//===- ConvertLaunchFuncToGpuRuntimeCalls.cpp - MLIR GPU lowering passes --===//
//
// 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 implements a pass to convert gpu.launch_func op into a sequence of
// GPU runtime calls. As most of GPU runtimes does not have a stable published
// ABI, this pass uses a slim runtime layer that builds on top of the public
// API from GPU runtime headers.
//
//===----------------------------------------------------------------------===//

#include "mlir/Conversion/GPUCommon/GPUCommonPass.h"

#include "mlir/Conversion/ArithToLLVM/ArithToLLVM.h"
#include "mlir/Conversion/AsyncToLLVM/AsyncToLLVM.h"
#include "mlir/Conversion/ControlFlowToLLVM/ControlFlowToLLVM.h"
#include "mlir/Conversion/FuncToLLVM/ConvertFuncToLLVM.h"
#include "mlir/Conversion/FuncToLLVM/ConvertFuncToLLVMPass.h"
#include "mlir/Conversion/LLVMCommon/ConversionTarget.h"
#include "mlir/Conversion/LLVMCommon/Pattern.h"
#include "mlir/Conversion/MemRefToLLVM/MemRefToLLVM.h"
#include "mlir/Conversion/VectorToLLVM/ConvertVectorToLLVM.h"
#include "mlir/Dialect/Async/IR/Async.h"
#include "mlir/Dialect/GPU/IR/GPUDialect.h"
#include "mlir/Dialect/GPU/Transforms/Passes.h"
#include "mlir/Dialect/LLVMIR/LLVMDialect.h"
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/IR/Attributes.h"
#include "mlir/IR/Builders.h"
#include "mlir/IR/BuiltinOps.h"
#include "mlir/IR/BuiltinTypes.h"

#include "llvm/ADT/STLExtras.h"
#include "llvm/Support/Error.h"
#include "llvm/Support/FormatVariadic.h"

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

using namespace mlir;

static constexpr const char *kGpuBinaryStorageSuffix = "_gpubin_cst";

namespace {

class GpuToLLVMConversionPass
    : public impl::GpuToLLVMConversionPassBase<GpuToLLVMConversionPass> {
public:
  GpuToLLVMConversionPass() = default;

  GpuToLLVMConversionPass(bool kernelBarePtrCallConv)
      : GpuToLLVMConversionPass() {
    if (this->kernelBarePtrCallConv.getNumOccurrences() == 0)
      this->kernelBarePtrCallConv = kernelBarePtrCallConv;
  }

  GpuToLLVMConversionPass(const GpuToLLVMConversionPass &other)
      : GpuToLLVMConversionPassBase(other) {}

  // Run the dialect converter on the module.
  void runOnOperation() override;

private:
  Option<std::string> gpuBinaryAnnotation{
      *this, "gpu-binary-annotation",
      llvm::cl::desc("Annotation attribute string for GPU binary"),
      llvm::cl::init(gpu::getDefaultGpuBinaryAnnotation())};
  Option<bool> kernelBarePtrCallConv{
      *this, "use-bare-pointers-for-kernels",
      llvm::cl::desc("Use bare pointers to pass memref arguments to kernels. "
                     "The kernel must use the same setting for this option."),
      llvm::cl::init(false)};
};

struct FunctionCallBuilder {
  FunctionCallBuilder(StringRef functionName, Type returnType,
                      ArrayRef<Type> argumentTypes)
      : functionName(functionName),
        functionType(LLVM::LLVMFunctionType::get(returnType, argumentTypes)) {}
  LLVM::CallOp create(Location loc, OpBuilder &builder,
                      ArrayRef<Value> arguments) const;

  StringRef functionName;
  LLVM::LLVMFunctionType functionType;
};

template <typename OpTy>
class ConvertOpToGpuRuntimeCallPattern : public ConvertOpToLLVMPattern<OpTy> {
public:
  explicit ConvertOpToGpuRuntimeCallPattern(LLVMTypeConverter &typeConverter)
      : ConvertOpToLLVMPattern<OpTy>(typeConverter) {}

protected:
  Value getNumElements(ConversionPatternRewriter &rewriter, Location loc,
                       MemRefType type, MemRefDescriptor desc) const {
    return type.hasStaticShape()
               ? ConvertToLLVMPattern::createIndexConstant(
                     rewriter, loc, type.getNumElements())
               // For identity maps (verified by caller), the number of
               // elements is stride[0] * size[0].
               : rewriter.create<LLVM::MulOp>(loc,
                                              desc.stride(rewriter, loc, 0),
                                              desc.size(rewriter, loc, 0));
  }

  MLIRContext *context = &this->getTypeConverter()->getContext();

  Type llvmVoidType = LLVM::LLVMVoidType::get(context);
  Type llvmPointerType =
      LLVM::LLVMPointerType::get(IntegerType::get(context, 8));
  Type llvmPointerPointerType = LLVM::LLVMPointerType::get(llvmPointerType);
  Type llvmInt8Type = IntegerType::get(context, 8);
  Type llvmInt32Type = IntegerType::get(context, 32);
  Type llvmInt64Type = IntegerType::get(context, 64);
  Type llvmIntPtrType = IntegerType::get(
      context, this->getTypeConverter()->getPointerBitwidth(0));

