path: root/mlir/lib/Dialect/XeGPU/Transforms/XeGPUUnroll.cpp

//===- XeGPUUnroll.cpp - patterns to do unrolling ---------------*- C++ -*-===//
//
// 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
//
//===----------------------------------------------------------------------===//
//
// This file contains patterns for unrolling XeGPU operations. It follows a
// similar concept and design as vector unroll patterns, serving as a complement
// to them.
//
//===----------------------------------------------------------------------===//

#include "mlir/Dialect/Utils/IndexingUtils.h"
#include "mlir/Dialect/XeGPU/IR/XeGPU.h"
#include "mlir/Dialect/XeGPU/Transforms/Transforms.h"
#include "mlir/Dialect/XeGPU/Utils/XeGPUUtils.h"
#include "llvm/ADT/STLExtras.h"
#include "llvm/Support/DebugLog.h"

namespace mlir {
namespace xegpu {
#define GEN_PASS_DEF_XEGPUUNROLL
#include "mlir/Dialect/XeGPU/Transforms/Passes.h.inc"
} // namespace xegpu
} // namespace mlir

#define DEBUG_TYPE "xegpu-unroll"

using namespace mlir;

namespace {

template <typename SourceOp>
struct UnrollPattern : public OpRewritePattern<SourceOp> {
  UnrollPattern(MLIRContext *context, const xegpu::UnrollOptions &options,
                PatternBenefit benefit = 1)
      : OpRewritePattern<SourceOp>(context, benefit), options(options) {}

protected:
  /// Return the target shape for the given `op`. Return std::nullopt if the
  /// op shouldn't be or cannot be unrolled.
  std::optional<SmallVector<int64_t>> getTargetShape(Operation *op) const {
    LDBG() << "Get unroll shape for: " << *op;

    if (options.filterConstraint && failed(options.filterConstraint(op))) {
      LDBG() << "--no filter constraint -> BAIL";
      return std::nullopt;
    }

    assert(options.nativeShape &&
           "expects the native shape for native shape call back function.");
    auto nativeShape = options.nativeShape(op);
    return nativeShape;
  }

  SmallVector<Type> getUnrolledTypes(ShapedType type,
                                     ArrayRef<int64_t> tileShape,
                                     bool returnSingleType = false) const {
    return options.getUnrolledTypes(type, tileShape, returnSingleType);
  }

  /// Emulate the the unpack behavior using insert_strided_slice for VectorType
  /// values and unrealized_conversion_cast for TensorDescType values.
  Value unpack(ValueRange srcs, Type destTy, ArrayRef<int64_t> blockSize,
               Location loc, PatternRewriter &rewriter) const {
    if (auto vecTy = dyn_cast<VectorType>(destTy)) {
      assert(vecTy.getRank() == static_cast<int64_t>(blockSize.size()) &&
             "Expecting blockSize size to match the rank of destTy.");
      auto shape = vecTy.getShape();
      return xegpu::createVectorWithShapeFromValues(rewriter, loc, srcs, shape);
    }

    if (isa<xegpu::TensorDescType>(destTy)) {
      auto attr = NamedAttribute(rewriter.getStringAttr(unpackAttrName),
                                 rewriter.getUnitAttr());
      auto blkAttr = NamedAttribute(rewriter.getStringAttr(blockAttrName),
                                    rewriter.getDenseI64ArrayAttr(blockSize));
      auto castOp = UnrealizedConversionCastOp::create(
          rewriter, loc, destTy, srcs,
          ArrayRef<NamedAttribute>({attr, blkAttr}));
      return castOp.getResult(0);
    }

    llvm_unreachable("Unexpected destTy.");
    return Value();
  }

  /// Emulate the the pack behavior using extract_strided_slice for VectorType
  /// values and unrealized_conversion_cast for TensorDescType values.
  SmallVector<Value> pack(Value src, TypeRange destTypes,
                          ArrayRef<int64_t> blockSize, Location loc,
                          PatternRewriter &rewriter) const {
    if (auto vecTy = dyn_cast<VectorType>(src.getType())) {
      assert(vecTy.getRank() == static_cast<int64_t>(blockSize.size()) &&
             "Expecting blockSize size to match the rank of src.");
      return xegpu::extractVectorsWithShapeFromValue(rewriter, loc, src,
                                                     blockSize);
    }

    if (isa<xegpu::TensorDescType>(src.getType())) {
      auto attr = NamedAttribute(rewriter.getStringAttr(packAttrName),
                                 rewriter.getUnitAttr());
      auto blkAttr = NamedAttribute(rewriter.getStringAttr(blockAttrName),
                                    rewriter.getDenseI64ArrayAttr(blockSize));
      auto castOp = UnrealizedConversionCastOp::create(
          rewriter, loc, destTypes, src,
          ArrayRef<NamedAttribute>({attr, blkAttr}));
      return castOp.getResults();
    }

    llvm_unreachable("Unexpected src type.");
    return SmallVector<Value>();
  }

private:
  const char *const packAttrName = "__xegpu_blocking_pack__";
  const char *const unpackAttrName = "__xegpu_blocking_unpack__";
  const char *const blockAttrName = "__xegpu_blocking_tile_shape__";

  xegpu::UnrollOptions options;
};

