“Current loss scale already at minimum - cannot decrease scale anymore” when I enabled ZERO++

deepspeed 0.10.0
here is my config：

 json = {
    "train_batch_size": 2,
    "train_micro_batch_size_per_gpu": 2,
    "steps_per_print": 20,
    "zero_allow_untested_optimizer": true,
    "zero_optimization": {
        "stage": 3,
        "reduce_bucket_size": 1.000000e+07,
        "reduce_scatter": true,
        "zero_quantized_weights": true,
        "zero_hpz_partition_size": 16,
        "zero_quantized_gradients": true,
        "contiguous_gradients": true,
        "overlap_comm": true,
        "offload_param": {
            "device": "none"
        },
        "offload_optimizer": {
            "device": "none"
        },
        "stage3_param_persistence_threshold": 1.000000e+04,
        "stage3_max_live_parameters": 3.000000e+07,
        "stage3_prefetch_bucket_size": 3.000000e+07,
        "memory_efficient_linear": false
    },
    "fp16": {
        "enabled": true,
        "loss_scale_window": 100
    },
    "gradient_clipping": 1.0,
    "prescale_gradients": false,
    "wall_clock_breakdown": false,
    "hybrid_engine": {
        "enabled": false,
        "max_out_tokens": 2.048000e+03,
        "inference_tp_size": 1,
        "release_inference_cache": false,
        "pin_parameters": true,
        "tp_gather_partition_size": 8
    }
}

model and params:

model=LlamaForCausalLM.from_pretrained("decapoda-research/llama-13b-hf")
args.zero_stage=3
args.num_train_epochs=1
args.per_device_train_batch_size = 2
args.gradient_accumulation_steps =1
args.seed=1234
args.weight_decay=0.01
args.lr_scheduler_type="cosine"
args.num_warmup_steps=500
args.learning_rate=1e-5
args.output_dir = "."

Reproduce the report using DeepSpeed 0.10.0, the supplied ZERO++/fp16 configuration, and the LlamaForCausalLM model from decapoda-research/llama-13b-hf. Trace when the loss scale reaches its minimum and determine whether the reported configuration causes it; done should identify the cause and provide a confirmed resolution or workaround.