66808ab25d* Studio: fix per-GPU VRAM reporting on Windows ROCm On Windows ROCm without a HIP SDK, amd-smi is disabled and the System tab fell back to torch mem_get_info, which reports free==total there (ROCm/ROCm#1909), so used VRAM showed as 0. The perf-counter fallback also summed every adapter into a single device with only GPU 0's total, hiding the second GPU. Read per-adapter Dedicated Usage (LUID-instanced) for used and take each GPU's total from torch properties, and treat the free==total case as unknown rather than 0, so every GPU shows real usage. NVIDIA, Linux ROCm, Apple and CPU paths are unchanged. Final validation needs a real Windows AMD box. Fixes #7072 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Studio: report unknown VRAM instead of fabricating or zeroing it Two gaps in the Windows ROCm VRAM path. When more adapters are actively using VRAM than are visible to the process (a GPU outside the visibility mask), the per-adapter attribution paired usage by size and fabricated a per-GPU value; report unknown for every device in that case rather than mis-assign. And the System API turned an unknown (None) used value into 0 with ``or 0``, then reported the full card as free, re-hiding the exact case this change surfaces; keep None so the UI shows unknown. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Render unknown VRAM as Unknown instead of zero in the System tab The backend reports null usage when it is unknown (e.g. the Windows ROCm perf counter is unavailable or localized), but the System tab coerced null to 0 and derived free from it, fabricating a 0-used/full-free total. Preserve null and render the translated Unknown for per-device used, free and utilization, and mark the aggregate VRAM tile unknown when any device is unknown. * Render unknown VRAM as Unknown in the floating monitor and the util tile The floating VRAM monitor and the aggregate utilization ring both still coerced a null usage to 0, showing a fabricated 0.00 GiB / full free / 0% on the same Windows ROCm no-counter case the resources tab already handles. Guard both on whether every device reports a finite usage and render Unknown (value and percent) instead of a concrete 0. * Attribute per-adapter VRAM usage only when capacity forces the mapping On Windows/ROCm there is no shared key between LUID performance-counter instances and torch ordinals, so usage was paired to devices purely by capacity ranking. That pairing is only trustworthy when capacity forces it (a usage larger than every smaller device can sit on one card). When a smaller-capacity device could equally hold a strictly larger usage (for example an 8 GiB card near full beside a lightly used 48 GiB card), the two values are swappable without violating any capacity, so the ranking is a guess with no key to break the tie. A wrong guess both mislabels the System tab and feeds routes/training_vram.py a wrong per-index free value, driving a wrong keep-resident decision. Report unknown for every device when the assignment is ambiguous, keeping the attribution only for the capacity-forced case. Returning None is the conservative direction: training_vram treats a missing index as zero free, so it never keeps a chat model into an OOM. Add regression tests for the not-capacity-ordered, same-capacity, single-fits-both, and capacity-forced cases. * Report unknown VRAM usage when a hidden adapter survives the noise filter When HIP_VISIBLE_DEVICES exposes a subset of the physical adapters, the LUID usage counters cover cards outside the visibility mask too. The sub-64 MiB noise filter could drop a genuinely-idle visible card's real usage while keeping a hidden larger card's high usage, which was then clamped onto the smaller visible device and reported as fully used (for example a hidden 48 GiB card at 40 GiB shown as a visible 8 GiB card fully used, with its true 10 MiB usage filtered out). That fabricated reading also feeds routes/training_vram.py a wrong per-index free value. Flag extra adapters on the raw counter count (before the noise filter, since an idle visible card can itself fall below the floor) and, when a kept usage exceeds its ranked visible capacity, report unknown rather than clamp a hidden card's usage onto a visible device. The genuinely-idle-noise and capacity-forced single-model cases are unchanged. Add a regression test for the hidden high-use-adapter case in both counter orders. * Report unknown when only a placeholder adapter counter survives the noise filter When more raw counters than visible devices are present but every counter sits below the 64 MiB noise floor (an idle real GPU alongside a Windows Basic Render Driver placeholder), the non_trivial-or-raw fallback resurrected the raw magnitude-sorted counters and could attribute the placeholder to a real GPU while dropping a real card's reading. With a single visible device the swap-ambiguity check cannot catch it (it needs at least two ranks), so the fabricated value reached the System tab and automatic GPU selection. Return unknown for every device in that case instead of falling back to raw counters. With the earlier guards this completes the invariant: a concrete per-GPU usage is emitted only when the assignment is capacity-forced, and every ambiguous, extra-adapter, placeholder-fallback, or count-mismatch path reports unknown. Add a regression test for the placeholder fallback in both counter orders and the two-idle-GPU case. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * studio: attribute Windows/ROCm VRAM only when capacity forces a clean bijection With more raw adapter counters than visible devices, a survivor that merely fits a visible card was pinned to it by magnitude ranking, fabricating a hidden GPU's usage onto an idle visible card whose true reading was dropped by the sub-threshold noise filter (two visible 48/8 GiB cards using 40 GiB / 10 MiB beside a hidden 6 GiB adapter returned [40, 6]). Emit a concrete per-device value only when the supra-threshold counters number exactly the visible devices (every visible card has one real reading, the extras were sub-threshold placeholders) AND the ranked usage strictly exceeds every smaller visible card's capacity. When a visible card is idle (fewer supra-threshold counters than devices) a survivor could be the hidden GPU's usage, so every device reports unknown; more active counters than visible cards, the smallest card, and any merely-fitting usage stay unknown too. The reporter's loaded-card display is preserved (40 GiB / 0.5 GiB across 48/8 GiB -> [40, None]). Adds a regression test for the reported case plus an exhaustive capacity-forced/bijection matrix. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * studio: keep the unified-memory total when Windows-ROCm used is unknown _apply_unified_memory_correction gated both the total and the used update on torch_used_gb being known, so on a unified-memory APU (Strix Halo) where torch reports used=None (the Windows-ROCm free==total sentinel) but an authoritative full-GTT total, the device kept amd-smi's small dedicated carve-out and underreported its capacity on the System tab. Adopt torch's larger total independently of used; overwrite used only when torch's is known (otherwise keep amd-smi's dedicated-usage figure) and recompute utilization against the corrected total. Adds regression tests. * Tighten comments in the ROCm/Windows VRAM reporting path * Tighten comments further in the ROCm/Windows VRAM reporting path --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: danielhanchen <unslothshared@gmail.com>
2451 lines
92 KiB
Python
2451 lines
92 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
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"""
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Hardware detection — run once at startup, read everywhere.
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Usage:
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# At FastAPI lifespan startup:
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from utils.hardware import detect_hardware
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detect_hardware()
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# Anywhere else:
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from utils.hardware import DEVICE, DeviceType, is_apple_silicon
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if DEVICE == DeviceType.CUDA:
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import torch
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...
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"""
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import copy
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import gc
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import glob
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import os
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import platform
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import re
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import subprocess
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import sys
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import types
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from importlib.metadata import PackageNotFoundError, version as pkg_version
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import structlog
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from loggers import get_logger
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from enum import Enum
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from pathlib import Path
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from typing import Optional, Dict, Any
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logger = get_logger(__name__)
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# ── GPU index ordering ──────────────────────────────────────────────────────
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# CUDA defaults to CUDA_DEVICE_ORDER=FASTEST_FIRST, numbering GPUs by compute
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# performance. nvidia-smi -- and every free-VRAM probe in Unsloth -- numbers GPUs
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# by PCI bus id instead. On a mixed-GPU host (e.g. an RTX 5090 alongside an RTX
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# PRO 6000) the two orderings disagree, so an index picked from nvidia-smi data
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# ("the emptiest card is GPU 1") gets written into CUDA_VISIBLE_DEVICES and then
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# reinterpreted by CUDA against FASTEST_FIRST -- landing the model on a different
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# physical GPU than the one selected. Pinning PCI_BUS_ID makes torch, nvidia-smi,
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# and CUDA_VISIBLE_DEVICES share a single index space, matching what users see in
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# `nvidia-smi -L`. Set at import (before any torch.cuda call latches the order
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# at context creation) and inherited by child processes, since the llama-server
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# and spawn workers copy os.environ. setdefault so an explicit user override wins.
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os.environ.setdefault("CUDA_DEVICE_ORDER", "PCI_BUS_ID")
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# Unsloth workers can import MLX without importing unsloth first, so mirror the
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# package bootstrap here. Keep an explicit user value authoritative.
