torch-mlir/lib/Dialect/Torch/Transforms/DecomposeComplexOps.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 "PassDetail.h"

#include "mlir/IR/BuiltinDialect.h"
#include "mlir/Transforms/DialectConversion.h"
#include "torch-mlir/Dialect/Torch/IR/TorchDialect.h"
#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
#include "torch-mlir/Dialect/Torch/IR/TorchTypes.h"
#include "torch-mlir/Dialect/Torch/Transforms/Passes.h"
#include "torch-mlir/Dialect/Torch/Utils/Utils.h"
#include "llvm/ADT/ArrayRef.h"
#include "llvm/ADT/StringExtras.h"
#include <cstdint>

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

// Helper function to check whether the `dtype` is None or Float type.
static bool isNoneOrFloatDtype(MLIRContext *context, Value dtype) {
  if (dtype.getType().isa<Torch::NoneType>())
    return true;
  int64_t dtypeInt;
  if (!matchPattern(dtype, m_TorchConstantInt(&dtypeInt)))
    return false;
  Type resDtype =
      getTypeForScalarType(context, (torch_upstream::ScalarType)dtypeInt);
  return resDtype.isa<mlir::FloatType>();
}

// Helper function to compute the return type of the reduction function.
// `dim` specifies the dimension to reduce and `keepDim` preserves the rank of
// the input tensor.
static Type computeReductionType(PatternRewriter &rewriter, Operation *op,
                                 BaseTensorType tensorType, Value dim,
                                 bool keepDim) {
  SmallVector<int64_t> sizes;
  int64_t dimInt;
  if (tensorType.hasSizes()) {
    ArrayRef<int64_t> inputShape = tensorType.getSizes();
    int64_t inputRank = inputShape.size();
    if (matchPattern(dim, m_TorchConstantInt(&dimInt))) {
      dimInt = toPositiveDim(dimInt, inputRank);
      if (!isValidDim(dimInt, inputRank)) {
        (void)rewriter.notifyMatchFailure(op, "dim is not a valid dim");
        return nullptr;
      }
      sizes.append(inputShape.begin(), inputShape.end());
      // The dimension to be reduced is set to 1 when `keepDim` is true else it
      // is removed.
      if (keepDim)
        sizes[dimInt] = 1;
      else
        sizes.erase(sizes.begin() + dimInt - 1);
    } else {
      unsigned reducedRank = keepDim ? inputRank : inputRank - 1;
      sizes.resize(reducedRank, kUnknownSize);
    }
  }

  Type resultType = tensorType.getWithSizesAndDtype(
      sizes.size() == 0 ? Optional<ArrayRef<int64_t>>()
                        : llvm::makeArrayRef(sizes),
      tensorType.getDtype());
  return resultType;
}

// Reduction function to calculate sum along given `dim`.
static Value createSumAlongDimension(PatternRewriter &rewriter, Location loc,
                                     Operation *op, Value input, Value dim,
                                     bool keepDim) {
  Value dimList = rewriter.create<PrimListConstructOp>(
      loc, Torch::ListType::get(dim.getType()), dim);
  Value keepDimCst = rewriter.create<ConstantBoolOp>(loc, keepDim);
  Value dtype = rewriter.create<ConstantNoneOp>(loc);
  Type resultType = computeReductionType(
      rewriter, op, input.getType().cast<BaseTensorType>(), dim, keepDim);
  if (!resultType)
    return nullptr;
  return rewriter.create<AtenSumDimIntListOp>(loc, resultType, input, dimList,
                                              keepDimCst, dtype);
}

// Redunction function to calculate max along given `dim`.
static Value createMaxAlongDimension(PatternRewriter &rewriter, Location loc,
                                     Operation *op, Value input, Value dim,
                                     bool keepDim) {
  Value keepDimCst = rewriter.create<ConstantBoolOp>(loc, keepDim);
  BaseTensorType valueType =
      computeReductionType(rewriter, op, input.getType().cast<BaseTensorType>(),
                           dim, keepDim)
          .cast<BaseTensorType>();
  if (!valueType)
    return nullptr;
  BaseTensorType indexType =
      valueType
          .getWithSizesAndDtype(
              !valueType.hasSizes() ? Optional<ArrayRef<int64_t>>()
                                    : llvm::makeArrayRef(valueType.getSizes()),
              IntegerType::get(op->getContext(), 64, IntegerType::Signed))
          .cast<BaseTensorType>();
  return rewriter
      .create<AtenMaxDimOp>(loc, valueType, indexType, input, dim, keepDimCst)
      .values();
}

// Helper for creating `aten::sub_tensor_op`.
static Value createTensorSub(PatternRewriter &rewriter, Location loc,
                             Type tensorType, Value lhs, Value rhs) {
  Value alpha =
      rewriter.create<ConstantFloatOp>(loc, rewriter.getF64FloatAttr(1));
  Value sub =
      rewriter.create<AtenSubTensorOp>(loc, tensorType, lhs, rhs, alpha);
  return sub;
}

// Helper to create a tensor filled with the given scalar. Scalar would be
// converted the to the element type of the given tensor type.
static Value createInitTensor(PatternRewriter &rewriter, Location loc,
                              Type resultType, Value scalar, Value sizeList) {
  BaseTensorType tensorType = resultType.cast<BaseTensorType>();
  Value noneVal = rewriter.create<ConstantNoneOp>(loc);
  Value emptyTensor = rewriter.create<AtenEmptyMemoryFormatOp>(
      loc, tensorType, sizeList, /*dtype=*/noneVal, /*layout=*/noneVal,
      /*device=*/noneVal, /*pin_memory=*/noneVal, /*memory_format=*/noneVal);
  return rewriter.create<ValsemVariantAtenFillScalarOp>(loc, resultType,
                                                        emptyTensor, scalar);
}

// Helper to create a rank 0 tensor filled with the given `scalar`. `scalar`
// would be converted to the element type of the given `inputType`.
static Value createRank0Tensor(PatternRewriter &rewriter, Location loc,
                               BaseTensorType inputType, Value scalar) {
  SmallVector<int64_t> sizes;
  Type rank0TensorTy = inputType.getWithSizesAndDtype(
      makeArrayRef(sizes), inputType.getOptionalDtype());
  Value dimList = rewriter.create<PrimListConstructOp>(
      loc, Torch::ListType::get(Torch::IntType::get(inputType.getContext())),
      ValueRange{});
  return createInitTensor(rewriter, loc, rank0TensorTy, scalar, dimList);
}

// Share code between `softmax_backward` and `log_softmax_backward` ops.
// Returns x - y * sum(z, dim).
static Value createSoftmaxBackwardCommonKernel(PatternRewriter &rewriter,
                                               Location loc, Operation *op,
                                               Type tensorType, Value x,
                                               Value y, Value z, Value dim) {
  Value sum =
      createSumAlongDimension(rewriter, loc, op, z, dim, /*keepDim=*/true);
  if (!sum)
    return nullptr;
  auto broadcastSizeType =
      Torch::ListType::get(Torch::IntType::get(op->getContext()));
  Value broadcastSize = rewriter.create<AtenSizeOp>(loc, broadcastSizeType, z);
  Value sumBroadcast =
      rewriter.create<AtenBroadcastToOp>(loc, tensorType, sum, broadcastSize);
  Value temp =
      rewriter.create<AtenMulTensorOp>(loc, tensorType, y, sumBroadcast);

  Value sub = createTensorSub(rewriter, loc, tensorType, x, temp);
  return sub;
}

namespace {
class DecomposeAtenSizeOp : public OpRewritePattern<AtenSizeOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenSizeOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value self = op.self();
    MLIRContext *context = op.getContext();
    int64_t rank = getTensorRank(self);
    if (rank < 0)
      return rewriter.notifyMatchFailure(op, "Unimplemented: unranked tensor");
    SmallVector<Value> sizes;
    for (int i = 0; i < rank; i++) {
      Value dim = rewriter.create<Torch::ConstantIntOp>(
          loc, rewriter.getI64IntegerAttr(i));
      sizes.push_back(rewriter.create<AtenSizeIntOp>(loc, self, dim));
    }

    Value sizeList = rewriter.create<PrimListConstructOp>(
        loc, Torch::ListType::get(Torch::IntType::get(context)), sizes);
    rewriter.replaceOp(op, sizeList);
    return success();
  }
};
} // namespace

namespace {
class DecomposeAtenSelectIntOp : public OpRewritePattern<AtenSelectIntOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenSelectIntOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value start = op.index();
    Value dim = op.dim();
    Value self = op.self();

    Value one =
        rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(1));
    Value startPlusOne =
        rewriter.create<AtenAddIntOp>(loc, one.getType(), start, one);
    Value slice = rewriter.create<AtenSliceTensorOp>(
        loc,
        computeReductionType(rewriter, op,
                             self.getType().cast<BaseTensorType>(), dim,
                             /*keepDim=*/true),
        op.self(), dim, start, startPlusOne, /*step=*/one);

    // `aten.slice.tensor` doesn't squeeze the dim even when it's size 1 after
    // slicing, while `aten.select.int` does.
    rewriter.replaceOpWithNewOp<AtenSqueezeDimOp>(op, op.getResult().getType(),
                                                  slice, op.dim());
    return success();
  }
};
} // namespace

namespace {
class DecomposeAtenNarrowOp : public OpRewritePattern<AtenNarrowOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenNarrowOp op,
                                PatternRewriter &rewriter) const override {

    Location loc = op.getLoc();
    Value start = op.start();
    Value dim = op.dim();
    Value length = op.length();

    Value one =
        rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(1));
    Value startPlusLength =
        rewriter.create<AtenAddIntOp>(loc, one.getType(), start, length);
    
    rewriter.replaceOpWithNewOp<AtenSliceTensorOp>(
        op, op.getResult().getType(), op.self(), /*dim=*/dim, /*start=*/start,
        /*end=*/startPlusLength, /*step=*/one);

    return success();
  }
};
} // namespace

namespace {
class DecomposeAtenZeroOp
    : public OpRewritePattern<AtenZeroOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenZeroOp op,
                                PatternRewriter &rewriter) const override {
    Value zero = rewriter.create<ConstantIntOp>(op.getLoc(),
                                                rewriter.getI64IntegerAttr(0));
    rewriter.replaceOpWithNewOp<ValsemVariantAtenFillScalarOp>(op, op.getType(),
                                                               op.self(), zero);
    return success();
  }
};
} // namespace

namespace {
class DecomposeAtenReshapeOp : public OpRewritePattern<AtenReshapeOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenReshapeOp op,
                                PatternRewriter &rewriter) const override {
    Value input = op.self();
    // TODO: Handle non value tensor type operands.
    if (!input.getType().isa<ValueTensorType>()) {
      return rewriter.notifyMatchFailure(
          op, "unimplemented: only value tensor type operands are supported");
    }
    rewriter.replaceOpWithNewOp<AtenViewOp>(op, op.getType(), input,
                                            op.shape());
    return success();
  }
};
} // namespace

// Calculates the softmax function on the given `input` tensor. Softmax(x) =
// exp(x)/sum(exp(x)).
// To avoid overflow we use the following decomposition rule:
//     x_max = max(input, dim, keepdim = True)
//     unnorm = aten.exp(input - x_max)
//     softmax = unnorm / sum(unnorm, dim, keepdim = True)
template <typename OpTy>
static Value getSoftmaxResult(OpTy op, Type resultType,
                              PatternRewriter &rewriter) {
  Location loc = op.getLoc();
  Value dim = op.dim();
  Value self = op.self();
  Value xMax =
      createMaxAlongDimension(rewriter, loc, op, self, dim, /*keepDim=*/true);
  if (!xMax)
    return nullptr;
  Value unNormalized = createTensorSub(rewriter, loc, resultType, self, xMax);
  Value unNormalizedExp =
      rewriter.create<AtenExpOp>(loc, resultType, unNormalized);
  Value sum = createSumAlongDimension(rewriter, loc, op, unNormalizedExp, dim,
                                      /*keepDim=*/true);
  if (!sum)
    return nullptr;
  return rewriter.create<AtenDivTensorOp>(loc, resultType, unNormalizedExp,
                                          sum);
}

// Decompose softmax into: exp(x) / sum(exp(x))
namespace {
class DecomposeAtenSoftmaxIntOp : public OpRewritePattern<AtenSoftmaxIntOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenSoftmaxIntOp op,
                                PatternRewriter &rewriter) const override {
    Value self = op.self();
    if (!op.dtype().getType().isa<Torch::NoneType>())
      return rewriter.notifyMatchFailure(
          op, "Unimplemented non-None dtype for softmax");

    BaseTensorType tensorType = self.getType().cast<BaseTensorType>();
    if (!tensorType.hasDtype() || !tensorType.getDtype().isa<mlir::FloatType>())
      return rewriter.notifyMatchFailure(op, "Only support floating type");

    Value result = getSoftmaxResult(op, tensorType, rewriter);
    if (!result)
      return failure();
    rewriter.replaceOpWithNewOp<TensorStaticInfoCastOp>(op, op.getType(),
                                                        result);
    return success();
  }
};
} // namespace

namespace {
class DecomposeAten_SoftmaxOp : public OpRewritePattern<Aten_SoftmaxOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(Aten_SoftmaxOp op,
                                PatternRewriter &rewriter) const override {
    Value self = op.self();
    BaseTensorType tensorType = self.getType().cast<BaseTensorType>();
    if (!tensorType.hasDtype() || !tensorType.getDtype().isa<mlir::FloatType>())
      return rewriter.notifyMatchFailure(op, "Only support floating type");
    bool halfToFloat;
    if (!matchPattern(op.half_to_float(), m_TorchConstantBool(&halfToFloat)))
      return rewriter.notifyMatchFailure(
          op, "Expected a boolean value for half_to_float");

