195 lines
6.0 KiB
Python
195 lines
6.0 KiB
Python
"""Composition guard: TaskDispatcher composes a real runtime.task.TaskRunner (task 9.2).
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Runs a non-mocked, stub-driver-backed TaskRunner instance inside
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TaskDispatcher.dispatch()'s goal-based path. Guards against silent drift in
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agent-runtime's public ``run(task) -> Task`` contract this change composes over.
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"""
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from __future__ import annotations
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from datetime import UTC, datetime
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from cloud.config import CloudConfig
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from cloud.dispatch import Assignment, TaskDispatcher
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from cloud.scheduler import ScheduledTask, TaskConstraints
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from cloud.store import CloudStore
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from core.models import Bounds, Scene, SceneElement, Task
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from runtime.executor import Executor, ExecutorConfig
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from runtime.planner import PlannedStep, Planner
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from runtime.task import TaskRunner, TaskRunnerConfig
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from storage.artifact_store import ArtifactStore
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from storage.task_metadata import TaskMetadataStore
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from storage.timeline import Timeline
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from tests.fakes import PNG_10X20
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class _ScriptedPlanner(Planner):
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def __init__(self, steps: list[PlannedStep]) -> None:
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self.steps = steps
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def plan(self, *, goal, scene, context): # type: ignore[override]
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if len(context.step_results) >= len(self.steps):
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return []
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return [self.steps[len(context.step_results)]]
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def goal_reached(self, *, goal, scene, context): # type: ignore[override]
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return len(context.step_results) >= len(self.steps) and all(
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result.success for result in context.step_results
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)
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def _scene() -> Scene:
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return Scene(
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width=10,
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height=20,
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elements=[
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SceneElement(
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id="search",
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type="input",
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text="Search",
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bounds=Bounds(1, 2, 4, 4),
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)
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],
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)
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def _real_task_runner(tmp_path) -> TaskRunner:
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planner = _ScriptedPlanner(
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[
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PlannedStep(action="tap", description="tap search", args={"x": 3, "y": 4}),
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]
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)
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executor = Executor(
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tools={"tap": lambda **kwargs: {"ok": True, **kwargs}},
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config=ExecutorConfig(max_retries=1, backoff_seconds=0),
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)
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metadata = TaskMetadataStore(tmp_path / "tasks.sqlite3")
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timeline = Timeline(ArtifactStore(tmp_path / "history"))
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return TaskRunner(
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planner=planner,
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executor=executor,
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metadata_store=metadata,
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timeline=timeline,
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config=TaskRunnerConfig(max_steps=3),
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observer=lambda device_id: _scene(),
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screenshot_provider=lambda device_id: PNG_10X20,
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)
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def _config() -> CloudConfig:
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return CloudConfig(
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sync_interval_seconds=30,
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stale_after_seconds=60,
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max_queue_depth=100,
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default_assignment_strategy="fifo_match",
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api_version_prefix="/v1",
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db_path="cloud/cloud.sqlite3",
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)
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def test_dispatcher_runs_real_task_runner_to_completion(tmp_path) -> None:
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store = CloudStore(tmp_path / "cloud.sqlite3")
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runner = _real_task_runner(tmp_path)
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dispatcher = TaskDispatcher(
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local_host_id="host-local",
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task_runner_factory=lambda: runner,
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workflow_runner_factory=lambda: None,
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store=store,
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)
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# Enqueue a ScheduledTask in 'assigned' state (the precondition for dispatch).
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task_id = "task-real"
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store.enqueue_task(
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ScheduledTask(
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id=task_id,
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goal="tap the search field",
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workflow_definition_id=None,
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constraints=TaskConstraints(),
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status="assigned",
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created_at=datetime.now(UTC),
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)
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)
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dispatcher.dispatch(
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Assignment(
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task_id=task_id,
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device_id="dev-1",
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host_id="host-local",
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goal="tap the search field",
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workflow_definition_id=None,
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)
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)
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task = store.get_task(task_id)
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assert task is not None
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assert task.status == "done"
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# The TaskRunner must have observed the assignment's device_id.
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# We assert via the executor's recorded outcomes indirectly by confirming
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# the loop drove at least one step (metadata store now has the task as completed).
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def test_dispatcher_propagates_real_failure(tmp_path) -> None:
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"""If the real TaskRunner reports failure, dispatcher records ``failed``."""
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class _AlwaysFailingPlanner(Planner):
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def plan(self, *, goal, scene, context): # type: ignore[override]
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return [
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PlannedStep(action="boom", description="will fail", args={}),
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]
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def goal_reached(self, *, goal, scene, context): # type: ignore[override]
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return False
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executor = Executor(
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tools={
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"boom": lambda **kwargs: (_ for _ in ()).throw(RuntimeError("boom")),
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},
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config=ExecutorConfig(max_retries=1, backoff_seconds=0),
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)
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metadata = TaskMetadataStore(tmp_path / "tasks.sqlite3")
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timeline = Timeline(ArtifactStore(tmp_path / "history"))
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runner = TaskRunner(
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planner=_AlwaysFailingPlanner(),
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executor=executor,
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metadata_store=metadata,
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timeline=timeline,
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config=TaskRunnerConfig(max_steps=1),
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observer=lambda device_id: _scene(),
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screenshot_provider=lambda device_id: PNG_10X20,
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)
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store = CloudStore(tmp_path / "cloud.sqlite3")
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dispatcher = TaskDispatcher(
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local_host_id="host-local",
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task_runner_factory=lambda: runner,
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workflow_runner_factory=lambda: None,
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store=store,
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)
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task_id = "task-fail"
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store.enqueue_task(
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ScheduledTask(
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id=task_id,
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goal="doomed",
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workflow_definition_id=None,
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constraints=TaskConstraints(),
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status="assigned",
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created_at=datetime.now(UTC),
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)
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)
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dispatcher.dispatch(
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Assignment(
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task_id=task_id,
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device_id="dev-1",
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host_id="host-local",
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goal="doomed",
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workflow_definition_id=None,
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)
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)
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task = store.get_task(task_id)
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assert task is not None
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assert task.status == "failed"
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