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if platform.system() == "Darwin" and platform.machine() == "arm64":
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os.environ.setdefault("AGX_RELAX_CDM_CTXSTORE_TIMEOUT", "1")
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# ========== Device Enum ==========
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class DeviceType(str, Enum):
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"""Supported compute backends. str subclass for clean JSON serialization."""
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CUDA = "cuda"
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XPU = "xpu"
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MLX = "mlx"
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CPU = "cpu"
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# ========== Global State (set once by detect_hardware) ==========
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DEVICE: Optional[DeviceType] = None
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CHAT_ONLY: bool = True # No CUDA GPU -> GGUF chat only (Mac, CPU-only, etc.)
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# Why CHAT_ONLY is True (Train/Export disabled). None when training is enabled.
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# "mlx_unavailable": Apple Silicon but the MLX stack is missing, too old, or broken
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# (the usual cause of "Train/Export greyed out" on Macs after a reinstall dropped MLX);
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# "intel_mac": Intel Mac (no PyTorch/MLX); "no_gpu": CPU-only non-Mac host.
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CHAT_ONLY_REASON: Optional[str] = None
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IS_ROCM: bool = False # True when running on AMD ROCm (HIP) -- routes GPU monitoring to amd.py
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def _backend_label(device: DeviceType) -> str:
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"""Return the user-facing backend name for API responses.
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ROCm hosts stay ``DeviceType.CUDA`` internally (ROCm reuses ``torch.cuda.*``),
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but "cuda" is misleading in JSON, so swap to ``"rocm"`` when ``IS_ROCM`` is set.
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"""
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if IS_ROCM and device == DeviceType.CUDA:
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return "rocm"
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return device.value
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# ========== Detection ==========
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def is_apple_silicon() -> bool:
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"""True on Apple Silicon (pure platform check, no ML imports)."""
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return platform.system() == "Darwin" and platform.machine() == "arm64"
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def _has_torch() -> bool:
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"""True if PyTorch is importable."""
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try:
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import torch
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return True
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except ImportError:
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return False
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def _has_mlx() -> bool:
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"""True if MLX is importable."""
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try:
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import mlx.core
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return True
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except ImportError:
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return False
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def _has_usable_mlx_stack() -> bool:
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"""True only when the FULL Unsloth MLX training/export stack is usable
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(mlx + mlx-lm + mlx-vlm at the minimum versions unsloth-zoo requires), not
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just a bare ``import mlx.core``. A backtracked/old mlx-vlm still imports but
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breaks VLM Train/Export, so the training gate must match the self-heal's own
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criterion (utils.mlx_repair.mlx_stack_available) -- otherwise detect_hardware
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would enable Train/Export on exactly the inadequate stack the MLX self-heal
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is trying to repair, leaving the user with greyed-in-but-broken buttons."""
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try:
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from utils.mlx_repair import mlx_stack_available
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return mlx_stack_available()
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except Exception as exc:
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# mlx_repair should always import; if it somehow cannot, fall back to the
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# bare import check rather than forcing a working host into chat-only.
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logger.debug("MLX stack availability check failed, using bare import: %s", exc)
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return _has_mlx()
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def _print_cuda_device_list(is_rocm: bool) -> None:
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"""List every visible CUDA/ROCm GPU with its index at startup.
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The "Hardware detected" banner names only device 0, which hides the other
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cards on a multi-GPU host. This lists the full visible set in CUDA-ordinal
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order, matching `nvidia-smi -L` when no CUDA_VISIBLE_DEVICES mask is set
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(under a mask the indices are visible ordinals, not physical PCI ids).
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CUDA_DEVICE_ORDER governs only CUDA, so it is shown for CUDA but not ROCm.
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No-ops on single-GPU hosts and never raises -- it is purely informational.
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"""
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try:
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import torch
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count = torch.cuda.device_count()
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if count <= 1:
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return
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if is_rocm:
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header = f"ROCm devices ({count}):"
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else:
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order = os.environ.get("CUDA_DEVICE_ORDER", "default")
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header = f"CUDA devices ({count}, CUDA_DEVICE_ORDER={order}):"
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lines = [header]
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for i in range(count):
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try:
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name = torch.cuda.get_device_properties(i).name
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except Exception as e:
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logger.debug("CUDA device %d property probe failed: %s", i, e)
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name = "<unavailable>"
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lines.append(f" [{i}] {name}")
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print("\n".join(lines))
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except Exception:
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return # purely informational; never disrupt startup
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def detect_hardware() -> DeviceType:
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"""
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Detect the best compute device and set the module-level DEVICE global.
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Call once at FastAPI lifespan startup; idempotent.
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Detection order:
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1. CUDA (NVIDIA GPU, requires torch)
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2. MLX (Apple Silicon via MLX framework)
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3. CPU (fallback)
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"""
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global DEVICE, CHAT_ONLY, CHAT_ONLY_REASON, IS_ROCM
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CHAT_ONLY = True # reset -- only CUDA/ROCm/XPU/MLX sets it to False
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CHAT_ONLY_REASON = None
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IS_ROCM = False
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# --- CUDA / ROCm: try PyTorch ---
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if _has_torch():
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import torch
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if torch.cuda.is_available():
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DEVICE = DeviceType.CUDA
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CHAT_ONLY = False
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try:
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device_name = torch.cuda.get_device_properties(0).name
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except Exception as e:
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logger.debug("CUDA device 0 property probe failed: %s", e)
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device_name = "<unavailable>"
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# Distinguish ROCm from CUDA for display only (DeviceType stays CUDA).
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# AMD SDK wheels don't set torch.version.hip, so fall back to __version__.
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_hip_ver = getattr(torch.version, "hip", None)
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if _hip_ver is not None or "rocm" in torch.__version__.lower():
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IS_ROCM = True
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_hip_label = _hip_ver or torch.__version__
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print(f"Hardware detected: ROCm (HIP {_hip_label}) -- {device_name}")
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else:
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print(f"Hardware detected: CUDA -- {device_name}")
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_print_cuda_device_list(IS_ROCM)
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return DEVICE
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# --- XPU: Intel GPU ---
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if _has_torch():
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import torch
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if hasattr(torch, "xpu") and torch.xpu.is_available():
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DEVICE = DeviceType.XPU
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CHAT_ONLY = False
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device_name = torch.xpu.get_device_name(0)
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print(f"Hardware detected: XPU — {device_name}")
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return DEVICE
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# --- MLX: Apple Silicon ---
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# Require the full mlx/mlx-lm/mlx-vlm stack (not a bare `import mlx.core`) so
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# the gate matches utils.mlx_repair: a partial/backtracked stack stays
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# chat-only (reason "mlx_unavailable") and the background self-heal repairs it.
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if is_apple_silicon() and _has_usable_mlx_stack():
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DEVICE = DeviceType.MLX
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CHAT_ONLY = False
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# Use platform.machine() ("arm64"); platform.processor() returns "i386"
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# on universal2 / Rosetta builds even on native arm64.
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chip = platform.machine() or "arm64"
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print(f"Hardware detected: MLX — Apple Silicon ({chip})")
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return DEVICE
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# --- Fallback ---
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DEVICE = DeviceType.CPU
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# CHAT_ONLY is still True here (every training-capable branch returned early),
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# so record WHY so the UI can explain the greyed-out Train/Export instead of
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# silently disabling them.
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if is_apple_silicon():
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# Reached the CPU fallback on Apple Silicon, so the MLX stack is missing,
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# too old, or broken. This is usually an environment problem recoverable
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# with `unsloth studio update`.
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CHAT_ONLY_REASON = "mlx_unavailable"
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logger.warning(
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"Apple Silicon detected but the MLX stack is incomplete or too old; "
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"Train/Export disabled (chat-only). Run `unsloth studio update` to "
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"restore MLX training."
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)
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elif platform.system() == "Darwin":
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CHAT_ONLY_REASON = "intel_mac" # Intel Mac: no PyTorch/MLX -> GGUF-only by design.
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else:
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CHAT_ONLY_REASON = "no_gpu"
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print("Hardware detected: CPU (no GPU backend available)")
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return DEVICE
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# ========== Convenience helpers ==========
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def get_device() -> DeviceType:
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"""
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Return the detected device, auto-detecting if detect_hardware() hasn't run.
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Prefer calling detect_hardware() explicitly at startup.
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"""
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global DEVICE
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if DEVICE is None:
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detect_hardware()
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return DEVICE
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def export_capability() -> dict:
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"""Whether model export can run here, with a torch-aware reason when it cannot.