  FunctionCallBuilder moduleLoadCallBuilder = {
      "mgpuModuleLoad",
      llvmPointerType /* void *module */,
      {llvmPointerType /* void *cubin */}};
  FunctionCallBuilder moduleUnloadCallBuilder = {
      "mgpuModuleUnload", llvmVoidType, {llvmPointerType /* void *module */}};
  FunctionCallBuilder moduleGetFunctionCallBuilder = {
      "mgpuModuleGetFunction",
      llvmPointerType /* void *function */,
      {
          llvmPointerType, /* void *module */
          llvmPointerType  /* char *name   */
      }};
  FunctionCallBuilder launchKernelCallBuilder = {
      "mgpuLaunchKernel",
      llvmVoidType,
      {
          llvmPointerType,        /* void* f */
          llvmIntPtrType,         /* intptr_t gridXDim */
          llvmIntPtrType,         /* intptr_t gridyDim */
          llvmIntPtrType,         /* intptr_t gridZDim */
          llvmIntPtrType,         /* intptr_t blockXDim */
          llvmIntPtrType,         /* intptr_t blockYDim */
          llvmIntPtrType,         /* intptr_t blockZDim */
          llvmInt32Type,          /* unsigned int sharedMemBytes */
          llvmPointerType,        /* void *hstream */
          llvmPointerPointerType, /* void **kernelParams */
          llvmPointerPointerType  /* void **extra */
      }};
  FunctionCallBuilder streamCreateCallBuilder = {
      "mgpuStreamCreate", llvmPointerType /* void *stream */, {}};
  FunctionCallBuilder streamDestroyCallBuilder = {
      "mgpuStreamDestroy", llvmVoidType, {llvmPointerType /* void *stream */}};
  FunctionCallBuilder streamSynchronizeCallBuilder = {
      "mgpuStreamSynchronize",
      llvmVoidType,
      {llvmPointerType /* void *stream */}};
  FunctionCallBuilder streamWaitEventCallBuilder = {
      "mgpuStreamWaitEvent",
      llvmVoidType,
      {llvmPointerType /* void *stream */, llvmPointerType /* void *event */}};
  FunctionCallBuilder eventCreateCallBuilder = {
      "mgpuEventCreate", llvmPointerType /* void *event */, {}};
  FunctionCallBuilder eventDestroyCallBuilder = {
      "mgpuEventDestroy", llvmVoidType, {llvmPointerType /* void *event */}};
  FunctionCallBuilder eventSynchronizeCallBuilder = {
      "mgpuEventSynchronize",
      llvmVoidType,
      {llvmPointerType /* void *event */}};
  FunctionCallBuilder eventRecordCallBuilder = {
      "mgpuEventRecord",
      llvmVoidType,
      {llvmPointerType /* void *event */, llvmPointerType /* void *stream */}};
  FunctionCallBuilder hostRegisterCallBuilder = {
      "mgpuMemHostRegisterMemRef",
      llvmVoidType,
      {llvmIntPtrType /* intptr_t rank */,
       llvmPointerType /* void *memrefDesc */,
       llvmIntPtrType /* intptr_t elementSizeBytes */}};
  FunctionCallBuilder allocCallBuilder = {
      "mgpuMemAlloc",
      llvmPointerType /* void * */,
      {llvmIntPtrType /* intptr_t sizeBytes */,
       llvmPointerType /* void *stream */}};
  FunctionCallBuilder deallocCallBuilder = {
      "mgpuMemFree",
      llvmVoidType,
      {llvmPointerType /* void *ptr */, llvmPointerType /* void *stream */}};
  FunctionCallBuilder memcpyCallBuilder = {
      "mgpuMemcpy",
      llvmVoidType,
      {llvmPointerType /* void *dst */, llvmPointerType /* void *src */,
       llvmIntPtrType /* intptr_t sizeBytes */,
       llvmPointerType /* void *stream */}};
  FunctionCallBuilder memsetCallBuilder = {
      "mgpuMemset32",
      llvmVoidType,
      {llvmPointerType /* void *dst */, llvmInt32Type /* unsigned int value */,
       llvmIntPtrType /* intptr_t sizeBytes */,
       llvmPointerType /* void *stream */}};
  FunctionCallBuilder setDefaultDeviceCallBuilder = {
      "mgpuSetDefaultDevice",
      llvmVoidType,
      {llvmInt32Type /* uint32_t devIndex */}};
};

/// A rewrite pattern to convert gpu.host_register operations into a GPU runtime
/// call. Currently it supports CUDA and ROCm (HIP).
class ConvertHostRegisterOpToGpuRuntimeCallPattern
    : public ConvertOpToGpuRuntimeCallPattern<gpu::HostRegisterOp> {
public:
  ConvertHostRegisterOpToGpuRuntimeCallPattern(LLVMTypeConverter &typeConverter)
      : ConvertOpToGpuRuntimeCallPattern<gpu::HostRegisterOp>(typeConverter) {}

private:
  LogicalResult
  matchAndRewrite(gpu::HostRegisterOp hostRegisterOp, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override;
};

/// A rewrite pattern to convert gpu.alloc operations into a GPU runtime
/// call. Currently it supports CUDA and ROCm (HIP).
class ConvertAllocOpToGpuRuntimeCallPattern
    : public ConvertOpToGpuRuntimeCallPattern<gpu::AllocOp> {
public:
  ConvertAllocOpToGpuRuntimeCallPattern(LLVMTypeConverter &typeConverter)
      : ConvertOpToGpuRuntimeCallPattern<gpu::AllocOp>(typeConverter) {}

private:
  LogicalResult
  matchAndRewrite(gpu::AllocOp allocOp, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override;
};

