PowerPC is IBM’s architecture and nobody but IBM is using it anymore. IBM have their own build of PyTorch and TensorFlow to powerpc, which called the powerai bundle, however the last update was 4 years ago and it only supports pytorch 1.3.
So I HAVE to build PyTorch myself. Building softwares is fairly easy on Linux, as long as you know to put the right thing in the right place .
I choose 1.12.1 because it seems that it’s the last version that supports cuda 11.2. With some modification to the code, pytorch 2.0.1 seems to compile but eventually fail with an error that I don’t have time to figure out how to solve.
These steps is used inside a cluster that I don’t have sudo access. Running cuda installer will result in segmentation fault but luckily the system admin have cuda 11.2 installed at /usr/local/cuda. The system don’t have CUDNN though, however it is possible to use CUDNN from conda-forge.
install miniforge
create a new mamba environment, with mamba create -n <your_env_name_here> python=3.10 cudatoolkit=11.2 cudnn libopenblas blas cmake ninja
clone PyTorch; git checkout v1.12.1; IGNORE pytorch document that asks you to install mkl.
1 2 3 cd pytorchgit submodule sync git submodule update --init --recursive
1 2 export CUDA_HOME=/usr/local/cudaexport LD_LIBRARY_PATH=$CUDA_HOME /lib64:$CONDA_PREFIX /lib:$LD_LIBRARY_PATH
1 pip install -r requirements.txt
1 2 3 export MAX_JOBS=40 python setup.py build
Make sure USE_CUDA is on:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 -- ******** Summary ******** -- General: -- CMake version : 3.27.8 -- CMake command : $CONDA_PREFIX/bin/cmake -- System : Linux -- C++ compiler : /usr/bin/c++ -- C++ compiler id : GNU -- C++ compiler version : 8.4.1 -- Using ccache if found : ON -- Found ccache : CCACHE_PROGRAM-NOTFOUND -- CXX flags : -Wno-deprecated -fvisibility-inlines-hidden -fopenmp -DNDEBUG -DUSE_KINETO -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow -- Build type : Release -- Compile definitions : ONNX_ML=1;ONNXIFI_ENABLE_EXT=1;ONNX_NAMESPACE=onnx_torch;HAVE_MMAP=1;_FILE_OFFSET_BITS=64;HAVE_SHM_OPEN=1;HAVE_SHM_UNLINK=1;HAVE_MALLOC_USABLE_SIZE=1;USE_EXTERNAL_MZCRC;MINIZ_DISABLE_ZIP_READER_CRC32_CHECKS -- CMAKE_PREFIX_PATH : $CONDA_PREFIX/lib/python3.10/site-packages;/usr/local/cuda -- CMAKE_INSTALL_PREFIX : pytorch/torch -- USE_GOLD_LINKER : OFF -- -- TORCH_VERSION : 1.12.0 -- CAFFE2_VERSION : 1.12.0 -- BUILD_CAFFE2 : OFF -- BUILD_CAFFE2_OPS : OFF -- BUILD_CAFFE2_MOBILE : OFF -- BUILD_STATIC_RUNTIME_BENCHMARK: OFF -- BUILD_TENSOREXPR_BENCHMARK: OFF -- BUILD_NVFUSER_BENCHMARK: ON -- BUILD_BINARY : OFF -- BUILD_CUSTOM_PROTOBUF : ON -- Link local protobuf : ON -- BUILD_DOCS : OFF -- BUILD_PYTHON : True -- Python version : 3.10.13 -- Python executable : $CONDA_PREFIX/bin/python -- Pythonlibs version : 3.10.13 -- Python library : $CONDA_PREFIX/lib/libpython3.10.a -- Python includes : $CONDA_PREFIX/include/python3.10 -- Python site-packages: lib/python3.10/site-packages -- BUILD_SHARED_LIBS : ON -- CAFFE2_USE_MSVC_STATIC_RUNTIME : OFF -- BUILD_TEST : True -- BUILD_JNI : OFF -- BUILD_MOBILE_AUTOGRAD : OFF -- BUILD_LITE_INTERPRETER: OFF -- INTERN_BUILD_MOBILE : -- USE_BLAS : 1 -- BLAS : open -- BLAS_HAS_SBGEMM : 1 -- USE_LAPACK : 1 -- LAPACK : open -- USE_ASAN : OFF -- USE_CPP_CODE_COVERAGE : OFF -- USE_CUDA : ON -- Split CUDA : OFF -- CUDA static link : OFF -- USE_CUDNN : ON -- USE_EXPERIMENTAL_CUDNN_V8_API: ON -- CUDA version : 