torch-mlir/lib/Conversion/TorchToLinalg/DataMovement.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 "mlir/IR/BuiltinTypes.h"
#include "mlir/IR/TypeSupport.h"
#include "mlir/Support/LogicalResult.h"
#include "mlir/Transforms/DialectConversion.h"
#include "torch-mlir/Conversion/TorchToLinalg/TorchToLinalg.h"

#include "../PassDetail.h"
#include "PopulatePatterns.h"
#include "Utils.h"
#include "mlir/Dialect/Arithmetic/IR/Arithmetic.h"
#include "mlir/Dialect/ControlFlow/IR/ControlFlowOps.h"
#include "mlir/Dialect/Linalg/IR/Linalg.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/IR/Matchers.h"
#include "torch-mlir/Conversion/Utils/Utils.h"
#include "torch-mlir/Dialect/Torch/IR/TorchDialect.h"
#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
#include "torch-mlir/Dialect/Torch/Utils/TorchUpstream.h"
#include "torch-mlir/Dialect/Torch/Utils/Utils.h"

#include <numeric>

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

static Value toPositiveValidDim(ConversionPatternRewriter &rewriter,
                                Location loc, Value torchOptionalInt,
                                Value builtinInt, Value defaultValue,
                                Value dimSize) {
  if (torchOptionalInt.getType().isa<Torch::NoneType>())
    return defaultValue;
  auto dimSizeAsInt = castIndexToInt64(rewriter, loc, dimSize);
  Value positiveDim =
      toPositiveDimDynamic(rewriter, loc, builtinInt, dimSizeAsInt);
  // positveDim < 0 ? 0 : positiveDim
  Value cst0 = rewriter.create<arith::ConstantOp>(
      loc, rewriter.getZeroAttr(dimSizeAsInt.getType()));
  Value predDimSltZero = rewriter.create<arith::CmpIOp>(
      loc, arith::CmpIPredicate::slt, positiveDim, cst0);
  Value atLeastZero =
      rewriter.create<arith::SelectOp>(loc, predDimSltZero, cst0, positiveDim);
  // atLeastZero > dimSizeAsInt ? dimSizeAsInt : atLeastZero
  Value sgtDimSize = rewriter.create<arith::CmpIOp>(
      loc, arith::CmpIPredicate::sgt, atLeastZero, dimSizeAsInt);
  Value boundedByDimSize = rewriter.create<arith::SelectOp>(
      loc, sgtDimSize, dimSizeAsInt, atLeastZero);

  return castIntToIndex(rewriter, loc, boundedByDimSize);
}

template <typename OpTy, typename OpAdaptor>
LogicalResult prepareArgumentsForSlicingOp(OpTy op, OpAdaptor adaptor,
                                           ConversionPatternRewriter &rewriter,
                                           SmallVector<Value> &resultShape,
                                           SmallVector<Value> &offsets,
                                           SmallVector<Value> &strides) {
  Location loc = op.getLoc();
  auto input = adaptor.self();
  RankedTensorType inputType =
      input.getType().template cast<RankedTensorType>();

  Value zero = rewriter.create<arith::ConstantIndexOp>(loc, 0);
  Value one = rewriter.create<arith::ConstantIndexOp>(loc, 1);

  int64_t dim;
  if (!matchPattern(op.dim(), m_TorchConstantInt(&dim)))
    return op->emitError("unimplemented: dim is not constant");

  int64_t inputRank = inputType.getRank();
  dim = toPositiveDim(dim, inputRank);
  if (!isValidDim(dim, inputRank))
    return rewriter.notifyMatchFailure(op, "dim is statically invalid");

  SmallVector<Value> inputShape = getTensorSizes(rewriter, loc, input);
  Value dimSize = inputShape[dim];

  Value torchTypeStart = op.start();
  Value torchTypeEnd = op.end();
  Value builtinTypeStart = adaptor.start();
  Value builtinTypeEnd = adaptor.end();

  if (torchTypeStart.getType().isa<OptionalType>() ||
      torchTypeEnd.getType().isa<OptionalType>())
    return rewriter.notifyMatchFailure(op, "unimplemented optional type arg");

  int64_t step;
  if (!matchPattern(op.step(), m_TorchConstantInt(&step))) {
    if (!op.step().getType().template isa<Torch::NoneType>())
      return op->emitError("unimplemented: step is not constant");
    step = 1;
  }

  Value start = toPositiveValidDim(rewriter, loc, torchTypeStart,
                                   builtinTypeStart, zero, dimSize);
  Value end = toPositiveValidDim(rewriter, loc, torchTypeEnd, builtinTypeEnd,
                                 dimSize, dimSize);

  // end >= start ? end : start
  Value endSgeStart = rewriter.create<arith::CmpIOp>(
      loc, arith::CmpIPredicate::sge, end, start);
  end = rewriter.create<arith::SelectOp>(loc, endSgeStart, end, start);
  Value stepIndex = rewriter.create<arith::ConstantIndexOp>(loc, step);

  // Slice logic: resultSize = floordiv(end - start + step - 1,  step)
  resultShape = getTensorSizes(rewriter, loc, input);
  Value len = rewriter.create<arith::SubIOp>(loc, end, start);
  Value resultSize = rewriter.create<arith::AddIOp>(loc, len, stepIndex);
  resultSize = rewriter.create<arith::SubIOp>(loc, resultSize, one);
  resultSize = rewriter.create<arith::FloorDivSIOp>(loc, resultSize, stepIndex);
  resultShape[dim] = resultSize;

  strides.resize(inputType.getRank(), one);
  offsets.resize(inputType.getRank(), zero);

  offsets[dim] = start;
  strides[dim] = rewriter.create<arith::MulIOp>(loc, strides[dim], stepIndex);
  return success();
}

namespace {
class ConvertAtenFlattenUsingIntsOp
    : public OpConversionPattern<AtenFlattenUsingIntsOp> {
public:
  using OpConversionPattern::OpConversionPattern;
  LogicalResult
  matchAndRewrite(AtenFlattenUsingIntsOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
      return failure();
    int64_t startDim;
    if (!matchPattern(op.start_dim(), m_TorchConstantInt(&startDim)))
      return rewriter.notifyMatchFailure(op, "start_dim must be constant");
    int64_t endDim;
    if (!matchPattern(op.end_dim(), m_TorchConstantInt(&endDim)))
      return rewriter.notifyMatchFailure(op, "end_dim must be constant");
    auto type = adaptor.self().getType().cast<RankedTensorType>();
    auto inputRank = type.getRank();
    auto resultType =
        getTypeConverter()->convertType(op.getType()).cast<RankedTensorType>();
    if (startDim < 0)
      startDim += inputRank;
    if (endDim < 0)
      endDim += inputRank;

