Exemplo n.º 1
0
    def run(self):
        # Print build options
        if WITH_NUMPY:
            print('-- Building with NumPy bindings')
        else:
            print('-- NumPy not found')
        if WITH_CUDNN:
            print('-- Detected cuDNN at ' + CUDNN_LIB_DIR + ', ' + CUDNN_INCLUDE_DIR)
        else:
            print('-- Not using cuDNN')
        if WITH_CUDA:
            print('-- Detected CUDA at ' + CUDA_HOME)
        else:
            print('-- Not using CUDA')
        if WITH_NCCL and WITH_SYSTEM_NCCL:
            print('-- Using system provided NCCL library at ' +
                  NCCL_LIB_DIR + ', ' + NCCL_INCLUDE_DIR)
        elif WITH_NCCL:
            print('-- Building NCCL library')
        else:
            print('-- Not using NCCL')
        if WITH_DISTRIBUTED:
            print('-- Building with distributed package ')
            monkey_patch_THD_link_flags()
        else:
            print('-- Building without distributed package')

        # cwrap depends on pyyaml, so we can't import it earlier
        from tools.cwrap import cwrap
        from tools.cwrap.plugins.THPPlugin import THPPlugin
        from tools.cwrap.plugins.ArgcountSortPlugin import ArgcountSortPlugin
        from tools.cwrap.plugins.AutoGPU import AutoGPU
        from tools.cwrap.plugins.BoolOption import BoolOption
        from tools.cwrap.plugins.KwargsPlugin import KwargsPlugin
        from tools.cwrap.plugins.NullableArguments import NullableArguments
        from tools.cwrap.plugins.CuDNNPlugin import CuDNNPlugin
        from tools.cwrap.plugins.WrapDim import WrapDim
        from tools.cwrap.plugins.AssertNDim import AssertNDim
        from tools.cwrap.plugins.Broadcast import Broadcast
        from tools.cwrap.plugins.ProcessorSpecificPlugin import ProcessorSpecificPlugin
        from tools.autograd.gen_variable_type import gen_variable_type
        thp_plugin = THPPlugin()
        cwrap('torch/csrc/generic/TensorMethods.cwrap', plugins=[
            ProcessorSpecificPlugin(), BoolOption(), thp_plugin,
            AutoGPU(condition='IS_CUDA'), ArgcountSortPlugin(), KwargsPlugin(),
            AssertNDim(), WrapDim(), Broadcast()
        ])
        cwrap('torch/csrc/cudnn/cuDNN.cwrap', plugins=[
            CuDNNPlugin(), NullableArguments()
        ])
        # Build ATen based Variable classes
        autograd_gen_dir = 'torch/csrc/autograd/generated'
        if not os.path.exists(autograd_gen_dir):
            os.mkdir(autograd_gen_dir)
        gen_variable_type(
            'torch/lib/build/ATen/ATen/Declarations.yaml',
            autograd_gen_dir)

        # It's an old-style class in Python 2.7...
        setuptools.command.build_ext.build_ext.run(self)
Exemplo n.º 2
0
    def run(self):
        # Print build options
        if WITH_NUMPY:
            print('-- Building with NumPy bindings')
        else:
            print('-- NumPy not found')
        if WITH_CUDNN:
            print('-- Detected cuDNN at ' + CUDNN_LIB_DIR + ', ' +
                  CUDNN_INCLUDE_DIR)
        else:
            print('-- Not using cuDNN')
        if WITH_CUDA:
            print('-- Detected CUDA at ' + CUDA_HOME)
        else:
            print('-- Not using CUDA')
        if WITH_NCCL and SYSTEM_NCCL:
            print('-- Using system provided NCCL library')
        elif WITH_NCCL:
            print('-- Building NCCL library')
        else:
            print('-- Not using NCCL')
        if WITH_DISTRIBUTED:
            print('-- Building with distributed package ')
        else:
            print('-- Building without distributed package')

