def create_extension(): srcs = [] srcs += glob.glob('srcs/cpp/src/torch/common.cpp') srcs += glob.glob('srcs/cpp/src/torch/ops/cpu/*.cpp') include_dirs = [ # FIXME: use tmp dir of pip os.path.join(os.path.dirname(__file__), './srcs/cpp/include') ] library_dirs = [kungfu_library_dir()] libraries = ['kungfu', 'kungfu_python'] with_cuda = None import torch if torch.cuda.is_available(): srcs += glob.glob('srcs/cpp/src/cuda/*.cpp') srcs += glob.glob('srcs/cpp/src/torch/ops/cuda/*.cpp') with_cuda = True include_dirs += [os.path.join(find_cuda(), 'include')] library_dirs += [os.path.join(find_cuda(), 'lib64')] libraries += ['cudart'] srcs += ['srcs/cpp/src/torch/module_cuda.cpp'] else: srcs += ['srcs/cpp/src/torch/module_cpu.cpp'] return cpp_extension.CppExtension( 'kungfu_torch_ops', srcs, include_dirs=include_dirs, library_dirs=library_dirs, libraries=libraries, with_cuda=with_cuda, )
def main() -> None: # Spatially-Varying Filtering name = 'pysrwarp' name_cuda = 'svf_cuda' target_dir = 'cuda' setuptools.setup( name=name, version='1.0.0', author='Sanghyun Son', author_email='*****@*****.**', packages=setuptools.find_packages(), ext_modules=[ cpp_extension.CppExtension( name='srwarp_cuda', sources=[path.join('cuda', name_cuda + '.cpp')], libraries=[ name_cuda + '_kernel', name_cuda + '_half_kernel', name_cuda + '_projective_grid_kernel', ], library_dirs=[path.join('.', target_dir)], extra_compile_args=['-g', '-fPIC'], ) ], cmdclass={'build_ext': cpp_extension.BuildExtension}, ) return
def main(): extra_compile_args = [] extra_link_args = [] grpc_objects = [ f"{PREFIX}/lib/libgrpc++.a", f"{PREFIX}/lib/libgrpc.a", f"{PREFIX}/lib/libgpr.a", f"{PREFIX}/lib/libaddress_sorting.a", ] include_dirs = cpp_extension.include_paths() + [ np.get_include(), f"{PREFIX}/include", ] libraries = [] if sys.platform == "darwin": extra_compile_args += ["-stdlib=libc++", "-mmacosx-version-min=10.14"] extra_link_args += ["-stdlib=libc++", "-mmacosx-version-min=10.14"] # Relevant only when c-cares is not embedded in grpc, e.g. when # installing grpc via homebrew. libraries.append("cares") elif sys.platform == "linux": libraries.append("z") grpc_objects.append(f"{PREFIX}/lib/libprotobuf.a") libtorchbeast = cpp_extension.CppExtension( name="libtorchbeast._C", sources=[ "libtorchbeast/libtorchbeast.cc", "libtorchbeast/actorpool.cc", "libtorchbeast/rpcenv.cc", "libtorchbeast/rpcenv.pb.cc", "libtorchbeast/rpcenv.grpc.pb.cc", ], include_dirs=include_dirs, libraries=libraries, language="c++", extra_compile_args=["-std=c++17"] + extra_compile_args, extra_link_args=extra_link_args, extra_objects=grpc_objects, ) setuptools.setup( name="libtorchbeast", packages=["libtorchbeast"], version="0.0.14", ext_modules=[libtorchbeast], cmdclass={"build_ext": build_ext}, test_suite="setup.test_suite", install_requires=[ 'setuptools' ], # HACK: any package is ok, but somehow must not be empty )
def ext_modules(): extensions = [ cpp_extension.CppExtension( "torchsort.isotonic_cpu", sources=["torchsort/isotonic_cpu.cpp"], extra_compile_args=compile_args(), ), ] if cuda_toolkit_available(): extensions.append( cpp_extension.CUDAExtension( "torchsort.isotonic_cuda", sources=["torchsort/isotonic_cuda.cu"], )) return extensions
