Yueqian Lin
39e3fd3d12
Signed-off-by: Yueqian Lin <linyueqian@outlook.com> Signed-off-by: Yueqian Lin <70319226+linyueqian@users.noreply.github.com>
1012 lines
40 KiB
Python
1012 lines
40 KiB
Python
import asyncio
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import base64
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import contextlib
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import io
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import json
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import os
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import random
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import ssl
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import sys
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import time
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import traceback
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from collections.abc import Iterable
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from dataclasses import dataclass
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from datetime import datetime
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from typing import Literal
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import aiohttp
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from pydub import AudioSegment
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from tqdm.asyncio import tqdm
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from vllm.benchmarks import datasets
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from vllm.benchmarks.datasets import SampleRequest
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from vllm.benchmarks.lib.endpoint_request_func import (
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ASYNC_REQUEST_FUNCS,
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OPENAI_COMPATIBLE_BACKENDS,
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RequestFuncInput,
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RequestFuncOutput,
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StreamedResponseHandler,
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_get_chat_content,
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_update_headers_common,
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_update_payload_common,
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_validate_api_url,
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)
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from vllm.logger import init_logger
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from vllm.tokenizers import TokenizerLike
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logger = init_logger(__name__)
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from vllm_omni.benchmarks.data_modules.daily_omni_dataset import DailyOmniDataset, DailyOmniSampleRequest
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from vllm_omni.benchmarks.data_modules.random_multi_modal_dataset import OmniRandomMultiModalDataset
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from vllm_omni.benchmarks.data_modules.seed_tts_dataset import (
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SEED_TTS_DEFAULT_OMNI_SYSTEM_PROMPT,
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SeedTTSDataset,
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SeedTTSDesignDataset,
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SeedTTSSampleRequest,
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SeedTTSTextDataset,
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)
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get_samples_old = datasets.get_samples
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_DEFAULT_DAILY_OMNI_REPO = "liarliar/Daily-Omni"
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def _seed_tts_capture_pcm_for_wer() -> bool:
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return os.environ.get("SEED_TTS_WER_EVAL", "").lower() in (
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"1",
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"true",
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"yes",
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)
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def _merge_extra_body_mm_kwargs(base: dict | None, overlay: dict | None) -> dict | None:
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"""Shallow-merge ``extra_body`` dicts; deep-merge ``mm_processor_kwargs`` if both set."""
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if not base and not overlay:
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return None
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out = dict(base or {})
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if not overlay:
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return out
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for k, v in overlay.items():
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if k == "mm_processor_kwargs" and isinstance(v, dict):
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prev = out.get("mm_processor_kwargs")
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merged_kw = {**(prev if isinstance(prev, dict) else {}), **v}
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out["mm_processor_kwargs"] = merged_kw
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else:
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out[k] = v
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return out
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def _attach_daily_omni_to_request_func_input(sample: SampleRequest, rfi: RequestFuncInput) -> None:
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"""Apply per-request OpenAI fields (``mm_processor_kwargs``, messages) for Daily-Omni."""
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if not isinstance(sample, DailyOmniSampleRequest):
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return
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rfi.extra_body = _merge_extra_body_mm_kwargs(rfi.extra_body, sample.omni_extra_body)
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if sample.omni_chat_messages is not None:
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setattr(rfi, "omni_chat_messages", sample.omni_chat_messages)
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else:
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setattr(rfi, "mm_position", sample.omni_chat_mm_position)
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def _attach_seed_tts_to_request_func_input(sample: SampleRequest, rfi: RequestFuncInput) -> None:
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"""Merge Seed-TTS per-row TTS fields into ``extra_body`` and mark for PCM capture.
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Always sets ``seed_tts_row=True`` on the RequestFuncInput for any
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:class:`SeedTTSSampleRequest` subclass (including text-only and design
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variants that carry no ``ref_audio``). This enables PCM capture for WER /
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UTMOS evaluation even when there is no reference audio.
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"""
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if not isinstance(sample, SeedTTSSampleRequest):
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return
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# Mark for PCM capture (WER / UTMOS eval) regardless of extra body presence.
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setattr(rfi, "seed_tts_row", True)
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sys_prompt = (sample.seed_tts_system_prompt or "").strip() or SEED_TTS_DEFAULT_OMNI_SYSTEM_PROMPT
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setattr(
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rfi,
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"omni_chat_messages",
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[
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{"role": "system", "content": [{"type": "text", "text": sys_prompt}]},
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{"role": "user", "content": [{"type": "text", "text": sample.prompt}]},
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],
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)
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ex = sample.seed_tts_speech_extra
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if not ex:
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return # voice comes from --extra-body in config; no ref_audio to merge
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base = dict(rfi.extra_body) if rfi.extra_body else {}
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base.update(ex)
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rfi.extra_body = base
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def _daily_omni_repo_from_args(args) -> str | None:
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"""Resolve HuggingFace repo id for Daily-Omni from CLI args.
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vLLM allows ``--dataset-path`` to be a local path while the real HF id is
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passed via ``--hf-name``. Upstream ``get_samples`` for ``hf`` only matches
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a fixed elif-chain and never discovers Omni's loader, so we must detect
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Daily-Omni here using either field.