    if (inputRank == 0) {
      SmallVector<ReassociationIndices> reassociation;
      if (!(startDim >= -1 && startDim <= 0 && endDim >= -1 && endDim <= 0))
        return rewriter.notifyMatchFailure(
            op, "start_dim and end_dim must be in [-1, 0] when inputRank is 0");
      rewriter.replaceOpWithNewOp<tensor::ExpandShapeOp>(
          op, resultType, adaptor.self(), reassociation);
      return success();
    }

    if (startDim < 0 || startDim >= inputRank || endDim < 0 ||
        endDim >= inputRank || startDim > endDim)
      return rewriter.notifyMatchFailure(
          op, "statically invalid flattening dim range");

    SmallVector<ReassociationIndices> reassociation(resultType.getRank());
    int j = 0;
    for (auto i : llvm::seq<int64_t>(0, inputRank)) {
      reassociation[j].push_back(i);
      if (i < startDim || i >= endDim)
        j++;
    }
    Value collapsedTensor = rewriter.create<tensor::CollapseShapeOp>(
        op->getLoc(), adaptor.self(), reassociation);
    rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType,
                                                collapsedTensor);
    return success();
  }
};
} // namespace

namespace {
/// The `ConvertAtenViewOp` conversion pattern converts `aten.View` op to
/// one `linalg.TensorExpandShape` op for all expanded dimensions and one
/// `linalg.TensorCollapseShape` op for all collapsed dimensions. Cases where
/// there is neither an expand or collapse of dimensions (e.g. [2, 3] -> [3, 2])
/// is not handled. Additionally, certain dynamic dimension cases rely on naive
/// assumptions or aren't supported.
/// TODO: Handle all the other cases of `aten.View` op.
class ConvertAtenViewOp : public OpConversionPattern<AtenViewOp> {
public:
  using OpConversionPattern::OpConversionPattern;

  // Helper for filling in remaining un-collapsed dims when the
  // input/output dim is next to the next boundary dim. Additionally
  // computes the size of a collapsed dynamic dim if necessary.
  static LogicalResult
  collapseToSingleDimHelper(AtenViewOp op, ConversionPatternRewriter &rewriter,
                            int64_t collapseDim, int64_t maxCollapseDim,
                            int64_t startExpandDim, int64_t maxExpandDim,
                            SmallVector<int64_t> &collapseShape,
                            const SmallVector<int64_t> &expandShape,
                            ReassociationIndices &expandIndices) {
    int64_t collapseDimSize = 1;
    for (auto i : llvm::seq<int64_t>(startExpandDim, maxExpandDim)) {
      expandIndices.push_back(i);
      if (collapseDimSize == kUnknownSize)
        continue;

      int64_t expandedDimSize = expandShape[i];
      if (expandedDimSize == kUnknownSize) {
        collapseDimSize = kUnknownSize;
        continue;
      }
      collapseDimSize *= expandedDimSize;
    }
    int64_t rawCollapseDimSize = collapseShape[collapseDim];
    if (rawCollapseDimSize != kUnknownSize && collapseDimSize != kUnknownSize &&
        collapseDimSize != rawCollapseDimSize) {
      return rewriter.notifyMatchFailure(
          op, "desired size is not compatible with the input tensor size");
    }
    collapseShape[collapseDim] = collapseDimSize;
    return success();
  }

  // Helper to find the minimum set of dims to collapse with the
  // same number of elements as that of collapseDim. This function assumes
  // the size of the collapsed dim is never dynamic.
  static LogicalResult
  minimallyCollapseDimHelper(AtenViewOp op, ConversionPatternRewriter &rewriter,
                             int64_t collapseDim, int64_t maxCollapseDim,
                             int64_t startExpandDim, int64_t maxExpandDim,
                             const SmallVector<int64_t> &collapseShape,
                             const SmallVector<int64_t> &expandShape,
                             ReassociationIndices &expandIndices) {
    int64_t collapseDimSize = collapseShape[collapseDim];
    int64_t expandedSize = 1;

    for (auto i : llvm::seq<int64_t>(startExpandDim, maxExpandDim)) {
      int64_t expandDimSize = expandShape[i];
      if (expandDimSize == kUnknownSize ||
          collapseDimSize % (expandedSize *= expandDimSize)) {
        return rewriter.notifyMatchFailure(
            op, "desired size is not compatible with the input tensor size");
      }
      expandIndices.push_back(i);
      if (expandedSize == collapseDimSize)
        return success();

      if (expandedSize > collapseDimSize) {
        return rewriter.notifyMatchFailure(
            op, "unimplemented: only supports expanding and collapsing "
                "in view");
      }
    }

    return rewriter.notifyMatchFailure(
        op, "total number of elements mismatch in the expansion");
  }

  LogicalResult
  matchAndRewrite(AtenViewOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
      return failure();
    Location loc = op.getLoc();
    Value input = adaptor.self();
    auto inputType = input.getType().cast<RankedTensorType>();
    ArrayRef<int64_t> inputShape = inputType.getShape();
    int64_t inputRank = inputType.getRank();
    TypeConverter *typeConverter = getTypeConverter();
    auto resultType =
        typeConverter->convertType(op.getType()).cast<RankedTensorType>();
    int64_t resultRank = resultType.getRank();
    if (resultRank == 0)
      return rewriter.notifyMatchFailure(op,
                                         "result shape of rank 0 is invalid");

    // TODO: add support for case inputRank 0 expanded to size 1
    if (inputRank == 0)
      return rewriter.notifyMatchFailure(
          op, "unimplemented: input rank 0 is not supported");

    // Extract the desired output size as a list of integers. This list should
    // have been created using the operation `torch.prim.ListConstruct`.
    SmallVector<Value> outputSizeTorchInt;
    if (!getListConstructElements(op.size(), outputSizeTorchInt)) {
      return rewriter.notifyMatchFailure(op,
                                         "unimplemented: the target size is "
                                         "not constructed from ListConstruct");
    }
    SmallVector<Value> outputSizeInt = getTypeConvertedValues(
        rewriter, loc, typeConverter, outputSizeTorchInt);
    if (resultRank != (int64_t)outputSizeInt.size()) {
      return rewriter.notifyMatchFailure(
          op, "desired size list length mismatches with the result type rank");
    }

    // Currently, we only handle the cases where each dimension is either
    // being expanded or collapsed. We do not handle cases where it's neither
    // collapsing nor expanding like view of [2,3] for 3x2 tensor.
    // TODO: For neither collapsing nor expanding, we could find a intermediate
    // shape to collapse and then expanded to the target shape. Like [2,3] =>
    // [6] => [3, 2].