    // Currently, setting `halfToFloat` is not supported as the E2E testing for
    // the same is not present on CPU.
    if (halfToFloat)
      return rewriter.notifyMatchFailure(
          op, "halfToFloat is currently not supported.");

    Value result = getSoftmaxResult(op, tensorType, rewriter);
    if (!result)
      return op.emitError("failed to get softmax result");
    rewriter.replaceOpWithNewOp<TensorStaticInfoCastOp>(op, op.getType(),
                                                        result);
    return success();
  }
};
} // namespace

// Aten_SoftmaxBackwardDataOp(gradOutput, output, dim) =>
//    newGrad = gradOutput * output
//    result = newGrad - output * sum(newGrad, dim))
//
// Refer to
// https://github.com/pytorch/pytorch/blob/15fecc4c830a3907fde4b44c9962dc4144da50a4/torch/csrc/jit/codegen/cuda/ops/normalization.cpp#L31
namespace {
class DecomposeAten_SoftmaxBackwardDataOp
    : public OpRewritePattern<Aten_SoftmaxBackwardDataOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(Aten_SoftmaxBackwardDataOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value gradOutput = op.grad_output();
    Value output = op.output();
    Value dim = op.dim();

    BaseTensorType tensorType = gradOutput.getType().cast<BaseTensorType>();
    if (!tensorType.hasDtype() || !tensorType.getDtype().isa<mlir::FloatType>())
      return rewriter.notifyMatchFailure(op, "Only support floating type");

    Value newGrad =
        rewriter.create<AtenMulTensorOp>(loc, tensorType, gradOutput, output);
    Value result = createSoftmaxBackwardCommonKernel(
        rewriter, loc, op, tensorType, newGrad, output, newGrad, dim);
    if (!result)
      return rewriter.notifyMatchFailure(
          op,
          "nullptr returned by createSoftmaxBackwardCommonKernel function.");
    rewriter.replaceOp(op, result);
    return success();
  }
};
} // namespace

// AtenTanhBackwardOp(gradOutput, output) =>
//    result = gradOutput * (1 - output^2)
// To get away from broadcasts the above formula is expanded i.e.,
// result = gradOutput - (gradOutput * output^2)
namespace {
class DecomposeAtenTanhBackwardOp
    : public OpRewritePattern<AtenTanhBackwardOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenTanhBackwardOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value gradOutput = op.grad_output();

    // `output` is the value flowing out from tanh. Hence, tanh(x) = output.
    //  Since, dTanh(x) = (1 - tanh(x)^2) hence, dOutput = (1 - output^2).
    Value output = op.output();

    BaseTensorType tensorType = gradOutput.getType().cast<BaseTensorType>();
    if (!tensorType.hasDtype() || !tensorType.getDtype().isa<mlir::FloatType>())
      return rewriter.notifyMatchFailure(op, "Only support floating type");

    Value tanhSquare =
        rewriter.create<AtenMulTensorOp>(loc, tensorType, output, output);
    Value gradMulTanhSquare = rewriter.create<AtenMulTensorOp>(
        loc, tensorType, tanhSquare, gradOutput);

    Value newGrad = createTensorSub(rewriter, loc, tensorType, gradOutput,
                                    gradMulTanhSquare);
    rewriter.replaceOp(op, newGrad);
    return success();
  }
};
} // namespace

// Aten_LogSoftmaxBackwardDataOp(gradOutput, output, dim) =>
//    result = gradOutput - (exp(output) * sum(gradOutput, dim))
namespace {
class DecomposeAten_LogSoftmaxBackwardDataOp
    : public OpRewritePattern<Aten_LogSoftmaxBackwardDataOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(Aten_LogSoftmaxBackwardDataOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value gradOutput = op.grad_output();
    Value output = op.output();
    Value dim = op.dim();

    BaseTensorType tensorType = gradOutput.getType().cast<BaseTensorType>();
    if (!tensorType.hasDtype() || !tensorType.getDtype().isa<mlir::FloatType>())
      return rewriter.notifyMatchFailure(op, "Only support floating type");

    Value expOut = rewriter.create<AtenExpOp>(loc, tensorType, output);
    Value result = createSoftmaxBackwardCommonKernel(
        rewriter, loc, op, tensorType, gradOutput, expOut, gradOutput, dim);
    if (!result)
      return rewriter.notifyMatchFailure(
          op,
          "nullptr returned by createSoftmaxBackwardCommonKernel function.");
    rewriter.replaceOp(op, result);
    return success();
  }
};
} // namespace

// Decompose `AtenArgMaxOp` into `AtenMaxDimOp`.
namespace {
class DecomposeAtenArgMaxOp : public OpRewritePattern<AtenArgmaxOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenArgmaxOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value input = op.self();
    Value dim = op.dim();
    Value keepDim = op.keepdim();
    Value result = op.result();

    BaseTensorType inputType = input.getType().cast<BaseTensorType>();
    BaseTensorType indicesTensorType = result.getType().cast<BaseTensorType>();

    if (!indicesTensorType.hasSizes())
      return failure();
    BaseTensorType valueTensorType =
        inputType
            .getWithSizesAndDtype(indicesTensorType.getSizes(),
                                  inputType.getDtype())
            .cast<BaseTensorType>();

    // If the dim type is `NoneType` i.e. reduce along all the dimensions.
    // `AtenMaxDimOp` doesn't support dim as `NoneType` so first the input
    // tensor is flattened to 1d tensor and then the reduction happens on the
    // 0th dimension.
    if (dim.getType().isa<Torch::NoneType>()) {
      BaseTensorType flattenType =
          inputType.getWithSizesAndDtype({kUnknownSize}, inputType.getDtype())
              .cast<BaseTensorType>();
      dim = rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(0));
      Value end = rewriter.create<ConstantIntOp>(
          loc, rewriter.getI64IntegerAttr(getTensorRank(input) - 1));
      input = rewriter.create<AtenFlattenUsingIntsOp>(loc, flattenType, input,
                                                      dim, end);
    }
    Value maxResult =
        rewriter
            .create<AtenMaxDimOp>(loc, valueTensorType, indicesTensorType,
                                  input, dim, keepDim)
            .indices();

    rewriter.replaceOp(op, maxResult);
    return success();
  }
};
} // namespace

// To avoid overflow we use the following decomposition rule:
//  x_max = aten.max(x, dim, keepdim=True)[0]
//  shifted = x - x_max
//  shifted_logsumexp = aten.log(aten.sum(aten.exp(shifted), dim, keepdim=True))
//  log_softmax = shifted - shifted_logsumexp
template <typename OpTy>
static Value getLogSoftmaxResult(OpTy op, PatternRewriter &rewriter) {
  Location loc = op.getLoc();
  Value dim = op.dim();
  Value self = op.self();
  BaseTensorType tensorType = self.getType().cast<BaseTensorType>();
  Value xMax =
      createMaxAlongDimension(rewriter, loc, op, self, dim, /*keepDim=*/true);
  if (!xMax)
    return nullptr;

  Value shifted = createTensorSub(rewriter, loc, tensorType, self, xMax);
  Value shiftedExp = rewriter.create<AtenExpOp>(loc, tensorType, shifted);
  Value shiftedSumExp =
      createSumAlongDimension(rewriter, loc, op, shiftedExp, dim,
                              /*keepDim=*/true);
  if (!shiftedSumExp)
    return nullptr;

  Value shiftedLogSumExp =
      rewriter.create<AtenLogOp>(loc, shiftedSumExp.getType(), shiftedSumExp);
  Value result =
      createTensorSub(rewriter, loc, op.getType(), shifted, shiftedLogSumExp);
  return result;
}

namespace {
class DecomposeAtenLogSoftmaxIntOp
    : public OpRewritePattern<AtenLogSoftmaxIntOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenLogSoftmaxIntOp op,
                                PatternRewriter &rewriter) const override {
    Value self = op.self();
    if (!op.dtype().getType().isa<Torch::NoneType>())
      return rewriter.notifyMatchFailure(
          op, "Unimplemented non-None dtype for log_softmax");

    BaseTensorType tensorType = self.getType().cast<BaseTensorType>();
    if (!tensorType.hasDtype() || !tensorType.getDtype().isa<mlir::FloatType>())
      return rewriter.notifyMatchFailure(op, "Only support floating type");

    Value logSoftmax = getLogSoftmaxResult(op, rewriter);
    if (!logSoftmax)
      return rewriter.notifyMatchFailure(
          op, "getLogSoftmaxResult function returned nullptr");
    rewriter.replaceOp(op, logSoftmax);
    return success();
  }
};
} // namespace

namespace {
class DecomposeAten_LogSoftmaxOp : public OpRewritePattern<Aten_LogSoftmaxOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(Aten_LogSoftmaxOp op,
                                PatternRewriter &rewriter) const override {
    bool halfToFloat;
    if (!matchPattern(op.half_to_float(), m_TorchConstantBool(&halfToFloat)))
      return rewriter.notifyMatchFailure(
          op, "Expected a boolean value for half_to_float");

    // Currently, setting `halfToFloat` is not supported as the E2E testing for
    // the same is not present on CPU.
    if (halfToFloat)
      return rewriter.notifyMatchFailure(
          op, "halfToFloat is currently not supported.");
    Value _logSoftmax = getLogSoftmaxResult(op, rewriter);
    if (!_logSoftmax)
      return rewriter.notifyMatchFailure(
          op, "getLogSoftmaxResult function returned nullptr");
    rewriter.replaceOp(op, _logSoftmax);
    return success();
  }
};
} // namespace

// Decompose aten.matmul into: aten.mm and aten.bmm according to ranks.
namespace {
class DecomposeAtenMatmulOp : public OpRewritePattern<AtenMatmulOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenMatmulOp op,
                                PatternRewriter &rewriter) const override {
    Value lhs = op.self();
    Value rhs = op.other();

    int lhsRank = getTensorRank(lhs);
    int rhsRank = getTensorRank(rhs);

    // If both lhs and rhs ranks are 2 then map it to `aten.mm` op.
    if (lhsRank == 2 && rhsRank == 2)
      rewriter.replaceOpWithNewOp<AtenMmOp>(op, op.getType(), lhs, rhs);

    // If both lhs and rhs ranks are 3 then map it to `aten.bmm` op.
    if (lhsRank == 3 && rhsRank == 3)
      rewriter.replaceOpWithNewOp<AtenBmmOp>(op, op.getType(), lhs, rhs);

    return success();
  }
};
} // namespace

// ReLU6(x) = min(max(0, x), 6) = min(Relu(x), 6)
static Value getRelu6Results(PatternRewriter &rewriter, Location loc,
                             Value input) {
  BaseTensorType inputType = input.getType().cast<BaseTensorType>();

  Value relu = rewriter.create<AtenReluOp>(loc, inputType, input);
  Value cst6 =
      rewriter.create<Torch::ConstantIntOp>(loc, rewriter.getI64IntegerAttr(6));
  Value sixTensor = createRank0Tensor(rewriter, loc, inputType, cst6);
  Value relu6Out =
      rewriter.create<AtenMinimumOp>(loc, inputType, relu, sixTensor);
  return relu6Out;
}

// Hardswish(x) = x * Relu6(x+3)/6
namespace {
class DecomposeAtenHardswishOp : public OpRewritePattern<AtenHardswishOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenHardswishOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value input = op.self();
    Type inputType = input.getType();

    Value constantOne = rewriter.create<Torch::ConstantIntOp>(
        loc, rewriter.getI64IntegerAttr(1));
    Value constantThree = rewriter.create<Torch::ConstantIntOp>(
        loc, rewriter.getI64IntegerAttr(3));
    Value constantSix = rewriter.create<Torch::ConstantIntOp>(
        loc, rewriter.getI64IntegerAttr(6));
    Value inputPlusThree = rewriter.create<AtenAddScalarOp>(
        loc, inputType, input, constantThree, /*alpha=*/constantOne);
    Value relu6 = getRelu6Results(rewriter, loc, inputPlusThree);
    Value divTensor =
        rewriter.create<AtenDivScalarOp>(loc, inputType, relu6, constantSix);
    Value mulTensor =
        rewriter.create<AtenMulTensorOp>(loc, inputType, divTensor, input);

    rewriter.replaceOp(op, mulTensor);
    return success();
  }
};
} // namespace

namespace {
class DecomposeAtenTOp : public OpRewritePattern<AtenTOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenTOp op,
                                PatternRewriter &rewriter) const override {
    Value lhs = op.self();
    int lhsRank = getTensorRank(lhs);
    auto loc = op.getLoc();

    if (lhsRank > 2 || lhsRank < 0) {
      std::string errorMessage =
          "t() expects a tensor with <=2 dimensions, but self is " +
          std::to_string(lhsRank) + "D";
      return rewriter.notifyMatchFailure(op, errorMessage.c_str());
    } else if (lhsRank < 2)
      rewriter.replaceOp(op, lhs);
    else {
      Value zero =
          rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(0));
      Value one =
          rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(1));
      rewriter.replaceOpWithNewOp<AtenTransposeIntOp>(op, op.getType(), lhs,
                                                      zero, one);
    }
    return success();
  }
};
} // namespace

