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llvm-project/clang/lib/Driver/ToolChains/Cuda.cpp

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//===--- Cuda.cpp - Cuda Tool and ToolChain Implementations -----*- C++ -*-===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
#include "Cuda.h"
#include "InputInfo.h"
#include "CommonArgs.h"
#include "clang/Basic/Cuda.h"
#include "clang/Config/config.h"
#include "clang/Basic/VirtualFileSystem.h"
#include "clang/Driver/Distro.h"
#include "clang/Driver/Compilation.h"
#include "clang/Driver/Driver.h"
#include "clang/Driver/DriverDiagnostic.h"
#include "clang/Driver/Options.h"
#include "llvm/Option/ArgList.h"
#include "llvm/Support/Path.h"
#include <system_error>
using namespace clang::driver;
using namespace clang::driver::toolchains;
using namespace clang::driver::tools;
using namespace clang;
using namespace llvm::opt;
// Parses the contents of version.txt in an CUDA installation. It should
// contain one line of the from e.g. "CUDA Version 7.5.2".
static CudaVersion ParseCudaVersionFile(llvm::StringRef V) {
if (!V.startswith("CUDA Version "))
return CudaVersion::UNKNOWN;
V = V.substr(strlen("CUDA Version "));
int Major = -1, Minor = -1;
auto First = V.split('.');
auto Second = First.second.split('.');
if (First.first.getAsInteger(10, Major) ||
Second.first.getAsInteger(10, Minor))
return CudaVersion::UNKNOWN;
if (Major == 7 && Minor == 0) {
// This doesn't appear to ever happen -- version.txt doesn't exist in the
// CUDA 7 installs I've seen. But no harm in checking.
return CudaVersion::CUDA_70;
}
if (Major == 7 && Minor == 5)
return CudaVersion::CUDA_75;
if (Major == 8 && Minor == 0)
return CudaVersion::CUDA_80;
if (Major == 9 && Minor == 0)
return CudaVersion::CUDA_90;
return CudaVersion::UNKNOWN;
}
CudaInstallationDetector::CudaInstallationDetector(
const Driver &D, const llvm::Triple &HostTriple,
const llvm::opt::ArgList &Args)
: D(D) {
SmallVector<std::string, 4> CudaPathCandidates;
// In decreasing order so we prefer newer versions to older versions.
std::initializer_list<const char *> Versions = {"8.0", "7.5", "7.0"};
if (Args.hasArg(clang::driver::options::OPT_cuda_path_EQ)) {
CudaPathCandidates.push_back(
Args.getLastArgValue(clang::driver::options::OPT_cuda_path_EQ));
} else if (HostTriple.isOSWindows()) {
for (const char *Ver : Versions)
CudaPathCandidates.push_back(
D.SysRoot + "/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v" +
Ver);
} else {
CudaPathCandidates.push_back(D.SysRoot + "/usr/local/cuda");
for (const char *Ver : Versions)
CudaPathCandidates.push_back(D.SysRoot + "/usr/local/cuda-" + Ver);
if (Distro(D.getVFS()).IsDebian())
// Special case for Debian to have nvidia-cuda-toolkit work
// out of the box. More info on http://bugs.debian.org/882505
CudaPathCandidates.push_back(D.SysRoot + "/usr/lib/cuda");
}
for (const auto &CudaPath : CudaPathCandidates) {
if (CudaPath.empty() || !D.getVFS().exists(CudaPath))
continue;
InstallPath = CudaPath;
BinPath = CudaPath + "/bin";
IncludePath = InstallPath + "/include";
LibDevicePath = InstallPath + "/nvvm/libdevice";
auto &FS = D.getVFS();
if (!(FS.exists(IncludePath) && FS.exists(BinPath)))
continue;
// On Linux, we have both lib and lib64 directories, and we need to choose
// based on our triple. On MacOS, we have only a lib directory.