    // Iterate through the view op size list to do the following:
    //
    // 1. Combine output size list and input tensor type info to get the most
    // static outputShape.
    //
    // 2. Mark dims in unchangedDims for size list items where the output dim
    // size comes from a `torch.aten.size.int(inputTensor, inputDim)`. We
    // naively assume this means the corresponding dimension is not expanded or
    // collapsed. Note this may technically not always be true.
    // TODO: think of a way better way to at least detect when this assumption
    // is violated for the cases of dynamic dimensions.
    SmallVector<int64_t> outputShape(resultRank, kUnknownSize);
    SmallVector<ReassociationIndices> unchangedDims;
    llvm::Optional<int64_t> inferredDimension;
    for (auto en : llvm::enumerate(outputSizeTorchInt)) {
      int64_t inputDim;
      int64_t size;
      int64_t outputDim = en.index();
      // Match torch.aten.size.int(inputTensor, inputDim) with constant inputDim
      if (matchPattern(en.value(),
                       m_TorchTensorSizeInt(op.self(), &inputDim))) {
        unchangedDims.emplace_back();
        unchangedDims.back().push_back(inputDim);
        unchangedDims.back().push_back(outputDim);
        if (!inputType.isDynamicDim(inputDim)) {
          outputShape[outputDim] = inputShape[inputDim];
          continue;
        }
      } else if (matchPattern(en.value(), m_TorchConstantInt(&size))) {
        if (size != -1) {
          outputShape[outputDim] = size;
          continue;
        }

        if (inferredDimension.hasValue()) {
          return rewriter.notifyMatchFailure(
              op, "at most one element in size list is allowed to be -1");
        }
        inferredDimension = outputDim;
      }
    }

    // Mark the end of the input/output shapes
    unchangedDims.emplace_back();
    unchangedDims.back().push_back(inputRank);
    unchangedDims.back().push_back(resultRank);

    // Use static information of input tensor to determine size of inferred
    // dimension in output shape.
    //
    // If there is an inferred dimension and that is the only dimension
    // in the output shape (i.e. the tensor is getting fully flattened),
    // then we don't need to analyze the static information of the input
    // shape since the reassociation of dimensions only requires rank
    // information.
    if (inferredDimension.hasValue() && outputShape.size() > 1) {
      if (llvm::count(outputShape, kUnknownSize) != 1 ||
          llvm::count(inputShape, kUnknownSize) != 0) {
        return rewriter.notifyMatchFailure(
            op,
            "unimplemented: an inferred dimension is only supported when there "
            "is enough static shape information to determine its size, or when "
            "the input tensor is being flattened to a single dimension");
      }

      auto productReduceKnownSizes = [](const ArrayRef<int64_t> sizes) {
        auto knownSizes = llvm::make_filter_range(
            sizes, [](int64_t val) { return val != kUnknownSize; });
        return std::accumulate(knownSizes.begin(), knownSizes.end(), /*init=*/1,
                               std::multiplies<int64_t>());
      };

      int64_t numOfElements = productReduceKnownSizes(inputShape);
      int64_t outputKnownNumOfElements = productReduceKnownSizes(outputShape);
      if (numOfElements % outputKnownNumOfElements != 0) {
        return rewriter.notifyMatchFailure(
            op, "number of elements in input tensor must be divisible by "
                "product of non-inferred dimensions in size list");
      }
      outputShape[*inferredDimension] =
          numOfElements / outputKnownNumOfElements;
    }

    SmallVector<Value> inputSize = getTensorSizes(rewriter, loc, input);
    ArrayRef<Value> outputShapeInt = llvm::makeArrayRef(outputSizeInt);
    ArrayRef<Value> inputShapeInt = llvm::makeArrayRef(inputSize);

    // Association indices for expand/collapse ops. These two vectors
    // are populated such that two entries at the same index corresponds
    // to an expand or collapse. For example,
    //
    // inputAssociations:  [[0, 1], [2]]
    // outputAssociations: [[0],    [1, 2, 3]]
    //
    // indicates that the first two dims of the input tensor
    // are collapsed into the first dim of the output, and the
    // third dim of the input is expanded into the last three dims
    // of the output.
    SmallVector<ReassociationIndices> inputAssociations;
    SmallVector<ReassociationIndices> outputAssociations;

    SmallVector<int64_t> inputShapeVec = llvm::to_vector(inputShape);

    // The for loop does the following:
    // 1. Attempt to match the indices from inputDim and outputDim to the next
    // boundary found from `torch.aten.size.int(inputTensor, inputDim)`, or
    // until (inputRank, resultRank) if there is no such op. Look at the first
    // dimension of the input and output and collapse the larger one by finding
    // a minimal set of opposing indices with the same number of elements. If
    // the number of dims to the next boundary is 1, then we assume all
    // remaining opposing dims must collapse into it.
    // 2. For handling of dynamic dimensions, we first assume they are only
    // split if we can easily compute the correct size.
    //      e.g. [2, -1] -> [2, 3, 4]
    // This mainly happens at the edges of boundaries. Otherwise we try to match
    // the dynamic dimension with the one across from it and give up if we can't
    // reason about how the dimensions are associated.
    //      e.g. [-1, -1] -> [2, 3, 4]
    // 3. Set inputShapeVec and outputShape following the requirements by
    // tensor.expand_shape verification code:
    //    a. As long as one or more of the related dimensions in the expanded
    //    shape is dynamic the collapsed dimension is dynamic.
    //    b. If all of the related dimensions are static, the collapsed
    //    dimension must be static. In other words, if a collapsed dimension is
    //    dynamic, at least one of the related dimensions need to be dynamic.
    int64_t inputDim = 0, outputDim = 0;
    for (auto boundary : unchangedDims) {
      // We assume dims specified by AtenSizeInt ops are unchanged
      int64_t nextUnchangedInput = boundary[0];
      int64_t nextUnchangedOutput = boundary[1];

      bool hasDynamic = false;
      while (inputDim < nextUnchangedInput && outputDim < nextUnchangedOutput) {
        inputAssociations.emplace_back();
        outputAssociations.emplace_back();

