setup.py

# Copyright 2017 Uber Technologies, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
from __future__ import print_function

import os
from setuptools import setup, Extension, find_packages
from setuptools.command.build_ext import build_ext
from distutils.errors import CompileError, DistutilsError, DistutilsPlatformError, LinkError
from distutils.version import LooseVersion
import shlex
import subprocess
import sys
import textwrap
import traceback
import re

from horovod import __version__


tensorflow_mpi_lib = Extension('horovod.tensorflow.mpi_lib', [])
torch_mpi_lib = Extension('horovod.torch.mpi_lib', [])
torch_mpi_lib_impl = Extension('horovod.torch.mpi_lib_impl', [])
torch_mpi_lib_v2 = Extension('horovod.torch.mpi_lib_v2', [])


def is_build_action():
    if len(sys.argv) <= 1:
        return False

    if sys.argv[1].startswith('build'):
        return True

    if sys.argv[1].startswith('bdist'):
        return True

    if sys.argv[1].startswith('install'):
        return True


def check_tf_version():
    try:
        import tensorflow as tf
        if LooseVersion(tf.__version__) < LooseVersion('1.1.0'):
            raise DistutilsPlatformError(
                'Your TensorFlow version %s is outdated.  '
                'Horovod requires tensorflow>=1.1.0' % tf.__version__)
    except ImportError:
        raise DistutilsPlatformError(
            'import tensorflow failed, is it installed?\n\n%s' % traceback.format_exc())
    except AttributeError:
        # This means that tf.__version__ was not exposed, which makes it *REALLY* old.
        raise DistutilsPlatformError(
            'Your TensorFlow version is outdated.  Horovod requires tensorflow>=1.1.0')


def get_cpp_flags(build_ext):
    last_err = None
    default_flags = ['-std=c++11', '-fPIC', '-O2', '-Wall']
    avx_flags = ['-mf16c', '-mavx']
    if sys.platform == 'darwin':
        # Darwin most likely will have Clang, which has libc++.
        flags_to_try = [default_flags + ['-stdlib=libc++'] + avx_flags,
                        default_flags + avx_flags,
                        default_flags + ['-stdlib=libc++'],
                        default_flags]
    else:
        flags_to_try = [default_flags + avx_flags,
                        default_flags + ['-stdlib=libc++'] + avx_flags,
                        default_flags,
                        default_flags + ['-stdlib=libc++']]
    for cpp_flags in flags_to_try:
        try:
            test_compile(build_ext, 'test_cpp_flags', extra_compile_preargs=cpp_flags,
                         code=textwrap.dedent('''\
                    #include <unordered_map>
                    void test() {
                    }
                    '''))

            return cpp_flags
        except (CompileError, LinkError):
            last_err = 'Unable to determine C++ compilation flags (see error above).'
        except Exception:
            last_err = 'Unable to determine C++ compilation flags.  ' \
                       'Last error:\n\n%s' % traceback.format_exc()

    raise DistutilsPlatformError(last_err)


def get_link_flags(build_ext):
    last_err = None
    libtool_flags = ['-Wl,-exported_symbols_list,horovod.exp']
    ld_flags = ['-Wl,--version-script=horovod.lds']
    if sys.platform == 'darwin':
        flags_to_try = [libtool_flags, ld_flags]
    else:
        flags_to_try = [ld_flags, libtool_flags]
    for link_flags in flags_to_try:
        try:
            test_compile(build_ext, 'test_link_flags', extra_link_preargs=link_flags,
                         code=textwrap.dedent('''\
                    void test() {
                    }
                    '''))

            return link_flags
        except (CompileError, LinkError):
            last_err = 'Unable to determine C++ link flags (see error above).'
        except Exception:
            last_err = 'Unable to determine C++ link flags.  ' \
                       'Last error:\n\n%s' % traceback.format_exc()

    raise DistutilsPlatformError(last_err)


def get_tf_include_dirs():
    import tensorflow as tf
    tf_inc = tf.sysconfig.get_include()
    return [tf_inc, '%s/external/nsync/public' % tf_inc]


