8f869c8ea5
Signed-off-by: Megha Agarwal <agarwalmegha1308@gmail.com> Signed-off-by: meghaagr13 <divyanshsinghvi@gmail.com> Signed-off-by: Taichang Zhou <tzhouam@connect.ust.hk> Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com> Co-authored-by: meghaagr13 <divyanshsinghvi@gmail.com> Co-authored-by: Boao Shi <aoibosh@connect.hku.hk> Co-authored-by: Taichang Zhou <tzhouam@connect.ust.hk> Co-authored-by: Cursor <cursoragent@cursor.com>
973 lines
39 KiB
Python
973 lines
39 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""WebSocket handler for streaming video input understanding.
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Accepts video frames incrementally via WebSocket, buffers them, and
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generates text + optional audio responses using the existing Qwen3-Omni
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multi-stage pipeline (thinker -> talker -> code2wav).
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Protocol:
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Client -> Server:
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{"type": "session.config", ...} # Session config (sent once)
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{"type": "video.frame", "data": "..."} # base64 JPEG/PNG frame
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{"type": "audio.chunk", "data": "..."} # base64 PCM16 16kHz mono
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{"type": "video.query", "text": "..."} # Submit query about buffered frames
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{"type": "video.done"} # End of session
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Server -> Client:
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{"type": "response.start"}
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{"type": "response.text.delta", "delta": "..."}
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{"type": "response.text.done", "text": "..."}
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{"type": "response.audio.delta", "data": "...", "format": "wav"}
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{"type": "response.audio.done"}
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{"type": "session.done"}
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{"type": "error", "message": "..."}
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"""
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import asyncio
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import base64
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import hashlib
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import io
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import json
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import time as _time
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import uuid
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import wave
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from collections.abc import Mapping
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from typing import Any
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import torch
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from fastapi import WebSocket, WebSocketDisconnect
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from PIL import Image
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from pydantic import BaseModel, Field, ValidationError
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from vllm.logger import init_logger
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from vllm_omni.entrypoints.openai import video_stream_envs
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from vllm_omni.entrypoints.openai.video_frame_filter import FrameSimilarityFilter
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from vllm_omni.entrypoints.openai.video_stream_context import (
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text_only_message,
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)
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from vllm_omni.outputs import OmniRequestOutput
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logger = init_logger(__name__)
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_DEFAULT_IDLE_TIMEOUT = 60.0
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_DEFAULT_CONFIG_TIMEOUT = 10.0
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_MAX_FRAME_SIZE = 10 * 1024 * 1024 # 10MB per frame
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_MAX_BUFFER_FRAMES = 64
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_MAX_AUDIO_BUFFER_BYTES = 4 * 1024 * 1024
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_MAX_MSG_QUEUE = 200
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_CODEC_FRAME_SAMPLES = 1920 # CausalConv leading-edge artifact length
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_BAD_FRAME = object()
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def _decode_frame_bytes(raw_bytes: bytes) -> Any:
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return Image.open(io.BytesIO(raw_bytes)).convert("RGB")
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class StreamingVideoSessionConfig(BaseModel):
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"""Configuration sent as the first WebSocket message."""
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model: str | None = None
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modalities: list[str] = Field(
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default_factory=lambda: ["text", "audio"],
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description="Output modalities: 'text', 'audio', or both.",
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)
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num_frames: int = Field(
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default=4,
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ge=1,
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le=128,
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description="Max frames to sample from buffer for the model.",
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)
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max_frames: int = Field(
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default=50,
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ge=1,
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le=256,
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description="Max frames to keep in the buffer.",
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)
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system_prompt: str | None = Field(
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default=None,
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description="Custom system prompt.",
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)
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use_audio_in_video: bool = Field(
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default=True,
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description="Interleave audio chunks with video frames when audio input is present.",
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)
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sampling_params_list: list[dict[str, Any]] | None = Field(
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default=None,
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description="Per-stage sampling params [thinker, talker, code2wav].",
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)
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enable_frame_filter: bool = Field(
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default=True,
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description="EVS pixel-similarity pre-filter to drop near-duplicate frames.",
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)
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frame_filter_threshold: float = Field(
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default=0.95,
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ge=0.0,
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le=1.0,
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description="EVS similarity threshold (higher = keep more frames).",
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)
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class OmniStreamingVideoHandler:
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"""Handles WebSocket sessions for streaming video input.