// Generic helper function for unrolling operations with offsets.
//
// Iterates over tile offsets within the tensor descriptor shape and calls
// the provided createOp function for each computed offset. This is used by
// operations like LoadNd, StoreNd, CreateNdDesc, and PrefetchNd when they
// have explicit offsets that need to be adjusted for each unrolled tile.
SmallVector<Value> computeUnrolledOffsets(
    SmallVector<OpFoldResult> mixedOffsets, xegpu::TensorDescType tdescTy,
    ArrayRef<int64_t> targetShape,
    const std::function<Value(SmallVector<OpFoldResult>)> &createOp,
    Location loc, PatternRewriter &rewriter) {
  int64_t rank = tdescTy.getRank();
  ArrayRef<int64_t> shape = tdescTy.getShape();

  auto addi = [&](OpFoldResult a, int64_t b) -> Value {
    std::optional<int64_t> maybeInt = getConstantIntValue(a);
    if (maybeInt) {
      return arith::ConstantIndexOp::create(rewriter, loc, *maybeInt + b);
    } else {
      auto aV = llvm::cast<Value>(a);
      auto bV = arith::ConstantIndexOp::create(rewriter, loc, b);
      return rewriter.createOrFold<arith::AddIOp>(loc, aV, bV);
    }
  };

  SmallVector<OpFoldResult> oldOffsets = llvm::to_vector(
      llvm::drop_begin(mixedOffsets, mixedOffsets.size() - rank));
  auto validIdxes =
      llvm::seq<int64_t>(mixedOffsets.size() - rank, mixedOffsets.size());

  SmallVector<Value> newOps;
  for (SmallVector<int64_t> offsets :
       StaticTileOffsetRange(shape, targetShape)) {

    for (auto [idx, oldOff, offset] :
         llvm::zip(validIdxes, oldOffsets, offsets))
      mixedOffsets[idx] = addi(oldOff, offset);

    auto newOp = createOp(mixedOffsets);
    newOps.push_back(newOp);
  }
  return newOps;
}

struct UnrollCreateNdOp : public UnrollPattern<xegpu::CreateNdDescOp> {
  using UnrollPattern<xegpu::CreateNdDescOp>::UnrollPattern;
  LogicalResult matchAndRewrite(xegpu::CreateNdDescOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    xegpu::TensorDescType tdescTy = op.getType();

    std::optional<SmallVector<int64_t>> targetShape = getTargetShape(op);
    if (!targetShape)
      return failure();

    SmallVector<Value> newOps;

    auto newTdescTy = getUnrolledTypes(tdescTy, *targetShape)[0];
    bool hasOffsets = op.getMixedOffsets().size() != 0;
    if (!hasOffsets) {
      auto newOp = xegpu::CreateNdDescOp::create(
          rewriter, loc, newTdescTy, op.getSource(), op.getMixedSizes(),
          op.getMixedStrides());
      newOps.push_back(newOp);
    } else {
      auto createOp = [&](SmallVector<OpFoldResult> offsets) -> Value {
        return xegpu::CreateNdDescOp::create(
            rewriter, loc, newTdescTy, op.getSource(), offsets,
            op.getMixedSizes(), op.getMixedStrides());
      };

      newOps = computeUnrolledOffsets(op.getMixedOffsets(), tdescTy,
                                      *targetShape, createOp, loc, rewriter);
    }
    Value castOp = unpack(newOps, tdescTy, *targetShape, loc, rewriter);
    rewriter.replaceOp(op, castOp);

    return success();
  }
};

struct UnrollUpdateNdOffsetOp : public UnrollPattern<xegpu::UpdateNdOffsetOp> {
  using UnrollPattern<xegpu::UpdateNdOffsetOp>::UnrollPattern;
  LogicalResult matchAndRewrite(xegpu::UpdateNdOffsetOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    xegpu::TensorDescType tdescTy = op.getTensorDescType();

    std::optional<SmallVector<int64_t>> targetShape = getTargetShape(op);
    if (!targetShape)
      return failure();

    SmallVector<Type> convertedTdescTypes =
        getUnrolledTypes(tdescTy, *targetShape);
    SmallVector<Value> convertedTdesc = pack(
        op.getTensorDesc(), convertedTdescTypes, *targetShape, loc, rewriter);

    SmallVector<Value> newOps;
    for (auto t : convertedTdesc) {
      auto newOp = xegpu::UpdateNdOffsetOp::create(
          rewriter, loc, t.getType(), t, op.getOffsets(), op.getConstOffsets());
      newOps.push_back(newOp);
    }
    Value castOp = unpack(newOps, op.getType(), *targetShape, loc, rewriter);
    rewriter.replaceOp(op, castOp);
    return success();
  }
};

struct UnrollPrefetchNdOp : public UnrollPattern<xegpu::PrefetchNdOp> {
  using UnrollPattern<xegpu::PrefetchNdOp>::UnrollPattern;
  LogicalResult matchAndRewrite(xegpu::PrefetchNdOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    xegpu::TensorDescType tdescTy = op.getTensorDescType();

    std::optional<SmallVector<int64_t>> targetShape = getTargetShape(op);
    if (!targetShape)
      return failure();

    int64_t offsetSize = static_cast<int64_t>(op.getOffsets().size());
    bool hasOffsets = (offsetSize != 0) || op.getConstOffsetsAttr();

    SmallVector<Type> convertedTdescTypes = getUnrolledTypes(
        tdescTy, *targetShape, /*returnSingleType*/ hasOffsets);

