Making large AI models cheaper, faster and more accessible
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import os
from .builder import Builder
from .utils import append_nvcc_threads, get_cuda_cc_flag
class MultiHeadAttnBuilder(Builder):
NAME = "multihead_attention"
PREBUILT_IMPORT_PATH = "colossalai._C.multihead_attention"
def __init__(self):
super().__init__(name=MultiHeadAttnBuilder.NAME,
prebuilt_import_path=MultiHeadAttnBuilder.PREBUILT_IMPORT_PATH)
def include_dirs(self):
ret = [self.csrc_abs_path("kernels/include"), self.get_cuda_home_include()]
return ret
def sources_files(self):
ret = [
self.csrc_abs_path(fname) for fname in [
'multihead_attention_1d.cpp', 'kernels/cublas_wrappers.cu', 'kernels/transform_kernels.cu',
'kernels/dropout_kernels.cu', 'kernels/normalize_kernels.cu', 'kernels/softmax_kernels.cu',
'kernels/general_kernels.cu', 'kernels/cuda_util.cu'
]
]
return ret
def cxx_flags(self):
return ['-O3'] + self.version_dependent_macros
def nvcc_flags(self):
extra_cuda_flags = [
'-std=c++14', '-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__',
'-U__CUDA_NO_HALF2_OPERATORS__', '-DTHRUST_IGNORE_CUB_VERSION_CHECK'
]
extra_cuda_flags.extend(get_cuda_cc_flag())
ret = ['-O3', '--use_fast_math'] + self.version_dependent_macros + extra_cuda_flags
return append_nvcc_threads(ret)