//===----------------------------------------------------------------------===//
//
// 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/Transforms/Passes.h"
#include "torch-mlir/Dialect/Torch/Utils/Utils.h"
#include "llvm/ADT/StringExtras.h"

using namespace mlir;
using namespace mlir::torch;
using namespace mlir::torch::Torch;
using namespace mlir::torch::torch_upstream; // For ScalarType and type

// Helper funtion to get rank of `Base tensor type`.
// -1 is returned if the tensorRank can't be determined.
static int getTensorRank(Value tensor) {
  int tensorRank = -1;
  BaseTensorType tensorType = tensor.getType().cast<BaseTensorType>();

  if (tensorType.hasSizes()) {
    ArrayRef<int64_t> tensorShape = tensorType.getSizes();
    tensorRank = tensorShape.size();
  }
  return tensorRank;
}

// 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,
                                 Value input, Value dim, bool keepDim) {
  BaseTensorType tensorType = input.getType().cast<BaseTensorType>();
  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, 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, 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<PseudoAtenFillScalarOp>(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, 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 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.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.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

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

// Decompose aten.var into: sum(square(x - mean))/(numTensorElements-1)
// for unbiased and mean(square(x - mean)) for biased case.
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();
    BaseTensorType inputTensorTy = self.getType().cast<BaseTensorType>();
    if (!inputTensorTy.hasDtype() ||
        !inputTensorTy.getDtype().isa<mlir::FloatType>()) {
      return rewriter.notifyMatchFailure(op,
                                         "Only aten.var support floating type");
    }
    BaseTensorType rank0FloatTensorTy = op.getType().cast<BaseTensorType>();
    assert(rank0FloatTensorTy.getSizes().size() == 0 &&
           "Op should have rank 0 tensor type");

    bool unbiased;
    if (!matchPattern(op.unbiased(), m_TorchConstantBool(&unbiased))) {
      return rewriter.notifyMatchFailure(
          op, "Only support constant unbiased for aten.var");
    }

    Value dtype = rewriter.create<ConstantNoneOp>(loc);
    Value mean =
        rewriter.create<AtenMeanOp>(loc, rank0FloatTensorTy, self, dtype);
    Value subMean = createTensorSub(rewriter, loc, inputTensorTy, self, mean);
    Value square = rewriter.create<AtenSquareOp>(loc, inputTensorTy, subMean);
    Value var;
    if (unbiased) {
      // Bessel’s correction is used. Divide the square sum by
      // numTensorElements-1.
      Value squareSum =
          rewriter.create<AtenSumOp>(loc, rank0FloatTensorTy, square, dtype);
      Value numTensorElements = rewriter.create<AtenNumelOp>(loc, square);
      Value cst1 = rewriter.create<Torch::ConstantIntOp>(
          loc, rewriter.getI64IntegerAttr(1));
      Value numTensorElementsSub1 =
          rewriter.create<AtenSubIntOp>(loc, numTensorElements, cst1);
      var = rewriter.replaceOpWithNewOp<AtenDivScalarOp>(
          op, rank0FloatTensorTy, squareSum, numTensorElementsSub1);
    } else {
      var = rewriter.replaceOpWithNewOp<AtenMeanOp>(op, rank0FloatTensorTy,
                                                    square, dtype);
    }
    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

// 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();
    auto inputType = input.getType().cast<BaseTensorType>();
    if (!inputType.hasDtype() || !inputType.getDtype().isa<mlir::FloatType>())
      return rewriter.notifyMatchFailure(op,
                                         "only support floating-point type");

    // TODO: Add support for layout, pin_memory and memory_format features.
    // Only `none` layout is supported.
    if (!op.layout().getType().isa<Torch::NoneType>())
      return rewriter.notifyMatchFailure(
          op, "unimplemented: only default layout is supported");

    // The pin_memory should be either `none` or constant `False`.
    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");
    }

    // Only `none` memory_format is supported.
    if (!op.memory_format().getType().isa<Torch::NoneType>())
      return rewriter.notifyMatchFailure(
          op, "unimplemented: only default memory format is supported");

    // Create a uniform random op with low and high set to 0.0 and 1.0
    // respectively.
    Value none = rewriter.create<ConstantNoneOp>(loc);
    Value lb =
        rewriter.create<ConstantFloatOp>(loc, rewriter.getF64FloatAttr(0.0));
    Value ub =
        rewriter.create<ConstantFloatOp>(loc, rewriter.getF64FloatAttr(1.0));
    rewriter.replaceOpWithNewOp<PseudoAtenUniformOp>(
        op, op.getType(), input, lb, ub, /*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 DecomposePseudoAtenBernoulliFloatOp
    : public OpRewritePattern<PseudoAtenBernoulliFloatOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(PseudoAtenBernoulliFloatOp 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 DecomposePseudoAtenBernoulliTensorOp
    : public OpRewritePattern<PseudoAtenBernoulliTensorOp> {
public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(PseudoAtenBernoulliTensorOp 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);
    std::vector<int64_t> meanVarSizes;
    for (int i = normalizedShapeSizesTorchInt.size(); i < inputRank; i++)
      meanVarSizes.push_back(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<PseudoAtenFillScalarOp>(
        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

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 {
    rewriter.replaceOpWithNewOp<NewOpTy>(op, op.getType(), op.size(),
                                         op.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<PseudoAtenFillScalarOp>(
        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<PseudoAtenFillScalarOp>(
        op, op.getType(), emptyTensor, op.fill_value());
    return success();
  }
};
} // namespace

namespace {
class DecomposeComplexOpsPass
    : public DecomposeComplexOpsBase<DecomposeComplexOpsPass> {
  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<DecomposeAtenExpandOp>(context);
    target.addIllegalOp<AtenExpandOp>();
    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<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);
    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<DecomposeAtenBernoulliOp>(context);
    target.addIllegalOp<AtenBernoulliOp>();
    patterns.add<DecomposePseudoAtenBernoulliFloatOp>(context);
    target.addIllegalOp<PseudoAtenBernoulliFloatOp>();
    patterns.add<DecomposePseudoAtenBernoulliTensorOp>(context);
    target.addIllegalOp<PseudoAtenBernoulliTensorOp>();
    patterns.add<DecomposeAtenRandLikeOp>(context);
    target.addIllegalOp<AtenRandLikeOp>();
    patterns.add<DecomposeAtenHardsigmoidOp>(context);
    target.addIllegalOp<AtenHardsigmoidOp>();
    patterns.add<DecomposeAtenHardswishOp>(context);
    target.addIllegalOp<AtenHardswishOp>();
    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>();

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