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Export runs through Unsloth, which hard-requires an accelerator (it calls ``torch.cuda`` at
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import and has no CPU path), so it is supported iff ``get_device() in {CUDA, XPU, MLX}``. The
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reason distinguishes a --no-torch install from a bare-CPU host. Safe to call without torch.
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Returns {export_supported, export_unsupported_reason, export_unsupported_message}.
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"""
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if get_device() in (DeviceType.CUDA, DeviceType.XPU, DeviceType.MLX):
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return {
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"export_supported": True,
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"export_unsupported_reason": None,
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"export_unsupported_message": None,
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}
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# No accelerator: name the blocker. Apple Silicon first -- its path is MLX, so "install PyTorch"
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# would be wrong advice on a Mac even when torch is also absent.
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if is_apple_silicon():
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reason = "mlx_unavailable"
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message = (
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"Export on Apple Silicon requires the MLX stack, which is unavailable or too old. Run "
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"`unsloth studio update` to restore MLX and enable export."
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)
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elif not _has_torch():
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reason = "pytorch_not_installed"
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message = (
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"PyTorch is not installed. Model export requires PyTorch with a supported accelerator "
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"(NVIDIA, AMD, or Intel GPU) or Apple Silicon (MLX). Install PyTorch to enable export."
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)
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else:
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reason = "no_accelerator"
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message = (
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"Export requires an NVIDIA, AMD, or Intel GPU, or Apple Silicon (MLX). No supported "
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"accelerator was found on this host. (PyTorch is installed, but Unsloth cannot export "
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"on CPU only.)"
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)
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return {
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"export_supported": False,
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"export_unsupported_reason": reason,
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"export_unsupported_message": message,
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}
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||
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def clear_gpu_cache():
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"""
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Clear GPU memory cache for the current device.
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Safe on any platform — no-ops gracefully.
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"""
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gc.collect()
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device = get_device()
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if device == DeviceType.CUDA:
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import torch
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torch.cuda.synchronize()
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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elif device == DeviceType.XPU:
|
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import torch
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torch.xpu.synchronize()
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torch.xpu.empty_cache()
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elif device == DeviceType.MLX:
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# MLX manages memory automatically; gc.collect() above is enough.
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pass
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||
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def get_gpu_memory_info() -> Dict[str, Any]:
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"""
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Get GPU memory info.
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Supports CUDA (NVIDIA), MLX (Apple Silicon), and CPU-only.
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||
"""
|
||
device = get_device()
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||
|
||
# ---- CUDA path ----
|
||
if device == DeviceType.CUDA:
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||
try:
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||
import torch
|
||
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||
idx = torch.cuda.current_device()
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||
props = torch.cuda.get_device_properties(idx)
|
||
|
||
total = props.total_memory
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||
allocated = torch.cuda.memory_allocated(idx)
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||
reserved = torch.cuda.memory_reserved(idx)
|
||
|
||
return {
|
||
"available": True,
|
||
"backend": _backend_label(device),
|
||
"device": idx,
|
||
"device_name": props.name,
|
||
"total_gb": total / (1024**3),
|
||
"allocated_gb": allocated / (1024**3),
|
||
"reserved_gb": reserved / (1024**3),
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||
"free_gb": (total - allocated) / (1024**3),
|
||
"utilization_pct": (allocated / total) * 100,
|
||
}
|
||
except Exception as e:
|
||
logger.error(f"Error getting CUDA GPU info: {e}")
|
||
return {
|
||
"available": False,
|
||
"backend": _backend_label(device),
|
||
"error": str(e),
|
||
}
|
||
|
||
# ---- XPU path (Intel GPU) ----
|
||
if device == DeviceType.XPU:
|
||
try:
|
||
import torch
|
||
|
||
idx = torch.xpu.current_device()
|
||
props = torch.xpu.get_device_properties(idx)
|
||
|
||
total = props.total_memory
|
||
allocated = torch.xpu.memory_allocated(idx)
|
||
reserved = torch.xpu.memory_reserved(idx)
|
||
|
||
return {
|
||
"available": True,
|
||
"backend": _backend_label(device),
|
||
"device": idx,
|
||
"device_name": props.name,
|
||
"total_gb": total / (1024**3),
|
||
"allocated_gb": allocated / (1024**3),
|
||
"reserved_gb": reserved / (1024**3),
|
||
"free_gb": (total - allocated) / (1024**3),
|
||
"utilization_pct": (allocated / total) * 100,
|
||
}
|
||
except Exception as e:
|
||
logger.error("Error getting XPU GPU info: %s", e)
|
||
return {
|
||
"available": False,
|
||
"backend": _backend_label(device),
|
||
"error": str(e),
|
||
}
|
||
|
||
# ---- MLX path (Apple Silicon) ----
|
||
if device == DeviceType.MLX:
|
||
try:
|
||
import mlx.core as mx
|
||
import psutil
|
||
|
||
# Unified memory: total = system RAM, GPU used from IORegistry AGX.
|
||
total = psutil.virtual_memory().total
|
||
agx = _read_apple_gpu_stats()
|
||
allocated = agx.get("vram_used_bytes", 0) if agx else 0
|
||
|
||
try:
|
||
info = mx.device_info()
|
||
# prefer machine(); processor() can return "i386" on native arm64.
|
||
gpu_name = info.get("device_name") or platform.machine() or "arm64"
|
||
except Exception:
|
||
gpu_name = platform.machine() or "arm64"
|
||
|
||
return {
|
||
"available": True,
|
||
"backend": _backend_label(device),
|
||
"device": 0,
|
||
"device_name": f"Apple Silicon ({gpu_name})",
|
||
"total_gb": total / (1024**3),
|
||
"allocated_gb": allocated / (1024**3),
|
||
"reserved_gb": allocated / (1024**3),
|
||
"free_gb": (total - allocated) / (1024**3),
|
||
"utilization_pct": (allocated / total) * 100 if total else 0,
|
||
}
|
||
except Exception as e:
|
||
logger.error(f"Error getting MLX GPU info: {e}")
|
||
return {
|
||
"available": False,
|
||
"backend": _backend_label(device),
|
||
"error": str(e),
|
||
}
|
||
|
||
# ---- CPU-only ----
|
||
return {"available": False, "backend": "cpu"}
|
||
|
||
|
||
def log_gpu_memory(context: str):
|
||
"""Log GPU memory usage with context."""
|
||
memory_info = get_gpu_memory_info()
|
||
if memory_info.get("available"):
|
||
backend = memory_info.get("backend", "unknown").upper()
|
||
device_name = memory_info.get("device_name", "")
|
||
label = f"{backend}" + (f" ({device_name})" if device_name else "")
|
||
logger.info(
|
||
f"GPU Memory [{context}] {label}: "
|
||
f"{memory_info['allocated_gb']:.2f}GB/{memory_info['total_gb']:.2f}GB "
|
||
f"({memory_info['utilization_pct']:.1f}% used, "
|
||
f"{memory_info['free_gb']:.2f}GB free)"
|
||
)
|
||
else:
|
||
logger.info(f"GPU Memory [{context}]: No GPU available (CPU-only)")
|
||
|
||
|
||
# ========== GPU Summary & Package Versions ==========
|
||
|
||
|
||
def get_gpu_summary() -> Dict[str, Any]:
|
||
"""
|
||
Return a compact summary of the primary GPU.
|
||
|
||
Returns dict with keys:
|
||
gpu_name – e.g. "NVIDIA L4" (or None)
|
||
vram_total_gb – e.g. 22.17 (or None)
|
||
"""
|
||
mem = get_gpu_memory_info()
|
||
if mem.get("available"):
|
||
return {
|
||
"gpu_name": mem.get("device_name"),
|
||
"vram_total_gb": round(mem.get("total_gb", 0), 2),
|
||
"vram_free_gb": round(mem.get("free_gb", 0), 2),
|
||
}
|
||
return {"gpu_name": None, "vram_total_gb": None, "vram_free_gb": None}
|
||
|
||
|
||
def get_package_versions() -> Dict[str, Optional[str]]:
|
||
"""
|
||
Return installed versions of key ML packages.
|
||
|
||
Uses importlib.metadata (stdlib), no subprocess. CUDA version from
|
||
torch.version.cuda. Returns dict keyed unsloth/torch/transformers/cuda;
|
||
missing packages yield None.
