bench_serving.py 46.8 KB
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# Adapted from https://github.com/vllm-project/vllm/blob/6366efc67b0aedd2c1721c14385370e50b297fb3/benchmarks/backend_request_func.py
# Adapted from https://github.com/vllm-project/vllm/blob/6366efc67b0aedd2c1721c14385370e50b297fb3/benchmarks/benchmark_serving.py
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"""
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Benchmark online serving with dynamic requests.
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Usage:
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python3 -m sglang.bench_serving --backend sglang --num-prompt 10
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python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompts 3000 --random-input 1024 --random-output 1024 --random-range-ratio 0.5
python3 -m sglang.bench_serving --backend sglang --dataset-name random --request-rate-range 1,2,4,8,16,32 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --multi
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"""
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import argparse
import asyncio
import json
import os
import random
import resource
import sys
import time
import traceback
import warnings
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from argparse import ArgumentParser
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from dataclasses import dataclass, field
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from datetime import datetime
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from typing import Any, AsyncGenerator, Dict, List, Optional, Tuple, Union
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import aiohttp
import numpy as np
import requests
from tqdm.asyncio import tqdm
from transformers import (
    AutoTokenizer,
    PreTrainedTokenizer,
    PreTrainedTokenizerBase,
    PreTrainedTokenizerFast,
)

AIOHTTP_TIMEOUT = aiohttp.ClientTimeout(total=6 * 60 * 60)

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global args

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@dataclass
class RequestFuncInput:
    prompt: str
    api_url: str
    prompt_len: int
    output_len: int
    model: str
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    extra_request_body: Dict[str, Any]
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@dataclass
class RequestFuncOutput:
    generated_text: str = ""
    success: bool = False
    latency: float = 0.0
    ttft: float = 0.0  # Time to first token
    itl: List[float] = field(default_factory=list)  # List of inter-token latencies
    prompt_len: int = 0
    error: str = ""
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    output_len: int = 0
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def remove_prefix(text: str, prefix: str) -> str:
    return text[len(prefix) :] if text.startswith(prefix) else text


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# trt llm not support ignore_eos
# https://github.com/triton-inference-server/tensorrtllm_backend/issues/505
async def async_request_trt_llm(
    request_func_input: RequestFuncInput,
    pbar: Optional[tqdm] = None,
) -> RequestFuncOutput:
    api_url = request_func_input.api_url
    assert api_url.endswith("generate_stream")

    async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
        payload = {
            "accumulate_tokens": True,
            "text_input": request_func_input.prompt,
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            "temperature": 0.000001,
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            "top_p": 1.0,
            "max_tokens": request_func_input.output_len,
            "stream": True,
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            "min_length": request_func_input.output_len,
            "end_id": 1048576,
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            **request_func_input.extra_request_body,
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        }
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        if args.disable_ignore_eos:
            del payload["min_length"]
            del payload["end_id"]
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        output = RequestFuncOutput()
        output.prompt_len = request_func_input.prompt_len

        ttft = 0.0
        st = time.perf_counter()
        most_recent_timestamp = st
        try:
            async with session.post(url=api_url, json=payload) as response:
                if response.status == 200:
                    async for chunk_bytes in response.content:
                        chunk_bytes = chunk_bytes.strip()
                        if not chunk_bytes:
                            continue

                        chunk = remove_prefix(chunk_bytes.decode("utf-8"), "data:")

                        data = json.loads(chunk)
                        output.generated_text += data["text_output"]
                        timestamp = time.perf_counter()
                        # First token
                        if ttft == 0.0:
                            ttft = time.perf_counter() - st
                            output.ttft = ttft

                        # Decoding phase
                        else:
                            output.itl.append(timestamp - most_recent_timestamp)

                        most_recent_timestamp = timestamp

                    output.latency = most_recent_timestamp - st
                    output.success = True
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                    output.output_len = request_func_input.output_len
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                else:
                    output.error = response.reason or ""
                    output.success = False
        except Exception:
            output.success = False
            exc_info = sys.exc_info()
            output.error = "".join(traceback.format_exception(*exc_info))

        if pbar:
            pbar.update(1)
        return output


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# set ignore_eos True by default
async def async_request_openai_completions(
    request_func_input: RequestFuncInput,
    pbar: Optional[tqdm] = None,
) -> RequestFuncOutput:
    api_url = request_func_input.api_url
    assert api_url.endswith(
        "completions"
    ), "OpenAI Completions API URL must end with 'completions'."

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    prompt = request_func_input.prompt

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    async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
        payload = {
            "model": request_func_input.model,
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            "prompt": prompt,
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            "temperature": 0.0,
            "best_of": 1,
            "max_tokens": request_func_input.output_len,
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            "stream": not args.disable_stream,
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            "ignore_eos": not args.disable_ignore_eos,
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            **request_func_input.extra_request_body,
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        }
        headers = {"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"}

        output = RequestFuncOutput()
        output.prompt_len = request_func_input.prompt_len

        generated_text = ""
        ttft = 0.0
        st = time.perf_counter()
        most_recent_timestamp = st
        try:
            async with session.post(
                url=api_url, json=payload, headers=headers
            ) as response:
                if response.status == 200:
                    async for chunk_bytes in response.content:
                        chunk_bytes = chunk_bytes.strip()
                        if not chunk_bytes:
                            continue

                        chunk = remove_prefix(chunk_bytes.decode("utf-8"), "data: ")
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                        latency = time.perf_counter() - st
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                        if chunk == "[DONE]":
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                            pass
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                        else:
                            data = json.loads(chunk)

