Comments (8)
1代模型吗?你batchsize多少?
from chatglm-6b-qlora.
脚本参数 fp32 换成fp16 这样显存只有12.4G
再开quantinize = int4 来SFT 显存只用6.5G
from chatglm-6b-qlora.
脚本参数 fp32 换成fp16 这样显存只有12.4G 再开quantinize = int4 来SFT 显存只用6.5G
已经是fp16了,内存占用不断变化,有时到30g
from chatglm-6b-qlora.
@sxm7078 有修改代码吗?因为代码里默认是int4的加载模型,显存占用应该很小的。
之前2代模型刚出来的时候没有实现activate checkpointing,导致显存占用很大,后来修复了,拉最新的模型就OK了
from chatglm-6b-qlora.
@sxm7078 有修改代码吗?因为代码里默认是int4的加载模型,显存占用应该很小的。 之前2代模型刚出来的时候没有实现activate checkpointing,导致显存占用很大,后来修复了,拉最新的模型就OK
微调的chatglm-6b的模型,lora_rank改为8,compute_dtype改为fp16。transformers==4.30.2和accelerate==0.20.3安装的是这个版本,还是dev版本
from chatglm-6b-qlora.
@sxm7078 现在用这个环境transformers==4.30.2和accelerate==0.20.3,不用dev了,但我觉得你应该不是dev的版本问题
from chatglm-6b-qlora.
我也是这样,使用的是Author的默认参数配置,用的4090卡,直接报CUDA out of memory,看了下报错信息,好像是在prepare_model_for_kbit_training函数里的 param.data = param.data.to(torch.float32)这一步出现显存占用异常
File "/home/lc/anaconda3/envs/pytorch/lib/python3.11/site-packages/peft/utils/other.py", line 81, in prepare_model_for_kbit_training
param.data = param.data.to(torch.float32)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 428.00 MiB (GPU 0; 23.65 GiB total capacity; 21.69 GiB already allocated; 96.00 MiB free; 22.62 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
from chatglm-6b-qlora.
我也是这样,使用的是Author的默认参数配置,用的4090卡,直接报CUDA out of memory,看了下报错信息,好像是在prepare_model_for_kbit_training函数里的 param.data = param.data.to(torch.float32)这一步出现显存占用异常 File "/home/lc/anaconda3/envs/pytorch/lib/python3.11/site-packages/peft/utils/other.py", line 81, in prepare_model_for_kbit_training param.data = param.data.to(torch.float32) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 428.00 MiB (GPU 0; 23.65 GiB total capacity; 21.69 GiB already allocated; 96.00 MiB free; 22.62 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
请问这个问题解决了吗?
from chatglm-6b-qlora.
Related Issues (20)
- 数据集格式instruction,input,output时tokenized出错 HOT 2
- 训练结束后,数据展示出现问题
- merge后模型Loading checkpoint shards Killed HOT 6
- 调优后的模型是否可以在CPU服务上部署? HOT 1
- protobuf 和 icetk 版本不兼容 HOT 1
- ImportError: libcudnn.so.8: cannot open shared object file: No such file or directory HOT 12
- RuntimeError: mat1 and mat2 shapes cannot be multiplied (588x4096 and 1x9437184) ,ChatGLM2-6B做微调,请问如何设置参数?? HOT 1
- RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:1 and cuda:0! (when checking argument for argument target in method wrapper_CUDA_nll_loss_forward),请问如何调整训练代码,适配多卡训练?? HOT 1
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- BrokenPipeError: [Errno 32] Broken pipe HOT 7
- 训练结束后没有保存完整的adapter文件 HOT 2
- 用V100显卡微调chatglm2-6b,但是loss一直为0,eval_loss=nan
- 训练显存有些问题
- 微调后推理性能问题 HOT 1
- ChatGLM3-6B 微调报错 RuntimeError: CUDA error: invalid argument HOT 1
- ChatGLM2-6B微调后合并报错We need an `offload_dir` to dispatch this model according to this `device_map`, the following submodules need to be offloaded: HOT 2
- 合并模型的时候显存不够,用的4090,24G
- qlora微调训练随机性
- ValueError:
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