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Supports:
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- Concurrent frame reception during query processing (reader/processor split)
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- PCM audio input (``audio.chunk``)
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- Async-chunk incremental audio output via ``engine_client.generate()``
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- Multi-turn conversation history
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- Soft interrupt (new query cancels current generation)
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"""
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def __init__(
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self,
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chat_service: Any,
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idle_timeout: float = _DEFAULT_IDLE_TIMEOUT,
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config_timeout: float = _DEFAULT_CONFIG_TIMEOUT,
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engine_client: Any | None = None,
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) -> None:
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self._chat_service = chat_service
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self._idle_timeout = idle_timeout
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self._config_timeout = config_timeout
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self._engine_client = engine_client
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async def handle_session(self, websocket: WebSocket) -> None:
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"""Main session loop for a single WebSocket connection."""
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await websocket.accept()
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try:
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config = await self._receive_config(websocket)
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if config is None:
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return
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frame_buffer: list[str] = [] # base64-encoded JPEG frames
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# Per-frame PIL cache + uuid for mm_hash reuse. Aligned with frame_buffer by index.
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frame_pil_cache: dict[str, tuple[Any, str] | object] = {} # b64 -> (PIL.Image, uuid) or _BAD_FRAME
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frame_filter = (
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FrameSimilarityFilter(threshold=config.frame_filter_threshold) if config.enable_frame_filter else None
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)
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audio_buffer = bytearray() # raw PCM16 16kHz mono
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message_history: list[dict[str, Any]] = []
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active_request_id: str | None = None
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prev_request_id: str | None = None # abort target iff prev was interrupted
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prev_was_interrupted: bool = False
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interrupt_event = asyncio.Event()
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prewarm_tasks: set[asyncio.Task[Any]] = set()
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query_task: asyncio.Task[Any] | None = None
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msg_queue: asyncio.Queue[dict[str, Any] | None] = asyncio.Queue(maxsize=_MAX_MSG_QUEUE)
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async def _reader() -> None:
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"""Receive WebSocket messages and enqueue them."""
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try:
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while True:
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try:
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raw = await asyncio.wait_for(
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websocket.receive_text(),
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timeout=self._idle_timeout,
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)
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except asyncio.TimeoutError:
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await self._send_error(websocket, "Idle timeout")
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await msg_queue.put(None)
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return
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try:
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msg = json.loads(raw)
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except json.JSONDecodeError:
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await self._send_error(websocket, "Invalid JSON")
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continue
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if not isinstance(msg, dict):
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await self._send_error(websocket, "Messages must be JSON objects")
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continue
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msg_type = str(msg.get("type", ""))
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if msg_type.startswith("_internal."):
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await self._send_error(websocket, f"Unknown type: {msg_type}")
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continue
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await msg_queue.put(msg)
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if msg.get("type") == "video.done":
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return
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except WebSocketDisconnect:
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await msg_queue.put(None)
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except Exception:
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await msg_queue.put(None)
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raise
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async def _cancel_active_query(*, abort_now: bool = False) -> None:
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"""Signal soft interrupt for the active query."""
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nonlocal active_request_id, prev_was_interrupted, query_task
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if active_request_id is not None:
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interrupt_event.set()
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prev_was_interrupted = True
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logger.info("Interrupt signaled for %s", active_request_id)
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if abort_now and self._engine_client:
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try:
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await self._engine_client.abort(active_request_id)
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except Exception:
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logger.debug("Abort failed for %s", active_request_id, exc_info=True)
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if query_task is not None and not query_task.done():
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query_task.cancel()
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await asyncio.gather(query_task, return_exceptions=True)
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query_task = None
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async def _processor() -> None:
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"""Process enqueued messages."""
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nonlocal active_request_id, prev_request_id, prev_was_interrupted, query_task
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while True:
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msg = await msg_queue.get()
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if msg is None:
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await _cancel_active_query(abort_now=True)
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return
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msg_type = msg.get("type")
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if msg_type == "_internal.frame_decode_failed":
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frame_data = msg.get("b64", "")
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removed = frame_data in frame_buffer
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if removed:
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frame_buffer[:] = [f for f in frame_buffer if f != frame_data]
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if frame_pil_cache.get(frame_data) is _BAD_FRAME:
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frame_pil_cache.pop(frame_data, None)
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if removed:
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await self._send_error(websocket, "Frame decode failed")
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elif msg_type == "video.frame":
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frame_data = msg.get("data", "")
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if not frame_data:
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continue
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if len(frame_data) > _MAX_FRAME_SIZE:
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await self._send_error(websocket, "Frame too large")
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continue
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try:
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raw_bytes = base64.b64decode(frame_data, validate=True)
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except Exception:
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await self._send_error(websocket, "Invalid image data")
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continue
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if frame_filter is not None:
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try:
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if not frame_filter.should_retain(raw_bytes):
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continue
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except Exception:
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await self._send_error(websocket, "Invalid image data")
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continue
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max_buf = config.max_frames
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if len(frame_buffer) >= max_buf:
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dropped = frame_buffer.pop(0)
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frame_pil_cache.pop(dropped, None)
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frame_buffer.append(frame_data)
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# Prewarm: decode PIL off the event loop so query-time chat_template
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# can skip base64+Image.open. uuid=md5 lets mm_cache dedupe identical frames.
