import asyncio import contextlib import io import json import os import random import ssl import sys import time import traceback import wave from collections.abc import Iterable from dataclasses import dataclass from datetime import datetime from typing import Literal import aiohttp import pybase64 as base64 from pydub import AudioSegment from tqdm.asyncio import tqdm from vllm.benchmarks import datasets from vllm.benchmarks.datasets import SampleRequest from vllm.benchmarks.lib.endpoint_request_func import ( ASYNC_REQUEST_FUNCS, OPENAI_COMPATIBLE_BACKENDS, RequestFuncInput, RequestFuncOutput, StreamedResponseHandler, _get_chat_content, _update_headers_common, _update_payload_common, _validate_api_url, ) from vllm.logger import init_logger from vllm.tokenizers import TokenizerLike logger = init_logger(__name__) from vllm_omni.benchmarks.audio_continuity import compute_continuity_stats from vllm_omni.benchmarks.data_modules.daily_omni_dataset import DailyOmniDataset, DailyOmniSampleRequest from vllm_omni.benchmarks.data_modules.random_multi_modal_dataset import OmniRandomMultiModalDataset from vllm_omni.benchmarks.data_modules.seed_tts_dataset import ( SEED_TTS_DEFAULT_OMNI_SYSTEM_PROMPT, SeedTTSDataset, SeedTTSDesignDataset, SeedTTSSampleRequest, SeedTTSTextDataset, ) from vllm_omni.benchmarks.data_modules.sound_effect_dataset import SoundEffectDataset from vllm_omni.benchmarks.data_modules.ttsd_dataset import TTSDDataset _AUDIO_CONTINUITY_THRESHOLD_ENV = "VLLM_OMNI_BENCH_AUDIO_CONTINUITY_THRESHOLD_S" _DEFAULT_AUDIO_CONTINUITY_THRESHOLD_S = 0.1 def _audio_continuity_threshold_s() -> float: """Return the per-request underrun budget (s). Read from ``VLLM_OMNI_BENCH_AUDIO_CONTINUITY_THRESHOLD_S`` so users can re-aim the SLO without rebuilding. Defaults to 100 ms - the standard "audible gap" budget for streaming TTS. """ raw = os.environ.get(_AUDIO_CONTINUITY_THRESHOLD_ENV) if not raw: return _DEFAULT_AUDIO_CONTINUITY_THRESHOLD_S try: value = float(raw) except ValueError: logger.warning( "Invalid %s=%r; using default %.3fs", _AUDIO_CONTINUITY_THRESHOLD_ENV, raw, _DEFAULT_AUDIO_CONTINUITY_THRESHOLD_S, ) return _DEFAULT_AUDIO_CONTINUITY_THRESHOLD_S return max(value, 0.0) get_samples_old = datasets.get_samples _DEFAULT_DAILY_OMNI_REPO = "liarliar/Daily-Omni" def _seed_tts_capture_pcm_for_wer() -> bool: return os.environ.get("SEED_TTS_WER_EVAL", "").lower() in ( "1", "true", "yes", ) def _merge_extra_body_mm_kwargs(base: dict | None, overlay: dict | None) -> dict | None: """Shallow-merge ``extra_body`` dicts; deep-merge ``mm_processor_kwargs`` if both set.""" if not base and not overlay: return None out = dict(base or {}) if not overlay: return out for k, v in overlay.items(): if k == "mm_processor_kwargs" and isinstance(v, dict): prev = out.get("mm_processor_kwargs") merged_kw = {**(prev if isinstance(prev, dict) else {}), **v} out["mm_processor_kwargs"] = merged_kw else: out[k] = v return out def _attach_daily_omni_to_request_func_input(sample: SampleRequest, rfi: RequestFuncInput) -> None: """Apply per-request OpenAI fields (``mm_processor_kwargs``, messages) for Daily-Omni.""" if not isinstance(sample, DailyOmniSampleRequest): return rfi.extra_body = _merge_extra_body_mm_kwargs(rfi.extra_body, sample.omni_extra_body) if sample.omni_chat_messages is not None: setattr(rfi, "omni_chat_messages", sample.omni_chat_messages) else: setattr(rfi, "mm_position", sample.omni_chat_mm_position) def _attach_seed_tts_to_request_func_input(sample: SampleRequest, rfi: RequestFuncInput) -> None: """Merge Seed-TTS per-row TTS fields into ``extra_body`` and mark for PCM capture. Always sets ``seed_tts_row=True`` on the RequestFuncInput for any :class:`SeedTTSSampleRequest` subclass (including text-only and design variants that carry no ``ref_audio``). This enables PCM capture for WER / UTMOS evaluation even when there is no reference