//
// It's sufficient for our purposes to be flexible: If both lib and lib64
// exist, we choose whichever one matches our triple. Otherwise, if only
// lib exists, we use it.
if (HostTriple.isArch64Bit() && FS.exists(InstallPath + "/lib64"))
LibPath = InstallPath + "/lib64";
else if (FS.exists(InstallPath + "/lib"))
LibPath = InstallPath + "/lib";
else
continue;
llvm::ErrorOr<std::unique_ptr<llvm::MemoryBuffer>> VersionFile =
FS.getBufferForFile(InstallPath + "/version.txt");
if (!VersionFile) {
// CUDA 7.0 doesn't have a version.txt, so guess that's our version if
// version.txt isn't present.
Version = CudaVersion::CUDA_70;
} else {
Version = ParseCudaVersionFile((*VersionFile)->getBuffer());
}
if (Version == CudaVersion::CUDA_90) {
// CUDA-9 uses single libdevice file for all GPU variants.
std::string FilePath = LibDevicePath + "/libdevice.10.bc";
if (FS.exists(FilePath)) {
for (const char *GpuArch :
{"sm_20", "sm_30", "sm_32", "sm_35", "sm_50", "sm_52", "sm_53",
"sm_60", "sm_61", "sm_62", "sm_70"})
LibDeviceMap[GpuArch] = FilePath;
}
} else {
std::error_code EC;
for (llvm::sys::fs::directory_iterator LI(LibDevicePath, EC), LE;
!EC && LI != LE; LI = LI.increment(EC)) {
StringRef FilePath = LI->path();
StringRef FileName = llvm::sys::path::filename(FilePath);
// Process all bitcode filenames that look like
// libdevice.compute_XX.YY.bc
const StringRef LibDeviceName = "libdevice.";
if (!(FileName.startswith(LibDeviceName) && FileName.endswith(".bc")))
continue;
StringRef GpuArch = FileName.slice(
LibDeviceName.size(), FileName.find('.', LibDeviceName.size()));
LibDeviceMap[GpuArch] = FilePath.str();
// Insert map entries for specifc devices with this compute
// capability. NVCC's choice of the libdevice library version is
// rather peculiar and depends on the CUDA version.
if (GpuArch == "compute_20") {
LibDeviceMap["sm_20"] = FilePath;
LibDeviceMap["sm_21"] = FilePath;
LibDeviceMap["sm_32"] = FilePath;
} else if (GpuArch == "compute_30") {
LibDeviceMap["sm_30"] = FilePath;
if (Version < CudaVersion::CUDA_80) {
LibDeviceMap["sm_50"] = FilePath;
LibDeviceMap["sm_52"] = FilePath;
LibDeviceMap["sm_53"] = FilePath;
}
LibDeviceMap["sm_60"] = FilePath;
LibDeviceMap["sm_61"] = FilePath;
LibDeviceMap["sm_62"] = FilePath;
} else if (GpuArch == "compute_35") {
LibDeviceMap["sm_35"] = FilePath;
LibDeviceMap["sm_37"] = FilePath;
} else if (GpuArch == "compute_50") {
if (Version >= CudaVersion::CUDA_80) {
LibDeviceMap["sm_50"] = FilePath;
LibDeviceMap["sm_52"] = FilePath;
LibDeviceMap["sm_53"] = FilePath;
}
}
}
}
// Check that we have found at least one libdevice that we can link in if
// -nocudalib hasn't been specified.
if (LibDeviceMap.empty() && !Args.hasArg(options::OPT_nocudalib))
continue;
IsValid = true;
break;
}
}
void CudaInstallationDetector::AddCudaIncludeArgs(
const ArgList &DriverArgs, ArgStringList &CC1Args) const {
if (!DriverArgs.hasArg(options::OPT_nobuiltininc)) {
// Add cuda_wrappers/* to our system include path. This lets us wrap
// standard library headers.
