Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Unable to build on Windows #1675

Open
jrobcary opened this issue Sep 3, 2024 · 8 comments
Open

Unable to build on Windows #1675

jrobcary opened this issue Sep 3, 2024 · 8 comments

Comments

@jrobcary
Copy link

jrobcary commented Sep 3, 2024

I am trying to build windows static. I configure with

'/winsame/cary/dev3/contrib-llvm17_vs2022/cmake-3.26.4-ser/bin/cmake.exe'
-DCMAKE_INSTALL_PREFIX:PATH=D:/winsame/cary/dev3/contrib-llvm17_vs2022/ginkgo-1.8.0-ser
-DCMAKE_BUILD_TYPE:STRING=Release
-DCMAKE_COLOR_MAKEFILE:BOOL=FALSE
-DCMAKE_VERBOSE_MAKEFILE:BOOL=TRUE
-DCMAKE_C_COMPILER:FILEPATH='C:/Program Files/LLVM17/bin/clang-cl.exe'
-DCMAKE_CXX_COMPILER:FILEPATH='C:/Program Files/LLVM17/bin/clang-cl.exe'
-DCMAKE_C_FLAGS:STRING='/W3 -Wno-implicit-function-declaration'
-DCMAKE_CXX_FLAGS:STRING='/W3 /GR /EHsc /Zc:__cplusplus /D_HAS_AUTO_PTR_ETC /bigobj -fms-compatibility-version=19 -Wno-register -Wno-implicit-function-declaration -Wno-microsoft-include'
-DBUILD_SHARED_LIBS=OFF
-DCMAKE_CUDA_COMPILER:FILEPATH='C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.8/bin/nvcc.exe'
-DCMAKE_CUDA_ARCHITECTURES:STRING='61;70;80'
-DGINKGO_BUILD_HIP:BOOL=OFF
-DGINKGO_BUILD_OMP:BOOL=OFF
-DGINKGO_BUILD_MPI:BOOL=OFF
-G 'NMake Makefiles JOM' D:/winsame/cary/xsd3/builds/ginkgo-1.8.0

But when I build (with jom), it dies at


LINK: command "C:\PROGRA~1\LLVM17\bin\lld-link.exe @CMakeFiles\mixed-spmv.dir\objects1.rsp /out:mixed-spmv.exe /implib:mixed-spmv.lib /pdb:D:\winsame\cary\xsd3\builds\ginkgo-1.8.0\ser\examples\mixed-spmv\mixed-spmv.pdb /version:0.0 /machine:x64 /INCREMENTAL:NO /subsystem:console -LIBPATH:C:\PROGRA~1\NVIDIA~2\CUDA\v11.8\lib\x64 ..\..\lib\ginkgo.lib ..\..\lib\ginkgo_omp.lib ..\..\lib\ginkgo_cuda.lib C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib\x64\cudart.lib C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib\x64\cublas.lib C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib\x64\cublasLt.lib C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib\x64\cusparse.lib C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib\x64\curand.lib C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib\x64\cufft.lib ..\..\lib\ginkgo_reference.lib ..\..\lib\ginkgo_hip.lib ..\..\lib\ginkgo_dpcpp.lib ..\..\lib\ginkgo_device.lib cudadevrt.lib cudart_static.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib /MANIFEST /MANIFESTFILE:mixed-spmv.exe.manifest" failed (exit code 1) with the following output:
lld-link: error: undefined symbol: void __cdecl gko::kernels::reference::dense::add_scaled_identity<float, float>(class std::shared_ptr<class gko::ReferenceExecutor const>, class gko::matrix::Dense<float> const *, class gko::matrix::Dense<float> const *, class gko::matrix::Dense<float> *)^M
>>> referenced by ginkgo.lib(dense.cpp.obj):(public: virtual void __cdecl gko::detail::RegisteredOperation<class `<auto> __cdecl gko::matrix::dense::`anonymous namespace'::make_add_scaled_identity<class gko::matrix::Dense<float> const *&, class gko::matrix::Dense<float> const *&, class gko::matrix::Dense<float> *&>(class matrix::dense::Dense<float> const *&, class matrix::dense::Dense<float> const *&, class matrix::dense::<auto> *&)'::`1'::<lambda_1>>::run(class std::shared_ptr<class gko::ReferenceExecutor const>) const)^M
...
@upsj
Copy link
Member

upsj commented Sep 3, 2024

Just making sure, can you reproduce this when using MSBuild (default) or Ninja (-GNinja) as the CMake generator? We don't yet test compilation with clang-cl on Windows, so this might just be an issue that slipped through the cracks

@jrobcary
Copy link
Author

I can build with Ninja and visual studio 2022. However, hoping to use clang-cl to have a consistent build chain.