def build_cpu_extension(name, src_files=None): path_parts = name.split('.') base_path = os.path.join("src", *path_parts) src_path = os.path.join(base_path, "src") incl_path = os.path.join(base_path, "include") ext_args = dict() ext_args.update(default_extension_args_cpu) ext_name = f"{name}._cpp" if src_files is None: src_files = [f for f in os.listdir(src_path) if f.endswith(".cpp")] ext_args["sources"] = [os.path.join(src_path, f) for f in src_files] ext_args["include_dirs"] = [incl_path] extension = cpp_extension.CppExtension(ext_name, **ext_args) return extension
sources=["fairseq/data/token_block_utils_fast.pyx"], language="c++", extra_compile_args=extra_compile_args, ), ] cmdclass = {} try: # torch is not available when generating docs from torch.utils import cpp_extension extensions.extend([ cpp_extension.CppExtension( "fairseq.libnat", sources=[ "fairseq/clib/libnat/edit_dist.cpp", ], ) ]) if "CUDA_HOME" in os.environ: extensions.extend([ cpp_extension.CppExtension( "fairseq.libnat_cuda", sources=[ "fairseq/clib/libnat_cuda/edit_dist.cu", "fairseq/clib/libnat_cuda/binding.cpp", ], ), cpp_extension.CppExtension( "fairseq.ngram_repeat_block_cuda", sources=[
extension_sources = [str(p) for p in this_dir.joinpath("csrc").rglob("*.cpp")] # Npcomp bits. include_dirs = npcomp_build.get_include_dirs() lib_dirs = npcomp_build.get_lib_dirs() npcomp_libs = [npcomp_build.get_capi_link_library_name()] # TODO: Export this in some way from an npcomp config file include vs needing # it loose here. compile_args = ["-DMLIR_PYTHON_PACKAGE_PREFIX=npcomp."] setup( name="npcomp-torch", ext_modules=[ cpp_extension.CppExtension(name="_torch_mlir", sources=extension_sources, include_dirs=include_dirs, library_dirs=lib_dirs, libraries=npcomp_libs, extra_compile_args=compile_args), ], cmdclass={"build_ext": cpp_extension.BuildExtension}, package_dir={ "": "./python", }, packages=find_packages("./python", include=[ "torch_mlir", "torch_mlir.*", "torch_mlir_torchscript", "torch_mlir_torchscript.*", "torch_mlir_torchscript_e2e_test_configs", "torch_mlir_torchscript_e2e_test_configs.*",
# Build with # CXX=c++ python3 setup.py build develop import setuptools import sys from torch.utils import cpp_extension extra_compile_args = [] extra_link_args = [] if sys.platform == 'darwin': extra_compile_args += ['-stdlib=libc++', '-mmacosx-version-min=10.12'] extra_link_args += ['-stdlib=libc++'] tensorbug = cpp_extension.CppExtension( name='tensorbug', sources=['bug.cc'], language='c++', extra_compile_args=['-std=c++17'] + extra_compile_args, extra_link_args=extra_link_args, ) setuptools.setup(name='tensorbug', ext_modules=[tensorbug], cmdclass={'build_ext': cpp_extension.BuildExtension})
license=LICENSE, keywords='tensorflow machine learning rnn lstm gru custom op', packages=['haste_tf'], package_dir={'haste_tf': 'tf'}, package_data={'haste_tf': ['*.so']}, install_requires=[], zip_safe=False, distclass=BinaryDistribution, classifiers=CLASSIFIERS) elif sys.argv[1] == 'haste_pytorch': del sys.argv[1] from glob import glob from torch.utils import cpp_extension extension = cpp_extension.CppExtension( 'haste_pytorch_lib', sources=glob('pytorch/*.cc'), include_dirs=['lib', '/usr/local/cuda/include'], libraries=['haste'], library_dirs=['.']) setup(name='haste_pytorch', version=VERSION, description=DESCRIPTION, author=AUTHOR, author_email=AUTHOR_EMAIL, url=URL, license=LICENSE, keywords='pytorch machine learning rnn lstm gru custom op', packages=['haste_pytorch'], package_dir={'haste_pytorch': 'pytorch'}, install_requires=[], ext_modules=[extension], cmdclass={'build_ext': cpp_extension.BuildExtension},