        // outputDim is next to the boundary
        if (outputDim == nextUnchangedOutput - 1) {
          if (hasDynamic && inputDim != nextUnchangedInput - 1) {
            return rewriter.notifyMatchFailure(
                op, "found ambiguous collapse of dynamic input sizes (e.g. "
                    "[-1, -1, -1] -> [-1, -1])");
          }
          outputAssociations.back().push_back(outputDim);
          if (failed(collapseToSingleDimHelper(
                  op, rewriter, outputDim, nextUnchangedOutput, inputDim,
                  nextUnchangedInput, outputShape, inputShapeVec,
                  inputAssociations.back())))
            return failure();
          outputDim = nextUnchangedOutput;
          inputDim = nextUnchangedInput;
          continue;
        }

        // inputDim is next to the boundary
        if (inputDim == nextUnchangedInput - 1) {
          if (hasDynamic && inputShape[inputDim] == kUnknownSize) {
            return rewriter.notifyMatchFailure(
                op, "found ambiguous expand of dynamic sizes (e.g. [-1, -1] -> "
                    "[-1, -1, -1])");
          }
          inputAssociations.back().push_back(inputDim);
          if (failed(collapseToSingleDimHelper(
                  op, rewriter, inputDim, nextUnchangedInput, outputDim,
                  nextUnchangedOutput, inputShapeVec, outputShape,
                  outputAssociations.back())))
            return failure();
          outputDim = nextUnchangedOutput;
          inputDim = nextUnchangedInput;
          continue;
        }

        int64_t inputMatchingDimSize = inputShapeVec[inputDim];
        int64_t outputMatchingDimSize = outputShape[outputDim];

        // If the input is dynamic, first assume it is not split
        if (inputMatchingDimSize == kUnknownSize) {
          checkDimEqualHelper(rewriter, loc, inputShapeInt[inputDim],
                              outputShapeInt[outputDim]);
          outputShape[outputDim] = kUnknownSize;
          inputAssociations.back().push_back(inputDim++);
          outputAssociations.back().push_back(outputDim++);
          hasDynamic = true;
          continue;
        }

        // inputDim size is larger; try to collapse onto it
        if (inputMatchingDimSize >= outputMatchingDimSize) {
          inputAssociations.back().push_back(inputDim);
          if (failed(minimallyCollapseDimHelper(
                  op, rewriter, inputDim, nextUnchangedInput, outputDim,
                  nextUnchangedOutput, inputShapeVec, outputShape,
                  outputAssociations.back())))
            return failure();
          hasDynamic = false;
          outputDim = outputAssociations.back().back() + 1;
          inputDim++;
          continue;
        }

        // outputDim is larger; try to collapse onto it
        outputAssociations.back().push_back(outputDim);
        if (failed(minimallyCollapseDimHelper(
                op, rewriter, outputDim, nextUnchangedOutput, inputDim,
                nextUnchangedInput, outputShape, inputShapeVec,
                inputAssociations.back())))
          return failure();
        hasDynamic = false;
        inputDim = inputAssociations.back().back() + 1;
        outputDim++;
        continue;
      }

      if (inputDim != nextUnchangedInput || outputDim != nextUnchangedOutput) {
        return rewriter.notifyMatchFailure(
            op, "could not match input tensor shape to output shape; "
                "potentially unsupported view shape");
      }

      // Append the associations for the dims matching `aten.size.int`
      if (nextUnchangedInput != inputRank &&
          nextUnchangedOutput != resultRank) {
        inputAssociations.emplace_back();
        outputAssociations.emplace_back();
        inputAssociations.back().push_back(inputDim++);
        outputAssociations.back().push_back(outputDim++);
      }
    }

    // Check if the shapes already match up to dynamic sizes. If so, we can just
    // cast as the result type because the previous loop sets up the necessary
    // dim checks in case of dynamic sizes.
    if (llvm::all_of(
            inputAssociations,
            [](ReassociationIndices indices) { return indices.size() == 1; }) &&
        llvm::all_of(outputAssociations, [](ReassociationIndices indices) {
          return indices.size() == 1;
        })) {
      rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, input);
      return success();
    }

    Type adjustedResultType =
        RankedTensorType::get(outputShape, resultType.getElementType());
    Type adjustedInputType =
        RankedTensorType::get(inputShapeVec, resultType.getElementType());
    Value castedInput =
        rewriter.create<tensor::CastOp>(loc, adjustedInputType, input);
    llvm::Optional<Value> expandedInput;
    llvm::Optional<Value> collapsedInput;

    if (llvm::any_of(inputAssociations, [](ReassociationIndices indices) {
          return indices.size() > 1;
        })) {
      SmallVector<int64_t> intermediateShape;
      for (auto i : llvm::seq(0, (int)inputAssociations.size())) {
        if (inputAssociations[i].size() > 1) {
          intermediateShape.push_back(outputShape[outputAssociations[i][0]]);
        } else {
          intermediateShape.push_back(inputShapeVec[inputAssociations[i][0]]);
        }
      }
      Type intermediateResultType =
          RankedTensorType::get(intermediateShape, resultType.getElementType());
      expandedInput =
          rewriter
              .create<tensor::CollapseShapeOp>(loc, intermediateResultType,
                                               castedInput, inputAssociations)
              .result();
    }

    if (llvm::any_of(outputAssociations, [](ReassociationIndices indices) {
          return indices.size() > 1;
        })) {
      collapsedInput = rewriter
                           .create<tensor::ExpandShapeOp>(
                               loc, adjustedResultType,
                               expandedInput.hasValue() ? expandedInput.value()
                                                        : castedInput,
                               outputAssociations)
                           .result();
    }

