Error after many steps of Training (IndexError: list index out of range)

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I'm trying to train a model using train_mem.py and I get the following error after many steps of training (1269 steps). The error is shown below:

```
IndexError: list index out of range
WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 129493 closing signal SIGTERM
WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 129495 closing signal SIGTERM
WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 129496 closing signal SIGTERM
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 1 (pid: 129494)
```

Did anyone face a similar issue during training? If so how did you address this issue?

_More information about my bash script:_

```
#!/bin/bash --login
#SBATCH --job-name FastChat
#SBATCH --time=48:00:00
#SBATCH --gres=gpu:a100:4
#SBATCH --cpus-per-gpu=12
#SBATCH --mem=500G
#SBATCH -o Output/gpu.%A.out
#SBATCH -e Error/gpu.%A.err
#SBATCH --reservation=A100

source activate chatbot

module load gcc/11.1.0
module load cuda/11.7.0

torchrun --nnodes=1 --nproc_per_node=4 --master_port=4141 \
fastchat/train/train_mem.py \
--model_name_or_path "./LLaMA_13B" \
--data_path "my_data.json" \
--bf16 True \
--output_dir output_13b_x \
--num_train_epochs 3 \
--per_device_train_batch_size 3 \
--per_device_eval_batch_size 16 \
--gradient_accumulation_steps 4 \
--evaluation_strategy "steps" \
--eval_steps 1500 \
--save_strategy "steps" \
--save_steps 100 \
--save_total_limit 1 \
--learning_rate 2e-5 \
--weight_decay 0. \
--warmup_ratio 0.04 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--fsdp "full_shard auto_wrap offload" \
--fsdp_transformer_layer_cls_to_wrap 'LlamaDecoderLayer' \
--tf32 True \
--model_max_length 2048 \
--gradient_checkpointing True \
--lazy_preprocess True

```

The failure occurs while running fastchat/train/train_mem.py via torchrun with four processes and FSDP; start by capturing the complete traceback rather than only the final IndexError, then inspect the training step around 1269 and the data in my_data.json. Done means identifying the failing list access and confirming the same command completes training without the exception.

Written by the indexing model from the issue text.