mirror of
https://github.com/agent0ai/agent-zero.git
synced 2026-10-19 15:33:54 +00:00
cc098d3e98
Reuse Git version metadata within each chat-load operation and pass saved message token counts into reconstruction. Preserve per-agent settings and credential reads, initialization hooks, profiles and legacy token behavior. Give the existing deferred embedding preload up to 10 extra seconds after chat restoration, within extensible init_a0. Continue startup on timeout or task failure while allowing unfinished model loading to complete. Across five trials per version with the same chat workload and a cached embedding model, median HTTP readiness after the restart command returned fell from 38.1 to 27.8 seconds: 10.3 seconds saved, or 27% less waiting. Boot-to-first-token fell from 42.7 to 33.4 seconds. Post-send first-token latency did not improve in this sample (4.709 to 5.108 seconds). Validation: 146 focused framework tests passed, followed by 20 preload tests including seven new edge cases. Real ten-second stalled-loading/inference checks and offline missing/cached-model checks passed. Five DeferredTask lifecycle tests passed separately; an existing test-order failure when combined with preload imports also reproduces without the new cases. Fresh-install performance was not measured; the full repository suite was not run.
486 lines
17 KiB
Python
486 lines
17 KiB
Python
from concurrent.futures import ThreadPoolExecutor
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import os
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from pathlib import Path
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import subprocess
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import sys
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import threading
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import time
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from types import SimpleNamespace
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import pytest
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import models
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def test_framework_imports_do_not_load_embedding_or_speech_runtimes():
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result = subprocess.run(
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[sys.executable, "-c", """
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import sys
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import models
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import preload
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for name in (
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'sentence_transformers', 'torch', 'whisper',
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'plugins._kokoro_tts.helpers.runtime',
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'plugins._whisper_stt.helpers.runtime',
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):
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assert name not in sys.modules, name
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"""],
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cwd=Path(__file__).resolve().parents[1],
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env={**os.environ, "LITELLM_LOCAL_MODEL_COST_MAP": "True"},
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capture_output=True,
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text=True,
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timeout=60,
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)
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assert result.returncode == 0, result.stderr
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def _clear_local_embedding_models():
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with models._LOCAL_EMBEDDING_MODELS_LOCK:
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models._LOCAL_EMBEDDING_MODELS.clear()
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@pytest.mark.parametrize("skip_init", [False, True])
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def test_runtime_preload_overlaps_chat_restoration_within_extension_hooks(monkeypatch, skip_init):
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import run_ui
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calls = []
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def extension_hook(point, agent, data):
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calls.append(point.rsplit("/", 1)[-1])
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if skip_init and point.endswith("/start"):
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data["result"] = None
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monkeypatch.setattr(run_ui.extension, "call_extensions_sync", extension_hook)
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def preload():
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calls.append("preload")
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def wait(timeout):
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assert timeout == 10
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calls.append("preload_ready")
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return SimpleNamespace(result_sync=wait)
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monkeypatch.setattr(run_ui.initialize, "initialize_preload", preload)
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monkeypatch.setattr(
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run_ui.initialize, "initialize_chats",
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lambda: SimpleNamespace(result_sync=lambda: calls.append("chats")),
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)
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monkeypatch.setattr(run_ui.initialize, "initialize_mcp", lambda: calls.append("mcp"))
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monkeypatch.setattr(run_ui.initialize, "initialize_job_loop", lambda: calls.append("jobs"))
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run_ui.init_a0()
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assert calls == (["start", "end"] if skip_init else [
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"start", "preload", "chats", "preload_ready", "mcp", "jobs", "end",
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])
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@pytest.mark.parametrize("error", [None, TimeoutError(), RuntimeError("preload failed")])
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def test_runtime_preload_wait_remains_best_effort(monkeypatch, error):
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import run_ui
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calls = []
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errors = []
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def wait(timeout):
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assert timeout == 10
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raise error
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monkeypatch.setattr(run_ui.extension, "call_extensions_sync", lambda *_args, **_kwargs: None)
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monkeypatch.setattr(
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run_ui.initialize, "initialize_preload",
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lambda: SimpleNamespace(result_sync=wait) if error is not None else None,
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)
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monkeypatch.setattr(
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run_ui.initialize, "initialize_chats",
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lambda: SimpleNamespace(result_sync=lambda: calls.append("chats")),
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)
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monkeypatch.setattr(run_ui.initialize, "initialize_mcp", lambda: calls.append("mcp"))
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monkeypatch.setattr(run_ui.initialize, "initialize_job_loop", lambda: calls.append("jobs"))
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monkeypatch.setattr(run_ui, "PrintStyle", lambda: SimpleNamespace(error=errors.append))
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run_ui.init_a0()
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assert calls == ["chats", "mcp", "jobs"]
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assert errors == (["Error waiting for preload: preload failed"] if isinstance(error, RuntimeError) else [])
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@pytest.mark.parametrize("blocked_stage", ["construction", "encoding"])
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def test_startup_continues_after_ten_seconds_with_model_still_loading(monkeypatch, blocked_stage, record_property):
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import preload
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import run_ui
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from helpers import persist_chat
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from helpers.defer import EventLoopThread