|
||
"""
|
||
packages = ("unsloth", "torch", "transformers")
|
||
versions: Dict[str, Optional[str]] = {}
|
||
|
||
for name in packages:
|
||
try:
|
||
versions[name] = pkg_version(name)
|
||
except PackageNotFoundError:
|
||
versions[name] = None
|
||
|
||
# GPU runtime version bundled with torch
|
||
try:
|
||
import torch
|
||
versions["cuda"] = getattr(torch.version, "cuda", None)
|
||
versions["rocm"] = getattr(torch.version, "hip", None)
|
||
except Exception:
|
||
versions["cuda"] = None
|
||
versions["rocm"] = None
|
||
|
||
return versions
|
||
|
||
|
||
# ========== Torch-based GPU fallbacks (AMD ROCm, Intel XPU, nvidia-smi missing) ==========
|
||
|
||
|
||
def _torch_get_device_module():
|
||
"""Return the appropriate torch device module (cuda or xpu) and its name."""
|
||
device = get_device()
|
||
import torch
|
||
|
||
if device == DeviceType.CUDA:
|
||
return torch.cuda, "cuda"
|
||
if device == DeviceType.XPU and hasattr(torch, "xpu"):
|
||
return torch.xpu, "xpu"
|
||
return None, None
|
||
|
||
|
||
def _torch_get_physical_gpu_count() -> Optional[int]:
|
||
mod, _ = _torch_get_device_module()
|
||
if mod is None:
|
||
return None
|
||
try:
|
||
return mod.device_count()
|
||
except Exception:
|
||
return None
|
||
|
||
|
||
def _torch_get_per_device_info(device_indices: list[int]) -> list[Dict[str, Any]]:
|
||
"""Query torch for per-GPU name, total VRAM, and used VRAM.
|
||
|
||
``used_gb`` is ``None`` on Windows ROCm when ``hipMemGetInfo`` reports
|
||
``free == total`` (ROCm/ROCm#1909): that 0 means unknown, not empty.
|
||
"""
|
||
mod, _ = _torch_get_device_module()
|
||
if mod is None:
|
||
return []
|
||
|
||
# free==total is a Windows-ROCm-only quirk.
|
||
_win_rocm = sys.platform == "win32" and IS_ROCM
|
||
devices = []
|
||
for ordinal, phys_idx in enumerate(device_indices):
|
||
try:
|
||
# torch ordinals are 0-based relative to CUDA_VISIBLE_DEVICES.
|
||
props = mod.get_device_properties(ordinal)
|
||
total_bytes = props.total_memory
|
||
used_bytes: Optional[int]
|
||
# Prefer mem_get_info (system-wide) so auto-select sees other consumers.
|
||
if hasattr(mod, "mem_get_info"):
|
||
free_bytes, total_bytes = mod.mem_get_info(ordinal)
|
||
used_bytes = total_bytes - free_bytes
|
||
# free==total is the broken-API sentinel, not an idle GPU.
|
||
if _win_rocm and free_bytes == total_bytes:
|
||
used_bytes = None
|
||
else:
|
||
used_bytes = mod.memory_allocated(ordinal)
|
||
devices.append(
|
||
{
|
||
"index": phys_idx,
|
||
"visible_ordinal": ordinal,
|
||
"name": props.name,
|
||
"total_gb": round(total_bytes / (1024**3), 2),
|
||
"used_gb": round(used_bytes / (1024**3), 2) if used_bytes is not None else None,
|
||
}
|
||
)
|
||
except Exception as e:
|
||
logger.debug("torch device query failed for ordinal %d: %s", ordinal, e)
|
||
return devices
|
||
|
||
|
||
# ========== Live GPU Utilization ==========
|
||
|
||
|
||
def _smi_query(func_name: str, *args, **kwargs) -> Optional[Dict[str, Any]]:
|
||
"""Query the appropriate SMI backend (amd-smi or nvidia-smi).
|
||
|
||
Returns the result dict if available, else None.
|
||
"""
|
||
if IS_ROCM:
|
||
backend_name = "amd-smi"
|
||
try:
|
||
from . import amd as _backend
|
||
except Exception as e:
|
||
logger.warning("%s import failed: %s", backend_name, e)
|
||
return None
|
||
else:
|
||
backend_name = "nvidia-smi"
|
||
try:
|
||
from . import nvidia as _backend
|
||
except Exception as e:
|
||
logger.warning("%s import failed: %s", backend_name, e)
|
||
return None
|
||
try:
|
||
func = getattr(_backend, func_name)
|
||
result = func(*args, **kwargs)
|
||
if isinstance(result, dict) and result.get("available"):
|
||
return result
|
||
except Exception as e:
|
||
logger.warning("%s %s query failed: %s", backend_name, func_name, e)
|
||
return None
|
||
|
||
|
||
def _read_apple_gpu_stats() -> Dict[str, Any]:
|
||
"""Query macOS IORegistry for AGX (Apple GPU) live stats. No sudo needed.
|
||
|
||
Returns dict with utilization_pct, vram_used_bytes (system-wide GPU
|
||
memory), or empty dict on failure.
|
||
"""
|
||
try:
|
||
result = subprocess.run(
|
||
["ioreg", "-r", "-c", "AGXAccelerator"],
|
||
capture_output = True,
|
||
timeout = 2,
|
||
)
|
||
text = result.stdout.decode("utf-8", errors = "replace")
|
||
except Exception:
|
||
return {}
|
||
|
||
# PerformanceStatistics block has GPU utilization and in-use memory
|
||
m = re.search(r'"PerformanceStatistics" = \{([^}]+)\}', text)
|
||
if not m:
|
||
return {}
|
||
stats_str = m.group(1)
|
||
pairs = re.findall(r'"([^"]+)"=(\d+)', stats_str)
|
||
stats = {k: int(v) for k, v in pairs}
|
||
|
||
return {
|
||
"utilization_pct": stats.get("Device Utilization %", 0),
|
||
"vram_used_bytes": stats.get("In use system memory", 0),
|
||
}
|
||
|
||
|
||
def _rocm_linux_sysfs_gpu_busy_pct() -> Optional[float]:
|
||
"""Query AMD GPU compute utilization via Linux DRM sysfs gpu_busy_percent."""
|
||
if platform.system() != "Linux":
|
||
return None
|
||
try:
|
||
files = glob.glob("/sys/class/drm/card*/device/gpu_busy_percent")
|
||
if not files:
|
||
return None
|
||
values = [int(open(f).read().strip()) for f in files]
|
||
return round(sum(values) / len(values), 1)
|
||
except Exception:
|
||
return None
|
||
|
||
|
||
def _rocm_linux_sysfs_temp_c() -> Optional[float]:
|
||
"""Query AMD GPU edge temperature via Linux DRM hwmon sysfs (temp1_input, millidegrees C)."""
|
||
if platform.system() != "Linux":
|
||
return None
|
||
try:
|
||
files = glob.glob("/sys/class/drm/card*/device/hwmon/hwmon*/temp1_input")
|
||
if not files:
|
||
return None
|
||
temps = [int(open(f).read().strip()) / 1000.0 for f in files]
|
||
return round(max(temps), 1)
|
||
except Exception:
|
||
return None
|
||
|
||
|
||
def _rocm_linux_sysfs_power_w() -> Optional[float]:
|
||
"""Query AMD GPU average power draw via Linux DRM hwmon sysfs (microwatts)."""
|
||
if platform.system() != "Linux":
|
||
return None
|
||
try:
|
||
for pattern in (
|
||
"/sys/class/drm/card*/device/hwmon/hwmon*/power1_average",
|
||
"/sys/class/drm/card*/device/hwmon/hwmon*/power1_input",
|
||
):
|
||
files = glob.glob(pattern)
|
||
if files:
|
||
watts = sum(int(open(f).read().strip()) / 1_000_000.0 for f in files)
|
||
return round(watts, 1)
|
||
return None
|
||
except Exception:
|
||
return None
|
||
|
||
|
||
def _rocm_windows_perf_counter_gpu_util_pct() -> Optional[float]:
|
||
"""Query AMD GPU compute utilization via Windows Performance Counters (3D engine nodes)."""