                            # NOTE: Some completion API might have a last
                            # usage summary response without a token so we
                            # want to check a token was generated
                            if data["choices"][0]["text"]:
                                timestamp = time.perf_counter()
                                # First token
                                if ttft == 0.0:
                                    ttft = time.perf_counter() - st
                                    output.ttft = ttft

                                # Decoding phase
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                                else:
                                    output.itl.append(timestamp - most_recent_timestamp)
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                                most_recent_timestamp = timestamp
                                generated_text += data["choices"][0]["text"]

                    output.generated_text = generated_text
                    output.success = True
                    output.latency = latency
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                    output.output_len = request_func_input.output_len
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                else:
                    output.error = response.reason or ""
                    output.success = False
        except Exception:
            output.success = False
            exc_info = sys.exc_info()
            output.error = "".join(traceback.format_exception(*exc_info))

    if pbar:
        pbar.update(1)
    return output


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async def async_request_truss(
    request_func_input: RequestFuncInput,
    pbar: Optional[tqdm] = None,
) -> RequestFuncOutput:
    api_url = request_func_input.api_url

    prompt = request_func_input.prompt

    async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
        payload = {
            "model": request_func_input.model,
            "prompt": prompt,
            "temperature": 0.0,
            "best_of": 1,
            "max_tokens": request_func_input.output_len,
            "stream": not args.disable_stream,
            "ignore_eos": not args.disable_ignore_eos,
            **request_func_input.extra_request_body,
        }
        headers = {"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"}

        output = RequestFuncOutput()
        output.prompt_len = request_func_input.prompt_len

        generated_text = ""
        ttft = 0.0
        st = time.perf_counter()
        most_recent_timestamp = st
        try:
            async with session.post(
                url=api_url, json=payload, headers=headers
            ) as response:
                if response.status == 200:
                    async for chunk_bytes in response.content:
                        chunk_bytes = chunk_bytes.strip()
                        if not chunk_bytes:
                            continue

                        chunk = remove_prefix(chunk_bytes.decode("utf-8"), "data: ")
                        latency = time.perf_counter() - st
                        if chunk == "[DONE]":
                            pass
                        else:
                            data = json.loads(chunk)

                            # NOTE: Some completion API might have a last
                            # usage summary response without a token so we
                            # want to check a token was generated
                            if data["choices"][0]["delta"]["content"]:
                                timestamp = time.perf_counter()
                                # First token
                                if ttft == 0.0:
                                    ttft = time.perf_counter() - st
                                    output.ttft = ttft

                                # Decoding phase
                                else:
                                    output.itl.append(timestamp - most_recent_timestamp)

                                most_recent_timestamp = timestamp
                                generated_text += data["choices"][0]["delta"]["content"]

                    output.generated_text = generated_text
                    output.success = True
                    output.latency = latency
                    output.output_len = request_func_input.output_len
                else:
                    output.error = response.reason or ""
                    output.success = False
        except Exception:
            output.success = False
            exc_info = sys.exc_info()
            output.error = "".join(traceback.format_exception(*exc_info))

    if pbar:
        pbar.update(1)
    return output


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async def async_request_sglang_generate(
    request_func_input: RequestFuncInput,
    pbar: Optional[tqdm] = None,
) -> RequestFuncOutput:
    api_url = request_func_input.api_url
    prompt = request_func_input.prompt

    async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
        payload = {
            "text": prompt,
            "sampling_params": {
                "temperature": 0.0,
                "max_new_tokens": request_func_input.output_len,
                "ignore_eos": not args.disable_ignore_eos,
            },
            "stream": not args.disable_stream,
            **request_func_input.extra_request_body,
        }
        headers = {}

        output = RequestFuncOutput()
        output.prompt_len = request_func_input.prompt_len

        generated_text = ""
        ttft = 0.0
        st = time.perf_counter()
        most_recent_timestamp = st
        try:
            async with session.post(
                url=api_url, json=payload, headers=headers
            ) as response:
                if response.status == 200:
                    async for chunk_bytes in response.content:
                        chunk_bytes = chunk_bytes.strip()
                        if not chunk_bytes:
                            continue
                        # print(chunk_bytes)

                        chunk = remove_prefix(chunk_bytes.decode("utf-8"), "data: ")
                        latency = time.perf_counter() - st
                        if chunk == "[DONE]":
                            pass
                        else:
                            data = json.loads(chunk)

                            # NOTE: Some completion API might have a last
                            # usage summary response without a token so we
                            # want to check a token was generated
                            if data["text"]:
                                timestamp = time.perf_counter()
                                # First token
                                if ttft == 0.0:
                                    ttft = time.perf_counter() - st
                                    output.ttft = ttft

                                # Decoding phase
                                else:
                                    output.itl.append(timestamp - most_recent_timestamp)

                                most_recent_timestamp = timestamp
                                generated_text = data["text"]

                    output.generated_text = generated_text
                    output.success = True
                    output.latency = latency
                    output.output_len = request_func_input.output_len
                else:
                    output.error = response.reason or ""
                    output.success = False
        except Exception:
            output.success = False
            exc_info = sys.exc_info()
            output.error = "".join(traceback.format_exception(*exc_info))

    if pbar:
        pbar.update(1)
    return output


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async def async_request_gserver(
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    request_func_input: RequestFuncInput,
    pbar: Optional[tqdm] = None,
) -> RequestFuncOutput:
    raise NotImplementedError()