audio. """ if not isinstance(sample, SeedTTSSampleRequest): return # Mark for PCM capture (WER / UTMOS eval) regardless of extra body presence. setattr(rfi, "seed_tts_row", True) sys_prompt = (sample.seed_tts_system_prompt or "").strip() or SEED_TTS_DEFAULT_OMNI_SYSTEM_PROMPT setattr( rfi, "omni_chat_messages", [ {"role": "system", "content": [{"type": "text", "text": sys_prompt}]}, {"role": "user", "content": [{"type": "text", "text": sample.prompt}]}, ], ) ex = sample.seed_tts_speech_extra if not ex: return # voice comes from --extra-body in config; no ref_audio to merge base = dict(rfi.extra_body) if rfi.extra_body else {} base.update(ex) rfi.extra_body = base def _daily_omni_repo_from_args(args) -> str | None: """Resolve HuggingFace repo id for Daily-Omni from CLI args. vLLM allows ``--dataset-path`` to be a local path while the real HF id is passed via ``--hf-name``. Upstream ``get_samples`` for ``hf`` only matches a fixed elif-chain and never discovers Omni's loader, so we must detect Daily-Omni here using either field. """ dp = getattr(args, "dataset_path", None) hn = getattr(args, "hf_name", None) if dp in DailyOmniDataset.SUPPORTED_DATASET_PATHS: return dp if hn in DailyOmniDataset.SUPPORTED_DATASET_PATHS: return hn return None def get_samples(args, tokenizer): # Daily-Omni: explicit dataset name, or hf + matching path/hf-name is_daily_omni = args.dataset_name == "daily-omni" or ( args.dataset_name == "hf" and _daily_omni_repo_from_args(args) is not None ) is_seed_tts = args.dataset_name in ( "seed-tts", "seed-tts-text", "seed-tts-design", "ttsd", "sound-effect", ) # Check if we need to handle omni-related backends/datasets is_omni_backend = args.backend in ["openai-chat-omni", "openai-audio-speech", "daily-omni"] is_omni_dataset = is_daily_omni or is_seed_tts or args.dataset_name == "random-mm" if not is_omni_backend and not is_omni_dataset: # Not an omni-related request, delegate to original implementation return get_samples_old(args, tokenizer) # Handle Daily-Omni dataset if is_daily_omni: # Support: # --dataset-name daily-omni [--dataset-path liarliar/Daily-Omni] # --dataset-name daily-omni --daily-omni-qa-json /path/to/qa.json (offline QA) # --dataset-name hf --dataset-path liarliar/Daily-Omni # --dataset-name hf --hf-name liarliar/Daily-Omni (dataset-path may be local) # Validate backend supports multimodal (video) if args.backend not in ["openai-chat-omni", "daily-omni"]: raise ValueError( f"Daily-Omni dataset requires a multimodal backend that supports video. " f"Got backend='{args.backend}'. Please use '--backend openai-chat-omni'" ) # Determine video directory if specified (for local video files) video_dir = getattr(args, "daily_omni_video_dir", None) # Get HF split (default to "train"; unused when loading from local qa.json) dataset_split = getattr(args, "hf_split", None) or "train" qa_json = getattr(args, "daily_omni_qa_json", None) if isinstance(qa_json, str): qa_json = qa_json.strip() or None if qa_json is not None: logger.info( "Loading Daily-Omni dataset: qa_json=%s, video_dir=%s (Hub not used for QA)", qa_json, video_dir, ) dataset = DailyOmniDataset( qa_json_path=qa_json, dataset_path=None, dataset_split=dataset_split, random_seed=args.seed, video_dir=video_dir, input_mode=getattr(args, "daily_omni_input_mode", "all"), inline_local_video=getattr(args, "daily_omni_inline_local_video", False), trust_remote_code=getattr(args, "trust_remote_code", False), disable_shuffle=getattr(args, "disable_shuffle", False), ) else: repo_id = _daily_omni_repo_from_args(args) if args.dataset_name == "daily-omni": if repo_id is None: repo_id = _DEFAULT_DAILY_OMNI_REPO elif repo_id is None: raise ValueError( "Daily-Omni with --dataset-name hf requires " f"--dataset-path {_DEFAULT_DAILY_OMNI_REPO} or " f"--hf-name {_DEFAULT_DAILY_OMNI_REPO}." ) logger.info( "Loading Daily-Omni dataset: hf_repo=%s, split=%s, video_dir=%s", repo_id, dataset_split, video_dir, ) dataset = DailyOmniDataset( dataset_path=repo_id, dataset_split=dataset_split, dataset_subset=getattr(args, "hf_subset", None), random_seed=args.seed, video_dir=video_dir, input_mode=getattr(args, "daily_omni_input_mode", "all"), inline_local_video=getattr(args, "daily_omni_inline_local_video", False), trust_remote_code=getattr(args, "trust_remote_code", False), no_stream=getattr(args, "no_stream", False), disable_shuffle=getattr(args, "disable_shuffle", False), ) out_len = getattr(args, "output_len", None) if out_len is None: out_len = getattr(args, "hf_output_len", None) if out_len is None: out_len = DailyOmniDataset.DEFAULT_OUTPUT_LEN input_requests = dataset.sample( tokenizer=tokenizer, num_requests=args.num_prompts, output_len=out_len, request_id_prefix=args.request_id_prefix, no_oversample=args.no_oversample, ) return input_requests if is_seed_tts: if args.backend not in ("openai-audio-speech", "openai-chat-omni"): raise ValueError( "Seed-TTS requires --backend openai-audio-speech (POST /v1/audio/speech) or " "--backend openai-chat-omni (POST /v1/chat/completions with ref_audio/ref_text). " f"Got backend={args.backend!r}." ) repo_id = getattr(args, "dataset_path", None) or getattr(args, "hf_name", None) if not repo_id: raise ValueError( "Seed-TTS requires --dataset-path (HF dataset repo id or local directory) or " "--hf-name for the Hub dataset id." ) _cls_map = { "seed-tts": SeedTTSDataset, "seed-tts-text": SeedTTSTextDataset, "seed-tts-design": SeedTTSDesignDataset, "ttsd": TTSDDataset, "sound-effect": SoundEffectDataset, } DatasetCls = _cls_map[args.dataset_name] dataset = DatasetCls( dataset_path=repo_id, random_seed=args.seed, locale=getattr(args, "seed_tts_locale", "en"), inline_ref_audio=not getattr(args, "seed_tts_file_ref_audio", False), seed_tts_root=getattr(args, "seed_tts_root", None), system_prompt=getattr(args, "seed_tts_system_prompt", None), disable_shuffle=getattr(args, "disable_shuffle", False), ) out_len = getattr(args, "output_len", None) if out_len is None: out_len = getattr(args, "hf_output_len", None) if out_len is None: out_len = SeedTTSDataset.DEFAULT_OUTPUT_LEN return dataset.sample( tokenizer=tokenizer, num_requests=args.num_prompts, output_len=out_len, request_id_prefix=args.request_id_prefix, no_oversample=args.no_oversample, ) # Handle random-mm dataset (Omni's synthetic multimodal dataset) if args.dataset_name == "random-mm": dataset = OmniRandomMultiModalDataset(random_seed=args.seed, dataset_path=args.dataset_path) input_requests = dataset.sample( tokenizer=tokenizer, num_requests=args.num_prompts, prefix_len=args.random_prefix_len, range_ratio=args.random_range_ratio, input_len=args.random_input_len, output_len=args.random_output_len, base_items_per_request=args.random_mm_base_items_per_request, limit_mm_per_prompt=args.random_mm_limit_mm_per_prompt, num_mm_items_range_ratio=args.random_mm_num_mm_items_range_ratio, bucket_config=args.random_mm_bucket_config, request_id_prefix=args.request_id_prefix, no_oversample=args.no_oversample, ) return input_requests else: return get_samples_old(args, tokenizer) datasets.get_samples = get_samples _serve_mod = sys.modules.get("vllm.benchmarks.serve") if _serve_mod is not None: _serve_mod.get_samples = get_samples @dataclass class MixRequestFuncOutput(RequestFuncOutput): audio_ttfp: float = 0.0 audio_duration: float = 0.0 audio_frames: int = 0 audio_rtf: float = 0.0 text_latency: float = 0.0 #: Worst-case streaming-audio underrun (wall-clock seconds the player #: would have been starved). Populated by the audio-speech backend; ``0.0`` #: for backends that do not run continuity analysis. audio_underrun_s: float = 0.0 #: Whether the request stayed under the continuity threshold (default #: 100 ms). Mirrors ``audio_underrun_s <= threshold``. audio_continuity_ok: bool = True #: Number of