"""
Common utilities for testing and benchmarking on NPU.

This file contains the following weight path categories:
- LLM model weights path
- VLM model weights path
- Embedding model weights path
- Rerank model weights path
- Reward model weights path

Please remember to sort by variable name within each section.
"""

import asyncio
import copy
import os
import subprocess
from types import SimpleNamespace
from typing import Awaitable, Callable, NamedTuple, Optional

from sglang.bench_serving import run_benchmark
from sglang.srt.utils import kill_process_tree
from sglang.test.test_utils import (
    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
    DEFAULT_URL_FOR_TEST,
    auto_config_device,
    popen_launch_server,
)

# Model weights storage directory
MODEL_WEIGHTS_DIR = "/root/.cache/modelscope/hub/models/"
HF_MODEL_WEIGHTS_DIR = "/root/.cache/huggingface/hub/"

# LLM model weights path
AFM_4_5B_BASE_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "arcee-ai/AFM-4.5B-Base")
BAICHUAN2_13B_CHAT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "baichuan-inc/Baichuan2-13B-Chat"
)
C4AI_COMMAND_R_V01_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "CohereForAI/c4ai-command-r-v01"
)
C4AI_COMMAND_R_V01_CHAT_TEMPLATE_PATH = "/__w/sglang/sglang/test/registered/ascend/llm_models/tool_chat_template_c4ai_command_r_v01.jinja"
CHATGLM2_6B_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "ZhipuAI/chatglm2-6b")
DBRX_INSTRUCT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "AI-ModelScope/dbrx-instruct"
)
DEEPSEEK_V3_2_EXP_W8A8_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "DeepSeek-V3.2-Exp-W8A8"
)
DEEPSEEK_V3_2_W8A8_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "vllm-ascend/DeepSeek-V3.2-W8A8"
)
DEEPSEEK_CODER_V2_LITE_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct"
)
DEEPSEEK_CODER_1_3_B_BASE_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "deepseek-ai/deepseek-coder-1.3b-base"
)
ERNIE_4_5_21B_A3B_PT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "baidu/ERNIE-4.5-21B-A3B-PT"
)
EXAONE_3_5_7_8B_INSTRUCT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct"
)
GEMMA_3_4B_IT_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "google/gemma-3-4b-it")
GLM_4_9B_CHAT_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "ZhipuAI/glm-4-9b-chat")
GRANITE_3_0_3B_A800M_INSTRUCT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "ibm-granite/granite-3.0-3b-a800m-instruct"
)
GRANITE_3_1_8B_INSTRUCT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "ibm-granite/granite-3.1-8b-instruct"
)
GROK_2_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "huihui-ai/grok-2")
INTERNLM2_7B_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "Shanghai_AI_Laboratory/internlm2-7b"
)
KIMI_K2_THINKING_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "Kimi/Kimi-K2-Thinking")
LING_LITE_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "inclusionAI/Ling-lite")
LLAMA_2_7B_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "LLM-Research/Llama-2-7B")
LLAMA_3_1_8B_INSTRUCT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "AI-ModelScope/Llama-3.1-8B-Instruct"
)
LLAMA_3_2_1B_INSTRUCT_TOOL_CALLING_LORA_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "codelion/Llama-3.2-1B-Instruct-tool-calling-lora"
)
LLAMA_3_2_1B_INSTRUCT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "LLM-Research/Llama-3.2-1B-Instruct"
)
LLAMA_3_2_1B_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "LLM-Research/Llama-3.2-1B")
LLAMA_4_SCOUT_17B_16E_INSTRUCT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "meta-llama/Llama-4-Scout-17B-16E-Instruct"
)
META_LLAMA_3_1_8B_INSTRUCT = os.path.join(
    MODEL_WEIGHTS_DIR, "LLM-Research/Meta-Llama-3.1-8B-Instruct"
)
MIMO_7B_RL_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "XiaomiMiMo/MiMo-7B-RL")
MINICPM3_4B_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "OpenBMB/MiniCPM3-4B")
MISTRAL_7B_INSTRUCT_V0_2_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "mistralai/Mistral-7B-Instruct-v0.2"
)
OLMOE_1B_7B_0924_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "allenai/OLMoE-1B-7B-0924"
)
PERSIMMON_8B_CHAT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "Howeee/persimmon-8b-chat"
)
PHI_4_MULTIMODAL_INSTRUCT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "microsoft/Phi-4-multimodal-instruct"
)
QWEN2_5_7B_INSTRUCT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "Qwen/Qwen2.5-7B-Instruct"
)
QWEN3_0_6B_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "Qwen/Qwen3-0.6B")
QWEN3_1_7B_GPTQ_INT8_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "Qwen/Qwen3-1.7B-GPTQ-Int8"
)
QWEN3_235B_A22B_W8A8_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "vllm-ascend/Qwen3-235B-A22B-W8A8"
)
QWEN3_30B_A3B_INSTRUCT_2507_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "Qwen/Qwen3-30B-A3B-Instruct-2507"
)
QWEN3_8B_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "Qwen/Qwen3-8B")
QWEN3_8B_EAGLE3_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "Qwen/Qwen3-8B_eagle3")
QWEN3_32B_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "Qwen/Qwen3-32B")
QWEN3_CODER_480B_A35B_INSTRUCT_W8A8_QUAROT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "Qwen3-Coder-480B-A35B-Instruct-w8a8-QuaRot"
)
QWEN3_NEXT_80B_A3B_INSTRUCT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "Qwen/Qwen3-Next-80B-A3B-Instruct"
)
QWEN3_32B_EAGLE3_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "Qwen/Qwen3-32B-Eagle3")
QWEN3_32B_W8A8_MINDIE_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "aleoyang/Qwen3-32B-w8a8-MindIE"
)
QWEN3_235B_A22B_W8A8_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "vllm-ascend/Qwen3-235B-A22B-W8A8"
)
QWEN3_CODER_480B_A35B_INSTRUCT_W8A8_QUAROT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "Qwen3-Coder-480B-A35B-Instruct-w8a8-QuaRot"
)
QWEN3_NEXT_80B_A3B_INSTRUCT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "Qwen/Qwen3-Next-80B-A3B-Instruct"
)
QWQ_32B_W8A8_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "vllm-ascend/QWQ-32B-W8A8")
SMOLLM_1_7B_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "HuggingFaceTB/SmolLM-1.7B")
STABLELM_2_1_6B_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "stabilityai/stablelm-2-1_6b"
)
XVERSE_MOE_A36B_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "xverse/XVERSE-MoE-A36B")
MINIMAX_M2_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "cyankiwi/MiniMax-M2-BF16")