    SmallVector<Value> convertedTdesc = pack(
        op.getTensorDesc(), convertedTdescTypes, *targetShape, loc, rewriter);

    if (!hasOffsets) {
      for (auto t : convertedTdesc)
        xegpu::PrefetchNdOp::create(rewriter, loc, TypeRange(), t,
                                    op->getAttrs());
    } else {
      auto createPrefetch = [&](SmallVector<OpFoldResult> offsets) -> Value {
        xegpu::PrefetchNdOp::create(rewriter, loc, convertedTdesc[0], offsets,
                                    op.getL1HintAttr(), op.getL2HintAttr(),
                                    op.getL3HintAttr());
        // return dummy Value to satisfy function's signature
        return nullptr;
      };

      computeUnrolledOffsets(op.getMixedOffsets(), tdescTy, *targetShape,
                             createPrefetch, loc, rewriter);
    }

    rewriter.eraseOp(op);
    return success();
  }
};

struct UnrollLoadNdOp : public UnrollPattern<xegpu::LoadNdOp> {
  using UnrollPattern<xegpu::LoadNdOp>::UnrollPattern;
  LogicalResult matchAndRewrite(xegpu::LoadNdOp op,
                                PatternRewriter &rewriter) const override {

    Location loc = op.getLoc();
    VectorType valueTy = op.getType();
    xegpu::TensorDescType tdescTy = op.getTensorDescType();

    std::optional<SmallVector<int64_t>> targetShape = getTargetShape(op);
    if (!targetShape)
      return failure();

    int64_t offsetSize = static_cast<int64_t>(op.getOffsets().size());
    bool hasOffsets = (offsetSize != 0) || op.getConstOffsetsAttr();

    Type elemTy = tdescTy.getElementType();
    VectorType newValueTy = valueTy.cloneWith(*targetShape, elemTy);

    SmallVector<Type> convertedTdescTypes = getUnrolledTypes(
        tdescTy, *targetShape, /*returnSingleType*/ hasOffsets);

    SmallVector<Value> convertedTdescs = pack(
        op.getTensorDesc(), convertedTdescTypes, *targetShape, loc, rewriter);
    SmallVector<Value> newOps;

    if (!hasOffsets) {
      for (auto t : convertedTdescs) {
        auto newOp = xegpu::LoadNdOp::create(rewriter, loc, newValueTy, t,
                                             op->getAttrs());
        newOps.push_back(newOp);
      }
    } else {
      auto createLoad = [&](SmallVector<OpFoldResult> offsets) {
        return xegpu::LoadNdOp::create(
            rewriter, loc, newValueTy, convertedTdescs[0], offsets,
            op.getPackedAttr(), op.getTransposeAttr(), op.getL1HintAttr(),
            op.getL2HintAttr(), op.getL3HintAttr());
      };
      newOps = computeUnrolledOffsets(op.getMixedOffsets(), tdescTy,
                                      *targetShape, createLoad, loc, rewriter);
    }

    Value castOp = unpack(newOps, op.getType(), *targetShape, loc, rewriter);

    rewriter.replaceOp(op, castOp);
    return success();
  }
};

struct UnrollStoreNdOp : public UnrollPattern<xegpu::StoreNdOp> {
  using UnrollPattern<xegpu::StoreNdOp>::UnrollPattern;
  LogicalResult matchAndRewrite(xegpu::StoreNdOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    VectorType valueTy = op.getValueType();
    xegpu::TensorDescType tdescTy = op.getTensorDescType();

    std::optional<SmallVector<int64_t>> targetShape = getTargetShape(op);
    if (!targetShape)
      return failure();

    int64_t offsetSize = static_cast<int64_t>(op.getOffsets().size());
    bool hasOffsets = (offsetSize != 0) || op.getConstOffsetsAttr();

    SmallVector<Type> convertedValTypes =
        getUnrolledTypes(valueTy, *targetShape);
    SmallVector<Type> convertedTdescTypes = getUnrolledTypes(
        tdescTy, *targetShape, /*returnSingleType*/ hasOffsets);

    SmallVector<Value> convertedTdescs = pack(
        op.getTensorDesc(), convertedTdescTypes, *targetShape, loc, rewriter);

    SmallVector<Value> convertedValues =
        pack(op.getValue(), convertedValTypes, *targetShape, loc, rewriter);
    if (!hasOffsets) {
      for (auto [v, t] : llvm::zip(convertedValues, convertedTdescs))
        xegpu::StoreNdOp::create(rewriter, loc, v, t, op.getL1HintAttr(),
                                 op.getL2HintAttr(), op.getL3HintAttr());
    } else {
      size_t valueIndex = 0;
      auto createStore = [&](SmallVector<OpFoldResult> offsets) {
        xegpu::StoreNdOp::create(rewriter, loc, convertedValues[valueIndex++],
                                 convertedTdescs[0], offsets,
                                 op.getL1HintAttr(), op.getL2HintAttr(),
                                 op.getL3HintAttr());
        // return dummy Value to satisfy function's signature
        return nullptr;
      };

      computeUnrolledOffsets(op.getMixedOffsets(), tdescTy, *targetShape,
                             createStore, loc, rewriter);
    }

    rewriter.eraseOp(op);
    return success();
  }
};

struct UnrollDpasOp : public UnrollPattern<xegpu::DpasOp> {
  using UnrollPattern<xegpu::DpasOp>::UnrollPattern;
  LogicalResult matchAndRewrite(xegpu::DpasOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();

    // expecting every operands is a 2D Vector
    if (llvm::any_of(op->getOperandTypes(), [&](Type type) {
          auto vecTy = dyn_cast<VectorType>(type);
          return !vecTy || vecTy.getRank() != 2;
        }))
      return failure();

