xref: /llvm-project/mlir/lib/Conversion/NVGPUToNVVM/NVGPUToNVVM.cpp (revision 7a77f14c0abfbecbfb800ea8d974e66d81ee516a)

//===- NVGPUToNVVM.cpp - NVGPU to NVVM dialect conversion -----------------===//
//
// 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
//
//===----------------------------------------------------------------------===//

#include "mlir/Conversion/NVGPUToNVVM/NVGPUToNVVM.h"

#include "mlir/Conversion/GPUCommon/GPUCommonPass.h"
#include "mlir/Conversion/LLVMCommon/ConversionTarget.h"
#include "mlir/Conversion/LLVMCommon/Pattern.h"
#include "mlir/Conversion/LLVMCommon/VectorPattern.h"
#include "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/Dialect/GPU/IR/GPUDialect.h"
#include "mlir/Dialect/LLVMIR/LLVMDialect.h"
#include "mlir/Dialect/LLVMIR/LLVMTypes.h"
#include "mlir/Dialect/LLVMIR/NVVMDialect.h"
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/Dialect/NVGPU/IR/NVGPUDialect.h"
#include "mlir/Dialect/SCF/Transforms/Patterns.h"
#include "mlir/IR/BuiltinTypes.h"
#include "mlir/IR/ImplicitLocOpBuilder.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/IR/TypeUtilities.h"
#include "mlir/IR/Value.h"
#include "mlir/Pass/Pass.h"
#include "llvm/Support/Debug.h"
#include "llvm/Support/ErrorHandling.h"
#include "llvm/Support/raw_ostream.h"
#include <optional>

#define DEBUG_TYPE "nvgpu-to-nvvm"
#define DBGS() (llvm::dbgs() << '[' << DEBUG_TYPE << "] ")
#define DBGSE() (llvm::dbgs())

namespace mlir {
#define GEN_PASS_DEF_CONVERTNVGPUTONVVMPASS
#include "mlir/Conversion/Passes.h.inc"
} // namespace mlir

using namespace mlir;

/// Number of bits that needs to be excluded when building matrix descriptor for
/// wgmma operations.
constexpr int exclude4LSB = 4;

/// GPU has 32 bit registers, this function truncates values when larger width
/// is not needed.
static Value truncToI32(ImplicitLocOpBuilder &b, Value value) {
  Type type = value.getType();
  assert(llvm::isa<IntegerType>(type) && "expected an integer Value");
  if (type.getIntOrFloatBitWidth() <= 32)
    return value;
  return b.create<LLVM::TruncOp>(b.getI32Type(), value);
}

/// Returns the type for the intrinsic given the vectorResultType of the
/// `gpu.mma.sync` operation.
static Type inferIntrinsicResultType(Type vectorResultType) {
  MLIRContext *ctx = vectorResultType.getContext();
  auto a = cast<LLVM::LLVMArrayType>(vectorResultType);
  auto f16x2Ty = LLVM::getFixedVectorType(Float16Type::get(ctx), 2);
  auto i32Ty = IntegerType::get(ctx, 32);
  auto i32x2Ty = LLVM::getFixedVectorType(i32Ty, 2);
  Type f64Ty = Float64Type::get(ctx);
  Type f64x2Ty = LLVM::getFixedVectorType(f64Ty, 2);
  Type f32Ty = Float32Type::get(ctx);
  Type f32x2Ty = LLVM::getFixedVectorType(f32Ty, 2);
  if (a.getElementType() == f16x2Ty) {
    return LLVM::LLVMStructType::getLiteral(
        ctx, SmallVector<Type>(a.getNumElements(), f16x2Ty));
  }
  if (a.getElementType() == i32x2Ty) {
    return LLVM::LLVMStructType::getLiteral(
        ctx,
        SmallVector<Type>(static_cast<size_t>(a.getNumElements()) * 2, i32Ty));
  }
  if (a.getElementType() == f64x2Ty) {
    return LLVM::LLVMStructType::getLiteral(ctx, {f64Ty, f64Ty});
  }
  if (a.getElementType() == f32x2Ty) {
    return LLVM::LLVMStructType::getLiteral(
        ctx,
        SmallVector<Type>(static_cast<size_t>(a.getNumElements()) * 2, f32Ty));
  }
  if (a.getElementType() == LLVM::getFixedVectorType(f32Ty, 1)) {
    return LLVM::LLVMStructType::getLiteral(
        ctx, SmallVector<Type>(static_cast<size_t>(a.getNumElements()), f32Ty));
  }
  return vectorResultType;
}

/// Convert the SSA result of the NVVM intrinsic `nvvm.mma.sync` (which is
/// always an LLVM struct) into a fragment that is compatible with the vector
/// type of this operation. This involves extracting elements from the struct
/// and inserting them into an LLVM array. These extra data-movement
/// operations should be canonicalized away by the LLVM backend.
static Value convertIntrinsicResult(Location loc, Type intrinsicResultType,
                                    Type resultType, Value intrinsicResult,
                                    RewriterBase &rewriter) {
  MLIRContext *ctx = rewriter.getContext();
  auto structType = dyn_cast<LLVM::LLVMStructType>(intrinsicResultType);
  auto arrayType = dyn_cast<LLVM::LLVMArrayType>(resultType);
  Type i32Ty = rewriter.getI32Type();
  Type f32Ty = rewriter.getF32Type();
  Type f64Ty = rewriter.getF64Type();
  Type f16x2Ty = LLVM::getFixedVectorType(rewriter.getF16Type(), 2);
  Type i32x2Ty = LLVM::getFixedVectorType(i32Ty, 2);
  Type f64x2Ty = LLVM::getFixedVectorType(f64Ty, 2);
  Type f32x2Ty = LLVM::getFixedVectorType(f32Ty, 2);
  Type f32x1Ty = LLVM::getFixedVectorType(f32Ty, 1);

  auto makeConst = [&](int32_t index) -> Value {
    return rewriter.create<LLVM::ConstantOp>(loc, IntegerType::get(ctx, 32),
                                             rewriter.getI32IntegerAttr(index));
  };

  if (arrayType) {
    SmallVector<Value, 4> elements;

    // The intrinsic returns 32-bit wide elements in a form which can be
    // directly bitcasted and inserted into the result vector.
    if (arrayType.getElementType() == f16x2Ty ||
        arrayType.getElementType() == f32x1Ty) {
      for (unsigned i = 0; i < structType.getBody().size(); i++) {
        Value el =
            rewriter.create<LLVM::ExtractValueOp>(loc, intrinsicResult, i);
        el = rewriter.createOrFold<LLVM::BitcastOp>(
            loc, arrayType.getElementType(), el);
        elements.push_back(el);
      }
    }

    // The intrinsic returns i32, f64, and f32 values as individual scalars,
    // even when the result is notionally a 64-bit wide element (e.g. f32x2). We
    // need to extract them from the struct and pack them into the 64-bit wide
    // rows of the vector result.
    if (arrayType.getElementType() == i32x2Ty ||
        arrayType.getElementType() == f64x2Ty ||
        arrayType.getElementType() == f32x2Ty) {

      for (unsigned i = 0, e = structType.getBody().size() / 2; i < e; i++) {
        Value vec =
            rewriter.create<LLVM::UndefOp>(loc, arrayType.getElementType());
        Value x1 =
            rewriter.create<LLVM::ExtractValueOp>(loc, intrinsicResult, i * 2);
        Value x2 = rewriter.create<LLVM::ExtractValueOp>(loc, intrinsicResult,
                                                         i * 2 + 1);
        vec = rewriter.create<LLVM::InsertElementOp>(loc, vec.getType(), vec,
                                                     x1, makeConst(0));
        vec = rewriter.create<LLVM::InsertElementOp>(loc, vec.getType(), vec,
                                                     x2, makeConst(1));
        elements.push_back(vec);
      }
    }

    // Create the final vectorized result.
    Value result = rewriter.create<LLVM::UndefOp>(loc, arrayType);
    for (const auto &el : llvm::enumerate(elements)) {
      result = rewriter.create<LLVM::InsertValueOp>(loc, result, el.value(),
                                                    el.index());
    }
    return result;
  }

  return intrinsicResult;
}

/// The `gpu.mma.sync` converter below expects matrix fragment operands to be
/// given as 2D `vectors` where the rows are 32b or 64b wide. The
/// `nvvm.mma.sync` op expects these argments to be a given in a long list of
/// scalars of certain types. This function helps unpack the `vector` arguments
/// and cast them to the types expected by `nvvm.mma.sync`.
static SmallVector<Value> unpackOperandVector(ImplicitLocOpBuilder &b,
                                              Value operand,
                                              NVVM::MMATypes operandPtxType) {
  SmallVector<Value> result;
  Type i32Ty = b.getI32Type();
  Type f64Ty = b.getF64Type();
  Type f32Ty = b.getF32Type();
  Type i64Ty = b.getI64Type();
  Type i8x4Ty = LLVM::getFixedVectorType(b.getI8Type(), 4);
  Type i4x8Ty = LLVM::getFixedVectorType(b.getIntegerType(4), 8);
  Type f32x1Ty = LLVM::getFixedVectorType(f32Ty, 1);
  auto arrayTy = cast<LLVM::LLVMArrayType>(operand.getType());

  for (unsigned i = 0, e = arrayTy.getNumElements(); i < e; ++i) {
    Value toUse = b.create<LLVM::ExtractValueOp>(operand, i);

    // For 4xi8 vectors, the intrinsic expects these to be provided as i32
    // scalar types.
    if (arrayTy.getElementType() == i8x4Ty ||
        arrayTy.getElementType() == i4x8Ty ||
        (arrayTy.getElementType() == f32x1Ty &&
         operandPtxType == NVVM::MMATypes::tf32)) {
      result.push_back(b.create<LLVM::BitcastOp>(i32Ty, toUse));
      continue;
    }

    // For some element types (i32, f32, f64), we need to unpack the inner
    // vector/array type as well because the intrinsic expects individual
    // scalars to be provided.
    VectorType innerArrayTy = dyn_cast<VectorType>(arrayTy.getElementType());
    if (innerArrayTy && (innerArrayTy.getElementType() == i32Ty ||
                         innerArrayTy.getElementType() == f64Ty ||
                         innerArrayTy.getElementType() == f32Ty)) {
      for (unsigned idx = 0, innerSize = innerArrayTy.getNumElements();
           idx < innerSize; idx++) {
        result.push_back(b.create<LLVM::ExtractElementOp>(
            toUse,
            b.create<LLVM::ConstantOp>(i64Ty, b.getI64IntegerAttr(idx))));
      }
      continue;
    }
    result.push_back(toUse);
  }
  return result;
}

