feat: 批量分块流水线、本地模型显存让渡与任务列表分工
批量引擎改为「分块流水线」：视频按 WOV_BATCH_STAGE_GROUP_SIZE（默认 8）分组， 组内按 DAG 拓扑序跑完全部视频（全部 extract → 全部 ASR → 全部翻译 → 全部 ASS） 再进入下一组，本地模型每组只加载一次、卸载一次，而不是每个视频来回加载卸载； 产物仍按组增量落到视频旁。调度器新增 execute_run(run_id, stop_after=节点)： 该节点完成后任务保持 RUNNING 不收尾，下一次调用从产物表跳过已完成节点继续， 用于实现阶段边界。 - nodes/llm.py：翻译节点结束释放本机 Ollama 显存（node 参数 unload_after > LLM_UNLOAD_AFTER > 本机 loopback 端点默认卸载，云端端点不卸载；卸载失败只告警）， 新增 keep_model.flag 语义（阶段内保持常驻）与 release_local_model()； 新增节点内暂停（按批 20 行检查 paused.flag，抛 PauseRequested，调度器保持 PAUSED）。 - src/wov_app/batch.py：分组阶段执行与阶段末统一释放显存；失败视频只在它失败 节点的那个阶段重试（避免 LLM 已常驻时重跑 ASR 抢显存）；任务没有明细时保持 QUEUED 等登记完成、仍有未完成视频时置回 QUEUED 自愈（原先留 RUNNING 会卡死： 引擎只拾取 QUEUED，任务停在“运行中但没人推进”）；无失败视频时删除任务级空目录； 每个阶段开始前清理 paused.flag / keep_model.flag，避免强杀残留影响后续阶段。 - src/wov_app/config.py：新增 WOV_BATCH_STAGE_GROUP_SIZE（设为 1 即旧的每视频全链路）。 - 任务列表与批量页分工：GET /api/runs 默认排除 source=batch（一个批量任务会产生 N 条单视频 run，会把 20 条窗口占满；且任务管理页的暂停/重试/删除对批量 run 语义不成立），需要排查时用 include_batch=1；作为补偿批量页详情新增阶段列 （阶段 i/N · 中文标签，由该视频 run 的 current_node_id 在 DAG 拓扑序中的位置 推导，节点类型映射中文标签）。阶段只有节点边界粒度，句级进度不落库、只在日志。 - 顺带纳入此前未提交的批量僵尸状态恢复：recover_interrupted_batch_jobs 除 RUNNING 外也把「COMPLETED 但仍含未结束视频」的任务置回 QUEUED；fix_zombie_batch_jobs.py 改为按条件扫描并支持 --apply 预览；批量页明细只列本批真正处理过的视频。 测试新增/更新：分块流水线调用顺序（组内按节点跑完再下一组）、每组只释放一次模型、 阶段内保持常驻标志、翻译按批暂停、失败视频不跨阶段推进、任务无明细/中途登记视频时 置回 QUEUED、任务工作空间与残留信号清理、任务列表默认过滤批量 run、详情阶段字段、 前端阶段列渲染；全量 507 passed（唯一失败为既有素材缺失的 integration 用例）。
This commit is contained in:
@@ -8,12 +8,14 @@
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from __future__ import annotations
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import json
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from contextlib import contextmanager
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from pathlib import Path
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import pytest
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from wov_app import registry
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from wov_app.batch import (
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KEEP_MODEL_FLAG,
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MARKER_NAME,
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SUBTITLE_EXTENSIONS,
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VIDEO_EXTENSIONS,
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@@ -26,7 +28,7 @@ from wov_app.batch import (
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scan_videos,
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)
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from wov_app.db import Database
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from wov_sdk.models import WorkflowDefinition
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from wov_sdk.models import InvokeRequest, InvokeResponse, WorkflowDefinition
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@pytest.fixture(autouse=True)
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@@ -70,6 +72,78 @@ def _published_db(tmp_path: Path, workflow_id: str = "wf") -> Database:
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return db
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def _staged_definition() -> WorkflowDefinition:
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"""三节点分阶段链路：prep(echo) → translate(llm) → post(echo)，产物为 srt。"""
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return WorkflowDefinition.from_dict({
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"name": "分阶段流程",
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"version": 1,
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"nodes": [
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{"id": "prep", "node_type": "echo", "params": {"node_tag": "prep"},
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"inputs": {"file_uri": "input.video_uri"}},
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{"id": "translate", "node_type": "llm-translate", "params": {"node_tag": "translate"},
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"inputs": {"file_uri": "prep.file_uri"}},
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{"id": "post", "node_type": "echo", "params": {"node_tag": "post"},
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"inputs": {"file_uri": "translate.file_uri"}},
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],
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"edges": [{"from": "prep", "to": "translate"}, {"from": "translate", "to": "post"}],
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"entry_inputs": {"video_uri": "file"},
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"final_outputs": {"cn_srt": "post.file_uri"},