// Decompose aten.repeat into aten.expand and aten.view ops.
//
// Ref: https://pytorch.org/docs/stable/generated/torch.Tensor.repeat.html
//
// For shape [S1, S2, S3] and repeats [M0, M1, M2, M3]
//     MS0 = M0; MS1 = M1 * S1; MS2 = M2 * S2; MS3 = M3 * S3
//
// def aten_repeat(self, repeats):
//     sizes = self.size()
//     unsqueezed_sizes = []
//     expanded_sizes = []
//     reshape_sizes = []
//     leading_rank = repeats.size() - sizes.size()
//     for r in range(leading_rank):
//         unsqueezed_sizes.append(1)
//         expanded_sizes.append(repeats[r])
//         reshaped_sizes.append(repeats[r])
//
//     for s, m in zip(sizes, repeats[leading_rank:]):
//         unsqueezed_sizes += [1, s]
//         expanded_sizes += [m, s]
//         reshaped_sizes += [m * s]
//     return
//     self.view(unsqueezed_sizes).expand(expanded_sizes).view(reshaped_sizes)
//
namespace {
class DecomposeAtenRepeatOp : public OpRewritePattern<AtenRepeatOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenRepeatOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value self = op.self();
    MLIRContext *context = op.getContext();
    int rank = getTensorRank(self);
    if (rank < 0)
      return rewriter.notifyMatchFailure(op, "Unimplemented: unranked tensor");

    SmallVector<Value> repeats;
    if (!getListConstructElements(op.repeats(), repeats))
      return rewriter.notifyMatchFailure(
          op, "Unimplemented: repeats not list of Scalar");

    if (rank > (int)repeats.size()) {
      return rewriter.notifyMatchFailure(
          op, "repeats are not matched with self's rank");
    }

    auto insertDimSizes = [](SmallVector<Value> &dimSizes,
                             SmallVector<int64_t> &shape,
                             const ArrayRef<Value> &vals) {
      dimSizes.insert(dimSizes.end(), vals.begin(), vals.end());
      std::transform(vals.begin(), vals.end(), std::back_inserter(shape),
                     [&](Value val) -> int64_t {
                       int64_t cst_val;
                       if (matchPattern(val, m_TorchConstantInt(&cst_val))) {
                         return cst_val;
                       } else {
                         return ShapedType::kDynamicSize;
                       }
                     });
    };

    Value one = rewriter.create<Torch::ConstantIntOp>(
        loc, rewriter.getI64IntegerAttr(1));

    SmallVector<Value> unsqueezedSizes, expandedSizes, reshapedSizes;
    SmallVector<int64_t> unsqueezedIntSizes, expandedIntSizes;
    auto leadingRank = repeats.size() - rank;
    assert(leadingRank >= 0 && "leadingRank should greater than 0");
    for (size_t i = 0; i < leadingRank; ++i) {
      insertDimSizes(unsqueezedSizes, unsqueezedIntSizes, ArrayRef<Value>{one});
      insertDimSizes(expandedSizes, expandedIntSizes,
                     ArrayRef<Value>{repeats[i]});
      reshapedSizes.push_back(repeats[i]);
    }

    auto selfType = self.getType().dyn_cast<BaseTensorType>();
    auto selfShape = selfType.getSizes();
    for (int i = 0; i < rank; i++) {
      auto scale = repeats[i + leadingRank];
      Value dimSize;
      if (selfShape[i] == ShapedType::kDynamicSize) {
        Value dim = rewriter.create<Torch::ConstantIntOp>(
            loc, rewriter.getI64IntegerAttr(i));
        dimSize = rewriter.create<AtenSizeIntOp>(loc, self, dim);
      } else {
        dimSize = rewriter.create<Torch::ConstantIntOp>(
            loc, rewriter.getI64IntegerAttr(selfShape[i]));
      }

      insertDimSizes(unsqueezedSizes, unsqueezedIntSizes,
                     ArrayRef<Value>{one, dimSize});
      insertDimSizes(expandedSizes, expandedIntSizes,
                     ArrayRef<Value>{scale, dimSize});

      Value scaledSize = rewriter.create<AtenMulIntOp>(loc, dimSize, scale);
      reshapedSizes.push_back(scaledSize);
    }

    Type dtype = self.getType().cast<ValueTensorType>().getDtype();
    Type unsqueezedType = ValueTensorType::get(
        context, llvm::makeArrayRef(unsqueezedIntSizes), dtype);
    Type expandedType = ValueTensorType::get(
        context, llvm::makeArrayRef(expandedIntSizes), dtype);

    auto listType = Torch::ListType::get(Torch::IntType::get(op.getContext()));
    Value unsqueezedDims =
        rewriter.create<PrimListConstructOp>(loc, listType, unsqueezedSizes);
    Value expandedDims =
        rewriter.create<PrimListConstructOp>(loc, listType, expandedSizes);
    Value reshapedDims =
        rewriter.create<PrimListConstructOp>(loc, listType, reshapedSizes);
    auto reshaped = rewriter.create<AtenViewOp>(loc, unsqueezedType, op.self(),
                                                unsqueezedDims);
    auto expanded = rewriter.create<AtenBroadcastToOp>(loc, expandedType,
                                                       reshaped, expandedDims);

    rewriter.replaceOpWithNewOp<AtenViewOp>(op, op.getType(), expanded,
                                            reshapedDims);
    return success();
  }
};
} // namespace

// Decompose aten.expand into aten.broadcast_to op.
namespace {
class DecomposeAtenExpandOp : public OpRewritePattern<AtenExpandOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenExpandOp op,
                                PatternRewriter &rewriter) const override {
    bool implicit = false;
    if (!matchPattern(op.implicit(), m_TorchConstantBool(&implicit)) ||
        implicit) {
      return rewriter.notifyMatchFailure(
          op, "unimplemented: requires implicit to be false");
    }
    rewriter.replaceOpWithNewOp<AtenBroadcastToOp>(op, op.getType(), op.self(),
                                                   op.size());
    return success();
  }
};
} // namespace

// Decompose aten.where.Scalar into aten.where.self op.
namespace {
class DecomposeAtenWhereScalarOp : public OpRewritePattern<AtenWhereScalarOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenWhereScalarOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    auto resType = op.getType().cast<BaseTensorType>();
    Value selfTensor = createRank0Tensor(rewriter, loc, resType, op.self());
    Value otherTensor = createRank0Tensor(rewriter, loc, resType, op.other());
    rewriter.replaceOpWithNewOp<AtenWhereSelfOp>(op, resType, op.condition(),
                                                 selfTensor, otherTensor);
    return success();
  }
};
} // namespace

// Decompose aten.where.ScalarOther into aten.where.self op.
namespace {
class DecomposeAtenWhereScalarOtherOp
    : public OpRewritePattern<AtenWhereScalarOtherOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenWhereScalarOtherOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    auto resType = op.getType().cast<BaseTensorType>();
    Value otherTensor = createRank0Tensor(rewriter, loc, resType, op.other());
    rewriter.replaceOpWithNewOp<AtenWhereSelfOp>(op, resType, op.condition(),
                                                 op.self(), otherTensor);
    return success();
  }
};
} // namespace

// Decompose aten.where.ScalarSelf into aten.where.self op.
namespace {
class DecomposeAtenWhereScalarSelfOp
    : public OpRewritePattern<AtenWhereScalarSelfOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenWhereScalarSelfOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    auto resType = op.getType().cast<BaseTensorType>();
    Value selfTensor = createRank0Tensor(rewriter, loc, resType, op.self());
    rewriter.replaceOpWithNewOp<AtenWhereSelfOp>(op, resType, op.condition(),
                                                 selfTensor, op.other());
    return success();
  }
};
} // namespace

// Decompose aten.convolution_overrideable to aten.convolution
namespace {
class DecomposeAtenConvolutionOverrideableOp
    : public OpRewritePattern<AtenConvolutionOverrideableOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenConvolutionOverrideableOp op,
                                PatternRewriter &rewriter) const override {

    rewriter.replaceOpWithNewOp<AtenConvolutionOp>(
        op, op->getResultTypes(), op.input(), op.weight(), op.bias(),
        op.stride(), op.padding(), op.dilation(), op.transposed(),
        op.output_padding(), op.groups());

    return success();
  }
};
} // namespace

// Decompose aten.convolution_overrideable to aten.convolution
namespace {
class DecomposeAten_ConvolutionOp
    : public OpRewritePattern<Aten_ConvolutionOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(Aten_ConvolutionOp op,
                                PatternRewriter &rewriter) const override {

    rewriter.replaceOpWithNewOp<AtenConvolutionOp>(
        op, op->getResultTypes(), op.input(), op.weight(), op.bias(),
        op.stride(), op.padding(), op.dilation(), op.transposed(),
        op.output_padding(), op.groups());

    return success();
  }
};
} // namespace

// Decompose aten.conv2d to aten.convolution
namespace {
class DecomposeAtenConv2dOp : public OpRewritePattern<AtenConv2dOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenConv2dOp op,
                                PatternRewriter &rewriter) const override {

    Value emptyList = rewriter.create<PrimListConstructOp>(
        op.getLoc(), Torch::ListType::get(Torch::IntType::get(op.getContext())),
        SmallVector<Value>());
    Value cstFalse = rewriter.create<Torch::ConstantBoolOp>(op.getLoc(), false);
    rewriter.replaceOpWithNewOp<AtenConvolutionOp>(
        op, op->getResultTypes(), op.input(), op.weight(), op.bias(),
        op.stride(), op.padding(), op.dilation(), cstFalse, emptyList,
        op.groups());

    return success();
  }
};
} // namespace

// Decompose aten.addmm into aten.mm and aten.add.Tensor op.
namespace {
class DecomposeAtenAddmmOp : public OpRewritePattern<AtenAddmmOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenAddmmOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value input = op.self();
    Value mat1 = op.mat1();
    Value mat2 = op.mat2();

    // The operands `mat1`, `mat2` to aten.addmm must be of rank 2.
    if (getTensorRank(mat1) != 2 || getTensorRank(mat2) != 2) {
      return rewriter.notifyMatchFailure(
          op, "expected mat1, mat2 operands to aten.addmm to be rank 2");
    }

    // TODO: Handle integer type operands.
    if (!input.getType()
             .cast<ValueTensorType>()
             .getDtype()
             .isa<mlir::FloatType>()) {
      return rewriter.notifyMatchFailure(
          op, "unimplemented: non-floating point dtype");
    }

    // matrix multiplication: matmul = mat1 @ mat2
    Value matmul = rewriter.create<AtenMmOp>(loc, op.getType(), mat1, mat2);
    // scaledInput = self * beta
    Value scaledInput = rewriter.create<AtenMulScalarOp>(loc, input.getType(),
                                                         input, op.beta());
    // result = scaledInput + alpha * matmul
    rewriter.replaceOpWithNewOp<AtenAddTensorOp>(op, op.getType(), scaledInput,
                                                 matmul, op.alpha());
    return success();
  }
};
} // namespace

// Decompose aten.mean into: sum(x)/div(numTensorElements).
namespace {
class DecomposeAtenMeanOp : public OpRewritePattern<AtenMeanOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenMeanOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value input = op.self();
    Value output = op.result();
    BaseTensorType outputTensorType = output.getType().cast<BaseTensorType>();
    Value sum =
        rewriter.create<AtenSumOp>(loc, outputTensorType, input, op.dtype());
    Value numTensorElements = rewriter.create<AtenNumelOp>(loc, input);
    rewriter.replaceOpWithNewOp<AtenDivScalarOp>(op, outputTensorType, sum,
                                                 numTensorElements);
    return success();
  }
};
} // namespace

// productDimSize = product(size(dim) for dim in dims)
// aten.mean(x, dims) = aten.sum(x, dims) / productDimSize.
namespace {
class DecomposeAtenMeanDimOp : public OpRewritePattern<AtenMeanDimOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenMeanDimOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value input = op.self();
    unsigned inputRank = getTensorRank(input);
    Value dimList = op.dim();
    Value keepDim = op.keepdim();
    Value dtype = op.dtype();
    Type outputType = op.getType();
    MLIRContext *context = op.getContext();

    BaseTensorType inputType = input.getType().cast<BaseTensorType>();
    if (!inputType.hasDtype() || !inputType.getDtype().isa<mlir::FloatType>() ||
        !isNoneOrFloatDtype(context, dtype)) {
      return rewriter.notifyMatchFailure(
          op, "only floating-point type is supported");
    }

    SmallVector<Value> dimListElements;
    if (!getListConstructElements(dimList, dimListElements) &&
        !dimList.getType().isa<Torch::NoneType>()) {
      return rewriter.notifyMatchFailure(
          op, "expected `dim` to be `None` or constructed from list construct");
    }

    // Compute sum along dimensions specified in `dimList`.
    Value sumAlongDims = rewriter.create<AtenSumDimIntListOp>(
        loc, outputType, input, dimList, keepDim, dtype);

    // `productDimSize` is product of sizes of dimensions to be reduced.
    Value productDimSize;
    // Case: Reduce along all dims.
    if (dimListElements.empty() && inputRank != 0) {
      productDimSize = rewriter.create<AtenNumelOp>(loc, input);
    } else {
      productDimSize = rewriter.create<Torch::ConstantIntOp>(
          loc, rewriter.getI64IntegerAttr(1));
      for (Value dim : dimListElements) {
        Value dimSize = rewriter.create<AtenSizeIntOp>(loc, input, dim);
        productDimSize =
            rewriter.create<AtenMulIntOp>(loc, productDimSize, dimSize);
      }
    }
    rewriter.replaceOpWithNewOp<AtenDivScalarOp>(op, outputType, sumAlongDims,
                                                 productDimSize);
    return success();
  }
};
} // namespace

namespace {
class DecomposeAtenSquareOp : public OpRewritePattern<AtenSquareOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenSquareOp op,
                                PatternRewriter &rewriter) const override {
    Value self = op.self();
    rewriter.replaceOpWithNewOp<AtenMulTensorOp>(op, op.getType(), self, self);
    return success();
  }
};
} // namespace

