529 lines
24 KiB
Python
529 lines
24 KiB
Python
"""Live hardware budget probe.
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Budget-source rule: discrete cards may trust the device query (measured honest within rounding);
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unified-memory devices must budget from OS free physical memory minus headroom — their device
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queries have been observed off by 3x in both directions. Every probe here must work under a
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stripped PATH — gateway and service sessions don't inherit the interactive environment.
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"""
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from __future__ import annotations
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from contextlib import suppress
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import csv
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import logging
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import os
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import re
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import shutil
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import subprocess
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import sys
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import time
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from dataclasses import replace
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from pathlib import Path
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from hermes_cli.local_runtime.devices import GGML_DEVICE_GPU
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from hermes_cli.local_runtime.estimator import HardwareBudget
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from hermes_platform.host.facts import gpu_class
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logger = logging.getLogger(__name__)
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_GIB = 1 << 30
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# Reserve carved off the card before any grant: the desktop's own co-residents (compositor,
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# browser, Electron) measure ~2-2.5 GiB, and a window granted into that space demotes silently
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# under WDDM. 7% covers big cards; the 2 GiB floor is what a 512 MiB floor failed to cover (a
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# 221K grant measured 31.9/32.6 GiB with the desktop running — 'fits' by the math, demoted in
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# reality). Small cards give up window to this; spill mode is their path to big models.
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_MARGIN_FLOOR = 2 << 30
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_MARGIN_FRACTION = 0.09
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# Room left on top of what other programs hold when a model launches: a browser tab or a chat app
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# can take another GiB between launch and the next request.
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_LAUNCH_HEADROOM = 1 << 30
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# UMA headroom: on unified-memory machines the model shares physical memory with the OS and every
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# app, so budget from RAM minus this fraction.
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_UMA_HEADROOM_FRACTION = 0.20
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# Engine-fallback gates for the unified-pool quirk — BOTH must hold, and no discrete card can
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# meet either: (1) the allocator's pool exceeds the smi report by well past rounding/ECC slack
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# (discrete cards agree within ~2%; carve-out disagreement runs to whole multiples), and (2) the
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# pool is system-RAM-sized. The driver's INTEGRATED attribute, when readable, bypasses both gates
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# in whichever direction it points.
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_POOL_DISAGREEMENT_FACTOR = 1.5
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_POOL_RAM_FRACTION = 0.75
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# cuDeviceGetAttribute enum: device is integrated with host memory.
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_CU_DEVICE_ATTRIBUTE_INTEGRATED = 18
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_NVIDIA_VENDOR_ID = 0x10DE
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# Engines whose devices the ggml probe reports; the CPU, CUDA and Metal builds answer elsewhere.
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_GPU_ENGINE_BACKENDS = ("vulkan", "hip")
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# One probe per process once a device answers (silicon doesn't change); a miss retries after this
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# long so a runtime installed mid-session gets picked up by the engine fallback.
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_POOL_NEGATIVE_TTL_S = 60.0
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_pool_probe_cache: tuple[float, tuple[int, bool | None] | None] | None = None
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# ' CUDA0: NVIDIA Example Device (1234-core Example GPU) (46464 MiB, 46284 MiB free)'
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# — greedy .* pins the LAST parenthesized group, so device names with parentheses parse.
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_DEVICE_LINE_RE = re.compile(r"CUDA\d+:.*\((\d+)\s*MiB,\s*\d+\s*MiB free\)\s*$")
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def _stdout(*argv: str) -> str:
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return subprocess.run(
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list(argv), capture_output=True, text=True, encoding="utf-8", errors="replace", timeout=5,
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check=False,
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).stdout
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_LINUX_RAM_KEYS = frozenset({"MemTotal", "MemAvailable", "MemFree"})
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def _linux_meminfo_text() -> str | None:
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"""Raw /proc/meminfo, or None when procfs cannot be read."""
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try:
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return Path("/proc/meminfo").read_text(encoding="utf-8")
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except OSError:
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return None
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def _linux_ram_from_meminfo(text: str) -> tuple[int, int] | None:
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"""(total, available) from meminfo text, or None if it cannot be trusted.
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MemAvailable includes reclaimable page cache, which ``getconf _AVPHYS_PAGES``
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counts as used. Kernels that omit MemAvailable fall back to MemFree. Zero is
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a real available value — a missing-field check must not treat it as absent.
