      computeOpShape(reshapedOpShape, transposedOpDims, transposedOpShape);

      bool opNeedsTranspose = isTransposeRequired(transposedOpDims);

      // Perform reshape
      auto reshapedOpType =
          RankedTensorType::get(reshapedOpShape, outputElemTy);
      auto reshapedOp = rewriter.create<tosa::ReshapeOp>(
          op->getLoc(),
          OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
              reshapedOpType),
          mmOpResult, rewriter.getI64ArrayAttr(reshapedOpShape));

      if (opNeedsTranspose) {

        llvm::Optional<Value> transposedOpShapeConst =
            tosa::getConstTensor<int32_t>(
                rewriter, op,
                /*vec=*/transposedOpDims,
                /*shape=*/{static_cast<int32_t>(transposedOpDims.size())});

        auto transposedOpType =
            RankedTensorType::get(transposedOpShape, outputElemTy);
        auto transposedOp = rewriter.create<tosa::TransposeOp>(
            op->getLoc(),
            OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
                transposedOpType),
            reshapedOp.getResult(), transposedOpShapeConst.getValue());

        rewriter.replaceOpWithNewOp<tensor::CastOp>(op, outputTy, transposedOp);
      } else {
        rewriter.replaceOpWithNewOp<tensor::CastOp>(op, outputTy, reshapedOp);
      }
    } else {
      rewriter.replaceOpWithNewOp<tensor::CastOp>(op, outputTy, mmOpResult);
    }

    return success();
  }
};

} // namespace

// -----------------------------------------------------------------------------
// TorchToTosa Pass
// -----------------------------------------------------------------------------

namespace {
class ConvertTorchToTosa : public ConvertTorchToTosaBase<ConvertTorchToTosa> {
public:
  void getDependentDialects(DialectRegistry &registry) const override {
    registry.insert<tosa::TosaDialect>();
    registry.insert<tensor::TensorDialect>();
    registry.insert<arith::ArithmeticDialect>();
    TorchConversion::getBackendTypeConversionDependentDialects(registry);
  }

  void runOnOperation() override {
    MLIRContext *context = &getContext();
    ConversionTarget target(*context);
    target.addLegalDialect<tosa::TosaDialect, tensor::TensorDialect,
                           arith::ArithmeticDialect>();

    TypeConverter typeConverter;
    typeConverter.addConversion([](Type type) { return type; });
    TorchConversion::setupBackendTypeConversion(target, typeConverter);

    RewritePatternSet patterns(context);

#define INSERT_UNARY_FPONLY_PATTERN(AtenOp, TosaOp)                            \
  target.addIllegalOp<AtenOp>();                                               \
  patterns.add<ConvertAtenUnaryFPOnlyOp<AtenOp, TosaOp>>(typeConverter,        \
                                                         context);
    INSERT_UNARY_FPONLY_PATTERN(AtenLogOp, tosa::LogOp)
    INSERT_UNARY_FPONLY_PATTERN(AtenExpOp, tosa::ExpOp)
#undef INSERT_UNARY_FPONLY_PATTERN

#define INSERT_UNARY_PATTERN(AtenOp, TosaOp)                                   \
  target.addIllegalOp<AtenOp>();                                               \
  patterns.add<ConvertAtenUnaryOp<AtenOp, TosaOp>>(typeConverter, context);
    INSERT_UNARY_PATTERN(AtenNegOp, tosa::NegateOp)
    INSERT_UNARY_PATTERN(AtenFloorOp, tosa::FloorOp)
    INSERT_UNARY_PATTERN(AtenRsqrtOp, tosa::RsqrtOp)
    INSERT_UNARY_PATTERN(AtenBitwiseNotOp, tosa::BitwiseNotOp)
#undef INSERT_UNARY_PATTERN

#define INSERT_BINARY_PATTERN(AtenOp, TosaOp)                                  \
  target.addIllegalOp<AtenOp>();                                               \
  patterns.add<ConvertAtenBinaryOp<AtenOp, TosaOp>>(typeConverter, context);
    INSERT_BINARY_PATTERN(AtenMaximumOp, tosa::MaximumOp)
    INSERT_BINARY_PATTERN(AtenMinimumOp, tosa::MinimumOp)
#undef INSERT_BINARY_PATTERN

#define INSERT_BINARY_ADDSUB_PATTERN(AtenOp, TosaOp)                           \
  target.addIllegalOp<AtenOp>();                                               \
  patterns.add<ConvertAtenAddSubOp<AtenOp, TosaOp>>(typeConverter, context);
    INSERT_BINARY_ADDSUB_PATTERN(AtenAddTensorOp, tosa::AddOp)
    INSERT_BINARY_ADDSUB_PATTERN(AtenSubTensorOp, tosa::SubOp)
#undef INSERT_BINARY_ADDSUB_PATTERN