# VLM model weights path
DEEPSEEK_VL2_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "deepseek-ai/deepseek-vl2")
GLM_4_5V_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "ZhipuAI/GLM-4.5V")
JANUS_PRO_1B_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "deepseek-ai/Janus-Pro-1B")
JANUS_PRO_7B_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "deepseek-ai/Janus-Pro-7B")
KIMI_VL_A3B_INSTRUCT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "Kimi/Kimi-VL-A3B-Instruct"
)
LLAMA_3_2_11B_VISION_INSTRUCT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "LLM-Research/Llama-3.2-11B-Vision-Instruct"
)
LLAVA_NEXT_72B_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "lmms-lab/llava-next-72b")
LLAVA_ONEVISION_QWEN2_7B_OV_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "lmms-lab/llava-onevision-qwen2-7b-ov"
)
LLAVA_V1_6_34B_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "AI-ModelScope/llava-v1.6-34b"
)
LLAVA_V1_6_34B_TOKENIZER_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "AI-ModelScope/llava-v1.6-34b/llava-1.6v-34b-tokenizer"
)
MIMO_VL_7B_RL_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "XiaomiMiMo/MiMo-VL-7B-RL")
MINICPM_O_2_6_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "openbmb/MiniCPM-o-2_6")
MINICPM_V_2_6_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "openbmb/MiniCPM-V-2_6")
MISTRAL_SMALL_3_1_24B_INSTRUCT_2503_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
)
QWEN2_5_VL_3B_INSTRUCT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "Qwen/Qwen2.5-VL-3B-Instruct"
)
QWEN2_5_VL_72B_INSTRUCT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "Qwen/Qwen2.5-VL-72B-Instruct"
)
QWEN3_VL_4B_INSTRUCT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "Qwen/Qwen3-VL-4B-Instruct"
)
QWEN3_VL_8B_INSTRUCT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "Qwen/Qwen3-VL-8B-Instruct"
)
QWEN3_VL_30B_A3B_INSTRUCT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "Qwen/Qwen3-VL-30B-A3B-Instruct"
)
QWEN3_VL_235B_A22B_INSTRUCT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "Qwen/Qwen3-VL-235B-A22B-Instruct"
)
QWEN2_0_5B_INSTRUCT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "Qwen/Qwen2-0.5B-Instruct"
)