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"""
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fields: dict[str, int] = {}
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try:
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for line in text.splitlines():
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name, separator, value = line.partition(":")
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if not separator or name not in _LINUX_RAM_KEYS:
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continue
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parts = value.split()
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if len(parts) != 2 or parts[1] != "kB":
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continue
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fields[name] = int(parts[0]) * 1024
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total = fields.get("MemTotal")
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# Key presence, not truthiness: MemAvailable 0 must not fall through to MemFree.
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if "MemAvailable" in fields:
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available = fields["MemAvailable"]
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else:
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available = fields.get("MemFree")
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if (
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total is None
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or total <= 0
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or available is None
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or not 0 <= available <= total
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):
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return None
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return total, available
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except (AttributeError, TypeError, ValueError):
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return None
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def _ram_bytes() -> tuple[int, int]:
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"""(total, available) physical memory, cross-platform stdlib."""
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try:
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import ctypes
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class MEMORYSTATUSEX(ctypes.Structure):
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_fields_ = ([("dwLength", ctypes.c_ulong), ("dwMemoryLoad", ctypes.c_ulong)]
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+ [(name, ctypes.c_ulonglong) for name in (
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"ullTotalPhys", "ullAvailPhys", "ullTotalPageFile", "ullAvailPageFile",
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"ullTotalVirtual", "ullAvailVirtual", "ullAvailExtendedVirtual")])
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stat = MEMORYSTATUSEX()
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stat.dwLength = ctypes.sizeof(MEMORYSTATUSEX)
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ctypes.windll.kernel32.GlobalMemoryStatusEx(ctypes.byref(stat))
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return stat.ullTotalPhys, stat.ullAvailPhys
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except (AttributeError, OSError):
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pass
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try:
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if sys.platform == "darwin":
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# macOS getconf has no _PHYS_PAGES/_AVPHYS_PAGES (exit 64) — the POSIX branch would
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# return (0, 0) and every model would read unavailable. sysctl is the platform truth.
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total = int(_stdout("/usr/sbin/sysctl", "-n", "hw.memsize").strip() or 0)
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if total <= 0:
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return 0, 0
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avail = total // 2 # conservative fallback
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with suppress(OSError, ValueError):
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out = _stdout("/usr/bin/vm_stat")
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page_m = re.search(r"page size of (\d+)", out)
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page = int(page_m.group(1)) if page_m else 16384
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# free + inactive + purgeable ≈ reclaimable-on-demand; the speculative pool is
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# dropped by the OS under pressure too.
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pages = sum(int(m.group(1)) for key in (
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"Pages free", "Pages inactive", "Pages purgeable", "Pages speculative")
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if (m := re.search(rf"{key}:\s+(\d+)\.", out)))
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if pages > 0:
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avail = pages * page
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return total, avail
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if sys.platform.startswith("linux"):
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meminfo = _linux_meminfo_text()
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if meminfo is not None:
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linux_ram = _linux_ram_from_meminfo(meminfo)
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if linux_ram is not None:
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return linux_ram
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# POSIX
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page = int(_stdout("getconf", "PAGE_SIZE") or 4096)
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total = int(_stdout("getconf", "_PHYS_PAGES") or 0) * page
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avail = total // 2 # conservative when _AVPHYS is unavailable
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with suppress(OSError, ValueError):
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avail = int(_stdout("getconf", "_AVPHYS_PAGES") or 0) * page or avail
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return total, avail
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except (OSError, ValueError):
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return 0, 0
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# nvidia-smi lives at a fixed path under the driver install; PATH presence varies by session type
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# (services and gateways often run minimal environments) and by driver generation (the legacy
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# NVSMI dir was never on PATH). Cached: the driver doesn't move mid-process.
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_smi_path_cache: tuple[str | None] | None = None
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def _nvidia_smi_path() -> str | None:
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"""Absolute path to nvidia-smi, or None. PATH first (respects user overrides), then the
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driver's known install locations on Windows and WSL."""
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global _smi_path_cache
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if _smi_path_cache is not None:
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return _smi_path_cache[0]
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found = shutil.which("nvidia-smi")
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if found is None and os.name == "nt":
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windir = os.environ.get("SystemRoot", r"C:\Windows")
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candidates = (
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# DCH drivers (every modern install) place it in System32.
