benchmark_throughput.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Benchmark offline inference throughput."""

import argparse
import dataclasses
import json
import os
import random
import time

from pathlib import Path
import warnings
from typing import Any, Optional, Union

import numpy as np
import torch
import uvloop
from tqdm import tqdm

from vllm.inputs import PromptType
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerBase

from benchmark_dataset import (
    AIMODataset,
    BurstGPTDataset,
    ConversationDataset,
    InstructCoderDataset,
    RandomDataset,
    SampleRequest,
    ShareGPTDataset,
    SonnetDataset,
    VisionArenaDataset,
)
from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
from vllm.engine.arg_utils import AsyncEngineArgs, EngineArgs
from vllm.entrypoints.openai.api_server import (
    build_async_engine_client_from_engine_args,
)
from vllm.inputs import TextPrompt, TokensPrompt
from vllm.lora.request import LoRARequest
from vllm.outputs import RequestOutput
from vllm.sampling_params import BeamSearchParams
from vllm.utils import FlexibleArgumentParser, merge_async_iterators


def run_vllm(
    requests: list[SampleRequest],
    n: int,
    num_iters_warmup: int,
    engine_args: EngineArgs,
    disable_detokenize: bool = False,
) -> tuple[float, Optional[list[RequestOutput]]]:
    from vllm import LLM, SamplingParams

    llm = LLM(**dataclasses.asdict(engine_args))
    assert all(
        llm.llm_engine.model_config.max_model_len
        >= (request.prompt_len + request.expected_output_len)
        for request in requests
    ), (
        "Please ensure that max_model_len is greater than the sum of"
        " prompt_len and expected_output_len for all requests."
    )
    # Add the requests to the engine.
    prompts: list[Union[TextPrompt, TokensPrompt]] = []
    sampling_params: list[SamplingParams] = []
    for request in requests:
        prompts.append(
            TokensPrompt(
                prompt_token_ids=request.prompt["prompt_token_ids"],
                multi_modal_data=request.multi_modal_data,
            )
            if "prompt_token_ids" in request.prompt
            else TextPrompt(
                prompt=request.prompt, multi_modal_data=request.multi_modal_data
            )
        )
        sampling_params.append(
            SamplingParams(
                n=n,
                temperature=1.0,
                top_p=1.0,
                ignore_eos=True,
                max_tokens=request.expected_output_len,
                detokenize=not disable_detokenize,
            )
        )
    lora_requests: Optional[list[LoRARequest]] = None
    if engine_args.enable_lora:
        lora_requests = [request.lora_request for request in requests]

    # warmup
    warmup_sampling_params = SamplingParams(
        n=n,
        temperature=1.0,
        top_p=1.0,
        ignore_eos=True,
        max_tokens=10,
    )
    dummy_prompt_token_ids = np.random.randint(10000, size=(1,10))
    dummy_prompts: list[PromptType] = [{
        "prompt_token_ids": batch
    } for batch in dummy_prompt_token_ids.tolist()]
    
    use_beam_search = False
    
    print("Warming up...")
    for _ in tqdm(range(num_iters_warmup), desc="Warmup iterations"):
        if not use_beam_search:
            llm.generate(dummy_prompts, sampling_params=warmup_sampling_params, use_tqdm=False)
        else:
            llm.beam_search(
                dummy_prompts,
                BeamSearchParams(
                    beam_width=args.n,
                    max_tokens=args.output_len,
                    ignore_eos=True,
                ),
            )

    outputs = None
    if not use_beam_search:
        if args.profile:
            profile_dir = args.profile_result_dir
            if not profile_dir:
                profile_dir = Path(
                    "."
                ) / "vllm_benchmark_result" / f"latency_result_{time.time()}"
            print(f"Profiling (results will be saved to '{profile_dir}')...")
            with torch.profiler.profile(
                        activities=[torch.profiler.ProfilerActivity.CPU,
                                    torch.profiler.ProfilerActivity.CUDA,
                        ],record_shapes=True,
                        on_trace_ready=torch.profiler.tensorboard_trace_handler(str(profile_dir))
                        ) as prof:
                start = time.perf_counter()
                outputs = llm.generate(
                    prompts, sampling_params, lora_request=lora_requests, use_tqdm=True
                )
                end = time.perf_counter()
            print('Prepare time report')
            print(prof.key_averages(group_by_input_shape=True).table(sort_by="self_cuda_time_total", row_limit=-1))
        else:
            start = time.perf_counter()
            outputs = llm.generate(
                prompts, sampling_params, lora_request=lora_requests, use_tqdm=True
            )
            end = time.perf_counter()
    else:
        assert lora_requests is None, "BeamSearch API does not support LoRA"
        prompts = [request.prompt for request in requests]
        # output_len should be the same for all requests.
        output_len = requests[0].expected_output_len
        for request in requests:
            assert request.expected_output_len == output_len
        start = time.perf_counter()
        llm.beam_search(
            prompts,
            BeamSearchParams(
                beam_width=n,
                max_tokens=output_len,
                ignore_eos=True,
            ),
        )
        end = time.perf_counter()
    return end - start, outputs