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if frame_data not in frame_pil_cache:
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mm_uuid = hashlib.md5(raw_bytes, usedforsecurity=False).hexdigest()
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async def _prewarm(b64: str, b: bytes, u: str) -> None:
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try:
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pil = await asyncio.to_thread(_decode_frame_bytes, b)
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frame_pil_cache[b64] = (pil, u)
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except Exception:
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frame_pil_cache[b64] = _BAD_FRAME
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logger.warning("prewarm decode failed for frame (len=%d)", len(b))
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try:
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msg_queue.put_nowait({"type": "_internal.frame_decode_failed", "b64": b64})
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except asyncio.QueueFull:
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logger.warning(
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"frame decode failure event dropped because message queue is full"
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)
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task = asyncio.create_task(_prewarm(frame_data, raw_bytes, mm_uuid))
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prewarm_tasks.add(task)
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task.add_done_callback(prewarm_tasks.discard)
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elif msg_type == "audio.chunk":
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data_b64 = msg.get("data", "")
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try:
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pcm_bytes = base64.b64decode(data_b64)
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except Exception:
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continue
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if len(audio_buffer) + len(pcm_bytes) > _MAX_AUDIO_BUFFER_BYTES:
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await self._send_error(websocket, "Audio buffer overflow")
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audio_buffer.clear()
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continue
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audio_buffer.extend(pcm_bytes)
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elif msg_type == "video.query":
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await _cancel_active_query()
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query_text = msg.get("text", "")
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audio_data_b64 = msg.get("audio_data")
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if audio_data_b64:
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try:
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decoded = base64.b64decode(audio_data_b64)
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if len(audio_buffer) + len(decoded) <= _MAX_AUDIO_BUFFER_BYTES:
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audio_buffer.extend(decoded)
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else:
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await self._send_error(websocket, "Audio buffer overflow")
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audio_buffer.clear()
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except Exception:
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pass
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if not frame_buffer:
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await self._send_error(websocket, "No frames buffered")
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continue
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# Abort only if the previous turn was interrupted mid-flight.
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# A naturally-finished request is already released by the scheduler;
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# aborting it again can race with stage-1/2 tear-down and has been
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# observed to crash flash_attn with a mixed prefill+decode batch
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# (scheduler_metadata shape mismatch) under longer sessions.
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if prev_was_interrupted and prev_request_id and self._engine_client:
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try:
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await self._engine_client.abort(prev_request_id)
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except Exception:
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pass
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await asyncio.sleep(0.1)
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prev_was_interrupted = False
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request_id = f"video-{uuid.uuid4().hex[:12]}"
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active_request_id = request_id
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interrupt_event.clear()
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query_frames = list(frame_buffer)
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query_audio_buffer = bytearray(audio_buffer)
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audio_buffer.clear()
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query_prewarmed_frames = dict(frame_pil_cache)
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async def _run_query() -> None:
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nonlocal active_request_id, prev_request_id
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try:
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await self._process_query(
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websocket,
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config,
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query_frames,
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query_audio_buffer,
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message_history,
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query_text,
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request_id,
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interrupt_event,
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query_prewarmed_frames,
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)
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finally:
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if active_request_id == request_id:
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prev_request_id = request_id
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active_request_id = None
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query_task = asyncio.create_task(_run_query())
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elif msg_type == "video.done":
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if query_task is not None and not query_task.done():
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await asyncio.gather(query_task, return_exceptions=True)
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query_task = None
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await websocket.send_json({"type": "session.done"})
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return
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elif msg_type == "ping":
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try:
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await websocket.send_json({"type": "pong"})
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except Exception:
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pass
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else:
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await self._send_error(websocket, f"Unknown type: {msg_type}")
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reader_task = asyncio.create_task(_reader())
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try:
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await _processor()
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finally:
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reader_task.cancel()
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try:
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await reader_task
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except (asyncio.CancelledError, Exception):
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pass
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for t in list(prewarm_tasks):
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t.cancel()
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if prewarm_tasks:
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await asyncio.gather(*prewarm_tasks, return_exceptions=True)
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if query_task is not None and not query_task.done():
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await _cancel_active_query(abort_now=True)
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except WebSocketDisconnect:
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logger.info("Streaming video: client disconnected")
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except Exception as e:
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logger.exception("Streaming video session error: %s", e)
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try:
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await self._send_error(websocket, f"Internal error: {e}")
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except Exception:
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pass
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async def _receive_config(self, websocket: WebSocket) -> StreamingVideoSessionConfig | None:
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"""Wait for and validate the session.config message."""
