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setup.py
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setup.py
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import os
import torch
from pathlib import Path
from setuptools import setup, find_packages
from distutils.sysconfig import get_python_lib
from torch.utils.cpp_extension import BuildExtension, CUDA_HOME, CUDAExtension
os.environ["CC"] = "g++"
os.environ["CXX"] = "g++"
common_setup_kwargs = {
"version": "0.1.4",
"name": "autoawq",
"author": "Casper Hansen",
"license": "MIT",
"python_requires": ">=3.8.0",
"description": "AutoAWQ implements the AWQ algorithm for 4-bit quantization with a 2x speedup during inference.",
"long_description": (Path(__file__).parent / "README.md").read_text(encoding="UTF-8"),
"long_description_content_type": "text/markdown",
"url": "https://github.com/casper-hansen/AutoAWQ",
"keywords": ["awq", "autoawq", "quantization", "transformers"],
"platforms": ["linux", "windows"],
"classifiers": [
"Environment :: GPU :: NVIDIA CUDA :: 11.8",
"Environment :: GPU :: NVIDIA CUDA :: 12",
"License :: OSI Approved :: MIT License",
"Natural Language :: English",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: C++",
]
}
requirements = [
"torch>=2.0.0",
"transformers>=4.34.0",
"tokenizers>=0.12.1",
"accelerate",
"sentencepiece",
"lm_eval",
"texttable",
"toml",
"attributedict",
"protobuf",
"torchvision",
"tabulate"
]
def get_include_dirs():
include_dirs = []
conda_cuda_include_dir = os.path.join(get_python_lib(), "nvidia/cuda_runtime/include")
if os.path.isdir(conda_cuda_include_dir):
include_dirs.append(conda_cuda_include_dir)
this_dir = os.path.dirname(os.path.abspath(__file__))
include_dirs.append(this_dir)
return include_dirs
def get_generator_flag():
generator_flag = []
torch_dir = torch.__path__[0]
if os.path.exists(os.path.join(torch_dir, "include", "ATen", "CUDAGeneratorImpl.h")):
generator_flag = ["-DOLD_GENERATOR_PATH"]
return generator_flag
def check_dependencies():
if CUDA_HOME is None:
raise RuntimeError(
f"Cannot find CUDA_HOME. CUDA must be available to build the package.")
def get_compute_capabilities():
# Collect the compute capabilities of all available GPUs.
for i in range(torch.cuda.device_count()):
major, minor = torch.cuda.get_device_capability(i)
cc = major * 10 + minor
if cc < 75:
raise RuntimeError("GPUs with compute capability less than 7.5 are not supported.")
# figure out compute capability
compute_capabilities = {75, 80, 86, 89, 90}
capability_flags = []
for cap in compute_capabilities:
capability_flags += ["-gencode", f"arch=compute_{cap},code=sm_{cap}"]
return capability_flags
check_dependencies()
include_dirs = get_include_dirs()
generator_flags = get_generator_flag()
arch_flags = get_compute_capabilities()
if os.name == "nt":
include_arch = os.getenv("INCLUDE_ARCH", "1") == "1"
# Relaxed args on Windows
if include_arch:
extra_compile_args={"nvcc": arch_flags}
else:
extra_compile_args={}
else:
extra_compile_args={
"cxx": ["-g", "-O3", "-fopenmp", "-lgomp", "-std=c++17", "-DENABLE_BF16"],
"nvcc": [
"-O3",
"-std=c++17",
"-DENABLE_BF16",
"-U__CUDA_NO_HALF_OPERATORS__",
"-U__CUDA_NO_HALF_CONVERSIONS__",
"-U__CUDA_NO_BFLOAT16_OPERATORS__",
"-U__CUDA_NO_BFLOAT16_CONVERSIONS__",
"-U__CUDA_NO_BFLOAT162_OPERATORS__",
"-U__CUDA_NO_BFLOAT162_CONVERSIONS__",
"--expt-relaxed-constexpr",
"--expt-extended-lambda",
"--use_fast_math",
] + arch_flags + generator_flags
}
extensions = [
CUDAExtension(
"awq_inference_engine",
[
"awq_cuda/pybind_awq.cpp",
"awq_cuda/quantization/gemm_cuda_gen.cu",
"awq_cuda/layernorm/layernorm.cu",
"awq_cuda/position_embedding/pos_encoding_kernels.cu",
"awq_cuda/quantization/gemv_cuda.cu"
], extra_compile_args=extra_compile_args
)
]
if os.name != "nt":
extensions.append(
CUDAExtension(
"ft_inference_engine",
[
"awq_cuda/pybind_ft.cpp",
"awq_cuda/attention/ft_attention.cpp",
"awq_cuda/attention/decoder_masked_multihead_attention.cu"
], extra_compile_args=extra_compile_args
)
)
additional_setup_kwargs = {
"ext_modules": extensions,
"cmdclass": {'build_ext': BuildExtension}
}
common_setup_kwargs.update(additional_setup_kwargs)
setup(
packages=find_packages(),
install_requires=requirements,
include_dirs=include_dirs,
**common_setup_kwargs
)