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setup.py
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setup.py
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#!/usr/bin/env python
#
# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
# Written by Angelos Katharopoulos <[email protected]>,
# Apoorv Vyas <[email protected]>
#
"""Setup fast transformers"""
from functools import lru_cache
from itertools import dropwhile
import os
from os import path
from setuptools import find_packages, setup
from subprocess import DEVNULL, call
import sys
try:
import torch
from torch.utils.cpp_extension import BuildExtension, CppExtension
except ImportError as e:
raise ImportError(
("PyTorch is required to install pytorch-fast-transformers. Please "
"install your favorite version of PyTorch, we support 1.3.1, 1.5.0 "
"and >=1.6"),
name=e.name,
path=e.path
) from e
@lru_cache(None)
def cuda_toolkit_available():
try:
call(["nvcc"], stdout=DEVNULL, stderr=DEVNULL)
return True
except FileNotFoundError:
return False
def collect_docstring(lines):
"""Return document docstring if it exists"""
lines = dropwhile(lambda x: not x.startswith('"""'), lines)
doc = ""
for line in lines:
doc += line
if doc.endswith('"""\n'):
break
return doc[3:-4].replace("\r", "").replace("\n", " ")
def collect_metadata():
meta = {}
with open(path.join("fast_transformers", "__init__.py")) as f:
lines = iter(f)
meta["description"] = collect_docstring(lines)
for line in lines:
if line.startswith("__"):
key, value = map(lambda x: x.strip(), line.split("="))
meta[key[2:-2]] = value[1:-1]
return meta
@lru_cache()
def _get_cpu_extra_compile_args():
base_args = ["-fopenmp", "-ffast-math"]
if sys.platform == "darwin":
return ["-Xpreprocessor"] + base_args
else:
return base_args
@lru_cache()
def _get_gpu_extra_compile_args():
if torch.cuda.is_available():
return []
else:
return ["-arch=compute_60"]
def get_extensions():
extensions = [
CppExtension(
"fast_transformers.hashing.hash_cpu",
sources=[
"fast_transformers/hashing/hash_cpu.cpp"
],
extra_compile_args=_get_cpu_extra_compile_args()
),
CppExtension(
"fast_transformers.aggregate.aggregate_cpu",
sources=[
"fast_transformers/aggregate/aggregate_cpu.cpp"
],
extra_compile_args=_get_cpu_extra_compile_args()
),
CppExtension(
"fast_transformers.clustering.hamming.cluster_cpu",
sources=[
"fast_transformers/clustering/hamming/cluster_cpu.cpp"
],
extra_compile_args=_get_cpu_extra_compile_args()
),
CppExtension(
"fast_transformers.sparse_product.sparse_product_cpu",
sources=[
"fast_transformers/sparse_product/sparse_product_cpu.cpp"
],
extra_compile_args=_get_cpu_extra_compile_args()
),
CppExtension(
"fast_transformers.sparse_product.clustered_sparse_product_cpu",
sources=[
"fast_transformers/sparse_product/clustered_sparse_product_cpu.cpp"
],
extra_compile_args=_get_cpu_extra_compile_args()
),
CppExtension(
"fast_transformers.causal_product.causal_product_cpu",
sources=[
"fast_transformers/causal_product/causal_product_cpu.cpp"
],
extra_compile_args=_get_cpu_extra_compile_args()
),
CppExtension(
"fast_transformers.local_product.local_product_cpu",
sources=[
"fast_transformers/local_product/local_product_cpu.cpp"
],
extra_compile_args=_get_cpu_extra_compile_args()
)
]
if cuda_toolkit_available():
from torch.utils.cpp_extension import CUDAExtension
extensions += [
CUDAExtension(
"fast_transformers.hashing.hash_cuda",
sources=[
"fast_transformers/hashing/hash_cuda.cu",
],
extra_compile_args=_get_gpu_extra_compile_args()
),
CUDAExtension(
"fast_transformers.aggregate.aggregate_cuda",
sources=[
"fast_transformers/aggregate/aggregate_cuda.cu"
],
extra_compile_args=_get_gpu_extra_compile_args()
),
CUDAExtension(
"fast_transformers.aggregate.clustered_aggregate_cuda",
sources=[
"fast_transformers/aggregate/clustered_aggregate_cuda.cu"
],
extra_compile_args=_get_gpu_extra_compile_args()
),
CUDAExtension(
"fast_transformers.clustering.hamming.cluster_cuda",
sources=[
"fast_transformers/clustering/hamming/cluster_cuda.cu"
],
extra_compile_args=_get_gpu_extra_compile_args()
),
CUDAExtension(
"fast_transformers.sparse_product.sparse_product_cuda",
sources=[
"fast_transformers/sparse_product/sparse_product_cuda.cu"
],
extra_compile_args=_get_gpu_extra_compile_args()
),
CUDAExtension(
"fast_transformers.sparse_product.clustered_sparse_product_cuda",
sources=[
"fast_transformers/sparse_product/clustered_sparse_product_cuda.cu"
],
extra_compile_args=_get_gpu_extra_compile_args()
),
CUDAExtension(
"fast_transformers.causal_product.causal_product_cuda",
sources=[
"fast_transformers/causal_product/causal_product_cuda.cu"
],
extra_compile_args=_get_gpu_extra_compile_args()
),
CUDAExtension(
"fast_transformers.local_product.local_product_cuda",
sources=[
"fast_transformers/local_product/local_product_cuda.cu"
],
extra_compile_args=_get_gpu_extra_compile_args()
)
]
return extensions
def setup_package():
with open("README.rst") as f:
long_description = f.read()
meta = collect_metadata()
version_suffix = os.getenv("FAST_TRANSFORMERS_VERSION_SUFFIX", "")
setup(
name="pytorch-fast-transformers",
version=meta["version"] + version_suffix,
description=meta["description"],
long_description=long_description,
long_description_content_type="text/x-rst",
maintainer=meta["maintainer"],
maintainer_email=meta["email"],
url=meta["url"],
license=meta["license"],
classifiers=[
"Intended Audience :: Science/Research",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Topic :: Scientific/Engineering",
"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.6",
],
packages=find_packages(exclude=["docs", "tests", "scripts", "examples"]),
ext_modules=get_extensions(),
cmdclass={"build_ext": BuildExtension},
install_requires=["torch"]
)
if __name__ == "__main__":
setup_package()