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try:
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raw = await asyncio.wait_for(
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websocket.receive_text(),
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timeout=self._config_timeout,
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)
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except asyncio.TimeoutError:
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await self._send_error(websocket, "Timeout waiting for session.config")
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return None
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try:
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msg = json.loads(raw)
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except json.JSONDecodeError:
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await self._send_error(websocket, "Invalid JSON in session.config")
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return None
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|
|
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if not isinstance(msg, dict) or msg.get("type") != "session.config":
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await self._send_error(
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websocket,
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f"Expected session.config, got: {msg.get('type') if isinstance(msg, dict) else type(msg).__name__}",
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)
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return None
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config_data = {k: v for k, v in msg.items() if k != "type"}
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alias_map = {
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"num_sample_frames": "num_frames",
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"evs_enabled": "enable_frame_filter",
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"evs_threshold": "frame_filter_threshold",
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}
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for old_key, new_key in alias_map.items():
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if old_key in config_data and new_key not in config_data:
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config_data[new_key] = config_data[old_key]
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|
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try:
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config = StreamingVideoSessionConfig(**config_data)
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except ValidationError as e:
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await self._send_error(websocket, f"Invalid session config: {e}")
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return None
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|
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return config
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|
|
async def _process_query(
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self,
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websocket: WebSocket,
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config: StreamingVideoSessionConfig,
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frame_buffer: list[str],
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audio_buffer: bytearray,
|
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message_history: list[dict[str, Any]],
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query_text: str,
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request_id: str,
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interrupt_event: asyncio.Event,
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prewarmed_frames: dict[str, tuple[Any, str]],
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) -> None:
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"""Build prompt, run inference, stream text + audio response."""
|
|
|
|
if self._engine_client is None:
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await self._send_error(websocket, "Streaming video requires an engine client")
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return
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|
|
|
await self._process_query_engine(
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websocket,
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config,
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frame_buffer,
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audio_buffer,
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message_history,
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query_text,
|
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request_id,
|
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interrupt_event,
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prewarmed_frames,
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)
|
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|
|
# ------------------------------------------------------------------
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# Engine-client path (async_chunk audio streaming)
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# ------------------------------------------------------------------
|
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|
|
async def _process_query_engine(
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self,
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websocket: WebSocket,
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config: StreamingVideoSessionConfig,
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frame_buffer: list[str],
|
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audio_buffer: bytearray,
|
|
message_history: list[dict[str, Any]],
|
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query_text: str,
|
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request_id: str,
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interrupt_event: asyncio.Event,
|
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prewarmed_frames: dict[str, tuple[Any, str]],
|
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) -> None:
|
|
"""Direct engine_client.generate() path for async_chunk audio."""
|
|
from vllm.entrypoints.openai.chat_completion.protocol import (
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ChatCompletionRequest,
|
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)
|
|
|
|
messages, user_message = self._build_messages(
|
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config,
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frame_buffer,
|
|
audio_buffer,
|
|
message_history,
|
|
query_text,
|
|
prewarmed_frames,
|
|
)
|
|
|
|
request_kwargs: dict[str, Any] = {
|
|
"model": config.model or "default",
|
|
"messages": messages,
|
|
"stream": True,
|
|
"modalities": config.modalities,
|
|
"add_generation_prompt": True,
|
|
"continue_final_message": False,
|
|
"add_special_tokens": False,
|
|
}
|
|
if config.use_audio_in_video and len(audio_buffer) > 0:
|
|
request_kwargs["mm_processor_kwargs"] = {
|
|
"use_audio_in_video": True,
|
|
}
|
|
if config.sampling_params_list:
|
|
request_kwargs["sampling_params_list"] = config.sampling_params_list
|
|
|
|
try:
|
|
chat_request = ChatCompletionRequest(**request_kwargs)
|
|
except Exception as e:
|
|
await self._send_error(websocket, f"Failed to build request: {e}")
|
|
return
|
|
|
|
try:
|
|
engine_prompt = await self._preprocess_to_engine_prompt(chat_request)
|
|
except Exception as e:
|
|
await self._send_error(websocket, f"Preprocess failed: {e}")
|
|
return
|
|
|
|
await websocket.send_json({"type": "response.start"})
|
|
text_parts: list[str] = []
