# Transformer's Trainer Memory shared Error **URL:** \ **Category:** 🤗Transformers\ **Created:** [December 5, 2024, 10:07am UTC](https://discuss.huggingface.co/t/transformers-trainer-memory-shared-error/129381 "2024-12-05T10:07:56Z")\ **Posts on this page:** 1\ **Showing post:** 2
**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\ **Post date:** [December 5, 2024, 10:21am UTC](https://discuss.huggingface.co/t/transformers-trainer-memory-shared-error/129381/2 "2024-12-05T10:21:44Z")
Perhaps same error? > [@Transformer's trainer runtime error](https://discuss.huggingface.co/t/transformers-trainer-runtime-error/129375): > > I performed fine tuning flan-t5-base model using peft. here is my peft config code peft\_model = get\_peft\_model(original\_model, lora\_config).to('cuda') output\_dir = f'/kaggle/working/peft-dialogue-summary-lora-training-{str(int(time.time()))}' peft\_training\_args = TrainingArguments( output\_dir=output\_dir, per\_device\_train\_batch\_size=8, gradient\_accumulation\_steps=4, learning\_rate=1e-3, # Higher learning rate than full fine-tuning. num\_train\_epo… Edit: > > > \### Reminder > > \- \[X\] I have read the README and searched the existing issues. > > \##…# Reproduction > > WANDB\_DISABLED=1 NCCL\_P2P\_DISABLE=1 NCCL\_IB\_DISABLE=1 deepspeed --num\_gpus 2 --master\_port=9527 /workspace/projects/LLaMA-Factory/src/train\_bash.py \\ > --stage rm \\ > --do\_train \\ > --deepspeed xxxxxxxx/ds\_z3\_offload\_config.json \\ > --model\_name\_or\_path xxxxxxx/chatglm3-6b \\ > --adapter\_name\_or\_path /xxx/chatglm\_exp\_sft\_lora\_llamafactory \\ > --create\_new\_adapter \\ > --dataset comparison\_gpt4\_zh \\ > --dataset\_dir xxx/data \\ > --template chatglm3 \\ > --finetuning\_type lora \\ > --lora\_target query\_key\_value \\ > --output\_dir xxx/chatglm\_exp\_rm\_lora\_llamafactory \\ > --overwrite\_cache \\ > --overwrite\_output\_dir \\ > --cutoff\_len 1024 \\ > --preprocessing\_num\_workers 4 \\ > --per\_device\_train\_batch\_size 2 \\ > --per\_device\_eval\_batch\_size 1 \\ > --gradient\_accumulation\_steps 4 \\ > --lr\_scheduler\_type cosine \\ > --logging\_steps 10 \\ > --warmup\_steps 20 \\ > --save\_steps 10 \\ > --eval\_steps 20 \\ > --evaluation\_strategy steps \\ > --learning\_rate 1e-5 \\ > --num\_train\_epochs 2.0 \\ > --max\_samples 5000 \\ > --val\_size 0.1 \\ > --plot\_loss \\ > --fp16 > 可以正常训练，但是保存checkpoint时候提示如下错误： > \[INFO|trainer.py:3305\] 2024-04-23 16:56:46,579 \>\> Saving model checkpoint to /workspace/models/huggingface/chatglm32k\_rm\_sft\_lora\_llamafactory/checkpoint-10 > \[INFO|trainer.py:3319\] 2024-04-23 16:56:46,587 \>\> Trainer.model is not a \`PreTrainedModel\`, only saving its state dict. > Traceback (most recent call last): > File "/workspace/projects/LLaMA-Factory/src/train\_bash.py", line 14, in \ > main() > File "/workspace/projects/LLaMA-Factory/src/train\_bash.py", line 5, in main > run\_exp() > File "/workspace/projects/LLaMA-Factory/src/llmtuner/train/tuner.py", line 35, in run\_exp > run\_rm(model\_args, data\_args, training\_args, finetuning\_args, callbacks) > File "/workspace/projects/LLaMA-Factory/src/llmtuner/train/rm/workflow.py", line 50, in run\_rm > train\_result = trainer.train(resume\_from\_checkpoint=training\_args.resume\_from\_checkpoint) > File "/opt/conda/lib/python3.10/site-packages/transformers/trainer.py", line 1859, in train > return inner\_training\_loop( > File "/opt/conda/lib/python3.10/site-packages/transformers/trainer.py", line 2278, in \_inner\_training\_loop > self.\_maybe\_log\_save\_evaluate(tr\_loss, grad\_norm, model, trial, epoch, ignore\_keys\_for\_eval) > File "/opt/conda/lib/python3.10/site-packages/transformers/trainer.py", line 2673, in \_maybe\_log\_save\_evaluate > self.\_save\_checkpoint(model, trial, metrics=metrics) > File "/opt/conda/lib/python3.10/site-packages/transformers/trainer.py", line 2752, in \_save\_checkpoint > self.save\_model(output\_dir, \_internal\_call=True) > File "/opt/conda/lib/python3.10/site-packages/transformers/trainer.py", line 3239, in save\_model > self.\_save(output\_dir, state\_dict=state\_dict) > File "/opt/conda/lib/python3.10/site-packages/transformers/trainer.py", line 3321, in \_save > safetensors.torch.save\_file( > File "/opt/conda/lib/python3.10/site-packages/safetensors/torch.py", line 284, in save\_file > serialize\_file(\_flatten(tensors), filename, metadata=metadata) > File "/opt/conda/lib/python3.10/site-packages/safetensors/torch.py", line 480, in \_flatten > raise RuntimeError( > RuntimeError: > Some tensors share memory, this will lead to duplicate memory on disk and potential differences when loading them again: \[{'pretrained\_model.base\_model.model.lm\_head.weight', 'pretrained\_model.base\_model.model.transformer.output\_layer.weight'}\]. > A potential way to correctly save your model is to use \`save\_model\`. > More information at https://huggingface.co/docs/safetensors/torch\_shared\_tensors > > \### Expected behavior > > 能够保存rm的checkpoints并顺利完成训练 > > \### System Info > > Copy-and-paste the text below in your GitHub issue and FILL OUT the two last points. > > \- \`transformers\` version: 4.40.0 > \- Platform: Linux-5.15.0-101-generic-x86\_64-with-glibc2.31 > \- Python version: 3.10.11 > \- Huggingface\_hub version: 0.22.2 > \- Safetensors version: 0.4.3 > \- Accelerate version: 0.29.3 > \- Accelerate config: not found > \- PyTorch version (GPU?): 2.0.1 (True) > \- Tensorflow version (GPU?): not installed (NA) > \- Flax version (CPU?/GPU?/TPU?): not installed (NA) > \- Jax version: not installed > \- JaxLib version: not installed > \- Using GPU in script?: \ > \- Using distributed or parallel set-up in script?: \ > > \### Others > > 无 ```auto --save_safetensors False ``` --- _[View the full topic](https://discuss.huggingface.co/t/transformers-trainer-memory-shared-error/129381)._