    // A vector of 3 elements should be returned, representing M, K, N
    // respectively.
    std::optional<SmallVector<int64_t>> targetShape = getTargetShape(op);
    if (!targetShape || targetShape->size() != 3)
      return failure();
    auto M = (*targetShape)[0];
    auto K = (*targetShape)[1];
    auto N = (*targetShape)[2];

    int64_t aBlockSize[2] = {M, K};
    int64_t bBlockSize[2] = {K, N};
    int64_t cBlockSize[2] = {M, N};

    auto packWrapper = [&](TypedValue<VectorType> val,
                           ArrayRef<int64_t> blockSize) {
      VectorType type = val.getType();
      std::optional<SmallVector<int64_t>> grids =
          computeShapeRatio(type.getShape(), blockSize);
      assert(grids && "Expecting grids to be computed.");
      auto numNewOps = computeProduct(*grids);
      if (numNewOps == 1)
        return SmallVector<Value>({val});
      VectorType newVecTy = type.cloneWith(blockSize, type.getElementType());
      SmallVector<Type> convertedTypes(numNewOps, newVecTy);
      SmallVector<Value> values =
          pack(val, convertedTypes, blockSize, loc, rewriter);
      return values;
    };

    auto a = op.getLhs();
    auto b = op.getRhs();
    auto c = op.getAcc();

    auto aShape = a.getType().getShape();
    auto bShape = b.getType().getShape();

    SmallVector<Value> aVals, bVals, cVals;
    aVals = packWrapper(a, aBlockSize);
    bVals = packWrapper(b, bBlockSize);

    if (c)
      cVals = packWrapper(c, cBlockSize);

    // Skip the operation if every operand has an invalid blocking size (empty)
    // or if the original shape matches the blocking size (size == 1).
    auto ranges = c ? SmallVector<ValueRange>({aVals, bVals, cVals})
                    : SmallVector<ValueRange>({aVals, bVals});
    if (llvm::any_of(ranges, [](auto &v) { return v.size() == 0; }) ||
        llvm::all_of(ranges, [](auto &v) { return v.size() == 1; }))
      return failure();

    VectorType resultTy = op.getResult().getType();
    auto vecTy = VectorType::get(cBlockSize, resultTy.getElementType());

    int64_t mIters = aShape[0] / M;
    int64_t kIters = aShape[1] / K;
    int64_t nIters = bShape[1] / N;

    SmallVector<Value> newOps;
    for (int64_t i = 0; i < mIters; ++i) {
      for (int64_t j = 0; j < nIters; ++j) {
        Value tmpC;
        if (c)
          tmpC = cVals[i * nIters + j]; // init with acc

        for (int64_t k = 0; k < kIters; ++k) {
          Value aVec = aVals[i * kIters + k];
          Value bVec = bVals[k * nIters + j];
          SmallVector<Value> operands({aVec, bVec});
          if (tmpC)
            operands.push_back(tmpC);

          tmpC = xegpu::DpasOp::create(rewriter, loc, vecTy, operands,
                                       op->getAttrs());
        }
        newOps.push_back(tmpC);
      }
    }
    Value castOp = unpack(newOps, resultTy, cBlockSize, loc, rewriter);
    rewriter.replaceOp(op, castOp);
    return success();
  }
};

struct UnrollCreateDescOp : public UnrollPattern<xegpu::CreateDescOp> {
  using UnrollPattern<xegpu::CreateDescOp>::UnrollPattern;
  LogicalResult matchAndRewrite(xegpu::CreateDescOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    xegpu::TensorDescType tdescTy = op.getType();
    TypedValue<::mlir::VectorType> indiceVec = op.getOffsets();
    VectorType indiceVecTy = indiceVec.getType();

    if (!tdescTy.isScattered())
      return failure();

    std::optional<SmallVector<int64_t>> targetShape = getTargetShape(op);
    if (!targetShape)
      return failure();

    SmallVector<int64_t> targetIndiceShape(*targetShape);
    int64_t originalChunkSize = tdescTy.getChunkSizeAsInt();
    // IndiceVec is 1 dim lower than tdescTy when chunkSize is larger than 1.
    if (originalChunkSize > 1)
      targetIndiceShape.pop_back();

    auto newTdescTy = getUnrolledTypes(tdescTy, *targetShape)[0];
    SmallVector<Type> convertedIndiceTypes =
        getUnrolledTypes(indiceVecTy, targetIndiceShape);
    SmallVector<Value> convertedIndiceVec =
        pack(indiceVec, convertedIndiceTypes, targetIndiceShape, loc, rewriter);

    SmallVector<Value> newOps;

    // More indices is need when chunkSize > 1. Since a big load from one
    // address could be break into multiple small loads.
    if (originalChunkSize > 1) {
      int64_t blockedChunkSize = targetShape->back();
      int64_t numNewChunks = originalChunkSize / blockedChunkSize;

      for (auto [indice, indiceType] :
           llvm::zip(convertedIndiceVec, convertedIndiceTypes)) {
        for (int64_t i = 0; i < numNewChunks; ++i) {
          // Compute the offset
          Value inc = arith::ConstantIndexOp::create(rewriter, loc,
                                                     i * blockedChunkSize);
          Value incVec =
              vector::BroadcastOp::create(rewriter, loc, indiceType, inc);
          Value offsetIndice =
              arith::AddIOp::create(rewriter, loc, indice, incVec);

          auto newOp = xegpu::CreateDescOp::create(
              rewriter, loc, newTdescTy, op.getSource(), offsetIndice);

          newOps.push_back(newOp);
        }
      }
    } else {
      for (auto indice : convertedIndiceVec) {
        auto newOp = xegpu::CreateDescOp::create(rewriter, loc, newTdescTy,
                                                 op.getSource(), indice);
        newOps.push_back(newOp);
      }
    }

