sgl-project--sglang/python/sglang/benchmark/utils.py

import json
import os
import resource
from json import JSONDecodeError
from typing import Dict, List, Optional, Union

import requests
from tqdm.asyncio import tqdm
from transformers import (
    AutoProcessor,
    AutoTokenizer,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
)


def remove_prefix(text: str, prefix: str) -> str:
    return text[len(prefix) :] if text.startswith(prefix) else text


def remove_suffix(text: str, suffix: str) -> str:
    return text[: -len(suffix)] if text.endswith(suffix) else text


def parse_custom_headers(header_list: List[str]) -> Dict[str, str]:
    return {k: v for h in header_list for k, _, v in [h.partition("=")] if k and v}


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]:
    assert (
        pretrained_model_name_or_path is not None
        and pretrained_model_name_or_path != ""
    )
    if pretrained_model_name_or_path.endswith(
        ".json"
    ) or pretrained_model_name_or_path.endswith(".model"):
        from sglang.srt.utils.hf_transformers_utils import get_tokenizer

        return get_tokenizer(pretrained_model_name_or_path)

    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
    )


def get_processor(
    pretrained_model_name_or_path: str,
) -> AutoProcessor:
    assert (
        pretrained_model_name_or_path is not None
        and pretrained_model_name_or_path != ""
    )

    from sglang.srt.utils.hf_transformers_utils import (
        get_processor as _srt_get_processor,
    )

    if not pretrained_model_name_or_path.endswith(
        (".json", ".model")
    ) and not os.path.exists(pretrained_model_name_or_path):
        pretrained_model_name_or_path = get_model(pretrained_model_name_or_path)
    return _srt_get_processor(pretrained_model_name_or_path, trust_remote_code=True)


def download_and_cache_hf_file(
    repo_id: str,
    filename: str,
    repo_type: str = "dataset",
):
    """Download a file from Hugging Face and cache it locally."""
    from huggingface_hub import hf_hub_download

    return hf_hub_download(repo_id=repo_id, filename=filename, repo_type=repo_type)


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])

    # Check if the cache file already exists
    if is_file_valid_json(filename):
        return filename

    print(f"Downloading from {url} to {filename}")

    # Stream the response to show the progress bar
    response = requests.get(url, stream=True)
    response.raise_for_status()  # Check for request errors

    # 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


def is_file_valid_json(path):
    if not os.path.isfile(path):
        return False

    # TODO can fuse into the real file open later
    try:
        with open(path) as f:
            json.load(f)
        return True
    except JSONDecodeError as e:
        print(
            f"{path} exists but json loading fails ({e=}), thus treat as invalid file"
        )
        return False


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}")