// Silu(x) = sigmoid(x) * x
namespace {
class DecomposeAtenSiluOp : public OpRewritePattern<AtenSiluOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenSiluOp op,
                                PatternRewriter &rewriter) const override {
    Value self = op.self();
    Value sigmoid =
        rewriter.create<AtenSigmoidOp>(op.getLoc(), op.getType(), self);
    rewriter.replaceOpWithNewOp<AtenMulTensorOp>(op, op.getType(), sigmoid,
                                                 self);
    return success();
  }
};
} // namespace

// pDash = 1.0 - p
// boolMask = aten.rand_like(input) < pDash
// dropout(input, p, train=True) = (boolMask * input) / pDash
// dropout(input, p, train=False) = input
namespace {
class DecomposeAtenDropoutOp : public OpRewritePattern<AtenDropoutOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenDropoutOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value input = op.input();
    Value prob = op.p();
    bool train = false;
    if (!matchPattern(op.train(), m_TorchConstantBool(&train)))
      return rewriter.notifyMatchFailure(op,
                                         "train must be a boolean constant");
    if (!train) {
      rewriter.replaceOp(op, input);
      return success();
    }
    BaseTensorType inputType = input.getType().cast<BaseTensorType>();
    if (!inputType.hasDtype() || !inputType.getDtype().isa<mlir::FloatType>())
      return rewriter.notifyMatchFailure(
          op, "only support floating type input for training mode");
    Value noneVal = rewriter.create<ConstantNoneOp>(loc);
    Value floatOne =
        rewriter.create<ConstantFloatOp>(loc, rewriter.getF64FloatAttr(1.0));
    Value oneMinusP = rewriter.create<AtenSubFloatOp>(loc, floatOne, prob);
    Value boolMask = rewriter.create<ValsemVariantAtenBernoulliFloatOp>(
        loc, inputType, input, oneMinusP, /*generator=*/noneVal);
    Value maskedInput =
        rewriter.create<AtenMulTensorOp>(loc, inputType, boolMask, input);
    rewriter.replaceOpWithNewOp<AtenDivScalarOp>(op, op.getType(), maskedInput,
                                                 oneMinusP);
    return success();
  }
};
} // namespace

// Decompose aten.var into: aten.var.dim op.
namespace {
class DecomposeAtenVarOp : public OpRewritePattern<AtenVarOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenVarOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value self = op.self();
    unsigned inputRank = getTensorRank(self);
    BaseTensorType rank0FloatTensorTy = op.getType().cast<BaseTensorType>();
    if (!rank0FloatTensorTy.hasSizes() ||
        rank0FloatTensorTy.getSizes().size() != 0) {
      return rewriter.notifyMatchFailure(
          op, "expected aten.var to have a rank 0 tensor type");
    }

    SmallVector<Value> dims;
    for (unsigned i = 0; i < inputRank; i++)
      dims.push_back(rewriter.create<Torch::ConstantIntOp>(
          loc, rewriter.getI64IntegerAttr(i)));
    Value dimList = rewriter.create<PrimListConstructOp>(
        loc, Torch::ListType::get(Torch::IntType::get(op.getContext())), dims);

    Value cstFalse = rewriter.create<Torch::ConstantBoolOp>(op.getLoc(), false);
    rewriter.replaceOpWithNewOp<AtenVarDimOp>(op, rank0FloatTensorTy, self,
                                              dimList, op.unbiased(),
                                              /*keepdim=*/cstFalse);
    return success();
  }
};
} // namespace

// Decompose aten.std to sqrt(var(x))
namespace {
class DecomposeAtenStdOp : public OpRewritePattern<AtenStdOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenStdOp op,
                                PatternRewriter &rewriter) const override {
    Value self = op.self();
    BaseTensorType inputTensorTy = self.getType().cast<BaseTensorType>();
    if (!inputTensorTy.hasDtype() ||
        !inputTensorTy.getDtype().isa<mlir::FloatType>()) {
      return rewriter.notifyMatchFailure(op,
                                         "Only aten.std support floating type");
    }
    Value var = rewriter.create<AtenVarOp>(op->getLoc(), op.getType(),
                                           op.self(), op.unbiased());
    rewriter.replaceOpWithNewOp<AtenSqrtOp>(op, op.getType(), var);
    return success();
  }
};
} // namespace

// Softplus(x, beta, threshold) =
//   x * beta > threshold ? x : log(1 + exp(x * beta)) / beta
namespace {
class DecomposeAtenSoftplusOp : public OpRewritePattern<AtenSoftplusOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenSoftplusOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value input = op.self();
    BaseTensorType inputType = input.getType().cast<BaseTensorType>();

    Value inputTimesBeta =
        rewriter.create<AtenMulScalarOp>(loc, inputType, input, op.beta());

    // out = log1p(exp(input * beta)) / beta
    Value exp = rewriter.create<AtenExpOp>(loc, inputType, inputTimesBeta);
    Value log1p = rewriter.create<AtenLog1pOp>(loc, inputType, exp);
    Value out =
        rewriter.create<AtenDivScalarOp>(loc, inputType, log1p, op.beta());

    // Select where x * beta > threshold
    auto boolResType = inputType.getWithSizesAndDtype(inputType.getSizes(),
                                                      rewriter.getI1Type());
    Value condition = rewriter.create<AtenGtScalarOp>(
        loc, boolResType, inputTimesBeta, op.threshold());

    rewriter.replaceOpWithNewOp<AtenWhereSelfOp>(op, op.getType(), condition,
                                                 input, out);
    return success();
  }
};
} // namespace

// Decompose aten.std.dim to sqrt(var.dim(x))
namespace {
class DecomposeAtenStdDimOp : public OpRewritePattern<AtenStdDimOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenStdDimOp op,
                                PatternRewriter &rewriter) const override {
    Value self = op.self();
    BaseTensorType inputTensorType = self.getType().cast<BaseTensorType>();
    if (!inputTensorType.hasDtype() ||
        !inputTensorType.getDtype().isa<mlir::FloatType>()) {
      return rewriter.notifyMatchFailure(
          op, "aten.std.dim expects input tensor of floating-point type");
    }

    Value varDim =
        rewriter.create<AtenVarDimOp>(op->getLoc(), op.getType(), self,
                                      op.dim(), op.unbiased(), op.keepdim());
    rewriter.replaceOpWithNewOp<AtenSqrtOp>(op, op.getType(), varDim);
    return success();
  }
};
} // namespace

// Hardsigmoid(x) = max(0, min(1, (x+3)/6))
namespace {
class DecomposeAtenHardsigmoidOp : public OpRewritePattern<AtenHardsigmoidOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenHardsigmoidOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value input = op.self();
    BaseTensorType inputType = input.getType().cast<BaseTensorType>();

    // outputTensor = (input + 3) / 6.
    Value constantOne = rewriter.create<Torch::ConstantIntOp>(
        loc, rewriter.getI64IntegerAttr(1));
    Value constantThree = rewriter.create<Torch::ConstantIntOp>(
        loc, rewriter.getI64IntegerAttr(3));
    Value constantSix = rewriter.create<Torch::ConstantIntOp>(
        loc, rewriter.getI64IntegerAttr(6));
    Value inputPlusThree = rewriter.create<AtenAddScalarOp>(
        loc, inputType, input, constantThree, /*alpha=*/constantOne);
    Value outputTensor = rewriter.create<AtenDivScalarOp>(
        loc, inputType, inputPlusThree, constantSix);

    // result = max(0, min(1, (input+3)/6))
    Value constantZero = rewriter.create<Torch::ConstantIntOp>(
        loc, rewriter.getI64IntegerAttr(0));
    Value oneTensor = createRank0Tensor(rewriter, loc, inputType, constantOne);
    Value minResult =
        rewriter.create<AtenMinimumOp>(loc, inputType, oneTensor, outputTensor);
    Value zeroTensor =
        createRank0Tensor(rewriter, loc, inputType, constantZero);
    rewriter.replaceOpWithNewOp<AtenMaximumOp>(op, op.getType(), zeroTensor,
                                               minResult);
    return success();
  }
};
} // namespace

namespace {
class DecomposeAtenHardtanhOp : public OpRewritePattern<AtenHardtanhOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenHardtanhOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value input = op.self();
    BaseTensorType inputType = input.getType().cast<BaseTensorType>();

    // result = min(maxVal, max(minVal, x))
    Value minVal = createRank0Tensor(rewriter, loc, inputType, op.min_val());
    Value maxResult =
        rewriter.create<AtenMaximumOp>(loc, inputType, input, minVal);
    Value maxVal = createRank0Tensor(rewriter, loc, inputType, op.max_val());
    rewriter.replaceOpWithNewOp<AtenMinimumOp>(op, op.getType(), maxVal,
                                               maxResult);
    return success();
  }
};
} // namespace

namespace {
class DecomposeAtenRandLikeOp : public OpRewritePattern<AtenRandLikeOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenRandLikeOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value input = op.self();
    Type resultType = op.getType();
    auto inputType = input.getType().cast<BaseTensorType>();
    if (!inputType.hasDtype() || !inputType.getDtype().isa<mlir::FloatType>()) {
      return rewriter.notifyMatchFailure(op,
                                         "only support floating-point type");
    }

    // Create a uniform random op with low and high set to 0.0 and 1.0,
    // respectively.
    Value none = rewriter.create<ConstantNoneOp>(loc);
    Value zero =
        rewriter.create<ConstantFloatOp>(loc, rewriter.getF64FloatAttr(0.0));
    Value one =
        rewriter.create<ConstantFloatOp>(loc, rewriter.getF64FloatAttr(1.0));
    Value emptyTensor = rewriter.create<AtenEmptyLikeOp>(
        loc, resultType, input, op.dtype(), op.layout(), op.device(),
        op.pin_memory(), op.memory_format());
    rewriter.replaceOpWithNewOp<ValsemVariantAtenUniformOp>(
        op, resultType, emptyTensor, /*from=*/zero, /*to=*/one,
        /*generator=*/none);
    return success();
  }
};
} // namespace

namespace {
// Bernoulli(x, p) = (rand_like(float(x)) < p).cast(type(x)). Here,
// 1. p must be a float tensor.
// 2. The shape of p should be broadcastable to the shape of x.
// 3. Bernoulli(x, p) returns a tensor of the same type as that of x.
static LogicalResult decomposeBernoulliLikeOp(PatternRewriter &rewriter,
                                              Operation *op, Location loc,
                                              Value input, Value prob,
                                              Value &output) {
  auto inputType = input.getType().cast<BaseTensorType>();
  auto probType = prob.getType().cast<BaseTensorType>();
  // Both the `input` and `prob` must be ranked tensors.
  if (!inputType.hasSizes() || !inputType.hasDtype() || !probType.hasSizes() ||
      !probType.hasDtype()) {
    return rewriter.notifyMatchFailure(
        op, "can't decompose bernoulli like ops without sizes or dtype");
  }
  // The `prob` is expected to be a float type tensor.
  if (!probType.getDtype().isa<mlir::FloatType>()) {
    return rewriter.notifyMatchFailure(
        op, "probabilities must be a float type tensor");
  }

  // Since the `aten.rand_like` op expects float-type operand, create a
  // float-type tensor with the same shape as that of the `input`.
  Value floatTensor =
      convertTensorToDtype(rewriter, loc, input, rewriter.getF64Type());
  Value none = rewriter.create<ConstantNoneOp>(loc);
  Value randomVal = rewriter.create<AtenRandLikeOp>(
      loc, floatTensor.getType(), floatTensor, /*dtype=*/none, /*layout=*/none,
      /*device=*/none, /*pin_memory=*/none, /*memory_format=*/none);

  // Bernoulli(x, p) = rand_like(float(x)) < p.
  auto boolResType = inputType.getWithSizesAndDtype(inputType.getSizes(),
                                                    rewriter.getI1Type());
  Value lessThanP =
      rewriter.create<AtenLtTensorOp>(loc, boolResType, randomVal, prob);

  // As the `output` is expected to be of the `input` type, convert the boolean
  // tensor `lessThanP` to a `input` type tensor.
  output = convertTensorToDtype(rewriter, loc, lessThanP, inputType.getDtype());
  return success();
}

// aten.bernoulli(x) = rand_like(x) < x. Here, the input x is a tensor
// containing probabilities to be used for drawing the binary random number.
class DecomposeAtenBernoulliOp : public OpRewritePattern<AtenBernoulliOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenBernoulliOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value input = op.self();
    if (!op.generator().getType().isa<Torch::NoneType>())
      return rewriter.notifyMatchFailure(
          op, "The generator has to ben None because only global default "
              "generator is supported");
    Value output;
    if (failed(
            decomposeBernoulliLikeOp(rewriter, op, loc, input, input, output)))
      return rewriter.notifyMatchFailure(
          op, "decomposeBernoulliLikeOp failed to decompose the op");
    rewriter.replaceOp(op, output);
    return success();
  }
};