def run_vllm_chat(
    requests: list[SampleRequest],
    n: int,
    engine_args: EngineArgs,
    disable_detokenize: bool = False,
) -> tuple[float, list[RequestOutput]]:
    """
    Run vLLM chat benchmark. This function is recommended ONLY for benchmarking
    multimodal models as it properly handles multimodal inputs and chat
    formatting. For non-multimodal models, use run_vllm() instead.
    """
    from vllm import LLM, SamplingParams

    llm = LLM(**dataclasses.asdict(engine_args))

    assert all(
        llm.llm_engine.model_config.max_model_len
        >= (request.prompt_len + request.expected_output_len)
        for request in requests
    ), (
        "Please ensure that max_model_len is greater than the sum of "
        "prompt_len and expected_output_len for all requests."
    )

    prompts = []
    sampling_params: list[SamplingParams] = []
    for request in requests:
        prompts.append(request.prompt)
        sampling_params.append(
            SamplingParams(
                n=n,
                temperature=1.0,
                top_p=1.0,
                ignore_eos=True,
                max_tokens=request.expected_output_len,
                detokenize=not disable_detokenize,
            )
        )
    start = time.perf_counter()
    outputs = llm.chat(prompts, sampling_params, use_tqdm=True)
    end = time.perf_counter()
    return end - start, outputs


async def run_vllm_async(
    requests: list[SampleRequest],
    n: int,
    engine_args: AsyncEngineArgs,
    disable_frontend_multiprocessing: bool = False,
    disable_detokenize: bool = False,
) -> float:
    from vllm import SamplingParams

    async with build_async_engine_client_from_engine_args(
        engine_args, disable_frontend_multiprocessing
    ) as llm:
        model_config = await llm.get_model_config()
        assert all(
            model_config.max_model_len
            >= (request.prompt_len + request.expected_output_len)
            for request in requests
        ), (
            "Please ensure that max_model_len is greater than the sum of"
            " prompt_len and expected_output_len for all requests."
        )

        # Add the requests to the engine.
        prompts: list[Union[TextPrompt, TokensPrompt]] = []
        sampling_params: list[SamplingParams] = []
        lora_requests: list[Optional[LoRARequest]] = []
        for request in requests:
            prompts.append(
                TokensPrompt(
                    prompt_token_ids=request.prompt["prompt_token_ids"],
                    multi_modal_data=request.multi_modal_data,
                )
                if "prompt_token_ids" in request.prompt
                else TextPrompt(
                    prompt=request.prompt, multi_modal_data=request.multi_modal_data
                )
            )
            sampling_params.append(
                SamplingParams(
                    n=n,
                    temperature=1.0,
                    top_p=1.0,
                    ignore_eos=True,
                    max_tokens=request.expected_output_len,
                    detokenize=not disable_detokenize,
                )
            )
            lora_requests.append(request.lora_request)

        generators = []
        start = time.perf_counter()
        for i, (prompt, sp, lr) in enumerate(
            zip(prompts, sampling_params, lora_requests)
        ):
            generator = llm.generate(prompt, sp, lora_request=lr, request_id=f"test{i}")
            generators.append(generator)
        all_gens = merge_async_iterators(*generators)
        async for i, res in all_gens:
            pass
        end = time.perf_counter()
        return end - start


def run_hf(
    requests: list[SampleRequest],
    model: str,
    tokenizer: PreTrainedTokenizerBase,
    n: int,
    max_batch_size: int,
    trust_remote_code: bool,
    disable_detokenize: bool = False,
) -> float:
    llm = AutoModelForCausalLM.from_pretrained(
        model, torch_dtype=torch.float16, trust_remote_code=trust_remote_code
    )
    if llm.config.model_type == "llama":
        # To enable padding in the HF backend.
        tokenizer.pad_token = tokenizer.eos_token
    llm = llm.cuda()