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from plugins._model_config.helpers import model_config
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release = threading.Event()
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blocked = threading.Event()
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chats_loaded = threading.Event()
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warmed = threading.Event()
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preload_done = threading.Event()
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later_startup = []
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errors = []
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constructions = []
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config = models.ModelConfig(models.ModelType.EMBEDDING, "huggingface", "sentence-transformers/timeout-test")
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def extension_hook(point, agent, data):
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if point.endswith("/initialize_preload/end"):
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data["result"].add_done_callback(lambda _: preload_done.set())
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def hold():
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blocked.set()
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assert release.wait(20), "Test did not release the model operation"
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class HeldModel:
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def __init__(self, name, **kwargs):
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constructions.append(name)
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if blocked_stage == "construction":
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hold()
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def encode(self, inputs, **kwargs):
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if blocked_stage == "encoding":
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hold()
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warmed.set()
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return [[1.0] for _ in inputs]
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monkeypatch.setitem(sys.modules, "sentence_transformers", SimpleNamespace(SentenceTransformer=HeldModel))
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monkeypatch.setattr(model_config, "get_embedding_model_config_object", lambda: config)
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monkeypatch.setattr(models, "get_provider_config", lambda *_args: None)
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monkeypatch.setattr(models, "get_api_key_raw", lambda *_args: "None")
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monkeypatch.setattr(run_ui.extension, "call_extensions_sync", extension_hook)
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monkeypatch.setattr(persist_chat, "load_tmp_chats", chats_loaded.set)
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monkeypatch.setattr(run_ui.initialize, "initialize_mcp", lambda: later_startup.append("mcp"))
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monkeypatch.setattr(run_ui.initialize, "initialize_job_loop", lambda: later_startup.append("jobs"))
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monkeypatch.setattr(preload, "PrintStyle", lambda: SimpleNamespace(print=lambda _: None, error=errors.append))
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monkeypatch.setattr(run_ui, "PrintStyle", lambda: SimpleNamespace(error=errors.append))
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_clear_local_embedding_models()
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# Release blocked work even if a regression removes the startup timeout.
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with ThreadPoolExecutor(max_workers=1) as pool:
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started = time.monotonic()
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startup = pool.submit(run_ui.init_a0)
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try:
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assert blocked.wait(3)
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assert chats_loaded.wait(3)
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startup.result(timeout=12)
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elapsed = time.monotonic() - started
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record_property("startup_wait_seconds", round(elapsed, 3))
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assert 9.5 <= elapsed < 13, elapsed
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assert later_startup == ["mcp", "jobs"]
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assert not warmed.is_set()
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assert not release.is_set()
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release.set()
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assert preload_done.wait(3)
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assert warmed.is_set()
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runtime_model = models.get_embedding_model(config.provider, config.name, model_config=config)
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assert runtime_model.embed_query("first message") == [1.0]
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assert constructions == ["timeout-test"]
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assert not errors
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finally:
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release.set()
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startup.result(timeout=12)
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assert preload_done.wait(3)
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EventLoopThread().terminate()
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_clear_local_embedding_models()
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@pytest.mark.parametrize("scenario", ["cached", "missing", "broken", "remote", "busy_restoration"])
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def test_real_preload_startup_handles_ready_or_unavailable_models(monkeypatch, scenario, record_property):
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import preload
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import run_ui
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from helpers import persist_chat
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from helpers.defer import EventLoopThread
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from plugins._model_config.helpers import model_config
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chats_started = threading.Event()
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release_chats = threading.Event()
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model_constructed = threading.Event()
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preload_done = threading.Event()
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constructions = []
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errors = []
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calls = []
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config = models.ModelConfig(
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models.ModelType.EMBEDDING,
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"openai" if scenario == "remote" else "huggingface",
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"sentence-transformers/startup-test",
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)
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class Model:
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def __init__(self, name, **kwargs):
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constructions.append(name)
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if scenario in {"missing", "broken"}:
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raise OSError(scenario + " model")
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model_constructed.set()
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def encode(self, inputs, **kwargs):
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return [[1.0] for _ in inputs]
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def restore_chats():
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chats_started.set()
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if scenario == "busy_restoration":
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assert release_chats.wait(5)
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def extension_hook(point, agent, data):
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if point.endswith("/initialize_preload/end"):
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data["result"].add_done_callback(lambda _: preload_done.set())
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monkeypatch.setitem(sys.modules, "sentence_transformers", SimpleNamespace(SentenceTransformer=Model))