// aten.bernoulli.float(x, p) = (rand_like(float(x)) < tensor(p)).cast(type(x)).
// Since the input x can be an integer tensor, it's important to cast it to
// float type before passing it to the `aten.rand_like` op.
class DecomposeValsemVariantAtenBernoulliFloatOp
    : public OpRewritePattern<ValsemVariantAtenBernoulliFloatOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(ValsemVariantAtenBernoulliFloatOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value input = op.self();
    Value p = op.p();
    if (!op.generator().getType().isa<Torch::NoneType>())
      return rewriter.notifyMatchFailure(
          op, "The generator has to ben None because only global default "
              "generator is supported");

    auto inputType = input.getType().cast<BaseTensorType>();
    SmallVector<int64_t> empty;
    Type tensorType = inputType.getWithSizesAndDtype(llvm::makeArrayRef(empty),
                                                     rewriter.getF64Type());
    Value prob = rewriter.create<PrimNumToTensorScalarOp>(loc, tensorType, p);
    Value output;
    if (failed(
            decomposeBernoulliLikeOp(rewriter, op, loc, input, prob, output)))
      return rewriter.notifyMatchFailure(
          op, "decomposeBernoulliLikeOp failed to decompose the op");
    rewriter.replaceOp(op, output);
    return success();
  }
};

// aten.bernoulli.Tensor(x, p) = (rand_like(float(x)) < p).cast(type(x)).
// Since the input x can be an integer tensor, it's important to cast it to
// float type before passing it to the `aten.rand_like` op.
class DecomposeValsemVariantAtenBernoulliTensorOp
    : public OpRewritePattern<ValsemVariantAtenBernoulliTensorOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(ValsemVariantAtenBernoulliTensorOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value input = op.self();
    Value prob = op.p();
    if (!op.generator().getType().isa<Torch::NoneType>())
      return rewriter.notifyMatchFailure(
          op, "The generator has to ben None because only global default "
              "generator is supported");
    Value output;
    if (failed(
            decomposeBernoulliLikeOp(rewriter, op, loc, input, prob, output)))
      return rewriter.notifyMatchFailure(
          op, "decomposeBernoulliLikeOp failed to decompose the op");
    rewriter.replaceOp(op, output);
    return success();
  }
};
} // namespace

namespace {
template <typename OpTy, typename T1T2Op>
class DecomposeAtenAddCLikeOp : public OpRewritePattern<OpTy> {
  using OpRewritePattern<OpTy>::OpRewritePattern;
  LogicalResult matchAndRewrite(OpTy op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value input = op.self();
    Value tensor1 = op.tensor1();
    Value tensor2 = op.tensor2();
    Value value = op.value();

    Value product =
        rewriter.create<T1T2Op>(loc, op.getType(), tensor1, tensor2);
    rewriter.replaceOpWithNewOp<AtenAddTensorOp>(op, op.getType(), input,
                                                 product, value);
    return success();
  }
};

class DecomposeAtenLayerNormOp : public OpRewritePattern<AtenLayerNormOp> {
  using OpRewritePattern<AtenLayerNormOp>::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenLayerNormOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();

    auto input = op.input().getType().cast<BaseTensorType>();
    if (!input.hasSizes())
      return rewriter.notifyMatchFailure(
          op, "input tensor should have known sizes.");
    int64_t inputRank = input.getSizes().size();
    Value normalizedShape = op.normalized_shape();
    SmallVector<Value> normalizedShapeSizesTorchInt;
    getListConstructElements(normalizedShape, normalizedShapeSizesTorchInt);
    int64_t axis = inputRank - normalizedShapeSizesTorchInt.size();
    std::vector<int64_t> meanVarSizes(inputRank, 1);
    for (int i = 0; i < axis; i++)
      meanVarSizes[i] = input.getSizes()[i];
    auto meanVarType = input.getWithSizesAndDtype(
        llvm::makeArrayRef(meanVarSizes), input.getDtype());
    auto nativeLayerNorm = rewriter.create<AtenNativeLayerNormOp>(
        loc, op.getType(), meanVarType, meanVarType, op.input(),
        op.normalized_shape(), op.weight(), op.bias(), op.eps());
    rewriter.replaceOp(op, nativeLayerNorm.getResult(0));
    return success();
  }
};
} // namespace

namespace {
// Decompose `aten.empty_like` op into `aten.size` and `aten.empty` ops.
class DecomposeAtenEmptyLikeOp : public OpRewritePattern<AtenEmptyLikeOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenEmptyLikeOp op,
                                PatternRewriter &rewriter) const override {
    auto sizeListType =
        Torch::ListType::get(Torch::IntType::get(op.getContext()));
    Value sizeList =
        rewriter.create<AtenSizeOp>(op.getLoc(), sizeListType, op.self());
    rewriter.replaceOpWithNewOp<AtenEmptyMemoryFormatOp>(
        op, op.getType(), sizeList, op.dtype(), op.layout(), op.device(),
        op.pin_memory(), op.memory_format());
    return success();
  }
};
} // namespace

namespace {
// The `aten.arange` op is converted to `aten.arange.start_step` op.
class DecomposeAtenArangeOp : public OpRewritePattern<AtenArangeOp> {
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenArangeOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    // The AtenArangeOp doesn't have a start and step value. Therefore we set
    // them as default values 0 and 1, respectively.
    Value start, step;
    start = rewriter.create<Torch::ConstantIntOp>(
        loc, rewriter.getI64IntegerAttr(0));
    step = rewriter.create<Torch::ConstantIntOp>(loc,
                                                 rewriter.getI64IntegerAttr(1));
    rewriter.replaceOpWithNewOp<AtenArangeStartStepOp>(
        op, op.getType(), start, op.end(), step, op.dtype(), op.layout(),
        op.device(), op.pin_memory());
    return success();
  }
};
} // namespace

namespace {
// The `aten.arange.start` op is converted to `aten.arange.start_step` op.
class DecomposeAtenArangeStartOp : public OpRewritePattern<AtenArangeStartOp> {
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenArangeStartOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    // The AtenArangeStartOp doesn't have a step value. Therefore we set it as
    // default value 1.
    Value step;
    step = rewriter.create<Torch::ConstantIntOp>(loc,
                                                 rewriter.getI64IntegerAttr(1));
    rewriter.replaceOpWithNewOp<AtenArangeStartStepOp>(
        op, op.getType(), op.start(), op.end(), step, op.dtype(), op.layout(),
        op.device(), op.pin_memory());
    return success();
  }
};
} // namespace

namespace {
// Decompose constant tensor allocation like ops.
template <typename OpTy, int fillVal>
class DecomposeConstantTensorAllocLikeOp : public OpRewritePattern<OpTy> {
  using OpRewritePattern<OpTy>::OpRewritePattern;
  LogicalResult matchAndRewrite(OpTy op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    // Allocate a memory block.
    Value initTensor = rewriter.create<AtenEmptyLikeOp>(
        loc, op.getType(), op.self(), op.dtype(), op.layout(), op.device(),
        op.pin_memory(), op.memory_format());
    Value constVal = rewriter.create<Torch::ConstantIntOp>(
        loc, rewriter.getI64IntegerAttr(fillVal));
    // Initialize the allocated memory block with `fillVal`.
    rewriter.replaceOpWithNewOp<ValsemVariantAtenFillScalarOp>(
        op, initTensor.getType(), initTensor, constVal);
    return success();
  }
};
} // namespace

namespace {
class DecomposeAtenNativeBatchNormOp
    : public OpRewritePattern<AtenNativeBatchNormOp> {
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenNativeBatchNormOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    MLIRContext *context = op.getContext();
    Value input = op.input();
    Value weight = op.weight();
    Value bias = op.bias();
    Value runningMean = op.running_mean();
    Value runningVar = op.running_var();
    Value eps = op.eps();

    // TODO: Add support for `training` mode.
    bool training = false;
    if (!matchPattern(op.training(), m_TorchConstantBool(&training)) ||
        training)
      return rewriter.notifyMatchFailure(
          op, "unimplemented: training mode is not supported");

    // Rank of the input tensor must be greater than or equal to 2. The shape of
    // the `input` is supposed to be (N, C, D?, H?, W?).
    int64_t inputRank = getTensorRank(input);
    if (inputRank < 2)
      return rewriter.notifyMatchFailure(
          op, "input must have rank greater than or equal to 2");

    // In the inference mode, the `runningMean` and `runningVar` must not be
    // None.
    if (runningMean.getType().isa<Torch::NoneType>() ||
        runningVar.getType().isa<Torch::NoneType>())
      return rewriter.notifyMatchFailure(
          op, "running stats must not be None in inference mode");

    // Rank of `runningMean` and `runningVar` must be exactly 1.
    if (getTensorRank(runningMean) != 1 || getTensorRank(runningVar) != 1)
      return rewriter.notifyMatchFailure(
          op, "expected running_mean and running_var to be rank 1");

    Value zero =
        rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(0));
    Value one =
        rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(1));
    Value numFeatures = rewriter.create<AtenSizeIntOp>(loc, input, /*dim=*/one);
    // TODO: Add Runtime Asserts to check the shape of weight, bias,
    // running_mean and running_var to be (numFeatures).

    // The `runningMean` and `runningVar` must be reshaped to (1, C, 1?, 1?, 1?)
    // to make it broadcast-compatible with (N, C, D?, H?, W?).
    // 1. runningMean = runningMean.view(1, C, 1?, 1?, 1?)
    // 2. runningVar = runningVar.view(1, C, 1?, 1?, 1?)
    SmallVector<Value> runningStatsShape(inputRank, one);
    runningStatsShape[1] = numFeatures;
    Value runningStatsSizeList = rewriter.create<PrimListConstructOp>(
        loc, ListType::get(IntType::get(context)), runningStatsShape);

    SmallVector<int64_t> runningStatsShapeInt(inputRank, 1);
    runningStatsShapeInt[1] = ShapedType::kDynamicSize;
    Type dtype = input.getType().cast<ValueTensorType>().getDtype();
    Type reshapeType = ValueTensorType::get(
        context, llvm::makeArrayRef(runningStatsShapeInt), dtype);

    runningMean = rewriter.create<AtenViewOp>(loc, reshapeType, runningMean,
                                              runningStatsSizeList);
    runningVar = rewriter.create<AtenViewOp>(loc, reshapeType, runningVar,
                                             runningStatsSizeList);

    // normalizedInput = (input - runningMean) / (sqrt(runningVar + eps)).
    Value inputSubMean = rewriter.create<AtenSubTensorOp>(
        loc, input.getType(), input, runningMean, /*alpha=*/one);
    Value varEps = rewriter.create<AtenAddScalarOp>(
        loc, runningVar.getType(), runningVar, eps, /*alpha=*/one);
    Value invStd = rewriter.create<AtenRsqrtOp>(loc, varEps.getType(), varEps);
    Value normalizedInput = rewriter.create<AtenMulTensorOp>(
        loc, inputSubMean.getType(), inputSubMean, invStd);

    // The `weight` and `bias` must be reshaped to (1, C, 1?, 1?, 1?) to make it
    // broadcast-compatible with (N, C, D?, H?, W?).
    // 1. weight = weight.view(1, C, 1?, 1?, 1?)
    // 2. bias = bias.view(1, C, 1?, 1?, 1?)
    // 3. output = normalizedInput * weight + bias
    Value batchNormOutput = normalizedInput;
    if (!weight.getType().isa<Torch::NoneType>()) {
      // Rank of `weight` must be exactly 1.
      if (getTensorRank(weight) != 1)
        return rewriter.notifyMatchFailure(op, "expected weight to be rank 1");
      weight = rewriter.create<AtenViewOp>(loc, reshapeType, weight,
                                           runningStatsSizeList);
      batchNormOutput = rewriter.create<AtenMulTensorOp>(
          loc, batchNormOutput.getType(), batchNormOutput, weight);
    }
    if (!bias.getType().isa<Torch::NoneType>()) {
      // Rank of `bias` must be exactly 1.
      if (getTensorRank(bias) != 1)
        return rewriter.notifyMatchFailure(op, "expected bias to be rank 1");
      bias = rewriter.create<AtenViewOp>(loc, reshapeType, bias,
                                         runningStatsSizeList);
      batchNormOutput = rewriter.create<AtenAddTensorOp>(
          loc, batchNormOutput.getType(), batchNormOutput, bias, /*alpha=*/one);
    }

    // The `mean` and `invstd` outputs are empty tensors in inference mode.
    Value zeroList = rewriter.create<PrimListConstructOp>(
        loc, Torch::ListType::get(zero.getType()), zero);
    Value none = rewriter.create<ConstantNoneOp>(loc);
    Value emptyMeanTensor = rewriter.create<AtenEmptyMemoryFormatOp>(
        loc, op.getType(1), zeroList, /*dtype=*/none, /*layout=*/none,
        /*device=*/none, /*pin_memory=*/none, /*memory_format=*/none);
    Value emptyInvStdTensor = rewriter.create<AtenEmptyMemoryFormatOp>(
        loc, op.getType(2), zeroList, /*dtype=*/none, /*layout=*/none,
        /*device=*/none, /*pin_memory=*/none, /*memory_format=*/none);

    rewriter.replaceOp(op,
                       {batchNormOutput, emptyMeanTensor, emptyInvStdTensor});
    return success();
  }
};
} // namespace

// Decompse `Aten_UnsafeViewOp` into `AtenViewOp`. _unsafe_view() differs from
// view() in that the returned tensor isn't treated as a view for the purposes
// of automatic differentiation.  It's only safe to use if the `self` tensor is
// temporary. For example, the viewed tensor here (a + b) is discarded
// immediately after viewing:
//
//  res = _unsafe_view(a + b, size);
//
// This is a hack because in-place operations on tensors treated like views
// can be much more expensive than the same operations on non-view tensors.