    pbar = tqdm(total=len(requests))
    start = time.perf_counter()
    batch: list[str] = []
    max_prompt_len = 0
    max_output_len = 0
    for i in range(len(requests)):
        prompt = requests[i].prompt
        prompt_len = requests[i].prompt_len
        output_len = requests[i].expected_output_len
        # Add the prompt to the batch.
        batch.append(prompt)
        max_prompt_len = max(max_prompt_len, prompt_len)
        max_output_len = max(max_output_len, output_len)
        if len(batch) < max_batch_size and i != len(requests) - 1:
            # Check if we can add more requests to the batch.
            next_prompt_len = requests[i + 1].prompt_len
            next_output_len = requests[i + 1].expected_output_len
            if (
                max(max_prompt_len, next_prompt_len)
                + max(max_output_len, next_output_len)
            ) <= 2048:
                # We can add more requests to the batch.
                continue

        # Generate the sequences.
        input_ids = tokenizer(batch, return_tensors="pt", padding=True).input_ids
        llm_outputs = llm.generate(
            input_ids=input_ids.cuda(),
            do_sample=True,
            num_return_sequences=n,
            temperature=1.0,
            top_p=1.0,
            use_cache=True,
            max_new_tokens=max_output_len,
        )
        if not disable_detokenize:
            # Include the decoding time.
            tokenizer.batch_decode(llm_outputs, skip_special_tokens=True)
        pbar.update(len(batch))

        # Clear the batch.
        batch = []
        max_prompt_len = 0
        max_output_len = 0
    end = time.perf_counter()
    return end - start


def run_mii(
    requests: list[SampleRequest],
    model: str,
    tensor_parallel_size: int,
    output_len: int,
) -> float:
    from mii import client, serve

    llm = serve(model, tensor_parallel=tensor_parallel_size)
    prompts = [request.prompt for request in requests]

    start = time.perf_counter()
    llm.generate(prompts, max_new_tokens=output_len)
    end = time.perf_counter()
    client = client(model)
    client.terminate_server()
    return end - start


def save_to_pytorch_benchmark_format(
    args: argparse.Namespace, results: dict[str, Any]
) -> None:
    pt_records = convert_to_pytorch_benchmark_format(
        args=args,
        metrics={
            "requests_per_second": [results["requests_per_second"]],
            "tokens_per_second": [results["tokens_per_second"]],
        },
        extra_info={
            k: results[k] for k in ["elapsed_time", "num_requests", "total_num_tokens"]
        },
    )
    if pt_records:
        # Don't use json suffix here as we don't want CI to pick it up
        pt_file = f"{os.path.splitext(args.output_json)[0]}.pytorch.json"
        write_to_json(pt_file, pt_records)


def get_requests(args, tokenizer):
    # Common parameters for all dataset types.
    common_kwargs = {
        "dataset_path": args.dataset_path,
        "random_seed": args.seed,
    }
    sample_kwargs = {
        "tokenizer": tokenizer,
        "lora_path": args.lora_path,
        "max_loras": args.max_loras,
        "num_requests": args.num_prompts,
        "input_len": args.input_len,
        "output_len": args.output_len,
    }

    if args.dataset_path is None or args.dataset_name == "random":
        sample_kwargs["range_ratio"] = args.random_range_ratio
        sample_kwargs["prefix_len"] = args.prefix_len
        dataset_cls = RandomDataset
    elif args.dataset_name == "sharegpt":
        dataset_cls = ShareGPTDataset
        if args.backend == "vllm-chat":
            sample_kwargs["enable_multimodal_chat"] = True
    elif args.dataset_name == "sonnet":
        assert tokenizer.chat_template or tokenizer.default_chat_template, (
            "Tokenizer/model must have chat template for sonnet dataset."
        )
        dataset_cls = SonnetDataset
        sample_kwargs["prefix_len"] = args.prefix_len
        sample_kwargs["return_prompt_formatted"] = True
    elif args.dataset_name == "burstgpt":
        dataset_cls = BurstGPTDataset
    elif args.dataset_name == "hf":
        if args.dataset_path in VisionArenaDataset.SUPPORTED_DATASET_PATHS:
            dataset_cls = VisionArenaDataset
            common_kwargs["dataset_subset"] = None
            common_kwargs["dataset_split"] = "train"
            sample_kwargs["enable_multimodal_chat"] = True
        elif args.dataset_path in InstructCoderDataset.SUPPORTED_DATASET_PATHS:
            dataset_cls = InstructCoderDataset
            common_kwargs["dataset_split"] = "train"
        elif args.dataset_path in ConversationDataset.SUPPORTED_DATASET_PATHS:
            dataset_cls = ConversationDataset
            common_kwargs["dataset_subset"] = args.hf_subset
            common_kwargs["dataset_split"] = args.hf_split
            sample_kwargs["enable_multimodal_chat"] = True
        elif args.dataset_path in AIMODataset.SUPPORTED_DATASET_PATHS:
            dataset_cls = AIMODataset
            common_kwargs["dataset_subset"] = None
            common_kwargs["dataset_split"] = "train"
    else:
        raise ValueError(f"Unknown dataset name: {args.dataset_name}")
    # Remove None values
    sample_kwargs = {k: v for k, v in sample_kwargs.items() if v is not None}
    return dataset_cls(**common_kwargs).sample(**sample_kwargs)


