"""OmniModalityMetrics — per-modality Prometheus families (audio path only). 7 audio business-semantic metric families. Text-path metrics (TTFT / ITL / TPOT / e2e) are NOT here — they come from the upstream ``vllm:*{stage="thinker", ...}`` families exposed via the ``OmniPrometheusStatLogger`` wrap. Contents: - Audio family declarations (Histograms + Counters) - ``OmniModalityMetrics``: label-bound observe API for the audio family - ``observe_modality_at_finalize``: dispatcher called from omni_base's e2e finalize hook; currently handles the audio path only. - ``observe_audio_first_packet``: TTFP emit from the streaming SSE first audio packet. - ``observe_audio_streaming_finalize``: emits ``audio_underrun_s`` + ``audio_continuity_ok_total`` at SSE close using accumulated per-chunk arrival timestamps. - ``extract_mm_output`` / ``count_audio_frames`` (from ``metrics.utils``): shape-tolerant helpers for the heterogeneous multimodal_output payloads emitted by different audio pipelines. """ from __future__ import annotations from typing import Any from prometheus_client import Counter, Histogram from vllm_omni.metrics import definitions as defs from vllm_omni.metrics.utils import count_audio_frames, extract_mm_output _stage_labels = list(defs.STAGE_LABELS) # ---------------------------------------------------------------------------- # Audio family # ---------------------------------------------------------------------------- _audio_ttfp_family = Histogram( defs.AUDIO_TTFP_S, "Time from request arrival to first audio packet/frame, in seconds.", labelnames=_stage_labels, buckets=defs.SECONDS_BUCKETS, ) _audio_duration_family = Histogram( defs.AUDIO_DURATION_S, "Generated audio content duration in seconds (audio_frames / sample_rate).", labelnames=_stage_labels, buckets=defs.SECONDS_BUCKETS, ) _audio_rtf_family = Histogram( defs.AUDIO_RTF_METRIC, "Audio real-time factor (stage_gen_time_s / audio_duration_s); streaming TTS requires < 1.", labelnames=_stage_labels, buckets=defs.RTF_BUCKETS, ) _audio_frames_family = Counter( defs.AUDIO_FRAMES_METRIC, "Cumulative audio frame count; per-model rate (not summable across models). Throughput recovered via rate().", labelnames=_stage_labels, ) _audio_underrun_family = Histogram( defs.AUDIO_UNDERRUN_S, "Per-request worst-case player-deficit in seconds (max time the player " "ran out of buffered audio). > 0 indicates listener experienced silent gaps.", labelnames=_stage_labels, buckets=defs.SECONDS_FAST_BUCKETS, ) _audio_continuity_ok_family = Counter( defs.AUDIO_CONTINUITY_OK_METRIC, "Incremented when the request's worst underrun stayed below threshold_ms. " "Pair with requests_success_total to compute streaming-continuity health rate.", labelnames=list(defs.AUDIO_CONTINUITY_LABELS), ) _audio_skipped_family = Counter( defs.AUDIO_SKIPPED_REQUESTS_METRIC, "Silent-loss counter — code2wav rejected malformed codec input and returned 200 OK with empty audio.", labelnames=list(defs.AUDIO_SKIPPED_LABELS), ) # ---------------------------------------------------------------------------- # Diffusion family # ---------------------------------------------------------------------------- _diffusion_exec_family = Histogram( defs.DIFFUSION_EXEC_S, "DiT forward pass execution time per request in seconds.", labelnames=_stage_labels, buckets=defs.SECONDS_BUCKETS, ) _diffusion_exec_per_step_family = Histogram( defs.DIFFUSION_EXEC_PER_STEP_S, "DiT forward pass execution time per denoising step in seconds.", labelnames=_stage_labels, buckets=defs.SECONDS_FAST_BUCKETS, ) _diffusion_preprocess_family = Histogram( defs.DIFFUSION_PREPROCESS_S, "Diffusion input preprocessing time per request in