SmallString<128> P(D.ResourceDir);
llvm::sys::path::append(P, "include");
llvm::sys::path::append(P, "cuda_wrappers");
CC1Args.push_back("-internal-isystem");
CC1Args.push_back(DriverArgs.MakeArgString(P));
}
if (DriverArgs.hasArg(options::OPT_nocudainc))
return;
if (!isValid()) {
D.Diag(diag::err_drv_no_cuda_installation);
return;
}
CC1Args.push_back("-internal-isystem");
CC1Args.push_back(DriverArgs.MakeArgString(getIncludePath()));
CC1Args.push_back("-include");
CC1Args.push_back("__clang_cuda_runtime_wrapper.h");
}
void CudaInstallationDetector::CheckCudaVersionSupportsArch(
CudaArch Arch) const {
if (Arch == CudaArch::UNKNOWN || Version == CudaVersion::UNKNOWN ||
ArchsWithBadVersion.count(Arch) > 0)
return;
auto MinVersion = MinVersionForCudaArch(Arch);
auto MaxVersion = MaxVersionForCudaArch(Arch);
if (Version < MinVersion || Version > MaxVersion) {
ArchsWithBadVersion.insert(Arch);
D.Diag(diag::err_drv_cuda_version_unsupported)
<< CudaArchToString(Arch) << CudaVersionToString(MinVersion)
<< CudaVersionToString(MaxVersion) << InstallPath
<< CudaVersionToString(Version);
}
}
void CudaInstallationDetector::print(raw_ostream &OS) const {
if (isValid())
OS << "Found CUDA installation: " << InstallPath << ", version "
<< CudaVersionToString(Version) << "\n";
}
void NVPTX::Assembler::ConstructJob(Compilation &C, const JobAction &JA,
const InputInfo &Output,
const InputInfoList &Inputs,
const ArgList &Args,
const char *LinkingOutput) const {
const auto &TC =
static_cast<const toolchains::CudaToolChain &>(getToolChain());
assert(TC.getTriple().isNVPTX() && "Wrong platform");
StringRef GPUArchName;
// If this is an OpenMP action we need to extract the device architecture
// from the -march=arch option. This option may come from -Xopenmp-target
// flag or the default value.
if (JA.isDeviceOffloading(Action::OFK_OpenMP)) {
GPUArchName = Args.getLastArgValue(options::OPT_march_EQ);
assert(!GPUArchName.empty() && "Must have an architecture passed in.");
} else
GPUArchName = JA.getOffloadingArch();
// Obtain architecture from the action.
CudaArch gpu_arch = StringToCudaArch(GPUArchName);
assert(gpu_arch != CudaArch::UNKNOWN &&
"Device action expected to have an architecture.");
// Check that our installation's ptxas supports gpu_arch.
if (!Args.hasArg(options::OPT_no_cuda_version_check)) {
TC.CudaInstallation.CheckCudaVersionSupportsArch(gpu_arch);
}
ArgStringList CmdArgs;
CmdArgs.push_back(TC.getTriple().isArch64Bit() ? "-m64" : "-m32");
if (Args.hasFlag(options::OPT_cuda_noopt_device_debug,
options::OPT_no_cuda_noopt_device_debug, false)) {
// ptxas does not accept -g option if optimization is enabled, so
// we ignore the compiler's -O* options if we want debug info.
CmdArgs.push_back("-g");
CmdArgs.push_back("--dont-merge-basicblocks");
CmdArgs.push_back("--return-at-end");
} else if (Arg *A = Args.getLastArg(options::OPT_O_Group)) {
// Map the -O we received to -O{0,1,2,3}.
//
// TODO: Perhaps we should map host -O2 to ptxas -O3. -O3 is ptxas's
// default, so it may correspond more closely to the spirit of clang -O2.
// -O3 seems like the least-bad option when -Osomething is specified to
// clang but it isn't handled below.
StringRef OOpt = "3";
if (A->getOption().matches(options::OPT_O4) ||
A->getOption().matches(options::OPT_Ofast))
OOpt = "3";
else if (A->getOption().matches(options::OPT_O0))
OOpt = "0";
else if (A->getOption().matches(options::OPT_O)) {
// -Os, -Oz, and -O(anything else) map to -O2, for lack of better options.
