Making large AI models cheaper, faster and more accessible
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import torch
from .builder import Builder
from .utils import append_nvcc_threads
class SmoothquantBuilder(Builder):
NAME = "cu_smoothquant"
PREBUILT_IMPORT_PATH = "colossalai._C.cu_smoothquant"
def __init__(self):
super().__init__(name=SmoothquantBuilder.NAME, prebuilt_import_path=SmoothquantBuilder.PREBUILT_IMPORT_PATH)
def include_dirs(self):
ret = [self.csrc_abs_path("smoothquant"), self.get_cuda_home_include()]
return ret
def sources_files(self):
ret = [
self.csrc_abs_path(fname)
for fname in [
"smoothquant/binding.cpp",
"smoothquant/linear.cu",
]
]
return ret
def cxx_flags(self):
return ["-O3"] + self.version_dependent_macros
def nvcc_flags(self):
compute_capability = torch.cuda.get_device_capability()
cuda_arch = compute_capability[0] * 100 + compute_capability[1] * 10
extra_cuda_flags = [
"-v",
f"-DCUDA_ARCH={cuda_arch}",
"-std=c++17",
"-U__CUDA_NO_HALF_OPERATORS__",
"-U__CUDA_NO_HALF_CONVERSIONS__",
"-U__CUDA_NO_HALF2_OPERATORS__",
"-DTHRUST_IGNORE_CUB_VERSION_CHECK",
]
ret = ["-O3", "--use_fast_math"] + self.version_dependent_macros + extra_cuda_flags
return append_nvcc_threads(ret)
def builder(self):
try:
super().builder()
except:
warnings.warn("build smoothquant lib not successful")