/// A rewrite pattern to convert gpu.dealloc operations into a GPU runtime
/// call. Currently it supports CUDA and ROCm (HIP).
class ConvertDeallocOpToGpuRuntimeCallPattern
    : public ConvertOpToGpuRuntimeCallPattern<gpu::DeallocOp> {
public:
  ConvertDeallocOpToGpuRuntimeCallPattern(LLVMTypeConverter &typeConverter)
      : ConvertOpToGpuRuntimeCallPattern<gpu::DeallocOp>(typeConverter) {}

private:
  LogicalResult
  matchAndRewrite(gpu::DeallocOp deallocOp, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override;
};

class ConvertAsyncYieldToGpuRuntimeCallPattern
    : public ConvertOpToGpuRuntimeCallPattern<async::YieldOp> {
public:
  ConvertAsyncYieldToGpuRuntimeCallPattern(LLVMTypeConverter &typeConverter)
      : ConvertOpToGpuRuntimeCallPattern<async::YieldOp>(typeConverter) {}

private:
  LogicalResult
  matchAndRewrite(async::YieldOp yieldOp, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override;
};

/// A rewrite pattern to convert gpu.wait operations into a GPU runtime
/// call. Currently it supports CUDA and ROCm (HIP).
class ConvertWaitOpToGpuRuntimeCallPattern
    : public ConvertOpToGpuRuntimeCallPattern<gpu::WaitOp> {
public:
  ConvertWaitOpToGpuRuntimeCallPattern(LLVMTypeConverter &typeConverter)
      : ConvertOpToGpuRuntimeCallPattern<gpu::WaitOp>(typeConverter) {}

private:
  LogicalResult
  matchAndRewrite(gpu::WaitOp waitOp, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override;
};

/// A rewrite pattern to convert gpu.wait async operations into a GPU runtime
/// call. Currently it supports CUDA and ROCm (HIP).
class ConvertWaitAsyncOpToGpuRuntimeCallPattern
    : public ConvertOpToGpuRuntimeCallPattern<gpu::WaitOp> {
public:
  ConvertWaitAsyncOpToGpuRuntimeCallPattern(LLVMTypeConverter &typeConverter)
      : ConvertOpToGpuRuntimeCallPattern<gpu::WaitOp>(typeConverter) {}

private:
  LogicalResult
  matchAndRewrite(gpu::WaitOp waitOp, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override;
};

/// A rewrite patter to convert gpu.launch_func operations into a sequence of
/// GPU runtime calls. Currently it supports CUDA and ROCm (HIP).
///
/// In essence, a gpu.launch_func operations gets compiled into the following
/// sequence of runtime calls:
///
/// * moduleLoad        -- loads the module given the cubin / hsaco data
/// * moduleGetFunction -- gets a handle to the actual kernel function
/// * getStreamHelper   -- initializes a new compute stream on GPU
/// * launchKernel      -- launches the kernel on a stream
/// * streamSynchronize -- waits for operations on the stream to finish
///
/// Intermediate data structures are allocated on the stack.
class ConvertLaunchFuncOpToGpuRuntimeCallPattern
    : public ConvertOpToGpuRuntimeCallPattern<gpu::LaunchFuncOp> {
public:
  ConvertLaunchFuncOpToGpuRuntimeCallPattern(LLVMTypeConverter &typeConverter,
                                             StringRef gpuBinaryAnnotation,
                                             bool kernelBarePtrCallConv)
      : ConvertOpToGpuRuntimeCallPattern<gpu::LaunchFuncOp>(typeConverter),
        gpuBinaryAnnotation(gpuBinaryAnnotation),
        kernelBarePtrCallConv(kernelBarePtrCallConv) {}

private:
  Value generateParamsArray(gpu::LaunchFuncOp launchOp, OpAdaptor adaptor,
                            OpBuilder &builder) const;
  Value generateKernelNameConstant(StringRef moduleName, StringRef name,
                                   Location loc, OpBuilder &builder) const;

  LogicalResult
  matchAndRewrite(gpu::LaunchFuncOp launchOp, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override;

  llvm::SmallString<32> gpuBinaryAnnotation;
  bool kernelBarePtrCallConv;
};

class EraseGpuModuleOpPattern : public OpRewritePattern<gpu::GPUModuleOp> {
  using OpRewritePattern<gpu::GPUModuleOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(gpu::GPUModuleOp op,
                                PatternRewriter &rewriter) const override {
    // GPU kernel modules are no longer necessary since we have a global
    // constant with the CUBIN, or HSACO data.
    rewriter.eraseOp(op);
    return success();
  }
};

/// A rewrite pattern to convert gpu.memcpy operations into a GPU runtime
/// call. Currently it supports CUDA and ROCm (HIP).
class ConvertMemcpyOpToGpuRuntimeCallPattern
    : public ConvertOpToGpuRuntimeCallPattern<gpu::MemcpyOp> {
public:
  ConvertMemcpyOpToGpuRuntimeCallPattern(LLVMTypeConverter &typeConverter)
      : ConvertOpToGpuRuntimeCallPattern<gpu::MemcpyOp>(typeConverter) {}

private:
  LogicalResult
  matchAndRewrite(gpu::MemcpyOp memcpyOp, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override;
};