|
||
if platform.system() != "Windows":
|
||
return None
|
||
try:
|
||
ps = (
|
||
"$s=(Get-Counter '\\GPU Engine(*engtype_3D*)\\Utilization Percentage'"
|
||
" -ErrorAction SilentlyContinue).CounterSamples;"
|
||
"if($s){[math]::Min(($s|Measure-Object CookedValue -Sum).Sum,100)}else{-1}"
|
||
)
|
||
r = subprocess.run(
|
||
["powershell", "-NoProfile", "-NonInteractive", "-Command", ps],
|
||
capture_output = True,
|
||
text = True,
|
||
timeout = 5,
|
||
)
|
||
if r.returncode != 0 or not r.stdout.strip():
|
||
return None
|
||
val = float(r.stdout.strip())
|
||
return round(val, 1) if val >= 0 else None
|
||
except Exception:
|
||
return None
|
||
|
||
|
||
def _rocm_linux_sysfs_vram_gb() -> tuple[Optional[float], Optional[float]]:
|
||
"""Query system-wide AMD GPU VRAM via Linux DRM sysfs.
|
||
|
||
Reads /sys/class/drm/card*/device/mem_info_vram_*, which the kernel
|
||
updates in real-time across all processes. No tools required.
|
||
Returns (used_gb, total_gb) or (None, None) on failure.
|
||
"""
|
||
if platform.system() != "Linux":
|
||
return None, None
|
||
try:
|
||
used_files = glob.glob("/sys/class/drm/card*/device/mem_info_vram_used")
|
||
total_files = glob.glob("/sys/class/drm/card*/device/mem_info_vram_total")
|
||
if not used_files or not total_files:
|
||
return None, None
|
||
used_bytes = sum(int(open(f).read().strip()) for f in used_files)
|
||
total_bytes = sum(int(open(f).read().strip()) for f in total_files)
|
||
if total_bytes == 0:
|
||
return None, None
|
||
return round(used_bytes / (1024**3), 2), round(total_bytes / (1024**3), 2)
|
||
except Exception:
|
||
return None, None
|
||
|
||
|
||
# ── Windows AMD/ROCm per-adapter VRAM (issue #7072) ──────────────────────────
|
||
# amd-smi is disabled and hipMemGetInfo reports free==total, so read used from the
|
||
# per-LUID "GPU Adapter Memory" perf counters and take each total from torch, so
|
||
# every GPU shows instead of one fake device with GPU 0's total.
|
||
# Placeholder adapters (Basic Render Driver / idle iGPU) drop only when they would
|
||
# outnumber the real torch devices.
|
||
_ROCM_WIN_ADAPTER_MIN_BYTES = 64 * 1024 * 1024 # 64 MiB
|
||
|
||
|
||
def _rocm_windows_perf_counter_vram_by_adapter() -> Optional[list[tuple[str, float]]]:
|
||
"""Per-adapter dedicated VRAM usage on Windows via Performance Counters.
|
||
|
||
Returns ``[(instance_name, used_bytes)]`` (one per LUID-named adapter), or
|
||
``None`` when the counter is unavailable/localized/empty so callers fall back.
|
||
"""
|
||
if platform.system() != "Windows":
|
||
return None
|
||
try:
|
||
# Emit "<InstanceName>|<CookedValue>" per sample, or a __NONE__ sentinel.
|
||
ps = (
|
||
"$s=(Get-Counter '\\GPU Adapter Memory(*)\\Dedicated Usage'"
|
||
" -ErrorAction SilentlyContinue).CounterSamples;"
|
||
"if($s){$s|ForEach-Object{'{0}|{1}' -f $_.InstanceName,[int64]$_.CookedValue}}"
|
||
"else{'__NONE__'}"
|
||
)
|
||
r = subprocess.run(
|
||
["powershell", "-NoProfile", "-NonInteractive", "-Command", ps],
|
||
capture_output = True,
|
||
text = True,
|
||
timeout = 5,
|
||
)
|
||
if r.returncode != 0 or not r.stdout.strip():
|
||
return None
|
||
adapters: list[tuple[str, float]] = []
|
||
for line in r.stdout.splitlines():
|
||
line = line.strip()
|
||
if not line or line == "__NONE__" or "|" not in line:
|
||
continue
|
||
instance, _, raw = line.rpartition("|")
|
||
try:
|
||
used = float(raw.strip())
|
||
except (ValueError, TypeError):
|
||
continue
|
||
if used < 0:
|
||
continue
|
||
adapters.append((instance.strip(), used))
|
||
return adapters or None
|
||
except Exception:
|
||
return None
|
||
|
||
|
||
def _match_adapter_used_to_devices(
|
||
adapter_useds: list[float], device_totals: list[float]
|
||
) -> list[Optional[float]]:
|
||
"""Attribute per-adapter used bytes to torch devices by capacity ranking.
|
||
|
||
Windows shares no key between LUID counters and torch ordinals, so usages are
|
||
ranked against device totals and each is trusted only when capacity *forces* it
|
||
(it exceeds every smaller device); an ambiguous ranking reports unknown
|
||
(``None``) rather than fabricate a per-index free.
|
||
|
||
Extra counters mean a hidden/display adapter, and the noise filter may have
|
||
dropped a real reading, so values are emitted only when the supra-threshold
|
||
counters number EXACTLY the visible devices AND capacity forces the mapping;
|
||
otherwise every device is unknown. Best-effort but correct for the common
|
||
loaded-card case (#7072). Returns a list aligned to ``device_totals``.
|
||
"""
|
||
n = len(device_totals)
|
||
if n == 0:
|
||
return []
|
||
useds = sorted(adapter_useds, reverse = True)
|
||
ranked_positions = sorted(range(n), key = lambda i: -device_totals[i])
|
||
ranked_totals = [device_totals[pos] for pos in ranked_positions]
|
||
assigned: list[Optional[float]]
|
||
# More counters than devices -> a hidden/display adapter (check before noise filter).
|
||
if len(useds) > n:
|
||
non_trivial = [u for u in useds if u >= _ROCM_WIN_ADAPTER_MIN_BYTES]
|
||
if len(non_trivial) != n:
|
||
# Not a clean bijection (a masked GPU is busy or a visible card idle):
|
||
# no counter maps to a specific card, so report unknown.
|
||
return [None] * n
|
||
# Exactly n supra-threshold counters: extras were placeholders, so a
|
||
# capacity-ranked bijection is plausible.
|
||
useds = non_trivial
|
||
ranked_useds = [useds[rank] for rank in range(n)]
|
||
# A usage above its ranked capacity is a hidden larger GPU; clamping onto the
|
||
# smaller card would fabricate a fully-used reading.
|
||
for rank in range(n):
|
||
if ranked_useds[rank] > ranked_totals[rank]:
|
||
return [None] * n
|
||
# Capacity forces the mapping only when the usage exceeds the next-smaller
|
||
# capacity; the smallest card and merely-fitting usages stay unknown.
|
||
# Keeps 40 GiB over 48/8 GiB -> [40, None].
|
||
assigned = [None] * n
|
||
for rank, pos in enumerate(ranked_positions):
|
||
if rank + 1 < n and ranked_useds[rank] > ranked_totals[rank + 1]:
|
||
assigned[pos] = min(ranked_useds[rank], device_totals[pos])
|
||
return assigned
|
||
# No hidden adapters: every counter is a visible card, so ranking is a permutation.
|
||
ranked_useds = [useds[rank] if rank < len(useds) else 0.0 for rank in range(n)]
|
||
# Ambiguous if a strictly larger usage also fits the next smaller card: the two
|
||
# could be swapped without breaking capacity, so ranking can't tell them apart.
|
||
for rank in range(n - 1):
|
||
upper, lower = ranked_useds[rank], ranked_useds[rank + 1]
|
||
if upper > lower and upper <= ranked_totals[rank + 1]:
|
||
return [None] * n
|
||
assigned = [None] * n
|
||
for rank, pos in enumerate(ranked_positions):
|
||
if rank < len(useds):
|
||
assigned[pos] = min(useds[rank], device_totals[pos])
|
||
return assigned
|
||
|
||
|
||
def _rocm_windows_per_device_vram(device_indices: list[int]) -> list[Dict[str, Any]]:
|
||
"""Per-GPU VRAM on Windows AMD/ROCm: total from torch properties (reliable),
|
||
used from the per-adapter Dedicated Usage counter.
|
||
|
||
Returns ``{index, visible_ordinal, name, used_gb, total_gb}`` per visible GPU
|
||
(``used_gb`` may be ``None`` when the counter is unavailable), or ``[]`` when
|
||
torch can't enumerate devices so callers fall through to the torch last resort.