    Value castOp = unpack(newOps, tdescTy, *targetShape, loc, rewriter);
    rewriter.replaceOp(op, castOp);

    return success();
  }
};

struct UnrollLoadGatherOp : public UnrollPattern<xegpu::LoadGatherOp> {
  using UnrollPattern<xegpu::LoadGatherOp>::UnrollPattern;
  LogicalResult matchAndRewrite(xegpu::LoadGatherOp op,
                                PatternRewriter &rewriter) const override {

    Location loc = op.getLoc();
    VectorType valueTy = llvm::dyn_cast<VectorType>(op.getValue().getType());
    xegpu::TensorDescType tdescTy = op.getTensorDescType();

    // TODO: handle the unstructure source case (!tdesTy)
    if (!tdescTy || op.getOffsets())
      return failure();

    std::optional<SmallVector<int64_t>> targetShape = getTargetShape(op);
    if (!targetShape)
      return failure();

    SmallVector<int64_t> targetMaskShape(*targetShape);
    int64_t originalChunkSize = tdescTy.getChunkSizeAsInt();

    VectorType maskTy = llvm::dyn_cast<VectorType>(op.getMask().getType());

    Type elemTy = tdescTy.getElementType();
    VectorType newValueTy = valueTy.cloneWith(*targetShape, elemTy);

    SmallVector<Type> convertedTdescTypes =
        getUnrolledTypes(tdescTy, *targetShape);
    SmallVector<Value> convertedTdescs = pack(
        op.getTensorDesc(), convertedTdescTypes, *targetShape, loc, rewriter);

    SmallVector<Type> convertedMaskTypes;
    SmallVector<Value> convertedMasks;

    if (originalChunkSize > 1) {
      targetMaskShape.pop_back();
      convertedMaskTypes = getUnrolledTypes(maskTy, targetMaskShape);
      int64_t blockedChunkSize = targetShape->back();
      int64_t numNewChunks = originalChunkSize / blockedChunkSize;

      // the mask is reused across the chunk_size dimension
      for (auto mask : pack(op.getMask(), convertedMaskTypes, targetMaskShape,
                            loc, rewriter))
        convertedMasks.append(numNewChunks, mask);

      newValueTy = valueTy.cloneWith(*targetShape, elemTy);
    } else {
      convertedMaskTypes = getUnrolledTypes(maskTy, targetMaskShape);
      convertedMasks = pack(op.getMask(), convertedMaskTypes, targetMaskShape,
                            loc, rewriter);
    }

    SmallVector<Value> newOps;
    for (auto [t, m] : llvm::zip(convertedTdescs, convertedMasks)) {
      auto newOp = xegpu::LoadGatherOp::create(
          rewriter, loc, newValueTy, t, m, op.getL1HintAttr(),
          op.getL2HintAttr(), op.getL3HintAttr());
      newOps.push_back(newOp);
    }

    Value castOp = unpack(newOps, op.getType(), *targetShape, loc, rewriter);
    rewriter.replaceOp(op, castOp);
    return success();
  }
};

/// This pattern handles the unrolling of LoadGatherOp with offsets (gathered
/// load).
/// It unrolls the offsets and mask operands accordingly, and creates multiple
/// LoadGatherOp with the unrolled operands.
struct UnrollLoadGatherOpWithOffset
    : public UnrollPattern<xegpu::LoadGatherOp> {
  using UnrollPattern<xegpu::LoadGatherOp>::UnrollPattern;
  LogicalResult matchAndRewrite(xegpu::LoadGatherOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    VectorType valueTy = llvm::dyn_cast<VectorType>(op.getType());
    Value offsets = op.getOffsets();
    Value mask = op.getMask();

    // Only handle the case where offsets are present (scattered load)
    if (!offsets)
      return failure();

    std::optional<SmallVector<int64_t>> targetShape = getTargetShape(op);
    if (!targetShape)
      return failure();

    SmallVector<int64_t> targetMaskShape(*targetShape);
    int64_t chunkSize = 1;
    if (auto chunkSizeAttr = op->getAttr("chunk_size")) {
      if (auto intAttr = llvm::dyn_cast<IntegerAttr>(chunkSizeAttr))
        chunkSize = intAttr.getInt();
    }

    // Unroll mask and offsets with correct shape
    VectorType maskTy = llvm::dyn_cast<VectorType>(mask.getType());
    VectorType offsetsTy = llvm::dyn_cast<VectorType>(offsets.getType());
    Type elemTy = valueTy.getElementType();
    VectorType newValueTy = valueTy.cloneWith(*targetShape, elemTy);

    SmallVector<Type> convertedMaskTypes;
    SmallVector<Value> convertedMasks;
    SmallVector<Type> convertedOffsetTypes;
    SmallVector<Value> convertedOffsets;

    if (chunkSize > 1) {
      // For chunked loads, mask and offsets have one less dimension
      targetMaskShape.pop_back();
      int64_t blockedChunkSize = targetShape->back();
      int64_t numNewChunks = chunkSize / blockedChunkSize;
      chunkSize = blockedChunkSize;

      convertedMaskTypes = getUnrolledTypes(maskTy, targetMaskShape);
      convertedOffsetTypes = getUnrolledTypes(offsetsTy, targetMaskShape);

      SmallVector<Value> convertedMasksBase =
          pack(mask, convertedMaskTypes, targetMaskShape, loc, rewriter);
      SmallVector<Value> convertedOffsetsBase =
          pack(offsets, convertedOffsetTypes, targetMaskShape, loc, rewriter);

      for (auto maskVal : convertedMasksBase)
        convertedMasks.append(numNewChunks, maskVal);

      for (auto [baseOffset, offsetType] :
           llvm::zip(convertedOffsetsBase, convertedOffsetTypes)) {
        for (int64_t i = 0; i < numNewChunks; ++i) {
          Value inc = arith::ConstantIndexOp::create(rewriter, loc,
                                                     i * blockedChunkSize);
          Value incVec =
              vector::BroadcastOp::create(rewriter, loc, offsetType, inc);
          Value offsetVal =
              arith::AddIOp::create(rewriter, loc, baseOffset, incVec);
          convertedOffsets.push_back(offsetVal);
        }
      }
    } else {
      convertedMaskTypes = getUnrolledTypes(maskTy, targetMaskShape);
      convertedMasks =
          pack(mask, convertedMaskTypes, targetMaskShape, loc, rewriter);

      convertedOffsetTypes = getUnrolledTypes(offsetsTy, *targetShape);
      convertedOffsets =
          pack(offsets, convertedOffsetTypes, *targetShape, loc, rewriter);
    }

    SmallVector<Value> newOps;
    for (auto [o, m] : llvm::zip(convertedOffsets, convertedMasks)) {
      auto newOp = xegpu::LoadGatherOp::create(
          rewriter, loc, newValueTy, op.getSource(), o, m,
          rewriter.getI64IntegerAttr(chunkSize), op.getL1HintAttr(),
          op.getL2HintAttr(), op.getL3HintAttr());
      newOps.push_back(newOp);
    }

    Value castOp = unpack(newOps, op.getType(), *targetShape, loc, rewriter);
    rewriter.replaceOp(op, castOp);
    return success();
  }
};

/// This pattern handles the unrolling of StoreScatterOp with offsets (scattered
/// store).
/// It unrolls the offsets and mask operands accordingly, and creates multiple
/// StoreScatterOp with the unrolled operands.
struct UnrollStoreScatterOpWithOffsets
    : public UnrollPattern<xegpu::StoreScatterOp> {
  using UnrollPattern<xegpu::StoreScatterOp>::UnrollPattern;
  LogicalResult matchAndRewrite(xegpu::StoreScatterOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    VectorType valueTy = llvm::dyn_cast<VectorType>(op.getValue().getType());
    Value offsets = op.getOffsets();
    Value mask = op.getMask();

    // Only handle the case where offsets are present (scattered store)
    if (!offsets)
      return failure();

    std::optional<SmallVector<int64_t>> targetShape = getTargetShape(op);
    if (!targetShape)
      return failure();

    int64_t chunkSize = 1;
    if (auto chunkSizeAttr = op->getAttr("chunk_size")) {
      if (auto intAttr = llvm::dyn_cast<IntegerAttr>(chunkSizeAttr))
        chunkSize = intAttr.getInt();
    }

    SmallVector<int64_t> targetMaskShape(*targetShape);
    VectorType maskTy = llvm::dyn_cast<VectorType>(mask.getType());
    VectorType offsetsTy = llvm::dyn_cast<VectorType>(offsets.getType());

    SmallVector<Type> convertedMaskTypes;
    SmallVector<Value> convertedMasks;
    SmallVector<Type> convertedOffsetTypes;
    SmallVector<Value> convertedOffsets;

    if (chunkSize > 1) {
      targetMaskShape.pop_back();
      int64_t blockedChunkSize = targetShape->back();
      int64_t numNewChunks = chunkSize / blockedChunkSize;
      chunkSize = blockedChunkSize;

      convertedMaskTypes = getUnrolledTypes(maskTy, targetMaskShape);
      convertedOffsetTypes = getUnrolledTypes(offsetsTy, targetMaskShape);

      SmallVector<Value> convertedMasksBase =
          pack(mask, convertedMaskTypes, targetMaskShape, loc, rewriter);
      SmallVector<Value> convertedOffsetsBase =
          pack(offsets, convertedOffsetTypes, targetMaskShape, loc, rewriter);

      for (auto maskVal : convertedMasksBase)
        convertedMasks.append(numNewChunks, maskVal);

      for (auto [baseOffset, offsetType] :
           llvm::zip(convertedOffsetsBase, convertedOffsetTypes)) {
        for (int64_t i = 0; i < numNewChunks; ++i) {
          Value inc = arith::ConstantIndexOp::create(rewriter, loc,
                                                     i * blockedChunkSize);
          Value incVec =
              vector::BroadcastOp::create(rewriter, loc, offsetType, inc);
          Value offsetVal =
              arith::AddIOp::create(rewriter, loc, baseOffset, incVec);
          convertedOffsets.push_back(offsetVal);
        }
      }
    } else {
      convertedMaskTypes = getUnrolledTypes(maskTy, targetMaskShape);
      convertedMasks =
          pack(mask, convertedMaskTypes, targetMaskShape, loc, rewriter);

      convertedOffsetTypes = getUnrolledTypes(offsetsTy, *targetShape);
      convertedOffsets =
          pack(offsets, convertedOffsetTypes, *targetShape, loc, rewriter);
    }