/// A rewrite pattern to convert gpu.memset operations into a GPU runtime
/// call. Currently it supports CUDA and ROCm (HIP).
class ConvertMemsetOpToGpuRuntimeCallPattern
    : public ConvertOpToGpuRuntimeCallPattern<gpu::MemsetOp> {
public:
  ConvertMemsetOpToGpuRuntimeCallPattern(LLVMTypeConverter &typeConverter)
      : ConvertOpToGpuRuntimeCallPattern<gpu::MemsetOp>(typeConverter) {}

private:
  LogicalResult
  matchAndRewrite(gpu::MemsetOp memsetOp, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override;
};

/// A rewrite pattern to convert gpu.set_default_device to a GPU runtime call.
/// Currently supports CUDA and ROCm (HIP)
class ConvertSetDefaultDeviceOpToGpuRuntimeCallPattern
    : public ConvertOpToGpuRuntimeCallPattern<gpu::SetDefaultDeviceOp> {
public:
  ConvertSetDefaultDeviceOpToGpuRuntimeCallPattern(
      LLVMTypeConverter &typeConverter)
      : ConvertOpToGpuRuntimeCallPattern<gpu::SetDefaultDeviceOp>(
            typeConverter) {}

  LogicalResult
  matchAndRewrite(gpu::SetDefaultDeviceOp op, OpAdaptor adaptor,
                  ConversionPatternRewriter &rewriter) const override;
};
} // namespace

void GpuToLLVMConversionPass::runOnOperation() {
  LLVMTypeConverter converter(&getContext());
  RewritePatternSet patterns(&getContext());
  LLVMConversionTarget target(getContext());

  target.addIllegalDialect<gpu::GPUDialect>();

  mlir::arith::populateArithToLLVMConversionPatterns(converter, patterns);
  mlir::cf::populateControlFlowToLLVMConversionPatterns(converter, patterns);
  populateVectorToLLVMConversionPatterns(converter, patterns);
  populateMemRefToLLVMConversionPatterns(converter, patterns);
  populateFuncToLLVMConversionPatterns(converter, patterns);
  populateAsyncStructuralTypeConversionsAndLegality(converter, patterns,
                                                    target);
  populateGpuToLLVMConversionPatterns(converter, patterns, gpuBinaryAnnotation,
                                      kernelBarePtrCallConv);

  if (failed(
          applyPartialConversion(getOperation(), target, std::move(patterns))))
    signalPassFailure();
}

LLVM::CallOp FunctionCallBuilder::create(Location loc, OpBuilder &builder,
                                         ArrayRef<Value> arguments) const {
  auto module = builder.getBlock()->getParent()->getParentOfType<ModuleOp>();
  auto function = [&] {
    if (auto function = module.lookupSymbol<LLVM::LLVMFuncOp>(functionName))
      return function;
    return OpBuilder::atBlockEnd(module.getBody())
        .create<LLVM::LLVMFuncOp>(loc, functionName, functionType);
  }();
  return builder.create<LLVM::CallOp>(loc, function, arguments);
}

// Returns whether all operands are of LLVM type.
static LogicalResult areAllLLVMTypes(Operation *op, ValueRange operands,
                                     ConversionPatternRewriter &rewriter) {
  if (!llvm::all_of(operands, [](Value value) {
        return LLVM::isCompatibleType(value.getType());
      }))
    return rewriter.notifyMatchFailure(
        op, "Cannot convert if operands aren't of LLVM type.");
  return success();
}

static LogicalResult
isAsyncWithOneDependency(ConversionPatternRewriter &rewriter,
                         gpu::AsyncOpInterface op) {
  if (op.getAsyncDependencies().size() != 1)
    return rewriter.notifyMatchFailure(
        op, "Can only convert with exactly one async dependency.");

  if (!op.getAsyncToken())
    return rewriter.notifyMatchFailure(op, "Can convert only async version.");

  return success();
}

LogicalResult ConvertHostRegisterOpToGpuRuntimeCallPattern::matchAndRewrite(
    gpu::HostRegisterOp hostRegisterOp, OpAdaptor adaptor,
    ConversionPatternRewriter &rewriter) const {
  auto *op = hostRegisterOp.getOperation();
  if (failed(areAllLLVMTypes(op, adaptor.getOperands(), rewriter)))
    return failure();

  Location loc = op->getLoc();

  auto memRefType = hostRegisterOp.getValue().getType();
  auto elementType = memRefType.cast<UnrankedMemRefType>().getElementType();
  auto elementSize = getSizeInBytes(loc, elementType, rewriter);

  auto arguments = getTypeConverter()->promoteOperands(
      loc, op->getOperands(), adaptor.getOperands(), rewriter);
  arguments.push_back(elementSize);
  hostRegisterCallBuilder.create(loc, rewriter, arguments);

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

LogicalResult ConvertAllocOpToGpuRuntimeCallPattern::matchAndRewrite(
    gpu::AllocOp allocOp, OpAdaptor adaptor,
    ConversionPatternRewriter &rewriter) const {
  if (adaptor.getHostShared())
    return rewriter.notifyMatchFailure(
        allocOp, "host_shared allocation is not supported");

  MemRefType memRefType = allocOp.getType();

  if (failed(areAllLLVMTypes(allocOp, adaptor.getOperands(), rewriter)) ||
      !isConvertibleAndHasIdentityMaps(memRefType) ||
      failed(isAsyncWithOneDependency(rewriter, allocOp)))
    return failure();

  auto loc = allocOp.getLoc();

  // Get shape of the memref as values: static sizes are constant
  // values and dynamic sizes are passed to 'alloc' as operands.
  SmallVector<Value, 4> shape;
  SmallVector<Value, 4> strides;
  Value sizeBytes;
  getMemRefDescriptorSizes(loc, memRefType, adaptor.getDynamicSizes(), rewriter,
                           shape, strides, sizeBytes);