|
|
text_done_sent = False
|
|
audio_chunk_count = 0
|
|
# Number of per-step tensors in OmniRequestOutput.audio_data already
|
|
# drained. Used by the fast path to skip already-emitted history.
|
|
audio_chunks_drained = 0
|
|
previous_text = ""
|
|
interrupted = False
|
|
t_start = _time.monotonic()
|
|
t_first_text = None
|
|
t_first_audio = None
|
|
|
|
# Wire-level async-chunk switch. "off" means
|
|
# buffer all deltas server-side and flush once at the end; the engine
|
|
# pipeline still overlaps internally.
|
|
async_chunk_mode = video_stream_envs.VLLM_VIDEO_ASYNC_CHUNK
|
|
streaming = async_chunk_mode == "on"
|
|
audio_tail_tensors: list[Any] = []
|
|
|
|
try:
|
|
result_gen = self._engine_client.generate(
|
|
prompt=engine_prompt,
|
|
request_id=request_id,
|
|
output_modalities=config.modalities,
|
|
)
|
|
|
|
async for output in result_gen:
|
|
# Soft interrupt: drain without sending
|
|
if interrupt_event.is_set():
|
|
if not interrupted:
|
|
logger.info("Generation interrupted — draining")
|
|
interrupted = True
|
|
continue
|
|
|
|
if not isinstance(output, OmniRequestOutput):
|
|
continue
|
|
|
|
out_type = getattr(output, "final_output_type", "text")
|
|
|
|
if out_type == "audio":
|
|
if streaming and not text_done_sent:
|
|
full_text = "".join(text_parts)
|
|
await websocket.send_json({"type": "response.text.done", "text": full_text})
|
|
text_done_sent = True
|
|
|
|
if t_first_audio is None:
|
|
t_first_audio = _time.monotonic()
|
|
audio_chunk_count += 1
|
|
if streaming:
|
|
b64, audio_chunks_drained = self._extract_audio_delta_b64(
|
|
output,
|
|
audio_chunks_drained,
|
|
)
|
|
if b64:
|
|
await websocket.send_json(
|
|
{
|
|
"type": "response.audio.delta",
|
|
"data": b64,
|
|
"format": "wav",
|
|
}
|
|
)
|
|
else:
|
|
audio_data = self._get_audio_data(output)
|
|
if audio_data is not None:
|
|
if isinstance(audio_data, list):
|
|
audio_tail_tensors = list(audio_data)
|
|
else:
|
|
audio_tail_tensors = [audio_data]
|
|
else:
|
|
delta_text, previous_text = self._extract_text_delta(
|
|
output,
|
|
previous_text,
|
|
)
|
|
if delta_text:
|
|
if t_first_text is None:
|
|
t_first_text = _time.monotonic()
|
|
text_parts.append(delta_text)
|
|
if streaming:
|
|
await websocket.send_json({"type": "response.text.delta", "delta": delta_text})
|
|
|
|
if not text_done_sent:
|
|
full_text = "".join(text_parts)
|
|
await websocket.send_json({"type": "response.text.done", "text": full_text})
|
|
text_done_sent = True
|
|
|
|
if not streaming and audio_tail_tensors:
|
|
try:
|
|
coalesced = (
|
|
audio_tail_tensors[0] if len(audio_tail_tensors) == 1 else torch.cat(audio_tail_tensors, dim=-1)
|
|
)
|
|
tail_np = self._tensor_to_1d_np(coalesced)
|
|
b64, _ = self._encode_tail(
|
|
tail_np,
|
|
0,
|
|
new_drained=len(audio_tail_tensors),
|
|
is_first=True,
|
|
)
|
|
if b64:
|
|
await websocket.send_json(
|
|
{
|
|
"type": "response.audio.delta",
|
|
"data": b64,
|
|
"format": "wav",
|
|
}
|
|
)
|
|
except Exception:
|
|
logger.exception("Failed to coalesce off-path audio")
|
|
|
|
if audio_chunk_count > 0:
|
|
await websocket.send_json({"type": "response.audio.done"})
|
|
|
|
response_text = "".join(text_parts)
|
|
message_history.append(user_message)
|
|
message_history.append({"role": "assistant", "content": response_text})
|
|
|
|
t_end = _time.monotonic()
|
|
logger.info(
|
|
"[TIMING] mode=%s total=%.2fs first_text=%.2fs first_audio=%.2fs audio_chunks=%d",
|
|
async_chunk_mode,
|
|
t_end - t_start,
|
|
(t_first_text - t_start) if t_first_text else -1,
|
|
(t_first_audio - t_start) if t_first_audio else -1,
|
|
audio_chunk_count,
|
|
)
|
|
|
|
except Exception:
|
|
logger.exception("Engine query failed")
|
|
await self._send_error(websocket, "Query processing failed")
|
|
|
|
if not text_done_sent:
|
|
full_text = "".join(text_parts)
|
|
await websocket.send_json({"type": "response.text.done", "text": full_text})
|
|
|
|
# ------------------------------------------------------------------
|
|
# Message building
|
|
# ------------------------------------------------------------------
|
|
|
|
def _build_messages(
|
|
self,
|
|
config: StreamingVideoSessionConfig,
|
|
frame_buffer: list[str],
|
|
audio_buffer: bytearray,
|
|
message_history: list[dict[str, Any]],
|
|
query_text: str,
|
|
prewarmed_frames: dict[str, tuple[Any, str]],
|
|
) -> tuple[list[dict[str, Any]], dict[str, Any]]:
|
|
"""Build OpenAI-style messages list and the current user message.