def get_tf_lib_dirs():
    import tensorflow as tf
    tf_lib = tf.sysconfig.get_lib()
    return [tf_lib]


def get_tf_libs(build_ext, lib_dirs, cpp_flags):
    last_err = None
    for tf_libs in [['tensorflow_framework'], []]:
        try:
            lib_file = test_compile(build_ext, 'test_tensorflow_libs',
                                    library_dirs=lib_dirs, libraries=tf_libs,
                                    extra_compile_preargs=cpp_flags,
                                    code=textwrap.dedent('''\
                    void test() {
                    }
                    '''))

            from tensorflow.python.framework import load_library
            load_library.load_op_library(lib_file)

            return tf_libs
        except (CompileError, LinkError):
            last_err = 'Unable to determine -l link flags to use with TensorFlow (see error above).'
        except Exception:
            last_err = 'Unable to determine -l link flags to use with TensorFlow.  ' \
                       'Last error:\n\n%s' % traceback.format_exc()

    raise DistutilsPlatformError(last_err)


def get_tf_abi(build_ext, include_dirs, lib_dirs, libs, cpp_flags):
    last_err = None
    cxx11_abi_macro = '_GLIBCXX_USE_CXX11_ABI'
    for cxx11_abi in ['0', '1']:
        try:
            lib_file = test_compile(build_ext, 'test_tensorflow_abi',
                                    macros=[(cxx11_abi_macro, cxx11_abi)],
                                    include_dirs=include_dirs, library_dirs=lib_dirs,
                                    libraries=libs, extra_compile_preargs=cpp_flags,
                                    code=textwrap.dedent('''\
                #include <string>
                #include "tensorflow/core/framework/op.h"
                #include "tensorflow/core/framework/op_kernel.h"
                #include "tensorflow/core/framework/shape_inference.h"
                void test() {
                    auto ignore = tensorflow::strings::StrCat("a", "b");
                }
                '''))

            from tensorflow.python.framework import load_library
            load_library.load_op_library(lib_file)

            return cxx11_abi_macro, cxx11_abi
        except (CompileError, LinkError):
            last_err = 'Unable to determine CXX11 ABI to use with TensorFlow (see error above).'
        except Exception:
            last_err = 'Unable to determine CXX11 ABI to use with TensorFlow.  ' \
                       'Last error:\n\n%s' % traceback.format_exc()

    raise DistutilsPlatformError(last_err)


def get_tf_flags(build_ext, cpp_flags):
    import tensorflow as tf
    try:
        return tf.sysconfig.get_compile_flags(), tf.sysconfig.get_link_flags()
    except AttributeError:
        # fallback to the previous logic
        tf_include_dirs = get_tf_include_dirs()
        tf_lib_dirs = get_tf_lib_dirs()
        tf_libs = get_tf_libs(build_ext, tf_lib_dirs, cpp_flags)
        tf_abi = get_tf_abi(build_ext, tf_include_dirs,
                            tf_lib_dirs, tf_libs, cpp_flags)

        compile_flags = []
        for include_dir in tf_include_dirs:
            compile_flags.append('-I%s' % include_dir)
        if tf_abi:
            compile_flags.append('-D%s=%s' % tf_abi)

        link_flags = []
        for lib_dir in tf_lib_dirs:
            link_flags.append('-L%s' % lib_dir)
        for lib in tf_libs:
            link_flags.append('-l%s' % lib)

