SolitaryThinker
cb46d63f07
Directly-constructed GenerationRequests marked every schema default as explicit (normalize_generation_request bound the full serialization when _fastvideo_explicit_paths was absent), so request_to_sampling_param overwrote model preset values with schema defaults: FastWan DMD ran 3->50 steps, TurboDiffusion 4->50, stable-audio 100 steps/CFG7 -> 50/CFG1, and 1.3B Wan examples rendered 720x1280x125f with CFG off. Fix at the root: SamplingConfig fields now default to None = "inherit the model preset". None leaves are never bound as explicit paths — for directly-constructed requests (normalize_generation_request) and for parsed YAML/JSON (parse_config treats explicit null as unset, so widening the types cannot let nulls stomp presets either). Dicts emptied by the pruning are dropped too, so None-valued extensions cannot resurface via the live-attribute read in explicit_request_updates. The _SCHEMA_DEFAULT_UPDATES tolerance hack is deleted (an explicitly-set field unsupported by a model's SamplingParam now always fails loudly), and return_state is skipped in the update loop since it is already translated to return_continuation_state. Consumers of raw (pre-resolution) sampling fields are guarded: the streaming server validates operator-pinned default_request.sampling before the multi-minute model boot (run_server) and in build_app; its segment negative_prompt merge preserves an explicit "" clear; the mock server supplies concrete fallbacks; generate_async resolves the preset so its advertised total_steps matches the actual run; the ray-serve gradio demo passes "" to keep its no-negative-prompt default. Stale schema-default claims in video_api/ServeConfig/openai.md docs updated. Tests: test_preset_inheritance.py locks the contract (bare request inherits preset; partial override; explicit value equal to an old schema default wins; negative_prompt None-inherits/""-clears; parsed nulls inherit; None-valued extensions are dropped; unsupported explicit field raises) plus a build_app pin-validation test.
130 lines
4.0 KiB
Python
130 lines
4.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""Mock streaming server — a frontend dev aid.
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Boots the same FastAPI app the real streaming server uses, but backs
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it with :class:`InProcessGpuPool` wrapping a synthetic generator that
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emits pre-baked RGB frames. No GPU or model weights required.
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Use cases:
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* Frontend development without a real model loaded.
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* Integration tests that exercise the WS protocol end-to-end.
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* Reproducing protocol bugs locally.
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Launch: ``python -m fastvideo.entrypoints.streaming.mock_server``.
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"""
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from __future__ import annotations
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import argparse
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import time
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from dataclasses import dataclass
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from typing import Any
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import numpy as np
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from fastvideo.api.schema import (
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ContinuationState,
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GenerationRequest,
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GeneratorConfig,
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SamplingConfig,
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ServeConfig,
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StreamingConfig,
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)
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from fastvideo.entrypoints.streaming.server import build_app
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@dataclass
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class MockGenerator:
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"""Generator stand-in that returns synthetic gradient frames.
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Each call produces one segment worth of frames whose pixels vary by
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a constant derived from the request seed and segment index. Latency
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is configurable via ``sleep_ms`` so the caller can exercise slow-
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generate scenarios without spinning a GPU.
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"""
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sleep_ms: float = 0.0
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def generate(self, request: GenerationRequest) -> dict[str, Any]:
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if self.sleep_ms:
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time.sleep(self.sleep_ms / 1000.0)
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# Requests carry None for unset sampling fields (normally resolved
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# against the model preset); the mock has no model, so fall back
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# to fixed values here.
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width = max(16, request.sampling.width or 16)
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height = max(16, request.sampling.height or 16)
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num_frames = max(1, request.sampling.num_frames or 1)
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seed = request.sampling.seed if request.sampling.seed is not None else 1024
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frames = [_gradient_frame(height, width, idx, seed=seed) for idx in range(num_frames)]
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state = ContinuationState(
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kind="ltx2.v1",
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payload={
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"schema_version": 1,
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"segment_index": 0,
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"source_prompt": request.prompt,
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},
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)
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return {
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"frames": frames,
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"audio_sample_rate": 24000,
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"state": state,
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}
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def _gradient_frame(height: int, width: int, idx: int, *, seed: int) -> np.ndarray:
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base = (idx * 17 + seed * 3) % 256
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row = np.linspace(base, (base + 64) % 256, width, dtype=np.uint8)
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frame = np.tile(row, (height, 1))
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stacked = np.stack([frame, np.roll(frame, 8, axis=1), np.roll(frame, 16, axis=1)], axis=-1)
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return stacked.astype(np.uint8)
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def build_mock_app(*, sleep_ms: float = 0.0):
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"""Build a FastAPI app backed by :class:`MockGenerator`."""
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serve_config = ServeConfig(
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generator=GeneratorConfig(model_path="/models/mock"),
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streaming=StreamingConfig(
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session_timeout_seconds=120,
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generation_segment_cap=6,
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),
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)
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serve_config.default_request.sampling = SamplingConfig(
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num_frames=24,
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height=256,
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width=256,
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fps=24,
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num_inference_steps=1,
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# The mock renders frames from the seed directly (no model, no
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# preset resolution), so it must be concrete here.
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seed=1024,
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)
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return build_app(serve_config, MockGenerator(sleep_ms=sleep_ms))
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def main() -> None: # pragma: no cover - CLI entry
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--host", default="127.0.0.1")
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parser.add_argument("--port", type=int, default=8000)
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parser.add_argument(
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"--sleep-ms",
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type=float,
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default=0.0,
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help="Per-segment artificial latency for testing slow paths",
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)
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args = parser.parse_args()
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import uvicorn
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app = build_mock_app(sleep_ms=args.sleep_ms)
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uvicorn.run(app, host=args.host, port=args.port)
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__all__ = [
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"MockGenerator",
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"build_mock_app",
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"main",
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]
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if __name__ == "__main__": # pragma: no cover - CLI entry
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main()
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