inter-chunk intervals during which the player buffer went #: negative. audio_underrun_event_count: int = 0 #: Raw PCM s16le mono at 24 kHz for Seed-TTS WER: from ``/v1/audio/speech`` stream or #: resampled export after ``openai-chat-omni`` audio deltas. tts_output_pcm_bytes: bytes | None = None async def async_request_openai_chat_omni_completions( request_func_input: RequestFuncInput, session: aiohttp.ClientSession, pbar: tqdm | None = None, mm_position: Literal["first", "last"] = "last", ) -> MixRequestFuncOutput: api_url = request_func_input.api_url _validate_api_url(api_url, "OpenAI Chat Completions API", "chat/completions") omni_messages = getattr(request_func_input, "omni_chat_messages", None) if omni_messages is not None: messages_payload = omni_messages else: effective_mm_position = getattr(request_func_input, "mm_position", mm_position) content = _get_chat_content(request_func_input, mm_position=effective_mm_position) messages_payload = [{"role": "user", "content": content}] payload = { "model": request_func_input.model_name if request_func_input.model_name else request_func_input.model, "messages": messages_payload, "temperature": 0.0, "max_tokens": request_func_input.output_len, "stream": True, "stream_options": { "include_usage": True, }, } _update_payload_common(payload, request_func_input) # Seed-TTS via chat: voice-clone fields live on the body; ensure audio is streamed. if getattr(request_func_input, "seed_tts_row", False): if payload.get("modalities") is None: payload["modalities"] = ["text", "audio"] response_format = payload.get("response_format", "wav") if response_format == "pcm": raise ValueError( "pcm response format is not supported yet. \ Please use other formats like wav, mp3, etc. instead." ) 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 max_retries = 3 retry_delay = 0.1 for attempt in range(max_retries + 1): # Reset per-attempt state so that retries do not mix partial # outputs or metrics from previous attempts. generated_text = "" generated_audio = None # For wav responses, accumulate decoded PCM bytes per chunk # to avoid repeated AudioSegment decode/concat. wav_pcm_buffer = bytearray() wav_audio_params: tuple[int, int, int] | None = None wav_inconsistent_chunk_count = 0 first_inconsistent_wav_params: tuple[int, int, int] | None = None # For non-wav responses, accumulate encoded bytes then decode once. audio_bytes_buffer = bytearray() ttft = 0.0 st = time.perf_counter() output.start_time = st most_recent_timestamp = st timestamp = st audio_generate_time = 0.0 output.itl = [] output.generated_text = "" output.ttft = 0.0 output.audio_ttfp = 0.0 output.audio_duration = 0.0 output.audio_frames = 0 output.audio_rtf = 0.0 output.text_latency = 0.0 output.output_tokens = 0 output.error = "" output.success = False try: async with session.post(url=api_url, json=payload, headers=headers) as response: if response.status == 200: handler = StreamedResponseHandler() async for chunk_bytes in response.content.iter_any(): # NOTE: Do NOT strip() here; TCP may fragment the SSE messages, # so stripping here can cause problems depending on how it is split. # # Simple example: [b'data: ', b'{json}\n\n'] <- stripping the first # chunk will break SSE parsing because the space after 'data:' is required. if not chunk_bytes: continue messages = handler.add_chunk(chunk_bytes) for message in messages: if type(message) is bytes: message = message.decode("utf-8") # NOTE: SSE comments (often used as pings) start with # a colon. These are not JSON data payload and should # be skipped. if message.startswith(":"): continue chunk = message.removeprefix("data: ") if chunk != "[DONE]": timestamp = time.perf_counter() data = json.loads(chunk) if choices := data.get("choices"): modality = data.get("modality") delta = choices[0].get("delta") or {} content = delta.get("content") if not content and