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"""
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dp = getattr(args, "dataset_path", None)
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hn = getattr(args, "hf_name", None)
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if dp in DailyOmniDataset.SUPPORTED_DATASET_PATHS:
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return dp
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if hn in DailyOmniDataset.SUPPORTED_DATASET_PATHS:
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return hn
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return None
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def get_samples(args, tokenizer):
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# Daily-Omni: explicit dataset name, or hf + matching path/hf-name
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is_daily_omni = args.dataset_name == "daily-omni" or (
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args.dataset_name == "hf" and _daily_omni_repo_from_args(args) is not None
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)
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is_seed_tts = args.dataset_name in ("seed-tts", "seed-tts-text", "seed-tts-design")
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# Check if we need to handle omni-related backends/datasets
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is_omni_backend = args.backend in ["openai-chat-omni", "openai-audio-speech", "daily-omni"]
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is_omni_dataset = is_daily_omni or is_seed_tts or args.dataset_name == "random-mm"
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if not is_omni_backend and not is_omni_dataset:
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# Not an omni-related request, delegate to original implementation
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return get_samples_old(args, tokenizer)
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# Handle Daily-Omni dataset
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if is_daily_omni:
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# Support:
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# --dataset-name daily-omni [--dataset-path liarliar/Daily-Omni]
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# --dataset-name daily-omni --daily-omni-qa-json /path/to/qa.json (offline QA)
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# --dataset-name hf --dataset-path liarliar/Daily-Omni
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# --dataset-name hf --hf-name liarliar/Daily-Omni (dataset-path may be local)
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# Validate backend supports multimodal (video)
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if args.backend not in ["openai-chat-omni", "daily-omni"]:
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raise ValueError(
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f"Daily-Omni dataset requires a multimodal backend that supports video. "
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f"Got backend='{args.backend}'. Please use '--backend openai-chat-omni'"
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)
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# Determine video directory if specified (for local video files)
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video_dir = getattr(args, "daily_omni_video_dir", None)
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# Get HF split (default to "train"; unused when loading from local qa.json)
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dataset_split = getattr(args, "hf_split", None) or "train"
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qa_json = getattr(args, "daily_omni_qa_json", None)
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if isinstance(qa_json, str):
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qa_json = qa_json.strip() or None
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if qa_json is not None:
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logger.info(
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"Loading Daily-Omni dataset: qa_json=%s, video_dir=%s (Hub not used for QA)",
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qa_json,
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video_dir,
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)
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dataset = DailyOmniDataset(
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qa_json_path=qa_json,
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dataset_path=None,
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dataset_split=dataset_split,
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random_seed=args.seed,
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video_dir=video_dir,
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input_mode=getattr(args, "daily_omni_input_mode", "all"),
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inline_local_video=getattr(args, "daily_omni_inline_local_video", False),
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trust_remote_code=getattr(args, "trust_remote_code", False),
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disable_shuffle=getattr(args, "disable_shuffle", False),
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)
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else:
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repo_id = _daily_omni_repo_from_args(args)
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if args.dataset_name == "daily-omni":
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if repo_id is None:
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repo_id = _DEFAULT_DAILY_OMNI_REPO
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elif repo_id is None:
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raise ValueError(
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"Daily-Omni with --dataset-name hf requires "
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f"--dataset-path {_DEFAULT_DAILY_OMNI_REPO} or "
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f"--hf-name {_DEFAULT_DAILY_OMNI_REPO}."
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)
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logger.info(
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"Loading Daily-Omni dataset: hf_repo=%s, split=%s, video_dir=%s",
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repo_id,
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dataset_split,
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video_dir,
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)
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dataset = DailyOmniDataset(
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dataset_path=repo_id,
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dataset_split=dataset_split,
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dataset_subset=getattr(args, "hf_subset", None),
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random_seed=args.seed,
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video_dir=video_dir,
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input_mode=getattr(args, "daily_omni_input_mode", "all"),
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inline_local_video=getattr(args, "daily_omni_inline_local_video", False),
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trust_remote_code=getattr(args, "trust_remote_code", False),
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no_stream=getattr(args, "no_stream", False),
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disable_shuffle=getattr(args, "disable_shuffle", False),
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)
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out_len = getattr(args, "output_len", None)
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if out_len is None:
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out_len = getattr(args, "hf_output_len", None)
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if out_len is None:
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out_len = DailyOmniDataset.DEFAULT_OUTPUT_LEN
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input_requests = dataset.sample(
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tokenizer=tokenizer,
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num_requests=args.num_prompts,
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output_len=out_len,
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request_id_prefix=args.request_id_prefix,
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no_oversample=args.no_oversample,
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)
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return input_requests
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if is_seed_tts:
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if args.backend not in ("openai-audio-speech", "openai-chat-omni"):
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raise ValueError(
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"Seed-TTS requires --backend openai-audio-speech (POST /v1/audio/speech) or "
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"--backend openai-chat-omni (POST /v1/chat/completions with ref_audio/ref_text). "
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f"Got backend={args.backend!r}."
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)
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repo_id = getattr(args, "dataset_path", None) or getattr(args, "hf_name", None)
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if not repo_id:
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raise ValueError(
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"Seed-TTS requires --dataset-path (HF dataset repo id or local directory) or "
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"--hf-name for the Hub dataset id."
