How to fix unstable loss. I am using wizardlm or Llama-X training code with vicuna style chat format for fine-tuning Llama-2-7b-hf model.

[apt-team-018]

I'm using the 'Llama-X' (https://github.com/AetherCortex/Llama-X) training code with the vicuna-style chat template to fine-tune the Llama-2-7b-hf model. However, I'm observing an unstable loss during the process.

Please find the detailed Weights & Biases report at (https://wandb.ai/arpitsh018/huggingface/reports/Untitled-Report--Vmlldzo1NjE2Njgz).

Training Parameters:

os.system(f'deepspeed train.py --model_name_or_path meta-llama/Llama-2-7b-hf --data_path ../data/dummy_conversation.json --output_dir ./checkpoint/finetuned-llama2-7b --num_train_epochs 1 --model_max_length 4096 --per_device_train_batch_size 1 --per_device_eval_batch_size 1 --gradient_accumulation_steps 1 --evaluation_strategy "no" --save_strategy "steps" --save_steps 1000 --save_total_limit 3 --learning_rate 2e-5 --warmup_steps 0 --logging_steps 1 --lr_scheduler_type "cosine" --report_to "wandb" --gradient_checkpointing True --deepspeed configs/deepspeed_config.json --bf16 True')

deepspeed_config.json:

{ "zero_optimization": { "stage": 2, "offload_optimizer": { "device": "cpu", "pin_memory": true }, "offload_param": { "device": "cpu", "pin_memory": true }, "overlap_comm": true, "contiguous_gradients": true, "sub_group_size": 0, "reduce_bucket_size": "auto", "stage3_prefetch_bucket_size": "auto", "stage3_param_persistence_threshold": "auto", "stage3_max_live_parameters": 0, "stage3_max_reuse_distance": 0, "stage3_gather_16bit_weights_on_model_save": true }, "bf16": { "enabled": true }, "optimizer": { "type": "AdamW", "params": { "lr": "auto", "betas": [0.9, 0.999], "eps": 1e-8, "weight_decay": 0 } }, "scheduler": { "type": "WarmupDecayLR", "params": { "warmup_min_lr": "auto", "warmup_max_lr": "auto", "warmup_num_steps": "auto", "total_num_steps": "auto" } }, "train_batch_size": "auto", "train_micro_batch_size_per_gpu": "auto", "gradient_accumulation_steps": "auto", "wall_clock_breakdown": false }

I'm eager to understand how to stabilize the loss for my training sessions. Any insights or recommendations would be greatly appreciated.

[scorixear]

Could you explain what you mean by unstable loss? If you refer to a continuous downscaling of the loss scale during training than #4017 might be a reason. This can occur, because the used GPUs are of an "older" kind that do not support Automatic Mixed Precision (AMP) and therefore do not support BF16 as a parameter datatype. During training deepspeed might fallback to the default fp16 configuration and continue training with that. Since Llama2 Models are originally trained with bf16, swapping to fp16 will result in this loss scale issue and in the end degraded performance.