  // Allocate the underlying buffer and store a pointer to it in the MemRef
  // descriptor.
  Type elementPtrType = this->getElementPtrType(memRefType);
  auto stream = adaptor.getAsyncDependencies().front();
  Value allocatedPtr =
      allocCallBuilder.create(loc, rewriter, {sizeBytes, stream}).getResult();
  allocatedPtr =
      rewriter.create<LLVM::BitcastOp>(loc, elementPtrType, allocatedPtr);

  // No alignment.
  Value alignedPtr = allocatedPtr;

  // Create the MemRef descriptor.
  auto memRefDescriptor = this->createMemRefDescriptor(
      loc, memRefType, allocatedPtr, alignedPtr, shape, strides, rewriter);

  rewriter.replaceOp(allocOp, {memRefDescriptor, stream});

  return success();
}

LogicalResult ConvertDeallocOpToGpuRuntimeCallPattern::matchAndRewrite(
    gpu::DeallocOp deallocOp, OpAdaptor adaptor,
    ConversionPatternRewriter &rewriter) const {
  if (failed(areAllLLVMTypes(deallocOp, adaptor.getOperands(), rewriter)) ||
      failed(isAsyncWithOneDependency(rewriter, deallocOp)))
    return failure();

  Location loc = deallocOp.getLoc();

  Value pointer =
      MemRefDescriptor(adaptor.getMemref()).allocatedPtr(rewriter, loc);
  auto casted = rewriter.create<LLVM::BitcastOp>(loc, llvmPointerType, pointer);
  Value stream = adaptor.getAsyncDependencies().front();
  deallocCallBuilder.create(loc, rewriter, {casted, stream});

  rewriter.replaceOp(deallocOp, {stream});
  return success();
}

static bool isGpuAsyncTokenType(Value value) {
  return value.getType().isa<gpu::AsyncTokenType>();
}

// Converts !gpu.async.token operands of `async.yield` to runtime calls. The
// !gpu.async.token are lowered to stream within the async.execute region, but
// are passed as events between them. For each !gpu.async.token operand, we
// create an event and record it on the stream.
LogicalResult ConvertAsyncYieldToGpuRuntimeCallPattern::matchAndRewrite(
    async::YieldOp yieldOp, OpAdaptor adaptor,
    ConversionPatternRewriter &rewriter) const {
  if (llvm::none_of(yieldOp.getOperands(), isGpuAsyncTokenType))
    return rewriter.notifyMatchFailure(yieldOp, "no gpu async token operand");

  Location loc = yieldOp.getLoc();
  SmallVector<Value, 4> newOperands(adaptor.getOperands());
  llvm::SmallDenseSet<Value> streams;
  for (auto &operand : yieldOp->getOpOperands()) {
    if (!isGpuAsyncTokenType(operand.get()))
      continue;
    auto idx = operand.getOperandNumber();
    auto stream = adaptor.getOperands()[idx];
    auto event = eventCreateCallBuilder.create(loc, rewriter, {}).getResult();
    eventRecordCallBuilder.create(loc, rewriter, {event, stream});
    newOperands[idx] = event;
    streams.insert(stream);
  }
  for (auto stream : streams)
    streamDestroyCallBuilder.create(loc, rewriter, {stream});

  rewriter.updateRootInPlace(yieldOp,
                             [&] { yieldOp->setOperands(newOperands); });
  return success();
}

// Returns whether `value` is the result of an LLVM::CallOp to `functionName`.
static bool isDefinedByCallTo(Value value, StringRef functionName) {
  assert(value.getType().isa<LLVM::LLVMPointerType>());
  if (auto defOp = value.getDefiningOp<LLVM::CallOp>())
    return defOp.getCallee()->equals(functionName);
  return false;
}

// Converts `gpu.wait` to runtime calls. The converted op synchronizes the host
// with the stream/event operands. The operands are destroyed. That is, it
// assumes that it is not used afterwards or elsewhere. Otherwise we will get a
// runtime error. Eventually, we should guarantee this property.
LogicalResult ConvertWaitOpToGpuRuntimeCallPattern::matchAndRewrite(
    gpu::WaitOp waitOp, OpAdaptor adaptor,
    ConversionPatternRewriter &rewriter) const {
  if (waitOp.getAsyncToken())
    return rewriter.notifyMatchFailure(waitOp, "Cannot convert async op.");

  Location loc = waitOp.getLoc();

  for (auto operand : adaptor.getOperands()) {
    if (isDefinedByCallTo(operand, streamCreateCallBuilder.functionName)) {
      // The converted operand's definition created a stream.
      streamSynchronizeCallBuilder.create(loc, rewriter, {operand});
      streamDestroyCallBuilder.create(loc, rewriter, {operand});
    } else {
      // Otherwise the converted operand is an event. This assumes that we use
      // events in control flow code as well.
      eventSynchronizeCallBuilder.create(loc, rewriter, {operand});
      eventDestroyCallBuilder.create(loc, rewriter, {operand});
    }
  }

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

// Converts `gpu.wait async` to runtime calls. The converted op creates a new
// stream that is synchronized with stream/event operands. The operands are
// destroyed. That is, it assumes that it is not used afterwards or elsewhere.
// Otherwise we will get a runtime error. Eventually, we should guarantee this
// property.
LogicalResult ConvertWaitAsyncOpToGpuRuntimeCallPattern::matchAndRewrite(
    gpu::WaitOp waitOp, OpAdaptor adaptor,
    ConversionPatternRewriter &rewriter) const {
  if (!waitOp.getAsyncToken())
    return rewriter.notifyMatchFailure(waitOp, "Can only convert async op.");

