[deepspeed] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 128, reducing to 64

[dopu2k16]

[INFO] [loss_scaler.py:183:update_scale] [deepspeed] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 128, reducing to 64

[loss_scaler.py:183:update_scale] [deepspeed] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 32, reducing to 16

[loss_scaler.py:190:update_scale] [deepspeed] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 0, but hysteresis is 2. Reducing hysteresis to 1
loss_scaler.py:183:update_scale] [deepspeed] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 0, reducing to 0

lr=2e-4
lora_rank=64
lora_alpha=128
lora_trainable="q_proj,v_proj,k_proj,o_proj,gate_proj,down_proj,up_proj"
modules_to_save="embed_tokens,lm_head"
lora_dropout=0.05
model_type="llama"
tokenizer_path_lang_vac="./vac_bpe.model"
dataset_dir_2=./data/vac/
data_cache=temp_data_cache_dir
per_device_train_batch_size=4
per_device_eval_batch_size=1
gradient_accumulation_steps=8
output_dir=vac_llama_peft_scratch_output_dir

deepspeed_config_file=ds_zero2_no_offload.json

torchrun --nnodes 1 --nproc_per_node 1 run_clm_llama_pretraining_peft.py
--deepspeed ${deepspeed_config_file}
--model_type ${model_type}
--tokenizer_name_or_path ${tokenizer_path_lang_vac}
--dataset_dir ${dataset_dir_2}
--data_cache_dir ${data_cache}
--validation_split_percentage 0.05
--per_device_train_batch_size ${per_device_train_batch_size}
--per_device_eval_batch_size ${per_device_eval_batch_size}
--do_train
--do_eval
--seed 42
--fp16
--num_train_epochs 1
--lr_scheduler_type linear
--learning_rate ${lr}
--warmup_ratio 0.05
--weight_decay 0.01
--logging_strategy steps
--logging_steps 10
--save_strategy steps
--save_total_limit 2
--save_steps 200
--gradient_accumulation_steps ${gradient_accumulation_steps}
--preprocessing_num_workers 8
--block_size 512
--output_dir ${output_dir}
--overwrite_output_dir True
--ddp_timeout 30000
--logging_first_step True
--lora_rank ${lora_rank}
--lora_alpha ${lora_alpha}
--trainable ${lora_trainable}
--lora_dropout ${lora_dropout}
--torch_dtype bfloat16
--gradient_checkpointing True
--ddp_find_unused_parameters False

May you tell me why there is overflow and the loss is not decreasing much, the model is not learning anything

[dopu2k16]

{
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 100,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1e-10
},

"zero_optimization": {
    "stage": 2,
    "allgather_partitions": true,
    "allgather_bucket_size": 1e8,
    "overlap_comm": true,
    "reduce_scatter": true,
    "reduce_bucket_size": 1e8,
    "contiguous_gradients": true
},

"gradient_accumulation_steps": "auto",
"gradient_clipping": "auto",
"steps_per_print": 2000,
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto",
"wall_clock_breakdown": false

}

Is it because bp16 is not enabled in the DeepSpeed configuration?

[dopu2k16]

Even with float16, I am getting the similar issue

[2023-09-03 22:41:56,720] [INFO] [loss_scaler.py:190:update_scale] [deepspeed] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 65536, but hysteresis is 2. Reducing hysteresis to 1

[dopu2k16]

HI, I was able to resolve it and now I don't have Overflow issues. Thank you.

[JianqiaoLu]

how do you solve it?

[yusufcakmakk]

Hi, could you share your solution fir this issue? @dopu2k16

[Chaseldot]

Could you share your solution?

[puppy2000]

so what's your solution? why not reply to us

[ghost]

can you please tell me ,how did you solve this issue?

When im facing this issue, i tried few method. 1st solution is i used to run in bf16. Second solution, i used A100 model to train llama2-7b,it solve the overflow issue