        # cwrap depends on pyyaml, so we can't import it earlier
        from tools.cwrap import cwrap
        from tools.cwrap.plugins.THPPlugin import THPPlugin
        from tools.cwrap.plugins.ArgcountSortPlugin import ArgcountSortPlugin
        from tools.cwrap.plugins.AutoGPU import AutoGPU
        from tools.cwrap.plugins.BoolOption import BoolOption
        from tools.cwrap.plugins.KwargsPlugin import KwargsPlugin
        from tools.cwrap.plugins.NullableArguments import NullableArguments
        from tools.cwrap.plugins.CuDNNPlugin import CuDNNPlugin
        from tools.cwrap.plugins.WrapDim import WrapDim
        from tools.cwrap.plugins.AssertNDim import AssertNDim
        from tools.cwrap.plugins.Broadcast import Broadcast
        from tools.cwrap.plugins.ProcessorSpecificPlugin import ProcessorSpecificPlugin
        thp_plugin = THPPlugin()
        cwrap('torch/csrc/generic/TensorMethods.cwrap',
              plugins=[
                  ProcessorSpecificPlugin(),
                  BoolOption(), thp_plugin,
                  AutoGPU(condition='IS_CUDA'),
                  ArgcountSortPlugin(),
                  KwargsPlugin(),
                  AssertNDim(),
                  WrapDim(),
                  Broadcast()
              ])
        cwrap('torch/csrc/cudnn/cuDNN.cwrap',
              plugins=[CuDNNPlugin(), NullableArguments()])
        # It's an old-style class in Python 2.7...
        setuptools.command.build_ext.build_ext.run(self)
Exemplo n.º 3
0
def generate_code(ninja_global=None):
    # if ninja is enabled, we just register this file as something
    # ninja will need to call if needed
    if ninja_global is not None:
        return generate_code_ninja(ninja_global)

    # cwrap depends on pyyaml, so we can't import it earlier
    root = os.path.dirname(
        os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
    sys.path.append(root)
    from tools.cwrap import cwrap
    from tools.cwrap.plugins.THPPlugin import THPPlugin
    from tools.cwrap.plugins.ArgcountSortPlugin import ArgcountSortPlugin
    from tools.cwrap.plugins.AutoGPU import AutoGPU
    from tools.cwrap.plugins.BoolOption import BoolOption
    from tools.cwrap.plugins.KwargsPlugin import KwargsPlugin
    from tools.cwrap.plugins.NullableArguments import NullableArguments

    from tools.cwrap.plugins.CuDNNPlugin import CuDNNPlugin
    from tools.cwrap.plugins.WrapDim import WrapDim
    from tools.cwrap.plugins.AssertNDim import AssertNDim

    from tools.cwrap.plugins.Broadcast import Broadcast
    from tools.cwrap.plugins.ProcessorSpecificPlugin import ProcessorSpecificPlugin
    from tools.autograd.gen_variable_type import gen_variable_type
    from tools.jit.gen_jit_dispatch import gen_jit_dispatch
    thp_plugin = THPPlugin()

    cwrap('torch/csrc/generic/TensorMethods.cwrap',
          plugins=[
              ProcessorSpecificPlugin(),
              BoolOption(), thp_plugin,
              AutoGPU(condition='IS_CUDA'),
              ArgcountSortPlugin(),
              KwargsPlugin(),
              AssertNDim(),
              WrapDim(),
              Broadcast()
          ])
    cwrap('torch/csrc/cudnn/cuDNN.cwrap',
          plugins=[CuDNNPlugin(), NullableArguments()])
    # Build ATen based Variable classes
    autograd_gen_dir = 'torch/csrc/autograd/generated'
    jit_gen_dir = 'torch/csrc/jit/generated'
    for d in (autograd_gen_dir, jit_gen_dir):
        if not os.path.exists(d):
            os.mkdir(d)
    gen_variable_type('torch/lib/tmp_install/share/ATen/Declarations.yaml',
                      autograd_gen_dir)
    gen_jit_dispatch('torch/lib/tmp_install/share/ATen/Declarations.yaml',
                     jit_gen_dir)
Exemplo n.º 4
0
    def run(self):