/// Returns whether mbarrier object has shared memory address space.
static bool isMbarrierShared(nvgpu::MBarrierGroupType barrierType) {
  return (mlir::nvgpu::NVGPUDialect::isSharedMemoryAddressSpace(
      barrierType.getMemorySpace()));
}

/// Returns the memory space attribute of the mbarrier object.
Attribute nvgpu::getMbarrierMemorySpace(MLIRContext *context,
                                        nvgpu::MBarrierGroupType barrierType) {
  Attribute memorySpace = {};
  if (isMbarrierShared(barrierType)) {
    memorySpace =
        IntegerAttr::get(IntegerType::get(context, 64),
                         nvgpu::NVGPUDialect::kSharedMemoryAddressSpace);
  }
  return memorySpace;
}

/// Returns memref type of the mbarrier object. The type is defined in the
/// MBarrierGroupType.
MemRefType nvgpu::getMBarrierMemrefType(MLIRContext *context,
                                        nvgpu::MBarrierGroupType barrierType) {
  Attribute memorySpace = nvgpu::getMbarrierMemorySpace(context, barrierType);
  MemRefLayoutAttrInterface layout;
  return MemRefType::get({barrierType.getNumBarriers()},
                         IntegerType::get(context, 64), layout, memorySpace);
}

namespace {

struct MmaLdMatrixOpToNVVM : public ConvertOpToLLVMPattern<nvgpu::LdMatrixOp> {
  using ConvertOpToLLVMPattern<nvgpu::LdMatrixOp>::ConvertOpToLLVMPattern;

  LogicalResult
  matchAndRewrite(nvgpu::LdMatrixOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    MLIRContext *ctx = getContext();
    ImplicitLocOpBuilder b(op.getLoc(), rewriter);

    // The result type of ldmatrix will always be a struct of 32bit integer
    // registers if more than one 32bit value is returned. Otherwise, the result
    // is a single i32. The result type of the GPU operation is always a vector
    // of shape (NumRegisters, VectorRegister) where VectorRegister is the
    // vector type of the result and always 32 bits long. We bitcast the result
    // of the NVVM::LdMatrix to this vector type.
    auto vectorResultType = dyn_cast<VectorType>(op->getResultTypes()[0]);
    if (!vectorResultType) {
      return failure();
    }
    Type innerVectorType = LLVM::getFixedVectorType(
        vectorResultType.getElementType(), vectorResultType.getDimSize(1));

    int64_t num32BitRegs = vectorResultType.getDimSize(0);

    Type ldMatrixResultType;
    if (num32BitRegs > 1) {
      ldMatrixResultType = LLVM::LLVMStructType::getLiteral(
          ctx, SmallVector<Type>(num32BitRegs, rewriter.getI32Type()));
    } else {
      ldMatrixResultType = rewriter.getI32Type();
    }

    auto srcMemrefType = cast<MemRefType>(op.getSrcMemref().getType());
    Value srcPtr =
        getStridedElementPtr(b.getLoc(), srcMemrefType, adaptor.getSrcMemref(),
                             adaptor.getIndices(), rewriter);
    Value ldMatrixResult = b.create<NVVM::LdMatrixOp>(
        ldMatrixResultType, srcPtr,
        /*num=*/op.getNumTiles(),
        /*layout=*/op.getTranspose() ? NVVM::MMALayout::col
                                     : NVVM::MMALayout::row);

    // The ldmatrix operation returns either a single i32 value or a struct of
    // i32 values. Here we unpack those values and cast them back to their
    // actual vector type (still of width 32b) and repack them into a result
    // struct.
    Type finalResultType = typeConverter->convertType(vectorResultType);
    Value result = b.create<LLVM::UndefOp>(finalResultType);
    for (int64_t i = 0, e = vectorResultType.getDimSize(0); i < e; i++) {
      Value i32Register =
          num32BitRegs > 1 ? b.create<LLVM::ExtractValueOp>(ldMatrixResult, i)
                           : ldMatrixResult;
      Value casted = b.create<LLVM::BitcastOp>(innerVectorType, i32Register);
      result = b.create<LLVM::InsertValueOp>(result, casted, i);
    }

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

/// Convert the given type into the corresponding PTX type (NVVM::MMATypes
/// enum).
static FailureOr<NVVM::MMATypes> getNvvmMmaType(Type t) {
  Type elType = getElementTypeOrSelf(t);
  if (elType.isInteger(8))
    return NVVM::MMATypes::s8;
  if (elType.isInteger(4))
    return NVVM::MMATypes::s4;
  if (elType.isF16())
    return NVVM::MMATypes::f16;
  if (elType.isF64())
    return NVVM::MMATypes::f64;
  if (elType.isF32())
    return NVVM::MMATypes::tf32;
  return failure();
}

struct MmaSyncOptoNVVM : public ConvertOpToLLVMPattern<nvgpu::MmaSyncOp> {
  using ConvertOpToLLVMPattern<nvgpu::MmaSyncOp>::ConvertOpToLLVMPattern;

  LogicalResult
  matchAndRewrite(nvgpu::MmaSyncOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    ImplicitLocOpBuilder b(op.getLoc(), rewriter);
    // Get the shapes of the MMAMatrix type being used. The shapes will
    // choose which intrinsic this op will be lowered to.
    VectorType aType = op.getMatrixA().getType();
    VectorType bType = op.getMatrixA().getType();
    VectorType cType = op.getMatrixC().getType();

    std::array<int64_t, 3> gemmShape = op.getMmaShapeAsArray();

    // Tensor Cores (mma.sync) on F32 works only with TensorFloat32 (TF32).
    bool tf32Enabled = op->hasAttr(op.getTf32EnabledAttrName());
    if (aType.getElementType().isF32() && !tf32Enabled)
      return failure();

    FailureOr<NVVM::MMATypes> ptxTypeA = getNvvmMmaType(aType);
    if (failed(ptxTypeA))
      return op->emitOpError("failed to deduce operand PTX types");
    FailureOr<NVVM::MMATypes> ptxTypeB = getNvvmMmaType(bType);
    if (failed(ptxTypeB))
      return op->emitOpError("failed to deduce operand PTX types");
    std::optional<NVVM::MMATypes> ptxTypeC =
        NVVM::MmaOp::inferOperandMMAType(cType.getElementType(),
                                         /*isAccumulator=*/true);
    if (!ptxTypeC)
      return op->emitError(
          "could not infer the PTX type for the accumulator/result");

    // TODO: add an attribute to the op to customize this behavior.
    std::optional<NVVM::MMAIntOverflow> overflow(std::nullopt);
    if (isa<IntegerType>(aType.getElementType()))
      overflow = NVVM::MMAIntOverflow::satfinite;

    SmallVector<Value> matA =
        unpackOperandVector(b, adaptor.getMatrixA(), *ptxTypeA);
    SmallVector<Value> matB =
        unpackOperandVector(b, adaptor.getMatrixB(), *ptxTypeB);
    SmallVector<Value> matC =
        unpackOperandVector(b, adaptor.getMatrixC(), *ptxTypeC);

    Type desiredRetTy = typeConverter->convertType(op->getResultTypes()[0]);
    Type intrinsicResTy = inferIntrinsicResultType(
        typeConverter->convertType(op->getResultTypes()[0]));
    Value intrinsicResult = b.create<NVVM::MmaOp>(
        intrinsicResTy, matA, matB, matC,
        /*shape=*/gemmShape,
        /*b1Op=*/std::nullopt,
        /*intOverflow=*/overflow,
        /*multiplicandPtxTypes=*/
        std::array<NVVM::MMATypes, 2>{*ptxTypeA, *ptxTypeB},
        /*multiplicandLayouts=*/
        std::array<NVVM::MMALayout, 2>{NVVM::MMALayout::row,
                                       NVVM::MMALayout::col});
    rewriter.replaceOp(op, convertIntrinsicResult(op.getLoc(), intrinsicResTy,
                                                  desiredRetTy, intrinsicResult,
                                                  rewriter));
    return success();
  }
};

struct ConvertNVGPUToNVVMPass
    : public impl::ConvertNVGPUToNVVMPassBase<ConvertNVGPUToNVVMPass> {
  using Base::Base;

  void getDependentDialects(DialectRegistry &registry) const override {
    registry.insert<memref::MemRefDialect, LLVM::LLVMDialect, NVVM::NVVMDialect,
                    arith::ArithDialect>();
  }

  void runOnOperation() override {
    LowerToLLVMOptions options(&getContext());
    RewritePatternSet patterns(&getContext());
    LLVMTypeConverter converter(&getContext(), options);
    IRRewriter rewriter(&getContext());
    populateGpuMemorySpaceAttributeConversions(
        converter, [](gpu::AddressSpace space) -> unsigned {
          switch (space) {
          case gpu::AddressSpace::Global:
            return static_cast<unsigned>(
                NVVM::NVVMMemorySpace::kGlobalMemorySpace);
          case gpu::AddressSpace::Workgroup:
            return static_cast<unsigned>(
                NVVM::NVVMMemorySpace::kSharedMemorySpace);
          case gpu::AddressSpace::Private:
            return 0;
          }
          llvm_unreachable("unknown address space enum value");
          return 0;
        });
    /// device-side async tokens cannot be materialized in nvvm. We just
    /// convert them to a dummy i32 type in order to easily drop them during
    /// conversion.
    converter.addConversion([&](nvgpu::DeviceAsyncTokenType type) -> Type {
      return converter.convertType(IntegerType::get(type.getContext(), 32));
    });
    converter.addConversion([&](nvgpu::WarpgroupAccumulatorType type) -> Type {
      Type elemType = type.getFragmented().getElementType();
      int64_t sizeM = type.getFragmented().getDimSize(0);
      int64_t sizeN = type.getFragmented().getDimSize(1);

      unsigned numMembers;
      if (elemType.isF32() || elemType.isInteger(32))
        numMembers = sizeN / 2;
      else if (elemType.isF16())
        numMembers = sizeN / 4;
      else
        llvm_unreachable("unsupported type for warpgroup accumulator");

      SmallVector<Type> innerStructBody;
      for (unsigned i = 0; i < numMembers; i++)
        innerStructBody.push_back(elemType);
      auto innerStructType =
          LLVM::LLVMStructType::getLiteral(type.getContext(), innerStructBody);