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})
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def _staged_db(tmp_path: Path, workflow_id: str = "wf") -> Database:
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"""建好已发布的三节点分阶段工作流库。"""
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db = Database(tmp_path / "wov.db")
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db.upsert_workflow({
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"id": workflow_id, "name": "分阶段流程", "description": "", "published": 1,
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"latest_version": 1,
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})
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db.create_workflow_version(workflow_id, 1, _staged_definition().to_dict())
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return db
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@contextmanager
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def _recording_nodes(
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trace: list[tuple[str, str]],
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llm_flag_state: list[bool] | None = None,
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fail_stage: tuple[str, str] | None = None,
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flag_trace: list[tuple[str, bool]] | None = None,
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):
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"""把 echo / llm-translate 节点换成记录调用顺序的假节点（覆盖真实注册表条目）。
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假节点把上游传来的视频名写成 payload.srt 透传给下一节点，因此每个阶段都
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知道自己在处理哪个视频；记录 (节点标签, 视频名)，llm 节点额外记录
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keep_model.flag 是否存在；fail_stage 指定的 (标签, 视频名) 组合返回失败。
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"""
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registry.register_all()
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def handler(request: InvokeRequest) -> InvokeResponse:
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tag = str(request.params.get("node_tag"))
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source = str(request.inputs.get("file_uri") or "")
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if source and Path(source).suffix.lower() in VIDEO_EXTENSIONS:
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# 首阶段的输入就是视频文件，后续阶段拿到的是上一阶段的 payload。
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video_name = Path(source).name
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else:
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video_name = Path(source).read_text(encoding="utf-8").strip() if source else ""
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trace.append((tag, video_name))
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run_root = Path(request.output_dir).parent.parent
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if llm_flag_state is not None and tag == "translate":
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llm_flag_state.append((run_root / KEEP_MODEL_FLAG).exists())
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if flag_trace is not None:
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flag_trace.append((tag, (run_root / KEEP_MODEL_FLAG).exists()))
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if fail_stage == (tag, video_name):
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return InvokeResponse(status="failed", error="模拟阶段失败")
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output_dir = Path(request.output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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output = output_dir / "payload.srt"
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output.write_text(video_name, encoding="utf-8")
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return InvokeResponse(status="completed", outputs={"file_uri": str(output)})
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for node_type in ("echo", "llm-translate"):
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registry.register(registry.get_node(node_type), handler)
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yield
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# ---------------------------------------------------------------------------
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# 扫描与旁挂字幕判定
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# ---------------------------------------------------------------------------
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@@ -412,3 +486,265 @@ def test_worker_start_stop_idempotent(tmp_path: Path) -> None:
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# 验证结果
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assert first is second
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assert worker._thread is None
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def test_worker_processes_recovered_zombie_job(tmp_path: Path, monkeypatch) -> None:
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"""僵尸任务（COMPLETED 但明细仍 PENDING）被恢复后能真正处理完剩余视频。
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曾出现「任务已完成、视频仍未处理」的僵尸状态（完成标记先于视频收尾写出），
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而引擎只拾取 QUEUED：不恢复就永远不会再处理那个视频。
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"""
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# 数据：已发布工作流 + 一个视频；创建任务后伪造成僵尸状态。
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folder = tmp_path / "videos"
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_make_video(folder / "movie.mp4")
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db = _published_db(tmp_path)
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monkeypatch.setattr("wov_app.batch.BATCH_WORK_ROOT", tmp_path / "storage" / "batch")
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registry.register_all()
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job_id = create_job(db, str(folder), "wf")
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db.update_batch_job(job_id, status="COMPLETED", progress=1.0,
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updated_at="2026-09-01T00:00:00+00:00")
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# 测试过程：重启恢复把僵尸任务放回队列，引擎拾起后处理剩余视频。
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db.recover_interrupted_batch_jobs("2026-09-01T01:00:00+00:00")
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worker = BatchWorker(db, interval_seconds=999)
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worker._process_job(db.get_batch_job(job_id))
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# 验证结果：视频真的处理完、产物放到视频旁、任务保持完成。
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item = db.list_batch_videos(job_id)[0]
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assert item["status"] == "COMPLETED"
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assert db.get_batch_job(job_id)["status"] == "COMPLETED"
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assert list(folder.glob("movie.*")), "应在视频旁放置最终产物"
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# ---------------------------------------------------------------------------
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# 引擎：分块流水线（阶段化执行）
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# ---------------------------------------------------------------------------
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def test_worker_runs_grouped_stage_pipeline(tmp_path: Path, monkeypatch) -> None:
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"""分块流水线：组内按节点顺序跑完全部视频，而不是每个视频跑完整链路。"""
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# 数据：3 个视频 + 三节点链路，分组大小 2（前两个一组、第三个一组）。
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folder = tmp_path / "videos"
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for name in ("a.mp4", "b.mp4", "c.mp4"):
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_make_video(folder / name)
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db = _staged_db(tmp_path)
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monkeypatch.setattr("wov_app.batch.BATCH_WORK_ROOT", tmp_path / "storage" / "batch")
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monkeypatch.setattr("wov_app.batch.BATCH_STAGE_GROUP_SIZE", 2)
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trace: list[tuple[str, str]] = []
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# 测试过程：用记录调用顺序的假节点驱动引擎跑一轮。
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with _recording_nodes(trace):
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job_id = create_job(db, str(folder), "wf")
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BatchWorker(db, interval_seconds=999)._process_job(db.get_batch_job(job_id))
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# 验证结果：组1 三个阶段各跑 a/b，再轮到组2 的 c。
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assert trace == [
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("prep", "a.mp4"), ("prep", "b.mp4"),
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("translate", "a.mp4"), ("translate", "b.mp4"),
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("post", "a.mp4"), ("post", "b.mp4"),
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("prep", "c.mp4"), ("translate", "c.mp4"), ("post", "c.mp4"),