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Path(windir) / "System32" / "nvidia-smi.exe",
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# Legacy standalone drivers used NVSMI, never on PATH.
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Path(os.environ.get("ProgramFiles", r"C:\Program Files"))
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/ "NVIDIA Corporation" / "NVSMI" / "nvidia-smi.exe",
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)
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found = next((str(c) for c in candidates if c.exists()), None)
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elif found is None and sys.platform.startswith("linux"):
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# The Windows display driver exposes nvidia-smi here inside WSL, but does not guarantee
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# that the directory is present on PATH.
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candidate = Path("/usr/lib/wsl/lib/nvidia-smi")
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if candidate.exists():
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found = str(candidate)
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_smi_path_cache = (found,)
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return found
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def _nvidia_vram() -> tuple[int, int, str, int | None] | None:
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"""(total bytes, free bytes, name, optional packed PCI ID) from the shared nvidia-smi query, or None."""
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query = _cached_nvidia_gpu_query()
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if query is None:
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return None
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return query["total_bytes"], query["free_bytes"], query["gpu_name"], query.get("gpu_pci_id")
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_gpu_query_cache: tuple[float, dict | None] | None = None
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# The statusbar polls /api/local-models/hardware every 5s and the endpoint needs
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# name/util/vram; the budget probe needs total/free. One shared query (with a TTL
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# shorter than the poll) serves both, so one poll = one nvidia-smi spawn (#120262)
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# instead of two, and the spawn carries CREATE_NO_WINDOW so a console-less backend
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# never flashes a window (#101895).
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_GPU_QUERY_TTL_S = 4.0
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def _cached_nvidia_gpu_query(ttl_s: float = _GPU_QUERY_TTL_S) -> dict | None:
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"""One nvidia-smi read shared by the budget probe and the hardware endpoint.
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Returns ``dict(gpu_name=, total_bytes=, free_bytes=, used_bytes=, gpu_util_percent=,
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gpu_pci_id=)`` or None when nvidia-smi is absent, fails, or is not an NVIDIA card.
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Cached for ``ttl_s`` (failures too — a missing smi must not spawn per poll).
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"""
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global _gpu_query_cache
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now = time.monotonic()
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if _gpu_query_cache is not None and now - _gpu_query_cache[0] < ttl_s:
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return _gpu_query_cache[1]
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exe = _nvidia_smi_path()
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if exe is None:
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_gpu_query_cache = (now, None)
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return None
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from hermes_cli._subprocess_compat import windows_hide_flags
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with suppress(OSError, ValueError, subprocess.TimeoutExpired):
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out = subprocess.run(
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[exe, "--query-gpu=memory.total,memory.free,name,pci.device_id,memory.used,utilization.gpu",
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"--format=csv,noheader,nounits"],
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capture_output=True, text=True, encoding="utf-8", errors="replace", timeout=10,
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creationflags=windows_hide_flags(), check=False)
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if out.returncode != 0 or not out.stdout.strip():
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_gpu_query_cache = (now, None)
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return None
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total_mib, free_mib, name, raw_id, used_mib, util = next(
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csv.reader(out.stdout.strip().splitlines(), skipinitialspace=True))
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pci_id = None
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with suppress(ValueError): # N/A or unsupported identity must not lose memory data.
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pci_id = int(raw_id, 16)
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data = {
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"gpu_name": name.strip(),
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"total_bytes": int(total_mib) << 20,
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"free_bytes": int(free_mib) << 20,
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"used_bytes": int(used_mib) << 20,
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"gpu_util_percent": int(util),
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"gpu_pci_id": pci_id,
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}
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_gpu_query_cache = (now, data)
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return data
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_gpu_query_cache = (now, None)
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return None
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def _cuda_driver_pool() -> tuple[int, bool | None] | None:
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"""(allocator_total_bytes, integrated_or_None) from the CUDA driver API via ctypes against the
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driver's own DLL/SO — no toolkit, no subprocess, ~ms. INTEGRATED is the vendor's own
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unified-memory declaration; total is the pool the allocator will actually hand out (on
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carve-out devices, several times what nvidia-smi reports)."""