      SmallVector<Type> structBody;
      for (int i = 0; i < sizeM; i += kWgmmaSizeM)
        structBody.push_back(innerStructType);

      auto convertedType =
          LLVM::LLVMStructType::getLiteral(type.getContext(), structBody);
      return converter.convertType(convertedType);
    });
    converter.addConversion([&](nvgpu::MBarrierTokenType type) -> Type {
      return converter.convertType(IntegerType::get(type.getContext(), 64));
    });
    converter.addConversion(
        [&](nvgpu::WarpgroupMatrixDescriptorType type) -> Type {
          return converter.convertType(IntegerType::get(type.getContext(), 64));
        });
    converter.addConversion([&](nvgpu::MBarrierGroupType type) -> Type {
      return converter.convertType(
          nvgpu::getMBarrierMemrefType(rewriter.getContext(), type));
    });
    converter.addConversion([&](nvgpu::TensorMapDescriptorType type) -> Type {
      return LLVM::LLVMPointerType::get(type.getContext());
    });
    populateNVGPUToNVVMConversionPatterns(converter, patterns);
    LLVMConversionTarget target(getContext());
    target.addLegalDialect<::mlir::LLVM::LLVMDialect>();
    target.addLegalDialect<::mlir::arith::ArithDialect>();
    target.addLegalDialect<::mlir::memref::MemRefDialect>();
    target.addLegalDialect<::mlir::NVVM::NVVMDialect>();
    mlir::scf::populateSCFStructuralTypeConversionsAndLegality(
        converter, patterns, target);
    if (failed(applyPartialConversion(getOperation(), target,
                                      std::move(patterns))))
      signalPassFailure();
  }
};

/// Returns the constraints for the sparse MMA inline assembly instruction.
static std::string buildMmaSparseAsmConstraintString(unsigned matASize,
                                                     unsigned matBSize,
                                                     unsigned matCSize) {
  std::string str;
  llvm::raw_string_ostream ss(str);
  for (unsigned i = 0; i < matCSize; i++)
    ss << "=r,";
  for (unsigned i = 0; i < matASize + matBSize + matCSize; i++)
    ss << "r,";
  // The final operand is for the sparsity metadata.
  // The sparsity selector appears as direct literal.
  ss << "r";
  return str;
}

/// Returns the string for the `mma.sp.sync` instruction that corresponds to
/// the given parameters. Note that this function doesn't do any validation,
/// it's expected that the provided parameters correspond to a valid
/// instruction.
static std::string buildMmaSparseAsmString(
    const std::array<int64_t, 3> &shape, unsigned matASize, unsigned matBSize,
    unsigned matCSize, NVVM::MMATypes ptxTypeA, NVVM::MMATypes ptxTypeB,
    NVVM::MMATypes ptxTypeC, NVVM::MMATypes ptxTypeD,
    std::optional<NVVM::MMAIntOverflow> overflow, unsigned metaDataSelector) {
  auto ptxTypeStr = [](NVVM::MMATypes ptxType) {
    return NVVM::stringifyMMATypes(ptxType);
  };

  std::string asmStr;
  llvm::raw_string_ostream ss(asmStr);
  ss << "mma.sp.sync.aligned.m" << shape[0] << "n" << shape[1] << "k"
     << shape[2] << ".row.col.";

  if (overflow)
    ss << NVVM::stringifyMMAIntOverflow(*overflow) << ".";

  ss << ptxTypeStr(ptxTypeD) << "." << ptxTypeStr(ptxTypeA) << "."
     << ptxTypeStr(ptxTypeB) << "." << ptxTypeStr(ptxTypeC) << " ";
  unsigned asmArgIdx = 0;

  // The operand string is structured into sections `{matC elements...},
  // {matA elements...}, {matB elements...}, {matC elements}`.
  for (const auto arrSize : {matCSize, matASize, matBSize, matCSize}) {
    ss << "{";
    for (unsigned i = 0; i < arrSize; i++)
      ss << "$" << asmArgIdx++ << (i < arrSize - 1 ? "," : "");
    ss << "},";
  }
  ss << "$" << asmArgIdx++ << ",";
  assert(metaDataSelector <= 1);
  ss << "0x" << metaDataSelector << ";";
  return asmStr;
}

/// Builds an inline assembly operation corresponding to the specified MMA
/// sparse sync operation.
static FailureOr<LLVM::InlineAsmOp> emitMmaSparseSyncOpAsm(
    ImplicitLocOpBuilder &b, NVVM::MMATypes ptxTypeA, NVVM::MMATypes ptxTypeB,
    NVVM::MMATypes ptxTypeC, NVVM::MMATypes ptxTypeD,
    std::optional<NVVM::MMAIntOverflow> overflow, ArrayRef<Value> unpackedAData,
    ArrayRef<Value> unpackedB, ArrayRef<Value> unpackedC, Value indexData,
    int64_t metadataSelector, const std::array<int64_t, 3> &shape,
    Type intrinsicResultType) {
  auto asmDialectAttr =
      LLVM::AsmDialectAttr::get(b.getContext(), LLVM::AsmDialect::AD_ATT);

  const unsigned matASize = unpackedAData.size();
  const unsigned matBSize = unpackedB.size();
  const unsigned matCSize = unpackedC.size();

  std::string asmStr = buildMmaSparseAsmString(
      shape, matASize, matBSize, matCSize, ptxTypeA, ptxTypeB, ptxTypeC,
      ptxTypeD, overflow, metadataSelector);
  std::string constraintStr =
      buildMmaSparseAsmConstraintString(matASize, matBSize, matCSize);

  SmallVector<Value> asmVals;
  asmVals.reserve(matASize + matBSize + matCSize + 1);
  for (ArrayRef<Value> args : {unpackedAData, unpackedB, unpackedC})
    llvm::append_range(asmVals, args);
  asmVals.push_back(indexData);

  return b.create<LLVM::InlineAsmOp>(
      /*resultTypes=*/intrinsicResultType,
      /*operands=*/asmVals,
      /*asm_string=*/asmStr,
      /*constraints=*/constraintStr,
      /*has_side_effects=*/true,
      /*is_align_stack=*/false,
      /*asm_dialect=*/asmDialectAttr,
      /*operand_attrs=*/ArrayAttr());
}

/// Lowers `nvgpu.mma.sp.sync` to inline assembly.
struct NVGPUMmaSparseSyncLowering
    : public ConvertOpToLLVMPattern<nvgpu::MmaSparseSyncOp> {
  using ConvertOpToLLVMPattern<nvgpu::MmaSparseSyncOp>::ConvertOpToLLVMPattern;

  LogicalResult
  matchAndRewrite(nvgpu::MmaSparseSyncOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    ImplicitLocOpBuilder b(op.getLoc(), rewriter);
    // Get the shapes of the MMAMatrix type being used. The shapes will
    // choose which intrinsic this op will be lowered to.
    VectorType aType = op.getMatrixA().getType();
    VectorType bType = op.getMatrixB().getType();
    VectorType cType = op.getMatrixC().getType();

    FailureOr<NVVM::MMATypes> ptxTypeA = getNvvmMmaType(aType);
    if (failed(ptxTypeA))
      return op->emitOpError("failed to deduce operand PTX types");
    FailureOr<NVVM::MMATypes> ptxTypeB = getNvvmMmaType(bType);
    if (failed(ptxTypeB))
      return op->emitOpError("failed to deduce operand PTX types");
    std::optional<NVVM::MMATypes> ptxTypeC =
        NVVM::MmaOp::inferOperandMMAType(cType.getElementType(),
                                         /*isAccumulator=*/true);
    if (!ptxTypeC)
      return op->emitError(
          "could not infer the PTX type for the accumulator/result");

    // Same as `mma.sync`, F32 works only with TensorFloat32 (TF32).
    bool tf32Enabled = op->hasAttr(op.getTf32EnabledAttrName());
    if (aType.getElementType().isF32() && !tf32Enabled)
      return failure();

    // TODO: add an attribute to the op to customize this behavior.
    std::optional<NVVM::MMAIntOverflow> overflow(std::nullopt);
    if (isa<IntegerType>(aType.getElementType()))
      overflow = NVVM::MMAIntOverflow::satfinite;

    SmallVector<Value> matA =
        unpackOperandVector(b, adaptor.getMatrixA(), *ptxTypeA);
    SmallVector<Value> matB =
        unpackOperandVector(b, adaptor.getMatrixB(), *ptxTypeB);
    SmallVector<Value> matC =
        unpackOperandVector(b, adaptor.getMatrixC(), *ptxTypeC);

    Type desiredRetTy = typeConverter->convertType(op->getResultTypes()[0]);
    Type intrinsicResTy = inferIntrinsicResultType(
        typeConverter->convertType(op->getResultTypes()[0]));

    // Bitcast the sparse metadata from vector<2xf16> to an i32.
    Value sparseMetadata = adaptor.getSparseMetadata();
    if (sparseMetadata.getType() !=
        LLVM::getFixedVectorType(rewriter.getI16Type(), 2))
      return op->emitOpError() << "Expected metadata type to be LLVM "
                                  "VectorType of 2 i16 elements";
    sparseMetadata =
        b.create<LLVM::BitcastOp>(rewriter.getI32Type(), sparseMetadata);

    FailureOr<LLVM::InlineAsmOp> intrinsicResult = emitMmaSparseSyncOpAsm(
        b, *ptxTypeA, *ptxTypeB, *ptxTypeC, *ptxTypeC, overflow, matA, matB,
        matC, sparseMetadata, op.getSparsitySelector(), op.getMmaShapeAsArray(),
        intrinsicResTy);
    if (failed(intrinsicResult))
      return failure();

    assert((*intrinsicResult).getNumResults() == 1 &&
           "expected inline asm op returns a single LLVM struct type");
    rewriter.replaceOp(
        op, convertIntrinsicResult(op.getLoc(), intrinsicResTy, desiredRetTy,
                                   (*intrinsicResult)->getResult(0), rewriter));
    return success();
  }
};

struct NVGPUAsyncCopyLowering
    : public ConvertOpToLLVMPattern<nvgpu::DeviceAsyncCopyOp> {
  using ConvertOpToLLVMPattern<
      nvgpu::DeviceAsyncCopyOp>::ConvertOpToLLVMPattern;

  LogicalResult
  matchAndRewrite(nvgpu::DeviceAsyncCopyOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    ImplicitLocOpBuilder b(op.getLoc(), rewriter);
    Location loc = op.getLoc();
    auto dstMemrefType = cast<MemRefType>(op.getDst().getType());
    Value dstPtr =
        getStridedElementPtr(b.getLoc(), dstMemrefType, adaptor.getDst(),
                             adaptor.getDstIndices(), rewriter);
    FailureOr<unsigned> dstAddressSpace =
        getTypeConverter()->getMemRefAddressSpace(dstMemrefType);
    if (failed(dstAddressSpace))
      return rewriter.notifyMatchFailure(
          loc, "destination memref address space not convertible to integer");

    auto srcMemrefType = cast<MemRefType>(op.getSrc().getType());
    FailureOr<unsigned> srcAddressSpace =
        getTypeConverter()->getMemRefAddressSpace(srcMemrefType);
    if (failed(srcAddressSpace))
      return rewriter.notifyMatchFailure(
          loc, "source memref address space not convertible to integer");

