Making large AI models cheaper, faster and more accessible
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from .builder import Builder
from .utils import append_nvcc_threads, get_cuda_cc_flag
class ScaledUpperTrainglemaskedSoftmaxBuilder(Builder):
NAME = "scaled_upper_triangle_masked_softmax"
PREBUILT_IMPORT_PATH = "colossalai._C.scaled_upper_triangle_masked_softmax"
def __init__(self):
super().__init__(
name=ScaledUpperTrainglemaskedSoftmaxBuilder.NAME,
prebuilt_import_path=ScaledUpperTrainglemaskedSoftmaxBuilder.PREBUILT_IMPORT_PATH,
)
def include_dirs(self):
return [self.csrc_abs_path("kernels/include"), self.get_cuda_home_include()]
def sources_files(self):
ret = [
self.csrc_abs_path(fname)
for fname in ["scaled_upper_triang_masked_softmax.cpp", "scaled_upper_triang_masked_softmax_cuda.cu"]
]
return ret
def cxx_flags(self):
return ["-O3"] + self.version_dependent_macros
def nvcc_flags(self):
extra_cuda_flags = [
"-U__CUDA_NO_HALF_OPERATORS__",
"-U__CUDA_NO_HALF_CONVERSIONS__",
"--expt-relaxed-constexpr",
"--expt-extended-lambda",
]
extra_cuda_flags.extend(get_cuda_cc_flag())
ret = ["-O3", "--use_fast_math"] + extra_cuda_flags
return append_nvcc_threads(ret)