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

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[Driver] Consolidate tools and toolchains by target platform. (NFC) Summary: (This is a move-only refactoring patch. There are no functionality changes.) This patch splits apart the Clang driver's tool and toolchain implementation files. Each target platform toolchain is moved to its own file, along with the closest-related tools. Each target platform toolchain has separate headers and implementation files, so the hierarchy of classes is unchanged. There are some remaining shared free functions, mostly from Tools.cpp. Several of these move to their own architecture-specific files, similar to r296056. Some of them are only used by a single target platform; since the tools and toolchains are now together, some helpers now live in a platform-specific file. The balance are helpers related to manipulating argument lists, so they are now in a new file pair, CommonArgs.h and .cpp. I've tried to cluster the code logically, which is fairly straightforward for most of the target platforms and shared architectures. I think I've made reasonable choices for these, as well as the various shared helpers; but of course, I'm happy to hear feedback in the review. There are some particular things I don't like about this patch, but haven't been able to find a better overall solution. The first is the proliferation of files: there are several files that are tiny because the toolchain is not very different from its base (usually the Gnu tools/toolchain). I think this is mostly a reflection of the true complexity, though, so it may not be "fixable" in any reasonable sense. The second thing I don't like are the includes like "../Something.h". I've avoided this largely by clustering into the current file structure. However, a few of these includes remain, and in those cases it doesn't make sense to me to sink an existing file any deeper. Reviewers: rsmith, mehdi_amini, compnerd, rnk, javed.absar Subscribers: emaste, jfb, danalbert, srhines, dschuff, jyknight, nemanjai, nhaehnle, mgorny, cfe-commits Differential Revision: https://reviews.llvm.org/D30372 llvm-svn: 297250
2017-03-08 09:02:16 +08:00
//===--- 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 "clang/Basic/Cuda.h"
#include "clang/Basic/VirtualFileSystem.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;
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);
}
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) &&
FS.exists(LibDevicePath)))
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());
}
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;
}
}
}
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 ||
ArchsWithVersionTooLowErrors.count(Arch) > 0)
return;
auto RequiredVersion = MinVersionForCudaArch(Arch);
if (Version < RequiredVersion) {
ArchsWithVersionTooLowErrors.insert(Arch);
D.Diag(diag::err_drv_cuda_version_too_low)
<< InstallPath << CudaArchToString(Arch) << CudaVersionToString(Version)
<< CudaVersionToString(RequiredVersion);
}
}
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");
// Obtain architecture from the action.
CudaArch gpu_arch = StringToCudaArch(JA.getOffloadingArch());
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");
}
CmdArgs.push_back("--gpu-name");
CmdArgs.push_back(Args.MakeArgString(CudaArchToString(gpu_arch)));
CmdArgs.push_back("--output-file");
CmdArgs.push_back(Args.MakeArgString(Output.getFilename()));
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));
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));
}
/// 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)
: ToolChain(D, Triple, Args), HostTC(HostTC),
CudaInstallation(D, HostTC.getTriple(), Args) {
if (CudaInstallation.isValid())
getProgramPaths().push_back(CudaInstallation.getBinPath());
}
void CudaToolChain::addClangTargetOptions(
const llvm::opt::ArgList &DriverArgs,
llvm::opt::ArgStringList &CC1Args) const {
HostTC.addClangTargetOptions(DriverArgs, CC1Args);
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;
StringRef GpuArch = DriverArgs.getLastArgValue(options::OPT_march_EQ);
assert(!GpuArch.empty() && "Must have an explicit GPU arch.");
std::string LibDeviceFile = CudaInstallation.getLibDeviceFile(GpuArch);
if (LibDeviceFile.empty()) {
getDriver().Diag(diag::err_drv_no_cuda_libdevice) << GpuArch;
return;
}
CC1Args.push_back("-mlink-cuda-bitcode");
CC1Args.push_back(DriverArgs.MakeArgString(LibDeviceFile));
// 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 (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 {
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);
}