OOpt = llvm::StringSwitch<const char *>(A->getValue())
.Case("1", "1")
.Case("2", "2")
.Case("3", "3")
.Case("s", "2")
.Case("z", "2")
.Default("2");
}
CmdArgs.push_back(Args.MakeArgString(llvm::Twine("-O") + OOpt));
} else {
// If no -O was passed, pass -O0 to ptxas -- no opt flag should correspond
// to no optimizations, but ptxas's default is -O3.
CmdArgs.push_back("-O0");
}
// Pass -v to ptxas if it was passed to the driver.
if (Args.hasArg(options::OPT_v))
CmdArgs.push_back("-v");
CmdArgs.push_back("--gpu-name");
CmdArgs.push_back(Args.MakeArgString(CudaArchToString(gpu_arch)));
CmdArgs.push_back("--output-file");
CmdArgs.push_back(Args.MakeArgString(TC.getInputFilename(Output)));
for (const auto& II : Inputs)
CmdArgs.push_back(Args.MakeArgString(II.getFilename()));
for (const auto& A : Args.getAllArgValues(options::OPT_Xcuda_ptxas))
CmdArgs.push_back(Args.MakeArgString(A));
// In OpenMP we need to generate relocatable code.
if (JA.isOffloading(Action::OFK_OpenMP) &&
Args.hasFlag(options::OPT_fopenmp_relocatable_target,
options::OPT_fnoopenmp_relocatable_target,
/*Default=*/ true))
CmdArgs.push_back("-c");
const char *Exec;
if (Arg *A = Args.getLastArg(options::OPT_ptxas_path_EQ))
Exec = A->getValue();
else
Exec = Args.MakeArgString(TC.GetProgramPath("ptxas"));
C.addCommand(llvm::make_unique<Command>(JA, *this, Exec, CmdArgs, Inputs));
}
// All inputs to this linker must be from CudaDeviceActions, as we need to look
// at the Inputs' Actions in order to figure out which GPU architecture they
// correspond to.
void NVPTX::Linker::ConstructJob(Compilation &C, const JobAction &JA,
const InputInfo &Output,
const InputInfoList &Inputs,
const ArgList &Args,
const char *LinkingOutput) const {
const auto &TC =
static_cast<const toolchains::CudaToolChain &>(getToolChain());
assert(TC.getTriple().isNVPTX() && "Wrong platform");
ArgStringList CmdArgs;
CmdArgs.push_back("--cuda");
CmdArgs.push_back(TC.getTriple().isArch64Bit() ? "-64" : "-32");
CmdArgs.push_back(Args.MakeArgString("--create"));
CmdArgs.push_back(Args.MakeArgString(Output.getFilename()));
for (const auto& II : Inputs) {
auto *A = II.getAction();
assert(A->getInputs().size() == 1 &&
"Device offload action is expected to have a single input");
const char *gpu_arch_str = A->getOffloadingArch();
assert(gpu_arch_str &&
"Device action expected to have associated a GPU architecture!");
CudaArch gpu_arch = StringToCudaArch(gpu_arch_str);
// We need to pass an Arch of the form "sm_XX" for cubin files and
// "compute_XX" for ptx.
const char *Arch =
(II.getType() == types::TY_PP_Asm)
? CudaVirtualArchToString(VirtualArchForCudaArch(gpu_arch))
: gpu_arch_str;
CmdArgs.push_back(Args.MakeArgString(llvm::Twine("--image=profile=") +
Arch + ",file=" + II.getFilename()));
}
for (const auto& A : Args.getAllArgValues(options::OPT_Xcuda_fatbinary))
CmdArgs.push_back(Args.MakeArgString(A));
const char *Exec = Args.MakeArgString(TC.GetProgramPath("fatbinary"));
C.addCommand(llvm::make_unique<Command>(JA, *this, Exec, CmdArgs, Inputs));
}
void NVPTX::OpenMPLinker::ConstructJob(Compilation &C, const JobAction &JA,
const InputInfo &Output,
const InputInfoList &Inputs,
const ArgList &Args,
const char *LinkingOutput) const {
const auto &TC =
static_cast<const toolchains::CudaToolChain &>(getToolChain());
assert(TC.getTriple().isNVPTX() && "Wrong platform");
ArgStringList CmdArgs;
// OpenMP uses nvlink to link cubin files. The result will be embedded in the
// host binary by the host linker.