        return compile_flags, link_flags


def get_mpi_flags():
    show_command = os.environ.get('HOROVOD_MPICXX_SHOW', 'mpicxx -show')
    try:
        mpi_show_output = subprocess.check_output(
            shlex.split(show_command), universal_newlines=True).strip()
        mpi_show_args = shlex.split(mpi_show_output)
        if not mpi_show_args[0].startswith('-'):
            # Open MPI and MPICH print compiler name as a first word, skip it
            mpi_show_args = mpi_show_args[1:]
        # strip off compiler call portion and always escape each arg
        return ' '.join(['"' + arg.replace('"', '"\'"\'"') + '"'
                         for arg in mpi_show_args])
    except Exception:
        raise DistutilsPlatformError(
            '%s failed (see error below), is MPI in $PATH?\n'
            'Note: If your version of MPI has a custom command to show compilation flags, '
            'please specify it with the HOROVOD_MPICXX_SHOW environment variable.\n\n'
            '%s' % (show_command, traceback.format_exc()))


def test_compile(build_ext, name, code, libraries=None, include_dirs=None, library_dirs=None,
                 macros=None, extra_compile_preargs=None, extra_link_preargs=None):
    test_compile_dir = os.path.join(build_ext.build_temp, 'test_compile')
    if not os.path.exists(test_compile_dir):
        os.makedirs(test_compile_dir)

    source_file = os.path.join(test_compile_dir, '%s.cc' % name)
    with open(source_file, 'w') as f:
        f.write(code)

    compiler = build_ext.compiler
    [object_file] = compiler.object_filenames([source_file])
    shared_object_file = compiler.shared_object_filename(
        name, output_dir=test_compile_dir)

    compiler.compile([source_file], extra_preargs=extra_compile_preargs,
                     include_dirs=include_dirs, macros=macros)
    compiler.link_shared_object(
        [object_file], shared_object_file, libraries=libraries, library_dirs=library_dirs,
        extra_preargs=extra_link_preargs)

    return shared_object_file


def get_cuda_dirs(build_ext, cpp_flags):
    cuda_include_dirs = []
    cuda_lib_dirs = []

    cuda_home = os.environ.get('HOROVOD_CUDA_HOME')
    if cuda_home:
        cuda_include_dirs += ['%s/include' % cuda_home]
        cuda_lib_dirs += ['%s/lib' % cuda_home, '%s/lib64' % cuda_home]

    cuda_include = os.environ.get('HOROVOD_CUDA_INCLUDE')
    if cuda_include:
        cuda_include_dirs += [cuda_include]

    cuda_lib = os.environ.get('HOROVOD_CUDA_LIB')
    if cuda_lib:
        cuda_lib_dirs += [cuda_lib]

    if not cuda_include_dirs and not cuda_lib_dirs:
        # default to /usr/local/cuda
        cuda_include_dirs += ['/usr/local/cuda/include']
        cuda_lib_dirs += ['/usr/local/cuda/lib', '/usr/local/cuda/lib64']

    try:
        test_compile(build_ext, 'test_cuda', libraries=['cudart'], include_dirs=cuda_include_dirs,
                     library_dirs=cuda_lib_dirs, extra_compile_preargs=cpp_flags,
                     code=textwrap.dedent('''\
            #include <cuda_runtime.h>
            void test() {
                cudaSetDevice(0);
            }
            '''))
    except (CompileError, LinkError):
        raise DistutilsPlatformError(
            'CUDA library was not found (see error above).\n'
            'Please specify correct CUDA location with the HOROVOD_CUDA_HOME '
            'environment variable or combination of HOROVOD_CUDA_INCLUDE and '
            'HOROVOD_CUDA_LIB environment variables.\n\n'
            'HOROVOD_CUDA_HOME - path where CUDA include and lib directories can be found\n'
            'HOROVOD_CUDA_INCLUDE - path to CUDA include directory\n'
            'HOROVOD_CUDA_LIB - path to CUDA lib directory')

    return cuda_include_dirs, cuda_lib_dirs


def get_nccl_vals(build_ext, cuda_include_dirs, cuda_lib_dirs, cpp_flags):
    nccl_include_dirs = []
    nccl_lib_dirs = []
    nccl_libs = []

    nccl_home = os.environ.get('HOROVOD_NCCL_HOME')
    if nccl_home:
        nccl_include_dirs += ['%s/include' % nccl_home]
        nccl_lib_dirs += ['%s/lib' % nccl_home, '%s/lib64' % nccl_home]