    Value result = collapsedInput.hasValue() ? collapsedInput.value()
                                             : expandedInput.value();
    rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, result);
    return success();
  }
};
} // namespace

namespace {
class ConvertAtenSqueezeOp : public OpConversionPattern<AtenSqueezeOp> {
public:
  using OpConversionPattern::OpConversionPattern;
  LogicalResult
  matchAndRewrite(AtenSqueezeOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
      return failure();
    Location loc = op.getLoc();
    Value input = adaptor.self();
    auto inputType = input.getType().cast<RankedTensorType>();
    int64_t inputRank = inputType.getRank();
    TypeConverter *typeConverter = getTypeConverter();
    auto resultType =
        typeConverter->convertType(op.getType()).cast<RankedTensorType>();
    int64_t resultRank = resultType.getRank();

    if (inputRank == 0) {
      return rewriter.notifyMatchFailure(
          op, "zero input rank should have been handled by the folder");
    }

    // In case the operand tensor type is statically shaped with all dimensions
    // being unit extent, it will be collapsed to a 0-D tensor.
    if (resultRank == 0) {
      SmallVector<ReassociationIndices> reassociation;
      rewriter.replaceOpWithNewOp<tensor::CollapseShapeOp>(
          op, resultType, input, reassociation);
      return success();
    }

    // All the static size-1 dimensions at the beginning(going from higher to
    // lower dimensions) will be collapsed into the first dynamic or first non
    // size-1 static dimension. All the other static size-1 dimensions will be
    // collapsed into its previous dynamic or non size-1 static dimension.
    SmallVector<ReassociationIndices> reassociation(resultRank);
    bool isSqueezed = false;
    int64_t headOnesCount = 0;
    while (headOnesCount < inputRank &&
           inputType.getDimSize(headOnesCount) == 1) {
      isSqueezed = true;
      reassociation[0].push_back(headOnesCount++);
    }

    // TODO: Add support for size-1 dynamic dimensions.
    Value one = rewriter.create<arith::ConstantOp>(
        loc, rewriter.getIntegerAttr(rewriter.getIndexType(), 1));
    int64_t j = -1;
    for (auto i : llvm::seq<int64_t>(headOnesCount, inputRank)) {
      if (inputType.isDynamicDim(i)) {
        // Make sure that size-1 dynamic dimension does not exist.
        Value dimSize = getDimOp(rewriter, loc, input, i);
        Value dimSizeNotOne = rewriter.create<arith::CmpIOp>(
            loc, arith::CmpIPredicate::ne, dimSize, one);
        rewriter.create<cf::AssertOp>(
            loc, dimSizeNotOne,
            rewriter.getStringAttr(
                "unimplemented: size 1 dynamic dimension is not supported"));
        ++j;
      } else if (inputType.getDimSize(i) != 1) {
        ++j;
      } else {
        // `isSqueezed` checks if the operand tensor type contains at least one
        // unit dimension.
        isSqueezed = true;
      }
      if (j == resultRank)
        break;
      reassociation[j].push_back(i);
    }

    // Make sure that result type rank is compatible with the squeezed size.
    if (j != resultRank - 1)
      return rewriter.notifyMatchFailure(
          op, "expected output size mismatches with the result type rank");

    if (isSqueezed) {
      rewriter.replaceOpWithNewOp<tensor::CollapseShapeOp>(
          op, resultType, input, reassociation);

    } else {
      // If the operand tensor type does not have any unit dimension,
      // `aten.squeeze` will behave as an identity operation.
      rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, input);
    }
    return success();
  }
};
} // namespace

namespace {
class ConvertAtenSqueezeDimOp : public OpConversionPattern<AtenSqueezeDimOp> {
public:
  using OpConversionPattern::OpConversionPattern;
  LogicalResult
  matchAndRewrite(AtenSqueezeDimOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
      return failure();
    Value input = adaptor.self();
    auto inputType = input.getType().cast<RankedTensorType>();
    int64_t inputRank = inputType.getRank();

    if (inputRank == 0) {
      return rewriter.notifyMatchFailure(
          op, "zero input rank should have been handled by the folder");
    }

    int64_t dim;
    if (!matchPattern(op.dim(), m_TorchConstantInt(&dim)))
      return rewriter.notifyMatchFailure(op, "dim must be constant");
    dim = toPositiveDim(dim, inputRank);
    if (!isValidDim(dim, inputRank))
      return rewriter.notifyMatchFailure(op, "dim is statically invalid");

    // TODO: Handle the case where the dim(th) dimension is dynamic.
    if (inputType.isDynamicDim(dim)) {
      return rewriter.notifyMatchFailure(
          op, "unimplemented: dim(th) dimension is not expected to be dynamic");
    }

    TypeConverter *typeConverter = getTypeConverter();
    auto resultType =
        typeConverter->convertType(op.getType()).cast<RankedTensorType>();
    int64_t resultRank = resultType.getRank();

    // If the dim(th) dimension of operand tensor type is not statically unit,
    // `aten.squeeze` will behave as an identity operation.
    if (inputType.getDimSize(dim) != 1) {
      rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, input);
      return success();
    }

    SmallVector<ReassociationIndices> reassociationMap(resultRank);
    bool alreadyCrossedSqueezedDim = false;
    for (int i = 0; i != resultRank; i++) {
      if (alreadyCrossedSqueezedDim) {
        reassociationMap[i].push_back(i + 1);
      } else {
        reassociationMap[i].push_back(i);
        if (dim != 0 && i != dim - 1)
          continue;

        alreadyCrossedSqueezedDim = true;
        if (dim == 0)
          reassociationMap[0].push_back(1);
        if (i == dim - 1)
          reassociationMap[i].push_back(dim);
      }
    }
    // Note: In case the operand tensor type is of unit rank and is statically
    // shaped with unit dimension, the `reassociationMap` will be empty and the
    // input will be collapsed to a 0-D tensor.
    rewriter.replaceOpWithNewOp<tensor::CollapseShapeOp>(op, resultType, input,
                                                         reassociationMap);
    return success();
  }
};
} // namespace

namespace {
class ConvertAtenUnsqueezeOp : public OpConversionPattern<AtenUnsqueezeOp> {
public:
  using OpConversionPattern::OpConversionPattern;
  LogicalResult
  matchAndRewrite(AtenUnsqueezeOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
      return failure();
    int64_t dim;
    if (!matchPattern(op.dim(), m_TorchConstantInt(&dim)))
      return rewriter.notifyMatchFailure(op, "dim must be constant");
    auto inputRank =
        adaptor.self().getType().cast<RankedTensorType>().getRank();
    if (dim < 0)
      dim += inputRank + 1;
    if (!(0 <= dim && dim <= inputRank))
      return rewriter.notifyMatchFailure(op, "statically invalid");