), ] cmdclass = {} try: # torch is not available when generating docs from torch.utils import cpp_extension extensions.extend( [ cpp_extension.CppExtension( "fairseq.libnat", sources=[ "fairseq/clib/libnat/edit_dist.cpp", ], ) ] ) if "CUDA_HOME" in os.environ: extensions.extend( [ cpp_extension.CppExtension( "fairseq.libnat_cuda", sources=[ "fairseq/clib/libnat_cuda/edit_dist.cu", "fairseq/clib/libnat_cuda/binding.cpp", ], )
from setuptools import setup, Extension from torch.utils import cpp_extension setup(name='q_par_cpp', ext_modules=[ cpp_extension.CppExtension('q_par_cpp', ['q_par.cpp'], extra_compile_args=["-fopenmp"]) ], cmdclass={'build_ext': cpp_extension.BuildExtension})
), NumpyExtension( 'fairseq.data.data_utils_fast', sources=['fairseq/data/data_utils_fast.pyx'], language='c++', extra_compile_args=extra_compile_args, ), NumpyExtension( 'fairseq.data.token_block_utils_fast', sources=['fairseq/data/token_block_utils_fast.pyx'], language='c++', extra_compile_args=extra_compile_args, ), cpp_extension.CppExtension( 'fairseq.libnat', sources=[ 'fairseq/clib/libnat/edit_dist.cpp', ], ) ] setup( name='fairseq', version='0.8.0', description='Facebook AI Research Sequence-to-Sequence Toolkit', url='https://github.com/pytorch/fairseq', classifiers=[ 'Intended Audience :: Science/Research', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Topic :: Scientific/Engineering :: Artificial Intelligence',
from setuptools import setup, Extension from setuptools.command.build_ext import build_ext import sys import setuptools from torch.utils import cpp_extension print(cpp_extension.include_paths()) __version__ = '0.0.1' extensions = [ cpp_extension.CppExtension( 'src.qp_fast', ["src/qp_fast.cpp"], language='c++', extra_compile_args=['-std=c++17'], ), ] setup(name='latent_decision_tree', version=__version__, author="VZ,MK,VN", ext_modules=extensions, setup_requires=['pybind11>=2.5.0'], cmdclass={'build_ext': cpp_extension.BuildExtension}, zip_safe=False)
plugin_compile_args.extend(["-g", "-O0"]) plugin_sources = ["src/torch_ucc.cpp", "src/torch_ucc_comm.cpp"] plugin_include_dirs = [ "{}/include/".format(ucc_plugin_dir), "{}/include/".format(ucx_home), "{}/include/".format(ucc_home) ] plugin_library_dirs = ["{}/lib/".format(ucx_home), "{}/lib/".format(ucc_home)] plugin_libraries = ["ucp", "uct", "ucm", "ucs", "ucc"] with_cuda = os.environ.get("WITH_CUDA") if with_cuda is None or with_cuda == "no": print("CUDA support is disabled") module = cpp_extension.CppExtension(name="torch_ucc", sources=plugin_sources, include_dirs=plugin_include_dirs, library_dirs=plugin_library_dirs, libraries=plugin_libraries, extra_compile_args=plugin_compile_args) else: print("CUDA support is enabled") plugin_compile_args.append("-DUSE_CUDA") module = cpp_extension.CUDAExtension( name="torch_ucc", sources=plugin_sources, include_dirs=plugin_include_dirs, library_dirs=plugin_library_dirs, libraries=plugin_libraries, extra_compile_args=plugin_compile_args) setup(name="torch-ucc", version="1.0.0", ext_modules=[module],
from setuptools import setup, Extension from torch.utils import cpp_extension setup(name='voxelizer_cpp', ext_modules=[ cpp_extension.CppExtension('voxelizer_cpp', ['voxelizer.cpp']) ], cmdclass={'build_ext': cpp_extension.BuildExtension})