isinstance(delta.get("audio"), dict): content = delta["audio"].get("data") if modality == "text": # First token if ttft == 0.0: ttft = timestamp - st output.ttft = ttft else: output.itl.append(timestamp - most_recent_timestamp) generated_text += content or "" most_recent_timestamp = timestamp output.text_latency = timestamp - st elif modality == "audio": if output.audio_ttfp == 0.0: output.audio_ttfp = timestamp - st audio_generate_time = timestamp - st if content: audio_bytes = base64.b64decode(content) if response_format == "wav": try: with wave.open(io.BytesIO(audio_bytes), "rb") as wav_reader: params = ( wav_reader.getnchannels(), wav_reader.getsampwidth(), wav_reader.getframerate(), ) if wav_audio_params is None: wav_audio_params = params elif wav_audio_params != params: wav_inconsistent_chunk_count += 1 if first_inconsistent_wav_params is None: first_inconsistent_wav_params = params continue wav_pcm_buffer.extend( wav_reader.readframes(wav_reader.getnframes()) ) except Exception as ex: logger.warning("Failed to parse wav audio chunk: %s", ex) else: audio_bytes_buffer.extend(audio_bytes) if metrics := data.get("metrics"): output.output_tokens = metrics.get("num_tokens_out", 0) if usage := data.get("usage"): if (pt := usage.get("prompt_tokens")) is not None: output.prompt_len = pt if wav_inconsistent_chunk_count > 0: logger.warning( "Dropped %d wav chunks with inconsistent params during benchmark " "(expected=%s, first_inconsistent=%s). " "Audio frames/duration may be undercounted.", wav_inconsistent_chunk_count, wav_audio_params, first_inconsistent_wav_params, ) output.latency = timestamp - st output.generated_text = generated_text if response_format == "wav" and wav_pcm_buffer and wav_audio_params is not None: channels, sample_width, frame_rate = wav_audio_params generated_audio = AudioSegment( data=bytes(wav_pcm_buffer), sample_width=sample_width, frame_rate=frame_rate, channels=channels, ) elif audio_bytes_buffer: try: generated_audio = AudioSegment.from_file( io.BytesIO(bytes(audio_bytes_buffer)), format=response_format, ) except Exception as ex: logger.warning("Failed to decode accumulated audio bytes: %s", ex) 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 chunk_arrival_times_s: list[float] = [] chunk_sizes: list[int] = [] 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: # TTS speech endpoint emits no text tokens, so TTFT is # not defined here; only audio TTFP is meaningful. output.audio_ttfp = timestamp - st total_pcm_bytes += len(chunk) chunk_arrival_times_s.append(timestamp - st) chunk_sizes.append(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") continuity = compute_continuity_stats( chunk_arrival_times_s=chunk_arrival_times_s, chunk_bytes=chunk_sizes, sample_rate=sample_rate, sample_width=sample_width, channels=channels, threshold_s=_audio_continuity_threshold_s(), ) output.audio_underrun_s = continuity.max_underrun_s output.audio_continuity_ok = continuity.is_continuous output.audio_underrun_event_count = continuity.underrun_event_count 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.lib.ready_checker import wait_for_endpoint from vllm.benchmarks.serve import TaskType, calculate_metrics_for_embeddings, get_request 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 # No text tokens generated (e.g. pure TTS speech endpoint): per-token # latency metrics (ttft/tpot/itl) are undefined, so skip them. is_text_token_metric = not (metric_attribute_name == "e2el" or metric_attribute_name.startswith("audio")) if is_text_token_metric and getattr(metrics, "total_output", 0) == 0: return is_audio_rtf = metric_attribute_name == "audio_rtf" is_audio_duration_or_underrun = metric_attribute_name in ("audio_duration", "audio_underrun") suffix = "_ms" if is_audio_duration_or_underrun: 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}", None) or []: 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