seconds.", labelnames=_stage_labels, buckets=defs.SECONDS_FAST_BUCKETS, ) _diffusion_postprocess_family = Histogram( defs.DIFFUSION_POSTPROCESS_S, "Diffusion output postprocessing (VAE decode) time per request in seconds.", labelnames=_stage_labels, buckets=defs.SECONDS_FAST_BUCKETS, ) class OmniModalityMetrics: """Per-modality observe API. Stage/replica are passed at observe time because a single OmniModalityMetrics instance per pipeline serves all stage+replica combinations. """ def __init__(self, model_name: str, log_stats: bool = True) -> None: self._model_name = model_name self._log_stats = log_stats # ---- Audio ------------------------------------------------------------ def observe_audio_ttfp(self, stage: str, replica: str, ttfp_seconds: float) -> None: if not self._log_stats: return _audio_ttfp_family.labels(model_name=self._model_name, stage=stage, replica=replica).observe(ttfp_seconds) def observe_audio_duration(self, stage: str, replica: str, duration_seconds: float) -> None: if not self._log_stats: return _audio_duration_family.labels(model_name=self._model_name, stage=stage, replica=replica).observe( duration_seconds ) def observe_audio_rtf(self, stage: str, replica: str, rtf: float) -> None: if not self._log_stats: return _audio_rtf_family.labels(model_name=self._model_name, stage=stage, replica=replica).observe(rtf) def inc_audio_frames(self, stage: str, replica: str, n_frames: int) -> None: if not self._log_stats or n_frames <= 0: return _audio_frames_family.labels(model_name=self._model_name, stage=stage, replica=replica).inc(n_frames) def observe_audio_underrun(self, stage: str, replica: str, underrun_s: float) -> None: if not self._log_stats: return _audio_underrun_family.labels(model_name=self._model_name, stage=stage, replica=replica).observe( max(underrun_s, 0.0) ) def inc_audio_continuity_ok(self, stage: str, replica: str, threshold_ms: int) -> None: if not self._log_stats: return _audio_continuity_ok_family.labels( model_name=self._model_name, stage=stage, replica=replica, threshold_ms=str(int(threshold_ms)), ).inc() def inc_audio_skipped(self, stage: str, replica: str, reason: str) -> None: if not self._log_stats: return _audio_skipped_family.labels( model_name=self._model_name, stage=stage, replica=replica, reason=reason or "unknown", ).inc() # ---- Diffusion -------------------------------------------------------- def observe_diffusion_exec(self, stage: str, replica: str, seconds: float) -> None: if not self._log_stats: return _diffusion_exec_family.labels( model_name=self._model_name, stage=stage, replica=replica, ).observe(seconds) def observe_diffusion_exec_per_step(self, stage: str, replica: str, seconds: float) -> None: if not self._log_stats: return _diffusion_exec_per_step_family.labels( model_name=self._model_name, stage=stage, replica=replica, ).observe(seconds) def observe_diffusion_preprocess(self, stage: str, replica: str, seconds: float) -> None: if not self._log_stats: return _diffusion_preprocess_family.labels( model_name=self._model_name, stage=stage, replica=replica, ).observe(seconds) def observe_diffusion_postprocess(self, stage: str, replica: str, seconds: float) -> None: if not self._log_stats: return _diffusion_postprocess_family.labels( model_name=self._model_name, stage=stage, replica=replica, ).observe(seconds) def observe_modality_at_finalize( mod_metrics: OmniModalityMetrics, *, output_type: str | None, stage_id: int, replica_id: int | None, stage_metrics: Any, engine_outputs: Any, ) -> None: """Route audio-path observations for a finalized request. Used by ``omni_base._process_single_result`` inside the e2e_done finalize guard so it fires once per request. Skips text path (covered by upstream ``vllm:*{stage="thinker", ...