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import ctypes
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for name in ("nvcuda.dll", "libcuda.so.1", "libcuda.so"):
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try:
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cuda = ctypes.CDLL(name)
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break
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except OSError:
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continue
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else:
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return None
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with suppress(OSError, AttributeError):
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if cuda.cuInit(0) != 0:
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return None
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dev = ctypes.c_int()
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if cuda.cuDeviceGet(ctypes.byref(dev), 0) != 0:
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return None
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total = ctypes.c_size_t()
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getter = getattr(cuda, "cuDeviceTotalMem_v2", None) or cuda.cuDeviceTotalMem
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if getter(ctypes.byref(total), dev) != 0 or total.value <= 0:
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return None
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integrated: bool | None = None
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attr = ctypes.c_int()
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if cuda.cuDeviceGetAttribute(
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ctypes.byref(attr), _CU_DEVICE_ATTRIBUTE_INTEGRATED, dev) == 0:
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integrated = bool(attr.value)
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return total.value, integrated
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return None
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def _configured_engine():
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"""The engine the probes below run: the managed server's own when one is up or booting, else
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the installed engine for ``local_runtime.backend``."""
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from hermes_cli.config import load_config_readonly
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from hermes_cli.local_runtime.binaries import installed_engine
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from hermes_cli.local_runtime.bootstrap import serving_engine
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engine = serving_engine()
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if engine is not None:
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return engine
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section = load_config_readonly().get("local_runtime") or {}
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return installed_engine(section.get("backend") or "auto")
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def _engine_device_pool() -> tuple[int, bool | None] | None:
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"""(engine_total_bytes, None) from the installed runtime's own --list-devices, or None. The
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fallback when the driver API is unreachable: asks the exact binary that will do the
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allocating. Carries no integrated verdict — callers must gate it."""
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with suppress(Exception): # a probe miss must never block budgeting
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engine = _configured_engine()
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if engine is None:
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return None
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exe = engine.binary
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out = subprocess.run([str(exe), "--list-devices"], capture_output=True,
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text=True, encoding="utf-8", errors="replace", timeout=30, cwd=str(exe.parent), check=False)
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if out.returncode != 0:
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return None
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for line in (out.stdout + out.stderr).splitlines():
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m = _DEVICE_LINE_RE.search(line)
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if m:
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return int(m.group(1)) << 20, None
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return None
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def _device_pool_view() -> tuple[int, bool | None] | None:
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"""Best available allocator-side view, cached: a hit is permanent for the process, a miss
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retries after a short TTL (the engine binary can appear mid-session via a pane install)."""
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global _pool_probe_cache
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now = time.monotonic()
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if _pool_probe_cache is not None:
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stamp, view = _pool_probe_cache
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if view is not None or now - stamp < _POOL_NEGATIVE_TTL_S:
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return view
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view = _cuda_driver_pool() or _engine_device_pool()
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_pool_probe_cache = (now, view)
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return view
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_accelerator_cache: dict[str, tuple[float, dict | None]] = {}
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def _accelerator_device(*, fresh: bool = False) -> dict | None:
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"""The device a Vulkan/HIP engine will place layers on, or None (no such engine, probe miss).
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Mirrors llama.cpp's own placement: discrete devices whenever one exists, an integrated one only
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otherwise; among discrete devices the largest. Cached per engine binary like
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``_device_pool_view`` (a hit lasts the process, a miss retries after the TTL): the hardware
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endpoint polls this every few seconds and each probe initializes the GPU driver. ``fresh`` skips
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the cache read for a launch-time free-memory answer.
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"""
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with suppress(Exception): # a probe miss must never block budgeting
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from hermes_cli.local_runtime.devices import probe_devices
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engine = _configured_engine()
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if engine is None or engine.backend not in _GPU_ENGINE_BACKENDS:
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return None
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key, now = str(engine.binary), time.monotonic()
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cached = _accelerator_cache.get(key)
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if not fresh and cached is not None and (cached[1] is not None or now - cached[0] < _POOL_NEGATIVE_TTL_S):
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return cached[1]
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devices = probe_devices(engine.binary.parent, engine.backend)
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device = max(devices, key=lambda d: (d["type"] == GGML_DEVICE_GPU, d["total"]), default=None)
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_accelerator_cache[key] = (now, device)
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return device
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return None
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def _unified_pool_bytes(smi_total: int, ram_total: int) -> int | None:
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"""The real pool size when this NVIDIA device is unified memory behind a carve-out, else None.
