[BUG]RuntimeError: the new group's world size should be less or equal to the world size set by init_process_group

Describe the bug
i have 4 gpus，but i set mp_size=3, it goes wrong

To Reproduce
Steps to reproduce the behavior:

model_name = "/data/share/rwq/Qwen-7B-Chat"
payload = "你好"
tokenizer = AutoTokenizer.from_pretrained(model_name,trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name)

ds_model = deepspeed.init_inference(
    model=model,      # Transformers模型
    mp_size=3,        # GPU数量
    dtype=torch.float16, # 权重类型(fp16)
    replace_method="auto", # 让DS自动替换层
    replace_with_kernel_inject=True, # 使用kernel injector替换
)
print(f"模型加载至设备{ds_model.module.device}\n")
#assert isinstance(ds_model.module.transformer.h[0], DeepSpeedTransformerInference) == True, "Model not sucessfully initalized"

the error info:

RuntimeError: the new group's world size should be less or equal to the world size set by init_process_group

ds_report output

[2024-01-17 19:27:54,435] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect)
--------------------------------------------------
DeepSpeed C++/CUDA extension op report
--------------------------------------------------
NOTE: Ops not installed will be just-in-time (JIT) compiled at
      runtime if needed. Op compatibility means that your system
      meet the required dependencies to JIT install the op.
--------------------------------------------------
JIT compiled ops requires ninja
ninja .................. [OKAY]
--------------------------------------------------
op name ................ installed .. compatible
--------------------------------------------------
 [WARNING]  async_io requires the dev libaio .so object and headers but these were not found.
 [WARNING]  async_io: please install the libaio-dev package with apt
 [WARNING]  If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
async_io ............... [NO] ....... [NO]
fused_adam ............. [NO] ....... [OKAY]
cpu_adam ............... [NO] ....... [OKAY]
cpu_adagrad ............ [NO] ....... [OKAY]
cpu_lion ............... [NO] ....... [OKAY]
 [WARNING]  Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
evoformer_attn ......... [NO] ....... [NO]
fused_lamb ............. [NO] ....... [OKAY]
fused_lion ............. [NO] ....... [OKAY]
inference_core_ops ..... [NO] ....... [OKAY]
cutlass_ops ............ [NO] ....... [OKAY]
quantizer .............. [NO] ....... [OKAY]
ragged_device_ops ...... [NO] ....... [OKAY]
ragged_ops ............. [NO] ....... [OKAY]
random_ltd ............. [NO] ....... [OKAY]
 [WARNING]  sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.1
 [WARNING]  using untested triton version (2.1.0), only 1.0.0 is known to be compatible
sparse_attn ............ [NO] ....... [NO]
spatial_inference ...... [NO] ....... [OKAY]
transformer ............ [NO] ....... [OKAY]
stochastic_transformer . [NO] ....... [OKAY]
transformer_inference .. [NO] ....... [OKAY]
--------------------------------------------------
DeepSpeed general environment info:
torch install path ............... ['/home/powerop/work/conda/envs/ds/lib/python3.10/site-packages/torch']
torch version .................... 2.1.2+cu118
deepspeed install path ........... ['/home/powerop/work/conda/envs/ds/lib/python3.10/site-packages/deepspeed']
deepspeed info ................... 0.12.3, unknown, unknown
torch cuda version ............... 11.8
torch hip version ................ None
nvcc version ..................... 11.8
deepspeed wheel compiled w. ...... torch 2.1, cuda 11.8
shared memory (/dev/shm) size .... 157.04 GB

Screenshots
If applicable, add screenshots to help explain your problem.

System info (please complete the following information):

• OS: Ubuntu 20.04
• GPU count and types 4 x A100s
• Python version: 3.10.12

Start at the provided deepspeed.init_inference call and inspect how its mp_size is applied to the initialized process group. Reproduce the snippet with four A100 GPUs and mp_size=3, then verify the resulting process-group behavior and error handling against the reported RuntimeError.