|
|
|
|
Returns (messages, user_message).
|
|
"""
|
|
# Stride sampling (index 0 anchor, last slot = newest). Covers full buffer + stable mm_hash.
|
|
n_buf = len(frame_buffer)
|
|
if n_buf <= config.num_frames:
|
|
frames = list(frame_buffer)
|
|
else:
|
|
stride = max(1, n_buf // config.num_frames)
|
|
idx = [i * stride for i in range(config.num_frames - 1)] + [n_buf - 1]
|
|
frames = [frame_buffer[i] for i in idx]
|
|
|
|
# Prefer prewarmed PIL + uuid so mm_cache can dedupe by hash.
|
|
prewarmed = prewarmed_frames or {}
|
|
user_content: list[dict] = []
|
|
for frame_b64 in frames:
|
|
cached = prewarmed.get(frame_b64)
|
|
if cached is _BAD_FRAME:
|
|
continue
|
|
if cached is not None:
|
|
pil, pil_uuid = cached
|
|
user_content.append(
|
|
{
|
|
"type": "image_pil",
|
|
"image_pil": pil,
|
|
"uuid": pil_uuid,
|
|
}
|
|
)
|
|
else:
|
|
user_content.append(
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {"url": f"data:image/jpeg;base64,{frame_b64}"},
|
|
}
|
|
)
|
|
|
|
if len(audio_buffer) > 0:
|
|
wav_b64 = self._pcm_to_wav_b64(bytes(audio_buffer))
|
|
user_content.append(
|
|
{
|
|
"type": "input_audio",
|
|
"input_audio": {
|
|
"data": wav_b64,
|
|
"format": "wav",
|
|
},
|
|
}
|
|
)
|
|
|
|
if query_text:
|
|
user_content.append({"type": "text", "text": query_text})
|
|
|
|
user_message: dict[str, Any] = {"role": "user", "content": user_content}
|
|
|
|
messages: list[dict[str, Any]] = []
|
|
if config.system_prompt:
|
|
messages.append({"role": "system", "content": config.system_prompt})
|
|
|
|
# Add text-only history (strip images/audio from old turns).
|
|
# Keep only the last turn (2 messages) to keep prompt short
|
|
# enough for single-step mm_encoder scheduling. When prompt
|
|
# exceeds ~50 tokens, the V1 scheduler splits mm_encoder and
|
|
# text prefill, causing incomplete thinker embeddings and
|
|
# garbled audio.
|
|
recent_history = message_history[-2:] if len(message_history) > 2 else message_history
|
|
for hist_msg in recent_history:
|
|
messages.append(self._text_only_message(hist_msg))
|
|
|
|
messages.append(user_message)
|
|
|
|
return messages, user_message
|
|
|
|
# ------------------------------------------------------------------
|
|
# Audio helpers
|
|
# ------------------------------------------------------------------
|
|
|
|
@staticmethod
|
|
def _pcm_to_wav_b64(pcm_data: bytes, sample_rate: int = 16000) -> str:
|
|
"""Wrap raw PCM16 mono in a WAV container and return base64."""
|
|
buf = io.BytesIO()
|
|
with wave.open(buf, "wb") as wf:
|
|
wf.setnchannels(1)
|
|
wf.setsampwidth(2)
|
|
wf.setframerate(sample_rate)
|
|
wf.writeframes(pcm_data)
|
|
return base64.b64encode(buf.getvalue()).decode()
|
|
|
|
@classmethod
|
|
def _extract_audio_delta_b64(
|
|
cls,
|
|
result: OmniRequestOutput,
|
|
chunks_drained: int,
|
|
) -> tuple[str | None, int]:
|
|
"""Return (base64 WAV of new samples, updated chunks_drained).