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The driver's INTEGRATED attribute decides in BOTH directions when readable; only the
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attribute-less engine fallback needs the two numeric gates, both of which must hold.
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"""
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view = _device_pool_view()
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if view is None:
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return None
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pool, integrated = view
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if integrated is not None:
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return pool if integrated else None
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if (smi_total > 0 and pool >= int(smi_total * _POOL_DISAGREEMENT_FACTOR)
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and ram_total > 0 and pool >= int(ram_total * _POOL_RAM_FRACTION)):
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return pool
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return None
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def _uma_budget(base: int, total: int, *, gpu_name: str = "",
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gpu_pci_id: int | None = None, lazy_reads: bool = True) -> HardwareBudget:
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usable = max(0, int(base * (1 - _UMA_HEADROOM_FRACTION)))
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return HardwareBudget(usable_vram_bytes=usable, total_device_bytes=total,
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ram_available_bytes=0, uma=True, gpu_name=gpu_name, platform=sys.platform,
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gpu_pci_id=gpu_pci_id, lazy_reads=lazy_reads)
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def _gpu_engine_runs_the_model() -> bool:
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"""Whether a Vulkan/HIP engine runs the model: the serving or installed engine, else the one
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setup would install for ``local_runtime.backend``. False when the engine can't be resolved."""
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with suppress(Exception):
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engine = _configured_engine()
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if engine is not None:
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return engine.backend in _GPU_ENGINE_BACKENDS
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from hermes_cli.config import load_config_readonly
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from hermes_cli.local_runtime.binaries import resolve_backend
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section = load_config_readonly().get("local_runtime") or {}
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return resolve_backend(section.get("backend") or "auto") in _GPU_ENGINE_BACKENDS
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return False
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def _windows_igpu_bytes(device: dict | None) -> int | None:
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"""What Windows lets the integrated GPU allocate (Task Manager's GPU memory), or None.
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The adapter is the one the engine named, else the only non-NVIDIA hardware adapter when a
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Vulkan/HIP engine will run the model; the CPU engine (chosen, or the only build for a Snapdragon)
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keeps the model in system RAM. Not the engine's own figure: ggml-vulkan sums every memory heap
|
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of an integrated GPU, host-visible ones included, so it can exceed what Windows grants.
|
|
"""
|
|
from hermes_platform.host.gpu_adapters import windows_gpu_adapters
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|
|
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hardware = [a for a in windows_gpu_adapters() if not a.software]
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named = [a for a in hardware if device is not None and a.description == device["description"]]
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candidates = named or [a for a in hardware if a.vendor_id != _NVIDIA_VENDOR_ID]
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if len(candidates) != 1 or (device is None and not _gpu_engine_runs_the_model()):
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return None
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return candidates[0].memory_bytes
|
|
|
|
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def probe_budget(*, planning: bool = False) -> HardwareBudget:
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|
"""Construct the budget per the source rules above.
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|
|
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``planning=False``: LIVE budget (free VRAM now) for launch-time fit and growth re-grants.
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|
``planning=True``: CAPACITY budget (total minus margin) for catalog pricing and quant
|
|
selection — pricing against live-free while a model was loaded made every row read as too
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|
large. The managed server unloads/relaunches itself, so capacity is real.
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|
"""
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ram_total, ram_avail = _ram_bytes()
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vram = _nvidia_vram()
