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})