|
|
|
|
`chunks_drained` is the number of per-step tensors in
|
|
``audio_data`` that have already been emitted. Each engine step appends
|
|
one tensor, so new samples are ``audio_data[chunks_drained:]`` — no
|
|
matter how many steps accumulated between reads (handles backpressure
|
|
cleanly, unlike a simple ``audio_data[-1]``).
|
|
|
|
Two paths, selected at runtime by ``VLLM_VIDEO_AUDIO_DELTA_MODE``:
|
|
* fast — only D2H the new tail. Per-call cost ∝ new chunks.
|
|
* slow — full cat + D2H each call. Per-call cost ∝ total history.
|
|
Retained for A/B; remove once downstream callers confirm.
|
|
"""
|
|
audio_data = cls._get_audio_data(result)
|
|
if audio_data is None:
|
|
return None, chunks_drained
|
|
|
|
if video_stream_envs.VLLM_VIDEO_AUDIO_DELTA_MODE == "slow":
|
|
return cls._delta_slow(audio_data, chunks_drained)
|
|
return cls._delta_fast(audio_data, chunks_drained)
|
|
|
|
@staticmethod
|
|
def _get_audio_data(result: OmniRequestOutput):
|
|
"""Navigate OmniRequestOutput → multimodal_output['audio']. None on miss."""
|
|
request_output = getattr(result, "request_output", None)
|
|
if request_output is None:
|
|
return None
|
|
outputs = getattr(request_output, "outputs", None)
|
|
if not isinstance(outputs, list) or not outputs:
|
|
return None
|
|
mm_output = getattr(outputs[0], "multimodal_output", None)
|
|
if not isinstance(mm_output, Mapping):
|
|
return None
|
|
return mm_output.get("audio")
|
|
|
|
@classmethod
|
|
def _delta_fast(
|
|
cls,
|
|
audio_data,
|
|
chunks_drained: int,
|
|
) -> tuple[str | None, int]:
|
|
"""Emit only tensors appended since the last call."""
|
|
# Single tensor: output_processor hands us one tensor before it becomes a
|
|
# list (see output_processor.py:89). Treat it as chunk #0.
|
|
if not isinstance(audio_data, list):
|
|
if chunks_drained >= 1:
|
|
return None, chunks_drained
|
|
tail_np = cls._tensor_to_1d_np(audio_data)
|
|
return cls._encode_tail(tail_np, chunks_drained, new_drained=1, is_first=True)
|
|
|
|
n = len(audio_data)
|
|
if n <= chunks_drained:
|
|
return None, chunks_drained
|
|
|
|
new_chunks = audio_data[chunks_drained:]
|
|
tail = new_chunks[0] if len(new_chunks) == 1 else torch.cat(new_chunks, dim=-1)
|
|
tail_np = cls._tensor_to_1d_np(tail)
|
|
return cls._encode_tail(tail_np, chunks_drained, new_drained=n, is_first=(chunks_drained == 0))
|
|
|
|
@classmethod
|
|
def _delta_slow(
|
|
cls,
|
|
audio_data,
|
|
chunks_drained: int,
|
|
) -> tuple[str | None, int]:
|
|
"""Pre-fix behaviour: concat everything each call and slice on CPU."""
|
|
if isinstance(audio_data, list):
|
|
if not audio_data:
|
|
return None, chunks_drained
|
|
audio_tensor = torch.cat(audio_data, dim=-1)
|
|
new_drained = len(audio_data)
|
|
else:
|
|
audio_tensor = audio_data
|
|
new_drained = 1
|
|
|
|
full_np = cls._tensor_to_1d_np(audio_tensor)
|
|
if full_np is None:
|
|
return None, chunks_drained
|
|
# chunks_drained doesn't map directly to sample offset without tracking
|
|
# per-chunk lengths, so we re-derive: replay the tail that corresponds
|
|
# to chunks appended since last call by slicing off the part produced
|
|
# by the already-drained prefix. For slow path this is intentionally
|
|
# wasteful — the point is to reproduce the pre-fix hot loop.
|
|
if chunks_drained == 0:
|
|
tail_np = full_np
|
|
else:
|
|
# Recover prefix length by re-concatenating the already-drained
|
|
# prefix tensors (cost intentionally identical to the baseline
|
|
# implementation this was lifted from).
|
|
if isinstance(audio_data, list) and chunks_drained < len(audio_data):
|
|
prefix_len = sum(int(t.shape[-1]) for t in audio_data[:chunks_drained])
|
|
tail_np = full_np[prefix_len:]
|
|
else:
|
|
tail_np = full_np[0:0]
|
|
return cls._encode_tail(tail_np, chunks_drained, new_drained=new_drained, is_first=(chunks_drained == 0))
|
|
|
|
@classmethod
|
|
def _encode_tail(
|
|
cls,
|
|
tail_np,
|
|
old_drained: int,
|
|
*,
|
|
new_drained: int,
|
|
is_first: bool,
|
|
) -> tuple[str | None, int]:
|
|
"""Strip the CausalConv leading artifact on first emit, then b64-encode."""
