torch-mlir/lib/Conversion/TorchToTosa/TorchToTosa.cpp

//===----------------------------------------------------------------------===//
//
// 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 {
    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 {
    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 {
    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 {
    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 {
    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();
}

} // 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_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);
#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>();
}