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|
|
|
# Unified-memory NVIDIA: the CUDA allocator pool is the real capacity. Classification comes
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|
# from the driver API/engine and must not require nvidia-smi (stripped-PATH sessions lose smi
|
|
# but nvcuda loads via the system loader). Crossing the carve-out costs nothing — it is an OS
|
|
# accounting knob, not a GPU limit. Deliberately NOT clamped to OS RAM: carved-out memory is
|
|
# invisible to GlobalMemoryStatusEx, so a RAM clamp would throw away exactly that capacity.
|
|
unified = _unified_pool_bytes(vram[0] if vram else 0, ram_total)
|
|
if unified is not None:
|
|
logger.info(
|
|
"unified-memory NVIDIA device: allocator pool %.1f GiB "
|
|
"(nvidia-smi carve-out: %s); budgeting from the pool",
|
|
unified / _GIB,
|
|
f"{vram[0] / _GIB:.1f} GiB" if vram else "unavailable")
|
|
if planning:
|
|
base = unified
|
|
else:
|
|
# Live: dedicated-free plus what the OS can still give. smi's free saturates at the
|
|
# carve-out so this under-counts a bit — the safe direction (the pool edge is a
|
|
# measured soft cliff: decode collapses ~3.5x when concurrent demand hits it).
|
|
live = (vram[1] + ram_avail) if vram else ram_avail
|
|
base = min(unified, live)
|
|
return _uma_budget(base, unified, gpu_name=vram[2] if vram else "",
|
|
gpu_pci_id=vram[3] if vram else None)
|
|
|
|
if vram is None:
|
|
device = _accelerator_device()
|
|
if device is not None and device["type"] == GGML_DEVICE_GPU:
|
|
# Discrete AMD/Intel card behind a Vulkan/HIP engine: its own memory, with system RAM
|
|
# as spill. Capacity serves the polled live view; launch_budget reads free memory.
|
|
total = device["total"]
|
|
margin = max(_MARGIN_FLOOR, int(total * _MARGIN_FRACTION))
|
|
return HardwareBudget(usable_vram_bytes=max(0, total - margin), total_device_bytes=total,
|
|
ram_available_bytes=ram_total if planning else ram_avail,
|
|
uma=False, gpu_name=device["description"], platform=sys.platform)
|
|
# Integrated GPU, Metal, CPU, or no answer: budget from RAM as unified memory, capped at what
|
|
# Windows lets an integrated GPU allocate (18 GB of 31.5 on an Arc B390). llama.cpp loads
|
|
# lazy tensors up front on an AMD/Intel integrated GPU, where reading them on demand halved
|
|
# prefill (#28160).
|
|
pool = min(ram_total, _windows_igpu_bytes(device) or ram_total)
|
|
return _uma_budget(pool if planning else min(pool, ram_avail), pool,
|
|
lazy_reads=gpu_class() not in ("amd", "intel"))
|
|
|
|
total, free, gpu_name, gpu_pci_id = vram
|
|
margin = max(_MARGIN_FLOOR, int(total * _MARGIN_FRACTION))
|
|
return HardwareBudget(usable_vram_bytes=max(0, (total if planning else free) - margin),
|
|
total_device_bytes=total,
|
|
ram_available_bytes=ram_total if planning else ram_avail,
|
|
uma=False, gpu_name=gpu_name, platform=sys.platform, gpu_pci_id=gpu_pci_id)
|
|
|
|
|
|
def launch_budget(capacity: HardwareBudget, *, own_bytes: int = 0) -> HardwareBudget | None:
|
|
"""Capacity less what other programs hold on the card right now, or None when that can't apply.
|
|
|
|
Capacity assumes other programs hold no more than the fixed margin. Beside a heavier desktop the
|
|
capacity-sized window doesn't fit, and Windows pages part of the model to host memory without
|
|
an error: a 32 GiB card with 4.5-6.3 GiB held by other apps decoded at ~24 tok/s at the
|
|
capacity window, and at ~90 tok/s at a window sized from free memory.
|
|
|
|
``own_bytes`` is what the managed server holds now and frees before the new instance loads.
|
|
A stopped server's memory reads as free by the time its process has exited (measured on
|
|
Windows: free memory was fully back at the first reading after exit). None for unified memory
|
|
(its live budget is already free memory) and when the device query fails, so callers keep the
|
|
capacity plan.
|
|
"""
|
|
if capacity.uma:
|
|
return None
|
|
vram = _nvidia_vram()
|
|
if vram is not None:
|
|
total, free = vram[0], vram[1]
|
|
else:
|
|
# A discrete card behind Vulkan/HIP: the driver's free count includes other programs.
|
|
device = _accelerator_device(fresh=True)
|
|
if device is None or device["type"] != GGML_DEVICE_GPU:
|
|
return None
|
|
total, free = device["total"], device["free"]
|
|
others = max(0, total - free - max(0, own_bytes))
|
|
usable = min(capacity.usable_vram_bytes, max(0, total - others - _LAUNCH_HEADROOM))
|
|
return replace(capacity, usable_vram_bytes=usable)
|