Making large AI models cheaper, faster and more accessible
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import re
import torch
from .builder import Builder
from .utils import append_nvcc_threads
class GPTQBuilder(Builder):
NAME = "cu_gptq"
PREBUILT_IMPORT_PATH = "colossalai._C.cu_gptq"
def __init__(self):
super().__init__(name=GPTQBuilder.NAME, prebuilt_import_path=GPTQBuilder.PREBUILT_IMPORT_PATH)
def include_dirs(self):
ret = [self.csrc_abs_path("gptq"), self.get_cuda_home_include()]
return ret
def sources_files(self):
ret = [
self.csrc_abs_path(fname)
for fname in [
"gptq/linear_gptq.cpp",
"gptq/column_remap.cu",
"gptq/cuda_buffers.cu",
"gptq/q4_matmul.cu",
"gptq/q4_matrix.cu",
]
]
return ret
def cxx_flags(self):
return ["-O3"] + self.version_dependent_macros
def nvcc_flags(self):
extra_cuda_flags = [
"-v",
"-std=c++14",
"-std=c++17",
"-U__CUDA_NO_HALF_OPERATORS__",
"-U__CUDA_NO_HALF_CONVERSIONS__",
"-U__CUDA_NO_HALF2_OPERATORS__",
"-DTHRUST_IGNORE_CUB_VERSION_CHECK",
"-lcublas",
]
for arch in torch.cuda.get_arch_list():
res = re.search(r"sm_(\d+)", arch)
if res:
arch_cap = res[1]
if int(arch_cap) >= 80:
extra_cuda_flags.extend(["-gencode", f"arch=compute_{arch_cap},code={arch}"])
ret = ["-O3", "--use_fast_math"] + self.version_dependent_macros + extra_cuda_flags
return append_nvcc_threads(ret)