QWEN3_30B_A3B_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "Qwen/Qwen3-30B-A3B")
QWEN3_30B_A3B_W8A8_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "Qwen/Qwen3-30B-A3B-w8a8"
)

DEEPSEEK_R1_DISTILL_QWEN_7B_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B"
)

QWEN3_30B_MODELSLIM_INT4_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "Eco-Tech/Qwen3-30B-A3B-w4a4-LAOS"
)

# Embedding model weights path
BGE_LARGE_EN_V1_5_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "bge-large-en-v1.5")
CLIP_VIT_LARGE_PATCH14_336_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "AI-ModelScope/clip-vit-large-patch14-336"
)
E5_MISTRAL_7B_INSTRUCT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "intfloat/e5-mistral-7b-instruct"
)
GME_QWEN2_VL_2B_INSTRUCT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "Alibaba-NLP/gme-Qwen2-VL-2B-Instruct"
)
GTE_QWEN2_1_5B_INSTRUCT_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "iic/gte_Qwen2-1.5B-instruct"
)
QWEN3_EMBEDDING_8B_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "Qwen/Qwen3-Embedding-8B"
)

# Rerank model weights path
BGE_RERANKER_V2_M3_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "BAAI/bge-reranker-v2-m3"
)

# Reward model weights path
INTERNLM2_7B_REWARD_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "Shanghai_AI_Laboratory/internlm2-7b-reward"
)
QWEN2_5_1_5B_APEACH_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "Howeee/Qwen2.5-1.5B-apeach"
)
QWEN2_5_MATH_RM_72B_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "Qwen/Qwen2.5-Math-RM-72B"
)
SKYWORK_REWARD_GEMMA_2_27B_V0_2_WEIGHTS_PATH = os.path.join(
    MODEL_WEIGHTS_DIR, "AI-ModelScope/Skywork-Reward-Gemma-2-27B-v0.2"
)
SKYWORK_REWARD_LLAMA_3_1_8B_V0_2_WEIGHTS_PATH = os.path.join(
    HF_MODEL_WEIGHTS_DIR,
    "models--Skywork--Skywork-Reward-Llama-3.1-8B-v0.2/snapshots/d4117fbfd81b72f41b96341238baa1e3e90a4ce1",
)
# fmt: on

# Other
DEEPSEEK_CODER_JSON_PATH = "/__w/sglang/sglang/test/registered/ascend/basic_function/parameter/deepseek_coder.json"


class ModelTestConfig(NamedTuple):
    """
    Configuration for model testing.

    Attributes:
        model_path: Path to the model weights directory
        mmlu_score: Weight for MMLU benchmark score
        gsm8k_accuracy: Weight for GSM8K benchmark score
        mmmu_accuracy: Weight for MMMU benchmark score
    """

    model_path: str
    mmlu_score: Optional[float] = None
    gsm8k_accuracy: Optional[float] = None
    mmmu_accuracy: Optional[float] = None


LLAMA_3_2_1B_INSTRUCT_WEIGHTS_FOR_TEST = ModelTestConfig(
    model_path=LLAMA_3_2_1B_INSTRUCT_WEIGHTS_PATH, mmlu_score=0.2
)

QWEN3_30B_A3B_INSTRUCT_2507_WEIGHTS_FOR_TEST = ModelTestConfig(
    model_path=QWEN3_30B_A3B_INSTRUCT_2507_WEIGHTS_PATH, gsm8k_accuracy=0.9
)