@jrobcary
Copy link
Author

jrobcary commented Sep 27, 2024

Also fails with LLVM19:

[10/560] Linking CXX executable reference\test\base\batch_multi_vector_kernels.exe
FAILED: reference/test/base/batch_multi_vector_kernels.exe
cmd.exe /C "cd . && D:\winsame\cary\dev2\contrib-llvm19_vs2022\cmake-3.26.4-ser\bin\cmake.exe -E vs_link_exe --intdir=reference\test\base\CMakeFiles\reference_test_base_batch_multi_vector_kernels.dir --rc=C:\PROGRA~2\WI3CF2~1\10\bin\100190~1.0\x64\rc.exe --mt=C:\PROGRA~2\WI3CF2~1\10\bin\100190~1.0\x64\mt.exe --manifests  -- C:\PROGRA~1\LLVM19\bin\lld-link.exe  reference\test\base\CMakeFiles\reference_test_base_batch_multi_vector_kernels.dir\batch_multi_vector_kernels.cpp.obj  /out:reference\test\base\batch_multi_vector_kernels.exe /implib:reference\test\base\batch_multi_vector_kernels.lib /pdb:reference\test\base\batch_multi_vector_kernels.pdb /version:0.0 /machine:x64 /INCREMENTAL:NO /subsystem:console -LIBPATH:C:\PROGRA~1\NVIDIA~2\CUDA\v11.8\lib\x64 lib\ginkgo_gtest_main.lib  lib\ginkgo.lib  lib\gtest.lib  lib\ginkgo_omp.lib  lib\ginkgo_cuda.lib  "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib\x64\cudart.lib"  "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib\x64\cublas.lib"  "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib\x64\cublasLt.lib"  "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib\x64\cusparse.lib"  "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib\x64\curand.lib"  "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib\x64\cufft.lib"  lib\ginkgo_reference.lib  lib\ginkgo_hip.lib  lib\ginkgo_dpcpp.lib  lib\ginkgo_device.lib  cudadevrt.lib  cudart_static.lib  kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib && cd ."
LINK: command "C:\PROGRA~1\LLVM19\bin\lld-link.exe reference\test\base\CMakeFiles\reference_test_base_batch_multi_vector_kernels.dir\batch_multi_vector_kernels.cpp.obj /out:reference\test\base\batch_multi_vector_kernels.exe /implib:reference\test\base\batch_multi_vector_kernels.lib /pdb:reference\test\base\batch_multi_vector_kernels.pdb /version:0.0 /machine:x64 /INCREMENTAL:NO /subsystem:console -LIBPATH:C:\PROGRA~1\NVIDIA~2\CUDA\v11.8\lib\x64 lib\ginkgo_gtest_main.lib lib\ginkgo.lib lib\gtest.lib lib\ginkgo_omp.lib lib\ginkgo_cuda.lib C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib\x64\cudart.lib C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib\x64\cublas.lib C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib\x64\cublasLt.lib C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib\x64\cusparse.lib C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib\x64\curand.lib C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib\x64\cufft.lib lib\ginkgo_reference.lib lib\ginkgo_hip.lib lib\ginkgo_dpcpp.lib lib\ginkgo_device.lib cudadevrt.lib cudart_static.lib kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib /MANIFEST /MANIFESTFILE:reference\test\base\batch_multi_vector_kernels.exe.manifest" failed (exit code 1) with the following output:
lld-link: error: undefined symbol: void __cdecl gko::kernels::reference::dense::add_scaled_identity<float, float>(class std::shared_ptr<class gko::ReferenceExecutor const>, class gko::matrix::Dense<float> const *, class gko::matrix::Dense<float> const *, class gko::matrix::Dense<float> *)
>>> referenced by ginkgo.lib(dense.cpp.obj):(public: virtual void __cdecl gko::detail::RegisteredOperation<class `<auto> __cdecl gko::matrix::dense::`anonymous namespace'::make_add_scaled_identity<class gko::matrix::Dense<float> const *&, class gko::matrix::Dense<float> const *&, class gko::matrix::Dense<float> *&>(class matrix::dense::Dense<float> const *&, class matrix::dense::Dense<float> const *&, class matrix::dense::<auto> *&)'::`1'::<lambda_1>>::run(class std::shared_ptr<class gko::ReferenceExecutor const>) const)

lld-link: error: undefined symbol: void __cdecl gko::kernels::reference::dense::add_scaled_identity<double, double>(class std::shared_ptr<class gko::ReferenceExecutor const>, class gko::matrix::Dense<double> const *, class gko::matrix::Dense<double> const *, class gko::matrix::Dense<double> *)
>>> referenced by ginkgo.lib(dense.cpp.obj):(public: virtual void __cdecl gko::detail::RegisteredOperation<class `<auto> __cdecl gko::matrix::dense::`anonymous namespace'::make_add_scaled_identity<class gko::matrix::Dense<double> const *&, class gko::matrix::Dense<double> const *&, class gko::matrix::Dense<double> *&>(class matrix::dense::Dense<double> const *&, class matrix::dense::Dense<double> const *&, class matrix::dense::<auto> *&)'::`1'::<lambda_1>>::run(class std::shared_ptr<class gko::ReferenceExecutor const>) const)