def main(args: argparse.Namespace):
    if args.seed is None:
        args.seed = 0
    print(args)
    random.seed(args.seed)
    # Sample the requests.
    tokenizer = AutoTokenizer.from_pretrained(
        args.tokenizer, trust_remote_code=args.trust_remote_code
    )
    requests = get_requests(args, tokenizer)
    is_multi_modal = any(request.multi_modal_data is not None for request in requests)
    request_outputs: Optional[list[RequestOutput]] = None
    if args.backend == "vllm":
        if args.async_engine:
            elapsed_time = uvloop.run(
                run_vllm_async(
                    requests,
                    args.n,
                    AsyncEngineArgs.from_cli_args(args),
                    args.disable_frontend_multiprocessing,
                    args.disable_detokenize,
                )
            )
        else:
            elapsed_time, request_outputs = run_vllm(
                requests,
                args.n,
                args.num_iters_warmup,
                EngineArgs.from_cli_args(args),
                args.disable_detokenize,
            )
    elif args.backend == "hf":
        assert args.tensor_parallel_size == 1
        elapsed_time = run_hf(
            requests,
            args.model,
            tokenizer,
            args.n,
            args.hf_max_batch_size,
            args.trust_remote_code,
            args.disable_detokenize,
        )
    elif args.backend == "mii":
        elapsed_time = run_mii(
            requests, args.model, args.tensor_parallel_size, args.output_len
        )
    elif args.backend == "vllm-chat":
        elapsed_time, request_outputs = run_vllm_chat(
            requests, args.n, EngineArgs.from_cli_args(args), args.disable_detokenize
        )
    else:
        raise ValueError(f"Unknown backend: {args.backend}")

    if request_outputs:
        # Note: with the vllm and vllm-chat backends,
        # we have request_outputs, which we use to count tokens.
        total_prompt_tokens = 0
        total_output_tokens = 0
        for ro in request_outputs:
            if not isinstance(ro, RequestOutput):
                continue
            total_prompt_tokens += (
                len(ro.prompt_token_ids) if ro.prompt_token_ids else 0
            )
            total_output_tokens += sum(len(o.token_ids) for o in ro.outputs if o)
        total_num_tokens = total_prompt_tokens + total_output_tokens
    else:
        total_num_tokens = sum(r.prompt_len + r.expected_output_len for r in requests)
        total_output_tokens = sum(r.expected_output_len for r in requests)
        total_prompt_tokens = total_num_tokens - total_output_tokens

    if is_multi_modal and args.backend != "vllm-chat":
        print(
            "\033[91mWARNING\033[0m: Multi-modal request with "
            f"{args.backend} backend detected. The "
            "following metrics are not accurate because image tokens are not"
            " counted. See vllm-project/vllm/issues/9778 for details."
        )
        # TODO(vllm-project/vllm/issues/9778): Count multi-modal token length.
        # vllm-chat backend counts the image tokens now

    print(f"Latency: {elapsed_time:.2f} s")
    print(
        f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
        f"{total_num_tokens / elapsed_time:.2f} total tokens/s, "
        f"{total_output_tokens / elapsed_time:.2f} output tokens/s"
    )
    print(f"Total num prompt tokens:  {total_prompt_tokens}")
    print(f"Total num output tokens:  {total_output_tokens}")