|
||
"""
|
||
if platform.system() != "Windows":
|
||
return []
|
||
mod, _ = _torch_get_device_module()
|
||
if mod is None:
|
||
return []
|
||
# Totals/names from torch properties (mem_get_info's free==total quirk zeroes used).
|
||
dev_meta: list[Dict[str, Any]] = []
|
||
for ordinal, phys_idx in enumerate(device_indices):
|
||
try:
|
||
props = mod.get_device_properties(ordinal)
|
||
dev_meta.append(
|
||
{
|
||
"index": phys_idx,
|
||
"visible_ordinal": ordinal,
|
||
"name": props.name,
|
||
"total_bytes": int(props.total_memory),
|
||
}
|
||
)
|
||
except Exception as e:
|
||
logger.debug("torch property probe failed for ordinal %d: %s", ordinal, e)
|
||
if not dev_meta:
|
||
return []
|
||
|
||
adapters = _rocm_windows_perf_counter_vram_by_adapter()
|
||
if adapters:
|
||
assigned = _match_adapter_used_to_devices(
|
||
[used for _, used in adapters],
|
||
[d["total_bytes"] for d in dev_meta],
|
||
)
|
||
else:
|
||
# Counter unavailable: show every GPU with a correct total, used unknown.
|
||
assigned = [None] * len(dev_meta)
|
||
|
||
devices: list[Dict[str, Any]] = []
|
||
for meta, used_bytes in zip(dev_meta, assigned):
|
||
total_gb = round(meta["total_bytes"] / (1024**3), 2)
|
||
used_gb = round(used_bytes / (1024**3), 2) if used_bytes is not None else None
|
||
devices.append(
|
||
{
|
||
"index": meta["index"],
|
||
"visible_ordinal": meta["visible_ordinal"],
|
||
"name": meta["name"],
|
||
"used_gb": used_gb,
|
||
"total_gb": total_gb,
|
||
}
|
||
)
|
||
return devices
|
||
|
||
|
||
def _rocm_windows_device_payload_entry(
|
||
device: DeviceType, dev: Dict[str, Any], gpu_util_pct: Optional[float]
|
||
) -> Dict[str, Any]:
|
||
"""Build a ``get_gpu_utilization`` device entry from a per-device VRAM dict."""
|
||
total_gb = dev["total_gb"]
|
||
used_gb = dev["used_gb"]
|
||
return {
|
||
"available": True,
|
||
"backend": _backend_label(device),
|
||
"index": dev["index"],
|
||
"visible_ordinal": dev["visible_ordinal"],
|
||
"name": dev.get("name", "Unknown"),
|
||
"gpu_utilization_pct": gpu_util_pct,
|
||
"temperature_c": None,
|
||
"vram_used_gb": used_gb,
|
||
"vram_total_gb": total_gb,
|
||
"vram_utilization_pct": round((used_gb / total_gb) * 100, 1)
|
||
if total_gb and total_gb > 0 and used_gb is not None
|
||
else None,
|
||
"power_draw_w": None,
|
||
"power_limit_w": None,
|
||
"power_utilization_pct": None,
|
||
}
|
||
|
||
|
||
def _gpu_utilization_payload(
|
||
device: DeviceType, devices: list[Dict[str, Any]], **metadata: Any
|
||
) -> Dict[str, Any]:
|
||
"""Keep the legacy primary-GPU shape and append all visible devices."""
|
||
backend = _backend_label(device)
|
||
normalized = []
|
||
for ordinal, raw in enumerate(devices):
|
||
dev = dict(raw)
|
||
dev.setdefault("available", True)
|
||
dev.setdefault("backend", backend)
|
||
if dev.get("visible_ordinal") is None:
|
||
dev["visible_ordinal"] = ordinal
|
||
normalized.append(dev)
|
||
|
||
normalized.sort(key = lambda dev: dev.get("visible_ordinal", dev.get("index", 0)))
|
||
payload: Dict[str, Any] = {
|
||
"available": bool(normalized),
|
||
"backend": backend,
|
||
"devices": normalized,
|
||
}
|
||
payload.update(metadata)
|
||
if normalized:
|
||
payload.update(normalized[0])
|
||
payload["available"] = True
|
||
payload["backend"] = normalized[0].get("backend", backend)
|
||
payload["devices"] = normalized
|
||
return payload
|
||
|
||
|
||
def get_gpu_utilization() -> Dict[str, Any]:
|
||
"""Live utilization snapshot for the primary GPU plus all visible GPUs."""
|
||
device = get_device()
|
||
|
||
if device == DeviceType.XPU:
|
||
result = get_visible_gpu_utilization()
|
||
return _gpu_utilization_payload(
|
||
device,
|
||
result.get("devices", []),
|
||
parent_visible_gpu_ids = result.get("parent_visible_gpu_ids", []),
|
||
index_kind = result.get("index_kind"),
|
||
)
|
||
|
||
if device == DeviceType.CUDA:
|
||
parent_visible_spec = _get_parent_visible_gpu_spec()
|
||
result = _smi_query(
|
||
"get_visible_gpu_utilization",
|
||
parent_visible_spec["numeric_ids"],
|
||
parent_cuda_visible_devices = parent_visible_spec["raw"],
|
||
)
|
||
if result is not None and "devices" in result:
|
||
devices = result["devices"]
|
||
numeric_ids = parent_visible_spec.get("numeric_ids")
|
||
if IS_ROCM and numeric_ids is not None:
|
||
_reconcile_rocm_unified_memory(result, numeric_ids)
|
||
|
||
return _gpu_utilization_payload(
|
||
device,
|
||
devices,
|
||
backend_cuda_visible_devices = result.get("backend_cuda_visible_devices"),
|
||
parent_visible_gpu_ids = result.get("parent_visible_gpu_ids", []),
|
||
index_kind = result.get("index_kind"),
|
||
)
|
||
|
||
# Fallback Windows ROCm: per-adapter VRAM attribution (issue #7072), so
|
||
# every visible GPU is shown instead of a sum collapsed onto one device.
|
||
if IS_ROCM and platform.system() == "Windows":
|
||
_win_ids = _get_parent_visible_gpu_spec().get("numeric_ids")
|
||
if not _win_ids:
|
||
_win_ids = list(range(_torch_get_physical_gpu_count() or 0))
|
||
_win_devices = _rocm_windows_per_device_vram(_win_ids)
|
||
if _win_devices:
|
||
# A single visible GPU can own the aggregate 3D-engine utilization;
|
||
# across several GPUs the sum isn't per-device, so leave it unset.
|
||
_win_util = (
|
||
_rocm_windows_perf_counter_gpu_util_pct() if len(_win_devices) == 1 else None
|
||
)
|
||
return _gpu_utilization_payload(
|
||
device,
|
||
[
|
||
_rocm_windows_device_payload_entry(device, _wd, _win_util)
|
||
for _wd in _win_devices
|
||
],
|
||
)
|
||
|
||
# Fallback Linux ROCm
|
||
if IS_ROCM and platform.system() == "Linux":
|
||
_linux_used, _linux_total = _rocm_linux_sysfs_vram_gb()
|
||
if _linux_used is not None and _linux_total is not None:
|
||
_linux_util = _rocm_linux_sysfs_gpu_busy_pct()
|
||
_linux_temp = _rocm_linux_sysfs_temp_c()
|
||
_linux_power = _rocm_linux_sysfs_power_w()
|
||
return _gpu_utilization_payload(
|
||
device,
|
||
[
|
||
{
|
||
"available": True,
|
||
"backend": _backend_label(device),
|
||
"index": 0,
|
||
"visible_ordinal": 0,
|
||
"gpu_utilization_pct": _linux_util,
|
||
"temperature_c": _linux_temp,
|
||
"vram_used_gb": _linux_used,
|
||
"vram_total_gb": _linux_total,
|
||
"vram_utilization_pct": round((_linux_used / _linux_total) * 100, 1)
|
||
if _linux_total > 0
|
||
else None,
|
||
"power_draw_w": _linux_power,
|
||
"power_limit_w": None,
|
||
"power_utilization_pct": None,
|
||
}
|
||
],
|
||
)
|
||
|
||
# Last resort: torch mem_get_info (process-local) for all visible GPUs
|
||
_visible_spec = _get_parent_visible_gpu_spec()
|
||
_numeric_ids = _visible_spec.get("numeric_ids") or []
|
||
if not _numeric_ids:
|
||
visible_count = _torch_get_physical_gpu_count() or 0
|
||
_numeric_ids = list(range(visible_count))
|
||
|
||
_torch_devices = _torch_get_per_device_info(_numeric_ids)
|
||
if _torch_devices:
|
||
gpu_array = []
|
||
for _td in _torch_devices:
|
||
_total = _td["total_gb"]
|
||
_used = _td["used_gb"]
|
||
gpu_array.append(
|
||
{
|
||
"available": True,
|
||
"backend": _backend_label(device),
|
||
"index": _td["index"],
|
||
"name": _td.get("name", "Unknown"),
|
||
"gpu_utilization_pct": None,
|
||
"temperature_c": None,
|
||
"vram_used_gb": _used,
|
||
"vram_total_gb": _total,
|
||
"vram_utilization_pct": round((_used / _total) * 100, 1)
|
||
if _total > 0 and _used is not None
|
||
else None,
|
||
"power_draw_w": None,
|
||
"power_limit_w": None,
|
||
"power_utilization_pct": None,
|
||
}
|
||
)
|
||
return _gpu_utilization_payload(device, gpu_array)
|
||
|
||
# MLX
|
||
if device == DeviceType.MLX:
|
||
try:
|
||
import psutil
|
||
agx = _read_apple_gpu_stats()
|
||
total_bytes = psutil.virtual_memory().total
|
||
except Exception as e:
|
||
logger.error(f"Error getting MLX GPU utilization: {e}")
|
||
return {"available": False, "backend": device.value, "devices": [], "error": str(e)}
|
||
|
||
allocated_bytes = agx.get("vram_used_bytes", 0) or 0
|
||
vram_used_gb = allocated_bytes / (1024**3)
|
||
total_gb = total_bytes / (1024**3)
|
||
|
||
try:
|
||
from core.training import get_training_backend
|
||
|
||
tb = get_training_backend()
|
||
tb_progress = getattr(tb, "_progress", None)
|
||
if tb_progress is not None and getattr(tb_progress, "is_training", False):
|
||
tb_peak = getattr(tb_progress, "peak_memory_gb", None)
|
||
if tb_peak is not None and tb_peak > 0:
|
||
vram_used_gb = float(tb_peak)
|
||
except Exception:
|
||
pass
|
||
|
||
from . import apple
|
||
|
||
return _gpu_utilization_payload(
|
||
device,
|
||
[
|
||
{
|
||
"available": True,
|
||
"backend": device.value,
|
||
"index": 0,
|
||
"visible_ordinal": 0,
|
||
"gpu_utilization_pct": agx.get("utilization_pct") if agx else None,
|
||
"temperature_c": apple.read_gpu_temperature_c(),
|
||
"vram_used_gb": round(vram_used_gb, 2),
|
||
"vram_total_gb": round(total_gb, 2),
|
||
"vram_utilization_pct": round((vram_used_gb / total_gb) * 100, 1)
|
||
if total_gb > 0
|
||
else None,
|
||
"power_draw_w": apple.read_gpu_power_w(),
|
||
"power_limit_w": None,
|
||
"power_utilization_pct": None,
|
||
}
|
||
],
|
||
)
|
||
|
||
mem = get_gpu_memory_info()
|
||
if device != DeviceType.CPU and mem.get("available"):
|
||
return _gpu_utilization_payload(
|
||
device,
|
||
[
|
||
{
|
||
"available": True,
|
||
"backend": _backend_label(device),
|
||
"index": mem.get("device", 0),
|
||
"visible_ordinal": 0,
|
||
"gpu_utilization_pct": None,
|
||
"temperature_c": None,
|
||
"vram_used_gb": round(mem.get("allocated_gb", 0), 2),
|
||
"vram_total_gb": round(mem.get("total_gb", 0), 2),
|
||
"vram_utilization_pct": round(mem.get("utilization_pct", 0), 1),
|
||
"power_draw_w": None,
|
||
"power_limit_w": None,
|
||
"power_utilization_pct": None,
|
||
}
|
||
],
|
||
)
|
||
|
||
return {"available": False, "backend": _backend_label(device), "devices": []}
|
||
|
||
|
||
def _apply_unified_memory_correction(
|
||
device_metrics: Dict[str, Any], torch_info: Dict[str, Any]
|
||
) -> None:
|
||
"""Per-device reconciliation: when torch reports a larger memory total
|
||
than amd-smi, overwrite the smi VRAM fields in place.
|
||
|
||
Used by both the multi-device and primary-device reconcilers so the two
|
||
endpoints stay in sync on AMD iGPUs with unified memory.
|
||
"""
|
||
torch_total_gb = torch_info["total_gb"]
|
||
torch_used_gb = torch_info.get("used_gb")
|
||
smi_total_gb = device_metrics.get("vram_total_gb") or 0.0
|
||
# torch sees the full unified (GTT) pool; amd-smi only the dedicated carve-out.
|
||
# Adopt torch's larger total regardless of used: on Windows ROCm torch_used is
|
||
# None (free==total sentinel) but its total stays authoritative. Overwrite used
|
||
# only when torch's is known, then recompute utilization against whatever remains.
|
||
if torch_total_gb > smi_total_gb:
|
||
device_metrics["vram_total_gb"] = torch_total_gb
|
||
if torch_used_gb is not None:
|
||
device_metrics["vram_used_gb"] = torch_used_gb
|
||
_used_for_pct = device_metrics.get("vram_used_gb")
|
||
device_metrics["vram_utilization_pct"] = (
|
||
round((_used_for_pct / torch_total_gb) * 100, 1)
|
||
if torch_total_gb > 0 and _used_for_pct is not None
|
||
else None
|
||
)
|
||
logger.debug(
|
||
"ROCm unified memory: adopted torch mem_get_info total (%.2f GB) over "
|
||
"amd-smi (%.2f GB) for device %s",
|
||
torch_total_gb,
|
||
smi_total_gb,
|
||
torch_info.get("index"),
|
||
)
|
||
|
||
|
||
def _reconcile_rocm_unified_memory(utilization: Dict[str, Any], device_indices: list[int]) -> None:
|
||
"""Fix amd-smi VRAM for ROCm unified-memory GPUs (e.g. Strix Halo).
|
||
|
||
amd-smi reports only the dedicated slice; torch sees the full GTT pool. When
|
||
torch total > smi total, overwrite per-device VRAM fields with the real value.
|
||
"""
|
||
torch_devices = _torch_get_per_device_info(device_indices)
|
||
if not torch_devices:
|
||
return
|
||
torch_by_index = {td["index"]: td for td in torch_devices}
|
||
for dev in utilization.get("devices", []):
|
||
td = torch_by_index.get(dev.get("index"))
|
||
if td is None:
|
||
continue
|
||
_apply_unified_memory_correction(dev, td)
|
||
|
||
|
||
def _reconcile_primary_rocm_unified_memory(
|
||
utilization: Dict[str, Any], parent_visible_spec: Dict[str, Any]
|
||
) -> None:
|
||
"""Same fix as _reconcile_rocm_unified_memory for the flat primary-GPU dict."""
|
||
numeric_ids = parent_visible_spec.get("numeric_ids")
|
||
if numeric_ids is None:
|
||
# No visibility env var set: torch ordinal 0 is the primary device.
|
||
primary_idx = [0]
|
||
elif len(numeric_ids) == 0:
|
||
# Empty mask: no GPU visible. Querying torch device 0 would raise or
|
||
# return stale data, so bail rather than write bad values.
|
||
return
|
||
else:
|
||
primary_idx = [int(numeric_ids[0])]
|
||
torch_devices = _torch_get_per_device_info(primary_idx)
|
||
if not torch_devices:
|
||
return
|
||
_apply_unified_memory_correction(utilization, torch_devices[0])
|
||
|
||
|
||
def get_visible_gpu_utilization() -> Dict[str, Any]:
|
||
device = get_device()
|
||
|
||
if device == DeviceType.CUDA:
|
||
parent_visible_spec = _get_parent_visible_gpu_spec()
|
||
result = _smi_query(
|
||
"get_visible_gpu_utilization",
|
||
parent_visible_spec["numeric_ids"],
|
||
parent_cuda_visible_devices = parent_visible_spec["raw"],
|
||
)
|
||
if result is not None:
|
||
result["backend"] = _backend_label(device)
|
||
numeric_ids = parent_visible_spec.get("numeric_ids")
|
||
if IS_ROCM and numeric_ids is not None:
|
||
# Fix unified-memory VRAM on AMD iGPUs (Strix Halo etc.).
|
||
_reconcile_rocm_unified_memory(result, numeric_ids)