  Location loc = waitOp.getLoc();

  auto insertionPoint = rewriter.saveInsertionPoint();
  SmallVector<Value, 1> events;
  for (auto pair :
       llvm::zip(waitOp.getAsyncDependencies(), adaptor.getOperands())) {
    auto operand = std::get<1>(pair);
    if (isDefinedByCallTo(operand, streamCreateCallBuilder.functionName)) {
      // The converted operand's definition created a stream. Insert an event
      // into the stream just after the last use of the original token operand.
      auto *defOp = std::get<0>(pair).getDefiningOp();
      rewriter.setInsertionPointAfter(defOp);
      auto event = eventCreateCallBuilder.create(loc, rewriter, {}).getResult();
      eventRecordCallBuilder.create(loc, rewriter, {event, operand});
      events.push_back(event);
    } else {
      // Otherwise the converted operand is an event. This assumes that we use
      // events in control flow code as well.
      events.push_back(operand);
    }
  }
  rewriter.restoreInsertionPoint(insertionPoint);
  auto stream = streamCreateCallBuilder.create(loc, rewriter, {}).getResult();
  for (auto event : events)
    streamWaitEventCallBuilder.create(loc, rewriter, {stream, event});
  for (auto event : events)
    eventDestroyCallBuilder.create(loc, rewriter, {event});
  rewriter.replaceOp(waitOp, {stream});

  return success();
}

// Creates a struct containing all kernel parameters on the stack and returns
// an array of type-erased pointers to the fields of the struct. The array can
// then be passed to the CUDA / ROCm (HIP) kernel launch calls.
// The generated code is essentially as follows:
//
// %struct = alloca(sizeof(struct { Parameters... }))
// %array = alloca(NumParameters * sizeof(void *))
// for (i : [0, NumParameters))
//   %fieldPtr = llvm.getelementptr %struct[0, i]
//   llvm.store parameters[i], %fieldPtr
//   %elementPtr = llvm.getelementptr %array[i]
//   llvm.store %fieldPtr, %elementPtr
// return %array
Value ConvertLaunchFuncOpToGpuRuntimeCallPattern::generateParamsArray(
    gpu::LaunchFuncOp launchOp, OpAdaptor adaptor, OpBuilder &builder) const {
  auto loc = launchOp.getLoc();
  auto numKernelOperands = launchOp.getNumKernelOperands();
  SmallVector<Value, 4> arguments;
  if (kernelBarePtrCallConv) {
    // Hack the bare pointer value on just for the argument promotion
    LLVMTypeConverter *converter = getTypeConverter();
    LowerToLLVMOptions options = converter->getOptions();
    LowerToLLVMOptions overrideToMatchKernelOpts = options;
    overrideToMatchKernelOpts.useBarePtrCallConv = true;
    converter->dangerousSetOptions(overrideToMatchKernelOpts);
    arguments = converter->promoteOperands(
        loc, launchOp.getOperands().take_back(numKernelOperands),
        adaptor.getOperands().take_back(numKernelOperands), builder);
    converter->dangerousSetOptions(options);
  } else {
    arguments = getTypeConverter()->promoteOperands(
        loc, launchOp.getOperands().take_back(numKernelOperands),
        adaptor.getOperands().take_back(numKernelOperands), builder);
  }

  auto numArguments = arguments.size();
  SmallVector<Type, 4> argumentTypes;
  argumentTypes.reserve(numArguments);
  for (auto argument : arguments)
    argumentTypes.push_back(argument.getType());
  auto structType = LLVM::LLVMStructType::getNewIdentified(context, StringRef(),
                                                           argumentTypes);
  auto one = builder.create<LLVM::ConstantOp>(loc, llvmInt32Type, 1);
  auto structPtr = builder.create<LLVM::AllocaOp>(
      loc, LLVM::LLVMPointerType::get(structType), one, /*alignment=*/0);
  auto arraySize =
      builder.create<LLVM::ConstantOp>(loc, llvmInt32Type, numArguments);
  auto arrayPtr = builder.create<LLVM::AllocaOp>(loc, llvmPointerPointerType,
                                                 arraySize, /*alignment=*/0);
  for (const auto &en : llvm::enumerate(arguments)) {
    auto fieldPtr = builder.create<LLVM::GEPOp>(
        loc, LLVM::LLVMPointerType::get(argumentTypes[en.index()]), structPtr,
        ArrayRef<LLVM::GEPArg>{0, en.index()});
    builder.create<LLVM::StoreOp>(loc, en.value(), fieldPtr);
    auto elementPtr =
        builder.create<LLVM::GEPOp>(loc, llvmPointerPointerType, arrayPtr,
                                    ArrayRef<LLVM::GEPArg>{en.index()});
    auto casted =
        builder.create<LLVM::BitcastOp>(loc, llvmPointerType, fieldPtr);
    builder.create<LLVM::StoreOp>(loc, casted, elementPtr);
  }
  return arrayPtr;
}