}``) and any case where required inputs are missing — caller should not need to pre-validate. audio_ttfp is intentionally NOT observed here; it's emitted by the streaming hook at first-packet time, not at finalize. """ if replica_id is None or stage_metrics is None or output_type is None: return stage_label = str(stage_id) replica_label = str(replica_id) gen_time_s = float(getattr(stage_metrics, "stage_gen_time_ms", 0.0)) / 1000.0 mm_out = extract_mm_output(engine_outputs) if output_type == "audio": sample_rate = defs.resolve_audio_sample_rate(mm_out) n_frames = int(getattr(stage_metrics, "audio_generated_frames", 0) or 0) if n_frames == 0: n_frames = count_audio_frames(mm_out) mod_metrics.inc_audio_frames(stage_label, replica_label, n_frames) duration_s = n_frames / sample_rate if sample_rate > 0 else 0.0 if duration_s > 0: mod_metrics.observe_audio_duration(stage_label, replica_label, duration_s) mod_metrics.observe_audio_rtf( stage_label, replica_label, defs.compute_audio_rtf(gen_time_s, duration_s), ) else: mod_metrics.inc_audio_skipped(stage_label, replica_label, "no_audio_data") dm = getattr(stage_metrics, "diffusion_metrics", None) if dm: _key_map = { "diffusion_engine_exec_time_s": mod_metrics.observe_diffusion_exec, "preprocess_time_s": mod_metrics.observe_diffusion_preprocess, "postprocess_time_s": mod_metrics.observe_diffusion_postprocess, } for key, observe_fn in _key_map.items(): val = dm.get(key) if val is not None: observe_fn(stage_label, replica_label, float(val)) exec_time = dm.get("diffusion_engine_exec_time_s") num_steps = dm.get("num_inference_steps") if exec_time is not None and num_steps and num_steps > 0: mod_metrics.observe_diffusion_exec_per_step( stage_label, replica_label, float(exec_time) / int(num_steps), ) def observe_audio_first_packet( mod_metrics: OmniModalityMetrics, *, stage_id: int, replica_id: int | None, arrival_ts: float, now_ts: float, ) -> None: """Observe audio_ttfp_s on a request's first audio packet. Caller is responsible for the once-per-request guard (e.g. checking ``ClientRequestState.first_audio_ts is None``) so this function fires at most once per request_id. Defensive-skips when ``replica_id`` or ``arrival_ts`` is insufficient — both can legitimately be missing in error paths and we'd rather drop the sample than emit a wrong (stage, replica). """ if replica_id is None or arrival_ts <= 0: return ttfp = max(now_ts - arrival_ts, 0.0) mod_metrics.observe_audio_ttfp(str(stage_id), str(replica_id), ttfp) def observe_audio_streaming_finalize( mod_metrics: OmniModalityMetrics, *, stage_id: int, replica_id: int | None, chunk_arrival_times_s: list[float], chunk_bytes: list[int], sample_rate: int, threshold_s: float = defs.AUDIO_CONTINUITY_DEFAULT_THRESHOLD_S, ) -> None: """Emit audio_underrun_s + audio_continuity_ok_total at request end. Reuses the math from ``vllm_omni.benchmarks.audio_continuity`` so the server-side and bench-side definitions stay aligned. Caller is responsible for collecting per-chunk arrival timestamps and byte sizes during the streaming response. """ if replica_id is None or not chunk_arrival_times_s: return # Local import to keep the bench module optional at import time. from vllm_omni.benchmarks.audio_continuity import compute_continuity_stats stats = compute_continuity_stats( chunk_arrival_times_s=chunk_arrival_times_s, chunk_bytes=chunk_bytes, sample_rate=sample_rate, threshold_s=threshold_s, ) stage_label = str(stage_id) replica_label = str(replica_id) mod_metrics.observe_audio_underrun(stage_label, replica_label, stats.max_underrun_s) if stats.is_continuous: mod_metrics.inc_audio_continuity_ok(stage_label, replica_label, int(threshold_s * 1000))