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def get_model(pretrained_model_name_or_path: str) -> str:
    if os.getenv("SGLANG_USE_MODELSCOPE", "False").lower() == "true":
        import huggingface_hub.constants
        from modelscope import snapshot_download

        model_path = snapshot_download(
            model_id=pretrained_model_name_or_path,
            local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
            ignore_file_pattern=[".*.pt", ".*.safetensors", ".*.bin"],
        )

        return model_path
    return pretrained_model_name_or_path


def get_tokenizer(
    pretrained_model_name_or_path: str,
) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]:
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    if pretrained_model_name_or_path.endswith(
        ".json"
    ) or pretrained_model_name_or_path.endswith(".model"):
        from sglang.srt.hf_transformers_utils import get_tokenizer

        return get_tokenizer(pretrained_model_name_or_path)

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    if pretrained_model_name_or_path is not None and not os.path.exists(
        pretrained_model_name_or_path
    ):
        pretrained_model_name_or_path = get_model(pretrained_model_name_or_path)
    return AutoTokenizer.from_pretrained(
        pretrained_model_name_or_path, trust_remote_code=True
    )


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def get_dataset(args, tokenizer):
    if args.dataset_name == "sharegpt":
        input_requests = sample_sharegpt_requests(
            dataset_path=args.dataset_path,
            num_requests=args.num_prompts,
            tokenizer=tokenizer,
            fixed_output_len=args.sharegpt_output_len,
        )
    elif args.dataset_name == "random":
        input_requests = sample_random_requests(
            input_len=args.random_input_len,
            output_len=args.random_output_len,
            num_prompts=args.num_prompts,
            range_ratio=args.random_range_ratio,
            tokenizer=tokenizer,
            dataset_path=args.dataset_path,
        )
    elif args.dataset_name == "generated-shared-prefix":
        input_requests = sample_generated_shared_prefix_requests(
            num_groups=args.gen_num_groups,
            prompts_per_group=args.gen_prompts_per_group,
            system_prompt_len=args.gen_system_prompt_len,
            question_len=args.gen_question_len,
            output_len=args.gen_output_len,
            tokenizer=tokenizer,
        )
    else:
        raise ValueError(f"Unknown dataset: {args.dataset_name}")
    return input_requests


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ASYNC_REQUEST_FUNCS = {
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    "sglang": async_request_sglang_generate,
    "sglang-native": async_request_sglang_generate,
    "sglang-oai": async_request_openai_completions,
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    "vllm": async_request_openai_completions,
    "lmdeploy": async_request_openai_completions,
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    "trt": async_request_trt_llm,
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    "gserver": async_request_gserver,
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    "truss": async_request_truss,
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}


@dataclass
class BenchmarkMetrics:
    completed: int
    total_input: int
    total_output: int
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    total_output_retokenized: int
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    request_throughput: float
    input_throughput: float
    output_throughput: float
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    output_throughput_retokenized: float
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    total_throughput: float
    total_throughput_retokenized: float
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    mean_ttft_ms: float
    median_ttft_ms: float
    std_ttft_ms: float
    p99_ttft_ms: float
    mean_tpot_ms: float
    median_tpot_ms: float
    std_tpot_ms: float
    p99_tpot_ms: float
    mean_itl_ms: float
    median_itl_ms: float
    std_itl_ms: float
    p99_itl_ms: float
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    mean_e2e_latency_ms: float
    median_e2e_latency_ms: float
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SHAREGPT_URL = "https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json"
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def download_and_cache_file(url: str, filename: Optional[str] = None):
    """Read and cache a file from a url."""
    if filename is None:
        filename = os.path.join("/tmp", url.split("/")[-1])
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    # Check if the cache file already exists
    if os.path.exists(filename):
        return filename
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    print(f"Downloading from {url} to {filename}")
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    # Stream the response to show the progress bar
    response = requests.get(url, stream=True)
    response.raise_for_status()  # Check for request errors
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    # Total size of the file in bytes
    total_size = int(response.headers.get("content-length", 0))
    chunk_size = 1024  # Download in chunks of 1KB

    # Use tqdm to display the progress bar
    with open(filename, "wb") as f, tqdm(
        desc=filename,
        total=total_size,
        unit="B",
        unit_scale=True,
        unit_divisor=1024,
    ) as bar:
        for chunk in response.iter_content(chunk_size=chunk_size):
            f.write(chunk)
            bar.update(len(chunk))

    return filename
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def sample_sharegpt_requests(
    dataset_path: str,
    num_requests: int,
    tokenizer: PreTrainedTokenizerBase,
    fixed_output_len: Optional[int] = None,
) -> List[Tuple[str, int, int]]:
    if fixed_output_len is not None and fixed_output_len < 4:
        raise ValueError("output_len too small")

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    # Download sharegpt if necessary
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    if not os.path.isfile(dataset_path):
        dataset_path = download_and_cache_file(SHAREGPT_URL)
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    # Load the dataset.
    with open(dataset_path) as f:
        dataset = json.load(f)
    # Filter out the conversations with less than 2 turns.
    dataset = [data for data in dataset if len(data["conversations"]) >= 2]
    # Only keep the first two turns of each conversation.
    dataset = [
        (data["conversations"][0]["value"], data["conversations"][1]["value"])
        for data in dataset
    ]

    # Shuffle the dataset.
    random.shuffle(dataset)

    # Filter out sequences that are too long or too short
    filtered_dataset: List[Tuple[str, int, int]] = []
    for i in range(len(dataset)):
        if len(filtered_dataset) == num_requests:
            break