    nccl_include_dir = os.environ.get('HOROVOD_NCCL_INCLUDE')
    if nccl_include_dir:
        nccl_include_dirs += [nccl_include_dir]

    nccl_lib_dir = os.environ.get('HOROVOD_NCCL_LIB')
    if nccl_lib_dir:
        nccl_lib_dirs += [nccl_lib_dir]

    nccl_link_mode = os.environ.get('HOROVOD_NCCL_LINK', 'STATIC')
    if nccl_link_mode.upper() == 'SHARED':
        nccl_libs += ['nccl']
    else:
        nccl_libs += ['nccl_static']

    try:
        test_compile(build_ext, 'test_nccl', libraries=nccl_libs, include_dirs=nccl_include_dirs + cuda_include_dirs,
                     library_dirs=nccl_lib_dirs + cuda_lib_dirs, extra_compile_preargs=cpp_flags,
                     code=textwrap.dedent('''\
            #include <nccl.h>
            #if NCCL_MAJOR < 2
            #error Horovod requires NCCL 2.0 or later version, please upgrade.
            #endif
            void test() {
                ncclUniqueId nccl_id;
                ncclGetUniqueId(&nccl_id);
            }
            '''))
    except (CompileError, LinkError):
        raise DistutilsPlatformError(
            'NCCL 2.0 library or its later version was not found (see error above).\n'
            'Please specify correct NCCL location with the HOROVOD_NCCL_HOME '
            'environment variable or combination of HOROVOD_NCCL_INCLUDE and '
            'HOROVOD_NCCL_LIB environment variables.\n\n'
            'HOROVOD_NCCL_HOME - path where NCCL include and lib directories can be found\n'
            'HOROVOD_NCCL_INCLUDE - path to NCCL include directory\n'
            'HOROVOD_NCCL_LIB - path to NCCL lib directory')

    return nccl_include_dirs, nccl_lib_dirs, nccl_libs


def get_ddl_dirs():
    # Default DDL home
    ddl_home = '/opt/DL/ddl'
    ddl_include_dir = '%s/include' % ddl_home
    ddl_lib_dir = '%s/lib' % ddl_home

    if not os.path.exists(ddl_lib_dir):
        raise DistutilsPlatformError(
            'DDL lib was not found. Please, make sure \'ddl\' package is installed.')
    if not os.path.exists(ddl_include_dir):
        raise DistutilsPlatformError(
            'DDL include was not found. Please, make sure \'ddl-dev\' package is installed.')

    return [ddl_include_dir], [ddl_lib_dir]


def get_common_options(build_ext):
    cpp_flags = get_cpp_flags(build_ext)
    link_flags = get_link_flags(build_ext)
    mpi_flags = get_mpi_flags()

    gpu_allreduce = os.environ.get('HOROVOD_GPU_ALLREDUCE')
    if gpu_allreduce and gpu_allreduce != 'MPI' and gpu_allreduce != 'NCCL' and \
       gpu_allreduce != 'DDL':
        raise DistutilsError('HOROVOD_GPU_ALLREDUCE=%s is invalid, supported '
                             'values are "", "MPI", "NCCL", "DDL".' % gpu_allreduce)

    gpu_allgather = os.environ.get('HOROVOD_GPU_ALLGATHER')
    if gpu_allgather and gpu_allgather != 'MPI':
        raise DistutilsError('HOROVOD_GPU_ALLGATHER=%s is invalid, supported '
                             'values are "", "MPI".' % gpu_allgather)

    gpu_broadcast = os.environ.get('HOROVOD_GPU_BROADCAST')
    if gpu_broadcast and gpu_broadcast != 'MPI':
        raise DistutilsError('HOROVOD_GPU_BROADCAST=%s is invalid, supported '
                             'values are "", "MPI".' % gpu_broadcast)