// Generates an LLVM IR dialect global that contains the name of the given
// kernel function as a C string, and returns a pointer to its beginning.
// The code is essentially:
//
// llvm.global constant @kernel_name("function_name\00")
// func(...) {
//   %0 = llvm.addressof @kernel_name
//   %1 = llvm.constant (0 : index)
//   %2 = llvm.getelementptr %0[%1, %1] : !llvm<"i8*">
// }
Value ConvertLaunchFuncOpToGpuRuntimeCallPattern::generateKernelNameConstant(
    StringRef moduleName, StringRef name, Location loc,
    OpBuilder &builder) const {
  // Make sure the trailing zero is included in the constant.
  std::vector<char> kernelName(name.begin(), name.end());
  kernelName.push_back('\0');

  std::string globalName =
      std::string(llvm::formatv("{0}_{1}_kernel_name", moduleName, name));
  return LLVM::createGlobalString(
      loc, builder, globalName, StringRef(kernelName.data(), kernelName.size()),
      LLVM::Linkage::Internal);
}

// Emits LLVM IR to launch a kernel function. Expects the module that contains
// the compiled kernel function as a cubin in the 'nvvm.cubin' attribute, or a
// hsaco in the 'rocdl.hsaco' attribute of the kernel function in the IR.
//
// %0 = call %binarygetter
// %1 = call %moduleLoad(%0)
// %2 = <see generateKernelNameConstant>
// %3 = call %moduleGetFunction(%1, %2)
// %4 = call %streamCreate()
// %5 = <see generateParamsArray>
// call %launchKernel(%3, <launchOp operands 0..5>, 0, %4, %5, nullptr)
// call %streamSynchronize(%4)
// call %streamDestroy(%4)
// call %moduleUnload(%1)
//
// If the op is async, the stream corresponds to the (single) async dependency
// as well as the async token the op produces.
LogicalResult ConvertLaunchFuncOpToGpuRuntimeCallPattern::matchAndRewrite(
    gpu::LaunchFuncOp launchOp, OpAdaptor adaptor,
    ConversionPatternRewriter &rewriter) const {
  if (failed(areAllLLVMTypes(launchOp, adaptor.getOperands(), rewriter)))
    return failure();

  if (launchOp.getAsyncDependencies().size() > 1)
    return rewriter.notifyMatchFailure(
        launchOp, "Cannot convert with more than one async dependency.");

  // Fail when the synchronous version of the op has async dependencies. The
  // lowering destroys the stream, and we do not want to check that there is no
  // use of the stream after this op.
  if (!launchOp.getAsyncToken() && !launchOp.getAsyncDependencies().empty())
    return rewriter.notifyMatchFailure(
        launchOp, "Cannot convert non-async op with async dependencies.");

  Location loc = launchOp.getLoc();

  // Create an LLVM global with CUBIN extracted from the kernel annotation and
  // obtain a pointer to the first byte in it.
  auto kernelModule = SymbolTable::lookupNearestSymbolFrom<gpu::GPUModuleOp>(
      launchOp, launchOp.getKernelModuleName());
  assert(kernelModule && "expected a kernel module");

  auto binaryAttr =
      kernelModule->getAttrOfType<StringAttr>(gpuBinaryAnnotation);
  if (!binaryAttr) {
    kernelModule.emitOpError()
        << "missing " << gpuBinaryAnnotation << " attribute";
    return failure();
  }

  SmallString<128> nameBuffer(kernelModule.getName());
  nameBuffer.append(kGpuBinaryStorageSuffix);
  Value data =
      LLVM::createGlobalString(loc, rewriter, nameBuffer.str(),
                               binaryAttr.getValue(), LLVM::Linkage::Internal);

  auto module = moduleLoadCallBuilder.create(loc, rewriter, data);
  // Get the function from the module. The name corresponds to the name of
  // the kernel function.
  auto kernelName = generateKernelNameConstant(
      launchOp.getKernelModuleName().getValue(),
      launchOp.getKernelName().getValue(), loc, rewriter);
  auto function = moduleGetFunctionCallBuilder.create(
      loc, rewriter, {module.getResult(), kernelName});
  Value zero = rewriter.create<LLVM::ConstantOp>(loc, llvmInt32Type, 0);
  Value stream =
      adaptor.getAsyncDependencies().empty()
          ? streamCreateCallBuilder.create(loc, rewriter, {}).getResult()
          : adaptor.getAsyncDependencies().front();
  // Create array of pointers to kernel arguments.
  auto kernelParams = generateParamsArray(launchOp, adaptor, rewriter);
  auto nullpointer = rewriter.create<LLVM::NullOp>(loc, llvmPointerPointerType);
  Value dynamicSharedMemorySize = launchOp.getDynamicSharedMemorySize()
                                      ? launchOp.getDynamicSharedMemorySize()
                                      : zero;
  launchKernelCallBuilder.create(
      loc, rewriter,
      {function.getResult(), adaptor.getGridSizeX(), adaptor.getGridSizeY(),
       adaptor.getGridSizeZ(), adaptor.getBlockSizeX(), adaptor.getBlockSizeY(),
       adaptor.getBlockSizeZ(), dynamicSharedMemorySize, stream, kernelParams,
       /*extra=*/nullpointer});

  if (launchOp.getAsyncToken()) {
    // Async launch: make dependent ops use the same stream.
    rewriter.replaceOp(launchOp, {stream});
  } else {
    // Synchronize with host and destroy stream. This must be the stream created
    // above (with no other uses) because we check that the synchronous version
    // does not have any async dependencies.
    streamSynchronizeCallBuilder.create(loc, rewriter, stream);
    streamDestroyCallBuilder.create(loc, rewriter, stream);
    rewriter.eraseOp(launchOp);
  }
  moduleUnloadCallBuilder.create(loc, rewriter, module.getResult());

  return success();
}

LogicalResult ConvertMemcpyOpToGpuRuntimeCallPattern::matchAndRewrite(
    gpu::MemcpyOp memcpyOp, OpAdaptor adaptor,
    ConversionPatternRewriter &rewriter) const {
  auto memRefType = memcpyOp.getSrc().getType().cast<MemRefType>();

  if (failed(areAllLLVMTypes(memcpyOp, adaptor.getOperands(), rewriter)) ||
      !isConvertibleAndHasIdentityMaps(memRefType) ||
      failed(isAsyncWithOneDependency(rewriter, memcpyOp)))
    return failure();

  auto loc = memcpyOp.getLoc();