    SmallVector<ReassociationIndices> reassociationMap(inputRank);
    // From the perspective of the reassociation map, the situation of
    // unsqueezing before or after the last dimension is symmetrical.
    // Normalize it to the "before" case.
    // The 0 case is special here, since there is no last dimension to insert
    // before -- we simply rely on the loop below iterating 0 times.
    if (dim == inputRank && inputRank != 0)
      dim = inputRank - 1;
    bool alreadyCrossedExpandedDim = false;
    for (int i = 0; i != inputRank; i++) {
      if (alreadyCrossedExpandedDim) {
        reassociationMap[i].push_back(i + 1);
      } else {
        reassociationMap[i].push_back(i);
        if (i == dim) {
          reassociationMap[i].push_back(i + 1);
          alreadyCrossedExpandedDim = true;
        }
      }
    }
    auto resultType = getTypeConverter()
                          ->convertType(op->getResult(0).getType())
                          .cast<RankedTensorType>();
    rewriter.replaceOpWithNewOp<tensor::ExpandShapeOp>(
        op, resultType, adaptor.self(), reassociationMap);
    return success();
  }
};
} // namespace

namespace {
class ConvertAtenTransposeIntOp
    : public OpConversionPattern<AtenTransposeIntOp> {
public:
  using OpConversionPattern::OpConversionPattern;
  LogicalResult
  matchAndRewrite(AtenTransposeIntOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
      return failure();

    int64_t dim0;
    if (!matchPattern(op.dim0(), m_TorchConstantInt(&dim0)))
      return rewriter.notifyMatchFailure(op, "dim0 must be constant");
    int64_t dim1;
    if (!matchPattern(op.dim1(), m_TorchConstantInt(&dim1)))
      return rewriter.notifyMatchFailure(op, "dim1 must be constant");

    auto inVector = adaptor.self();
    auto inType = inVector.getType().cast<RankedTensorType>();
    auto inputRank = inType.getRank();
    auto outType = getTypeConverter()
                       ->convertType(op->getResult(0).getType())
                       .cast<RankedTensorType>();
    auto elementType = inType.getElementType();

    dim0 = toPositiveDim(dim0, inputRank);
    if (!isValidDim(dim0, inputRank))
      return rewriter.notifyMatchFailure(op, "dim0 out of range");
    dim1 = toPositiveDim(dim1, inputRank);
    if (!isValidDim(dim1, inputRank))
      return rewriter.notifyMatchFailure(op, "dim1 out of range");

    auto loc = op.getLoc();

    SmallVector<Value> outputDims;
    for (auto i = 0; i < inputRank; i++)
      outputDims.push_back(getDimOp(rewriter, loc, adaptor.self(), i));
    std::swap(outputDims[dim0], outputDims[dim1]);

    Value outVector =
        rewriter.create<linalg::InitTensorOp>(loc, outputDims, elementType);
    SmallVector<AffineExpr> idExprs;
    SmallVector<AffineExpr> swapExprs;
    for (auto i = 0; i < inputRank; i++)
      idExprs.push_back(getAffineDimExpr(i, rewriter.getContext()));
    for (auto i = 0; i < inputRank; i++) {
      if (i == dim0)
        swapExprs.push_back(idExprs[dim1]);
      else if (i == dim1)
        swapExprs.push_back(idExprs[dim0]);
      else
        swapExprs.push_back(idExprs[i]);
    }

    SmallVector<AffineMap> indexingMaps = {
        AffineMap::get(inputRank, 0, idExprs, op.getContext()),
        AffineMap::get(inputRank, 0, swapExprs, op.getContext())};
    SmallVector<StringRef> iteratorTypes(inputRank, "parallel");
    auto transpose = rewriter
                         .create<linalg::GenericOp>(
                             loc, outVector.getType(), inVector, outVector,
                             indexingMaps, iteratorTypes,
                             [](OpBuilder &b, Location loc, ValueRange args) {
                               b.create<linalg::YieldOp>(loc, args[0]);
                             })
                         .getResult(0);
    rewriter.replaceOpWithNewOp<tensor::CastOp>(op, outType, transpose);
    return success();
  }
};
} // namespace

namespace {
class ConvertAtenPermuteOp : public OpConversionPattern<AtenPermuteOp> {
public:
  using OpConversionPattern::OpConversionPattern;
  LogicalResult
  matchAndRewrite(AtenPermuteOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
      return failure();

    SmallVector<int64_t> dimensions;
    if (!matchPattern(op.dims(), m_TorchConstantIntList(dimensions)))
      return rewriter.notifyMatchFailure(op, "all dimensions must be constant");

    Value inVector = adaptor.self();
    auto inType = inVector.getType().cast<RankedTensorType>();
    int64_t inputRank = inType.getRank();
    auto outType = getTypeConverter()
                       ->convertType(op->getResult(0).getType())
                       .cast<RankedTensorType>();
    Type elementType = inType.getElementType();

    // Check if the dimensions are a valid constants.
    int64_t numDimensions = dimensions.size();
    if (inputRank != numDimensions)
      return rewriter.notifyMatchFailure(
          op, "size of `dims` must be equal to the rank of the input");
    for (unsigned i = 0; i < numDimensions; i++) {
      if (dimensions[i] < 0)
        dimensions[i] = toPositiveDim(dimensions[i], inputRank);
      if (!isValidDim(dimensions[i], inputRank))
        return rewriter.notifyMatchFailure(op, "dimension out of range");
    }

    Location loc = op.getLoc();

    SmallVector<Value> outputDims;
    for (unsigned i = 0; i < inputRank; i++)
      outputDims.push_back(getDimOp(rewriter, loc, inVector, dimensions[i]));

    Value outVector =
        rewriter.create<linalg::InitTensorOp>(loc, outputDims, elementType);
    SmallVector<AffineExpr> idExprs;
    SmallVector<AffineExpr> swapExprs;
    for (unsigned i = 0; i < inputRank; i++)
      idExprs.push_back(getAffineDimExpr(i, rewriter.getContext()));
    for (unsigned i = 0; i < inputRank; i++)
      swapExprs.push_back(idExprs[dimensions[i]]);