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)
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_cls_map = {
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"seed-tts": SeedTTSDataset,
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"seed-tts-text": SeedTTSTextDataset,
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"seed-tts-design": SeedTTSDesignDataset,
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}
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DatasetCls = _cls_map[args.dataset_name]
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dataset = DatasetCls(
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dataset_path=repo_id,
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random_seed=args.seed,
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locale=getattr(args, "seed_tts_locale", "en"),
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inline_ref_audio=not getattr(args, "seed_tts_file_ref_audio", False),
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seed_tts_root=getattr(args, "seed_tts_root", None),
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system_prompt=getattr(args, "seed_tts_system_prompt", None),
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disable_shuffle=getattr(args, "disable_shuffle", False),
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)
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out_len = getattr(args, "output_len", None)
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if out_len is None:
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out_len = getattr(args, "hf_output_len", None)
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if out_len is None:
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out_len = SeedTTSDataset.DEFAULT_OUTPUT_LEN
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return dataset.sample(
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tokenizer=tokenizer,
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num_requests=args.num_prompts,
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output_len=out_len,
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request_id_prefix=args.request_id_prefix,
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no_oversample=args.no_oversample,
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)
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# Handle random-mm dataset (Omni's synthetic multimodal dataset)
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if args.dataset_name == "random-mm":
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dataset = OmniRandomMultiModalDataset(random_seed=args.seed, dataset_path=args.dataset_path)
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input_requests = dataset.sample(
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tokenizer=tokenizer,
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num_requests=args.num_prompts,
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prefix_len=args.random_prefix_len,
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range_ratio=args.random_range_ratio,
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input_len=args.random_input_len,
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output_len=args.random_output_len,
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base_items_per_request=args.random_mm_base_items_per_request,
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limit_mm_per_prompt=args.random_mm_limit_mm_per_prompt,
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num_mm_items_range_ratio=args.random_mm_num_mm_items_range_ratio,
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bucket_config=args.random_mm_bucket_config,
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request_id_prefix=args.request_id_prefix,
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no_oversample=args.no_oversample,
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)
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return input_requests
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else:
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return get_samples_old(args, tokenizer)
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datasets.get_samples = get_samples
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_serve_mod = sys.modules.get("vllm.benchmarks.serve")
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if _serve_mod is not None:
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_serve_mod.get_samples = get_samples
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@dataclass
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class MixRequestFuncOutput(RequestFuncOutput):
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audio_ttfp: float = 0.0
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audio_duration: float = 0.0
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audio_frames: int = 0
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audio_rtf: float = 0.0
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text_latency: float = 0.0
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#: Raw PCM s16le mono at 24 kHz for Seed-TTS WER: from ``/v1/audio/speech`` stream or
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#: resampled export after ``openai-chat-omni`` audio deltas.
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tts_output_pcm_bytes: bytes | None = None
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async def async_request_openai_chat_omni_completions(
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request_func_input: RequestFuncInput,
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session: aiohttp.ClientSession,
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pbar: tqdm | None = None,
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mm_position: Literal["first", "last"] = "last",
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) -> MixRequestFuncOutput:
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api_url = request_func_input.api_url
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_validate_api_url(api_url, "OpenAI Chat Completions API", "chat/completions")
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omni_messages = getattr(request_func_input, "omni_chat_messages", None)
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if omni_messages is not None:
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messages_payload = omni_messages
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else:
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effective_mm_position = getattr(request_func_input, "mm_position", mm_position)
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content = _get_chat_content(request_func_input, mm_position=effective_mm_position)
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messages_payload = [{"role": "user", "content": content}]
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payload = {
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"model": request_func_input.model_name if request_func_input.model_name else request_func_input.model,
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"messages": messages_payload,
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"temperature": 0.0,
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"max_tokens": request_func_input.output_len,
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"stream": True,
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"stream_options": {
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"include_usage": True,
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},
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}
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_update_payload_common(payload, request_func_input)
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# Seed-TTS via chat: voice-clone fields live on the body; ensure audio is streamed.
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if getattr(request_func_input, "seed_tts_row", False):
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if payload.get("modalities") is None:
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payload["modalities"] = ["text", "audio"]
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response_format = payload.get("response_format", "wav")
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if response_format == "pcm":
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raise ValueError(
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"pcm response format is not supported yet. \
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Please use other formats like wav, mp3, etc. instead."
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)
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
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}
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_update_headers_common(headers, request_func_input)
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output = MixRequestFuncOutput()
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output.prompt_len = request_func_input.prompt_len
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max_retries = 3
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retry_delay = 0.1
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for attempt in range(max_retries + 1):
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# Reset per-attempt state so that retries do not mix partial
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# outputs or metrics from previous attempts.
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generated_text = ""
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generated_audio = None
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ttft = 0.0
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st = time.perf_counter()
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output.start_time = st
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most_recent_timestamp = st
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timestamp = st
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audio_generate_time = 0.0
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output.itl = []
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output.generated_text = ""
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output.ttft = 0.0
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output.audio_ttfp = 0.0
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output.audio_duration = 0.0
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output.audio_frames = 0
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output.audio_rtf = 0.0
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output.text_latency = 0.0
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output.output_tokens = 0
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output.error = ""
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output.success = False
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try:
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async with session.post(url=api_url, json=payload, headers=headers) as response:
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if response.status == 200:
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handler = StreamedResponseHandler()
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async for chunk_bytes in response.content.iter_any():
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# NOTE: Do NOT strip() here; TCP may fragment the SSE messages,
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# so stripping here can cause problems depending on how it is split.
