当你保存checkpoint时,zero_to_fp32.py脚本会自动生成。注意:目前该脚本使用的内存(通用RAM)是最终checkpoint大小的两倍。 或者,如果你有足够的CPU内存,并且想要将模型更新为其fp32权重,您可以在训练结束时执行以下操作: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 from deepspeed.utils.z
是从优化器状态上读取fp32原始权重(因为直接保存的权重可能是bf16),然后再还原回完整模型权重。 中间可能有注意不到的精度误差。 发布于 2025-02-13 20:19・浙江 deepspeed 深度学习(Deep Learning) 分布式训练 赞同1添加评论 分享喜欢收藏申请转载 ...
zero_to_fp32.py文件 Thezero_to_fp32.pyscript is typically used in the context of training deep learning models using mixed precision, particularly with libraries like Microsoft's DeepSpeed. The script converts a model's checkpoint saved in mixed precision format to the standard single precision ...
you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with the checkpoint. A typical usage might be :: from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint # do the training and checkpoint saving state_dict = ...
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective. - DeepSpeed/deepspeed/utils/zero_to_fp32.py at b647fb2470f8f6fefe5cab0ea84a2d89696eb898 · deepspeedai/DeepSpeed
流程是先使用zero_to_fp32.py生成pytorch_model.bin,转为float16,然后重命名成adapter_model.bin,最后用原版baichuan2-base和这个25G的adapter_model.bin合并。 hiyouga commented on Sep 11, 2023 hiyouga on Sep 11, 2023 Owner @zzy99 更新代码后无需转换 👍1 zzy99 commented on Sep 11, 2023 zzy...
模型参数(fp16)、模型梯度(fp16)和Adam状态(fp32的模型参数备份,fp32的momentum和fp32的variance)。假设模型参数量 Φ ,则共需要 2Φ+2Φ+(4Φ+4Φ+4Φ)=4Φ+12Φ=16Φ 字节存储。 优化剩余空间 优化了模型的空间利用率之后,接下来就要优化激活值、临时缓冲区和不可用空间碎片。
ZeRO-1 非常适合使用类似Adam进行优化的模型训练,因为Adam拥有额外的参数m(momentum)与v(variance),特别是FP16混合精度训练。 ZeRO-1 不适合使用SGD类似的优化器进行模型训练,因为SGD只有较少的参数内存,并且由于需要更新模型参数,导致额外的通讯成本。 1.3.2 ZeRO-2 ...
zero_to_fp32.py文件 Thezero_to_fp32.pyscript is typically used in the context of training deep learning models using mixed precision, particularly with libraries like Microsoft's DeepSpeed. The script converts a model's checkpoint saved in mixed precision format to the standard single precision...
you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with the checkpoint. A typical usage might be :: from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint # do the training and checkpoint saving state_dict = ...