    AffineMap inputMap = AffineMap::get(inputRank, /*symbolCount=*/0, idExprs,
                                        op->getContext());
    AffineMap outputMap = AffineMap::get(inputRank, /*symbolCount=*/0, swapExprs,
                                         op->getContext());
    SmallVector<AffineMap> indexingMaps{inputMap, outputMap};
    SmallVector<StringRef> iteratorTypes(inputRank, getParallelIteratorTypeName());
    auto transpose = rewriter
                         .create<linalg::GenericOp>(
                             loc, outVector.getType(), inVector, outVector,
                             indexingMaps, iteratorTypes,
                             [](OpBuilder &b, Location loc, ValueRange args) {
                               b.create<linalg::YieldOp>(loc, args[0]);
                             })
                         .getResult(0);
    rewriter.replaceOpWithNewOp<tensor::CastOp>(op, outType, transpose);
    return success();
  }
};
} // namespace

namespace {
class ConvertAtenSliceTensorOp : public OpConversionPattern<AtenSliceTensorOp> {
public:
  using OpConversionPattern::OpConversionPattern;
  LogicalResult
  matchAndRewrite(AtenSliceTensorOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
      return failure();

    Location loc = op.getLoc();
    TypeConverter *typeConverter = getTypeConverter();

    auto input = adaptor.self();
    RankedTensorType resultType =
        typeConverter->convertType(op->getResult(0).getType())
            .cast<RankedTensorType>();

    SmallVector<Value> resultShape;
    SmallVector<Value> offsets;
    SmallVector<Value> strides;
    if (failed(prepareArgumentsForSlicingOp<AtenSliceTensorOp,
                                            AtenSliceTensorOpAdaptor>(
            op, adaptor, rewriter, resultShape, offsets, strides))) {
      return failure();
    }

    Value result = rewriter.create<tensor::ExtractSliceOp>(
        loc, input, offsets, resultShape, strides);

    rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, result);
    return success();
  }
};
} // namespace

namespace {
class ConvertAtenCatOp : public OpConversionPattern<AtenCatOp> {
public:
  using OpConversionPattern::OpConversionPattern;
  LogicalResult
  matchAndRewrite(AtenCatOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
      return failure();
    Location loc = op.getLoc();
    TypeConverter *typeConverter = getTypeConverter();

    Value dimValue = op.dim();
    int64_t dim;
    if (!matchPattern(dimValue, m_TorchConstantInt(&dim)))
      return op.emitError("unimplemented: dim is not constant");

    // Collect all the tensors to be concatenated.
    auto tensorList = op.tensors();
    SmallVector<Value> tensorsTorchType;
    if (!getListConstructElements(tensorList, tensorsTorchType))
      return op.emitError(
          "unimplemented: the tensor list is not from list construct");
    auto tensors =
        getTypeConvertedValues(rewriter, loc, typeConverter, tensorsTorchType);

    RankedTensorType newResultType =
        typeConverter->convertType(op.getType()).cast<RankedTensorType>();
    int rank = newResultType.getRank();
    SmallVector<Value> offsets, sizes, strides;
    sizes.reserve(rank);
    strides.resize(rank, rewriter.create<arith::ConstantIndexOp>(loc, 1));
    offsets.resize(rank, rewriter.create<arith::ConstantIndexOp>(loc, 0));

    for (int i = 0; i < rank; ++i)
      sizes.push_back(rewriter.createOrFold<tensor::DimOp>(loc, tensors[0], i));

    dim = toPositiveDim(dim, rank);
    if (!isValidDim(dim, rank))
      return rewriter.notifyMatchFailure(op, "dim is statically invalid");

    // Calculate the size of the `dim` result dimension by adding the dim size
    // of each tensor together.
    Value resultDimSize = sizes[dim];

    Value dimIndex = rewriter.createOrFold<arith::ConstantOp>(
        loc, rewriter.getIndexAttr(dim));
    for (auto tensor : makeArrayRef(tensors).drop_front()) {
      auto size = rewriter.createOrFold<tensor::DimOp>(loc, tensor, dimIndex);
      resultDimSize =
          rewriter.createOrFold<arith::AddIOp>(loc, resultDimSize, size);
    }
    sizes[dim] = resultDimSize;

    auto toOpFoldResult = [](Value v) -> OpFoldResult {
      auto op = v.getDefiningOp<arith::ConstantIndexOp>();
      if (!op)
        return v;
      return op.getValue();
    };

    Value result = rewriter.create<linalg::InitTensorOp>(
        loc, sizes, newResultType.getElementType());
    for (auto tensor : tensors) {
      SmallVector<Value> sizes = getTensorSizes(rewriter, loc, tensor);
      result = rewriter.createOrFold<tensor::InsertSliceOp>(
          loc, tensor, result,
          llvm::to_vector(llvm::map_range(offsets, toOpFoldResult)),
          llvm::to_vector(llvm::map_range(sizes, toOpFoldResult)),
          llvm::to_vector(llvm::map_range(strides, toOpFoldResult)));
      offsets[dim] =
          rewriter.createOrFold<arith::AddIOp>(loc, offsets[dim], sizes[dim]);
    }

    rewriter.replaceOpWithNewOp<tensor::CastOp>(op, newResultType, result);
    return success();
  }
};
} // namespace

namespace {
class ConvertAtenBroadcastToOp : public OpConversionPattern<AtenBroadcastToOp> {
public:
  using OpConversionPattern::OpConversionPattern;
  LogicalResult
  matchAndRewrite(AtenBroadcastToOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {

    if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
      return failure();
    Value self = adaptor.self();

    SmallVector<Value> inShape;
    if (!getListConstructElements(adaptor.size(), inShape)) {
      return rewriter.notifyMatchFailure(
          op, "unimplemented: the size list is not from list construct");
    }
    SmallVector<Value> inShapeConverted = getTypeConvertedValues(
        rewriter, op.getLoc(), getTypeConverter(), inShape);

    Value result;
    if (failed(torch_to_linalg::broadcastToGivenShape(
            op, rewriter, self, inShapeConverted, result))) {
      return rewriter.notifyMatchFailure(
          op, "unable to perform broadcast operation");
    }

    Type newResultType = getTypeConverter()->convertType(op.getType());
    rewriter.replaceOpWithNewOp<tensor::CastOp>(op, newResultType, result);
    return success();
  }
};
} // namespace

namespace {
class ConvertAtenContiguousOp : public OpConversionPattern<AtenContiguousOp> {
public:
  using OpConversionPattern::OpConversionPattern;
  LogicalResult
  matchAndRewrite(AtenContiguousOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {

    if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
      return failure();

