Comments (7)
单卡应该没有问题。
多卡我手边还没有机器搞……
from chatglm-6b-qlora.
单卡应该没有问题。 多卡我手边还没有机器搞……
好的,大佬看看以下报错要怎么修改
`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...
Traceback (most recent call last):
File "/media/ubuntu/chat/chatGLM-6B-QLoRA-main/train_qlora.py", line 206, in <module>
train(args)
File "/media/ubuntu/chat/chatGLM-6B-QLoRA-main/train_qlora.py", line 200, in train
trainer.train(resume_from_checkpoint=resume_from_checkpoint)
File "/root/anaconda3/envs/ql/lib/python3.9/site-packages/transformers/trainer.py", line 1645, in train
return inner_training_loop(
File "/root/anaconda3/envs/ql/lib/python3.9/site-packages/transformers/trainer.py", line 1938, in _inner_training_loop
tr_loss_step = self.training_step(model, inputs)
File "/root/anaconda3/envs/ql/lib/python3.9/site-packages/transformers/trainer.py", line 2759, in training_step
loss = self.compute_loss(model, inputs)
File "/root/anaconda3/envs/ql/lib/python3.9/site-packages/transformers/trainer.py", line 2784, in compute_loss
outputs = model(**inputs)
File "/root/anaconda3/envs/ql/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/root/anaconda3/envs/ql/lib/python3.9/site-packages/peft/peft_model.py", line 857, in forward
return self.base_model(
File "/root/anaconda3/envs/ql/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/root/anaconda3/envs/ql/lib/python3.9/site-packages/accelerate/hooks.py", line 165, in new_forward
output = old_forward(*args, **kwargs)
File "/root/.cache/huggingface/modules/transformers_modules/chatglm2-6b/modeling_chatglm.py", line 954, in forward
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
File "/root/anaconda3/envs/ql/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/root/anaconda3/envs/ql/lib/python3.9/site-packages/torch/nn/modules/loss.py", line 1174, in forward
return F.cross_entropy(input, target, weight=self.weight,
File "/root/anaconda3/envs/ql/lib/python3.9/site-packages/torch/nn/functional.py", line 3029, in cross_entropy
return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothing)
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:2 and cuda:0! (when checking argument for argument target in method wrapper_CUDA_nll_loss_forward)
from chatglm-6b-qlora.
看了下别人的代码,https://github.com/beyondguo/LLM-Tuning/blob/master/chatglm2_lora_tuning.py
102-106行的注释,似乎是和你一样的问题,看看能不能解决,但我觉得后面这块官方代码应该会更新解决的吧。
等我搞到机器我再试试多卡的……
from chatglm-6b-qlora.
看了下别人的代码,https://github.com/beyondguo/LLM-Tuning/blob/master/chatglm2_lora_tuning.py 102-106行的注释,似乎是和你一样的问题,看看能不能解决,但我觉得后面这块官方代码应该会更新解决的吧。 等我搞到机器我再试试多卡的……
辛苦拉,他这个我跑通了....
大佬加油
from chatglm-6b-qlora.
看了下别人的代码,https://github.com/beyondguo/LLM-Tuning/blob/master/chatglm2_lora_tuning.py 102-106行的注释,似乎是和你一样的问题,看看能不能解决,但我觉得后面这块官方代码应该会更新解决的吧。 等我搞到机器我再试试多卡的……
per_device_train_batch_size 填2都能爆内存,大佬有什么建议优化一下么
from chatglm-6b-qlora.
@shenmadouyaowen 他这个代码好像是lora的,8bit训练,我这个是qlora,4bit的,理论上我这个代码应该显存占用比他小,两个建议:
1、chatglm2-6b的官方模型代码重新拉一下最新的,因为之前的代码没有实现activation checkpointing,最新的代码实现了
2、如果1完成了,仍然爆显存,把那几段注释的里的代码加到我这个代码里试试,我明天估计能搞到多卡的机器,到时候我也试试
3、相对的,也可以把我qlora的配置加到他的代码里
其实我觉得就单机的话,24G显存我这个qlora应该可以很大batchsize了吧,我在12G的卡上,跑ADGEN数据集都可以到8的batchsize,都没用满
from chatglm-6b-qlora.
是的,我用他代码加入到你的代码了,然后显示爆内存,修改per_device_train_batch_size=1就可以跑了,已经拉取了最新文件,坐等大佬优化.
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
- transformers>4.30.2时,模型不会进行量化,是因为什么?
- 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:
- ChatGLM2-6b模型合并时报错ValueError: We need an `offload_dir` to dispatch this model according to this `device_map`, the following submodules need to be offloaded
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from chatglm-6b-qlora.