SolitaryThinker
58223c0c41
Answers "what's the right way to name/register custom pipelines like T2I→I2V": treat a Workflow like a card — a stable namespaced id in the same servable namespace, declared dependencies, and a two-level registry. No new concepts; mirrors how cards are registered (and vllm-omni's pipeline_registry). - program/workflow.py: Workflow gains `requires` (the cards it composes, derived from stages) and `validate(engine)` (fail-fast if a required card is absent, P7). New WorkflowRegistry: declarative workflow_id -> builder catalog for out-of-tree/ad hoc use. - runtime/engine.py: `_workflows` registry + register_workflow (validates deps, rejects id collision with a model_id) + serves(); engine.run routes a request whose model_id is a workflow to workflow.run — addressable exactly like a model. Single-model hot path untouched. - runtime/async_engine.py + serving/server.py: serves() and /models include workflows (discoverable as servables). - models/__init__.py: declarative `_WORKFLOWS` catalog (the cross-model analog of _BUILDERS) + register_workflows() helper; build_image_video_engine now registers the workflow too. Adding a custom pipeline = one catalog line. - Naming convention: dotted/namespaced workflow_id (`image_video.t2i_i2v`), distinct from kebab model ids, collision-checked. Renamed from `t2i_then_i2v`. - tests (+5, 12 total in the file): addressable by id, requires/validate, id collision, registry catalog, register_workflows helper. Full suite 114 passed. - designv4 §9.6: the naming & registration convention documented.
212 lines
9.8 KiB
Python
212 lines
9.8 KiB
Python
"""OmniOpenAIServer — our own OpenAI-compatible worker server (design.md §6.3.5; vllm-omni pattern).
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Fronts ONE AsyncEngine (the per-pool worker surface). Routes OpenAI-shaped endpoints to typed
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OmniRequests, streams chat via SSE, and serves video as an async job (POST returns a job id;
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GET polls) plus a /sync variant — mirroring vllm-omni's `/v1/videos` + `/v1/videos/sync`. The fleet
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(deploy/fleet.py) or Dynamo routes across multiple such servers/engines above this layer.
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Unlike vllm-omni's request-scheduled opaque DIFFUSION stage, the engine underneath is step-scheduled
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— the endpoint is a thin shim; the runtime-visible loop scheduler is the substance.
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"""
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from __future__ import annotations
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import asyncio
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import base64
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import itertools
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import time
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from typing import Any
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import numpy as np
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from ..request.artifacts import Output
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from ..request.streams import StreamChunk
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from .http import HttpServer, Request, Response
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def _json_default(o: Any) -> Any:
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"""Make SSE event payloads JSON-safe (media.chunk carries a StreamChunk with an array)."""
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if isinstance(o, StreamChunk):
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shape = list(np.asarray(o.data).shape) if o.data is not None else None
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return {"stream_id": o.stream_id, "modality": o.modality, "seq": o.seq,
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"preview": o.preview, "final": o.final, "shape": shape}
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if isinstance(o, np.ndarray):
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return {"shape": list(o.shape), "dtype": str(o.dtype)}
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return str(o)
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from .protocol import ChatCompletionRequest, ImageGenerationRequest, VideoGenerationRequest
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_job_ctr = itertools.count(1)
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_chat_ctr = itertools.count(1)
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def _payload(art: Any) -> Any:
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for attr in ("text", "frames", "samples", "tensor", "latent", "token_ids"):
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if hasattr(art, attr) and getattr(art, attr) is not None:
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return getattr(art, attr)
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return None
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def _ser_artifacts(out: Output) -> dict:
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res: dict = {}
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for name, art in out.artifacts.items():
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p = _payload(art)
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if isinstance(p, str):
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res[name] = {"type": "text", "text": p}
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elif p is None:
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res[name] = {"type": "empty"}
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else:
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a = np.asarray(p)
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res[name] = {"type": "tensor", "shape": list(a.shape), "dtype": str(a.dtype),
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"b64_json": base64.b64encode(np.ascontiguousarray(a).tobytes()).decode()}
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return res
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class OmniOpenAIServer:
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def __init__(self, engine: Any, *, engine_id: str = "v2", max_jobs: int = 512):
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self.engine = engine # an AsyncEngine
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self.engine_id = engine_id
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self.jobs: dict[str, dict] = {} # video job store
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self.http = HttpServer(self.dispatch)
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self.served = 0
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self.max_jobs = max_jobs
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self._tasks: set = set() # tracked video-job tasks (no fire-and-forget leak)
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def _evict_jobs(self) -> None:
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terminal = ("completed", "failed")
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for jid in list(self.jobs):
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if len(self.jobs) <= self.max_jobs:
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break
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if self.jobs[jid].get("status") in terminal:
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self.jobs.pop(jid, None)
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# --- routing ------------------------------------------------------------ #
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async def dispatch(self, req: Request) -> Response:
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m, p = req.method, req.path
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if m == "GET" and p == "/health":
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return Response.json(self._health())
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if m == "GET" and p == "/metrics":
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return Response.json(self._metrics())
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if m == "GET" and p == "/v1/models":
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return Response.json(self._models())
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if m == "POST" and p == "/v1/images/generations":
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return await self._images(req)
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if m == "POST" and p == "/v1/videos":
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return await self._video_create(req)
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if m == "POST" and p == "/v1/videos/sync":
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return await self._video_sync(req)
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if m == "GET" and p.startswith("/v1/videos/"):
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return self._video_get(p.rsplit("/", 1)[-1])
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if m == "POST" and p == "/v1/chat/completions":
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return await self._chat(req)
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return Response.json({"error": f"no route for {m} {p}"}, status=404)
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# --- metadata endpoints ------------------------------------------------- #
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def _health(self) -> dict:
