380 lines
16 KiB
Python
380 lines
16 KiB
Python
"""Live hardware budget probe.
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Budget-source rule: discrete cards may trust the device query (measured
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honest within rounding); unified-memory devices must budget from OS free
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physical memory minus headroom — their device queries have been observed
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off by 3x in both directions. The probe classifies the device and
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constructs the right HardwareBudget for the estimator.
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Vendor probe quirk (WDDM carve-out): on unified-memory NVIDIA devices
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under Windows, nvidia-smi answers from the legacy dedicated-VRAM
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carve-out — a fraction of the pool the CUDA allocator actually
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addresses uniformly at full bandwidth. The CUDA driver API is
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the tiebreaker: cuDeviceGetAttribute(INTEGRATED) is the vendor's own
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declaration and always wins — 1 budgets unified, 0 stays discrete no
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matter what any other number says. Only when the driver API is
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unreachable does the engine's --list-devices view apply, and then only
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behind two independent conditions no discrete card can meet.
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Every probe here must work under a stripped PATH — gateway and service
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sessions don't inherit the interactive environment. nvcuda/libcuda load
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through the system loader (PATH plays no part), so classification never
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depends on PATH; nvidia-smi resolves through an explicit candidate
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ladder (PATH first, then the driver's known install locations) and its
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absence only softens the live number, never the verdict.
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"""
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from __future__ import annotations
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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 pathlib import Path
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from hermes_cli.local_runtime.estimator import HardwareBudget
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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
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# co-residents (compositor, browser, Electron) measure ~2-2.5 GiB on a
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# working machine, 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 the margin's
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# old 512 MiB floor failed to cover in practice (a 221K grant measured
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# 31.9/32.6 GiB with the desktop running — 'fits' by the math, demoted
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# in reality). Small cards give up window to this; spill mode is their
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# path to big models regardless.
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_MARGIN_FLOOR = 2 << 30
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_MARGIN_FRACTION = 0.09
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# UMA headroom: on unified-memory machines (Apple Silicon, unified-memory
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# NVIDIA) the model shares physical memory with the OS and every app, so
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# 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
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# no discrete card can meet either: (1) the allocator's pool exceeds the
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# smi report by well past rounding/ECC slack (discrete cards agree within
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# ~2%; carve-out disagreement runs to whole multiples), and (2) the pool is
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# system-RAM-sized — a workstation card in a RAM-matched box fails (1)
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# because its smi and allocator AGREE, and a big discrete card in a
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# bigger box fails (2). The driver's INTEGRATED attribute, when
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# readable, bypasses both gates 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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# One probe per process once a device answers (silicon doesn't change);
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# a miss retries after this long so a runtime installed mid-session gets
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# 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 carrying
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# their own parentheses parse correctly.
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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 _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),
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("dwMemoryLoad", ctypes.c_ulong),
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("ullTotalPhys", ctypes.c_ulonglong),
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("ullAvailPhys", ctypes.c_ulonglong),
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("ullTotalPageFile", ctypes.c_ulonglong),
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("ullAvailPageFile", ctypes.c_ulonglong),
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("ullTotalVirtual", ctypes.c_ulonglong),
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("ullAvailVirtual", ctypes.c_ulonglong),
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("ullAvailExtendedVirtual", ctypes.c_ulonglong)]
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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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if sys.platform == "darwin":
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# macOS getconf has no _PHYS_PAGES/_AVPHYS_PAGES (exit 64, "no such
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# configuration parameter") — the POSIX branch below returns (0, 0)
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# and every model reads unavailable. sysctl is the platform truth.
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try:
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total = int(subprocess.run(
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["/usr/sbin/sysctl", "-n", "hw.memsize"],
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capture_output=True, text=True, timeout=5).stdout.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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try:
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out = subprocess.run(["/usr/bin/vm_stat"], capture_output=True,
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text=True, timeout=5).stdout
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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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pages = 0
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# free + inactive + purgeable ≈ reclaimable-on-demand; the
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# speculative pool is dropped by the OS under pressure too.
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for key in ("Pages free", "Pages inactive", "Pages purgeable",
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"Pages speculative"):
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m = re.search(rf"{key}:\s+(\d+)\.", out)
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if m:
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pages += int(m.group(1))
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if pages > 0:
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avail = pages * page
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except (OSError, ValueError):
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pass
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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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# POSIX
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try:
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page = int(subprocess.run(["getconf", "PAGE_SIZE"], capture_output=True,
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text=True, timeout=5).stdout or 4096)
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total = int(subprocess.run(["getconf", "_PHYS_PAGES"], capture_output=True,
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text=True, timeout=5).stdout or 0) * page
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avail = total // 2 # conservative when _AVPHYS is unavailable
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try:
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avail = int(subprocess.run(["getconf", "_AVPHYS_PAGES"],
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capture_output=True, text=True,
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timeout=5).stdout or 0) * page or avail
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except (OSError, ValueError):
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pass
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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
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# varies by session type (services and gateways often run with a minimal
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# environment) and by driver generation (legacy NVSMI dir was never on
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# PATH). Resolution result is 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
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overrides), then the driver's known install locations on Windows;
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on Linux/WSL the PATH lookup is the whole ladder."""