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monkeypatch.setattr(models, "get_provider_config", lambda *_args: None)
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monkeypatch.setattr(models, "get_api_key_raw", lambda *_args: "None")
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monkeypatch.setattr(model_config, "get_embedding_model_config_object", lambda: config)
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monkeypatch.setattr(run_ui.extension, "call_extensions_sync", extension_hook)
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monkeypatch.setattr(persist_chat, "load_tmp_chats", restore_chats)
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monkeypatch.setattr(run_ui.initialize, "initialize_mcp", lambda: calls.append("mcp"))
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monkeypatch.setattr(run_ui.initialize, "initialize_job_loop", lambda: calls.append("jobs"))
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monkeypatch.setattr(preload, "PrintStyle", lambda: SimpleNamespace(print=lambda _: None, error=errors.append))
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monkeypatch.setattr(run_ui, "PrintStyle", lambda: SimpleNamespace(error=errors.append))
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_clear_local_embedding_models()
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if scenario == "cached":
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models.get_embedding_model(config.provider, config.name)
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with ThreadPoolExecutor(max_workers=1) as pool:
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started = time.monotonic()
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startup = pool.submit(run_ui.init_a0)
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try:
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assert chats_started.wait(2)
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if scenario == "busy_restoration":
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assert model_constructed.wait(2)
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assert not startup.done()
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release_chats.set()
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startup.result(timeout=2)
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elapsed = time.monotonic() - started
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record_property("startup_wait_seconds", round(elapsed, 3))
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assert elapsed < 2, elapsed
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assert preload_done.wait(2)
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assert calls == ["mcp", "jobs"]
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assert len(constructions) == (0 if scenario == "remote" else 1)
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assert errors == (["Error in preload: " + scenario + " model"] if scenario in {"missing", "broken"} else [])
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finally:
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release_chats.set()
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startup.result(timeout=12)
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assert preload_done.wait(2)
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EventLoopThread().terminate()
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_clear_local_embedding_models()
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def test_local_embedding_preload_is_reused_with_runtime_model_config(monkeypatch):
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created = []
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class FakeSentenceTransformer:
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def __init__(self, model, **kwargs):
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created.append((model, kwargs))
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monkeypatch.setitem(
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sys.modules, "sentence_transformers",
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SimpleNamespace(SentenceTransformer=FakeSentenceTransformer),
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)
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_clear_local_embedding_models()
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try:
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preload = models.LocalSentenceTransformerWrapper(
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"huggingface",
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"sentence-transformers/example",
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device="cpu",
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model_kwargs={"revision": "stable", "trust_remote_code": False},
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)
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runtime_config = SimpleNamespace(name="runtime")
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runtime = models.LocalSentenceTransformerWrapper(
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"huggingface",
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"sentence-transformers/example",
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model_config=runtime_config,
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model_kwargs={"trust_remote_code": False, "revision": "stable"},
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device="cpu",
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)
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assert runtime.model is preload.model
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assert runtime.a0_model_conf is runtime_config
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assert created == [
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(
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"example",
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{
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"device": "cpu",
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"model_kwargs": {
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"revision": "stable",
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"trust_remote_code": False,
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},
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},
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)
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]
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finally:
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_clear_local_embedding_models()
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def test_local_embedding_cache_tracks_effective_constructor_options(monkeypatch):
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created = []
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class FakeSentenceTransformer:
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def __init__(self, model, **kwargs):
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created.append((model, kwargs))
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monkeypatch.setitem(
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sys.modules, "sentence_transformers",
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SimpleNamespace(SentenceTransformer=FakeSentenceTransformer),
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)
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_clear_local_embedding_models()
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try:
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first = models.LocalSentenceTransformerWrapper(
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"huggingface", "sentence-transformers/example", device="cpu"
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)
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second = models.LocalSentenceTransformerWrapper(
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"huggingface", "sentence-transformers/example", device="cuda"
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)
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assert second.model is not first.model
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assert created == [
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("example", {"device": "cpu"}),
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("example", {"device": "cuda"}),
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]
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assert len(models._LOCAL_EMBEDDING_MODELS) == 1
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finally:
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_clear_local_embedding_models()
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def test_concurrent_preload_and_runtime_share_one_model(monkeypatch):
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created = []
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construction_started = threading.Event()
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release_construction = threading.Event()