// Refer to
// https://github.com/pytorch/pytorch/blob/364055b2771ecf9b54f1d67a8bf44bb5496476d4/aten/src/ATen/native/TensorShape.cpp#L2072
namespace {
class DecomposeAten_UnsafeViewOp : public OpRewritePattern<Aten_UnsafeViewOp> {
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(Aten_UnsafeViewOp op,
                                PatternRewriter &rewriter) const override {
    rewriter.replaceOpWithNewOp<AtenViewOp>(op, op.getType(), op.self(),
                                            op.size());
    return success();
  }
};
} // namespace

// In PyTorch, _reshape_alias just uses an already computed stride.
// See
// https://github.com/pytorch/pytorch/blob/d8c31a819d4a65e732b5901e3b994e1869851f1a/aten/src/ATen/native/TensorShape.cpp#L1153
// Note that this is the same decomposition as in AOTAutograd
// https://github.com/pytorch/functorch/blob/a3042d94e616d4143813668b1372d9d4545be14e/functorch/_src/aot_autograd.py#L104
namespace {
class DecomposeAten_ReshapeAliasOp
    : public OpRewritePattern<Aten_ReshapeAliasOp> {
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(Aten_ReshapeAliasOp op,
                                PatternRewriter &rewriter) const override {
    rewriter.replaceOpWithNewOp<AtenViewOp>(op, op.getType(), op.self(),
                                            op.size());
    return success();
  }
};
} // namespace

namespace {
// Decompose constant tensor like ops.
template <typename OpTy, typename NewOpTy>
class DecomposeConstantTensorNewLikeOp : public OpRewritePattern<OpTy> {
  using OpRewritePattern<OpTy>::OpRewritePattern;
  LogicalResult matchAndRewrite(OpTy op,
                                PatternRewriter &rewriter) const override {
    Value dtype = op.dtype();
    if (dtype.getType().isa<Torch::NoneType>()) {
      BaseTensorType tensorType =
          op.self().getType().template cast<BaseTensorType>();
      dtype =
          getDtypeIntValueForType(rewriter, op.getLoc(), tensorType.getDtype());
    }
    rewriter.replaceOpWithNewOp<NewOpTy>(op, op.getType(), op.size(), dtype,
                                         op.layout(), op.device(),
                                         op.pin_memory());
    return success();
  }
};
} // namespace

namespace {
// Decompose `aten.full` op into `aten.empty` and `aten.fill` ops.
class DecomposeAtenFullOp : public OpRewritePattern<AtenFullOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenFullOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value noneVal = rewriter.create<Torch::ConstantNoneOp>(loc);
    Value emptyTensor = rewriter.create<AtenEmptyMemoryFormatOp>(
        loc, op.getType(), op.size(), op.dtype(), op.layout(), op.device(),
        op.pin_memory(), /*memory_format=*/noneVal);
    rewriter.replaceOpWithNewOp<ValsemVariantAtenFillScalarOp>(
        op, op.getType(), emptyTensor, op.fill_value());
    return success();
  }
};
} // namespace

namespace {
// Decompose `aten.full_like` op into `aten.empty_like` and `aten.fill` ops.
class DecomposeAtenFullLikeOp : public OpRewritePattern<AtenFullLikeOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenFullLikeOp op,
                                PatternRewriter &rewriter) const override {
    Value emptyTensor = rewriter.create<AtenEmptyLikeOp>(
        op.getLoc(), op.getType(), op.self(), op.dtype(), op.layout(),
        op.device(), op.pin_memory(), op.memory_format());
    rewriter.replaceOpWithNewOp<ValsemVariantAtenFillScalarOp>(
        op, op.getType(), emptyTensor, op.fill_value());
    return success();
  }
};
} // namespace

namespace {
// Decompose `aten.index_put` op into `valsem.aten.index_put_impl` op.
class DecomposeAtenIndexPutOp : public OpRewritePattern<AtenIndexPutOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenIndexPutOp op,
                                PatternRewriter &rewriter) const override {
    Value cstFalse = rewriter.create<Torch::ConstantBoolOp>(op.getLoc(), false);
    rewriter.replaceOpWithNewOp<ValsemVariantAtenIndexPutImplOp>(
        op, op.getType(), op.self(), op.indices(), op.values(), op.accumulate(),
        /*unsafe=*/cstFalse);
    return success();
  }
};
} // namespace

namespace {
class DecomposeAtenExpandAsOp : public OpRewritePattern<AtenExpandAsOp> {
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenExpandAsOp op,
                                PatternRewriter &rewriter) const override {

    auto sizeListType =
        Torch::ListType::get(Torch::IntType::get(op.getContext()));
    Value sizeList =
        rewriter.create<AtenSizeOp>(op.getLoc(), sizeListType, op.other());
    rewriter.replaceOpWithNewOp<AtenBroadcastToOp>(op, op.getType(), op.self(),
                                                   sizeList);
    return success();
  }
};
} // namespace

namespace {
// Decompose `aten._to_copy` op into `valsem.aten.copy` op.
class DecomposeAten_ToCopyOp : public OpRewritePattern<Aten_ToCopyOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(Aten_ToCopyOp op,
                                PatternRewriter &rewriter) const override {
    Value emptyTensor = rewriter.create<AtenEmptyLikeOp>(
        op.getLoc(), op.getType(), op.self(), op.dtype(), op.layout(),
        op.device(), op.pin_memory(), op.memory_format());
    rewriter.replaceOpWithNewOp<ValsemVariantAtenCopyOp>(
        op, op.getType(), emptyTensor, op.self(), op.non_blocking());
    return success();
  }
};
} // namespace

namespace {
// Decompose `aten.new_empty` op into `aten.empty.memory_format` op.
class DecomposeAtenNewEmptyOp : public OpRewritePattern<AtenNewEmptyOp> {
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenNewEmptyOp op,
                                PatternRewriter &rewriter) const override {
    Value noneVal = rewriter.create<ConstantNoneOp>(op.getLoc());
    Value dtype = op.dtype();
    if (dtype.getType().isa<Torch::NoneType>()) {
      BaseTensorType tensorType = op.self().getType().cast<BaseTensorType>();
      dtype =
          getDtypeIntValueForType(rewriter, op.getLoc(), tensorType.getDtype());
    }
    rewriter.replaceOpWithNewOp<AtenEmptyMemoryFormatOp>(
        op, op.getType(), op.size(), dtype, op.layout(), op.device(),
        op.pin_memory(), /*memory_format=*/noneVal);
    return success();
  }
};
} // namespace

namespace {
// Decompose `aten.index_put.hacked_twin` op into `valsem.aten.index_put_impl`
// op.
class DecomposeAtenIndexPutHackedTwinOp
    : public OpRewritePattern<AtenIndexPutHackedTwinOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenIndexPutHackedTwinOp op,
                                PatternRewriter &rewriter) const override {
    Value cstFalse = rewriter.create<Torch::ConstantBoolOp>(op.getLoc(), false);
    rewriter.replaceOpWithNewOp<ValsemVariantAtenIndexPutImplOp>(
        op, op.getType(), op.self(), op.indices(), op.values(), op.accumulate(),
        /*unsafe=*/cstFalse);
    return success();
  }
};
} // namespace

namespace {
// Decompose `aten.pad` op into `aten.constant_pad_nd` op.
class DecomposeAtenPadOp : public OpRewritePattern<AtenPadOp> {
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenPadOp op,
                                PatternRewriter &rewriter) const override {

    Value value = op.value();
    if (value.getType().isa<Torch::OptionalType>())
      return rewriter.notifyMatchFailure(op, "optional type not supported");
    if (value.getType().isa<Torch::NoneType>())
      value = rewriter.create<Torch::ConstantFloatOp>(
          op.getLoc(), rewriter.getF64FloatAttr(0));

    rewriter.replaceOpWithNewOp<AtenConstantPadNdOp>(
        op, op.getType(), op.self(), op.pad(), value);
    return success();
  }
};
} // namespace

namespace {
// Decompose `aten.to.dtype_layout` op into `aten.to.dtype` op.
class DecomposeAtenToDtypeLayoutOp
    : public OpRewritePattern<AtenToDtypeLayoutOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenToDtypeLayoutOp op,
                                PatternRewriter &rewriter) const override {
    // TODO: Add support for pin_memory arg equal to `True`.
    if (!op.pin_memory().getType().isa<Torch::NoneType>()) {
      bool pinMemory;
      if (!matchPattern(op.pin_memory(), m_TorchConstantBool(&pinMemory)))
        return rewriter.notifyMatchFailure(
            op, "unimplemented: pin_memory must be a constant");
      else if (pinMemory)
        return rewriter.notifyMatchFailure(
            op, "unimplemented: pin_memory is expected to be false");
    }

    // TODO: Add support for non-None device arg.
    if (!op.device().getType().isa<Torch::NoneType>()) {
      return rewriter.notifyMatchFailure(
          op, "unimplemented: device arg must be None");
    }

    // TODO: Add support for non-strided layout.
    // torch.layout is by default strided i.e. 0.
    if (!op.layout().getType().isa<Torch::NoneType>()) {
      int64_t tensorLayout;
      if (!matchPattern(op.layout(), m_TorchConstantInt(&tensorLayout)))
        return rewriter.notifyMatchFailure(
            op, "unimplemented: layout must be a constant");
      else if (tensorLayout != torch_upstream::Layout::Strided)
        return rewriter.notifyMatchFailure(
            op, "unimplemented: layout is expected to be strided");
    }

    rewriter.replaceOpWithNewOp<AtenToDtypeOp>(op, op.getType(), op.self(),
                                               op.dtype(), op.non_blocking(),
                                               op.copy(), op.memory_format());
    return success();
  }
};
} // namespace

namespace {
// Decompose `aten.to.device` op into `aten.to.dtype` op.
class DecomposeAtenToDeviceOp : public OpRewritePattern<AtenToDeviceOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenToDeviceOp op,
                                PatternRewriter &rewriter) const override {

    // Device information isn't relevant to torch-mlir, so we can drop that info
    // here.
    rewriter.replaceOpWithNewOp<AtenToDtypeOp>(op, op.getType(), op.self(),
                                               op.dtype(), op.non_blocking(),
                                               op.copy(), op.memory_format());

    return success();
  }
};
} // namespace

namespace {
// Decompose `aten.adaptive_avg_pool2d` op into `aten.avg_pool2d` op.
//
// For AdaptiveAvgPool2d op, when the input size is an integer multiple of
// output size the kernel_size, stride and padding is calculated as follows:
// strideH = inH // outH
// strideW = inH // outH
// kernelH = inH - [(outH - 1) * strideH]
// kernelW = inW - [(outW - 1) * strideW]
// paddingH = 0, paddingW = 0
//
// For the special case, when the output size is one for all dimensions,
// the kernel size is same as the input size.
class DecomposeAtenAdaptiveAvgPool2dOp
    : public OpRewritePattern<AtenAdaptiveAvgPool2dOp> {
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenAdaptiveAvgPool2dOp op,
                                PatternRewriter &rewriter) const override {

    Location loc = op.getLoc();
    MLIRContext *context = op.getContext();

    Value input = op.self();
    int64_t rank = getTensorRank(input);
    SmallVector<Value, 2> inputHW;
    Value dimH = rewriter.create<Torch::ConstantIntOp>(
        loc, rewriter.getI64IntegerAttr(rank - 2));
    inputHW.push_back(
        /*inH=*/rewriter.create<AtenSizeIntOp>(loc, input, dimH));
    Value dimW = rewriter.create<Torch::ConstantIntOp>(
        loc, rewriter.getI64IntegerAttr(rank - 1));
    inputHW.push_back(
        /*inW=*/rewriter.create<AtenSizeIntOp>(loc, input, dimW));

    Value outputShape = op.output_size();
    SmallVector<Value> outputShapeSizesTorchInt;
    getListConstructElements(outputShape, outputShapeSizesTorchInt);

    // TODO: Add support for cases other than:
    // 1.) inH == outH and inW == outW.
    // 2.) outH == outW == 1
    bool unitOutputSize = true;
    for (Value outShape : outputShapeSizesTorchInt) {
      int64_t outShapeInt;
      if (!matchPattern(outShape, m_TorchConstantInt(&outShapeInt))) {
        return rewriter.notifyMatchFailure(
            op, "output size is expected to be a constant");
      }
      if (outShapeInt != 1) {
        unitOutputSize = false;
        break;
      }
    }

    Value constantOne = rewriter.create<Torch::ConstantIntOp>(
        loc, rewriter.getI64IntegerAttr(1));
    Value constantZero = rewriter.create<Torch::ConstantIntOp>(
        loc, rewriter.getI64IntegerAttr(0));
    Value constantFalse = rewriter.create<Torch::ConstantBoolOp>(loc, false);
    Value constantTrue = rewriter.create<Torch::ConstantBoolOp>(loc, true);
    Value constantNone = rewriter.create<Torch::ConstantNoneOp>(loc);
    SmallVector<Value, 2> kernelSize;

    for (unsigned i = 0; i < inputHW.size(); i++) {
      if (unitOutputSize) {
        BaseTensorType inputTensorType = input.getType().cast<BaseTensorType>();
        ArrayRef<int64_t> inputShape = inputTensorType.getSizes();
        kernelSize.push_back(inputShape[rank - 2 + i] == kUnknownSize
                                 ? inputHW[i]
                                 : rewriter.create<Torch::ConstantIntOp>(
                                       loc, rewriter.getI64IntegerAttr(
                                                inputShape[rank - 2 + i])));
      } else {
        Value cond = rewriter.create<AtenEqIntOp>(loc, inputHW[i],
                                                  outputShapeSizesTorchInt[i]);
        rewriter.create<RuntimeAssertOp>(
            loc, cond,
            "unimplemented: only support cases where input and output size are "
            "equal for non-unit output size");