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#
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# Simple example: [b'data: ', b'{json}\n\n'] <- stripping the first
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# chunk will break SSE parsing because the space after 'data:' is required.
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if not chunk_bytes:
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continue
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messages = handler.add_chunk(chunk_bytes)
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for message in messages:
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if type(message) is bytes:
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message = message.decode("utf-8")
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# NOTE: SSE comments (often used as pings) start with
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# a colon. These are not JSON data payload and should
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# be skipped.
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if message.startswith(":"):
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continue
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chunk = message.removeprefix("data: ")
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if chunk != "[DONE]":
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timestamp = time.perf_counter()
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data = json.loads(chunk)
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if choices := data.get("choices"):
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modality = data.get("modality")
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delta = choices[0].get("delta") or {}
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content = delta.get("content")
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if not content and isinstance(delta.get("audio"), dict):
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content = delta["audio"].get("data")
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if modality == "text":
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# First token
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if ttft == 0.0:
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ttft = timestamp - st
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output.ttft = ttft
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else:
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output.itl.append(timestamp - most_recent_timestamp)
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generated_text += content or ""
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most_recent_timestamp = timestamp
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output.text_latency = timestamp - st
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elif modality == "audio":
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if output.audio_ttfp == 0.0:
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output.audio_ttfp = timestamp - st
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audio_generate_time = timestamp - st
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if content:
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audio_bytes = base64.b64decode(content)
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seg = AudioSegment.from_file(io.BytesIO(audio_bytes))
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if seg is not None:
|
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if generated_audio is None:
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generated_audio = seg
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else:
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generated_audio = generated_audio + seg
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if metrics := data.get("metrics"):
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output.output_tokens = metrics.get("num_tokens_out", 0)
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|
|
|
if usage := data.get("usage"):
|
|
if (pt := usage.get("prompt_tokens")) is not None:
|
|
output.prompt_len = pt
|
|
|
|
output.latency = timestamp - st
|
|
output.generated_text = generated_text
|
|
if generated_audio is not None:
|
|
output.audio_duration = len(generated_audio) / 1000.0
|
|
frame_width = generated_audio.frame_width
|
|
if frame_width > 0:
|
|
output.audio_frames = len(generated_audio.raw_data) // frame_width
|
|
else:
|
|
output.audio_frames = 0
|
|
logger.warning("Audio frame width is zero")
|
|
audio_duration = output.audio_duration
|
|
if audio_duration > 0:
|
|
output.audio_rtf = audio_generate_time / output.audio_duration
|
|
else:
|
|
output.audio_rtf = 0
|
|
logger.warning("Audio duration is zero")
|
|
if _seed_tts_capture_pcm_for_wer() and getattr(request_func_input, "seed_tts_row", False):
|
|
try:
|
|
seg = generated_audio.set_frame_rate(24000).set_channels(1).set_sample_width(2)
|
|
output.tts_output_pcm_bytes = bytes(seg.raw_data)
|
|
except Exception as ex:
|
|
logger.warning("seed_tts WER PCM export failed: %s", ex)
|
|
output.success = True
|
|
else:
|
|
output.error = response.reason or ""
|
|
output.success = False
|
|
break
|
|
except aiohttp.ClientError as e:
|
|
# transient transport error: may retry
|
|
output.success = False
|
|
output.error = traceback.format_exc()
|
|
if attempt < max_retries:
|
|
logger.warning(
|
|
"ClientError in omni benchmark request (will retry): attempt=%d/%d delay=%.2fs: %s",
|
|
attempt + 1,
|
|
max_retries + 1,
|
|
retry_delay,
|
|
str(e),
|
|
)
|
|
await asyncio.sleep(retry_delay)
|
|
continue
|
|
logger.error(
|
|
"ClientError in omni benchmark request (giving up):\n%s",
|
|
output.error,
|
|
)
|
|
break
|
|
except Exception:
|
|
output.success = False
|
|
output.error = traceback.format_exc()
|
|
logger.error(f"ERROR: send request failed, reason is: {output.error}")
|
|
break
|
|
|
|
if pbar:
|
|
pbar.update(1)
|
|
return output
|
|
|
|
|
|
async def async_request_openai_audio_speech(
|
|
request_func_input: RequestFuncInput, session: aiohttp.ClientSession, pbar: tqdm | None = None
|
|
) -> MixRequestFuncOutput:
|
|
"""Streaming request to /v1/audio/speech endpoint.
|
|
|
|
Sends ``stream=true`` with ``response_format=pcm`` so the server returns
|
|
raw PCM chunks as they are decoded. This allows measuring TTFP (time to
|
|
first audio packet) separately from E2EL.