  MemRefDescriptor srcDesc(adaptor.getSrc());
  Value numElements = getNumElements(rewriter, loc, memRefType, srcDesc);

  Type elementPtrType = getElementPtrType(memRefType);
  Value nullPtr = rewriter.create<LLVM::NullOp>(loc, elementPtrType);
  Value gepPtr =
      rewriter.create<LLVM::GEPOp>(loc, elementPtrType, nullPtr, numElements);
  auto sizeBytes =
      rewriter.create<LLVM::PtrToIntOp>(loc, getIndexType(), gepPtr);

  auto src = rewriter.create<LLVM::BitcastOp>(
      loc, llvmPointerType, srcDesc.alignedPtr(rewriter, loc));
  auto dst = rewriter.create<LLVM::BitcastOp>(
      loc, llvmPointerType,
      MemRefDescriptor(adaptor.getDst()).alignedPtr(rewriter, loc));

  auto stream = adaptor.getAsyncDependencies().front();
  memcpyCallBuilder.create(loc, rewriter, {dst, src, sizeBytes, stream});

  rewriter.replaceOp(memcpyOp, {stream});

  return success();
}

LogicalResult ConvertMemsetOpToGpuRuntimeCallPattern::matchAndRewrite(
    gpu::MemsetOp memsetOp, OpAdaptor adaptor,
    ConversionPatternRewriter &rewriter) const {
  auto memRefType = memsetOp.getDst().getType().cast<MemRefType>();

  if (failed(areAllLLVMTypes(memsetOp, adaptor.getOperands(), rewriter)) ||
      !isConvertibleAndHasIdentityMaps(memRefType) ||
      failed(isAsyncWithOneDependency(rewriter, memsetOp)))
    return failure();

  auto loc = memsetOp.getLoc();

  Type valueType = adaptor.getValue().getType();
  if (!valueType.isIntOrFloat() || valueType.getIntOrFloatBitWidth() != 32) {
    return rewriter.notifyMatchFailure(memsetOp,
                                       "value must be a 32 bit scalar");
  }

  MemRefDescriptor dstDesc(adaptor.getDst());
  Value numElements = getNumElements(rewriter, loc, memRefType, dstDesc);

  auto value =
      rewriter.create<LLVM::BitcastOp>(loc, llvmInt32Type, adaptor.getValue());
  auto dst = rewriter.create<LLVM::BitcastOp>(
      loc, llvmPointerType, dstDesc.alignedPtr(rewriter, loc));

  auto stream = adaptor.getAsyncDependencies().front();
  memsetCallBuilder.create(loc, rewriter, {dst, value, numElements, stream});

  rewriter.replaceOp(memsetOp, {stream});
  return success();
}

LogicalResult ConvertSetDefaultDeviceOpToGpuRuntimeCallPattern::matchAndRewrite(
    gpu::SetDefaultDeviceOp op, OpAdaptor adaptor,
    ConversionPatternRewriter &rewriter) const {
  Location loc = op.getLoc();
  setDefaultDeviceCallBuilder.create(loc, rewriter, {adaptor.getDevIndex()});
  rewriter.replaceOp(op, {});
  return success();
}

std::unique_ptr<mlir::OperationPass<mlir::ModuleOp>>
mlir::createGpuToLLVMConversionPass(bool kernelBarePtrCallConv) {
  return std::make_unique<GpuToLLVMConversionPass>(kernelBarePtrCallConv);
}

void mlir::populateGpuToLLVMConversionPatterns(LLVMTypeConverter &converter,
                                               RewritePatternSet &patterns,
                                               StringRef gpuBinaryAnnotation,
                                               bool kernelBarePtrCallConv) {
  converter.addConversion(
      [context = &converter.getContext()](gpu::AsyncTokenType type) -> Type {
        return LLVM::LLVMPointerType::get(IntegerType::get(context, 8));
      });
  patterns.add<ConvertAllocOpToGpuRuntimeCallPattern,
               ConvertDeallocOpToGpuRuntimeCallPattern,
               ConvertHostRegisterOpToGpuRuntimeCallPattern,
               ConvertMemcpyOpToGpuRuntimeCallPattern,
               ConvertMemsetOpToGpuRuntimeCallPattern,
               ConvertSetDefaultDeviceOpToGpuRuntimeCallPattern,
               ConvertWaitAsyncOpToGpuRuntimeCallPattern,
               ConvertWaitOpToGpuRuntimeCallPattern,
               ConvertAsyncYieldToGpuRuntimeCallPattern>(converter);
  patterns.add<ConvertLaunchFuncOpToGpuRuntimeCallPattern>(
      converter, gpuBinaryAnnotation, kernelBarePtrCallConv);
  patterns.add<EraseGpuModuleOpPattern>(&converter.getContext());
}