from setuptools import setup, Extension from torch.utils import cpp_extension setup(name='dirpg_cpp', ext_modules=[cpp_extension.CppExtension('dirpg_cpp', sources=['dirpg.cpp', 'retsp/batched_graphs.cpp', 'retsp/batched_graphs_tsp.cpp', 'retsp/a_star_sampling.cpp', 'retsp/batched_heaps.cpp', 'retsp/batched_trajectories.cpp', 'retsp/node_allocator.cpp', 'retsp/mst_node.cpp', 'retsp/info_node.cpp', 'retsp/gumbel_state.cpp', 'retsp/union_find.cpp', ], include_dirs=['retsp'])], headers=[ 'retsp/batched_graphs.h', 'retsp/batched_graphs_tsp.h', 'retsp/a_star_sampling.h', 'retsp/batched_heaps.h', 'retsp/batched_trajectories.h', 'retsp/node_allocator.h', 'retsp/mst_node.h', 'retsp/info_node.h', 'retsp/gumbel_state.h', 'retsp/union_find.h'], cmdclass={'build_ext': cpp_extension.BuildExtension})
from setuptools import setup, Extension from torch.utils import cpp_extension setup( name='kruskals_cpp', ext_modules=[cpp_extension.CppExtension('kruskals_cpp', ['kruskals.cpp'])], cmdclass={'build_ext': cpp_extension.BuildExtension})
from setuptools import setup, Extension from torch.utils import cpp_extension setup(name='shift_kernel', ext_modules=[ cpp_extension.CppExtension('shift_kernel', ['shift_kernel.cpp'], extra_compile_args=['-fopenmp', '-O3']) ], cmdclass={'build_ext': cpp_extension.BuildExtension})
# ------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. # -------------------------------------------------------------------------- import os from setuptools import setup from torch.utils import cpp_extension filename = os.path.join(os.path.dirname(__file__), "aten_op_executor.cc") setup( name="aten_op_executor", ext_modules=[ cpp_extension.CppExtension(name="aten_op_executor", sources=[filename]) ], cmdclass={"build_ext": cpp_extension.BuildExtension}, )
from setuptools import setup, Extension from torch.utils import cpp_extension setup(name='sparse_coo_tensor_cpp', ext_modules=[ cpp_extension.CppExtension('sparse_coo_tensor_cpp', ['sparse_coo_tensor.cpp'], extra_compile_args=["-lcusparse"]) ], cmdclass={'build_ext': cpp_extension.BuildExtension})
from setuptools import setup, Extension from torch.utils import cpp_extension setup(name='lltm_cpp', ext_modules=[ cpp_extension.CppExtension('lltm_cpp', ['./models/ops/lltm.cpp']) ], cmdclass={'build_ext': cpp_extension.BuildExtension}) setup(name='lltm_cuda', ext_modules=[ cpp_extension.CUDAExtension('lltm_cuda', [ './models/ops/lltm_cuda.cpp', './models/ops/lltm_cuda_kernel.cu', ]) ], cmdclass={'build_ext': cpp_extension.BuildExtension})
'fairseq.data.token_block_utils_fast', sources=['fairseq/data/token_block_utils_fast.pyx'], language='c++', extra_compile_args=extra_compile_args, ), ] cmdclass = {} try: # torch is not available when generating docs from torch.utils import cpp_extension extensions.extend([ cpp_extension.CppExtension( 'fairseq.libnat', sources=[ 'fairseq/clib/libnat/edit_dist.cpp', ], ) ]) if 'CUDA_HOME' in os.environ: extensions.extend([ cpp_extension.CppExtension( 'fairseq.libnat_cuda', sources=[ 'fairseq/clib/libnat_cuda/edit_dist.cu', 'fairseq/clib/libnat_cuda/binding.cpp' ], ) ]) cmdclass['build_ext'] = cpp_extension.BuildExtension
from setuptools import setup, Extension from torch.utils import cpp_extension import os LIBXSMM_ROOT=os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.getcwd())))) print("LIBXSMM root directory path: ", LIBXSMM_ROOT) setup(name='conv1dopti-layer', ext_modules=[cpp_extension.CppExtension('Conv1dOpti_cpp', ['Conv1dOpti.cpp'], \ author="Narendra Chaudhary", \ author_email="*****@*****.**", \ description="PyTorch Extension for optimized 1D dilated convolutional layer", \ extra_compile_args=['-O3', '-g', \ '-fopenmp-simd', '-fopenmp', '-march=native',\ # '-mprefer-vector-width=512', '-mavx512f', '-mavx512cd', '-mavx512bw', \ # '-mavx512dq', '-mavx512vl', '-mavx512ifma', '-mavx512vbmi' \ ], \ include_dirs=['{}/include/'.format(LIBXSMM_ROOT)], \ library_dirs=['{}/lib/'.format(LIBXSMM_ROOT)], \ libraries=['xsmm'], \ )], py_modules=['Conv1dOpti_ext'], cmdclass={'build_ext': cpp_extension.BuildExtension})