QWEN3_32B_WEIGHTS_FOR_TEST = ModelTestConfig(
    model_path=QWEN3_32B_WEIGHTS_PATH, gsm8k_accuracy=0.82
)

QWEN3_NEXT_80B_A3B_INSTRUCT_WEIGHTS_FOR_TEST = ModelTestConfig(
    model_path=QWEN3_NEXT_80B_A3B_INSTRUCT_WEIGHTS_PATH, gsm8k_accuracy=0.92
)

QWQ_32B_W8A8_WEIGHTS_FOR_TEST = ModelTestConfig(
    model_path=QWQ_32B_W8A8_WEIGHTS_PATH, gsm8k_accuracy=0.59
)

# Default configuration for testing
DEFAULT_WEIGHTS_FOR_TEST = LLAMA_3_2_1B_INSTRUCT_WEIGHTS_FOR_TEST


def run_command(cmd, shell=True):
    """Execute system command and return stdout

    parameter:
        cmd: command to execute
        shell:
        True, Execute command in shell
        False, Commands are invoked directly without shell parsing
    return:
        The result of executing the command
    """
    try:
        result = subprocess.run(
            cmd, shell=shell, capture_output=True, text=True, check=True
        )
        return result.stdout
    except subprocess.CalledProcessError as e:
        print(f"execute command error: {e}")
        return None


def get_benchmark_args(
    base_url="",
    backend="sglang",
    dataset_name="",
    dataset_path="",
    tokenizer="",
    num_prompts=500,
    sharegpt_output_len=None,
    random_input_len=4096,
    random_output_len=2048,
    sharegpt_context_len=None,
    request_rate=float("inf"),
    disable_stream=False,
    disable_ignore_eos=False,
    seed: int = 0,
    device="auto",
    pd_separated: bool = False,
    lora_name=None,
    lora_request_distribution="uniform",
    lora_zipf_alpha=1.5,
    gsp_num_groups=4,
    gsp_prompts_per_group=4,
    gsp_system_prompt_len=128,
    gsp_question_len=32,
    gsp_output_len=32,
    gsp_num_turns=1,
    header=None,
    max_concurrency=None,
):
    """Constructing the parameter objects needed for inference tests

    Parameters:
        base_url: url
        backend: Inference backend
        dataset_name: Data set name
        dataset_path: Dataset path
        tokenizer: tokenizer
        num_prompts: Total number of test requests
        sharegpt_output_len: Output the number of tokens
        random_input_len: The length of the randomly generated input prompt
        random_output_len: The length of the randomly generated output prompt
        sharegpt_context_len: Sharegpt dataset context length
        request_rate: Request rate
        disable_stream: Disable streaming output
        disable_ignore_eos: Should eos_token be ignored?
        seed: random seed
        device: Device type
        pd_separated: Enable PD separation
        lora_name: LoRA fine-tuning model path
        lora_request_distribution: LoRA request distribution strategy
        lora_zipf_alpha: Control request distribution skewness
        gsp_num_groups: Grouped Sequence Parallelism
        gsp_prompts_per_group: Number of parallel prompts within each group
        gsp_system_prompt_len: GSP system prompts length
        gsp_question_len: GSP question length
        gsp_output_len: GSP output length
        gsp_num_turns: GSP Dialogue Rounds
        header: HTTP request header
        max_concurrency: Maximum number of concurrent requests
    Returns:
        The return parameter is the same as the input.
    """

    return SimpleNamespace(
        backend=backend,
        base_url=base_url,
        host=None,
        port=None,
        dataset_name=dataset_name,
        dataset_path=dataset_path,
        model=None,
        tokenizer=tokenizer,
        num_prompts=num_prompts,
        sharegpt_output_len=sharegpt_output_len,
        sharegpt_context_len=sharegpt_context_len,
        random_input_len=random_input_len,
        random_output_len=random_output_len,
        random_range_ratio=0.0,
        request_rate=request_rate,
        multi=None,
        output_file=None,
        disable_tqdm=False,
        disable_stream=disable_stream,
        return_logprob=False,
        return_routed_experts=False,
        seed=seed,
        disable_ignore_eos=disable_ignore_eos,
        extra_request_body=None,
        apply_chat_template=False,
        profile=None,
        lora_name=lora_name,
        lora_request_distribution=lora_request_distribution,
        lora_zipf_alpha=lora_zipf_alpha,
        prompt_suffix="",
        device=device,
        pd_separated=pd_separated,
        gsp_num_groups=gsp_num_groups,
        gsp_prompts_per_group=gsp_prompts_per_group,
        gsp_system_prompt_len=gsp_system_prompt_len,
        gsp_question_len=gsp_question_len,
        gsp_output_len=gsp_output_len,
        gsp_num_turns=gsp_num_turns,
        header=header,
        max_concurrency=max_concurrency,
    )