lld-link: error: undefined symbol: void __cdecl gko::kernels::reference::dense::add_scaled_identity<class std::complex<float>, class std::complex<float>>(class std::shared_ptr<class gko::ReferenceExecutor const>, class gko::matrix::Dense<class std::complex<float>> const *, class gko::matrix::Dense<class std::complex<float>> const *, class gko::matrix::Dense<class std::complex<float>> *)
>>> referenced by ginkgo.lib(dense.cpp.obj):(public: virtual void __cdecl gko::detail::RegisteredOperation<class `<auto> __cdecl gko::matrix::dense::`anonymous namespace'::make_add_scaled_identity<class gko::matrix::Dense<class std::complex<float>> const *&, class gko::matrix::Dense<class std::complex<float>> const *&, class gko::matrix::Dense<class std::complex<float>> *&>(class matrix::dense::Dense<class std::complex<float>> const *&, class matrix::dense::Dense<class std::complex<float>> const *&, class matrix::dense::<auto> *&)'::`1'::<lambda_1>>::run(class std::shared_ptr<class gko::ReferenceExecutor const>) const)

lld-link: error: undefined symbol: void __cdecl gko::kernels::reference::dense::add_scaled_identity<class std::complex<double>, class std::complex<double>>(class std::shared_ptr<class gko::ReferenceExecutor const>, class gko::matrix::Dense<class std::complex<double>> const *, class gko::matrix::Dense<class std::complex<double>> const *, class gko::matrix::Dense<class std::complex<double>> *)
>>> referenced by ginkgo.lib(dense.cpp.obj):(public: virtual void __cdecl gko::detail::RegisteredOperation<class `<auto> __cdecl gko::matrix::dense::`anonymous namespace'::make_add_scaled_identity<class gko::matrix::Dense<class std::complex<double>> const *&, class gko::matrix::Dense<class std::complex<double>> const *&, class gko::matrix::Dense<class std::complex<double>> *&>(class matrix::dense::Dense<class std::complex<double>> const *&, class matrix::dense::Dense<class std::complex<double>> const *&, class matrix::dense::<auto> *&)'::`1'::<lambda_1>>::run(class std::shared_ptr<class gko::ReferenceExecutor const>) const)
ninja: build stopped: subcommand failed.

Configured as

'/winsame/cary/dev2/contrib-llvm19_vs2022/cmake-3.26.4-ser/bin/cmake.exe' \
  -DCMAKE_INSTALL_PREFIX:PATH=D:/winsame/cary/dev2/contrib-llvm19_vs2022/ginkgo-1.8.0-ser \
  -DCMAKE_BUILD_TYPE:STRING=Release \
  -DCMAKE_COLOR_MAKEFILE:BOOL=FALSE \
  -DCMAKE_VERBOSE_MAKEFILE:BOOL=TRUE \
  -DCMAKE_C_COMPILER:FILEPATH='C:/Program Files/LLVM19/bin/clang-cl.exe' \
  -DCMAKE_CXX_COMPILER:FILEPATH='C:/Program Files/LLVM19/bin/clang-cl.exe' \
  -DCMAKE_C_FLAGS:STRING='/W3 -Wno-implicit-function-declaration' \
  -DCMAKE_CXX_FLAGS:STRING='/W3 /GR /EHsc /Zc:__cplusplus /D_HAS_AUTO_PTR_ETC /bigobj -fms-compatibility-version=19 -Wno-register -Wno-implicit-function-declaration -Wno-microsoft-include' \
  -DBUILD_SHARED_LIBS=OFF \
  -DCMAKE_CUDA_COMPILER:FILEPATH='C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.8/bin/nvcc.exe' \
  -DCMAKE_CUDA_ARCHITECTURES:STRING='61;70;80' \
  -DGINKGO_BUILD_HIP:BOOL=OFF \
  -DGINKGO_BUILD_OMP:BOOL=OFF \
  -DGINKGO_BUILD_MPI:BOOL=OFF \
  -G 'Ninja' D:/winsame/cary/xsd2/builds/ginkgo-1.8.0

@jrobcary
Copy link
Author

jrobcary commented Oct 4, 2024

@upsj , any ideas?

@upsj
Copy link
Member

upsj commented Oct 5, 2024

I can reproduce the issue with clang-cl, I'll take a closer look.

@upsj
Copy link
Member

upsj commented Oct 5, 2024

Seems like a more complex issue, but I get the feeling this would be solved with Ginkgo 2.0 and another attempt at #715

@jrobcary
Copy link
Author

jrobcary commented Oct 5, 2024

How do I get 2.0?

@upsj
Copy link
Member

upsj commented Oct 5, 2024

Sorry for the misleading wording - Ginkgo 2.0 is a future release where we need to make a few interface breaks to fix architectural issues that we can't address with a minor release.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants