mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-04-28 19:55:29 +00:00
611a5a80ca
* [inference] add int8 rotary embedding kernel for smoothquant (#4843) * [inference] add smoothquant llama attention (#4850) * add smoothquant llama attention * remove uselss code * remove useless code * fix import error * rename file name * [inference] add silu linear fusion for smoothquant llama mlp (#4853) * add silu linear * update skip condition * catch smoothquant cuda lib exception * prcocess exception for tests * [inference] add llama mlp for smoothquant (#4854) * add llama mlp for smoothquant * fix down out scale * remove duplicate lines * add llama mlp check * delete useless code * [inference] add smoothquant llama (#4861) * add smoothquant llama * fix attention accuracy * fix accuracy * add kv cache and save pretrained * refactor example * delete smooth * refactor code * [inference] add smooth function and delete useless code for smoothquant (#4895) * add smooth function and delete useless code * update datasets * remove duplicate import * delete useless file * refactor codes (#4902) * rafactor code * add license * add torch-int and smoothquant license
53 lines
1.5 KiB
Python
53 lines
1.5 KiB
Python
import torch
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from .builder import Builder
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from .utils import append_nvcc_threads
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class SmoothquantBuilder(Builder):
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NAME = "cu_smoothquant"
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PREBUILT_IMPORT_PATH = "colossalai._C.cu_smoothquant"
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def __init__(self):
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super().__init__(name=SmoothquantBuilder.NAME, prebuilt_import_path=SmoothquantBuilder.PREBUILT_IMPORT_PATH)
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def include_dirs(self):
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ret = [self.csrc_abs_path("smoothquant"), self.get_cuda_home_include()]
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return ret
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def sources_files(self):
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ret = [
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self.csrc_abs_path(fname)
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for fname in [
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"smoothquant/binding.cpp",
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"smoothquant/linear.cu",
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]
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]
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return ret
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def cxx_flags(self):
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return ["-O3"] + self.version_dependent_macros
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def nvcc_flags(self):
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compute_capability = torch.cuda.get_device_capability()
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cuda_arch = compute_capability[0] * 100 + compute_capability[1] * 10
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extra_cuda_flags = [
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"-v",
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f"-DCUDA_ARCH={cuda_arch}",
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"-std=c++17",
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"-U__CUDA_NO_HALF_OPERATORS__",
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"-U__CUDA_NO_HALF_CONVERSIONS__",
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"-U__CUDA_NO_HALF2_OPERATORS__",
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"-DTHRUST_IGNORE_CUB_VERSION_CHECK",
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]
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ret = ["-O3", "--use_fast_math"] + self.version_dependent_macros + extra_cuda_flags
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return append_nvcc_threads(ret)
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def builder(self):
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try:
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super().builder()
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except:
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warnings.warn("build smoothquant lib not successful")
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