#encoding: utf-8 from setuptools import setup, Extension from torch.utils import cpp_extension setup(name='res_attn_cpp', ext_modules=[ cpp_extension.CppExtension('res_attn_cpp', ['modules/cpp/base/resattn/attn.cpp']) ], cmdclass={'build_ext': cpp_extension.BuildExtension})
#encoding: utf-8 from setuptools import setup, Extension from torch.utils import cpp_extension setup(name='lgate_cpp', ext_modules=[cpp_extension.CppExtension('lgate_cpp', ['modules/cpp/hplstm/lgate.cpp'])], cmdclass={'build_ext': cpp_extension.BuildExtension})
# ------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. # -------------------------------------------------------------------------- import os from setuptools import setup, Extension from torch.utils import cpp_extension filename = os.path.join(os.path.dirname(__file__), 'torch_interop_utils.cc') setup(name='torch_interop_utils', ext_modules=[cpp_extension.CppExtension(name='torch_interop_utils', sources=[filename])], cmdclass={'build_ext': cpp_extension.BuildExtension})
sources=["fairseq/data/token_block_utils_fast.pyx"], language="c++", extra_compile_args=extra_compile_args, ), ] cmdclass = {} try: # torch is not available when generating docs from torch.utils import cpp_extension extensions.extend([ cpp_extension.CppExtension( "fairseq.libbase", sources=[ "fairseq/clib/libbase/balanced_assignment.cpp", ], ) ]) extensions.extend([ cpp_extension.CppExtension( "fairseq.libnat", sources=[ "fairseq/clib/libnat/edit_dist.cpp", ], ), cpp_extension.CppExtension( "alignment_train_cpu_binding", sources=[ "examples/operators/alignment_train_cpu.cpp",
from setuptools import setup from torch.utils import cpp_extension setup( name='fasth', version='0.0.1', license='LICENSE', description='', packages=["fasth"], long_description=open('README.md').read(), long_description_content_type="text/markdown", ext_modules=[cpp_extension.CppExtension('fasth', ['fasth/fasth_cuda.cu', 'fasth/fasth.cpp'])], cmdclass={'build_ext': cpp_extension.BuildExtension}, python_requires=">=3.6", install_requires=[ "torch>=1.3.1", "numpy", "ninja", ], )
from setuptools import setup, Extension from torch.utils import cpp_extension setup(name="cctc2", ext_modules=[ cpp_extension.CppExtension( "cctc2", ["cctc2.cpp"], extra_compile_args=["-g"], ) ], cmdclass={"build_ext": cpp_extension.BuildExtension})
sourceFiles = ['hingetree.cpp'] extraCflags = ['-O2'] extraCudaFlags = ['-O2'] if torch.cuda.is_available(): sourceFiles.append('hingetree_gpu.cu') extraCflags.append('-DWITH_CUDA=1') setup(name='hingetree_cpp', ext_modules=[ cpp_extension.CUDAExtension(name='hingetree_cpp', sources=sourceFiles, extra_compile_args={ 'cxx': extraCflags, 'nvcc': extraCudaFlags }) ], cmdclass={'build_ext': cpp_extension.BuildExtension}) else: setup(name='hingetree_cpp', ext_modules=[ cpp_extension.CppExtension(name='hingetree_cpp', sources=sourceFiles, extra_compile_args={ 'cxx': extraCflags, 'nvcc': extraCudaFlags }) ], cmdclass={'build_ext': cpp_extension.BuildExtension})