    if gpu_allreduce or gpu_allgather or gpu_broadcast:
        have_cuda = True
        cuda_include_dirs, cuda_lib_dirs = get_cuda_dirs(build_ext, cpp_flags)
    else:
        have_cuda = False
        cuda_include_dirs = cuda_lib_dirs = []

    if gpu_allreduce == 'NCCL':
        have_nccl = True
        nccl_include_dirs, nccl_lib_dirs, nccl_libs = get_nccl_vals(
            build_ext, cuda_include_dirs, cuda_lib_dirs, cpp_flags)
    else:
        have_nccl = False
        nccl_include_dirs = nccl_lib_dirs = nccl_libs = []

    if gpu_allreduce == 'DDL':
        have_ddl = True
        ddl_include_dirs, ddl_lib_dirs = get_ddl_dirs()
    else:
        have_ddl = False
        ddl_include_dirs = ddl_lib_dirs = []

    if (gpu_allreduce == 'NCCL' and (gpu_allgather == 'MPI' or gpu_broadcast == 'MPI')
            and not os.environ.get('HOROVOD_ALLOW_MIXED_GPU_IMPL')):
        raise DistutilsError('You should not mix NCCL and MPI GPU due to a possible deadlock.\n'
                             'If you\'re sure you want to mix them, set the '
                             'HOROVOD_ALLOW_MIXED_GPU_IMPL environment variable to \'1\'.')

    MACROS = [('EIGEN_MPL2_ONLY', 1)]
    INCLUDES = ['third_party/eigen',
                'third_party/lbfgs/include']
    SOURCES = ['horovod/common/common.cc',
               'horovod/common/fusion_buffer_manager.cc',
               'horovod/common/mpi_message.cc',
               'horovod/common/half.cc',
               'horovod/common/operations.cc',
               'horovod/common/parameter_manager.cc',
               'horovod/common/timeline.cc',
               'horovod/common/optim/bayesian_optimization.cc',
               'horovod/common/optim/gaussian_process.cc',
               'horovod/common/logging.cc']
    COMPILE_FLAGS = cpp_flags + shlex.split(mpi_flags)
    LINK_FLAGS = link_flags + shlex.split(mpi_flags)
    LIBRARY_DIRS = []
    LIBRARIES = []

    if have_cuda:
        MACROS += [('HAVE_CUDA', '1')]
        INCLUDES += cuda_include_dirs
        LIBRARY_DIRS += cuda_lib_dirs
        LIBRARIES += ['cudart']

    if have_nccl:
        MACROS += [('HAVE_NCCL', '1')]
        INCLUDES += nccl_include_dirs
        LIBRARY_DIRS += nccl_lib_dirs
        LIBRARIES += nccl_libs

    if have_ddl:
        MACROS += [('HAVE_DDL', '1')]
        INCLUDES += ddl_include_dirs
        LIBRARY_DIRS += ddl_lib_dirs
        LIBRARIES += ['ddl', 'ddl_pack']

    if gpu_allreduce:
        MACROS += [('HOROVOD_GPU_ALLREDUCE', "'%s'" % gpu_allreduce[0])]

    if gpu_allgather:
        MACROS += [('HOROVOD_GPU_ALLGATHER', "'%s'" % gpu_allgather[0])]

    if gpu_broadcast:
        MACROS += [('HOROVOD_GPU_BROADCAST', "'%s'" % gpu_broadcast[0])]

    return dict(MACROS=MACROS,
                INCLUDES=INCLUDES,
                SOURCES=SOURCES,
                COMPILE_FLAGS=COMPILE_FLAGS,
                LINK_FLAGS=LINK_FLAGS,
                LIBRARY_DIRS=LIBRARY_DIRS,
                LIBRARIES=LIBRARIES)


def build_tf_extension(build_ext, options):
    check_tf_version()
    tf_compile_flags, tf_link_flags = get_tf_flags(
        build_ext, options['COMPILE_FLAGS'])