|
|
"""
|
|
api_url = request_func_input.api_url
|
|
_validate_api_url(api_url, "OpenAI Audio Speech API", "audio/speech")
|
|
|
|
payload = {
|
|
"model": request_func_input.model_name if request_func_input.model_name else request_func_input.model,
|
|
"input": request_func_input.prompt,
|
|
"stream": True,
|
|
"response_format": "pcm",
|
|
}
|
|
_update_payload_common(payload, request_func_input)
|
|
# Seed-TTS + WER: ``--extra-body`` may set stream=false / other formats; speech must stream PCM.
|
|
if getattr(request_func_input, "seed_tts_row", False) and _seed_tts_capture_pcm_for_wer():
|
|
payload["stream"] = True
|
|
payload["response_format"] = "pcm"
|
|
|
|
headers = {
|
|
"Content-Type": "application/json",
|
|
"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
|
|
}
|
|
_update_headers_common(headers, request_func_input)
|
|
|
|
output = MixRequestFuncOutput()
|
|
output.prompt_len = request_func_input.prompt_len
|
|
|
|
# PCM format: 16-bit signed, 24 kHz, mono
|
|
sample_rate = 24000
|
|
sample_width = 2 # 16-bit = 2 bytes
|
|
channels = 1
|
|
|
|
st = time.perf_counter()
|
|
output.start_time = st
|
|
total_pcm_bytes = 0
|
|
capture_wer_pcm = _seed_tts_capture_pcm_for_wer() and getattr(request_func_input, "seed_tts_row", False)
|
|
pcm_capture = bytearray() if capture_wer_pcm else None
|
|
try:
|
|
async with session.post(url=api_url, json=payload, headers=headers) as response:
|
|
if response.status == 200:
|
|
async for chunk in response.content.iter_any():
|
|
if not chunk:
|
|
continue
|
|
timestamp = time.perf_counter()
|
|
if output.audio_ttfp == 0.0:
|
|
output.audio_ttfp = timestamp - st
|
|
output.ttft = output.audio_ttfp
|
|
total_pcm_bytes += len(chunk)
|
|
if pcm_capture is not None:
|
|
pcm_capture.extend(chunk)
|
|
|
|
end_time = time.perf_counter()
|
|
output.latency = end_time - st
|
|
|
|
total_samples = total_pcm_bytes // (sample_width * channels)
|
|
output.audio_duration = total_samples / sample_rate
|
|
output.audio_frames = total_samples
|
|
if output.audio_duration > 0:
|
|
output.audio_rtf = output.latency / output.audio_duration
|
|
else:
|
|
output.audio_rtf = 0
|
|
logger.warning("Audio duration is zero")
|
|
if pcm_capture is not None and pcm_capture:
|
|
output.tts_output_pcm_bytes = bytes(pcm_capture)
|
|
elif capture_wer_pcm:
|
|
ct = response.headers.get("Content-Type", "")
|
|
logger.warning(
|
|
"Seed-TTS WER: HTTP 200 but no PCM bytes (Content-Type=%r, url=%s). "
|
|
"Check stream=true and response_format=pcm on the server.",
|
|
ct,
|
|
api_url,
|
|
)
|
|
output.success = True
|
|
else:
|
|
output.error = response.reason or ""
|
|
output.success = False
|
|
except Exception:
|
|
output.success = False
|
|
output.error = traceback.format_exc()
|
|
logger.error(f"ERROR: send request failed, reason is: {output.error}")
|
|
|
|
if pbar:
|
|
pbar.update(1)
|
|
return output
|
|
|
|
|
|
ASYNC_REQUEST_FUNCS["openai-chat-omni"] = async_request_openai_chat_omni_completions
|
|
if "openai-chat-omni" not in OPENAI_COMPATIBLE_BACKENDS:
|
|
OPENAI_COMPATIBLE_BACKENDS.append("openai-chat-omni")
|
|
|
|
ASYNC_REQUEST_FUNCS["openai-audio-speech"] = async_request_openai_audio_speech
|
|
if "openai-audio-speech" not in OPENAI_COMPATIBLE_BACKENDS:
|
|
OPENAI_COMPATIBLE_BACKENDS.append("openai-audio-speech")
|
|
|
|
# Daily-Omni backend for audio-visual reasoning benchmark
|
|
# Reuses openai-chat-omni completions for video+text understanding
|
|
ASYNC_REQUEST_FUNCS["daily-omni"] = async_request_openai_chat_omni_completions
|
|
if "daily-omni" not in OPENAI_COMPATIBLE_BACKENDS:
|
|
OPENAI_COMPATIBLE_BACKENDS.append("daily-omni")
|
|
|
|
# ruff: noqa: E402
|
|
# Prevent import order from causing patch failures
|
|
from vllm.benchmarks import serve
|
|
from vllm.benchmarks.serve import TaskType, calculate_metrics_for_embeddings, get_request, wait_for_endpoint
|
|
|
|
from vllm_omni.benchmarks.metrics.metrics import MultiModalsBenchmarkMetrics, calculate_metrics
|
|
|
|
# ruff: noqa: E402
|
|
|
|
benchmark_old = serve.benchmark
|
|
|
|
|
|
async def benchmark(
|
|
task_type: TaskType,
|
|
endpoint_type: str,
|
|
api_url: str,
|
|
base_url: str,
|
|
model_id: str,
|
|
model_name: str,
|
|
tokenizer: TokenizerLike | None,
|
|
input_requests: list[SampleRequest],
|
|
logprobs: int | None,
|
|
request_rate: float,
|
|
burstiness: float,
|
|
disable_tqdm: bool,
|
|
num_warmups: int,
|
|
profile: bool,
|
|
selected_percentile_metrics: list[str],
|
|
selected_percentiles: list[float],
|
|
ignore_eos: bool,
|
|
goodput_config_dict: dict[str, float],
|
|
max_concurrency: int | None,
|
|
lora_modules: Iterable[str] | None,
|
|
extra_headers: dict | None,
|
|
extra_body: dict | None,
|
|
lora_assignment: Literal["random", "round-robin"] = "random",
|
|
ramp_up_strategy: Literal["linear", "exponential"] | None = None,
|
|
ramp_up_start_rps: int | None = None,
|
|
ramp_up_end_rps: int | None = None,
|
|
ready_check_timeout_sec: int = 600,
|
|
ssl_context: ssl.SSLContext | bool | None = None,
|
|
):
|
|
try:
|
|
request_func = ASYNC_REQUEST_FUNCS[endpoint_type]
|
|
except KeyError:
|
|
raise ValueError(f"Unknown backend: {endpoint_type}") from None
|
|
|
|
# Reuses connections across requests to reduce TLS handshake overhead.
|
|
ssl_setting = ssl_context if ssl_context is not None else ("https://" in api_url)
|
|
connector = aiohttp.TCPConnector(
|
|
limit=max_concurrency or 0,
|
|
limit_per_host=max_concurrency or 0,
|
|
ttl_dns_cache=300,
|
|
use_dns_cache=True,
|
|
enable_cleanup_closed=True,
|
|
force_close=True,
|
|
ssl=ssl_setting,
|
|
)
|
|
|
|
session = aiohttp.ClientSession(
|
|
connector=connector,
|
|
trust_env=True,
|
|
timeout=aiohttp.ClientTimeout(total=6 * 60 * 60),
|
|
)
|
|
|
|
print("Starting initial single prompt test run...")
|
|
test_prompt, test_prompt_len, test_output_len, test_mm_content = (
|
|
input_requests[0].prompt,
|
|
input_requests[0].prompt_len,
|
|
input_requests[0].expected_output_len,
|
|
input_requests[0].multi_modal_data,
|
|
)
|
|
|
|
assert (
|
|
test_mm_content is None
|
|
or isinstance(test_mm_content, dict)
|
|
or (isinstance(test_mm_content, list) and all(isinstance(item, dict) for item in test_mm_content))
|
|
), "multi_modal_data must be a dict or list[dict]"
|
|
test_input = RequestFuncInput(
|
|
model=model_id,
|
|
model_name=model_name,
|
|
prompt=test_prompt,
|
|
api_url=api_url,
|
|
prompt_len=test_prompt_len,
|
|
output_len=test_output_len,
|
|
logprobs=logprobs,
|
|
multi_modal_content=test_mm_content,
|
|
ignore_eos=ignore_eos,
|
|
extra_headers=extra_headers,
|
|
extra_body=extra_body,
|
|
)
|
|
_attach_daily_omni_to_request_func_input(input_requests[0], test_input)
|
|
_attach_seed_tts_to_request_func_input(input_requests[0], test_input)
|
|
|
|
if ready_check_timeout_sec > 0:
|
|
test_output = await wait_for_endpoint(
|
|
request_func,
|
|
test_input,
|
|
session,
|
|
timeout_seconds=ready_check_timeout_sec,
|
|
)
|
|
if not test_output.success:
|
|
raise ValueError(
|
|
"Initial test run failed - Please make sure benchmark "
|
|
"arguments are correctly specified. "
|
|
f"Error: {test_output.error}"
|
|
)
|
|
else:
|
|
print("Initial test run completed.")
|
|
else:
|
|
print("Skipping endpoint ready check.")
|
|
|
|
if num_warmups > 0:
|
|
print(f"Warming up with {num_warmups} requests...")
|
|
warmup_pbar = None if disable_tqdm else tqdm(total=num_warmups)
|
|
warmup_semaphore = asyncio.Semaphore(max_concurrency) if max_concurrency else contextlib.nullcontext()
|
|
warmup_tasks = []
|
|
|
|
async def warmup_limited_request_func():
|
|
async with warmup_semaphore:
|
|
return await request_func(request_func_input=test_input, session=session, pbar=warmup_pbar)
|
|
|
|
for _ in range(num_warmups):
|
|
request_task = asyncio.create_task(warmup_limited_request_func())
|
|
warmup_tasks.append(request_task)
|
|
_ = await asyncio.gather(*warmup_tasks)
|
|
|
|
if warmup_pbar is not None:
|
|
warmup_pbar.close()
|
|
print("Warmup run completed.")
|
|
|
|
print("Starting main benchmark run...")
|
|
|
|
if lora_modules:
|
|
lora_modules_list = list(lora_modules)
|
|
if lora_assignment == "round-robin":
|
|
lora_modules = iter([lora_modules_list[i % len(lora_modules_list)] for i in range(len(input_requests))])
|
|
else:
|
|
lora_modules = iter([random.choice(lora_modules_list) for _ in range(len(input_requests))])
|
|
|
|
if profile:
|
|
print("Starting profiler...")