        Value outMinusOne = rewriter.create<AtenSubIntOp>(
            loc, outputShapeSizesTorchInt[i], constantOne);
        kernelSize.push_back(
            rewriter.create<AtenSubIntOp>(loc, inputHW[i], outMinusOne));
      }
    }

    Value kernelSizeList = rewriter.create<PrimListConstructOp>(
        loc, Torch::ListType::get(Torch::IntType::get(context)), kernelSize);
    // Currently we only support cases where input size is equal to the output
    // size or unit output size. For the former case, stride is always equal to
    // one and for the latter the stride value doesn't matter, since the kernel
    // size is same as the input size. Therfore, keeping the stride as one for
    // the latter case as well for the ease of implementation.
    Value strideList = rewriter.create<PrimListConstructOp>(
        loc, Torch::ListType::get(Torch::IntType::get(context)),
        ValueRange{constantOne, constantOne});
    Value paddingSizeList = rewriter.create<PrimListConstructOp>(
        loc, Torch::ListType::get(Torch::IntType::get(context)),
        ValueRange{constantZero, constantZero});

    rewriter.replaceOpWithNewOp<AtenAvgPool2dOp>(
        op, op.getType(), input, kernelSizeList, strideList, paddingSizeList,
        /*ceil_mode=*/constantFalse, /*count_include_pad=*/constantTrue,
        /*divisor_override=*/constantNone);
    return success();
  }
};
} // namespace

namespace {
// Decompose `aten.clamp_min` op into `aten.clamp` op.
class DecomposeAtenClampMinOp : public OpRewritePattern<AtenClampMinOp> {
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenClampMinOp op,
                                PatternRewriter &rewriter) const override {
    Value constantNone = rewriter.create<Torch::ConstantNoneOp>(op.getLoc());
    rewriter.replaceOpWithNewOp<AtenClampOp>(op, op.getType(), op.self(),
                                             op.min(), /*max=*/constantNone);
    return success();
  }
};
} // namespace

namespace {
// Decompose `aten.clamp_max` op into `aten.clamp` op.
class DecomposeAtenClampMaxOp : public OpRewritePattern<AtenClampMaxOp> {
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenClampMaxOp op,
                                PatternRewriter &rewriter) const override {
    Value constantNone = rewriter.create<Torch::ConstantNoneOp>(op.getLoc());
    rewriter.replaceOpWithNewOp<AtenClampOp>(op, op.getType(), op.self(),
                                             /*min=*/constantNone, op.max());
    return success();
  }
};
} // namespace

namespace {
// Decompose `aten.baddbmm` op into `aten.bmm`, `aten.mul.Scalar`, and
// `aten.add.Tensor` op.
class DecomposeAtenBaddbmmOp : public OpRewritePattern<AtenBaddbmmOp> {
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenBaddbmmOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value bmm =
        rewriter.create<AtenBmmOp>(loc, op.getType(), op.batch1(), op.batch2());
    Value alphaTimesBmm =
        rewriter.create<AtenMulScalarOp>(loc, op.getType(), bmm, op.alpha());
    Value input = op.self();
    BaseTensorType inputType = input.getType().cast<BaseTensorType>();
    BaseTensorType resultType =
        op->getResult(0).getType().cast<BaseTensorType>();
    if (inputType.hasDtype() && resultType.hasDtype() &&
        inputType.getDtype() != resultType.getDtype()) {
      input = convertTensorToDtype(rewriter, loc, input, resultType.getDtype());
    }
    rewriter.replaceOpWithNewOp<AtenAddTensorOp>(
        op, op.getType(), alphaTimesBmm, op.self(), op.beta());
    return success();
  }
};
} // namespace

namespace {
// Decompose `aten.floor_divide` op into `aten.div.Tensor_mode` op.
class DecomposeAtenFloorDivideOp : public OpRewritePattern<AtenFloorDivideOp> {
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenFloorDivideOp op,
                                PatternRewriter &rewriter) const override {
    // https://pytorch.org/docs/stable/generated/torch.floor_divide.html
    // PyTorch aten.floor_divide is a misnomer because it actually rounds
    // the quotient towards zero instead of taking its floor.
    Value cstStrFloor =
        rewriter.create<Torch::ConstantStrOp>(op.getLoc(), "trunc");
    rewriter.replaceOpWithNewOp<AtenDivTensorModeOp>(
        op, op.getType(), op.self(), op.other(),
        /*rounding_mode=*/cstStrFloor);
    return success();
  }
};
} // namespace

namespace {
// Decompose `aten.numpy_T` op into `aten.permute` op.
class DecomposeAtenNumpyTOp : public OpRewritePattern<AtenNumpyTOp> {
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenNumpyTOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value self = op.self();
    int64_t inputRank = getTensorRank(self);

    SmallVector<Value> dimListElements;
    for (int64_t i = inputRank - 1; i >= 0; i--)
      dimListElements.push_back(rewriter.create<Torch::ConstantIntOp>(
          loc, rewriter.getI64IntegerAttr(i)));
    Value dimList = rewriter.create<PrimListConstructOp>(
        loc, Torch::ListType::get(Torch::IntType::get(op->getContext())),
        dimListElements);
    rewriter.replaceOpWithNewOp<AtenPermuteOp>(op, op.getType(), self, dimList);
    return success();
  }
};
} // namespace

template <typename OpTy>
static LogicalResult calculateVariance(OpTy op, PatternRewriter &rewriter,
                                       bool unbiased, int64_t correction) {
  Location loc = op.getLoc();
  Value self = op.self();
  Value dimList = op.dim();
  Value keepDim = op.keepdim();
  BaseTensorType inputTensorTy = self.getType().cast<BaseTensorType>();
  Type outputType = op.getType();
  BaseTensorType outputTensorType = outputType.cast<BaseTensorType>();
  Type newOutputType = outputTensorType.getWithSizesAndDtype(
      outputTensorType.getSizes(), rewriter.getF64Type());
  if (!inputTensorTy.hasDtype() ||
      !inputTensorTy.getDtype().isa<mlir::FloatType>()) {
    return rewriter.notifyMatchFailure(
        op, "support floating-point type input only");
  }

  // Upcasting the input tensor to `F64` dtype for higher precision during the
  // computation of the result.
  if (inputTensorTy.getDtype().getIntOrFloatBitWidth() != 64) {
    self = convertTensorToDtype(rewriter, loc, self, rewriter.getF64Type());
    inputTensorTy = self.getType().cast<BaseTensorType>();
  }

  unsigned inputRank = getTensorRank(self);
  SmallVector<Value> dimListElements;
  bool isNoneOrEmpty = true;
  if (!dimList.getType().template isa<Torch::NoneType>()) {
    if (!getListConstructElements(dimList, dimListElements))
      return rewriter.notifyMatchFailure(
          op, "expect dimList to be constructed from list construct");
    if (!dimListElements.empty() || inputRank == 0)
      isNoneOrEmpty = false;
  }
  if (isNoneOrEmpty) {
    for (unsigned i = 0; i < inputRank; i++)
      dimListElements.push_back(rewriter.create<Torch::ConstantIntOp>(
          loc, rewriter.getI64IntegerAttr(i)));
    dimList = rewriter.create<PrimListConstructOp>(
        loc, Torch::ListType::get(Torch::IntType::get(op.getContext())),
        dimListElements);
  }
  Type meanDimResultType = inputTensorTy;
  for (unsigned i = 0; i < dimListElements.size(); i++)
    meanDimResultType = computeReductionType(
        rewriter, op, meanDimResultType.cast<BaseTensorType>(),
        dimListElements[i],
        /*keepDim=*/true);

  Value constantNone = rewriter.create<ConstantNoneOp>(loc);
  Value constantTrue = rewriter.create<ConstantBoolOp>(loc, true);
  Value meanAlongDims = rewriter.create<AtenMeanDimOp>(
      loc, meanDimResultType, self, dimList, /*keepDim=*/constantTrue,
      /*dtype=*/constantNone);
  Value subMean =
      createTensorSub(rewriter, loc, inputTensorTy, self, meanAlongDims);
  Value square = rewriter.create<AtenSquareOp>(loc, inputTensorTy, subMean);

  if (!unbiased) {
    Value result = rewriter.create<AtenMeanDimOp>(
        loc, newOutputType, square, dimList, keepDim, /*dtype=*/constantNone);
    result = convertTensorToDtype(rewriter, loc, result,
                                  outputTensorType.getDtype());
    rewriter.replaceOp(op, result);
    return success();
  }
  // Divide the square sum by productDimSize - correction.
  Value squareSum = rewriter.create<AtenSumDimIntListOp>(
      loc, newOutputType, square, dimList, keepDim, /*dtype=*/constantNone);

  // `productDimSize` is product of sizes of dimensions to be reduced.
  Value constantOne =
      rewriter.create<Torch::ConstantIntOp>(loc, rewriter.getI64IntegerAttr(1));
  Value productDimSize = constantOne;
  for (Value dim : dimListElements) {
    Value dimSize = rewriter.create<AtenSizeIntOp>(loc, self, dim);
    productDimSize =
        rewriter.create<AtenMulIntOp>(loc, productDimSize, dimSize);
  }
  Value cstCorrection = rewriter.create<Torch::ConstantIntOp>(
      loc, rewriter.getI64IntegerAttr(correction));
  // The `correction` value should be less than or equal to `productDimSize +
  // 1`.
  Value productDimSizePlusOne =
      rewriter.create<AtenAddIntOp>(loc, productDimSize, constantOne);
  Value cond =
      rewriter.create<AtenGeIntOp>(loc, productDimSizePlusOne, cstCorrection);
  rewriter.create<RuntimeAssertOp>(
      loc, cond,
      "correction value should be less than or equal to productDimSize + 1");
  Value productDimSizeSubCorrection =
      rewriter.create<AtenSubIntOp>(loc, productDimSize, cstCorrection);
  Value result = rewriter.create<AtenDivScalarOp>(loc, newOutputType, squareSum,
                                                  productDimSizeSubCorrection);
  result =
      convertTensorToDtype(rewriter, loc, result, outputTensorType.getDtype());
  rewriter.replaceOp(op, result);
  return success();
}

// Decompose aten.var(x, dims) into:
// sub = aten.sub(x, aten.mean(x, dims))
// square = aten.square(sub)
// For Unbiased case:
// out = aten.sum(square, dims) / (productDimSize-1)
// For Biased case:
// out = aten.mean(square, dims)
namespace {
class DecomposeAtenVarDimOp : public OpRewritePattern<AtenVarDimOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenVarDimOp op,
                                PatternRewriter &rewriter) const override {
    bool unbiased;
    if (!matchPattern(op.unbiased(), m_TorchConstantBool(&unbiased))) {
      return rewriter.notifyMatchFailure(
          op, "Only support constant unbiased for aten.var");
    }
    int64_t correction = unbiased ? 1 : 0;
    if (failed(calculateVariance<AtenVarDimOp>(op, rewriter, unbiased,
                                               correction)))
      return rewriter.notifyMatchFailure(op, "invalid variance parameters");
    return success();
  }
};
} // namespace

// Decompose aten.var(x, dims) into:
// sub = aten.sub(x, aten.mean(x, dims))
// square = aten.square(sub)
// out = aten.sum(square, dims) / (productDimSize - correction)
namespace {
class DecomposeAtenVarCorrectionOp
    : public OpRewritePattern<AtenVarCorrectionOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenVarCorrectionOp op,
                                PatternRewriter &rewriter) const override {
    int64_t correction;
    if (!op.correction().getType().isa<Torch::NoneType>()) {
      if (!matchPattern(op.correction(), m_TorchConstantInt(&correction)))
        return rewriter.notifyMatchFailure(
            op, "Only support constant int correction for aten.var");
    } else {
      // The default value in case of `correction` being None is 1.
      correction = 1;
    }
    bool unbiased = correction == 0 ? false : true;
    if (failed(calculateVariance<AtenVarCorrectionOp>(op, rewriter, unbiased,
                                                      correction)))
      return rewriter.notifyMatchFailure(op, "invalid variance parameters");
    return success();
  }
};
} // namespace

namespace {
// Decompose the `aten.select_scatter` operation into `aten.slice_scatter` op.
class DecomposeAtenSelectScatterOp
    : public OpRewritePattern<AtenSelectScatterOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AtenSelectScatterOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value start = op.index();
    Value dim = op.dim();
    Value self = op.self();
    Value src = op.src();

    Value one =
        rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(1));
    Value startPlusOne =
        rewriter.create<AtenAddIntOp>(loc, one.getType(), start, one);
    BaseTensorType srcTensorType = src.getType().cast<BaseTensorType>();
    SmallVector<int64_t> sizes;
    if (!srcTensorType.hasSizes())
      return rewriter.notifyMatchFailure(op, "src tensor must have size");