    tensorflow_mpi_lib.define_macros = options['MACROS']
    tensorflow_mpi_lib.include_dirs = options['INCLUDES']
    tensorflow_mpi_lib.sources = options['SOURCES'] + \
        ['horovod/tensorflow/mpi_ops.cc']
    tensorflow_mpi_lib.extra_compile_args = options['COMPILE_FLAGS'] + \
        tf_compile_flags
    tensorflow_mpi_lib.extra_link_args = options['LINK_FLAGS'] + tf_link_flags
    tensorflow_mpi_lib.library_dirs = options['LIBRARY_DIRS']
    tensorflow_mpi_lib.libraries = options['LIBRARIES']

    build_ext.build_extension(tensorflow_mpi_lib)


def parse_version(version_str):
    m = re.match('^(\d+)(?:\.(\d+))?(?:\.(\d+))?(?:\.(\d+))?', version_str)
    if m is None:
        return None

    # turn version string to long integer
    version = int(m.group(1)) * 10 ** 9
    if m.group(2) is not None:
        version += int(m.group(2)) * 10 ** 6
    if m.group(3) is not None:
        version += int(m.group(3)) * 10 ** 3
    if m.group(4) is not None:
        version += int(m.group(4))
    return version


def dummy_import_torch():
    try:
        import torch
    except:
        pass


def check_torch_version():
    try:
        import torch
        if LooseVersion(torch.__version__) < LooseVersion('0.4.0'):
            raise DistutilsPlatformError(
                'Your PyTorch version %s is outdated.  '
                'Horovod requires torch>=0.4.0' % torch.__version__)
    except ImportError:
        raise DistutilsPlatformError(
            'import torch failed, is it installed?\n\n%s' % traceback.format_exc())

    # parse version
    version = parse_version(torch.__version__)
    if version is None:
        raise DistutilsPlatformError(
            'Unable to determine PyTorch version from the version string \'%s\'' % torch.__version__)
    return version


def is_torch_cuda():
    try:
        from torch.utils.ffi import create_extension
        cuda_test_ext = create_extension(
            name='horovod.torch.test_cuda',
            headers=['horovod/torch/dummy.h'],
            sources=[],
            with_cuda=True,
            extra_compile_args=['-std=c11', '-fPIC', '-O2']
        )
        cuda_test_ext.build()
        return True
    except:
        print('INFO: Above error indicates that this PyTorch installation does not support CUDA.')
        return False


def is_torch_cuda_v2(build_ext, include_dirs, extra_compile_args):
    try:
        from torch.utils.cpp_extension import include_paths
        test_compile(build_ext, 'test_torch_cuda', include_dirs=include_dirs + include_paths(cuda=True),
                     extra_compile_preargs=extra_compile_args, code=textwrap.dedent('''\
            #include <THC/THC.h>
            void test() {
            }
            '''))
        return True
    except (CompileError, LinkError, EnvironmentError):
        print('INFO: Above error indicates that this PyTorch installation does not support CUDA.')
        return False


def check_macro(macros, key):
    return any(k == key and v for k, v in macros)


def set_macro(macros, key, new_value):
    if any(k == key for k, _ in macros):
        return [(k, new_value if k == key else v) for k, v in macros]
    else:
        return macros + [(key, new_value)]


class protect_files(object):
    def __init__(self, *files):
        self.files = files

    def __enter__(self):
        for file in self.files:
            os.rename(file, file + '.protected')

    def __exit__(self, type, value, traceback):
        for file in self.files:
            os.rename(file + '.protected', file)


def build_torch_extension(build_ext, options, torch_version):
    have_cuda = is_torch_cuda()
    if not have_cuda and check_macro(options['MACROS'], 'HAVE_CUDA'):
        raise DistutilsPlatformError(
            'Horovod build with GPU support was requested, but this PyTorch '
            'installation does not support CUDA.')