        # Tokenize the prompts and completions.
        prompt = dataset[i][0]
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        prompt_token_ids = tokenizer.encode(prompt)
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        completion = dataset[i][1]
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        completion_token_ids = tokenizer.encode(completion)
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        prompt_len = len(prompt_token_ids)
        output_len = (
            len(completion_token_ids) if fixed_output_len is None else fixed_output_len
        )
        if prompt_len < 4 or output_len < 4:
            # Prune too short sequences.
            continue
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        if prompt_len > 1024 or (
            prompt_len + output_len > 2048 and fixed_output_len is None
        ):
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            # Prune too long sequences.
            continue
        filtered_dataset.append((prompt, prompt_len, output_len))

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    print(f"#Input tokens: {np.sum([x[1] for x in filtered_dataset])}")
    print(f"#Output tokens: {np.sum([x[2] for x in filtered_dataset])}")
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    return filtered_dataset


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def sample_random_requests(
    input_len: int,
    output_len: int,
    num_prompts: int,
    range_ratio: float,
    tokenizer: PreTrainedTokenizerBase,
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    dataset_path: str,
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) -> List[Tuple[str, int, int]]:

    input_lens = np.random.randint(
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        max(int(input_len * range_ratio), 1),
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        input_len + 1,
        size=num_prompts,
    )
    output_lens = np.random.randint(
        int(output_len * range_ratio),
        output_len + 1,
        size=num_prompts,
    )
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    if True:
        # Sample token ids from ShareGPT and repeat/truncate them to satisfy the input_lens

        # Download sharegpt if necessary
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        if not os.path.isfile(dataset_path):
            dataset_path = download_and_cache_file(SHAREGPT_URL)
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        # Load the dataset.
        with open(dataset_path) as f:
            dataset = json.load(f)
        # Filter out the conversations with less than 2 turns.
        dataset = [data for data in dataset if len(data["conversations"]) >= 2]
        # Only keep the first two turns of each conversation.
        dataset = [
            (data["conversations"][0]["value"], data["conversations"][1]["value"])
            for data in dataset
        ]
        # Shuffle the dataset.
        random.shuffle(dataset)

        # Filter out sequences that are too long or too short
        input_requests: List[Tuple[str, int, int]] = []
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        for data in dataset:
            i = len(input_requests)
            if i == num_prompts:
                break

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            # Tokenize the prompts and completions.
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            prompt = data[0]
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            prompt_token_ids = tokenizer.encode(prompt)
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            prompt_len = len(prompt_token_ids)

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            # Skip empty prompt
            if prompt_len == 0:
                continue

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            if prompt_len > input_lens[i]:
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                input_ids = prompt_token_ids[: input_lens[i]]
            else:
                ratio = (input_lens[i] + prompt_len - 1) // prompt_len
                input_ids = (prompt_token_ids * ratio)[: input_lens[i]]
            prompt = tokenizer.decode(input_ids)
            input_requests.append((prompt, int(input_lens[i]), int(output_lens[i])))
    else:
        # Sample token ids from random integers. This can cause some NaN issues.
        offsets = np.random.randint(0, tokenizer.vocab_size, size=num_prompts)
        input_requests = []
        for i in range(num_prompts):
            prompt = tokenizer.decode(
                [
                    (offsets[i] + i + j) % tokenizer.vocab_size
                    for j in range(input_lens[i])
                ]
            )
            input_requests.append((prompt, int(input_lens[i]), int(output_lens[i])))
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    print(f"#Input tokens: {np.sum(input_lens)}")
    print(f"#Output tokens: {np.sum(output_lens)}")
    return input_requests


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def gen_prompt(tokenizer, token_num):
    """Generate a random prompt of specified token length using tokenizer vocabulary."""
    all_available_tokens = list(tokenizer.get_vocab().values())
    selected_tokens = random.choices(all_available_tokens, k=token_num)
    return tokenizer.decode(selected_tokens)


def sample_generated_shared_prefix_requests(
    num_groups: int,
    prompts_per_group: int,
    system_prompt_len: int,
    question_len: int,
    output_len: int,
    tokenizer: PreTrainedTokenizerBase,
) -> List[Tuple[str, int, int]]:
    """Generate benchmark requests with shared system prompts using random tokens."""
    # Generate system prompts for each group
    system_prompts = []
    for _ in range(num_groups):
        system_prompt = gen_prompt(tokenizer, system_prompt_len)
        system_prompts.append(system_prompt)

    # Generate questions
    questions = []
    for _ in range(num_groups * prompts_per_group):
        question = gen_prompt(tokenizer, question_len)
        questions.append(question)

    # Combine system prompts with questions
    input_requests = []
    total_input_tokens = 0
    total_output_tokens = 0

    for group_idx in range(num_groups):
        system_prompt = system_prompts[group_idx]
        for prompt_idx in range(prompts_per_group):
            question = questions[group_idx * prompts_per_group + prompt_idx]
            full_prompt = f"{system_prompt}\n\n{question}"
            prompt_len = len(tokenizer.encode(full_prompt))

            input_requests.append((full_prompt, prompt_len, output_len))
            total_input_tokens += prompt_len
            total_output_tokens += output_len

    print(f"\nGenerated shared prefix dataset statistics:")
    print(f"Number of groups: {num_groups}")
    print(f"Prompts per group: {prompts_per_group}")
    print(f"Total prompts: {len(input_requests)}")
    print(f"Total input tokens: {total_input_tokens}")
    print(f"Total output tokens: {total_output_tokens}")
    print(
        f"Average system prompt length: {sum(len(tokenizer.encode(sp)) for sp in system_prompts) / len(system_prompts):.1f} tokens"
    )
    print(
        f"Average question length: {sum(len(tokenizer.encode(q)) for q in questions) / len(questions):.1f} tokens\n"
    )

    return input_requests


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async def get_request(
    input_requests: List[Tuple[str, int, int]],
    request_rate: float,
) -> AsyncGenerator[Tuple[str, int, int], None]:
    input_requests = iter(input_requests)
    for request in input_requests:
        yield request

        if request_rate == float("inf"):
            # If the request rate is infinity, then we don't need to wait.
            continue