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]
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# 三个视频都完成且产物按约定名落到视频旁。
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assert db.get_batch_job(job_id)["status"] == "COMPLETED"
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assert sorted(p.name for p in folder.glob("*.srt")) == ["a.CN.srt", "b.CN.srt", "c.CN.srt"]
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def test_worker_releases_local_llm_once_per_group(tmp_path: Path, monkeypatch) -> None:
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"""LLM 阶段结束由引擎统一释放显存：每组一次，而不是每个视频一次。"""
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# 数据：3 个视频 + 分组 2，记录释放调用与 LLM 调用时的常驻信号状态。
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folder = tmp_path / "videos"
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for name in ("a.mp4", "b.mp4", "c.mp4"):
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_make_video(folder / name)
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db = _staged_db(tmp_path)
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monkeypatch.setattr("wov_app.batch.BATCH_WORK_ROOT", tmp_path / "storage" / "batch")
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monkeypatch.setattr("wov_app.batch.BATCH_STAGE_GROUP_SIZE", 2)
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releases: list[str | None] = []
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monkeypatch.setattr(
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"wov_app.batch.release_local_model",
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lambda model=None: releases.append(model),
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)
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trace: list[tuple[str, str]] = []
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flag_state: list[bool] = []
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# 测试过程
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with _recording_nodes(trace, llm_flag_state=flag_state):
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job_id = create_job(db, str(folder), "wf")
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BatchWorker(db, interval_seconds=999)._process_job(db.get_batch_job(job_id))
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# 验证结果：两组各释放一次；每次 LLM 调用都在“保持常驻”信号下执行；信号已清理。
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assert releases == [None, None]
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assert flag_state == [True, True, True]
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assert not list((tmp_path / "storage").rglob(KEEP_MODEL_FLAG))
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def test_worker_defers_video_failed_in_earlier_stage(tmp_path: Path, monkeypatch) -> None:
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"""上一阶段失败的视频不在后续阶段重跑（避免 LLM 已常驻时重跑 ASR 抢显存）。"""
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# 数据：2 个视频（同一组）+ 三节点链路，prep 阶段让 a 失败。
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folder = tmp_path / "videos"
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for name in ("a.mp4", "b.mp4"):
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_make_video(folder / name)
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db = _staged_db(tmp_path)
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monkeypatch.setattr("wov_app.batch.BATCH_WORK_ROOT", tmp_path / "storage" / "batch")
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monkeypatch.setattr("wov_app.batch.BATCH_STAGE_GROUP_SIZE", 2)
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monkeypatch.setattr("wov_app.batch.release_local_model", lambda model=None: None)
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trace: list[tuple[str, str]] = []
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# 测试过程
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with _recording_nodes(trace, fail_stage=("prep", "a.mp4")):
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job_id = create_job(db, str(folder), "wf")
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BatchWorker(db, interval_seconds=999)._process_job(db.get_batch_job(job_id))
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# 验证结果：a 只在 prep 出现一次并记为 FAILED；b 三阶段跑完并落地产物。
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assert [entry for entry in trace if entry[1] == "a.mp4"] == [("prep", "a.mp4")]
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videos = {Path(v["video_path"]).name: v for v in db.list_batch_videos(job_id)}
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assert videos["a.mp4"]["status"] == "FAILED"