|
|
if tail_np is None or len(tail_np) == 0:
|
|
return None, new_drained
|
|
if is_first and len(tail_np) > _CODEC_FRAME_SAMPLES * 2:
|
|
tail_np = tail_np[_CODEC_FRAME_SAMPLES:]
|
|
if len(tail_np) == 0:
|
|
return None, new_drained
|
|
try:
|
|
return cls._encode_audio_wav_b64(tail_np), new_drained
|
|
except Exception:
|
|
logger.exception("Failed to encode audio delta WAV")
|
|
return None, old_drained
|
|
|
|
@staticmethod
|
|
def _tensor_to_1d_np(t):
|
|
"""Tensor → flat float32 numpy on CPU. None on failure."""
|
|
if t is None or not hasattr(t, "float"):
|
|
return None
|
|
arr = t.float().detach().cpu().numpy()
|
|
if arr.ndim > 1:
|
|
arr = arr.flatten()
|
|
return arr
|
|
|
|
@staticmethod
|
|
def _encode_audio_wav_b64(audio_np) -> str:
|
|
"""Encode numpy float32 audio to base64 WAV (24kHz)."""
|
|
from vllm_omni.entrypoints.openai.audio_utils_mixin import AudioMixin
|
|
from vllm_omni.entrypoints.openai.protocol.audio import CreateAudio
|
|
|
|
audio_obj = CreateAudio(
|
|
audio_tensor=audio_np,
|
|
sample_rate=24000,
|
|
response_format="wav",
|
|
speed=1.0,
|
|
stream_format="audio",
|
|
base64_encode=True,
|
|
)
|
|
mixin = AudioMixin()
|
|
resp = mixin.create_audio(audio_obj)
|
|
return resp.audio_data
|
|
|
|
@staticmethod
|
|
def _extract_text_delta(
|
|
result: OmniRequestOutput,
|
|
previous_text: str,
|
|
) -> tuple[str, str]:
|
|
"""Extract incremental text delta from OmniRequestOutput."""
|
|
if result.final_output_type != "text":
|
|
return "", previous_text
|
|
|
|
request_output = getattr(result, "request_output", None)
|
|
if request_output is None:
|
|
return "", previous_text
|
|
|
|
outputs = getattr(request_output, "outputs", None)
|
|
if not isinstance(outputs, list) or not outputs:
|
|
return "", previous_text
|
|
|
|
text = getattr(outputs[0], "text", None)
|
|
if not isinstance(text, str) or not text:
|
|
return "", previous_text
|
|
|
|
if text.startswith(previous_text):
|
|
return text[len(previous_text) :], text
|
|
return text, text
|
|
|
|
# ------------------------------------------------------------------
|
|
# Preprocessing
|
|
# ------------------------------------------------------------------
|
|
|
|
async def _preprocess_to_engine_prompt(self, request) -> Any:
|
|
"""Use the chat handler's preprocessing to build an engine prompt."""
|
|
handler = self._chat_service
|
|
renderer = handler.renderer
|
|
|
|
_conversation, engine_prompts = await handler._preprocess_chat(
|
|
request,
|
|
request.messages,
|
|
default_template=getattr(request, "chat_template", None) or handler.chat_template,
|
|
default_template_content_format=handler.chat_template_content_format,
|
|
renderer=renderer,
|
|
add_generation_prompt=request.add_generation_prompt,
|
|
continue_final_message=request.continue_final_message,
|
|
add_special_tokens=request.add_special_tokens,
|
|
)
|
|
return engine_prompts[0]
|
|
|
|
# ------------------------------------------------------------------
|
|
# Utilities
|
|
# ------------------------------------------------------------------
|
|
|
|
_text_only_message = staticmethod(text_only_message)
|
|
|
|
async def _send_error(self, websocket: WebSocket, message: str) -> None:
|
|
"""Send an error message to the client."""
|
|
try:
|
|
await websocket.send_json({"type": "error", "message": message})
|
|
except Exception:
|
|
pass
|