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210 lines
6.5 KiB
Python
210 lines
6.5 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
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"""Parent-side training Xet->HTTP fallback: a model-load stall respawns the
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worker once with Xet disabled, preserving the DB run row. Driven via
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_handle_event with a fake spawn context; no GPU, no network, no real subprocess.
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"""
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from __future__ import annotations
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import contextlib
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import logging
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import queue
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import sys
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import threading
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import types as _types
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from pathlib import Path
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import pytest
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_BACKEND_DIR = str(Path(__file__).resolve().parent.parent)
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if _BACKEND_DIR not in sys.path:
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sys.path.insert(0, _BACKEND_DIR)
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# Stub the heavy module-level imports of core/training/training.py so it imports
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# under CPU-only/no-network, then restore them (see the restore loop below).
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_SAVED: dict = {}
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def _stub(name, mod):
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_SAVED[name] = sys.modules.get(name)
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sys.modules[name] = mod
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_lg = _types.ModuleType("loggers")
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_lg.get_logger = lambda name: logging.getLogger(name)
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_stub("loggers", _lg)
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_stub("structlog", _types.ModuleType("structlog"))
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_mpl = _types.ModuleType("matplotlib")
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_plt = _types.ModuleType("matplotlib.pyplot")
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_plt.Figure = type("Figure", (), {}) # referenced in a class-def annotation
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_mpl.pyplot = _plt
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_stub("matplotlib", _mpl)
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_stub("matplotlib.pyplot", _plt)
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_hw = _types.ModuleType("utils.hardware")
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_hw.prepare_gpu_selection = lambda *a, **k: (None, None)
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_stub("utils.hardware", _hw)
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_npl = _types.ModuleType("utils.native_path_leases")
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_npl.native_path_secret_removed_for_child_start = lambda: contextlib.nullcontext()
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_npl.run_without_native_path_secret = lambda fn: fn
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_stub("utils.native_path_leases", _npl)
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_pth = _types.ModuleType("utils.paths")
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_pth.outputs_root = lambda *a, **k: "/tmp/outputs"
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_stub("utils.paths", _pth)
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import core.training.training as training_mod
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from core.training.training import TrainingBackend
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# Restore every stubbed module so this file never pollutes the shared session: a
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# leaked bare ``structlog`` (no ``get_logger``) would break every later module
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# that logs at import. training_mod already bound the stubs it needs at runtime.
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for _name in (
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"loggers",
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"structlog",
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"matplotlib",
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"matplotlib.pyplot",
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"utils.hardware",
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"utils.native_path_leases",
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"utils.paths",
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):
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_prev = _SAVED.get(_name)
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if _prev is None:
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sys.modules.pop(_name, None)
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else:
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sys.modules[_name] = _prev
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@pytest.fixture(autouse = True)
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def _stub_worker_module():
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"""Stub ``core.training.worker`` so the respawn's lazy import of the
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torch-heavy worker is never required."""
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prev = sys.modules.get("core.training.worker")
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stub = _types.ModuleType("core.training.worker")
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stub.run_training_process = lambda **kwargs: None
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sys.modules["core.training.worker"] = stub
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yield
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if prev is None:
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sys.modules.pop("core.training.worker", None)
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else:
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sys.modules["core.training.worker"] = prev
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class _FakeProc:
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def __init__(self, **kwargs):
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self._alive = True
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self.pid = 4321
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self.kwargs = kwargs
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def start(self):
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pass
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def is_alive(self):
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return self._alive
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def terminate(self):
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self._alive = False
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def kill(self):
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self._alive = False
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def join(self, timeout = None):
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self._alive = False
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class _FakeQueue:
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def put(self, *a, **k):
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pass
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def get(self, *a, **k):
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raise queue.Empty
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class _FakeCtx:
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def __init__(self):
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self.spawned: list = []
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def Queue(self):
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return _FakeQueue()
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def Process(self, **kwargs):
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self.spawned.append(kwargs)
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return _FakeProc(**kwargs)
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def _backend_mid_load():
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b = TrainingBackend()
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b._last_full_config = {"model_name": "org/model", "disable_xet": False, "hf_token": "tok"}
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b._in_model_load = True
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b._xet_fallback_used = False
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proc = _FakeProc()
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b._proc = proc
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return b, proc
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def test_stall_during_load_arms_respawn_and_terminates_worker():
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b, proc = _backend_mid_load()
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b._handle_event({"type": "stall", "message": "no progress for 180s"})
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assert b._needs_xet_respawn is True
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assert b._xet_fallback_used is True
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assert proc.is_alive() is False, "stalled worker must be terminated"
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def test_respawn_uses_disable_xet_and_preserves_run_row(monkeypatch):
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b, _ = _backend_mid_load()
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b._handle_event({"type": "stall", "message": "x"})
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fake_ctx = _FakeCtx()
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monkeypatch.setattr(training_mod, "_CTX", fake_ctx)
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monkeypatch.setattr(b, "_pump_loop", lambda: None) # neutralize the new pump
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created = {"n": 0}
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finalized = {"n": 0}
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monkeypatch.setattr(
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b, "_ensure_db_run_created", lambda: created.__setitem__("n", created["n"] + 1)
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)
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monkeypatch.setattr(
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b, "_finalize_run_in_db", lambda **k: finalized.__setitem__("n", finalized["n"] + 1)
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)
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b._respawn_worker_disable_xet()
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assert len(fake_ctx.spawned) == 1, "respawn must start exactly one worker"
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cfg = fake_ctx.spawned[0]["kwargs"]["config"]
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assert cfg["disable_xet"] is True, "respawned worker must run with Xet disabled"
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assert cfg["model_name"] == "org/model"
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assert created["n"] == 0, "respawn must not recreate the DB run row"
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assert finalized["n"] == 0, "a successful respawn must not finalize the run as error"
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def test_second_stall_surfaces_error_without_respawn():
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b, proc = _backend_mid_load()
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b._xet_fallback_used = True # HTTP fallback already spent
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b._handle_event({"type": "stall", "message": "stalled again over http"})
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assert b._needs_xet_respawn is False
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assert b._progress.error and "stalled" in b._progress.error.lower()
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assert proc.is_alive() is False
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def test_model_load_completed_disarms_recovery():
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b, _ = _backend_mid_load()
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b._handle_event({"type": "model_load_completed"})
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assert b._in_model_load is False
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# A stall after the load finished is not a transport stall to recover from.
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b._handle_event({"type": "stall", "message": "post-load"})
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assert b._needs_xet_respawn is False
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def test_model_load_started_arms_recovery_window():
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b = TrainingBackend()
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assert b._in_model_load is False
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b._handle_event({"type": "model_load_started"})
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assert b._in_model_load is True
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def test_child_should_disable_xet_truth_table():
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from utils.hf_xet_fallback import child_should_disable_xet
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assert child_should_disable_xet({"disable_xet": True}) is True
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assert child_should_disable_xet({"disable_xet": False}) is False
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assert child_should_disable_xet({}) is False
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