def run_bench_serving(
    model,
    num_prompts,
    request_rate,
    other_server_args,
    dataset_name="random",
    dataset_path="",
    tokenizer=None,
    random_input_len=4096,
    random_output_len=2048,
    sharegpt_context_len=None,
    disable_stream=False,
    disable_ignore_eos=False,
    need_warmup=False,
    seed: int = 0,
    device="auto",
    gsp_num_groups=None,
    gsp_prompts_per_group=None,
    gsp_system_prompt_len=None,
    gsp_question_len=None,
    gsp_output_len=None,
    max_concurrency=None,
    background_task: Optional[Callable[[str, asyncio.Event], Awaitable[None]]] = None,
    lora_name: Optional[str] = None,
):
    """Start the service and obtain the inference results.

    Parameters:
        model: Model name
        num_prompts: Total number of test requests
        request_rate: Request rate
        other_server_args: Additional configuration when starting the service
        dataset_name: Data set name
        dataset_path: Dataset path
        tokenizer: tokenizer
        random_input_len: The length of the randomly generated input prompt
        random_output_len: The length of the randomly generated output prompt
        sharegpt_context_len: Sharegpt dataset context length
        disable_stream: Disable streaming output
        disable_ignore_eos: Should eos_token be ignored?
        need_warmup: Preheating required
        seed: random seed
        device: Device type
        gsp_num_groups: Grouped Sequence Parallelism
        gsp_prompts_per_group: Number of parallel prompts within each group
        gsp_system_prompt_len: GSP system prompts length
        gsp_question_len: GSP question length
        gsp_output_len: GSP output length
        max_concurrency: Maximum number of concurrent requests
        background_task: Background tasks
        lora_name: LoRA fine-tuning model path
    Returns:
        res: Number of requests successfully completed

    """

    if device == "auto":
        device = auto_config_device()
    # Launch the server
    base_url = DEFAULT_URL_FOR_TEST
    process = popen_launch_server(
        model,
        base_url,
        timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
        other_args=other_server_args,
    )

    # Run benchmark
    args = get_benchmark_args(
        base_url=base_url,
        dataset_name=dataset_name,
        dataset_path=dataset_path,
        tokenizer=tokenizer,
        num_prompts=num_prompts,
        random_input_len=random_input_len,
        random_output_len=random_output_len,
        sharegpt_context_len=sharegpt_context_len,
        request_rate=request_rate,
        disable_stream=disable_stream,
        disable_ignore_eos=disable_ignore_eos,
        seed=seed,
        device=device,
        lora_name=lora_name,
        gsp_num_groups=gsp_num_groups,
        gsp_prompts_per_group=gsp_prompts_per_group,
        gsp_system_prompt_len=gsp_system_prompt_len,
        gsp_question_len=gsp_question_len,
        gsp_output_len=gsp_output_len,
        max_concurrency=max_concurrency,
    )

    async def _run():
        if need_warmup:
            warmup_args = copy.deepcopy(args)
            warmup_args.num_prompts = 16
            await asyncio.to_thread(run_benchmark, warmup_args)

        start_event = asyncio.Event()
        stop_event = asyncio.Event()
        task_handle = (
            asyncio.create_task(background_task(base_url, start_event, stop_event))
            if background_task
            else None
        )

        try:
            start_event.set()
            result = await asyncio.to_thread(run_benchmark, args)
        finally:
            if task_handle:
                stop_event.set()
                await task_handle

        return result

    try:
        res = asyncio.run(_run())
    finally:
        kill_process_tree(process.pid)

    assert res["completed"] == num_prompts
    return res