    SmallVector<Type> convertedValTypes =
        getUnrolledTypes(valueTy, *targetShape);
    SmallVector<Value> convertedValues =
        pack(op.getValue(), convertedValTypes, *targetShape, loc, rewriter);

    for (auto [v, o, m] :
         llvm::zip(convertedValues, convertedOffsets, convertedMasks)) {
      xegpu::StoreScatterOp::create(rewriter, loc, v, op.getDest(), o, m,
                                    rewriter.getI64IntegerAttr(chunkSize),
                                    op.getL1HintAttr(), op.getL2HintAttr(),
                                    op.getL3HintAttr());
    }

    rewriter.eraseOp(op);
    return success();
  }
};

struct UnrollPrefetchOp : public UnrollPattern<xegpu::PrefetchOp> {
  using UnrollPattern<xegpu::PrefetchOp>::UnrollPattern;
  LogicalResult matchAndRewrite(xegpu::PrefetchOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    xegpu::TensorDescType tdescTy = op.getTensorDescType();

    // TODO: handle the unstructure source case (!tdesTy)
    if (!tdescTy || op.getOffsets())
      return failure();

    std::optional<SmallVector<int64_t>> targetShape = getTargetShape(op);
    if (!targetShape)
      return failure();

    SmallVector<Type> convertedTdescTypes =
        getUnrolledTypes(tdescTy, *targetShape);
    SmallVector<Value> convertedTdesc = pack(
        op.getTensorDesc(), convertedTdescTypes, *targetShape, loc, rewriter);

    for (auto t : convertedTdesc)
      xegpu::PrefetchOp::create(rewriter, loc, TypeRange(), t, op->getAttrs());

    rewriter.eraseOp(op);
    return success();
  }
};

struct UnrollStoreScatterOp : public UnrollPattern<xegpu::StoreScatterOp> {
  using UnrollPattern<xegpu::StoreScatterOp>::UnrollPattern;
  LogicalResult matchAndRewrite(xegpu::StoreScatterOp op,
                                PatternRewriter &rewriter) const override {

    Location loc = op.getLoc();
    VectorType valueTy = llvm::dyn_cast<VectorType>(op.getValue().getType());
    xegpu::TensorDescType tdescTy = op.getTensorDescType();

    // TODO: handle the unstructure source case (!tdesTy)
    if (!tdescTy || op.getOffsets())
      return failure();

    std::optional<SmallVector<int64_t>> targetShape = getTargetShape(op);
    if (!targetShape)
      return failure();

    SmallVector<int64_t> targetMaskShape(*targetShape);
    int64_t originalChunkSize = tdescTy.getChunkSizeAsInt();

    VectorType maskTy = llvm::dyn_cast<VectorType>(op.getMask().getType());

    SmallVector<Type> convertedTdescTypes =
        getUnrolledTypes(tdescTy, *targetShape);
    SmallVector<Value> convertedTdescs = pack(
        op.getTensorDesc(), convertedTdescTypes, *targetShape, loc, rewriter);

    SmallVector<Type> convertedMaskTypes;
    SmallVector<Value> convertedMasks;

    if (originalChunkSize > 1) {
      targetMaskShape.pop_back();
      int64_t blockedChunkSize = targetShape->back();
      int64_t numNewChunks = originalChunkSize / blockedChunkSize;
      convertedMaskTypes = getUnrolledTypes(maskTy, targetMaskShape);

      // the mask is reused across the chunk_size dimension
      for (auto mask : pack(op.getMask(), convertedMaskTypes, targetMaskShape,
                            loc, rewriter))
        convertedMasks.append(numNewChunks, mask);
    } else {
      convertedMaskTypes = getUnrolledTypes(maskTy, targetMaskShape);
      convertedMasks = pack(op.getMask(), convertedMaskTypes, targetMaskShape,
                            loc, rewriter);
    }

    SmallVector<Type> convertedValTypes =
        getUnrolledTypes(valueTy, *targetShape);
    SmallVector<Value> convertedValues =
        pack(op.getValue(), convertedValTypes, *targetShape, loc, rewriter);

    for (size_t i = 0; i < convertedValues.size(); ++i) {
      Value v = convertedValues[i];
      Value t = convertedTdescs[i];
      Value m = op.getMask() ? convertedMasks[i] : nullptr;
      xegpu::StoreScatterOp::create(rewriter, loc, v, t, m, op.getL1HintAttr(),
                                    op.getL2HintAttr(), op.getL3HintAttr());
    }

    rewriter.eraseOp(op);
    return success();
  }
};

struct UnrollUpdateOffsetOp : public UnrollPattern<xegpu::UpdateOffsetOp> {
  using UnrollPattern<xegpu::UpdateOffsetOp>::UnrollPattern;
  LogicalResult matchAndRewrite(xegpu::UpdateOffsetOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    xegpu::TensorDescType tdescTy = op.getTensorDescType();

    if (!tdescTy.isScattered())
      return failure();

    std::optional<SmallVector<int64_t>> targetShape = getTargetShape(op);
    if (!targetShape)
      return failure();

    SmallVector<Type> convertedTdescTypes =
        getUnrolledTypes(tdescTy, *targetShape);
    SmallVector<Value> convertedTdesc = pack(
        op.getTensorDesc(), convertedTdescTypes, *targetShape, loc, rewriter);

