command error: ''Adafactor is already registered in optimizer at torch.optim''

xtuner train cfg-gamma7b/gemma_7b_it_qlora_alpaca_e3_copy.py --deepspeed deepspeed_zero2
10/28 18:19:05 - mmengine - WARNING - WARNING: command error: ''Adafactor is already registered in optimizer at torch.optim''!
10/28 18:19:05 - mmengine - WARNING -
Arguments received: ['xtuner', 'train', 'cfg-gamma7b/gemma_7b_it_qlora_alpaca_e3_copy.py', '--deepspeed', 'deepspeed_zero2']. xtuner commands use the following syntax:

xtuner MODE MODE_ARGS ARGS

Where MODE (required) is one of ('list-cfg', 'copy-cfg', 'log-dataset', 'check-custom-dataset', 'train', 'test', 'chat', 'convert', 'preprocess', 'mmbench', 'eval_refcoco')
MODE_ARG (optional) is the argument for specific mode
ARGS (optional) are the arguments for specific command

Some usages for xtuner commands: (See more by using -h for specific command!)

1. List all predefined configs:
xtuner list-cfg
2. Copy a predefined config to a given path:
xtuner copy-cfg $CONFIG $SAVE_FILE
3-1. Fine-tune LLMs by a single GPU:
xtuner train $CONFIG
3-2. Fine-tune LLMs by multiple GPUs:
NPROC_PER_NODE=$NGPUS NNODES=$NNODES NODE_RANK=$NODE_RANK PORT=$PORT ADDR=$ADDR xtuner dist_train $CONFIG $GPUS
4-1. Convert the pth model to HuggingFace's model:
xtuner convert pth_to_hf $CONFIG $PATH_TO_PTH_MODEL $SAVE_PATH_TO_HF_MODEL
4-2. Merge the HuggingFace's adapter to the pretrained base model:
xtuner convert merge $LLM $ADAPTER $SAVE_PATH
xtuner convert merge $CLIP $ADAPTER $SAVE_PATH --is-clip
4-3. Split HuggingFace's LLM to the smallest sharded one:
xtuner convert split $LLM $SAVE_PATH
5-1. Chat with LLMs with HuggingFace's model and adapter:
xtuner chat $LLM --adapter $ADAPTER --prompt-template $PROMPT_TEMPLATE --system-template $SYSTEM_TEMPLATE
5-2. Chat with VLMs with HuggingFace's model and LLaVA:
xtuner chat $LLM --llava $LLAVA --visual-encoder $VISUAL_ENCODER --image $IMAGE --prompt-template $PROMPT_TEMPLATE --system-template $SYSTEM_TEMPLATE
6-1. Preprocess arxiv dataset:
xtuner preprocess arxiv $SRC_FILE $DST_FILE --start-date $START_DATE --categories $CATEGORIES
6-2. Preprocess refcoco dataset:
xtuner preprocess refcoco --ann-path $RefCOCO_ANN_PATH --image-path $COCO_IMAGE_PATH --save-path $SAVE_PATH
7-1. Log processed dataset:
xtuner log-dataset $CONFIG
7-2. Verify the correctness of the config file for the custom dataset:
xtuner check-custom-dataset $CONFIG
8. MMBench evaluation:
xtuner mmbench $LLM --llava $LLAVA --visual-encoder $VISUAL_ENCODER --prompt-template $PROMPT_TEMPLATE --data-path $MMBENCH_DATA_PATH
9. Refcoco evaluation:
xtuner eval_refcoco $LLM --llava $LLAVA --visual-encoder $VISUAL_ENCODER --prompt-template $PROMPT_TEMPLATE --data-path $REFCOCO_DATA_PATH
10. List all dataset formats which are supported in XTuner

Run special commands:

xtuner help
xtuner version

GitHub: https://github.com/InternLM/xtuner

**libs used:**

datasets 3.0.2
torch 2.5.0
torchvision 0.20.0
transformers 4.46.0
xtuner 0.1.23
peft 0.13.2
numpy 2.1.2
nvidia-cublas-cu12 12.4.5.8
nvidia-cuda-cupti-cu12 12.4.127
nvidia-cuda-nvrtc-cu12 12.4.127
nvidia-cuda-runtime-cu12 12.4.127
nvidia-cudnn-cu12 9.1.0.70
nvidia-cufft-cu12 11.2.1.3
nvidia-curand-cu12 10.3.5.147
nvidia-cusolver-cu12 11.6.1.9
nvidia-cusparse-cu12 12.3.1.170
nvidia-ml-py 12.560.30
nvidia-nccl-cu12 2.21.5
nvidia-nvjitlink-cu12 12.4.127
nvidia-nvtx-cu12 12.4.127