    # Update HAVE_CUDA to mean that PyTorch supports CUDA. Internally, we will be checking
    # HOROVOD_GPU_(ALLREDUCE|ALLGATHER|BROADCAST) to decide whether we should use GPU
    # version or transfer tensors to CPU memory for those operations.
    updated_macros = set_macro(
        options['MACROS'], 'HAVE_CUDA', str(int(have_cuda)))

    # Export TORCH_VERSION equal to our representation of torch.__version__. Internally it's
    # used for backwards compatibility checks.
    updated_macros = set_macro(updated_macros, 'TORCH_VERSION', str(torch_version))

    # Create_extension overwrites these files which are customized, we need to protect them.
    with protect_files('horovod/torch/mpi_lib/__init__.py',
                       'horovod/torch/mpi_lib_impl/__init__.py'):
        from torch.utils.ffi import create_extension
        ffi_iface = create_extension(
            name='horovod.torch.mpi_lib',
            headers=['horovod/torch/interface.h'] +
            (['horovod/torch/interface_cuda.h'] if have_cuda else []),
            with_cuda=have_cuda,
            language='c',
            package=True,
            sources=[],
            extra_compile_args=['-std=c11', '-fPIC', '-O2']
        )
        ffi_impl = create_extension(
            name='horovod.torch.mpi_lib_impl',
            headers=[],
            with_cuda=have_cuda,
            language='c++',
            package=True,
            source_extension='.cc',
            define_macros=updated_macros,
            include_dirs=options['INCLUDES'],
            sources=options['SOURCES'] + ['horovod/torch/mpi_ops.cc',
                                          'horovod/torch/handle_manager.cc',
                                          'horovod/torch/ready_event.cc',
                                          'horovod/torch/tensor_util.cc',
                                          'horovod/torch/cuda_util.cc',
                                          'horovod/torch/adapter.cc'],
            extra_compile_args=options['COMPILE_FLAGS'],
            extra_link_args=options['LINK_FLAGS'],
            library_dirs=options['LIBRARY_DIRS'],
            libraries=options['LIBRARIES']
        )

    for ffi, setuptools_ext in [(ffi_iface, torch_mpi_lib),
                                (ffi_impl, torch_mpi_lib_impl)]:
        ffi_ext = ffi.distutils_extension()
        # ffi_ext is distutils Extension, not setuptools Extension
        for k, v in ffi_ext.__dict__.items():
            setuptools_ext.__dict__[k] = v
        build_ext.build_extension(setuptools_ext)


def build_torch_extension_v2(build_ext, options, torch_version):
    have_cuda = is_torch_cuda_v2(build_ext, include_dirs=options['INCLUDES'],
                                 extra_compile_args=options['COMPILE_FLAGS'])
    if not have_cuda and check_macro(options['MACROS'], 'HAVE_CUDA'):
        raise DistutilsPlatformError(
            'Horovod build with GPU support was requested, but this PyTorch '
            'installation does not support CUDA.')

    # Update HAVE_CUDA to mean that PyTorch supports CUDA. Internally, we will be checking
    # HOROVOD_GPU_(ALLREDUCE|ALLGATHER|BROADCAST) to decide whether we should use GPU
    # version or transfer tensors to CPU memory for those operations.
    updated_macros = set_macro(
        options['MACROS'], 'HAVE_CUDA', str(int(have_cuda)))

    # Export TORCH_VERSION equal to our representation of torch.__version__. Internally it's
    # used for backwards compatibility checks.
    updated_macros = set_macro(updated_macros, 'TORCH_VERSION', str(torch_version))

    # Always set _GLIBCXX_USE_CXX11_ABI, since PyTorch can only detect whether it was set to 1.
    import torch
    updated_macros = set_macro(updated_macros, '_GLIBCXX_USE_CXX11_ABI',
                               str(int(torch.compiled_with_cxx11_abi())))