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assert videos["b.mp4"]["status"] == "COMPLETED"
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assert (folder / "b.CN.srt").is_file()
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assert not (folder / "a.CN.srt").exists()
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assert db.get_batch_job(job_id)["failed"] == 1
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# ---------------------------------------------------------------------------
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# 引擎：创建期间拾起任务（明细未登记完）的自愈
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# ---------------------------------------------------------------------------
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def test_worker_requeues_job_without_details(tmp_path: Path, monkeypatch) -> None:
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"""任务行先于明细写入：拾起到无明细的任务时保持 QUEUED，不按空任务收尾。"""
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# 数据：只有任务行、还没写任何视频明细的批量任务。
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db = _published_db(tmp_path)
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monkeypatch.setattr("wov_app.batch.BATCH_WORK_ROOT", tmp_path / "storage" / "batch")
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db.create_batch_job({
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"id": "batch-registering", "folder_path": str(tmp_path), "workflow_id": "wf",
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"recursive": 1, "status": "QUEUED", "progress": 0, "total": 0, "done": 0,
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"failed": 0, "current_video": None, "error": None,
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"created_at": "2026-09-01T00:00:00+00:00", "updated_at": "2026-09-01T00:00:00+00:00",
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})
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# 测试过程
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worker = BatchWorker(db, interval_seconds=999)
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worker._process_job(db.get_batch_job("batch-registering"))
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# 验证结果：任务仍在排队等待登记完成，而不是被标成 COMPLETED。
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assert db.get_batch_job("batch-registering")["status"] == "QUEUED"
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assert db.next_queued_batch_job() is not None
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def test_worker_requeues_job_when_video_registered_mid_pass(tmp_path: Path, monkeypatch) -> None:
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"""明细在引擎处理中途才登记进来：本轮结束后置回 QUEUED，下一轮续跑完成。"""
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# 数据：1 个视频 + 单节点工作流；处理首个阶段时登记第二个视频（模拟创建中拾起）。
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folder = tmp_path / "videos"
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_make_video(folder / "first.mp4")
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db = _published_db(tmp_path)
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monkeypatch.setattr("wov_app.batch.BATCH_WORK_ROOT", tmp_path / "storage" / "batch")
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registry.register_all()
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job_id = create_job(db, str(folder), "wf")
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worker = BatchWorker(db, interval_seconds=999)
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original_stage = worker._run_stage
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injected = {"done": False}
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def stage_with_late_video(*args, **kwargs):
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# 模拟 create_job 仍在写明细：引擎快照之后新视频才出现在数据库里。
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if not injected["done"]:
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injected["done"] = True
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late_video = _make_video(folder / "second.mp4")
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db.create_batch_video({
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"id": "bv_late", "job_id": job_id, "video_path": str(late_video),
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"work_dir": str(tmp_path / "storage" / "batch" / job_id / "bv_late"),
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"run_id": None, "status": "PENDING", "error": None,
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"created_at": "2026-09-01T00:00:01+00:00", "updated_at": "2026-09-01T00:00:01+00:00",
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})
