Files
hao-ai-lab-FastVideo/v2/serving/server.py
T
Will Lin 1fe50c0092 [refactor] v2: group flat top-level into planes; isolate vendored under _vendor/
The v2 top level had grown to ~27 dirs + 13 loose files — half of them
single-concept abstraction shells (memory/ 86 LOC, transport/ 222, parity/ 178,
extend/ 226) and half vendored fastvideo code sitting as peers to the actual v2
design. Regroup to mirror README section 3 "Planes & dependency order":

  core/     enums+types, card, loop, program, parity, request, parallel
            (the model-native contracts; no kernels)
  runtime/  + folded-in substrate: cache, memory, transport, extend
            (the import graph shows only runtime consumes them; compile/cudagraph
            already lived here)
  serving/  + deploy/ (products / fleet)
  _vendor/  all copied fastvideo: models, layers, attention, configs, distributed,
            platforms, api, hooks, logging_utils, third_party + fastvideo_args/
            utils/logger/envs/forward_context — internal layout unchanged, still
            mirrors upstream for diffing

28 dirs + 13 root files -> 8 dirs + 6 root files. Pure mechanical move: 947
absolute-import paths rewritten (v2.X -> v2.{core,runtime,serving,_vendor}.X),
boundary-anchored so platform/ (native dispatch) and platforms/ (vendored CUDA
detect) no longer collide and neither does hooks/ vs extend/. Deleted the empty
v2/loader/. README section 3 + 16 updated; stale test count corrected
(34 files/216 tests -> 22 files/143 tests).

Validated: `import v2` stays torch-free; `v2/run_tests.py` and `pytest v2/tests/`
-> 143 passed, 2 skipped on the numpy toy backend. (On a GPU box force the toy
backend with CUDA_VISIBLE_DEVICES="" or detect() picks cuda.) No external importer
changed — examples use the re-exported `from v2 import VideoGenerator`.
2026-07-04 17:15:34 +00:00