    # PyTorch requires -DTORCH_API_INCLUDE_EXTENSION_H
    updated_macros = set_macro(updated_macros, 'TORCH_API_INCLUDE_EXTENSION_H', '1')

    if have_cuda:
        from torch.utils.cpp_extension import CUDAExtension as TorchExtension
    else:
        # CUDAExtension fails with `ld: library not found for -lcudart` if CUDA is not present
        from torch.utils.cpp_extension import CppExtension as TorchExtension
    ext = TorchExtension(torch_mpi_lib_v2.name,
                         define_macros=updated_macros,
                         include_dirs=options['INCLUDES'],
                         sources=options['SOURCES'] + ['horovod/torch/mpi_ops_v2.cc',
                                                       'horovod/torch/handle_manager.cc',
                                                       'horovod/torch/ready_event.cc',
                                                       'horovod/torch/cuda_util.cc',
                                                       'horovod/torch/adapter_v2.cc'],
                         extra_compile_args=options['COMPILE_FLAGS'],
                         extra_link_args=options['LINK_FLAGS'],
                         library_dirs=options['LIBRARY_DIRS'],
                         libraries=options['LIBRARIES'])

    # Patch an existing torch_mpi_lib_v2 extension object.
    for k, v in ext.__dict__.items():
        torch_mpi_lib_v2.__dict__[k] = v
    build_ext.build_extension(torch_mpi_lib_v2)


# run the customize_compiler
class custom_build_ext(build_ext):
    def build_extensions(self):
        options = get_common_options(self)
        built_plugins = []
        # If PyTorch is installed, it must be imported before TensorFlow, otherwise
        # we may get an error: dlopen: cannot load any more object with static TLS
        if not os.environ.get('HOROVOD_WITHOUT_PYTORCH'):
            dummy_import_torch()
        if not os.environ.get('HOROVOD_WITHOUT_TENSORFLOW'):
            try:
                build_tf_extension(self, options)
                built_plugins.append(True)
            except:
                if not os.environ.get('HOROVOD_WITH_TENSORFLOW'):
                    print('INFO: Unable to build TensorFlow plugin, will skip it.\n\n'
                          '%s' % traceback.format_exc(), file=sys.stderr)
                    built_plugins.append(False)
                else:
                    raise
        if not os.environ.get('HOROVOD_WITHOUT_PYTORCH'):
            try:
                torch_version = check_torch_version()
                if torch_version >= 1000000000:
                    build_torch_extension_v2(self, options, torch_version)
                else:
                    build_torch_extension(self, options, torch_version)
                built_plugins.append(True)
            except:
                if not os.environ.get('HOROVOD_WITH_PYTORCH'):
                    print('INFO: Unable to build PyTorch plugin, will skip it.\n\n'
                          '%s' % traceback.format_exc(), file=sys.stderr)
                    built_plugins.append(False)
                else:
                    raise
        if not built_plugins:
            raise DistutilsError(
                'Both TensorFlow and PyTorch plugins were excluded from build. Aborting.')
        if not any(built_plugins):
            raise DistutilsError(
                'Neither TensorFlow nor PyTorch plugins were built. See errors above.')


setup(name='horovod',
      version=__version__,
      packages=find_packages(),
      description='Distributed training framework for TensorFlow, Keras, and PyTorch.',
      author='Uber Technologies, Inc.',
      long_description=textwrap.dedent('''\
          Horovod is a distributed training framework for TensorFlow, Keras, and PyTorch.
          The goal of Horovod is to make distributed Deep Learning fast and easy to use.'''),
      url='https://github.com/uber/horovod',
      classifiers=[
          'License :: OSI Approved :: Apache Software License'
      ],
      ext_modules=[tensorflow_mpi_lib, torch_mpi_lib, torch_mpi_lib_impl, torch_mpi_lib_v2],
      cmdclass={'build_ext': custom_build_ext},
      # cffi is required for PyTorch
      # If cffi is specified in setup_requires, it will need libffi to be installed on the machine,
      # which is undesirable.  Luckily, `install` action will install cffi before executing build,
      # so it's only necessary for `build*` or `bdist*` actions.
      setup_requires=['cffi>=1.4.0', 'cloudpickle', 'psutil', 'six'] if is_build_action() else [],
      install_requires=['cffi>=1.4.0', 'cloudpickle', 'psutil', 'six'],
      zip_safe=False)