    ArrayRef<int64_t> srcShape = srcTensorType.getSizes();
    // `src` has a reduced rank. Hence add 1.
    int64_t srcRank = srcShape.size() + 1;
    int64_t dimInt = 0;
    if (matchPattern(dim, m_TorchConstantInt(&dimInt))) {
      dimInt = toPositiveDim(dimInt, srcRank);
      if (!isValidDim(dimInt, srcRank))
        return rewriter.notifyMatchFailure(op, "dim is not a valid dim");

      sizes.append(srcShape.begin(), srcShape.end());
      sizes.insert(sizes.begin() + dimInt, 1);

    } else {
      sizes.resize(srcShape.size() + 1, kUnknownSize);
    }
    Type srcType = srcTensorType.getWithSizesAndDtype(llvm::makeArrayRef(sizes),
                                                      srcTensorType.getDtype());
    src = rewriter.create<AtenUnsqueezeOp>(loc, srcType, src, dim);
    rewriter.replaceOpWithNewOp<AtenSliceScatterOp>(
        op, op.self().getType(), self, src, dim, start, startPlusOne,
        /*step=*/one);
    return success();
  }
};
} // namespace

namespace {
class DecomposeAten_EmbeddingBagOp
    : public OpRewritePattern<Aten_EmbeddingBagOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(Aten_EmbeddingBagOp op,
                                PatternRewriter &rewriter) const override {
    Value weight = op.weight();
    Value indices = op.indices();
    Value offsets = op.offsets();
    Value scaleGradByFreq = op.scale_grad_by_freq();
    Value mode = op.mode();
    Value sparse = op.sparse();
    Value perSampleWeights = op.per_sample_weights();
    Value includeLastOffset = op.include_last_offset();
    Value paddingIdx = op.padding_idx();

    auto resultType0 = op->getResult(0).getType();
    auto resultType1 = op->getResult(1).getType();
    auto resultType2 = op->getResult(2).getType();
    auto resultType3 = op->getResult(3).getType();

    mlir::TypeRange returnTypes{resultType0, resultType1, resultType2,
                                resultType3};

    rewriter.replaceOpWithNewOp<AtenEmbeddingBagPaddingIdxOp>(
        op, returnTypes, weight, indices, offsets, scaleGradByFreq, mode,
        sparse, perSampleWeights, includeLastOffset, paddingIdx);

    return success();
  }
};
} // namespace

namespace {
class DecomposeComplexOpsPass
    : public DecomposeComplexOpsBase<DecomposeComplexOpsPass> {
public:
  DecomposeComplexOpsPass() = default;
  DecomposeComplexOpsPass(ArrayRef<std::string> legalOps) {
    this->legalOps = legalOps;
  }
  void runOnOperation() override {
    MLIRContext *context = &getContext();
    RewritePatternSet patterns(context);
    ConversionTarget target(*context);
    target.addLegalDialect<Torch::TorchDialect>();

    patterns.add<DecomposeAtenSoftmaxIntOp>(context);
    target.addIllegalOp<AtenSoftmaxIntOp>();
    patterns.add<DecomposeAten_SoftmaxOp>(context);
    target.addIllegalOp<Aten_SoftmaxOp>();
    patterns.add<DecomposeAten_LogSoftmaxOp>(context);
    target.addIllegalOp<Aten_LogSoftmaxOp>();
    patterns.add<DecomposeAtenLogSoftmaxIntOp>(context);
    target.addIllegalOp<AtenLogSoftmaxIntOp>();
    patterns.add<DecomposeAtenEmptyLikeOp>(context);
    target.addIllegalOp<AtenEmptyLikeOp>();
    patterns.add<DecomposeConstantTensorAllocLikeOp<AtenOnesLikeOp, 1>>(
        context);
    target.addIllegalOp<AtenOnesLikeOp>();
    patterns.add<DecomposeConstantTensorAllocLikeOp<AtenZerosLikeOp, 0>>(
        context);
    target.addIllegalOp<AtenZerosLikeOp>();
    patterns.add<DecomposeAtenRepeatOp>(context);
    target.addIllegalOp<AtenRepeatOp>();
    patterns.add<DecomposeAtenExpandOp>(context);
    target.addIllegalOp<AtenExpandOp>();
    patterns.add<DecomposeAtenWhereScalarOp>(context);
    target.addIllegalOp<AtenWhereScalarOp>();
    patterns.add<DecomposeAtenWhereScalarOtherOp>(context);
    target.addIllegalOp<AtenWhereScalarOtherOp>();
    patterns.add<DecomposeAtenWhereScalarSelfOp>(context);
    target.addIllegalOp<AtenWhereScalarSelfOp>();
    patterns.add<DecomposeAtenSizeOp>(context);
    target.addIllegalOp<AtenSizeOp>();
    patterns.add<DecomposeAtenReshapeOp>(context);
    target.addIllegalOp<AtenReshapeOp>();
    patterns.add<DecomposeAten_SoftmaxBackwardDataOp>(context);
    target.addIllegalOp<Aten_SoftmaxBackwardDataOp>();
    patterns.add<DecomposeAtenTanhBackwardOp>(context);
    target.addIllegalOp<AtenTanhBackwardOp>();
    patterns.add<DecomposeAtenAddmmOp>(context);
    target.addIllegalOp<AtenAddmmOp>();
    patterns.add<DecomposeAtenMeanOp>(context);
    target.addIllegalOp<AtenMeanOp>();
    patterns.add<DecomposeAtenMeanDimOp>(context);
    target.addIllegalOp<AtenMeanDimOp>();
    patterns.add<DecomposeAtenSelectIntOp>(context);
    target.addIllegalOp<AtenSelectIntOp>();
    patterns.add<DecomposeAtenMatmulOp>(context);
    target.addIllegalOp<AtenTOp>();
    patterns.add<DecomposeAtenTOp>(context);
    patterns.add<DecomposeAten_LogSoftmaxBackwardDataOp>(context);
    target.addIllegalOp<Aten_LogSoftmaxBackwardDataOp>();
    target.addDynamicallyLegalOp<AtenMatmulOp>([](AtenMatmulOp op) {
      int lhsRank = getTensorRank(op.self());
      int rhsRank = getTensorRank(op.other());

      // Make aten.matmul legal if the following condition is satisfied.
      return (lhsRank != 2 || rhsRank != 2) && (lhsRank != 3 || rhsRank != 3);
    });
    patterns.add<DecomposeAtenAddCLikeOp<AtenAddcmulOp, AtenMulTensorOp>>(
        context);
    target.addIllegalOp<AtenAddcmulOp>();
    patterns.add<DecomposeAtenAddCLikeOp<AtenAddcdivOp, AtenDivTensorOp>>(
        context);
    target.addIllegalOp<AtenAddcdivOp>();
    target.addIllegalOp<AtenLayerNormOp>();
    patterns.add<DecomposeAtenLayerNormOp>(context);
    target.addIllegalOp<AtenNativeBatchNormOp>();
    patterns.add<DecomposeAtenNativeBatchNormOp>(context);
    target.addIllegalOp<AtenConvolutionOverrideableOp>();
    patterns.add<DecomposeAtenConvolutionOverrideableOp>(context);
    target.addIllegalOp<Aten_ConvolutionOp>();
    patterns.add<DecomposeAten_ConvolutionOp>(context);
    target.addIllegalOp<AtenConv2dOp>();
    patterns.add<DecomposeAtenConv2dOp>(context);
    patterns.add<DecomposeAtenArangeOp>(context);
    target.addIllegalOp<AtenArangeOp>();
    patterns.add<DecomposeAtenArangeStartOp>(context);
    target.addIllegalOp<AtenArangeStartOp>();
    patterns.add<DecomposeAtenArgMaxOp>(context);
    target.addIllegalOp<AtenArgmaxOp>();
    patterns.add<DecomposeAtenSquareOp>(context);
    target.addIllegalOp<AtenSquareOp>();
    patterns.add<DecomposeAtenVarOp>(context);
    target.addIllegalOp<AtenVarOp>();
    patterns.add<DecomposeAtenStdOp>(context);
    target.addIllegalOp<AtenStdOp>();
    patterns.add<DecomposeAten_UnsafeViewOp>(context);
    target.addIllegalOp<Aten_UnsafeViewOp>();
    patterns.add<DecomposeAten_ReshapeAliasOp>(context);
    target.addIllegalOp<Aten_ReshapeAliasOp>();
    patterns.add<DecomposeAtenBernoulliOp>(context);
    target.addIllegalOp<AtenBernoulliOp>();
    patterns.add<DecomposeValsemVariantAtenBernoulliFloatOp>(context);
    target.addIllegalOp<ValsemVariantAtenBernoulliFloatOp>();
    patterns.add<DecomposeValsemVariantAtenBernoulliTensorOp>(context);
    target.addIllegalOp<ValsemVariantAtenBernoulliTensorOp>();
    patterns.add<DecomposeAtenZeroOp>(context);
    target.addIllegalOp<AtenZeroOp>();
    patterns.add<DecomposeAtenRandLikeOp>(context);
    target.addIllegalOp<AtenRandLikeOp>();
    patterns.add<DecomposeAtenHardsigmoidOp>(context);
    target.addIllegalOp<AtenHardsigmoidOp>();
    patterns.add<DecomposeAtenHardswishOp>(context);
    target.addIllegalOp<AtenHardswishOp>();
    patterns.add<DecomposeAtenSoftplusOp>(context);
    target.addIllegalOp<AtenSoftplusOp>();
    patterns.add<DecomposeAtenSiluOp>(context);
    target.addIllegalOp<AtenSiluOp>();
    patterns.add<DecomposeConstantTensorNewLikeOp<AtenNewZerosOp, AtenZerosOp>>(
        context);
    target.addIllegalOp<AtenNewZerosOp>();
    patterns.add<DecomposeConstantTensorNewLikeOp<AtenNewOnesOp, AtenOnesOp>>(
        context);
    target.addIllegalOp<AtenNewOnesOp>();
    patterns.add<DecomposeAtenHardtanhOp>(context);
    target.addIllegalOp<AtenHardtanhOp>();
    patterns.add<DecomposeAtenFullOp>(context);
    target.addIllegalOp<AtenFullOp>();
    patterns.add<DecomposeAtenFullLikeOp>(context);
    target.addIllegalOp<AtenFullLikeOp>();
    patterns.add<DecomposeAtenIndexPutOp>(context);
    target.addIllegalOp<AtenIndexPutOp>();
    patterns.add<DecomposeAtenExpandAsOp>(context);
    target.addIllegalOp<AtenExpandAsOp>();
    patterns.add<DecomposeAten_ToCopyOp>(context);
    target.addIllegalOp<Aten_ToCopyOp>();
    patterns.add<DecomposeAtenDropoutOp>(context);
    target.addIllegalOp<AtenDropoutOp>();
    target.addIllegalOp<AtenNewEmptyOp>();
    patterns.add<DecomposeAtenNewEmptyOp>(context);
    patterns.add<DecomposeAtenIndexPutHackedTwinOp>(context);
    target.addIllegalOp<AtenIndexPutHackedTwinOp>();
    target.addIllegalOp<AtenPadOp>();
    patterns.add<DecomposeAtenPadOp>(context);
    patterns.add<DecomposeAtenToDtypeLayoutOp>(context);
    target.addIllegalOp<AtenToDtypeLayoutOp>();
    patterns.add<DecomposeAtenToDeviceOp>(context);
    target.addIllegalOp<AtenToDeviceOp>();
    patterns.add<DecomposeAtenAdaptiveAvgPool2dOp>(context);
    target.addIllegalOp<AtenAdaptiveAvgPool2dOp>();
    patterns.add<DecomposeAtenClampMinOp>(context);
    target.addIllegalOp<AtenClampMinOp>();
    patterns.add<DecomposeAtenClampMaxOp>(context);
    target.addIllegalOp<AtenClampMaxOp>();
    patterns.add<DecomposeAtenBaddbmmOp>(context);
    target.addIllegalOp<AtenBaddbmmOp>();
    patterns.add<DecomposeAtenFloorDivideOp>(context);
    target.addIllegalOp<AtenFloorDivideOp>();
    patterns.add<DecomposeAtenNumpyTOp>(context);
    target.addIllegalOp<AtenNumpyTOp>();
    patterns.add<DecomposeAtenSelectScatterOp>(context);
    target.addIllegalOp<AtenSelectScatterOp>();
    patterns.add<DecomposeAtenVarDimOp>(context);
    target.addIllegalOp<AtenVarDimOp>();
    patterns.add<DecomposeAtenVarCorrectionOp>(context);
    target.addIllegalOp<AtenVarCorrectionOp>();
    patterns.add<DecomposeAtenStdDimOp>(context);
    target.addIllegalOp<AtenStdDimOp>();
    patterns.add<DecomposeAtenNarrowOp>(context);
    target.addIllegalOp<AtenNarrowOp>();
    patterns.add<DecomposeAten_EmbeddingBagOp>(context);
    target.addIllegalOp<Aten_EmbeddingBagOp>();

    for (std::string opName : legalOps) {
      target.addLegalOp(OperationName(opName, context));
    }

    if (failed(applyPartialConversion(getOperation(), target,
                                      std::move(patterns)))) {
      return signalPassFailure();
    }
  }
};
} // namespace
std::unique_ptr<OperationPass<func::FuncOp>>
mlir::torch::Torch::createDecomposeComplexOpsPass() {
  return std::make_unique<DecomposeComplexOpsPass>();
}