unsloth/tests/conftest.py

# 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

# --- torch.compile cache isolation -------------------------------------------------
# Must run before torch is imported anywhere below, so it is here rather than in a
# fixture. See tests/_shared/compile_cache_isolation.py for what it does and why.
import importlib.util as _ilu  # noqa: E402
import pathlib as _pathlib  # noqa: E402

_iso = _pathlib.Path(__file__).resolve()
for _up in _iso.parents:
    _candidate = _up / "tests" / "_shared" / "compile_cache_isolation.py"
    if _candidate.is_file():
        _spec = _ilu.spec_from_file_location("_unsloth_compile_cache_isolation", _candidate)
        _mod = _ilu.module_from_spec(_spec)
        _spec.loader.exec_module(_mod)  # sets the env vars on import
        break
# -----------------------------------------------------------------------------------

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

        # (free, total). Zero free is an exhausted card, which callers that size
        # against it treat as fatal.
        _cuda_memory.mem_get_info = lambda *a, **k: (60 * 1024**3, 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


def _preimport_bitsandbytes() -> None:
    """Bind bitsandbytes against the real torch before the CUDA spoof below.

    `bitsandbytes/__init__.py` runs `if torch.cuda.is_available(): from .backends.cuda
    import ops`, and that module reads `torch._C._cuda_getCurrentRawStream`, which a
    CPU-only torch build does not expose. `_preload_device_type` patches
    `torch.cuda.is_available` to return True, so a bitsandbytes import landing inside
    that window takes the CUDA branch and dies with AttributeError.

    Python then drops `bitsandbytes` from sys.modules but leaves `bitsandbytes.functional`
    and the rest of its submodules cached, so the next import re-executes __init__ against
    those cached submodules, re-binds nothing, and hands back a module with no
    `.functional`. `unsloth/kernels/utils.py` reads `bnb.functional.get_ptr` at module
    scope, so every later `import unsloth` in that process dies with
    "module 'bitsandbytes' has no attribute 'functional'".

    Importing first, outside the window, keeps bitsandbytes on its CPU backend and fully
    usable. Must stay ahead of the `_preload_device_type` calls below.
    """
    try:
        import bitsandbytes  # noqa: F401
    except Exception:
        # A genuinely absent or broken wheel is unsloth's own degradation path.
        pass


if not _has_real_accelerator():
    _preimport_bitsandbytes()
    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()