|
|
profile_input = RequestFuncInput(
|
|
model=model_id,
|
|
model_name=model_name,
|
|
prompt=test_prompt,
|
|
api_url=base_url + "/start_profile",
|
|
prompt_len=test_prompt_len,
|
|
output_len=test_output_len,
|
|
logprobs=logprobs,
|
|
multi_modal_content=test_mm_content,
|
|
ignore_eos=ignore_eos,
|
|
extra_headers=extra_headers,
|
|
extra_body=extra_body,
|
|
)
|
|
_attach_daily_omni_to_request_func_input(input_requests[0], profile_input)
|
|
_attach_seed_tts_to_request_func_input(input_requests[0], profile_input)
|
|
profile_output = await request_func(request_func_input=profile_input, session=session)
|
|
if profile_output.success:
|
|
print("Profiler started")
|
|
|
|
distribution = "Poisson process" if burstiness == 1.0 else "Gamma distribution"
|
|
|
|
if ramp_up_strategy is not None:
|
|
print(f"Traffic ramp-up strategy: {ramp_up_strategy}.")
|
|
print(
|
|
f"Will increase RPS from {ramp_up_start_rps} to {ramp_up_end_rps} RPS over the duration of the benchmark."
|
|
)
|
|
else:
|
|
print(f"Traffic request rate: {request_rate}")
|
|
|
|
print(f"Burstiness factor: {burstiness} ({distribution})")
|
|
print(f"Maximum request concurrency: {max_concurrency}")
|
|
|
|
pbar = None if disable_tqdm else tqdm(total=len(input_requests))
|
|
|
|
semaphore = asyncio.Semaphore(max_concurrency) if max_concurrency else contextlib.nullcontext()
|
|
|
|
async def limited_request_func(request_func_input, session, pbar):
|
|
async with semaphore:
|
|
return await request_func(request_func_input=request_func_input, session=session, pbar=pbar)
|
|
|
|
benchmark_start_time = time.perf_counter()
|
|
tasks: list[asyncio.Task] = []
|
|
|
|
rps_change_events = []
|
|
last_int_rps = -1
|
|
if ramp_up_strategy is not None and ramp_up_start_rps is not None:
|
|
last_int_rps = ramp_up_start_rps
|
|
rps_change_events.append(
|
|
{
|
|
"rps": last_int_rps,
|
|
"timestamp": datetime.now().isoformat(),
|
|
}
|
|
)
|
|
|
|
async for request, current_request_rate in get_request(
|
|
input_requests,
|
|
request_rate,
|
|
burstiness,
|
|
ramp_up_strategy,
|
|
ramp_up_start_rps,
|
|
ramp_up_end_rps,
|
|
):
|
|
if ramp_up_strategy is not None:
|
|
current_int_rps = int(current_request_rate)
|
|
if current_int_rps > last_int_rps:
|
|
timestamp = datetime.now().isoformat()
|
|
for rps_val in range(last_int_rps + 1, current_int_rps + 1):
|
|
rps_change_events.append({"rps": rps_val, "timestamp": timestamp})
|
|
last_int_rps = current_int_rps
|
|
prompt, prompt_len, output_len, mm_content, request_id = (
|
|
request.prompt,
|
|
request.prompt_len,
|
|
request.expected_output_len,
|
|
request.multi_modal_data,
|
|
request.request_id,
|
|
)
|
|
req_model_id, req_model_name = model_id, model_name
|
|
if lora_modules:
|
|
req_lora_module = next(lora_modules)
|
|
req_model_id, req_model_name = req_lora_module, req_lora_module
|
|
|
|
request_func_input = RequestFuncInput(
|
|
model=req_model_id,
|
|
model_name=req_model_name,
|
|
prompt=prompt,
|
|
api_url=api_url,
|
|
prompt_len=prompt_len,
|
|
output_len=output_len,
|
|
logprobs=logprobs,
|
|
multi_modal_content=mm_content,
|
|
ignore_eos=ignore_eos,
|
|
extra_headers=extra_headers,
|
|
extra_body=extra_body,
|
|
request_id=request_id,
|
|
)
|
|
_attach_daily_omni_to_request_func_input(request, request_func_input)
|
|
_attach_seed_tts_to_request_func_input(request, request_func_input)
|
|
tasks.append(
|
|
asyncio.create_task(limited_request_func(request_func_input=request_func_input, session=session, pbar=pbar))
|
|
)
|
|
outputs: list[MixRequestFuncOutput] = await asyncio.gather(*tasks)
|
|
|
|
if pbar is not None:
|
|
pbar.close()
|
|
|
|
benchmark_duration = time.perf_counter() - benchmark_start_time
|
|
|
|
if task_type == TaskType.GENERATION:
|
|
metrics, actual_output_lens = calculate_metrics(
|
|
input_requests=input_requests,
|
|
outputs=outputs,
|
|
dur_s=benchmark_duration,
|
|
tokenizer=tokenizer,
|
|
selected_percentiles=selected_percentiles,
|
|
goodput_config_dict=goodput_config_dict,
|
|
task_type=task_type,
|
|
selected_percentile_metrics=selected_percentile_metrics,
|
|
max_concurrency=max_concurrency,
|
|
request_rate=request_rate,
|
|
benchmark_duration=benchmark_duration,
|
|
)
|
|
else:
|
|
metrics = calculate_metrics_for_embeddings(
|
|