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return original_stage(*args, **kwargs)
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monkeypatch.setattr(worker, "_run_stage", stage_with_late_video)
|
||||
|
||||
# 测试过程：第一轮只看到 first.mp4。
|
||||
worker._process_job(db.get_batch_job(job_id))
|
||||
|
||||
# 验证结果：job 被置回 QUEUED 等待下一轮，新视频还没被处理。
|
||||
assert db.get_batch_job(job_id)["status"] == "QUEUED"
|
||||
statuses = {Path(v["video_path"]).name: v["status"] for v in db.list_batch_videos(job_id)}
|
||||
assert statuses == {"first.mp4": "COMPLETED", "second.mp4": "PENDING"}
|
||||
|
||||
# 测试过程：下一轮引擎拾起后处理剩余视频并收尾。
|
||||
second = BatchWorker(db, interval_seconds=999)
|
||||
second._process_job(db.get_batch_job(job_id))
|
||||
|
||||
# 验证结果：两个视频都完成、任务完成、产物都在视频旁（真实 echo 节点产物为 echo.txt）。
|
||||
statuses = {Path(v["video_path"]).name: v["status"] for v in db.list_batch_videos(job_id)}
|
||||
assert statuses == {"first.mp4": "COMPLETED", "second.mp4": "COMPLETED"}
|
||||
assert db.get_batch_job(job_id)["status"] == "COMPLETED"
|
||||
# 真实 echo 节点的最终产物保留原扩展名（非 .srt/.ass），按视频主名放置。
|
||||
assert list(folder.glob("first.*")) and list(folder.glob("second.*"))
|
||||
|
||||
|
||||
def test_worker_removes_empty_job_workspace_after_completion(tmp_path: Path, monkeypatch) -> None:
|
||||
"""任务全部完成后删掉任务级工作空间目录（每视频工作空间已各自清理）。"""
|
||||
# 数据：一个视频的批量任务。
|
||||
folder = tmp_path / "videos"
|
||||
_make_video(folder / "movie.mp4")
|
||||
db = _published_db(tmp_path)
|
||||
monkeypatch.setattr("wov_app.batch.BATCH_WORK_ROOT", tmp_path / "storage" / "batch")
|
||||
registry.register_all()
|
||||
job_id = create_job(db, str(folder), "wf")
|
||||
|
||||
# 测试过程
|
||||
BatchWorker(db, interval_seconds=999)._process_job(db.get_batch_job(job_id))
|
||||
|
||||
# 验证结果：任务完成，任务级目录（收尾后只剩空壳）被删除。
|
||||
assert db.get_batch_job(job_id)["status"] == "COMPLETED"
|
||||
assert not (tmp_path / "storage" / "batch" / job_id).exists()
|
||||
|
||||
|
||||
def test_worker_keeps_job_workspace_when_video_failed(tmp_path: Path, monkeypatch) -> None:
|
||||
"""有失败视频时保留任务工作空间（失败视频的中间产物供断点重试）。"""
|
||||
# 数据：三节点链路，prep 阶段让视频失败。
|
||||
folder = tmp_path / "videos"
|
||||
_make_video(folder / "a.mp4")
|
||||
db = _staged_db(tmp_path)
|
||||
monkeypatch.setattr("wov_app.batch.BATCH_WORK_ROOT", tmp_path / "storage" / "batch")
|
||||
monkeypatch.setattr("wov_app.batch.BATCH_STAGE_GROUP_SIZE", 2)
|
||||
monkeypatch.setattr("wov_app.batch.release_local_model", lambda model=None: None)
|
||||
|
||||
# 测试过程
|
||||
with _recording_nodes([], fail_stage=("prep", "a.mp4")):
|
||||
job_id = create_job(db, str(folder), "wf")
|
||||
BatchWorker(db, interval_seconds=999)._process_job(db.get_batch_job(job_id))
|
||||
|
||||
# 验证结果：视频失败、任务工作空间仍在（可重试）。
|
||||
assert db.get_batch_job(job_id)["failed"] == 1
|
||||
assert (tmp_path / "storage" / "batch" / job_id).exists()
|
||||
|
||||
|
||||
def test_worker_clears_stale_keep_model_flag_before_stage(tmp_path: Path, monkeypatch) -> None:
|
||||
"""强杀残留的 keep_model.flag 不会带到后续阶段：非 LLM 节点不应看到它。"""
|
||||
# 数据：两节点链路 + 已存在的 run（工作空间里残留强杀时的 keep_model.flag）。
|
||||
folder = tmp_path / "videos"
|
||||
_make_video(folder / "a.mp4")
|
||||
db = _staged_db(tmp_path)
|
||||
work_root = tmp_path / "storage" / "batch"
|
||||
monkeypatch.setattr("wov_app.batch.BATCH_WORK_ROOT", work_root)
|
||||
monkeypatch.setattr("wov_app.batch.BATCH_STAGE_GROUP_SIZE", 8)
|
||||
monkeypatch.setattr("wov_app.batch.release_local_model", lambda model=None: None)
|
||||
job_id = create_job(db, str(folder), "wf")
|
||||
video = db.list_batch_videos(job_id)[0]
|
||||
run_id = "run_stale_flag"
|
||||
db.update_batch_video(video["id"], run_id=run_id, updated_at="2026-09-01T00:00:00+00:00")
|
||||
db.create_run({
|
||||
"id": run_id, "workflow_id": "wf", "workflow_version": 1, "status": "QUEUED",
|
||||
"current_node_id": None, "progress": 0.0, "error": None,
|
||||
"input_uri": str(folder / "a.mp4"), "param_overrides": None, "source": "batch",
|
||||
"created_at": "2026-09-01T00:00:00+00:00", "updated_at": "2026-09-01T00:00:00+00:00",
|
||||
})
|
||||
run_dir = Path(video["work_dir"]) / "runs" / run_id
|
||||
run_dir.mkdir(parents=True, exist_ok=True)
|
||||
(run_dir / KEEP_MODEL_FLAG).write_text("", encoding="utf-8")
|
||||
flag_trace: list[tuple[str, bool]] = []
|
||||
|
||||
# 测试过程
|
||||
with _recording_nodes([], flag_trace=flag_trace):
|
||||
BatchWorker(db, interval_seconds=999)._process_job(db.get_batch_job(job_id))
|
||||
|
||||
# 验证结果：prep（非 LLM）看不到残留标志；translate（LLM 阶段）才写入；
|
||||
# post（非 LLM）不再看到它。
|
||||
assert flag_trace == [("prep", False), ("translate", True), ("post", False)]
|
||||
|
||||
Reference in New Issue
Block a user