        # Sample the request interval from the exponential distribution.
        interval = np.random.exponential(1.0 / request_rate)
        # The next request will be sent after the interval.
        await asyncio.sleep(interval)


def calculate_metrics(
    input_requests: List[Tuple[str, int, int]],
    outputs: List[RequestFuncOutput],
    dur_s: float,
    tokenizer: PreTrainedTokenizerBase,
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    backend: str,
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) -> Tuple[BenchmarkMetrics, List[int]]:
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    output_lens: List[int] = []
    retokenized_output_lens: List[int] = []
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    total_input = 0
    completed = 0
    itls: List[float] = []
    tpots: List[float] = []
    ttfts: List[float] = []
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    e2e_latencies: List[float] = []
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    for i in range(len(outputs)):
        if outputs[i].success:
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            output_len = outputs[i].output_len
            output_lens.append(output_len)
            retokenized_output_len = len(
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                tokenizer.encode(outputs[i].generated_text, add_special_tokens=False)
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            )
            retokenized_output_lens.append(retokenized_output_len)
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            total_input += input_requests[i][1]
            if output_len > 1:
                tpots.append((outputs[i].latency - outputs[i].ttft) / (output_len - 1))
            itls += outputs[i].itl
            ttfts.append(outputs[i].ttft)
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            e2e_latencies.append(outputs[i].latency)

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            completed += 1
        else:
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            output_lens.append(0)
            retokenized_output_lens.append(0)
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    if completed == 0:
        warnings.warn(
            "All requests failed. This is likely due to a misconfiguration "
            "on the benchmark arguments.",
            stacklevel=2,
        )
    metrics = BenchmarkMetrics(
        completed=completed,
        total_input=total_input,
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        total_output=sum(output_lens),
        total_output_retokenized=sum(retokenized_output_lens),
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        request_throughput=completed / dur_s,
        input_throughput=total_input / dur_s,
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        output_throughput=sum(output_lens) / dur_s,
        output_throughput_retokenized=sum(retokenized_output_lens) / dur_s,
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        total_throughput=(total_input + sum(output_lens)) / dur_s,
        total_throughput_retokenized=(total_input + sum(retokenized_output_lens))
        / dur_s,
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        mean_ttft_ms=np.mean(ttfts or 0)
        * 1000,  # ttfts is empty if streaming is not supported by backend
        median_ttft_ms=np.median(ttfts or 0) * 1000,
        std_ttft_ms=np.std(ttfts or 0) * 1000,
        p99_ttft_ms=np.percentile(ttfts or 0, 99) * 1000,
        mean_tpot_ms=np.mean(tpots or 0) * 1000,
        median_tpot_ms=np.median(tpots or 0) * 1000,
        std_tpot_ms=np.std(tpots or 0) * 1000,
        p99_tpot_ms=np.percentile(tpots or 0, 99) * 1000,
        mean_itl_ms=np.mean(itls or 0) * 1000,
        median_itl_ms=np.median(itls or 0) * 1000,
        std_itl_ms=np.std(itls or 0) * 1000,
        p99_itl_ms=np.percentile(itls or 0, 99) * 1000,
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        mean_e2e_latency_ms=np.mean(e2e_latencies) * 1000,
        median_e2e_latency_ms=np.median(e2e_latencies) * 1000,
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    )

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    return metrics, output_lens
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async def benchmark(
    backend: str,
    api_url: str,
    model_id: str,
    tokenizer: PreTrainedTokenizerBase,
    input_requests: List[Tuple[str, int, int]],
    request_rate: float,
    disable_tqdm: bool,
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    extra_request_body: Dict[str, Any],
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):
    if backend in ASYNC_REQUEST_FUNCS:
        request_func = ASYNC_REQUEST_FUNCS[backend]
    else:
        raise ValueError(f"Unknown backend: {backend}")

    print("Starting initial single prompt test run...")
    test_prompt, test_prompt_len, test_output_len = input_requests[0]
    test_input = RequestFuncInput(
        model=model_id,
        prompt=test_prompt,
        api_url=api_url,
        prompt_len=test_prompt_len,
        output_len=test_output_len,
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        extra_request_body=extra_request_body,
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    )
    test_output = await request_func(request_func_input=test_input)
    if not test_output.success:
        raise ValueError(
            "Initial test run failed - Please make sure benchmark arguments "
            f"are correctly specified. Error: {test_output.error}"
        )
    else:
        print("Initial test run completed. Starting main benchmark run...")