    # Output JSON results if specified
    if args.output_json:
        results = {
            "elapsed_time": elapsed_time,
            "num_requests": len(requests),
            "total_num_tokens": total_num_tokens,
            "requests_per_second": len(requests) / elapsed_time,
            "tokens_per_second": total_num_tokens / elapsed_time,
        }
        with open(args.output_json, "w") as f:
            json.dump(results, f, indent=4)
        save_to_pytorch_benchmark_format(args, results)


def validate_args(args):
    """
    Validate command-line arguments.
    """

    # === Deprecation and Defaulting ===
    if args.dataset is not None:
        warnings.warn(
            "The '--dataset' argument will be deprecated in the next release. "
            "Please use '--dataset-name' and '--dataset-path' instead.",
            stacklevel=2,
        )
        args.dataset_path = args.dataset

    if not getattr(args, "tokenizer", None):
        args.tokenizer = args.model

    # === Backend Validation ===
    valid_backends = {"vllm", "hf", "mii", "vllm-chat"}
    if args.backend not in valid_backends:
        raise ValueError(f"Unsupported backend: {args.backend}")

    # === Dataset Configuration ===
    if not args.dataset and not args.dataset_path:
        print("When dataset path is not set, it will default to random dataset")
        args.dataset_name = "random"
        if args.input_len is None:
            raise ValueError("input_len must be provided for a random dataset")

    # === Dataset Name Specific Checks ===
    # --hf-subset and --hf-split: only used
    # when dataset_name is 'hf'
    if args.dataset_name != "hf" and (
        getattr(args, "hf_subset", None) is not None
        or getattr(args, "hf_split", None) is not None
    ):
        warnings.warn(
            "--hf-subset and --hf-split will be ignored \
                since --dataset-name is not 'hf'.",
            stacklevel=2,
        )
    elif args.dataset_name == "hf":
        if args.dataset_path in (
            VisionArenaDataset.SUPPORTED_DATASET_PATHS.keys()
            | ConversationDataset.SUPPORTED_DATASET_PATHS
        ):
            assert args.backend == "vllm-chat", (
                f"{args.dataset_path} needs to use vllm-chat as the backend."
            )  # noqa: E501
        elif args.dataset_path in (
            InstructCoderDataset.SUPPORTED_DATASET_PATHS
            | AIMODataset.SUPPORTED_DATASET_PATHS
        ):
            assert args.backend == "vllm", (
                f"{args.dataset_path} needs to use vllm as the backend."
            )  # noqa: E501
        else:
            raise ValueError(f"{args.dataset_path} is not supported by hf dataset.")

    # --random-range-ratio: only used when dataset_name is 'random'
    if args.dataset_name != "random" and args.random_range_ratio is not None:
        warnings.warn(
            "--random-range-ratio will be ignored since \
                --dataset-name is not 'random'.",
            stacklevel=2,
        )

    # --prefix-len: only used when dataset_name is 'random', 'sonnet', or not
    # set.
    if (
        args.dataset_name not in {"random", "sonnet", None}
        and args.prefix_len is not None
    ):
        warnings.warn(
            "--prefix-len will be ignored since --dataset-name\
                 is not 'random', 'sonnet', or not set.",
            stacklevel=2,
        )

    # === LoRA Settings ===
    if getattr(args, "enable_lora", False) and args.backend != "vllm":
        raise ValueError("LoRA benchmarking is only supported for vLLM backend")
    if getattr(args, "enable_lora", False) and args.lora_path is None:
        raise ValueError("LoRA path must be provided when enable_lora is True")

    # === Backend-specific Validations ===
    if args.backend == "hf" and args.hf_max_batch_size is None:
        raise ValueError("HF max batch size is required for HF backend")
    if args.backend != "hf" and args.hf_max_batch_size is not None:
        raise ValueError("HF max batch size is only for HF backend.")

    if (
        args.backend in {"hf", "mii"}
        and getattr(args, "quantization", None) is not None
    ):
        raise ValueError("Quantization is only for vLLM backend.")

    if args.backend == "mii" and args.dtype != "auto":
        raise ValueError("dtype must be auto for MII backend.")
    if args.backend == "mii" and args.n != 1:
        raise ValueError("n must be 1 for MII backend.")
    if args.backend == "mii" and args.tokenizer != args.model:
        raise ValueError("Tokenizer must be the same as the model for MII backend.")