        # Print build options
        if WITH_NUMPY:
            print('-- Building with NumPy bindings')
        else:
            print('-- NumPy not found')
        if WITH_CUDNN:
            print('-- Detected cuDNN at ' + CUDNN_LIB_DIR + ', ' +
                  CUDNN_INCLUDE_DIR)
        else:
            print('-- Not using cuDNN')
        if WITH_CUDA:
            print('-- Detected CUDA at ' + CUDA_HOME)
        else:
            print('-- Not using CUDA')
        if WITH_NCCL and WITH_SYSTEM_NCCL:
            print('-- Using system provided NCCL library at ' +
                  NCCL_SYSTEM_LIB + ', ' + NCCL_INCLUDE_DIR)
        elif WITH_NCCL:
            print('-- Building NCCL library')
        else:
            print('-- Not using NCCL')
        if WITH_DISTRIBUTED:
            print('-- Building with distributed package ')
            monkey_patch_THD_link_flags()
        else:
            print('-- Building without distributed package')

        # Do we actually need this here?
        if WITH_NNPACK:
            nnpack_dir = NNPACK_LIB_PATHS[0]
            print('-- Detected NNPACK at ' + nnpack_dir)
        else:
            print('-- Not using NNPACK')
        # cwrap depends on pyyaml, so we can't import it earlier
        from tools.cwrap import cwrap
        from tools.cwrap.plugins.THPPlugin import THPPlugin
        from tools.cwrap.plugins.ArgcountSortPlugin import ArgcountSortPlugin
        from tools.cwrap.plugins.AutoGPU import AutoGPU
        from tools.cwrap.plugins.BoolOption import BoolOption
        from tools.cwrap.plugins.KwargsPlugin import KwargsPlugin
        from tools.cwrap.plugins.NullableArguments import NullableArguments

        from tools.cwrap.plugins.CuDNNPlugin import CuDNNPlugin
        from tools.cwrap.plugins.WrapDim import WrapDim
        from tools.cwrap.plugins.AssertNDim import AssertNDim

        from tools.cwrap.plugins.Broadcast import Broadcast
        from tools.cwrap.plugins.ProcessorSpecificPlugin import ProcessorSpecificPlugin
        from tools.autograd.gen_variable_type import gen_variable_type
        from tools.jit.gen_jit_dispatch import gen_jit_dispatch
        thp_plugin = THPPlugin()

        cwrap('torch/csrc/generic/TensorMethods.cwrap',
              plugins=[
                  ProcessorSpecificPlugin(),
                  BoolOption(), thp_plugin,
                  AutoGPU(condition='IS_CUDA'),
                  ArgcountSortPlugin(),
                  KwargsPlugin(),
                  AssertNDim(),
                  WrapDim(),
                  Broadcast()
              ])
        cwrap('torch/csrc/cudnn/cuDNN.cwrap',
              plugins=[CuDNNPlugin(), NullableArguments()])
        # Build ATen based Variable classes
        autograd_gen_dir = 'torch/csrc/autograd/generated'
        jit_gen_dir = 'torch/csrc/jit/generated'
        for d in (autograd_gen_dir, jit_gen_dir):
            if not os.path.exists(d):
                os.mkdir(d)
        gen_variable_type('torch/lib/tmp_install/share/ATen/Declarations.yaml',
                          autograd_gen_dir)
        gen_jit_dispatch('torch/lib/tmp_install/share/ATen/Declarations.yaml',
                         jit_gen_dir)

        if IS_WINDOWS:
            build_temp = self.build_temp
            build_dir = 'torch/csrc'

            ext_filename = self.get_ext_filename('_C')
            lib_filename = '.'.join(ext_filename.split('.')[:-1]) + '.lib'

            _C_LIB = os.path.join(build_temp, build_dir,
                                  lib_filename).replace('\\', '/')

            THNN.extra_link_args += [_C_LIB]
            if WITH_CUDA:
                THCUNN.extra_link_args += [_C_LIB]
            else:
                # To generate .obj files for AutoGPU for the export class
                # a header file cannot build, so it has to be copied to someplace as a source file
                if os.path.exists("torch/csrc/generated/AutoGPU_cpu_win.cpp"):
                    os.remove("torch/csrc/generated/AutoGPU_cpu_win.cpp")
                shutil.copyfile("torch/csrc/cuda/AutoGPU.h",
                                "torch/csrc/generated/AutoGPU_cpu_win.cpp")

        # It's an old-style class in Python 2.7...
        setuptools.command.build_ext.build_ext.run(self)