11.2 -- cuDNN version : 8.8.0 -- CUDA root directory : /usr/local/cuda -- CUDA library : /usr/local/cuda/lib64/stubs/libcuda.so -- cudart library : /usr/local/cuda/lib64/libcudart.so -- cublas library : /usr/local/cuda/lib64/libcublas.so -- cufft library : /usr/local/cuda/lib64/libcufft.so -- curand library : /usr/local/cuda/lib64/libcurand.so -- cuDNN library : $CONDA_PREFIX/lib/libcudnn.so -- nvrtc : /usr/local/cuda/lib64/libnvrtc.so -- CUDA include path : /usr/local/cuda/include -- NVCC executable : /usr/local/cuda/bin/nvcc -- CUDA compiler : /usr/local/cuda/bin/nvcc -- CUDA flags : -Xfatbin -compress-all -DONNX_NAMESPACE=onnx_torch -gencode arch=compute_70,code=sm_70 -Xcudafe --diag_suppress=cc_clobber_ignored,--diag_suppress=integer_sign_change,--diag_suppress=useless_using_declaration,--diag_suppress=set_but_not_used,--diag_suppress=field_without_dll_interface,--diag_suppress=base_class_has_different_dll_interface,--diag_suppress=dll_interface_conflict_none_assumed,--diag_suppress=dll_interface_conflict_dllexport_assumed,--diag_suppress=implicit_return_from_non_void_function,--diag_suppress=unsigned_compare_with_zero,--diag_suppress=declared_but_not_referenced,--diag_suppress=bad_friend_decl --expt-relaxed-constexpr --expt-extended-lambda -Wno-deprecated-gpu-targets --expt-extended-lambda -DCUDA_HAS_FP16=1 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -- CUDA host compiler : -- CUDA --device-c : OFF -- USE_TENSORRT : OFF -- USE_ROCM : OFF -- USE_EIGEN_FOR_BLAS : ON -- USE_FBGEMM : OFF -- USE_FAKELOWP : OFF -- USE_KINETO : ON -- USE_FFMPEG : OFF -- USE_GFLAGS : OFF -- USE_GLOG : OFF -- USE_LEVELDB : OFF -- USE_LITE_PROTO : OFF -- USE_LMDB : OFF -- USE_METAL : OFF -- USE_PYTORCH_METAL : OFF -- USE_PYTORCH_METAL_EXPORT : OFF -- USE_MPS : OFF -- USE_FFTW : OFF -- USE_MKL : OFF -- USE_MKLDNN : OFF -- USE_NCCL : ON -- USE_SYSTEM_NCCL : OFF -- USE_NCCL_WITH_UCC : OFF -- USE_NNPACK : OFF -- USE_NUMPY : ON -- USE_OBSERVERS : ON -- USE_OPENCL : OFF -- USE_OPENCV : OFF -- USE_OPENMP : ON -- USE_TBB : OFF -- USE_VULKAN : OFF -- USE_PROF : OFF -- USE_QNNPACK : OFF -- USE_PYTORCH_QNNPACK : OFF -- USE_XNNPACK : OFF -- USE_REDIS : OFF -- USE_ROCKSDB : OFF -- USE_ZMQ : OFF -- USE_DISTRIBUTED : ON -- USE_MPI : OFF -- USE_GLOO : ON -- USE_GLOO_WITH_OPENSSL : OFF -- USE_TENSORPIPE : ON -- USE_DEPLOY : OFF -- Public Dependencies : caffe2::Threads -- Private Dependencies : cpuinfo;fp16;tensorpipe;gloo;foxi_loader;rt;fmt::fmt-header-only;kineto;gcc_s;gcc;dl -- USE_COREML_DELEGATE : OFF -- BUILD_LAZY_TS_BACKEND : ON -- Configuring done (34.5s)
1 2 3 4 python3 setup.py bdist_wheel pip install dist/torch*.whl cd .. python3 -c 'import torch'
Verify installation
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 $ python3 Python 3.10.13 | packaged by conda-forge | (main, Oct 26 2023, 18:10:55) [GCC 12.3.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import torch >>> torch.__version__ '1.12.0a0+git664058f' >>> torch.cuda.is_available() True >>> a = torch.tensor([1,2,3]).cuda() >>> for i in range(torch.cuda.device_count()): ... a = a.to(f'cuda:{i}') ... b = torch.tensor([2,3,4], device=a.device) ... a*b ... tensor([ 2, 6, 12], device='cuda:0') tensor([ 2, 6, 12], device='cuda:1') tensor([ 2, 6, 12], device='cuda:2') tensor([ 2, 6, 12], device='cuda:3') tensor([ 2, 6, 12], device='cuda:4') tensor([ 2, 6, 12], device='cuda:5') >>>
Enjoy an empty cluster that nobody but you know how to use!