    Value scrPtr = getStridedElementPtr(loc, srcMemrefType, adaptor.getSrc(),
                                        adaptor.getSrcIndices(), rewriter);
    // Intrinsics takes a global pointer so we need an address space cast.
    auto srcPointerGlobalType = LLVM::LLVMPointerType::get(
        op->getContext(), NVVM::NVVMMemorySpace::kGlobalMemorySpace);
    scrPtr = b.create<LLVM::AddrSpaceCastOp>(srcPointerGlobalType, scrPtr);
    int64_t dstElements = adaptor.getDstElements().getZExtValue();
    int64_t sizeInBytes =
        (dstMemrefType.getElementTypeBitWidth() * dstElements) / 8;
    // When the optional SrcElements argument is *not* present, the regular
    // CpAsyncOp is generated. CopyAsyncOp reads bytes from source (global
    // memory) to fill DstElements number of elements in the destination
    // (shared memory).
    Value srcBytes = adaptor.getSrcElements();
    if (srcBytes) {
      // When the optional SrcElements argument is present, the source (global
      // memory) of CpAsyncOp is read only for SrcElements number of elements.
      // The rest of the DstElements in the destination (shared memory) are
      // filled with zeros.
      Value c3I32 =
          b.create<LLVM::ConstantOp>(b.getI32Type(), b.getI32IntegerAttr(3));
      Value bitwidth = b.create<LLVM::ConstantOp>(
          b.getI32Type(),
          b.getI32IntegerAttr(srcMemrefType.getElementTypeBitWidth()));
      Value srcElementsI32 = b.create<LLVM::TruncOp>(b.getI32Type(), srcBytes);
      srcBytes = b.create<LLVM::LShrOp>(
          b.create<LLVM::MulOp>(bitwidth, srcElementsI32), c3I32);
    }
    // Cache global (.cg) for 16 dst bytes, Cache all (.ca) for sizes other than
    // 16 dst bytes.
    NVVM::LoadCacheModifierKind cacheModifier =
        (op.getBypassL1().value_or(false) && sizeInBytes == 16)
            ? NVVM::LoadCacheModifierKind::CG
            : NVVM::LoadCacheModifierKind::CA;

    b.create<NVVM::CpAsyncOp>(
        dstPtr, scrPtr, rewriter.getI32IntegerAttr(sizeInBytes),
        NVVM::LoadCacheModifierKindAttr::get(op->getContext(), cacheModifier),
        srcBytes);

    // Drop the result token.
    Value zero = b.create<LLVM::ConstantOp>(
        IntegerType::get(op.getContext(), 32), rewriter.getI32IntegerAttr(0));
    rewriter.replaceOp(op, zero);
    return success();
  }
};

struct NVGPUAsyncCreateGroupLowering
    : public ConvertOpToLLVMPattern<nvgpu::DeviceAsyncCreateGroupOp> {
  using ConvertOpToLLVMPattern<
      nvgpu::DeviceAsyncCreateGroupOp>::ConvertOpToLLVMPattern;

  LogicalResult
  matchAndRewrite(nvgpu::DeviceAsyncCreateGroupOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    rewriter.create<NVVM::CpAsyncCommitGroupOp>(op.getLoc());
    // Drop the result token.
    Value zero = rewriter.create<LLVM::ConstantOp>(
        op->getLoc(), IntegerType::get(op.getContext(), 32),
        rewriter.getI32IntegerAttr(0));
    rewriter.replaceOp(op, zero);
    return success();
  }
};

struct NVGPUAsyncWaitLowering
    : public ConvertOpToLLVMPattern<nvgpu::DeviceAsyncWaitOp> {
  using ConvertOpToLLVMPattern<
      nvgpu::DeviceAsyncWaitOp>::ConvertOpToLLVMPattern;

  LogicalResult
  matchAndRewrite(nvgpu::DeviceAsyncWaitOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    // If numGroup is not present pick 0 as a conservative correct value.
    int32_t numGroups = adaptor.getNumGroups().value_or(0);
    rewriter.create<NVVM::CpAsyncWaitGroupOp>(op.getLoc(), numGroups);
    rewriter.eraseOp(op);
    return success();
  }
};

/// Creates mbarrier object in shared memory
struct NVGPUMBarrierCreateLowering
    : public ConvertOpToLLVMPattern<nvgpu::MBarrierCreateOp> {
  using ConvertOpToLLVMPattern<nvgpu::MBarrierCreateOp>::ConvertOpToLLVMPattern;

  template <typename moduleT>
  memref::GlobalOp generateGlobalBarrier(ConversionPatternRewriter &rewriter,
                                         Operation *funcOp, moduleT moduleOp,
                                         MemRefType barrierType) const {
    SymbolTable symbolTable(moduleOp);
    OpBuilder::InsertionGuard guard(rewriter);
    rewriter.setInsertionPoint(&moduleOp.front());
    auto global = rewriter.create<memref::GlobalOp>(
        funcOp->getLoc(), "__mbarrier",
        /*sym_visibility=*/rewriter.getStringAttr("private"),
        /*type=*/barrierType,
        /*initial_value=*/ElementsAttr(),
        /*constant=*/false,
        /*alignment=*/rewriter.getI64IntegerAttr(8));
    symbolTable.insert(global);
    return global;
  }

  LogicalResult
  matchAndRewrite(nvgpu::MBarrierCreateOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    Operation *funcOp = op->getParentOp();
    MemRefType barrierType = nvgpu::getMBarrierMemrefType(
        rewriter.getContext(), op.getBarriers().getType());

    memref::GlobalOp global;
    if (auto moduleOp = funcOp->getParentOfType<gpu::GPUModuleOp>())
      global = generateGlobalBarrier(rewriter, funcOp, moduleOp, barrierType);
    else if (auto moduleOp = funcOp->getParentOfType<ModuleOp>())
      global = generateGlobalBarrier(rewriter, funcOp, moduleOp, barrierType);

    rewriter.setInsertionPoint(op);
    rewriter.replaceOpWithNewOp<memref::GetGlobalOp>(op, barrierType,
                                                     global.getName());
    return success();
  }
};

/// Base class for lowering mbarrier operations to nvvm intrinsics.
template <typename SourceOp>
struct MBarrierBasePattern : public ConvertOpToLLVMPattern<SourceOp> {
public:
  using ConvertOpToLLVMPattern<SourceOp>::ConvertOpToLLVMPattern;
  /// Returns the base pointer of the mbarrier object.
  Value getMbarrierPtr(ImplicitLocOpBuilder &b,
                       nvgpu::MBarrierGroupType mbarType, Value memrefDesc,
                       Value mbarId,
                       ConversionPatternRewriter &rewriter) const {
    MemRefType mbarrierMemrefType =
        nvgpu::getMBarrierMemrefType(rewriter.getContext(), mbarType);
    return ConvertToLLVMPattern::getStridedElementPtr(
        b.getLoc(), mbarrierMemrefType, memrefDesc, {mbarId}, rewriter);
  }
};

/// Lowers `nvgpu.mbarrier.init` to `nvvm.mbarrier.init`
struct NVGPUMBarrierInitLowering
    : public MBarrierBasePattern<nvgpu::MBarrierInitOp> {
  using MBarrierBasePattern<nvgpu::MBarrierInitOp>::MBarrierBasePattern;

  LogicalResult
  matchAndRewrite(nvgpu::MBarrierInitOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    ImplicitLocOpBuilder b(op->getLoc(), rewriter);
    nvgpu::MBarrierGroupType mbarrierType = op.getBarriers().getType();
    rewriter.setInsertionPoint(op);
    Value barrier = getMbarrierPtr(b, mbarrierType, adaptor.getBarriers(),
                                   adaptor.getMbarId(), rewriter);
    Value count = truncToI32(b, adaptor.getCount());
    if (isMbarrierShared(mbarrierType)) {
      rewriter.replaceOpWithNewOp<NVVM::MBarrierInitSharedOp>(
          op, barrier, count, adaptor.getPredicate());
    } else {
      rewriter.replaceOpWithNewOp<NVVM::MBarrierInitOp>(op, barrier, count,
                                                        adaptor.getPredicate());
    }
    return success();
  }
};

/// Lowers `nvgpu.mbarrier.arrive` to `nvvm.mbarrier.arrive`
struct NVGPUMBarrierArriveLowering
    : public MBarrierBasePattern<nvgpu::MBarrierArriveOp> {
  using MBarrierBasePattern<nvgpu::MBarrierArriveOp>::MBarrierBasePattern;
  LogicalResult
  matchAndRewrite(nvgpu::MBarrierArriveOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    ImplicitLocOpBuilder b(op->getLoc(), rewriter);
    Value barrier =
        getMbarrierPtr(b, op.getBarriers().getType(), adaptor.getBarriers(),
                       adaptor.getMbarId(), rewriter);
    Type tokenType = getTypeConverter()->convertType(
        nvgpu::MBarrierTokenType::get(op->getContext()));
    if (isMbarrierShared(op.getBarriers().getType())) {
      rewriter.replaceOpWithNewOp<NVVM::MBarrierArriveSharedOp>(op, tokenType,
                                                                barrier);
    } else {
      rewriter.replaceOpWithNewOp<NVVM::MBarrierArriveOp>(op, tokenType,
                                                          barrier);
    }
    return success();
  }
};

/// Lowers `nvgpu.mbarrier.arrive.nocomplete` to
/// `nvvm.mbarrier.arrive.nocomplete`
struct NVGPUMBarrierArriveNoCompleteLowering
    : public MBarrierBasePattern<nvgpu::MBarrierArriveNoCompleteOp> {
  using MBarrierBasePattern<
      nvgpu::MBarrierArriveNoCompleteOp>::MBarrierBasePattern;
  LogicalResult
  matchAndRewrite(nvgpu::MBarrierArriveNoCompleteOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    ImplicitLocOpBuilder b(op->getLoc(), rewriter);
    Value barrier =
        getMbarrierPtr(b, op.getBarriers().getType(), adaptor.getBarriers(),
                       adaptor.getMbarId(), rewriter);
    Type tokenType = getTypeConverter()->convertType(
        nvgpu::MBarrierTokenType::get(op->getContext()));
    Value count = truncToI32(b, adaptor.getCount());
    if (isMbarrierShared(op.getBarriers().getType())) {
      rewriter.replaceOpWithNewOp<NVVM::MBarrierArriveNocompleteSharedOp>(
          op, tokenType, barrier, count);
    } else {
      rewriter.replaceOpWithNewOp<NVVM::MBarrierArriveNocompleteOp>(
          op, tokenType, barrier, count);
    }
    return success();
  }
};

/// Lowers `nvgpu.mbarrier.test.wait` to `nvvm.mbarrier.test.wait`
struct NVGPUMBarrierTestWaitLowering
    : public MBarrierBasePattern<nvgpu::MBarrierTestWaitOp> {
  using MBarrierBasePattern<nvgpu::MBarrierTestWaitOp>::MBarrierBasePattern;
  LogicalResult
  matchAndRewrite(nvgpu::MBarrierTestWaitOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    ImplicitLocOpBuilder b(op->getLoc(), rewriter);
    Value barrier =
        getMbarrierPtr(b, op.getBarriers().getType(), adaptor.getBarriers(),
                       adaptor.getMbarId(), rewriter);
    Type retType = rewriter.getI1Type();
    if (isMbarrierShared(op.getBarriers().getType())) {
      rewriter.replaceOpWithNewOp<NVVM::MBarrierTestWaitSharedOp>(
          op, retType, barrier, adaptor.getToken());
    } else {
      rewriter.replaceOpWithNewOp<NVVM::MBarrierTestWaitOp>(
          op, retType, barrier, adaptor.getToken());
    }
    return success();
  }
};

struct NVGPUMBarrierArriveExpectTxLowering
    : public MBarrierBasePattern<nvgpu::MBarrierArriveExpectTxOp> {
  using MBarrierBasePattern<
      nvgpu::MBarrierArriveExpectTxOp>::MBarrierBasePattern;
  LogicalResult
  matchAndRewrite(nvgpu::MBarrierArriveExpectTxOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    ImplicitLocOpBuilder b(op->getLoc(), rewriter);
    Value barrier =
        getMbarrierPtr(b, op.getBarriers().getType(), adaptor.getBarriers(),
                       adaptor.getMbarId(), rewriter);
    Value txcount = truncToI32(b, adaptor.getTxcount());

    if (isMbarrierShared(op.getBarriers().getType())) {
      rewriter.replaceOpWithNewOp<NVVM::MBarrierArriveExpectTxSharedOp>(
          op, barrier, txcount, adaptor.getPredicate());
      return success();
    }

    rewriter.replaceOpWithNewOp<NVVM::MBarrierArriveExpectTxOp>(
        op, barrier, txcount, adaptor.getPredicate());
    return success();
  }
};

struct NVGPUMBarrierTryWaitParityLowering
    : public MBarrierBasePattern<nvgpu::MBarrierTryWaitParityOp> {
  using MBarrierBasePattern<
      nvgpu::MBarrierTryWaitParityOp>::MBarrierBasePattern;
  LogicalResult
  matchAndRewrite(nvgpu::MBarrierTryWaitParityOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    ImplicitLocOpBuilder b(op->getLoc(), rewriter);
    Value barrier =
        getMbarrierPtr(b, op.getBarriers().getType(), adaptor.getBarriers(),
                       adaptor.getMbarId(), rewriter);
    Value ticks = truncToI32(b, adaptor.getTicks());
    Value phase =
        b.create<LLVM::ZExtOp>(b.getI32Type(), adaptor.getPhaseParity());

    if (isMbarrierShared(op.getBarriers().getType())) {
      rewriter.replaceOpWithNewOp<NVVM::MBarrierTryWaitParitySharedOp>(
          op, barrier, phase, ticks);
      return success();
    }

    rewriter.replaceOpWithNewOp<NVVM::MBarrierTryWaitParityOp>(op, barrier,
                                                               phase, ticks);
    return success();
  }
};

struct NVGPUTmaAsyncLoadOpLowering
    : public MBarrierBasePattern<nvgpu::TmaAsyncLoadOp> {
  using MBarrierBasePattern<nvgpu::TmaAsyncLoadOp>::MBarrierBasePattern;
  LogicalResult
  matchAndRewrite(nvgpu::TmaAsyncLoadOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    ImplicitLocOpBuilder b(op->getLoc(), rewriter);
    auto srcMemrefType = cast<MemRefType>(op.getDst().getType());
    Value dest = getStridedElementPtr(op->getLoc(), srcMemrefType,
                                      adaptor.getDst(), {}, rewriter);
    Value barrier =
        getMbarrierPtr(b, op.getBarriers().getType(), adaptor.getBarriers(),
                       adaptor.getMbarId(), rewriter);

    SmallVector<Value> coords = adaptor.getCoordinates();
    for (auto [index, value] : llvm::enumerate(coords)) {
      coords[index] = truncToI32(b, value);
    }
    rewriter.replaceOpWithNewOp<NVVM::CpAsyncBulkTensorGlobalToSharedClusterOp>(
        op, dest, adaptor.getTensorMapDescriptor(), coords, barrier,
        ValueRange{}, adaptor.getMulticastMask(), Value{},
        adaptor.getPredicate());
    return success();
  }
};

struct NVGPUTmaAsyncStoreOpLowering
    : public MBarrierBasePattern<nvgpu::TmaAsyncStoreOp> {
  using MBarrierBasePattern<nvgpu::TmaAsyncStoreOp>::MBarrierBasePattern;
  LogicalResult
  matchAndRewrite(nvgpu::TmaAsyncStoreOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    ImplicitLocOpBuilder b(op->getLoc(), rewriter);
    auto srcMemrefType = cast<MemRefType>(op.getSrc().getType());
    Value dest = getStridedElementPtr(op->getLoc(), srcMemrefType,
                                      adaptor.getSrc(), {}, rewriter);
    SmallVector<Value> coords = adaptor.getCoordinates();
    for (auto [index, value] : llvm::enumerate(coords)) {
      coords[index] = truncToI32(b, value);
    }

    rewriter.replaceOpWithNewOp<NVVM::CpAsyncBulkTensorSharedCTAToGlobalOp>(
        op, adaptor.getTensorMapDescriptor(), dest, coords,
        adaptor.getPredicate());
    return success();
  }
};

struct NVGPUGenerateWarpgroupDescriptorLowering
    : public ConvertOpToLLVMPattern<nvgpu::WarpgroupGenerateDescriptorOp> {
  using ConvertOpToLLVMPattern<
      nvgpu::WarpgroupGenerateDescriptorOp>::ConvertOpToLLVMPattern;

  LogicalResult
  matchAndRewrite(nvgpu::WarpgroupGenerateDescriptorOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {

    ImplicitLocOpBuilder b(op->getLoc(), rewriter);

    nvgpu::TensorMapSwizzleKind swizzleKind =
        op.getTensorMap().getType().getSwizzle();

    unsigned layout =
        (swizzleKind == nvgpu::TensorMapSwizzleKind::SWIZZLE_128B)  ? 128
        : (swizzleKind == nvgpu::TensorMapSwizzleKind::SWIZZLE_64B) ? 64
        : (swizzleKind == nvgpu::TensorMapSwizzleKind::SWIZZLE_32B) ? 32
                                                                    : 1;
    unsigned swizzle =
        (swizzleKind == nvgpu::TensorMapSwizzleKind::SWIZZLE_128B)  ? 1
        : (swizzleKind == nvgpu::TensorMapSwizzleKind::SWIZZLE_64B) ? 2
        : (swizzleKind == nvgpu::TensorMapSwizzleKind::SWIZZLE_32B) ? 3
                                                                    : 0;

    auto ti64 = b.getIntegerType(64);
    auto makeConst = [&](uint64_t index) -> Value {
      return b.create<LLVM::ConstantOp>(ti64, b.getI64IntegerAttr(index));
    };
    auto shiftLeft = [&](Value value, unsigned shift) -> Value {
      return b.create<LLVM::ShlOp>(ti64, value, makeConst(shift));
    };
    auto shiftRight = [&](Value value, unsigned shift) -> Value {
      return b.create<LLVM::LShrOp>(ti64, value, makeConst(shift));
    };
    auto insertBit = [&](Value desc, Value val, int startBit) {
      return b.create<LLVM::OrOp>(ti64, desc, shiftLeft(val, startBit));
    };

    int64_t sizeN = op.getTensorMap().getType().getTensor().getDimSize(0);
    uint64_t strideDimVal = (layout << 3) >> exclude4LSB;
    uint64_t leadDimVal = (sizeN * layout) >> exclude4LSB;
    uint64_t offsetVal = 0;

    Value strideDim = makeConst(strideDimVal);
    Value leadDim = makeConst(leadDimVal);

    Value baseAddr = getStridedElementPtr(
        op->getLoc(), cast<MemRefType>(op.getTensor().getType()),
        adaptor.getTensor(), {}, rewriter);
    Value basePtr = b.create<LLVM::PtrToIntOp>(ti64, baseAddr);
    // Just use 14 bits for base address
    Value basePtr14bit = shiftRight(shiftLeft(basePtr, 46), 50);

    int startSwizzleBit = 62, startOffsetBit = 49, startStrideBit = 32,
        startLeadBit = 16, startBaseAddrBit = 0;
    Value dsc = makeConst(0);
    // // [62,64)  swizzle type
    dsc = insertBit(dsc, makeConst(swizzle), startSwizzleBit);
    // // [49,52)  base_offset
    dsc = insertBit(dsc, makeConst(offsetVal), startOffsetBit);
    // // [32,46)  stride
    dsc = insertBit(dsc, strideDim, startStrideBit);
    // // [16,30)  leading dimension
    dsc = insertBit(dsc, leadDim, startLeadBit);
    // // [0,14)   start_address
    dsc = insertBit(dsc, basePtr14bit, startBaseAddrBit);

    LLVM_DEBUG(DBGS() << "Generating warpgroup.descriptor: "
                      << "leading_off:" << leadDimVal << "\t"
                      << "stride_off :" << strideDimVal << "\t"
                      << "base_offset:" << offsetVal << "\t"
                      << "layout_type:" << swizzle << " ("
                      << nvgpu::stringifyTensorMapSwizzleKind(swizzleKind)
                      << ")\n start_addr :  " << baseAddr << "\n");

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

static Value makeI64Const(ImplicitLocOpBuilder &b, int32_t index) {
  return b.create<LLVM::ConstantOp>(b.getIntegerType(64),
                                    b.getI32IntegerAttr(index));
}

/// Returns a Value that holds data type enum that is expected by CUDA driver.
static Value elementTypeAsLLVMConstant(ImplicitLocOpBuilder &b, Type type) {
  // Enum is from CUDA driver API
  // https://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__TYPES.html
  enum CUtensorMapDataTypeEnum {
    CU_TENSOR_MAP_DATA_TYPE_UINT8 = 0,
    CU_TENSOR_MAP_DATA_TYPE_UINT16,
    CU_TENSOR_MAP_DATA_TYPE_UINT32,
    CU_TENSOR_MAP_DATA_TYPE_INT32,
    CU_TENSOR_MAP_DATA_TYPE_UINT64,
    CU_TENSOR_MAP_DATA_TYPE_INT64,
    CU_TENSOR_MAP_DATA_TYPE_FLOAT16,
    CU_TENSOR_MAP_DATA_TYPE_FLOAT32,
    CU_TENSOR_MAP_DATA_TYPE_FLOAT64,
    CU_TENSOR_MAP_DATA_TYPE_BFLOAT16,
    CU_TENSOR_MAP_DATA_TYPE_FLOAT32_FTZ,
    CU_TENSOR_MAP_DATA_TYPE_TFLOAT32,
    CU_TENSOR_MAP_DATA_TYPE_TFLOAT32_FTZ
  };

  if (type.isUnsignedInteger(8))
    return makeI64Const(b, CU_TENSOR_MAP_DATA_TYPE_UINT8);
  if (type.isUnsignedInteger(16))
    return makeI64Const(b, CU_TENSOR_MAP_DATA_TYPE_UINT16);
  if (type.isUnsignedInteger(32))
    return makeI64Const(b, CU_TENSOR_MAP_DATA_TYPE_UINT32);
  if (type.isUnsignedInteger(64))
    return makeI64Const(b, CU_TENSOR_MAP_DATA_TYPE_UINT64);
  if (type.isSignlessInteger(32))
    return makeI64Const(b, CU_TENSOR_MAP_DATA_TYPE_INT32);
  if (type.isSignlessInteger(64))
    return makeI64Const(b, CU_TENSOR_MAP_DATA_TYPE_INT64);
  if (type.isF16())
    return makeI64Const(b, CU_TENSOR_MAP_DATA_TYPE_FLOAT16);
  if (type.isF32())
    return makeI64Const(b, CU_TENSOR_MAP_DATA_TYPE_FLOAT32);
  if (type.isF64())
    return makeI64Const(b, CU_TENSOR_MAP_DATA_TYPE_FLOAT64);
  if (type.isBF16())
    return makeI64Const(b, CU_TENSOR_MAP_DATA_TYPE_BFLOAT16);

  llvm_unreachable("Not supported data type");
}

struct NVGPUTmaCreateDescriptorOpLowering
    : public ConvertOpToLLVMPattern<nvgpu::TmaCreateDescriptorOp> {
  using ConvertOpToLLVMPattern<
      nvgpu::TmaCreateDescriptorOp>::ConvertOpToLLVMPattern;
  LogicalResult
  matchAndRewrite(nvgpu::TmaCreateDescriptorOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    ImplicitLocOpBuilder b(op->getLoc(), rewriter);
    auto llvmPointerType = LLVM::LLVMPointerType::get(op->getContext());
    Type llvmInt64Type = IntegerType::get(op->getContext(), 64);

    Value tensorElementType =
        elementTypeAsLLVMConstant(b, op.getTensor().getType().getElementType());
    auto promotedOperands = getTypeConverter()->promoteOperands(
        b.getLoc(), op->getOperands(), adaptor.getOperands(), b);

    Value boxArrayPtr = b.create<LLVM::AllocaOp>(llvmPointerType, llvmInt64Type,
                                                 makeI64Const(b, 5));
    for (auto [index, value] : llvm::enumerate(adaptor.getBoxDimensions())) {
      Value gep = b.create<LLVM::GEPOp>(llvmPointerType, llvmPointerType,
                                        boxArrayPtr, makeI64Const(b, index));
      b.create<LLVM::StoreOp>(value, gep);
    }

    nvgpu::TensorMapDescriptorType desc = op.getTensorMap().getType();
    // Set Arguments for the function call
    SmallVector<Value> arguments;
    arguments.push_back(promotedOperands[0]); // rank
    arguments.push_back(promotedOperands[1]); // descriptor
    arguments.push_back(tensorElementType);   // data type
    arguments.push_back(
        makeI64Const(b, (int)desc.getInterleave()));              // interleave
    arguments.push_back(makeI64Const(b, (int)desc.getSwizzle())); // swizzle
    arguments.push_back(makeI64Const(b, (int)desc.getL2promo())); // l2promo
    arguments.push_back(makeI64Const(b, (int)desc.getOob()));     // oob
    arguments.push_back(boxArrayPtr); // box dimensions

    // Set data types of the arguments
    SmallVector<Type> argTypes = {
        llvmInt64Type,   /* int64_t tensorRank */
        llvmPointerType, /* ptr */
        llvmInt64Type,   /* int64_t */
        llvmInt64Type,   /* int64_t */
        llvmInt64Type,   /* int64_t */
        llvmInt64Type,   /* int64_t */
        llvmInt64Type,   /* int64_t */
        llvmPointerType  /* ptr  */
    };
    FunctionCallBuilder hostRegisterCallBuilder = {
        "mgpuTensorMapEncodeTiledMemref", llvmPointerType, argTypes};
    Value tensorMap =
        hostRegisterCallBuilder.create(b.getLoc(), b, arguments).getResult();

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

struct NVGPUWarpgroupMmaOpLowering
    : public ConvertOpToLLVMPattern<nvgpu::WarpgroupMmaOp> {
  using ConvertOpToLLVMPattern<nvgpu::WarpgroupMmaOp>::ConvertOpToLLVMPattern;

  /// This is a helper class to generate required NVVM Ops for warp-group level
  /// matrix multiplication.
  /// When the given GEMM shape is larger than the shape of
  /// a wgmma instrution in PTX, it can generate multiple NVVM::WgmmaMmaAsyncOp
  /// Op(s), group and execute them asynchronously. The class also handles
  /// waiting for completion and iterates through WarpgroupMatrixDescriptor to
  /// create descriptors for each instruction.
  ///
  /// For example this is the case when the shape of GEMM is 128x128x128
  ///
  ///    nvvm.wgmma.fence.aligned
  ///
  ///    nvvm.wgmma.mma.async descA, descB
  ///    iterate(descA, descB)
  ///    nvvm.wgmma.mma.async descA, descB
  ///    [6x times more]
  ///
  ///    nvvm.wgmma.group.sync.aligned
  ///    nvvm.wgmma.wait.group.sync [groupId]
  ///
  class WarpgroupGemm {
    nvgpu::WarpgroupMmaOp op;
    ImplicitLocOpBuilder b;
    OpAdaptor adaptor;

    // Entire shape of the given Op
    int64_t totalM, totalN, totalK;

    // Shape of one wgmma instruction
    int wgmmaM = 0, wgmmaN = 0, wgmmaK = 0;

    // Iteration counts for GEMM
    int iterationM = 0, iterationN = 0, iterationK = 0;

    /// The function returns the shape of wgmma instruction that is defined in
    /// PTX programming guide.
    /// https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#asynchronous-warpgroup-level-matrix-shape
    void findWgmmaShape(int64_t sizeM, int64_t sizeN, Type inputElemType) {
      wgmmaM = 64;
      wgmmaN = sizeN;
      if (inputElemType.isTF32()) {
        wgmmaK = 8;
      } else if (inputElemType.isF16() || inputElemType.isBF16()) {
        wgmmaK = 16;
      } else if (isa<Float8E4M3FNType, Float8E5M2Type>(inputElemType) ||
                 inputElemType.isInteger(16)) {
        wgmmaK = 32;
      } else if (inputElemType.isInteger(1)) {
        wgmmaK = 256;
      } else {
        llvm_unreachable("msg: not supported K shape");
      }
      LLVM_DEBUG(DBGS() << "Generating WgmmaMmaAsyncOp shape[m = " << wgmmaM
                        << ", n = " << wgmmaN << ", k = " << wgmmaK << "]\n");
    }

    /// Generates WGMMATypesAttr from MLIR Type
    NVVM::WGMMATypesAttr generateWgmmaType(Type type,
                                           bool useF32 = false) const {
      auto getWgmmaType = [=](Type elemType) {
        if (elemType.isF32() || elemType.isTF32())
          return useF32 ? NVVM::WGMMATypes::f32 : NVVM::WGMMATypes::tf32;
        if (elemType.isF16())
          return NVVM::WGMMATypes::f16;
        if (elemType.isBF16())
          return NVVM::WGMMATypes::bf16;
        if (isa<Float8E4M3FNType>(elemType))
          return NVVM::WGMMATypes::e4m3;
        if (isa<Float8E5M2Type>(elemType))
          return NVVM::WGMMATypes::e5m2;
        if (elemType.isInteger(1))
          return NVVM::WGMMATypes::b1;
        if (elemType.isInteger(8))
          return NVVM::WGMMATypes::s8;
        if (elemType.isUnsignedInteger(8))
          return NVVM::WGMMATypes::u8;
        if (elemType.isInteger(32))
          return NVVM::WGMMATypes::s32;
        llvm_unreachable("unsupported type");
      };
      return NVVM::WGMMATypesAttr::get(op->getContext(), getWgmmaType(type));
    }

    /// Generates layout attribute for the input matrix for wgmma instruction
    NVVM::MMALayoutAttr
    generateWgmmaLayout(std::optional<bool> transpose) const {
      if (transpose.value_or(false))
        return NVVM::MMALayoutAttr::get(op->getContext(), NVVM::MMALayout::col);
      return NVVM::MMALayoutAttr::get(op->getContext(), NVVM::MMALayout::row);
    }

    /// Generates shape attribute for wgmma instruction
    NVVM::MMAShapeAttr generateWgmmaShape() const {
      return NVVM::MMAShapeAttr::get(op->getContext(), wgmmaM, wgmmaN, wgmmaK);
    }

    /// Generates scale attributes of output matrix for wgmma instruction
    NVVM::WGMMAScaleOutAttr generateScaleOut() const {
      return NVVM::WGMMAScaleOutAttr::get(op->getContext(),
                                          NVVM::WGMMAScaleOut::one);
    }
    /// Generates scale attributes of input matrix for wgmma instruction
    NVVM::WGMMAScaleInAttr generateScaleIn() const {
      return NVVM::WGMMAScaleInAttr::get(op->getContext(),
                                         NVVM::WGMMAScaleIn::one);
    }

    /// Basic function to generate Add
    Value makeAdd(Value lhs, Value rhs) {
      return b.create<LLVM::AddOp>(lhs.getType(), lhs, rhs);
    };

    /// Moves the descriptor pointer of matrix-A for the next wgmma instruction.
    /// Currently, it only handles row-major.
    ///
    /// It moves the pointer like below for [128][64] size:
    ///                 +2 +4 +6
    ///                  ↓  ↓  ↓
    /// descA    ---> +--+--+--+--+
    ///               |->|->|->|->|
    ///               |  |  |  |  |
    ///               |  |  |  |  |
    ///               |  |  |  |  |
    /// descA+512---> +-----------+
    ///               |  |  |  |  |
    ///               |  |  |  |  |
    ///               |  |  |  |  |
    ///               |  |  |  |  |
    ///               +-----------+
    ///
    Value iterateDescriptorA(Value desc, int i, int j, int k) {
      MemRefType matrixTypeA = op.getDescriptorA().getType().getTensor();
      Type elemA = matrixTypeA.getElementType();
      int byte = elemA.getIntOrFloatBitWidth() / 8;
      int tileShapeA = matrixTypeA.getDimSize(1);
      int incrementVal = ((wgmmaK * k) + (totalK * tileShapeA * i)) * byte;
      incrementVal = incrementVal >> exclude4LSB;
      LLVM_DEBUG(DBGS() << "\t\t[m: " << i << " n: " << j << " k: " << k
                        << "] [wgmma descriptors] Descriptor A + "
                        << incrementVal << " | \t ");
      if (!incrementVal)
        return desc;
      return makeAdd(desc, makeI64Const(b, incrementVal));
    }

    /// Moves the descriptor pointer of matrix-B for the next wgmma instruction.
    /// Currently, it only handles column-major.
    ///
    /// It moves the pointer like below for [128][64] size:
    /// descB     ---> +--+--+--+--+--+--+--+--+
    ///                |↓ |  |  |  |  |  |  |  |
    ///                |↓ |  |  |  |  |  |  |  |
    ///                |↓ |  |  |  |  |  |  |  |
    ///                |↓ |  |  |  |  |  |  |  |
    ///                +--+--+--+--+--+--+--+--+
    ///
    Value iterateDescriptorB(Value desc, int i, int j, int k) {
      MemRefType matrixTypeB = op.getDescriptorB().getType().getTensor();
      Type elemB = matrixTypeB.getElementType();
      int byte = elemB.getIntOrFloatBitWidth() / 8;
      int incrementVal = matrixTypeB.getDimSize(0) * wgmmaK * k * byte;
      incrementVal = incrementVal >> exclude4LSB;
      LLVM_DEBUG(DBGSE() << "Descriptor B + " << incrementVal << "\n");
      if (!incrementVal)
        return desc;
      return makeAdd(desc, makeI64Const(b, incrementVal));
    }

    /// This function generates a WgmmaMmaAsyncOp using provided GMMA matrix
    /// descriptors and arranges them based on induction variables: i, j, and k.
    Value generateWgmma(int i, int j, int k, Value matrixC) {
      LLVM_DEBUG(DBGS() << "\t wgmma."
                        << "m" << wgmmaM << "n" << wgmmaN << "k" << wgmmaK
                        << "(A[" << (iterationM * wgmmaM) << ":"
                        << (iterationM * wgmmaM) + wgmmaM << "]["
                        << (iterationK * wgmmaK) << ":"
                        << (iterationK * wgmmaK + wgmmaK) << "] * "
                        << " B[" << (iterationK * wgmmaK) << ":"
                        << (iterationK * wgmmaK + wgmmaK) << "][" << 0 << ":"
                        << wgmmaN << "])\n");

      Value descriptorA = iterateDescriptorA(adaptor.getDescriptorA(), i, j, k);
      Value descriptorB = iterateDescriptorB(adaptor.getDescriptorB(), i, j, k);

      Type elemA = op.getDescriptorA().getType().getTensor().getElementType();
      NVVM::WGMMATypesAttr itypeA = generateWgmmaType(elemA);

      Type elemB = op.getDescriptorB().getType().getTensor().getElementType();
      NVVM::WGMMATypesAttr itypeB = generateWgmmaType(elemB);

      Type elemD = op.getMatrixC().getType().getFragmented().getElementType();
      NVVM::WGMMATypesAttr itypeD = generateWgmmaType(elemD, true);

      NVVM::MMAShapeAttr shape = generateWgmmaShape();
      NVVM::WGMMAScaleOutAttr scaleOut = generateScaleOut();
      NVVM::WGMMAScaleInAttr scaleIn = generateScaleIn();
      NVVM::MMALayoutAttr layoutA = generateWgmmaLayout(op.getTransposeA());
      NVVM::MMALayoutAttr layoutB = generateWgmmaLayout(!op.getTransposeB());

      auto overflow = NVVM::MMAIntOverflowAttr::get(
          op->getContext(), NVVM::MMAIntOverflow::wrapped);

      return b.create<NVVM::WgmmaMmaAsyncOp>(
          matrixC.getType(), matrixC, descriptorA, descriptorB, shape, itypeA,
          itypeB, itypeD, scaleOut, scaleIn, scaleIn, layoutA, layoutB,
          overflow);
    }

    /// Generates multiple wgmma instructions to complete the given GEMM shape
    Value generateWgmmaGroup() {
      Value wgmmaResult =
          b.create<LLVM::UndefOp>(adaptor.getMatrixC().getType());

      // Perform GEMM
      SmallVector<Value> wgmmaResults;
      for (int i = 0; i < iterationM; ++i) {
        Value matrixC = b.create<LLVM::ExtractValueOp>(adaptor.getMatrixC(), i);
        for (int j = 0; j < iterationN; ++j)
          for (int k = 0; k < iterationK; ++k)
            matrixC = generateWgmma(i, j, k, matrixC);
        wgmmaResults.push_back(matrixC);
      }
      for (auto [idx, matrix] : llvm::enumerate(wgmmaResults)) {
        wgmmaResult = b.create<LLVM::InsertValueOp>(wgmmaResult.getType(),
                                                    wgmmaResult, matrix, idx);
      }
      return wgmmaResult;
    }

  public:
    WarpgroupGemm(nvgpu::WarpgroupMmaOp op, ImplicitLocOpBuilder &b,
                  OpAdaptor adaptor)
        : op(op), b(b), adaptor(adaptor) {
      // Find the entire GEMM Shape
      totalM = op.getDescriptorA().getType().getTensor().getDimSize(0);
      totalN = op.getDescriptorB().getType().getTensor().getDimSize(1);
      totalK = op.getDescriptorA().getType().getTensor().getDimSize(1);
      LLVM_DEBUG(DBGS() << "===--- GEMM D[" << totalM << "][" << totalN
                        << "] += A[" << totalM << "][" << totalK << "] * B["
                        << totalK << "][" << totalN << "] ---===\n");

      // Find the shape for one wgmma instruction
      findWgmmaShape(
          totalM, totalN,
          op.getDescriptorA().getType().getTensor().getElementType());

      // Iterations counts to complete the given shape with wgmma shape
      iterationM = totalM / wgmmaM;
      iterationN = totalN / wgmmaN;
      iterationK = totalK / wgmmaK;
    }

    /// Generates WgmmaMmaAsync Ops to complete the specified GEMM  shape. It
    /// includes generating a fence Op (WgmmaFenceAlignedOp) before the
    /// instructions and group synchronization, as well as waiting
    /// (WgmmaGroupSyncAlignedOp) for group synchronization
    /// (WgmmaWaitGroupSyncOp) after the instructions.
    Value generateWarpgroupMma() {
      b.create<NVVM::WgmmaFenceAlignedOp>();
      Value wgmmaResult = generateWgmmaGroup();
      b.create<NVVM::WgmmaGroupSyncAlignedOp>();
      b.create<NVVM::WgmmaWaitGroupSyncOp>(op.getWaitGroup());
      return wgmmaResult;
    }
  };
  LogicalResult
  matchAndRewrite(nvgpu::WarpgroupMmaOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    ImplicitLocOpBuilder b(op->getLoc(), rewriter);

    // Step 1. Build a helper class
    WarpgroupGemm warpgroupGemm(op, b, adaptor);

    // Step 2. Get the entire GEMM Shape
    Value wgmmaResult = warpgroupGemm.generateWarpgroupMma();

    // Step 3. Replace fragmented result struct with the op results
    rewriter.replaceOp(op, wgmmaResult);
    return success();
  }
};

struct NVGPUWarpgroupMmaStoreOpLowering
    : public ConvertOpToLLVMPattern<nvgpu::WarpgroupMmaStoreOp> {
  using ConvertOpToLLVMPattern<
      nvgpu::WarpgroupMmaStoreOp>::ConvertOpToLLVMPattern;

  /// This function stores a fragmented register matrix owned by a warp group
  /// (128 threads) into a memref. Each thread has 64 registers, each the size
  /// of a struct.
  /// Here is what each threads (T) holds, each `d` is struct value with a
  /// number.
  ///
  /// Threads in warp-group (128 threads) and what they owns in the matrixD:
  /// 0-31 	  Warp-0  -> MatrixD[0:15 ][0:N]
  /// 32-63 	Warp-1  -> MatrixD[16:31][0:N]
  /// 64-95 	Warp-2  -> MatrixD[32:47][0:N]
  /// 96-127 	Warp-3  -> MatrixD[48:64][0:N]
  ///
  /// Matrix-D:
  ///   +______________________________________________________________________+
  ///   |     0-1  |    2-3  |    4-5  |    6-7  |   8-9  |   10-11|..|N-8,N-7 |
  /// 0 | T0:d0-d1 |T1:d0-d1 |T2:d0-d1 |T3:d0-d1 |T0:d4-d5| T1:d4-d5..|T0:dX-dY|
  /// 1 | T4:d0-d1 |T5:d0-d1 |T6:d0-d1 |T7:d0-d1 |T4:d4-d5| T5:d4-d5..|T4:dX-dY|
  /// ..| .........|.........|.........|.........|........|...........|........|
  /// 8 | T0:d2-d3 |T1:d2-d3 |T2:d2-d3 |T3:d2-d3 |T0:d6-d7|T1:d6-d7,..|T0:dZ-dW|
  /// 9 | T4:d2-d3 |T5:d2-d3 |T6:d2-d3 |T7:d2-d3 |T4:d6-d7| T5:d6-d7..|T4:dZ-dW|
  /// ..| .........|.........|.........|.........|........|...........|........|
  /// 15| T28:d2-d3|T29:d2-d3|T30:d2-d3|T31:d2-d3|........|...........|........|
  /// 16| T32:d2-d3|T33:d2-d3|T34:d2-d3|T35:d2-d3|........|...........|........|
  /// ..| .........|.........|.........|.........|........|...........|........|
  /// 32| T64:d2-d3|T65:d2-d3|T66:d2-d3|T67:d2-d3|........|...........|........|
  /// ..| .........|.........|.........|.........|........|...........|........|
  /// 48| T96:d2-d3|T97:d2-d3|T98:d2-d3|T99:d2-d3|........|...........|........|
  /// ..| .........|.........|.........|.........|........|...........|........|
  ///   +______________________________________________________________________+
  ///
  /// \param rewriter: The pattern rewriter.
  /// \param matrixD: Result of the warp-group MMA operation (fragmented
  /// matrix). It is holded by a thread and a struct with 64 elements.
  /// \param dstMemref: The memref where the registers will be stored.
  /// \param offset: the offset within the memref where the registers will be
  /// stored.
  void storeFragmentedMatrix(ImplicitLocOpBuilder &b, Value matrixD,
                             TypedValue<MemRefType> dstMemref,
                             int offset) const {
    Type i32 = b.getI32Type();