outputs=outputs,
|
|
dur_s=benchmark_duration,
|
|
selected_percentiles=selected_percentiles,
|
|
)
|
|
actual_output_lens = 0
|
|
|
|
if isinstance(metrics, MultiModalsBenchmarkMetrics):
|
|
result = {
|
|
"duration": benchmark_duration,
|
|
"completed": metrics.completed,
|
|
"failed": metrics.failed,
|
|
"total_input_tokens": metrics.total_input,
|
|
"total_output_tokens": metrics.total_output,
|
|
"request_throughput": metrics.request_throughput,
|
|
"request_goodput": metrics.request_goodput if goodput_config_dict else None,
|
|
"output_throughput": metrics.output_throughput,
|
|
"total_token_throughput": metrics.total_token_throughput,
|
|
"total_audio_duration_s": metrics.total_audio_duration_s,
|
|
"total_audio_frames": metrics.total_audio_frames,
|
|
"audio_throughput": metrics.audio_throughput,
|
|
"input_lens": [output.prompt_len for output in outputs],
|
|
"output_lens": actual_output_lens,
|
|
"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],
|
|
"max_output_tokens_per_s": metrics.max_output_tokens_per_s,
|
|
"max_concurrent_requests": metrics.max_concurrent_requests,
|
|
"rtfx": metrics.rtfx,
|
|
}
|
|
else:
|
|
result = {
|
|
"duration": benchmark_duration,
|
|
"completed": metrics.completed,
|
|
"total_input_tokens": metrics.total_input,
|
|
"request_throughput": metrics.request_throughput,
|
|
"total_token_throughput": metrics.total_token_throughput,
|
|
"input_lens": [output.prompt_len for output in outputs],
|
|
"errors": [output.error for output in outputs],
|
|
}
|
|
|
|
from vllm_omni.benchmarks.data_modules.daily_omni_eval import (
|
|
compute_daily_omni_accuracy_metrics,
|
|
print_daily_omni_accuracy_summary,
|
|
)
|
|
|
|
_save_items = os.environ.get("DAILY_OMNI_SAVE_EVAL_ITEMS", "").lower() in (
|
|
"1",
|
|
"true",
|
|
"yes",
|
|
)
|
|
_daily_acc = compute_daily_omni_accuracy_metrics(input_requests, outputs, include_per_item=_save_items)
|
|
if _daily_acc is not None:
|
|
result.update(_daily_acc)
|
|
print_daily_omni_accuracy_summary(_daily_acc)
|
|
|
|
if _seed_tts_capture_pcm_for_wer():
|
|
from vllm_omni.benchmarks.data_modules.seed_tts_eval import (
|
|
compute_seed_tts_wer_metrics,
|
|
print_seed_tts_wer_summary,
|
|
)
|
|
|
|
_save_wer = os.environ.get("SEED_TTS_WER_SAVE_ITEMS", "").lower() in (
|
|
"1",
|
|
"true",
|
|
"yes",
|
|
)
|
|
_wer_m = compute_seed_tts_wer_metrics(input_requests, outputs, include_per_item=_save_wer)
|
|
if _wer_m is not None:
|
|
result.update(_wer_m)
|
|
print_seed_tts_wer_summary(_wer_m)
|
|
|
|
if rps_change_events:
|
|
result["rps_change_events"] = rps_change_events
|
|
|
|
def process_one_metric(
|
|
# E.g., "ttft"
|
|
metric_attribute_name: str,
|
|
):
|
|
# This function prints and adds statistics of the specified
|
|
# metric.
|
|
if metric_attribute_name not in selected_percentile_metrics:
|
|
return
|
|
is_audio_rtf = metric_attribute_name == "audio_rtf"
|
|
is_audio_duration = metric_attribute_name == "audio_duration"
|
|
|
|
suffix = "_ms"
|
|
if is_audio_duration:
|
|
suffix = "_s"
|
|
elif is_audio_rtf:
|
|
suffix = ""
|
|
mean_attr_name = f"mean_{metric_attribute_name}{suffix}"
|
|
mean_value = getattr(metrics, mean_attr_name, 0.0)
|
|
result[mean_attr_name] = mean_value
|
|
|
|
median_attr_name = f"median_{metric_attribute_name}{suffix}"
|
|
median_value = getattr(metrics, median_attr_name, 0.0)
|
|
result[median_attr_name] = median_value
|
|
for p, value in getattr(metrics, f"percentiles_{metric_attribute_name}{suffix}"):
|
|
p_word = str(int(p)) if int(p) == p else str(p)
|
|
result[f"p{p_word}_{metric_attribute_name}{suffix}"] = value
|
|
|
|
if task_type == TaskType.GENERATION:
|
|
for metric in selected_percentile_metrics:
|
|
process_one_metric(metric)
|
|
else:
|
|
process_one_metric("e2el")
|
|
|
|
if profile:
|
|
print("Stopping profiler...")
|
|
profile_input = RequestFuncInput(
|
|
model=model_id,
|
|
prompt=test_prompt,
|
|
api_url=base_url + "/stop_profile",
|
|
prompt_len=test_prompt_len,
|
|
output_len=test_output_len,
|
|
logprobs=logprobs,
|
|
)
|
|
profile_output = await request_func(request_func_input=profile_input, session=session)
|
|
if profile_output.success:
|
|
print("Profiler stopped")
|
|
|
|
await session.close()
|
|
return result
|
|
|
|
|
|
serve.benchmark = benchmark
|