    TypedValue<::mlir::VectorType> offsetVec = op.getOffsets();
    VectorType offsetVecTy = offsetVec.getType();
    SmallVector<Type> convertedOffsetTypes;
    SmallVector<Value> convertedOffsetVec;
    SmallVector<Value> newOps;
    int64_t originalChunkSize = tdescTy.getChunkSizeAsInt();
    if (originalChunkSize > 1) {
      auto targetOffsetShape = ArrayRef<int64_t>(*targetShape).drop_back();
      convertedOffsetTypes = getUnrolledTypes(offsetVecTy, targetOffsetShape);

      int64_t blockedChunkSize = targetShape->back();
      int64_t numNewChunks = originalChunkSize / blockedChunkSize;
      // the offset is reused across the chunk_size dimension
      for (auto offset : pack(offsetVec, convertedOffsetTypes,
                              targetOffsetShape, loc, rewriter))
        convertedOffsetVec.append(numNewChunks, offset);

    } else {
      convertedOffsetTypes = getUnrolledTypes(offsetVecTy, *targetShape);
      convertedOffsetVec =
          pack(offsetVec, convertedOffsetTypes, *targetShape, loc, rewriter);
    }

    for (auto [t, o] : llvm::zip(convertedTdesc, convertedOffsetVec)) {
      auto newOp =
          xegpu::UpdateOffsetOp::create(rewriter, loc, t.getType(), t, o);
      newOps.push_back(newOp);
    }
    Value castOp = unpack(newOps, op.getType(), *targetShape, loc, rewriter);
    rewriter.replaceOp(op, castOp);
    return success();
  }
};

struct UnrollLoadMatrixOp : public UnrollPattern<xegpu::LoadMatrixOp> {
  using UnrollPattern<xegpu::LoadMatrixOp>::UnrollPattern;
  LogicalResult matchAndRewrite(xegpu::LoadMatrixOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    VectorType valueTy = op.getType();
    std::optional<SmallVector<int64_t>> targetShape = getTargetShape(op);
    if (!targetShape || targetShape->size() != (size_t)valueTy.getRank())
      return failure();

    Type elemTy = valueTy.getElementType();
    ArrayRef<int64_t> shape = valueTy.getShape();
    auto layout = dyn_cast<xegpu::LayoutAttr>(op.getLayoutAttr());

    VectorType newValueTy = valueTy.cloneWith(*targetShape, elemTy);

    SmallVector<OpFoldResult> mixedOffsets = op.getMixedOffsets();
    SmallVector<SmallVector<OpFoldResult>> offsetsList;
    for (SmallVector<int64_t> offsets :
         StaticTileOffsetRange(shape, *targetShape)) {
      auto adds = xegpu::addElementwise(
          rewriter, loc, mixedOffsets,
          getAsIndexOpFoldResult(op.getContext(), offsets));
      offsetsList.push_back(adds);
    }

    SmallVector<Value> newOps;
    layout = layout.dropInstData();
    for (SmallVector<OpFoldResult> offsets : offsetsList) {
      auto newOp = xegpu::LoadMatrixOp::create(
          rewriter, op.getLoc(), newValueTy, op.getMemDesc(), offsets, layout);
      newOps.push_back(newOp);
    }
    Value castOp = unpack(newOps, op.getType(), *targetShape, loc, rewriter);
    rewriter.replaceOp(op, castOp);
    return success();
  }
};

struct UnrollStoreMatrixOp : public UnrollPattern<xegpu::StoreMatrixOp> {
  using UnrollPattern<xegpu::StoreMatrixOp>::UnrollPattern;
  LogicalResult matchAndRewrite(xegpu::StoreMatrixOp op,
                                PatternRewriter &rewriter) const override {
    std::optional<SmallVector<int64_t>> targetShape = getTargetShape(op);
    if (!targetShape)
      return failure();

    Location loc = op.getLoc();
    VectorType valueTy = op.getData().getType();
    ArrayRef<int64_t> shape = valueTy.getShape();
    auto layout = dyn_cast<xegpu::LayoutAttr>(op.getLayoutAttr());

    SmallVector<Type> convertedValTypes =
        getUnrolledTypes(valueTy, *targetShape);
    SmallVector<Value> convertedValues =
        pack(op.getData(), convertedValTypes, *targetShape, loc, rewriter);

    SmallVector<OpFoldResult> mixedOffsets = op.getMixedOffsets();
    SmallVector<SmallVector<OpFoldResult>> offsetsList;
    for (SmallVector<int64_t> offsets :
         StaticTileOffsetRange(shape, *targetShape)) {
      auto adds = xegpu::addElementwise(
          rewriter, loc, mixedOffsets,
          getAsIndexOpFoldResult(op.getContext(), offsets));
      offsetsList.push_back(adds);
    }

    for (auto [v, offsets] : llvm::zip_equal(convertedValues, offsetsList))
      xegpu::StoreMatrixOp::create(rewriter, loc, v, op.getMemDesc(), offsets,
                                   layout.dropInstData());

    rewriter.eraseOp(op);
    return success();
  }
};

} // namespace

void mlir::xegpu::populateXeGPUUnrollPatterns(
    RewritePatternSet &patterns, const xegpu::UnrollOptions &options) {
  patterns
      .add<UnrollCreateNdOp, UnrollUpdateNdOffsetOp, UnrollPrefetchNdOp,
           UnrollLoadNdOp, UnrollStoreNdOp, UnrollDpasOp, UnrollCreateDescOp,
           UnrollLoadGatherOp, UnrollStoreScatterOp, UnrollPrefetchOp,
           UnrollUpdateOffsetOp, UnrollLoadMatrixOp, UnrollStoreMatrixOp,
           UnrollLoadGatherOpWithOffset, UnrollStoreScatterOpWithOffsets>(
          patterns.getContext(), options);
}