unsloth/tests/conftest.py
Daniel Han a6dc10dad2
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Reduce and tighten comments and docstrings across the test suite (#6429)
* Reduce and tighten comments and docstrings in tests

Shorten verbose comments and docstrings across the test suite without
changing any test logic. Remove narration that restates the next line,
collapse long module and test docstrings to a single line, and drop banner
separators. Keep regression context (issue and PR references, run ids),
skip reasons, mocking and timing rationale, license headers, lint and type
directives, and commented-out code.

Comments and docstrings only: an AST signature check confirms no code,
assertions, or string literals changed, and the suite byte-compiles cleanly.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
2026-06-18 01:07:09 -07:00

148 lines
5 KiB
Python

# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved.
"""GPU-free test harness.
unsloth_zoo.device_type calls get_device_type() at import time and raises
NotImplementedError on CI runners with no CUDA/XPU/HIP. Pre-load it under a
mocked torch.cuda.is_available()==True so its @cache permanently captures
"cuda"; on a real accelerator the pre-load is skipped.
Mirrors the conftest harness in unslothai/unsloth-zoo PR #624.
"""
from __future__ import annotations
import importlib.util
import os
import sys
import types
def _has_real_accelerator() -> bool:
try:
import torch
except Exception:
return False
for probe in (
lambda: hasattr(torch, "cuda") and torch.cuda.is_available(),
lambda: hasattr(torch, "xpu") and torch.xpu.is_available(),
lambda: hasattr(torch, "accelerator") and torch.accelerator.is_available(),
):
try:
if probe():
return True
except Exception:
pass
return False
def _preload_device_type(package: str, prereqs: tuple[str, ...] = ()) -> bool:
"""Pre-load <package>.device_type under a mocked is_available()==True so its
@cache captures "cuda"; prereqs are submodules to load first (e.g. 'utils').
Returns False if anything is unimportable, so the caller falls back to a stub."""
target = f"{package}.device_type"
if target in sys.modules:
return True
pkg_spec = importlib.util.find_spec(package)
if pkg_spec is None or not pkg_spec.submodule_search_locations:
return False
pkg_path = pkg_spec.submodule_search_locations[0]
skeleton_already = package in sys.modules
if not skeleton_already:
skel = types.ModuleType(package)
skel.__path__ = [pkg_path]
skel.__spec__ = pkg_spec
skel.__package__ = package
sys.modules[package] = skel
try:
for prereq in prereqs:
full = f"{package}.{prereq}"
if full in sys.modules:
continue
prereq_path = os.path.join(pkg_path, f"{prereq}.py")
prereq_spec = importlib.util.spec_from_file_location(full, prereq_path)
prereq_mod = importlib.util.module_from_spec(prereq_spec)
sys.modules[full] = prereq_mod
prereq_spec.loader.exec_module(prereq_mod)
device_type_path = os.path.join(pkg_path, "device_type.py")
dt_spec = importlib.util.spec_from_file_location(target, device_type_path)
dt_mod = importlib.util.module_from_spec(dt_spec)
sys.modules[target] = dt_mod
import torch
_orig_is_avail = torch.cuda.is_available
torch.cuda.is_available = lambda: True # type: ignore[assignment]
try:
dt_spec.loader.exec_module(dt_mod)
finally:
torch.cuda.is_available = _orig_is_avail
except Exception:
sys.modules.pop(target, None)
return False
finally:
if not skeleton_already:
sys.modules.pop(package, None)
return True
def _patch_torch_cuda_for_import() -> None:
"""Stub the torch.cuda.* probes fired at import time once DEVICE_TYPE is
forced to "cuda"; returning plausible Ampere values lets the import finish
(real-tensor tests still run on CPU)."""
try:
import torch.cuda.memory as _cuda_memory # type: ignore
_cuda_memory.mem_get_info = lambda *a, **k: (0, 80 * 1024**3)
except Exception:
pass
try:
import torch
torch.cuda.get_device_capability = lambda *a, **k: (8, 0)
torch.cuda.is_bf16_supported = lambda *a, **k: True
except Exception:
pass
def _install_device_type_stub(name: str) -> None:
stub = types.ModuleType(name)
stub.DEVICE_TYPE = "cuda"
stub.DEVICE_TYPE_TORCH = "cuda"
stub.DEVICE_COUNT = 1
stub.ALLOW_PREQUANTIZED_MODELS = False
stub.is_hip = lambda: False
stub.get_device_type = lambda: "cuda"
stub.get_device_count = lambda: 1
stub.device_synchronize = lambda *a, **k: None
stub.device_empty_cache = lambda *a, **k: None
stub.device_is_bf16_supported = lambda *a, **k: False
sys.modules[name] = stub
if not _has_real_accelerator():
if not _preload_device_type("unsloth_zoo", prereqs = ("utils",)):
_install_device_type_stub("unsloth_zoo.device_type")
if not _preload_device_type("unsloth"):
_install_device_type_stub("unsloth.device_type")
_patch_torch_cuda_for_import()
# ---------------------------------------------------------------------------
# Apply upstream-drift fixes (vllm/triton/peft) by triggering ``import unsloth``
# (they run at import time in unsloth/import_fixes.py). The harness above lets
# the import survive CPU-only runners; the ImportError is swallowed otherwise.
# ---------------------------------------------------------------------------
def _apply_upstream_import_fixes_for_tests() -> None:
try:
import unsloth # noqa: F401 # runs unsloth/import_fixes.py
except Exception:
pass
_apply_upstream_import_fixes_for_tests()