#define INSERT_NDIMS_REDUCTION_OP_PATTERN(AtenOp, ConversionFunc)              \
  target.addIllegalOp<AtenOp>();                                               \
  patterns.add<ConvertAtenMultipleDimsReductionOp<AtenOp, ConversionFunc>>(    \
      typeConverter, context);
    INSERT_NDIMS_REDUCTION_OP_PATTERN(AtenMeanDimOp,
                                      mlir::tosa::convertReduceMeanOp)
    INSERT_NDIMS_REDUCTION_OP_PATTERN(AtenSumDimIntListOp,
                                      mlir::tosa::convertReduceSumOp)
#undef INSERT_NDIMS_REDUCTION_OP_PATTERN

#define INSERT_ONEDIM_REDUCTION_OP_PATTERN(AtenOp, ConversionFunc)             \
  target.addIllegalOp<AtenOp>();                                               \
  patterns.add<ConvertAtenOneDimReductionOp<AtenOp, ConversionFunc>>(          \
      typeConverter, context);
    INSERT_ONEDIM_REDUCTION_OP_PATTERN(AtenAnyDimOp,
                                       mlir::tosa::convertReduceAnyOp)
#undef INSERT_ONEDIM_REDUCTION_OP_PATTERN

#define INSERT_ALLDIMS_REDUCTION_OP_PATTERN(AtenOp, ConversionFunc)            \
  target.addIllegalOp<AtenOp>();                                               \
  patterns.add<ConvertAtenAllDimsReductionOp<AtenOp, ConversionFunc>>(         \
      typeConverter, context);
    INSERT_ALLDIMS_REDUCTION_OP_PATTERN(AtenAllOp,
                                        mlir::tosa::convertReduceAllOp)
    INSERT_ALLDIMS_REDUCTION_OP_PATTERN(AtenAnyOp,
                                        mlir::tosa::convertReduceAnyOp)
    INSERT_ALLDIMS_REDUCTION_OP_PATTERN(AtenSumOp,
                                        mlir::tosa::convertReduceSumOp)
#undef INSERT_ALLDIMS_REDUCTION_OP_PATTERN

#define INSERT_SQUEEZE_OP_PATTERN(AtenOp, TemplateForm)                        \
  target.addIllegalOp<AtenOp>();                                               \
  patterns.add<TemplateForm<AtenOp>>(typeConverter, context);
    INSERT_SQUEEZE_OP_PATTERN(AtenSqueezeOp, ConvertAtenSqueezeAllDimsOp)
    INSERT_SQUEEZE_OP_PATTERN(AtenSqueezeDimOp, ConvertAtenSqueezeOneDimOp)
#undef INSERT_SQUEEZE_OP_PATTERN

#define INSERT_MATMUL_ATENOP_PATTERN(AtenOp)                                   \
  target.addIllegalOp<AtenOp>();                                               \
  patterns.add<ConvertAtenMatMulOp<AtenOp>>(typeConverter, context);
    INSERT_MATMUL_ATENOP_PATTERN(AtenMatmulOp);
    INSERT_MATMUL_ATENOP_PATTERN(AtenMmOp);
    INSERT_MATMUL_ATENOP_PATTERN(AtenBmmOp);
#undef INSERT_MATMUL_ATEMOP_PATTERN

#define INSERT_ATENOP_PATTERN(AtenOp)                                          \
  target.addIllegalOp<AtenOp>();                                               \
  patterns.add<ConvertAtenOp<AtenOp>>(typeConverter, context);
    INSERT_ATENOP_PATTERN(AtenTanhOp);
    INSERT_ATENOP_PATTERN(AtenSigmoidOp);
    INSERT_ATENOP_PATTERN(AtenReluOp);
    INSERT_ATENOP_PATTERN(AtenMulTensorOp);
    INSERT_ATENOP_PATTERN(AtenDivTensorOp);
    INSERT_ATENOP_PATTERN(AtenArgmaxOp);
    INSERT_ATENOP_PATTERN(AtenPowTensorScalarOp);
#undef INSERT_ATENOP_PATTERN

    if (failed(applyPartialConversion(getOperation(), target,
                                      std::move(patterns))))
      return signalPassFailure();
  }
};
} // namespace

std::unique_ptr<OperationPass<FuncOp>>
mlir::torch::createConvertTorchToTosaPass() {
  return std::make_unique<ConvertTorchToTosa>();
}