    Type resultType = getTypeConverter()->convertType(op.getType());
    rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, adaptor.self());
    return success();
  }
};
} // namespace

namespace {
class ConvertValsemVariantAtenCopyOp
    : public OpConversionPattern<ValsemVariantAtenCopyOp> {
public:
  using OpConversionPattern::OpConversionPattern;
  LogicalResult
  matchAndRewrite(ValsemVariantAtenCopyOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {

    if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
      return failure();

    Location loc = op.getLoc();
    Value self = adaptor.self();
    Value src = adaptor.src();
    RankedTensorType selfType = self.getType().cast<RankedTensorType>();

    // The non_blocking should be a constant `False`.
    bool nonBlocking;
    if (!matchPattern(op.non_blocking(), m_TorchConstantBool(&nonBlocking))) {
      return rewriter.notifyMatchFailure(
          op, "unimplemented: non_blocking must be a constant");
    } else if (nonBlocking) {
      return rewriter.notifyMatchFailure(
          op, "unimplemented: non_blocking is expected to be false");
    }

    // The size of the src tensor can be different from the self but should be
    // broadcastable. Therefore, broadcasting the src tensor to match the size
    // of the self tensor.
    SmallVector<Value> selfSizes = getTensorSizes(rewriter, loc, self);
    for (unsigned i = 0; i < selfSizes.size(); i++)
      selfSizes[i] = castIndexToInt64(rewriter, loc, selfSizes[i]);
    Value broadcastedSrc;
    if (failed(torch_to_linalg::broadcastToGivenShape(
            op, rewriter, src, selfSizes, broadcastedSrc))) {
      return rewriter.notifyMatchFailure(
          op, "unable to perform broadcast operation");
    }

    AffineMap id = AffineMap::getMultiDimIdentityMap(selfType.getRank(),
                                                     rewriter.getContext());
    SmallVector<StringRef> iteratorTypes(selfType.getRank(),
                                         getParallelIteratorTypeName());
    Value result = rewriter
                       .create<linalg::GenericOp>(
                           loc,
                           /*resultType=*/selfType,
                           /*inputs=*/broadcastedSrc,
                           /*outputs=*/self,
                           /*indexingMaps=*/llvm::makeArrayRef({id, id}),
                           /*iteratorTypes=*/iteratorTypes,
                           [](OpBuilder &b, Location loc, ValueRange args) {
                             Value result = args[0];
                             if (args[0].getType() != args[1].getType()) {
                               result = convertScalarToDtype(b, loc, args[0],
                                                             args[1].getType());
                             }
                             b.create<linalg::YieldOp>(loc, result);
                           })
                       ->getResult(0);

    Type resultType = getTypeConverter()->convertType(op.getType());
    rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, result);
    return success();
  }
};
} // namespace

namespace {
class ConvertAtenSliceScatterOp
    : public OpConversionPattern<AtenSliceScatterOp> {
public:
  using OpConversionPattern::OpConversionPattern;
  LogicalResult
  matchAndRewrite(AtenSliceScatterOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {

    if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
      return failure();

    Location loc = op.getLoc();
    TypeConverter *typeConverter = getTypeConverter();

    auto input = adaptor.self();

    RankedTensorType resultType =
        typeConverter->convertType(op->getResult(0).getType())
            .cast<RankedTensorType>();

    SmallVector<Value> resultShape;
    SmallVector<Value> offsets;
    SmallVector<Value> strides;
    if (failed(prepareArgumentsForSlicingOp<AtenSliceScatterOp,
                                            AtenSliceScatterOpAdaptor>(
            op, adaptor, rewriter, resultShape, offsets, strides))) {
      return failure();
    }

    Value src = adaptor.src();
    auto srcType = src.getType().cast<RankedTensorType>();
    int64_t srcRank = srcType.getRank();
    SmallVector<int64_t> srcAbstractSizes(srcRank, kUnknownSize);
    auto abstractSrcType =
        RankedTensorType::get(srcAbstractSizes, srcType.getElementType());
    Value abstractSrc =
        rewriter.create<tensor::CastOp>(loc, abstractSrcType, src);

    Value result = rewriter.create<tensor::InsertSliceOp>(
        loc, abstractSrc, input, offsets, resultShape, strides);

    rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, result);

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

void mlir::torch::torch_to_linalg::populateDataMovementPatternsAndLegality(
    TypeConverter &typeConverter, RewritePatternSet &patterns,
    ConversionTarget &target) {
  MLIRContext *context = patterns.getContext();
  target.addIllegalOp<AtenFlattenUsingIntsOp>();
  patterns.add<ConvertAtenFlattenUsingIntsOp>(typeConverter, context);
  target.addIllegalOp<AtenViewOp>();
  patterns.add<ConvertAtenViewOp>(typeConverter, context);
  target.addIllegalOp<AtenSqueezeOp>();
  patterns.add<ConvertAtenSqueezeOp>(typeConverter, context);
  target.addIllegalOp<AtenSqueezeDimOp>();
  patterns.add<ConvertAtenSqueezeDimOp>(typeConverter, context);
  target.addIllegalOp<AtenUnsqueezeOp>();
  patterns.add<ConvertAtenUnsqueezeOp>(typeConverter, context);
  target.addIllegalOp<AtenTransposeIntOp>();
  patterns.add<ConvertAtenTransposeIntOp>(typeConverter, context);
  target.addIllegalOp<AtenPermuteOp>();
  patterns.add<ConvertAtenPermuteOp>(typeConverter, context);
  target.addIllegalOp<AtenSliceTensorOp>();
  patterns.add<ConvertAtenSliceTensorOp>(typeConverter, context);
  target.addIllegalOp<AtenCatOp>();
  patterns.add<ConvertAtenCatOp>(typeConverter, context);
  target.addIllegalOp<AtenBroadcastToOp>();
  patterns.add<ConvertAtenBroadcastToOp>(typeConverter, context);
  target.addIllegalOp<AtenContiguousOp>();
  patterns.add<ConvertAtenContiguousOp>(typeConverter, context);
  target.addIllegalOp<ValsemVariantAtenCopyOp>();
  patterns.add<ConvertValsemVariantAtenCopyOp>(typeConverter, context);
  target.addIllegalOp<AtenSliceScatterOp>();
  patterns.add<ConvertAtenSliceScatterOp>(typeConverter, context);
}