    # --data-parallel is not supported currently.
    # https://github.com/vllm-project/vllm/issues/16222
    if args.data_parallel_size > 1:
        raise ValueError(
            "Data parallel is not supported in offline benchmark, \
            please use benchmark serving instead"
        )


def create_argument_parser():
    parser = FlexibleArgumentParser(description="Benchmark the throughput.")
    parser.add_argument(
        "--backend",
        type=str,
        choices=["vllm", "hf", "mii", "vllm-chat"],
        default="vllm",
    )
    parser.add_argument(
        "--dataset-name",
        type=str,
        choices=["sharegpt", "random", "sonnet", "burstgpt", "hf"],
        help="Name of the dataset to benchmark on.",
        default="sharegpt",
    )
    parser.add_argument(
        "--dataset",
        type=str,
        default=None,
        help="Path to the ShareGPT dataset, will be deprecated in\
            the next release. The dataset is expected to "
        "be a json in form of list[dict[..., conversations: "
        "list[dict[..., value: <prompt_or_response>]]]]",
    )
    parser.add_argument(
        "--dataset-path", type=str, default=None, help="Path to the dataset"
    )
    parser.add_argument(
        "--input-len",
        type=int,
        default=None,
        help="Input prompt length for each request",
    )
    parser.add_argument(
        "--output-len",
        type=int,
        default=None,
        help="Output length for each request. Overrides the "
        "output length from the dataset.",
    )
    parser.add_argument(
        "--n", type=int, default=1, help="Number of generated sequences per prompt."
    )
    parser.add_argument(
        "--num-iters-warmup", type=int, default=1, help="Number of iterations to run for warmup."
    )
    parser.add_argument(
        "--num-prompts", type=int, default=1000, help="Number of prompts to process."
    )
    parser.add_argument(
        '--profile',
        action='store_true',
        help='profile the generation process of a single batch')
    parser.add_argument(
        '--profile-result-dir',
        type=str,
        default=None,
        help=('path to save the pytorch profiler output. Can be visualized '
              'with ui.perfetto.dev or Tensorboard.'))
    parser.add_argument(
        "--hf-max-batch-size",
        type=int,
        default=None,
        help="Maximum batch size for HF backend.",
    )
    parser.add_argument(
        "--output-json",
        type=str,
        default=None,
        help="Path to save the throughput results in JSON format.",
    )
    parser.add_argument(
        "--async-engine",
        action="store_true",
        default=False,
        help="Use vLLM async engine rather than LLM class.",
    )
    parser.add_argument(
        "--disable-frontend-multiprocessing",
        action="store_true",
        default=False,
        help="Disable decoupled async engine frontend.",
    )
    parser.add_argument(
        "--disable-detokenize",
        action="store_true",
        help=(
            "Do not detokenize the response (i.e. do not include "
            "detokenization time in the measurement)"
        ),
    )
    # LoRA
    parser.add_argument(
        "--lora-path",
        type=str,
        default=None,
        help="Path to the LoRA adapters to use. This can be an absolute path, "
        "a relative path, or a Hugging Face model identifier.",
    )
    parser.add_argument(
        "--prefix-len",
        type=int,
        default=None,
        help=f"Number of prefix tokens to be used in RandomDataset "
        "and SonnetDataset. For RandomDataset, the total input "
        "length is the sum of prefix-len (default: "
        f"{RandomDataset.DEFAULT_PREFIX_LEN}) and a random context length "
        "sampled from [input_len * (1 - range_ratio), "
        "input_len * (1 + range_ratio)]. For SonnetDataset, "
        f"prefix_len (default: {SonnetDataset.DEFAULT_PREFIX_LEN}) "
        "controls how much of the input is fixed lines versus "
        "random lines, but the total input length remains approximately "
        "input_len tokens.",
    )
    # random dataset
    parser.add_argument(
        "--random-range-ratio",
        type=float,
        default=None,
        help=f"Range ratio (default : {RandomDataset.DEFAULT_RANGE_RATIO}) "
        "for sampling input/output length, "
        "used only for RandomDataset. Must be in the range [0, 1) to "
        "define a symmetric sampling range "
        "[length * (1 - range_ratio), length * (1 + range_ratio)].",
    )

    # hf dtaset
    parser.add_argument(
        "--hf-subset", type=str, default=None, help="Subset of the HF dataset."
    )
    parser.add_argument(
        "--hf-split", type=str, default=None, help="Split of the HF dataset."
    )

    parser = AsyncEngineArgs.add_cli_args(parser)

    return parser


if __name__ == "__main__":
    parser = create_argument_parser()
    args = parser.parse_args()
    if args.tokenizer is None:
        args.tokenizer = args.model
    validate_args(args)
    main(args)