276 lines
10 KiB
Python

"""OmniOpenAIServer — OpenAI-compatible worker server (vllm-omni pattern).
Fronts ONE AsyncEngine (the per-pool worker surface). Routes OpenAI-shaped endpoints to typed
OmniRequests, streams chat via SSE, and serves video as an async job (POST returns a job id;
GET polls) plus a /sync variant — mirroring vllm-omni's `/v1/videos` + `/v1/videos/sync`. The fleet
(deploy/fleet.py) or Dynamo routes across multiple such servers/engines above this layer.
Unlike vllm-omni's request-scheduled opaque DIFFUSION stage, the engine underneath is step-scheduled;
the endpoint is a thin shim over the loop scheduler.
"""
from __future__ import annotations
import asyncio
import base64
import contextlib
import itertools
import time
from typing import Any
import numpy as np
from v2.core.request.artifacts import Output
from v2.core.request.streams import StreamChunk
from v2.serving.http import HttpServer, Request, Response
from v2.serving.protocol import ChatCompletionRequest, ImageGenerationRequest, VideoGenerationRequest
def _json_default(o: Any) -> Any:
"""Make SSE event payloads JSON-safe (media.chunk carries a StreamChunk with an array)."""
if isinstance(o, StreamChunk):
shape = list(np.asarray(o.data).shape) if o.data is not None else None
return {
"stream_id": o.stream_id,
"modality": o.modality,
"seq": o.seq,
"preview": o.preview,
"final": o.final,
"shape": shape
}
if isinstance(o, np.ndarray):
return {"shape": list(o.shape), "dtype": str(o.dtype)}
return str(o)
_job_ctr = itertools.count(1)
_chat_ctr = itertools.count(1)
def _payload(art: Any) -> Any:
for attr in ("text", "frames", "samples", "tensor", "latent", "token_ids"):
if hasattr(art, attr) and getattr(art, attr) is not None:
return getattr(art, attr)
return None
def _ser_artifacts(out: Output) -> dict:
res: dict = {}
for name, art in out.artifacts.items():
p = _payload(art)
if isinstance(p, str):
res[name] = {"type": "text", "text": p}
elif p is None:
res[name] = {"type": "empty"}
else:
a = np.asarray(p)
res[name] = {
"type": "tensor",
"shape": list(a.shape),
"dtype": str(a.dtype),
"b64_json": base64.b64encode(np.ascontiguousarray(a).tobytes()).decode()
}
return res
class OmniOpenAIServer:
def __init__(self, engine: Any, *, engine_id: str = "v2", max_jobs: int = 512):
self.engine = engine # an AsyncEngine
self.engine_id = engine_id
self.jobs: dict[str, dict] = {} # video job store
self.http = HttpServer(self.dispatch)
self.served = 0
self.max_jobs = max_jobs
self._tasks: set = set() # tracked video-job tasks (no fire-and-forget leak)
def _evict_jobs(self) -> None:
terminal = ("completed", "failed")
for jid in list(self.jobs):
if len(self.jobs) <= self.max_jobs:
break
if self.jobs[jid].get("status") in terminal:
self.jobs.pop(jid, None)
# --- routing ------------------------------------------------------------ #
async def dispatch(self, req: Request) -> Response:
m, p = req.method, req.path
if m == "GET" and p == "/health":
return Response.json(self._health())
if m == "GET" and p == "/metrics":
return Response.json(self._metrics())
if m == "GET" and p == "/v1/models":
return Response.json(self._models())
if m == "POST" and p == "/v1/images/generations":
return await self._images(req)
if m == "POST" and p == "/v1/videos":
return await self._video_create(req)
if m == "POST" and p == "/v1/videos/sync":
return await self._video_sync(req)
if m == "GET" and p.startswith("/v1/videos/"):
return self._video_get(p.rsplit("/", 1)[-1])
if m == "POST" and p == "/v1/chat/completions":
return await self._chat(req)
return Response.json({"error": f"no route for {m} {p}"}, status=404)
# --- metadata endpoints ------------------------------------------------- #
def _health(self) -> dict:
return {
"status": "healthy",
"engine_id": self.engine_id,
"in_flight": self.engine.in_flight,
"queue_depth": self.engine.queue_depth
}
def _metrics(self) -> dict:
sm = self.engine.engine.metrics
adm = self.engine.engine.admission.metrics
return {
"served": self.served,
"in_flight": self.engine.in_flight,
"stepped_units": sm.stepped_units,
"admitted": adm.admitted,
"deferred": adm.deferred,
"by_kind": dict(adm.by_kind)
}
def _models(self) -> dict:
ids = (list(self.engine.engine._registry) + list(self.engine._disagg) + list(self.engine.engine._workflows)
) # workflows are servables too
return {"object": "list", "data": [{"id": m, "object": "model"} for m in ids]}
# --- images ------------------------------------------------------------- #
async def _images(self, req: Request) -> Response:
ig = ImageGenerationRequest.from_json(req.json())
out = await self.engine.generate(ig.to_omni())
self.served += 1
arts = _ser_artifacts(out)
primary = arts.get("image") or arts.get("video") or arts.get("latents")
return Response.json({
"created": int(time.time()),
"model": ig.model,
"data": [primary] * max(1, ig.n),
"omni": arts,
"metrics": {
k: round(v, 6)
for k, v in out.metrics.items()
}
})
# --- video: async job + poll + sync ------------------------------------- #
async def _video_create(self, req: Request) -> Response:
vg = VideoGenerationRequest.from_json(req.json())
job_id = f"video-{next(_job_ctr)}"
self.jobs[job_id] = {"id": job_id, "status": "queued", "model": vg.model}
t = asyncio.create_task(self._run_video_job(job_id, vg))
self._tasks.add(t)
t.add_done_callback(self._tasks.discard) # tracked, not fire-and-forget
self._evict_jobs()
return Response.json({"id": job_id, "status": "queued", "model": vg.model}, status=202)
async def _run_video_job(self, job_id: str, vg: VideoGenerationRequest) -> None:
self.jobs[job_id]["status"] = "running"
try:
out = await self.engine.generate(vg.to_omni())
self.served += 1
self.jobs[job_id].update(status="completed",
result=_ser_artifacts(out),
metrics={
k: round(v, 6)
for k, v in out.metrics.items()
})
except Exception as e: # noqa: BLE001
self.jobs[job_id].update(status="failed", error=str(e))
def _video_get(self, job_id: str) -> Response:
job = self.jobs.get(job_id)
if job is None:
return Response.json({"error": f"no such job {job_id}"}, status=404)
return Response.json(job)
async def _video_sync(self, req: Request) -> Response:
vg = VideoGenerationRequest.from_json(req.json())
out = await self.engine.generate(vg.to_omni())
self.served += 1
return Response.json({
"model": vg.model,
"data": _ser_artifacts(out),
"metrics": {
k: round(v, 6)
for k, v in out.metrics.items()
}
})
# --- chat: SSE stream or JSON ------------------------------------------- #
async def _chat(self, req: Request) -> Response:
cc = ChatCompletionRequest.from_json(req.json())
omni = cc.to_omni()
if cc.stream:
return Response.sse(self._chat_sse(omni, cc.model))
out = await self.engine.generate(omni)
self.served += 1
arts = _ser_artifacts(out)
content = arts.get("text", {}).get("text", "") if "text" in arts else ""
return Response.json({
"id":
f"chatcmpl-{next(_chat_ctr)}",
"object":
"chat.completion",
"created":
int(time.time()),
"model":
cc.model,
"choices": [{
"index": 0,
"finish_reason": "stop",
"message": {
"role": "assistant",
"content": content
}
}],
"omni":
arts,
"metrics": {
k: round(v, 6)
for k, v in out.metrics.items()
}
})
async def _chat_sse(self, omni: Any, model: str):
import json
cid = f"chatcmpl-{next(_chat_ctr)}"
async for ev in self.engine.submit(omni): # live OmniEvents → SSE chunks
chunk = {"id": cid, "object": "chat.completion.chunk", "model": model, "event": ev.type, "data": ev.payload}
yield f"data: {json.dumps(chunk, default=_json_default)}\n\n"
self.served += 1
out = self.engine.result(omni.request_id)
arts = _ser_artifacts(out) if out is not None else {}
content = arts.get("text", {}).get("text", "") if "text" in arts else ""
final = {
"id": cid,
"object": "chat.completion.chunk",
"model": model,
"choices": [{
"index": 0,
"delta": {
"role": "assistant",
"content": content
},
"finish_reason": "stop"
}],
"omni": arts
}
yield f"data: {json.dumps(final, default=_json_default)}\n\n"
yield "data: [DONE]\n\n"
# --- lifecycle ---------------------------------------------------------- #
async def serve(self, host: str = "127.0.0.1", port: int = 0) -> tuple[str, int]:
return await self.http.start(host, port)
async def close(self) -> None:
for t in list(self._tasks): # cancel + drain in-flight video jobs on shutdown
t.cancel()
for t in list(self._tasks):
with contextlib.suppress(asyncio.CancelledError, Exception):
await t
await self.http.close()