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    time.sleep(1.5)

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    pbar = None if disable_tqdm else tqdm(total=len(input_requests))

    benchmark_start_time = time.perf_counter()
    tasks: List[asyncio.Task] = []
    async for request in get_request(input_requests, request_rate):
        prompt, prompt_len, output_len = request
        request_func_input = RequestFuncInput(
            model=model_id,
            prompt=prompt,
            api_url=api_url,
            prompt_len=prompt_len,
            output_len=output_len,
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            extra_request_body=extra_request_body,
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        )
        tasks.append(
            asyncio.create_task(
                request_func(request_func_input=request_func_input, pbar=pbar)
            )
        )
    outputs: List[RequestFuncOutput] = await asyncio.gather(*tasks)

    if pbar is not None:
        pbar.close()

    benchmark_duration = time.perf_counter() - benchmark_start_time

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    metrics, output_lens = calculate_metrics(
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        input_requests=input_requests,
        outputs=outputs,
        dur_s=benchmark_duration,
        tokenizer=tokenizer,
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        backend=backend,
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    )

    print("\n{s:{c}^{n}}".format(s=" Serving Benchmark Result ", n=50, c="="))
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    print("{:<40} {:<10}".format("Backend:", backend))
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    print("{:<40} {:<10}".format("Traffic request rate:", request_rate))
    print("{:<40} {:<10}".format("Successful requests:", metrics.completed))
    print("{:<40} {:<10.2f}".format("Benchmark duration (s):", benchmark_duration))
    print("{:<40} {:<10}".format("Total input tokens:", metrics.total_input))
    print("{:<40} {:<10}".format("Total generated tokens:", metrics.total_output))
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    print(
        "{:<40} {:<10}".format(
            "Total generated tokens (retokenized):", metrics.total_output_retokenized
        )
    )
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    print(
        "{:<40} {:<10.2f}".format(
            "Request throughput (req/s):", metrics.request_throughput
        )
    )
    print(
        "{:<40} {:<10.2f}".format(
            "Input token throughput (tok/s):", metrics.input_throughput
        )
    )
    print(
        "{:<40} {:<10.2f}".format(
            "Output token throughput (tok/s):", metrics.output_throughput
        )
    )
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    print(
        "{:<40} {:<10.2f}".format(
            "Total token throughput (tok/s):", metrics.total_throughput
        )
    )
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    print("{s:{c}^{n}}".format(s="End-to-End Latency", n=50, c="-"))
    print(
        "{:<40} {:<10.2f}".format("Mean E2E Latency (ms):", metrics.mean_e2e_latency_ms)
    )
    print(
        "{:<40} {:<10.2f}".format(
            "Median E2E Latency (ms):", metrics.median_e2e_latency_ms
        )
    )
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    print("{s:{c}^{n}}".format(s="Time to First Token", n=50, c="-"))
    print("{:<40} {:<10.2f}".format("Mean TTFT (ms):", metrics.mean_ttft_ms))
    print("{:<40} {:<10.2f}".format("Median TTFT (ms):", metrics.median_ttft_ms))
    print("{:<40} {:<10.2f}".format("P99 TTFT (ms):", metrics.p99_ttft_ms))
    print(
        "{s:{c}^{n}}".format(s="Time per Output Token (excl. 1st token)", n=50, c="-")
    )
    print("{:<40} {:<10.2f}".format("Mean TPOT (ms):", metrics.mean_tpot_ms))
    print("{:<40} {:<10.2f}".format("Median TPOT (ms):", metrics.median_tpot_ms))
    print("{:<40} {:<10.2f}".format("P99 TPOT (ms):", metrics.p99_tpot_ms))
    print("{s:{c}^{n}}".format(s="Inter-token Latency", n=50, c="-"))
    print("{:<40} {:<10.2f}".format("Mean ITL (ms):", metrics.mean_itl_ms))
    print("{:<40} {:<10.2f}".format("Median ITL (ms):", metrics.median_itl_ms))
    print("{:<40} {:<10.2f}".format("P99 ITL (ms):", metrics.p99_itl_ms))
    print("=" * 50)

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    if (
        metrics.median_ttft_ms is not None
        and metrics.mean_itl_ms is not None
        and metrics.output_throughput is not None
    ):
        result = {
            "backend": args.backend,
            "dataset_name": args.dataset_name,
            "request_rate": request_rate,
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            "total_input_tokens": metrics.total_input,
            "total_output_tokens": metrics.total_output,
            "total_output_tokens_retokenized": metrics.total_output_retokenized,
            "mean_e2e_latency_ms": metrics.mean_e2e_latency_ms,
            "median_e2e_latency_ms": metrics.median_e2e_latency_ms,
            "median_ttft_ms": metrics.median_ttft_ms,
            "median_itl_ms": metrics.median_itl_ms,
            "output_throughput": metrics.output_throughput,
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            "sharegpt_output_len": args.sharegpt_output_len,
            "random_input_len": args.random_input_len,
            "random_output_len": args.random_output_len,
            "random_range_ratio": args.random_range_ratio,
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            "duration": benchmark_duration,
            "completed": metrics.completed,
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        }
    else:
        print(f"Error running benchmark for request rate: {request_rate}")
        print("-" * 30)
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    # Determine output file name
    if args.output_file:
        output_file_name = args.output_file
    else:
        now = datetime.now().strftime("%m%d")
        if args.dataset_name == "random":
            output_file_name = f"{args.backend}_{now}_{args.num_prompts}_{args.random_input_len}_{args.random_output_len}.jsonl"
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        else:
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            output_file_name = f"{args.backend}_{now}_{args.num_prompts}_sharegpt.jsonl"
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    # Append results to a JSONL file
    with open(output_file_name, "a") as file:
        file.write(json.dumps(result) + "\n")
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    result = {
        "duration": benchmark_duration,
        "completed": metrics.completed,
        "total_input_tokens": metrics.total_input,
        "total_output_tokens": metrics.total_output,
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        "total_output_tokens_retokenized": metrics.total_output_retokenized,
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        "request_throughput": metrics.request_throughput,
        "input_throughput": metrics.input_throughput,
        "output_throughput": metrics.output_throughput,
        "mean_ttft_ms": metrics.mean_ttft_ms,
        "median_ttft_ms": metrics.median_ttft_ms,
        "std_ttft_ms": metrics.std_ttft_ms,
        "p99_ttft_ms": metrics.p99_ttft_ms,
        "mean_tpot_ms": metrics.mean_tpot_ms,
        "median_tpot_ms": metrics.median_tpot_ms,
        "std_tpot_ms": metrics.std_tpot_ms,
        "p99_tpot_ms": metrics.p99_tpot_ms,
        "mean_itl_ms": metrics.mean_itl_ms,
        "median_itl_ms": metrics.median_itl_ms,
        "std_itl_ms": metrics.std_itl_ms,
        "p99_itl_ms": metrics.p99_itl_ms,
        "input_lens": [output.prompt_len for output in outputs],
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        "output_lens": output_lens,
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        "ttfts": [output.ttft for output in outputs],
        "itls": [output.itl for output in outputs],
        "generated_texts": [output.generated_text for output in outputs],
        "errors": [output.error for output in outputs],
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        "mean_e2e_latency_ms": metrics.mean_e2e_latency_ms,
        "median_e2e_latency_ms": metrics.median_e2e_latency_ms,
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    }
    return result


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def parse_request_rate_range(request_rate_range):
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    if len(request_rate_range.split(",")) == 3:
        start, stop, step = map(int, request_rate_range.split(","))
        return list(range(start, stop, step))
    else:
        return list(map(int, request_rate_range.split(",")))
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def check_chat_template(model_path):
    try:
        tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
        return "chat_template" in tokenizer.init_kwargs
    except Exception as e:
        print(f"Fail to load tokenizer config with error={e}")
        return False


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def run_benchmark(args_: argparse.Namespace):
    global args
    args = args_

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    # Set global environments
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    set_ulimit()
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    random.seed(args.seed)
    np.random.seed(args.seed)

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    extra_request_body = {}
    if args.extra_request_body:
        extra_request_body = json.loads(args.extra_request_body)

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    # Set url
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    if args.port is None:
        args.port = {
            "sglang": 30000,
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            "sglang-native": 30000,
            "sglang-oai": 30000,
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            "lmdeploy": 23333,
            "vllm": 8000,
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            "trt": 8000,
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            "gserver": 9988,
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            "truss": 8080,
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        }.get(args.backend, 30000)

    model_url = (
        f"{args.base_url}/v1/models"
        if args.base_url
        else f"http://{args.host}:{args.port}/v1/models"
    )

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    if args.backend in ["sglang", "sglang-native"]:
        api_url = (
            f"{args.base_url}/generate"
            if args.base_url
            else f"http://{args.host}:{args.port}/generate"
        )
    elif args.backend in ["sglang-oai", "vllm", "lmdeploy"]:
        api_url = (
            f"{args.base_url}/v1/completions"
            if args.base_url
            else f"http://{args.host}:{args.port}/v1/completions"
        )
    elif args.backend == "trt":
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        api_url = (
            f"{args.base_url}/v2/models/ensemble/generate_stream"
            if args.base_url
            else f"http://{args.host}:{args.port}/v2/models/ensemble/generate_stream"
        )
        if args.model is None:
            print("Please provide a model using `--model` when using `trt` backend.")
            sys.exit(1)
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    elif args.backend == "gserver":
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        api_url = args.base_url if args.base_url else f"{args.host}:{args.port}"
        args.model = args.model or "default"
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    elif args.backend == "truss":
        api_url = (
            f"{args.base_url}/v1/models/model:predict"
            if args.base_url
            else f"http://{args.host}:{args.port}/v1/models/model:predict"
        )
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    # Get model name
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    if args.model is None:
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        if args.backend == "truss":
            print(
                "Please provide a model with `--model` when using truss backend. e.g. --model meta-llama/Llama-3.1-8B-Instruct"
            )
            sys.exit(1)
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        try:
            response = requests.get(model_url)
            model_list = response.json().get("data", [])
            args.model = model_list[0]["id"] if model_list else None
        except Exception as e:
            print(f"Failed to fetch model from {model_url}. Error: {e}")
            print(
                "Please specify the correct host and port using `--host` and `--port`."
            )
            sys.exit(1)

    if args.model is None:
        print("No model specified or found. Please provide a model using `--model`.")
        sys.exit(1)

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    if not check_chat_template(args.model):
        print(
            "\nWARNING It is recommended to use the `Chat` or `Instruct` model for benchmarking.\n"
            "Because when the tokenizer counts the output tokens, if there is gibberish, it might count incorrectly.\n"
        )

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    print(f"{args}\n")

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    # Read dataset
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    backend = args.backend
    model_id = args.model
    tokenizer_id = args.tokenizer if args.tokenizer is not None else args.model

    tokenizer = get_tokenizer(tokenizer_id)

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    input_requests = get_dataset(args, tokenizer)
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    if not args.multi:
        return asyncio.run(
            benchmark(
                backend=backend,
                api_url=api_url,
                model_id=model_id,
                tokenizer=tokenizer,
                input_requests=input_requests,
                request_rate=args.request_rate,
                disable_tqdm=args.disable_tqdm,
                extra_request_body=extra_request_body,
            )
        )
    else:
        # Benchmark multiple rps. TODO: use a fixed duration to compute num_prompts
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        request_rates = parse_request_rate_range(args.request_rate_range)

        for rate in request_rates:
            asyncio.run(
                benchmark(
                    backend=backend,
                    api_url=api_url,
                    model_id=model_id,
                    tokenizer=tokenizer,
                    input_requests=input_requests,
                    request_rate=rate,
                    disable_tqdm=args.disable_tqdm,
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                    extra_request_body=extra_request_body,
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                )
            )
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def set_ulimit(target_soft_limit=65535):
    resource_type = resource.RLIMIT_NOFILE
    current_soft, current_hard = resource.getrlimit(resource_type)

    if current_soft < target_soft_limit:
        try:
            resource.setrlimit(resource_type, (target_soft_limit, current_hard))
        except ValueError as e:
            print(f"Fail to set RLIMIT_NOFILE: {e}")


if __name__ == "__main__":
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    parser = ArgumentParser(description="Benchmark the online serving throughput.")
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    parser.add_argument(
        "--backend",
        type=str,
        choices=list(ASYNC_REQUEST_FUNCS.keys()),
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        default="sglang",
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        help="Must specify a backend, depending on the LLM Inference Engine.",
    )
    parser.add_argument(
        "--base-url",
        type=str,
        default=None,
        help="Server or API base url if not using http host and port.",
    )
    parser.add_argument(
        "--host", type=str, default="0.0.0.0", help="Default host is 0.0.0.0."
    )
    parser.add_argument(
        "--port",
        type=int,
        help="If not set, the default port is configured according to its default value for different LLM Inference Engines.",
    )
    parser.add_argument(
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        "--dataset-name",
        type=str,
        default="sharegpt",
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        choices=["sharegpt", "random", "generated-shared-prefix"],
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        help="Name of the dataset to benchmark on.",
    )
    parser.add_argument(
        "--dataset-path", type=str, default="", help="Path to the dataset."
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    )
    parser.add_argument(
        "--model",
        type=str,
        help="Name or path of the model. If not set, the default model will request /v1/models for conf.",
    )
    parser.add_argument(
        "--tokenizer",
        type=str,
        help="Name or path of the tokenizer. If not set, using the model conf.",
    )
    parser.add_argument(
        "--num-prompts",
        type=int,
        default=1000,
        help="Number of prompts to process. Default is 1000.",
    )
    parser.add_argument(
        "--sharegpt-output-len",
        type=int,
        default=None,
        help="Output length for each request. Overrides the output length from the ShareGPT dataset.",
    )
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    parser.add_argument(
        "--random-input-len",
        type=int,
        help="Number of input tokens per request, used only for random dataset.",
    )
    parser.add_argument(
        "--random-output-len",
        type=int,
        help="Number of output tokens per request, used only for random dataset.",
    )
    parser.add_argument(
        "--random-range-ratio",
        type=float,
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        default=0.0,
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        help="Range of sampled ratio of input/output length, "
        "used only for random dataset.",
    )
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    parser.add_argument(
        "--request-rate",
        type=float,
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        default=float("inf"),
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        help="Number of requests per second. If this is inf, then all the requests are sent at time 0. "
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        "Otherwise, we use Poisson process to synthesize the request arrival times. Default is inf.",
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    )
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    parser.add_argument("--seed", type=int, default=1, help="The random seed.")
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    parser.add_argument(
        "--multi",
        action="store_true",
        help="Use request rate range rather than single value.",
    )
    parser.add_argument(
        "--request-rate-range",
        type=str,
        default="2,34,2",
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        help="Range of request rates in the format start,stop,step. Default is 2,34,2. It also supports a list of request rates, requiring the parameters to not equal three.",
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    )
    parser.add_argument("--output-file", type=str, help="Output JSONL file name.")
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    parser.add_argument(
        "--disable-tqdm",
        action="store_true",
        help="Specify to disable tqdm progress bar.",
    )
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    parser.add_argument(
        "--disable-stream",
        action="store_true",
        help="Disable streaming mode.",
    )
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    parser.add_argument(
        "--disable-ignore-eos",
        action="store_true",
        help="Disable ignoring EOS.",
    )
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    parser.add_argument(
        "--extra-request-body",
        metavar='{"key1": "value1", "key2": "value2"}',
        type=str,
        help="Append given JSON object to the request payload. You can use this to specify"
        "additional generate params like sampling params.",
    )
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    group = parser.add_argument_group("generated-shared-prefix dataset arguments")
    group.add_argument(
        "--gen-num-groups",
        type=int,
        default=64,
        help="Number of system prompt groups for generated-shared-prefix dataset",
    )
    group.add_argument(
        "--gen-prompts-per-group",
        type=int,
        default=16,
        help="Number of prompts per system prompt group for generated-shared-prefix dataset",
    )
    group.add_argument(
        "--gen-system-prompt-len",
        type=int,
        default=2048,
        help="Target length in tokens for system prompts in generated-shared-prefix dataset",
    )
    group.add_argument(
        "--gen-question-len",
        type=int,
        default=128,
        help="Target length in tokens for questions in generated-shared-prefix dataset",
    )
    group.add_argument(
        "--gen-output-len",
        type=int,
        default=256,
        help="Target length in tokens for outputs in generated-shared-prefix dataset",
    )

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    args = parser.parse_args()
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    run_benchmark(args)