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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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for candidate in (
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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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if candidate.exists():
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found = str(candidate)
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break
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_smi_path_cache = (found,)
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return found
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def _nvidia_vram() -> tuple[int, int] | None:
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"""(total, free) MiB->bytes from nvidia-smi, or None."""
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exe = _nvidia_smi_path()
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if exe is None:
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return None
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try:
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out = subprocess.run(
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[exe, "--query-gpu=memory.total,memory.free",
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"--format=csv,noheader,nounits"],
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capture_output=True, text=True, timeout=10)
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if out.returncode != 0 or not out.stdout.strip():
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return None
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total_mib, free_mib = (int(x) for x in out.stdout.strip().splitlines()[0].split(","))
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return total_mib << 20, free_mib << 20
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except (OSError, ValueError, subprocess.TimeoutExpired):
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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
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API, or None when unreachable. ctypes against the driver's own DLL/SO
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— 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
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actually hand out (on carve-out devices, several times what
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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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try:
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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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except (OSError, AttributeError):
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return None
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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
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--list-devices, or None. The fallback truth source when the driver
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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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try:
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from hermes_cli.local_runtime.binaries import (
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installed_tags,
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runtimes_root,
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server_binary,
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)
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tags = installed_tags()
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if not tags:
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return None
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tag_dir = runtimes_root() / tags[0]
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backend_dirs = [d for d in tag_dir.iterdir() if d.is_dir()]
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if not backend_dirs:
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return None
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exe = server_binary(backend_dirs[0])
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out = subprocess.run([str(exe), "--list-devices"], capture_output=True,
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text=True, timeout=30, cwd=str(exe.parent))
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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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except Exception: # noqa: BLE001 — a probe miss must never block budgeting
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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
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the process, a miss retries after a short TTL (the engine binary can
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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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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
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a WDDM carve-out, else None (trust nvidia-smi as ever).
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The driver's INTEGRATED attribute decides when readable — in BOTH
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directions (0 pins discrete even if the numbers look weird; a driver
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that declares integrated is believed even at modest pool sizes). Only
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an attribute-less view (engine fallback) needs the two numeric gates;
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both must hold and no discrete card meets either.
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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 False:
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return None
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if integrated is True:
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return pool
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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 probe_budget(*, planning: bool = False) -> HardwareBudget:
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"""Construct the budget per the source rules above.
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``planning=False`` (default): LIVE budget — free VRAM right now. The
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right input for launch-time fit decisions and growth re-grants.
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``planning=True``: CAPACITY budget — what this machine can run once
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the runtime manages placement (total device memory minus the margin).
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The right input for catalog pricing and quant selection: pricing
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against live-free while a model is already loaded made every row read
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'larger than your GPU memory' and degraded quant picks to Q2 on a
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32 GiB card. The managed server
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unloads/relaunches models itself, so at load time the capacity is
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genuinely available.
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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
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# capacity. Classification comes from the driver API/engine — it
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# must not require nvidia-smi (stripped-PATH sessions lose smi but
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# nvcuda loads via the system loader regardless). Crossing the
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# carve-out costs nothing (effective bandwidth is flat through the
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# boundary; smi's used/total merely saturate at it) — the carve-out
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# is an OS accounting knob, not a GPU limit. Deliberately NOT
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# clamped to OS RAM: carved-out memory is invisible to
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# GlobalMemoryStatusEx (the OS reports correspondingly less total
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# RAM), so a RAM clamp would throw away exactly the carved capacity.
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unified = _unified_pool_bytes(vram[0] if vram else 0, ram_total)
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if unified is not None:
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logger.info(
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"unified-memory NVIDIA device: allocator pool %.1f GiB "
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"(nvidia-smi carve-out: %s); budgeting from the pool",
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unified / _GIB,
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f"{vram[0] / _GIB:.1f} GiB" if vram else "unavailable")
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if planning:
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base = unified
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else:
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# Live: dedicated-free plus what the OS can still give. smi's
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# free saturates at the carve-out so this under-counts a bit —
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# the safe direction (the pool edge is a measured soft cliff:
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# decode collapses ~3.5x when concurrent demand hits it).
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# Without smi, OS-available alone is the honest floor.
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live = (vram[1] + ram_avail) if vram else ram_avail
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base = min(unified, live)
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usable = max(0, int(base * (1 - _UMA_HEADROOM_FRACTION)))
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return HardwareBudget(usable_vram_bytes=usable,
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total_device_bytes=unified,
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ram_available_bytes=0, uma=True)
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if vram is None:
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# No NVIDIA device visible: Metal/Vulkan/CPU paths budget from RAM
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# as UMA (Apple Silicon) — conservative for discrete AMD until a
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# vendor probe lands (E3 hardware).
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base = ram_total if planning else ram_avail
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usable = max(0, int(base * (1 - _UMA_HEADROOM_FRACTION)))
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return HardwareBudget(usable_vram_bytes=usable,
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total_device_bytes=ram_total,
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ram_available_bytes=0, uma=True)
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total, free = vram
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margin = max(_MARGIN_FLOOR, int(total * _MARGIN_FRACTION))
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base = total if planning else free
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return HardwareBudget(usable_vram_bytes=max(0, base - margin),
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total_device_bytes=total,
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ram_available_bytes=ram_avail if not planning else ram_total,
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uma=False)
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