    auto makeConst = [&](int32_t index) -> Value {
      return b.create<LLVM::ConstantOp>(i32, b.getI32IntegerAttr(index));
    };
    Value c1 = makeConst(1);
    Value c2 = makeConst(2);
    Value c4 = makeConst(4);
    Value c8 = makeConst(8);
    Value c16 = makeConst(16);
    Value warpSize = makeConst(kWarpSize);

    auto makeMul = [&](Value lhs, Value rhs) -> Value {
      return b.create<LLVM::MulOp>(lhs.getType(), lhs, rhs);
    };
    auto makeAdd = [&](Value lhs, Value rhs) -> Value {
      return b.create<LLVM::AddOp>(lhs.getType(), lhs, rhs);
    };

    auto makeExtractAndStore = [&](int i, Value wgmmaResult, Value x, Value y,
                                   TypedValue<::mlir::MemRefType> memref) {
      Type it = b.getIndexType();
      Value idx = b.create<arith::IndexCastOp>(it, x);
      Value idy0 = b.create<arith::IndexCastOp>(it, y);
      Value idy1 = b.create<arith::IndexCastOp>(it, makeAdd(y, c1));
      Value d0 = b.create<LLVM::ExtractValueOp>(wgmmaResult, i);
      Value d1 = b.create<LLVM::ExtractValueOp>(wgmmaResult, i + 1);
      b.create<memref::StoreOp>(d0, memref, ValueRange{idx, idy0});
      b.create<memref::StoreOp>(d1, memref, ValueRange{idx, idy1});
    };

    Value tidx = b.create<NVVM::ThreadIdXOp>(i32);
    Value laneId = b.create<LLVM::URemOp>(i32, tidx, warpSize);
    Value warpId = b.create<LLVM::UDivOp>(i32, tidx, warpSize);
    Value lane4Id = b.create<LLVM::UDivOp>(i32, laneId, c4);
    Value lane4modId = b.create<LLVM::URemOp>(i32, laneId, c4);

    Value tj = makeMul(lane4modId, c2);
    Value ti = makeAdd(lane4Id, makeMul(warpId, c16));
    if (offset)
      ti = makeAdd(ti, makeConst(offset));

    auto structType = cast<LLVM::LLVMStructType>(matrixD.getType());

    // Number of 32-bit registers owns per thread
    constexpr unsigned numAdjacentRegisters = 2;
    // Number of 8x8 matrices one below another per warp
    constexpr unsigned numStackedMatrices = 2;

    size_t storeCount = (structType.getBody().size() /
                         (numStackedMatrices * numAdjacentRegisters));

    for (size_t i = 0; i < numStackedMatrices; ++i) {
      Value idx = makeAdd(ti, makeMul(makeConst(i), c8));
      for (size_t j = 0; j < storeCount; ++j) {
        Value idy = makeAdd(tj, makeMul(makeConst(j), c8));
        size_t structIndex = (i * numAdjacentRegisters) +
                             (j * (numStackedMatrices * numAdjacentRegisters));
        makeExtractAndStore(structIndex, matrixD, idx, idy, dstMemref);
      }
    }
  }

  LogicalResult
  matchAndRewrite(nvgpu::WarpgroupMmaStoreOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    int offset = 0;
    ImplicitLocOpBuilder b(op->getLoc(), rewriter);
    Value matriDValue = adaptor.getMatrixD();
    auto stype = cast<LLVM::LLVMStructType>(matriDValue.getType());
    for (auto [idx, matrixD] : llvm::enumerate(stype.getBody())) {
      auto structType = cast<LLVM::LLVMStructType>(matrixD);
      Value innerStructValue = b.create<LLVM::ExtractValueOp>(matriDValue, idx);
      storeFragmentedMatrix(b, innerStructValue, op.getDstMemref(), offset);
      offset += structType.getBody().size();
    }
    rewriter.eraseOp(op);
    return success();
  }
};

struct NVGPUWarpgroupMmaInitAccumulatorOpLowering
    : public ConvertOpToLLVMPattern<nvgpu::WarpgroupMmaInitAccumulatorOp> {
  using ConvertOpToLLVMPattern<
      nvgpu::WarpgroupMmaInitAccumulatorOp>::ConvertOpToLLVMPattern;
  LogicalResult
  matchAndRewrite(nvgpu::WarpgroupMmaInitAccumulatorOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    ImplicitLocOpBuilder b(op->getLoc(), rewriter);
    LLVM::LLVMStructType packStructType = cast<LLVM::LLVMStructType>(
        getTypeConverter()->convertType(op.getMatrixC().getType()));
    Type elemType = cast<LLVM::LLVMStructType>(packStructType.getBody().front())
                        .getBody()
                        .front();
    Value zero = b.create<LLVM::ConstantOp>(elemType, b.getZeroAttr(elemType));
    Value packStruct = b.create<LLVM::UndefOp>(packStructType);
    SmallVector<Value> innerStructs;
    // Unpack the structs and set all values to zero
    for (auto [idx, s] : llvm::enumerate(packStructType.getBody())) {
      auto structType = cast<LLVM::LLVMStructType>(s);
      Value structValue = b.create<LLVM::ExtractValueOp>(packStruct, idx);
      for (unsigned i = 0; i < structType.getBody().size(); ++i) {
        structValue = b.create<LLVM::InsertValueOp>(
            structType, structValue, zero, ArrayRef<int64_t>({i}));
      }
      innerStructs.push_back(structValue);
    }
    // Pack the inner structs into a single struct
    for (auto [idx, matrix] : llvm::enumerate(innerStructs)) {
      packStruct = b.create<LLVM::InsertValueOp>(packStruct.getType(),
                                                 packStruct, matrix, idx);
    }
    rewriter.replaceOp(op, packStruct);
    return success();
  }
};

struct NVGPUTmaPrefetchOpLowering
    : public ConvertOpToLLVMPattern<nvgpu::TmaPrefetchOp> {
  using ConvertOpToLLVMPattern<nvgpu::TmaPrefetchOp>::ConvertOpToLLVMPattern;
  LogicalResult
  matchAndRewrite(nvgpu::TmaPrefetchOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    rewriter.replaceOpWithNewOp<NVVM::PrefetchTensorMapOp>(
        op, adaptor.getTensorMapDescriptor(), adaptor.getPredicate());
    return success();
  }
};

struct NVGPURcpOpLowering : public ConvertOpToLLVMPattern<nvgpu::RcpOp> {
  using ConvertOpToLLVMPattern<nvgpu::RcpOp>::ConvertOpToLLVMPattern;
  LogicalResult
  matchAndRewrite(nvgpu::RcpOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override {
    ImplicitLocOpBuilder b(op->getLoc(), rewriter);
    auto i64Ty = b.getI64Type();
    auto f32Ty = b.getF32Type();
    VectorType inTy = op.getIn().getType();
    // apply rcp.approx.ftz.f on each element in vector.
    auto convert1DVec = [&](Type llvm1DVectorTy, Value inVec) {
      Value ret1DVec = b.create<LLVM::UndefOp>(llvm1DVectorTy);
      int numElems = llvm::cast<VectorType>(llvm1DVectorTy).getNumElements();
      for (int i = 0; i < numElems; i++) {
        Value idx = b.create<LLVM::ConstantOp>(i64Ty, b.getI64IntegerAttr(i));
        Value elem = b.create<LLVM::ExtractElementOp>(inVec, idx);
        Value dst = b.create<NVVM::RcpApproxFtzF32Op>(f32Ty, elem);
        ret1DVec = b.create<LLVM::InsertElementOp>(ret1DVec, dst, idx);
      }
      return ret1DVec;
    };
    if (inTy.getRank() == 1) {
      rewriter.replaceOp(op, convert1DVec(inTy, adaptor.getIn()));
      return success();
    }
    return LLVM::detail::handleMultidimensionalVectors(
        op.getOperation(), adaptor.getOperands(), *(this->getTypeConverter()),
        [&](Type llvm1DVectorTy, ValueRange operands) -> Value {
          OpAdaptor adaptor(operands);
          return convert1DVec(llvm1DVectorTy, adaptor.getIn());
        },
        rewriter);
  }
};
} // namespace

void mlir::populateNVGPUToNVVMConversionPatterns(
    const LLVMTypeConverter &converter, RewritePatternSet &patterns) {
  patterns.add<
      NVGPUMBarrierCreateLowering,           // nvgpu.mbarrier.create
      NVGPUMBarrierInitLowering,             // nvgpu.mbarrier.init
      NVGPUMBarrierArriveLowering,           // nvgpu.mbarrier.arrive
      NVGPUMBarrierArriveNoCompleteLowering, // nvgpu.mbarrier.arrive.no_complete
      NVGPUMBarrierTestWaitLowering,         // nvgpu.mbarrier.test_wait_parity
      NVGPUMBarrierTryWaitParityLowering,    // nvgpu.mbarrier.try_wait_parity
      NVGPUTmaAsyncLoadOpLowering,           // nvgpu.tma.async.load
      NVGPUTmaAsyncStoreOpLowering,          // nvgpu.tma.async.store
      NVGPUTmaCreateDescriptorOpLowering,    // nvgpu.tma.create.descriptor
      NVGPUTmaPrefetchOpLowering,            // nvgpu.tma.prefetch.descriptor
      NVGPUMBarrierArriveExpectTxLowering,   // nvgpu.mbarrier.arrive.expect_tx
      NVGPUGenerateWarpgroupDescriptorLowering, // nvgpu.warpgroup.generate.descriptor
      NVGPUWarpgroupMmaOpLowering,              // nvgpu.warpgroup.mma
      NVGPUWarpgroupMmaStoreOpLowering,         // nvgpu.warpgroup.mma.store
      NVGPUWarpgroupMmaInitAccumulatorOpLowering, // nvgpu.warpgroup.mma.init.accumulator
      MmaSyncOptoNVVM, MmaLdMatrixOpToNVVM, NVGPUAsyncCopyLowering,
      NVGPUAsyncCreateGroupLowering, NVGPUAsyncWaitLowering,
      NVGPUMmaSparseSyncLowering, NVGPURcpOpLowering>(converter);
}