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class FakeSentenceTransformer:
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def __init__(self, model, **kwargs):
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created.append((model, kwargs))
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construction_started.set()
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assert release_construction.wait(timeout=2)
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monkeypatch.setitem(
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sys.modules, "sentence_transformers",
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SimpleNamespace(SentenceTransformer=FakeSentenceTransformer),
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)
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_clear_local_embedding_models()
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try:
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with ThreadPoolExecutor(max_workers=2) as executor:
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first = executor.submit(
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models.LocalSentenceTransformerWrapper,
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"huggingface",
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"sentence-transformers/example",
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)
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assert construction_started.wait(timeout=2)
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second = executor.submit(
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models.LocalSentenceTransformerWrapper,
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"huggingface",
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"sentence-transformers/example",
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)
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release_construction.set()
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assert second.result().model is first.result().model
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assert created == [("example", {})]
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finally:
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release_construction.set()
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_clear_local_embedding_models()
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def test_failed_model_change_keeps_the_working_cached_model(monkeypatch):
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created = []
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class FakeSentenceTransformer:
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def __init__(self, model, **kwargs):
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created.append((model, kwargs))
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if model == "broken":
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raise RuntimeError("model unavailable")
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monkeypatch.setitem(
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sys.modules, "sentence_transformers",
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SimpleNamespace(SentenceTransformer=FakeSentenceTransformer),
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)
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_clear_local_embedding_models()
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try:
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working = models.LocalSentenceTransformerWrapper(
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"huggingface", "sentence-transformers/working"
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)
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with pytest.raises(RuntimeError, match="model unavailable"):
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models.LocalSentenceTransformerWrapper(
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"huggingface", "sentence-transformers/broken"
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)
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reused = models.LocalSentenceTransformerWrapper(
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"huggingface", "sentence-transformers/working"
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)
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assert reused.model is working.model
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assert created == [("working", {}), ("broken", {})]
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finally:
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_clear_local_embedding_models()
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@pytest.mark.asyncio
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async def test_preload_uses_the_runtime_embedding_configuration(monkeypatch):
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import preload
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from plugins._model_config.helpers import model_config
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config = SimpleNamespace(
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provider="huggingface",
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name="sentence-transformers/example",
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build_kwargs=lambda: {"device": "cpu"},
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)
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calls = []
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embedded = []
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loop_thread = threading.get_ident()
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class FakeEmbeddings:
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async def aembed_query(self, text):
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embedded.append(text)
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def get_embedding_model(provider, name, **kwargs):
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assert threading.get_ident() != loop_thread
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calls.append((provider, name, kwargs))
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return FakeEmbeddings()
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monkeypatch.setattr(
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model_config, "get_embedding_model_config_object", lambda: config
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)
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monkeypatch.setattr(preload.models, "get_embedding_model", get_embedding_model)
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await preload.preload()
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assert calls == [
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(
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"huggingface",
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"sentence-transformers/example",
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{"model_config": config, "device": "cpu"},
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)
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]
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assert embedded == ["test"]
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@pytest.mark.asyncio
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@pytest.mark.parametrize("provider", ["openai", "huggingface"])
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async def test_preload_skips_remote_models_and_contains_local_failures(monkeypatch, provider):
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import preload
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from plugins._model_config.helpers import model_config
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config = SimpleNamespace(provider=provider, name="example", build_kwargs=lambda: {})
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calls = []
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errors = []
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def fail_embedding(*args, **kwargs):
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calls.append(args)
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raise RuntimeError("model unavailable")
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monkeypatch.setattr(model_config, "get_embedding_model_config_object", lambda: config)
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monkeypatch.setattr(models, "get_embedding_model", fail_embedding)
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monkeypatch.setattr(
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preload, "PrintStyle",
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lambda: SimpleNamespace(print=lambda _: None, error=errors.append),
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)
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await preload.preload()
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|
|
if provider == "huggingface":
|
|
assert calls == [("huggingface", "example")]
|
|
assert errors == ["Error in preload: model unavailable"]
|
|
else:
|
|
assert calls == []
|
|
assert errors == []
|