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setup.py
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setup.py
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import glob
from pathlib import Path
import airspeed
from setuptools import setup, find_packages
from distutils.command.clean import clean
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
from torch.cuda import is_available as torch_cuda_available
from rational import __version__
import os
degrees = [(3, 3), (4, 4), (5, 5), (6, 6), (7, 7), (8, 8), (5, 4), (7, 6)]
# degrees = [(5, 4), (7, 6)]
name='rational-activations'
#import ipdb; ipdb.set_trace()
#find_packages(where="rational")
def is_torch_cuda_available():
"""Wrapper for torch cuda availability check (torch.cuda.is_available) that takes an environment variable
'FORCE_CUDA' into account and returns also true iff FORCE_CUDA=1.
This is necessary when building rational in a Dockerfile script since the docker build pass doesn't have
access to cuda and thus torch.cuda.is_available always returns false, even when the docker image which is
to be built in fact does have cuda.
"""
force_cuda = os.getenv("FORCE_CUDA", "0") == "1"
return force_cuda or torch_cuda_available()
def generate_cpp_module(fname, degrees=degrees, versions=None):
file_content = airspeed.Template("""
\#include <torch/extension.h>
\#include <vector>
\#include <iostream>
#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x " must be a CUDA tensor")
#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
#foreach ($vname in $versions)
#if( $vname == 'D' )
#set ($forward_header = 'const bool training, const unsigned long long iteration, torch::Tensor x, torch::Tensor n, torch::Tensor d')
#set ($backward_header = 'const bool training, const unsigned long long iteration, torch::Tensor grad_output, torch::Tensor x, torch::Tensor n, torch::Tensor d')
#set ($forward_invocation = 'training, iteration, x, n, d')
#set ($backward_invocation = 'training, iteration, grad_output, x, n, d')
#else
#set ($forward_header = 'torch::Tensor x, torch::Tensor n, torch::Tensor d')
#set ($backward_header = 'torch::Tensor grad_output, torch::Tensor x, torch::Tensor n, torch::Tensor d')
#set ($forward_invocation = 'x, n, d')
#set ($backward_invocation = 'grad_output, x, n, d')
#end
#foreach ($degs in $degrees)
at::Tensor rational_cuda_forward_${vname}_$degs[0]_$degs[1]($forward_header);
std::vector<torch::Tensor> rational_cuda_backward_${vname}_$degs[0]_$degs[1]($backward_header);
#end
#foreach ($degs in $degrees)
at::Tensor rational_forward_${vname}_$degs[0]_$degs[1]($forward_header) {
CHECK_INPUT(x);
CHECK_INPUT(n);
CHECK_INPUT(d);
return rational_cuda_forward_${vname}_$degs[0]_$degs[1]($forward_invocation);
}
std::vector<torch::Tensor> rational_backward_${vname}_$degs[0]_$degs[1]($backward_header) {
CHECK_INPUT(grad_output);
CHECK_INPUT(x);
CHECK_INPUT(n);
CHECK_INPUT(d);
return rational_cuda_backward_${vname}_$degs[0]_$degs[1]($backward_invocation);
}
#end
#end
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
#foreach ($degs in $degrees)
#foreach ($vname in $versions)
m.def("forward_${vname}_$degs[0]_$degs[1]", &rational_forward_${vname}_$degs[0]_$degs[1], "Rational forward ${vname}_$degs[0]_$degs[1]");
m.def("backward_${vname}_$degs[0]_$degs[1]", &rational_backward_${vname}_$degs[0]_$degs[1], "Rational backward ${vname}_$degs[0]_$degs[1]");
#end
#end
}
""")
content = file_content.merge(locals())
with open(fname, "w") as text_file:
text_file.write(content)
def generate_cpp_kernels_module(fname, degrees=degrees, template_contents=None):
degrees = [[e[0], e[1], max(e[0], e[1])] for e in degrees]
template = """
\#include <torch/extension.h>
\#include <ATen/cuda/CUDAContext.h>
\#include <cuda.h>
\#include <cuda_runtime.h>
\#include <vector>
\#include <stdlib.h>
\#include <curand.h>
\#include <curand_kernel.h>
\#include <curand_philox4x32_x.h>
constexpr uint32_t THREADS_PER_BLOCK = 512;
"""
file_content = airspeed.Template(template + template_contents)
content = file_content.merge(locals())
with open(fname, "w") as text_file:
text_file.write(content)
if is_torch_cuda_available():
version_names = []
template_contents = ""
for template_fname in sorted(glob.glob("rational/_cuda/versions/*.cu")):
version_names.append(Path(template_fname).stem)
with open(template_fname) as infile:
template_contents += infile.read()
generate_cpp_module(fname='rational/_cuda/rational_cuda.cpp', versions=version_names)
generate_cpp_kernels_module(fname='rational/_cuda/rational_cuda_kernels.cu', template_contents=template_contents)
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
with open("requirements.txt", "r", encoding="utf-8") as fh:
requirements = fh.readlines()
class clean_all(clean):
def run(self):
self.all = True
super().run()
import shutil
import os
egginf = name.replace('-', '_')
shutil.rmtree(egginf + '.egg-info')
shutil.rmtree('dist')
if os.path.exists("rational/cuda.cpython-36m-x86_64-linux-gnu.so"):
os.remove("rational/cuda.cpython-36m-x86_64-linux-gnu.so")
print("Cleaned everything")
setup(
name=name,
version=__version__,
author="Alejandro Molina, Quentin Delfosse, Patrick Schramowski",
author_email="molina@cs.tu-darmstadt.de, quentin.delfosse@cs.tu-darmstadt.de",
description="Rational Activations",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/ml-research/rational_activations",
packages=find_packages(exclude=["tests"]),
package_data={'': ['*.json']},
include_package_data=True,
classifiers=[
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"License :: OSI Approved :: Apache Software License"
],
install_requires=requirements,
ext_modules=[
CUDAExtension('rational.cuda', [
'rational/_cuda/rational_cuda.cpp',
'rational/_cuda/rational_cuda_kernels.cu',
],
extra_compile_args={'cxx': [],
'nvcc': ['-gencode=arch=compute_60,code="sm_60,compute_60"', '-lineinfo']
}
),
] if is_torch_cuda_available() else [],
cmdclass={
'build_ext': BuildExtension,
'clean': clean_all
},
setup_requires=['airspeed'],
python_requires='>=3.5.0',)