unsloth/studio/backend/utils/model_memory_settings.py

# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0

"""Persisted model-memory residency controls.

``keep_resident``  -- weights never go back to system RAM while loaded: no idle
auto-unload, and ``--mlock`` so the OS cannot page them out and re-fault them in.

``no_ram_reserve`` -- no full host-RAM copy: keeps llama.cpp's default mmap path
and drops ``--no-mmap`` / ``--mlock``.

Both on means "live in VRAM, keep no RAM copy, never idle-unload". ``--mlock`` is
itself a full-model RAM reservation, so ``no_ram_reserve`` wins on that flag.
"""

from __future__ import annotations

import threading
import time
from typing import Any, Optional

KEEP_RESIDENT_SETTING_KEY = "model_memory_keep_resident"
NO_RAM_RESERVE_SETTING_KEY = "model_memory_no_ram_reserve"

DEFAULT_KEEP_RESIDENT = False
DEFAULT_NO_RAM_RESERVE = False

# Read on the load path and every idle poll, so memo briefly to spare SQLite.
# Matches openai_auto_switch_settings.
_CACHE_TTL_S = 2.0
_cache_lock = threading.Lock()
_cache: dict[str, tuple[float, Any]] = {}
# Bumped on every write. A read that began before a write must not fill the
# cache with the value it already fetched, or the new setting would appear to
# revert for the rest of the TTL and a load could launch contradicting it.
_generation: dict[str, int] = {}


def _coerce_bool(value: Any) -> Optional[bool]:
    if isinstance(value, bool):
        return value
    if isinstance(value, str):
        normalized = value.strip().lower()
        if normalized in {"1", "true", "yes", "on"}:
            return True
        if normalized in {"0", "false", "no", "off", ""}:
            return False
    return None


# A write racing a read is rare, so a couple of retries always converges. The
# bound only exists so a pathological write storm cannot spin here forever.
_MAX_REREADS = 3


def _cached_setting(key: str) -> Any:
    for _attempt in range(_MAX_REREADS):
        with _cache_lock:
            hit = _cache.get(key)
            if hit is not None and time.monotonic() - hit[0] < _CACHE_TTL_S:
                return hit[1]
            generation = _generation.get(key, 0)
        try:
            from storage.studio_db import get_app_setting
            stored = get_app_setting(key, None)
        except Exception:
            # An unreadable DB must not fail a load; fall back to the default.
            return None
        with _cache_lock:
            if _generation.get(key, 0) == generation:
                _cache[key] = (time.monotonic(), stored)
                return stored
        # A write committed while this read was in flight, so `stored` predates
        # it. Returning it would let a load launch with flags contradicting the
        # setting that was just saved, so read again against the new generation.
    return stored


def _invalidate(*keys: str) -> None:
    """Drop these keys in ONE acquisition. The write commits the pair in one
    transaction, so invalidating them separately would let a load in between read
    a new keep_resident against a cached old no_ram_reserve and emit --mlock for
    a combination that was never stored."""
    with _cache_lock:
        for key in keys:
            _cache.pop(key, None)
            _generation[key] = _generation.get(key, 0) + 1


def get_keep_resident() -> bool:
    """True when the loaded model must stay in GPU memory while it is loaded."""
    parsed = _coerce_bool(_cached_setting(KEEP_RESIDENT_SETTING_KEY))
    return parsed if parsed is not None else DEFAULT_KEEP_RESIDENT


def get_no_ram_reserve() -> bool:
    """True when no full host-RAM copy of the weights may be held."""
    parsed = _coerce_bool(_cached_setting(NO_RAM_RESERVE_SETTING_KEY))
    return parsed if parsed is not None else DEFAULT_NO_RAM_RESERVE


def should_mlock() -> bool:
    """Whether to pass ``--mlock``.

    mlock pins the whole model in host RAM, so it is emitted only when residency
    is on and no-reserve is off. The two conflict, and no-reserve wins.
    """
    keep_resident, no_ram_reserve = get_model_memory_settings()
    return keep_resident and not no_ram_reserve


def _pair_generations() -> tuple[int, int]:
    with _cache_lock:
        return (
            _generation.get(KEEP_RESIDENT_SETTING_KEY, 0),
            _generation.get(NO_RAM_RESERVE_SETTING_KEY, 0),
        )


def get_model_memory_settings() -> tuple[bool, bool]:
    """``(keep_resident, no_ram_reserve)`` from ONE coherent snapshot.

    Read one after the other, a save landing in between returns a pair that was
    never stored, and the launch then strips for one setting while locking for
    the other. The write drops both keys in a single acquisition, so a bumped
    generation on either side is enough to spot it and read again.
    """
    pair = (get_keep_resident(), get_no_ram_reserve())
    for _attempt in range(_MAX_REREADS):
        before = _pair_generations()
        pair = (get_keep_resident(), get_no_ram_reserve())
        if _pair_generations() == before:
            return pair
    return pair


def set_model_memory_settings(
    keep_resident: Any = None, no_ram_reserve: Any = None
) -> tuple[bool, bool]:
    """One-transaction write; ``None`` leaves a stored value untouched."""
    updates: dict[str, bool] = {}

    if keep_resident is not None:
        parsed = _coerce_bool(keep_resident)
        if parsed is None:
            raise ValueError("Keep model in GPU memory must be true or false.")
        updates[KEEP_RESIDENT_SETTING_KEY] = parsed

    if no_ram_reserve is not None:
        parsed = _coerce_bool(no_ram_reserve)
        if parsed is None:
            raise ValueError("Do not reserve system RAM must be true or false.")
        updates[NO_RAM_RESERVE_SETTING_KEY] = parsed

    if updates:
        from storage.studio_db import upsert_app_settings
        upsert_app_settings(updates)
        _invalidate(*updates)

    return get_keep_resident(), get_no_ram_reserve()


def memlock_limit_bytes() -> Optional[int]:
    """Soft RLIMIT_MEMLOCK, or None when unlimited or unavailable.

    mlock cannot exceed this. Linux commonly defaults to 8 MB, where llama.cpp
    logs "failed to mlock" and carries on, so residency would silently do
    nothing. None on Windows (no RLIMIT_MEMLOCK) and on macOS (unlimited).
    """
    try:
        import resource
    except ImportError:
        return None
    try:
        soft, _hard = resource.getrlimit(resource.RLIMIT_MEMLOCK)
    except (AttributeError, ValueError, OSError):
        return None
    if soft < 0 or soft == resource.RLIM_INFINITY:
        return None
    return int(soft)