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
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)
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)