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return {"status": "healthy", "engine_id": self.engine_id,
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"in_flight": self.engine.in_flight, "queue_depth": self.engine.queue_depth}
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def _metrics(self) -> dict:
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sm = self.engine.engine.metrics
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adm = self.engine.engine.admission.metrics
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return {"served": self.served, "in_flight": self.engine.in_flight,
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"stepped_units": sm.stepped_units, "batches": sm.batches,
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"admitted": adm.admitted, "deferred": adm.deferred,
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"gpu_seconds": round(adm.gpu_seconds, 6), "by_kind": dict(adm.by_kind)}
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def _models(self) -> dict:
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ids = (list(self.engine.engine._registry) + list(self.engine._disagg)
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+ list(self.engine.engine._workflows)) # workflows are servables too
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return {"object": "list", "data": [{"id": m, "object": "model"} for m in ids]}
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# --- images ------------------------------------------------------------- #
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async def _images(self, req: Request) -> Response:
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ig = ImageGenerationRequest.from_json(req.json())
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out = await self.engine.generate(ig.to_omni())
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self.served += 1
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arts = _ser_artifacts(out)
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primary = arts.get("image") or arts.get("video") or arts.get("latents")
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return Response.json({"created": int(time.time()), "model": ig.model,
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"data": [primary] * max(1, ig.n), "omni": arts,
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"metrics": {k: round(v, 6) for k, v in out.metrics.items()}})
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# --- video: async job + poll + sync ------------------------------------- #
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async def _video_create(self, req: Request) -> Response:
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vg = VideoGenerationRequest.from_json(req.json())
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job_id = f"video-{next(_job_ctr)}"
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self.jobs[job_id] = {"id": job_id, "status": "queued", "model": vg.model}
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t = asyncio.create_task(self._run_video_job(job_id, vg))
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self._tasks.add(t)
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t.add_done_callback(self._tasks.discard) # tracked, not fire-and-forget
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self._evict_jobs()
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return Response.json({"id": job_id, "status": "queued", "model": vg.model}, status=202)
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async def _run_video_job(self, job_id: str, vg: VideoGenerationRequest) -> None:
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self.jobs[job_id]["status"] = "running"
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try:
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out = await self.engine.generate(vg.to_omni())
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self.served += 1
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self.jobs[job_id].update(status="completed", result=_ser_artifacts(out),
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metrics={k: round(v, 6) for k, v in out.metrics.items()})
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except Exception as e: # noqa: BLE001
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self.jobs[job_id].update(status="failed", error=str(e))
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def _video_get(self, job_id: str) -> Response:
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job = self.jobs.get(job_id)
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if job is None:
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return Response.json({"error": f"no such job {job_id}"}, status=404)
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return Response.json(job)
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async def _video_sync(self, req: Request) -> Response:
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vg = VideoGenerationRequest.from_json(req.json())
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out = await self.engine.generate(vg.to_omni())
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self.served += 1
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return Response.json({"model": vg.model, "data": _ser_artifacts(out),
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"metrics": {k: round(v, 6) for k, v in out.metrics.items()}})
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# --- chat: SSE stream or JSON ------------------------------------------- #
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async def _chat(self, req: Request) -> Response:
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cc = ChatCompletionRequest.from_json(req.json())
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omni = cc.to_omni()
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if cc.stream:
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return Response.sse(self._chat_sse(omni, cc.model))
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out = await self.engine.generate(omni)
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self.served += 1
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arts = _ser_artifacts(out)
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content = arts.get("text", {}).get("text", "") if "text" in arts else ""
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return Response.json({
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"id": f"chatcmpl-{next(_chat_ctr)}", "object": "chat.completion",
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"created": int(time.time()), "model": cc.model,
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"choices": [{"index": 0, "finish_reason": "stop",
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"message": {"role": "assistant", "content": content}}],
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"omni": arts, "metrics": {k: round(v, 6) for k, v in out.metrics.items()}})
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async def _chat_sse(self, omni: Any, model: str):
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import json
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cid = f"chatcmpl-{next(_chat_ctr)}"
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async for ev in self.engine.submit(omni): # live OmniEvents → SSE chunks
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chunk = {"id": cid, "object": "chat.completion.chunk", "model": model,
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"event": ev.type, "data": ev.payload}
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yield f"data: {json.dumps(chunk, default=_json_default)}\n\n"
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self.served += 1
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out = self.engine.result(omni.request_id)
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arts = _ser_artifacts(out) if out is not None else {}
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content = arts.get("text", {}).get("text", "") if "text" in arts else ""
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final = {"id": cid, "object": "chat.completion.chunk", "model": model,
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"choices": [{"index": 0, "delta": {"role": "assistant", "content": content},
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"finish_reason": "stop"}], "omni": arts}
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yield f"data: {json.dumps(final, default=_json_default)}\n\n"
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yield "data: [DONE]\n\n"
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# --- lifecycle ---------------------------------------------------------- #
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async def serve(self, host: str = "127.0.0.1", port: int = 0) -> tuple[str, int]:
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return await self.http.start(host, port)
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async def close(self) -> None:
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for t in list(self._tasks): # cancel + drain in-flight video jobs on shutdown
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t.cancel()
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for t in list(self._tasks):
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try:
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await t
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except (asyncio.CancelledError, Exception): # noqa: BLE001
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pass
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await self.http.close()
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