assert(!JA.isHostOffloading(Action::OFK_OpenMP) &&
"CUDA toolchain not expected for an OpenMP host device.");
if (Output.isFilename()) {
CmdArgs.push_back("-o");
CmdArgs.push_back(Output.getFilename());
} else
assert(Output.isNothing() && "Invalid output.");
if (Args.hasArg(options::OPT_g_Flag))
CmdArgs.push_back("-g");
if (Args.hasArg(options::OPT_v))
CmdArgs.push_back("-v");
StringRef GPUArch =
Args.getLastArgValue(options::OPT_march_EQ);
assert(!GPUArch.empty() && "At least one GPU Arch required for ptxas.");
CmdArgs.push_back("-arch");
CmdArgs.push_back(Args.MakeArgString(GPUArch));
// Add paths specified in LIBRARY_PATH environment variable as -L options.
addDirectoryList(Args, CmdArgs, "-L", "LIBRARY_PATH");
// Add paths for the default clang library path.
SmallString<256> DefaultLibPath =
llvm::sys::path::parent_path(TC.getDriver().Dir);
llvm::sys::path::append(DefaultLibPath, "lib" CLANG_LIBDIR_SUFFIX);
CmdArgs.push_back(Args.MakeArgString(Twine("-L") + DefaultLibPath));
// Add linking against library implementing OpenMP calls on NVPTX target.
CmdArgs.push_back("-lomptarget-nvptx");
for (const auto &II : Inputs) {
if (II.getType() == types::TY_LLVM_IR ||
II.getType() == types::TY_LTO_IR ||
II.getType() == types::TY_LTO_BC ||
II.getType() == types::TY_LLVM_BC) {
C.getDriver().Diag(diag::err_drv_no_linker_llvm_support)
<< getToolChain().getTripleString();
continue;
}
// Currently, we only pass the input files to the linker, we do not pass
// any libraries that may be valid only for the host.
if (!II.isFilename())
continue;
const char *CubinF = C.addTempFile(
C.getArgs().MakeArgString(getToolChain().getInputFilename(II)));
CmdArgs.push_back(CubinF);
}
AddOpenMPLinkerScript(getToolChain(), C, Output, Inputs, Args, CmdArgs, JA);
const char *Exec =
Args.MakeArgString(getToolChain().GetProgramPath("nvlink"));
C.addCommand(llvm::make_unique<Command>(JA, *this, Exec, CmdArgs, Inputs));
}
/// CUDA toolchain. Our assembler is ptxas, and our "linker" is fatbinary,
/// which isn't properly a linker but nonetheless performs the step of stitching
/// together object files from the assembler into a single blob.
CudaToolChain::CudaToolChain(const Driver &D, const llvm::Triple &Triple,
const ToolChain &HostTC, const ArgList &Args,
const Action::OffloadKind OK)
: ToolChain(D, Triple, Args), HostTC(HostTC),
CudaInstallation(D, HostTC.getTriple(), Args), OK(OK) {
if (CudaInstallation.isValid())
getProgramPaths().push_back(CudaInstallation.getBinPath());
// Lookup binaries into the driver directory, this is used to
// discover the clang-offload-bundler executable.
getProgramPaths().push_back(getDriver().Dir);
}
std::string CudaToolChain::getInputFilename(const InputInfo &Input) const {
// Only object files are changed, for example assembly files keep their .s
// extensions. CUDA also continues to use .o as they don't use nvlink but
// fatbinary.
if (!(OK == Action::OFK_OpenMP && Input.getType() == types::TY_Object))
return ToolChain::getInputFilename(Input);
// Replace extension for object files with cubin because nvlink relies on
// these particular file names.
SmallString<256> Filename(ToolChain::getInputFilename(Input));
llvm::sys::path::replace_extension(Filename, "cubin");
return Filename.str();
}
void CudaToolChain::addClangTargetOptions(
const llvm::opt::ArgList &DriverArgs,
llvm::opt::ArgStringList &CC1Args,
Action::OffloadKind DeviceOffloadingKind) const {
HostTC.addClangTargetOptions(DriverArgs, CC1Args, DeviceOffloadingKind);
StringRef GpuArch = DriverArgs.getLastArgValue(options::OPT_march_EQ);
assert(!GpuArch.empty() && "Must have an explicit GPU arch.");
assert((DeviceOffloadingKind == Action::OFK_OpenMP ||
DeviceOffloadingKind == Action::OFK_Cuda) &&
"Only OpenMP or CUDA offloading kinds are supported for NVIDIA GPUs.");
if (DeviceOffloadingKind == Action::OFK_Cuda) {
CC1Args.push_back("-fcuda-is-device");
if (DriverArgs.hasFlag(options::OPT_fcuda_flush_denormals_to_zero,
options::OPT_fno_cuda_flush_denormals_to_zero, false))
CC1Args.push_back("-fcuda-flush-denormals-to-zero");
if (DriverArgs.hasFlag(options::OPT_fcuda_approx_transcendentals,
options::OPT_fno_cuda_approx_transcendentals, false))
CC1Args.push_back("-fcuda-approx-transcendentals");
}
if (DriverArgs.hasArg(options::OPT_nocudalib))
return;
std::string LibDeviceFile = CudaInstallation.getLibDeviceFile(GpuArch);
if (LibDeviceFile.empty()) {
if (DeviceOffloadingKind == Action::OFK_OpenMP &&
DriverArgs.hasArg(options::OPT_S))
return;
getDriver().Diag(diag::err_drv_no_cuda_libdevice) << GpuArch;
return;
}
CC1Args.push_back("-mlink-cuda-bitcode");
CC1Args.push_back(DriverArgs.MakeArgString(LibDeviceFile));
if (CudaInstallation.version() >= CudaVersion::CUDA_90) {
// CUDA-9 uses new instructions that are only available in PTX6.0
CC1Args.push_back("-target-feature");
CC1Args.push_back("+ptx60");
} else {
// Libdevice in CUDA-7.0 requires PTX version that's more recent
// than LLVM defaults to. Use PTX4.2 which is the PTX version that
// came with CUDA-7.0.
CC1Args.push_back("-target-feature");
CC1Args.push_back("+ptx42");
}
}
void CudaToolChain::AddCudaIncludeArgs(const ArgList &DriverArgs,
ArgStringList &CC1Args) const {
// Check our CUDA version if we're going to include the CUDA headers.
if (!DriverArgs.hasArg(options::OPT_nocudainc) &&
!DriverArgs.hasArg(options::OPT_no_cuda_version_check)) {
StringRef Arch = DriverArgs.getLastArgValue(options::OPT_march_EQ);
assert(!Arch.empty() && "Must have an explicit GPU arch.");
CudaInstallation.CheckCudaVersionSupportsArch(StringToCudaArch(Arch));
}
CudaInstallation.AddCudaIncludeArgs(DriverArgs, CC1Args);
}
llvm::opt::DerivedArgList *
CudaToolChain::TranslateArgs(const llvm::opt::DerivedArgList &Args,
StringRef BoundArch,
Action::OffloadKind DeviceOffloadKind) const {
DerivedArgList *DAL =
HostTC.TranslateArgs(Args, BoundArch, DeviceOffloadKind);
if (!DAL)
DAL = new DerivedArgList(Args.getBaseArgs());
const OptTable &Opts = getDriver().getOpts();
// For OpenMP device offloading, append derived arguments. Make sure
// flags are not duplicated.
// Also append the compute capability.
if (DeviceOffloadKind == Action::OFK_OpenMP) {
for (Arg *A : Args) {
bool IsDuplicate = false;
for (Arg *DALArg : *DAL) {
if (A == DALArg) {
IsDuplicate = true;
break;
}
}
if (!IsDuplicate)
DAL->append(A);
}
StringRef Arch = DAL->getLastArgValue(options::OPT_march_EQ);
if (Arch.empty())
DAL->AddJoinedArg(nullptr, Opts.getOption(options::OPT_march_EQ),
CLANG_OPENMP_NVPTX_DEFAULT_ARCH);
return DAL;
}
for (Arg *A : Args) {
if (A->getOption().matches(options::OPT_Xarch__)) {
// Skip this argument unless the architecture matches BoundArch
if (BoundArch.empty() || A->getValue(0) != BoundArch)
continue;
unsigned Index = Args.getBaseArgs().MakeIndex(A->getValue(1));
unsigned Prev = Index;
std::unique_ptr<Arg> XarchArg(Opts.ParseOneArg(Args, Index));
// If the argument parsing failed or more than one argument was
// consumed, the -Xarch_ argument's parameter tried to consume
// extra arguments. Emit an error and ignore.
//
// We also want to disallow any options which would alter the
// driver behavior; that isn't going to work in our model. We
// use isDriverOption() as an approximation, although things
// like -O4 are going to slip through.
if (!XarchArg || Index > Prev + 1) {
getDriver().Diag(diag::err_drv_invalid_Xarch_argument_with_args)
<< A->getAsString(Args);
continue;
} else if (XarchArg->getOption().hasFlag(options::DriverOption)) {
getDriver().Diag(diag::err_drv_invalid_Xarch_argument_isdriver)
<< A->getAsString(Args);
continue;
}
XarchArg->setBaseArg(A);
A = XarchArg.release();
DAL->AddSynthesizedArg(A);
}
DAL->append(A);
}
if (!BoundArch.empty()) {
DAL->eraseArg(options::OPT_march_EQ);
DAL->AddJoinedArg(nullptr, Opts.getOption(options::OPT_march_EQ), BoundArch);
}
return DAL;
}
Tool *CudaToolChain::buildAssembler() const {
return new tools::NVPTX::Assembler(*this);
}
Tool *CudaToolChain::buildLinker() const {
if (OK == Action::OFK_OpenMP)
return new tools::NVPTX::OpenMPLinker(*this);
return new tools::NVPTX::Linker(*this);
}
void CudaToolChain::addClangWarningOptions(ArgStringList &CC1Args) const {
HostTC.addClangWarningOptions(CC1Args);
}
ToolChain::CXXStdlibType
CudaToolChain::GetCXXStdlibType(const ArgList &Args) const {
return HostTC.GetCXXStdlibType(Args);
}
void CudaToolChain::AddClangSystemIncludeArgs(const ArgList &DriverArgs,
ArgStringList &CC1Args) const {
HostTC.AddClangSystemIncludeArgs(DriverArgs, CC1Args);
}
void CudaToolChain::AddClangCXXStdlibIncludeArgs(const ArgList &Args,
ArgStringList &CC1Args) const {
HostTC.AddClangCXXStdlibIncludeArgs(Args, CC1Args);
}
void CudaToolChain::AddIAMCUIncludeArgs(const ArgList &Args,
ArgStringList &CC1Args) const {
HostTC.AddIAMCUIncludeArgs(Args, CC1Args);
}
SanitizerMask CudaToolChain::getSupportedSanitizers() const {
// The CudaToolChain only supports sanitizers in the sense that it allows
// sanitizer arguments on the command line if they are supported by the host
// toolchain. The CudaToolChain will actually ignore any command line
// arguments for any of these "supported" sanitizers. That means that no
// sanitization of device code is actually supported at this time.
//
// This behavior is necessary because the host and device toolchains
// invocations often share the command line, so the device toolchain must
// tolerate flags meant only for the host toolchain.
return HostTC.getSupportedSanitizers();
}
VersionTuple CudaToolChain::computeMSVCVersion(const Driver *D,
const ArgList &Args) const {
return HostTC.computeMSVCVersion(D, Args);
}
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