|
||
return result
|
||
|
||
# Windows AMD/ROCm (issue #7072): the System tab's VRAM source. The torch
|
||
# fallback below would report used==0 (free==total), so read per-adapter
|
||
# Dedicated Usage instead; total from torch properties.
|
||
if IS_ROCM and platform.system() == "Windows":
|
||
win_numeric_ids = parent_visible_spec.get("numeric_ids")
|
||
if win_numeric_ids:
|
||
win_ids = win_numeric_ids
|
||
win_index_kind = "physical"
|
||
else:
|
||
win_ids = list(range(_torch_get_physical_gpu_count() or 0))
|
||
win_index_kind = "relative"
|
||
win_devices = _rocm_windows_per_device_vram(win_ids)
|
||
if win_devices:
|
||
devices = []
|
||
for wd in win_devices:
|
||
total = wd["total_gb"]
|
||
used = wd["used_gb"]
|
||
devices.append(
|
||
{
|
||
"index": wd["index"],
|
||
"index_kind": win_index_kind,
|
||
"visible_ordinal": wd["visible_ordinal"],
|
||
"name": wd.get("name"),
|
||
"gpu_utilization_pct": None,
|
||
"temperature_c": None,
|
||
"vram_used_gb": used,
|
||
"vram_total_gb": total,
|
||
"vram_utilization_pct": round((used / total) * 100, 1)
|
||
if total and total > 0 and used is not None
|
||
else None,
|
||
"power_draw_w": None,
|
||
"power_limit_w": None,
|
||
"power_utilization_pct": None,
|
||
}
|
||
)
|
||
return {
|
||
"available": True,
|
||
"backend": _backend_label(device),
|
||
"parent_visible_gpu_ids": win_numeric_ids or [],
|
||
"devices": devices,
|
||
"index_kind": win_index_kind,
|
||
}
|
||
|
||
# Torch-based fallback for CUDA (nvidia-smi unavailable, AMD ROCm) and XPU (Intel)
|
||
if device in (DeviceType.CUDA, DeviceType.XPU):
|
||
parent_ids = get_parent_visible_gpu_ids()
|
||
# Empty parent_ids (UUID/MIG mask or no CVD): enumerate torch ordinals.
|
||
if parent_ids:
|
||
torch_indices = parent_ids
|
||
index_kind = "physical"
|
||
else:
|
||
visible_count = _torch_get_physical_gpu_count() or 0
|
||
torch_indices = list(range(visible_count))
|
||
index_kind = "relative"
|
||
torch_devices = _torch_get_per_device_info(torch_indices)
|
||
if torch_devices:
|
||
devices = []
|
||
for td in torch_devices:
|
||
total = td["total_gb"]
|
||
used = td["used_gb"]
|
||
devices.append(
|
||
{
|
||
"index": td["index"],
|
||
"index_kind": index_kind,
|
||
"visible_ordinal": td["visible_ordinal"],
|
||
"gpu_utilization_pct": None,
|
||
"temperature_c": None,
|
||
"vram_used_gb": used,
|
||
"vram_total_gb": total,
|
||
"vram_utilization_pct": round((used / total) * 100, 1)
|
||
if total > 0 and used is not None
|
||
else None,
|
||
"power_draw_w": None,
|
||
"power_limit_w": None,
|
||
"power_utilization_pct": None,
|
||
}
|
||
)
|
||
return {
|
||
"available": True,
|
||
"backend": _backend_label(device),
|
||
"parent_visible_gpu_ids": parent_ids,
|
||
"devices": devices,
|
||
"index_kind": index_kind,
|
||
}
|
||
|
||
if device == DeviceType.MLX:
|
||
mem = get_gpu_memory_info()
|
||
if not mem.get("available"):
|
||
return {
|
||
"available": False,
|
||
"backend": _backend_label(device),
|
||
"parent_visible_gpu_ids": [],
|
||
"devices": [],
|
||
"index_kind": "relative",
|
||
}
|
||
return {
|
||
"available": True,
|
||
"backend": _backend_label(device),
|
||
"parent_visible_gpu_ids": [0],
|
||
"devices": [
|
||
{
|
||
"index": 0,
|
||
"index_kind": "relative",
|
||
"visible_ordinal": 0,
|
||
"gpu_utilization_pct": None,
|
||
"temperature_c": None,
|
||
"vram_used_gb": round(mem.get("allocated_gb", 0), 2),
|
||
"vram_total_gb": round(mem.get("total_gb", 0), 2),
|
||
"vram_utilization_pct": round(mem.get("utilization_pct", 0), 1),
|
||
"power_draw_w": None,
|
||
"power_limit_w": None,
|
||
"power_utilization_pct": None,
|
||
}
|
||
],
|
||
"index_kind": "relative",
|
||
}
|
||
|
||
return {
|
||
"available": False,
|
||
"backend": _backend_label(device),
|
||
"parent_visible_gpu_ids": [],
|
||
"devices": [],
|
||
"index_kind": "relative",
|
||
}
|
||
|
||
|
||
# ========== Multi-GPU Detection & Safe num_proc ==========
|
||
|
||
_physical_gpu_count: Optional[int] = None
|
||
_visible_gpu_count: Optional[int] = None
|
||
|
||
|
||
def _get_parent_visible_gpu_spec() -> Dict[str, Any]:
|
||
# ROCm uses HIP/ROCR_VISIBLE_DEVICES on top of CUDA_VISIBLE_DEVICES; check
|
||
# them first. Explicit None checks (not `or`) so "" reads as "no visible GPUs".
|
||
cuda_visible = None
|
||
# Prefer ROCm masks only on a ROCm host or when no CUDA mask is set, so a
|
||
# stale HIP_VISIBLE_DEVICES on NVIDIA can't override CUDA_VISIBLE_DEVICES.
|
||
_is_rocm_spec = IS_ROCM or (
|
||
"CUDA_VISIBLE_DEVICES" not in os.environ
|
||
and ("HIP_VISIBLE_DEVICES" in os.environ or "ROCR_VISIBLE_DEVICES" in os.environ)
|
||
)
|
||
if _is_rocm_spec:
|
||
hip_vis = os.environ.get("HIP_VISIBLE_DEVICES")
|
||
rocr_vis = os.environ.get("ROCR_VISIBLE_DEVICES")
|
||
if hip_vis is not None:
|
||
cuda_visible = hip_vis
|
||
elif rocr_vis is not None:
|
||
cuda_visible = rocr_vis
|
||
if cuda_visible is None:
|
||
cuda_visible = os.environ.get("CUDA_VISIBLE_DEVICES")
|
||
|
||
if cuda_visible is None:
|
||
return {
|
||
"raw": None,
|
||
"numeric_ids": list(range(get_physical_gpu_count())),
|
||
"supports_explicit_gpu_ids": True,
|
||
}
|
||
|
||
cuda_visible = cuda_visible.strip()
|
||
if cuda_visible == "" or cuda_visible == "-1":
|
||
return {
|
||
"raw": cuda_visible,
|
||
"numeric_ids": [],
|
||
"supports_explicit_gpu_ids": True,
|
||
}
|
||
|
||
tokens = [value.strip() for value in cuda_visible.split(",") if value.strip()]
|
||
try:
|
||
numeric_ids = [int(value) for value in tokens]
|
||
except ValueError:
|
||
return {
|
||
"raw": cuda_visible,
|
||
"numeric_ids": None,
|
||
"supports_explicit_gpu_ids": False,
|
||
}
|
||
|
||
return {
|
||
"raw": cuda_visible,
|
||
"numeric_ids": numeric_ids,
|
||
"supports_explicit_gpu_ids": True,
|
||
}
|
||
|
||
|
||
def get_parent_visible_gpu_ids() -> list[int]:
|
||
parent_visible_ids = _get_parent_visible_gpu_spec()["numeric_ids"]
|
||
return list(parent_visible_ids) if parent_visible_ids is not None else []
|
||
|
||
|
||
def resolve_requested_gpu_ids(gpu_ids: Optional[list[int]]) -> list[int]:
|
||
parent_visible_spec = _get_parent_visible_gpu_spec()
|
||
parent_visible_ids = get_parent_visible_gpu_ids()
|
||
physical_gpu_count = get_physical_gpu_count()
|
||
|
||
if gpu_ids is None:
|
||
return parent_visible_ids
|
||
|
||
requested_ids = list(gpu_ids)
|
||
if len(requested_ids) == 0:
|
||
return parent_visible_ids
|
||
|
||
if not parent_visible_spec["supports_explicit_gpu_ids"]:
|
||
raise ValueError(
|
||
f"Invalid gpu_ids {requested_ids}: explicit physical GPU IDs are "
|
||
f"unsupported when CUDA_VISIBLE_DEVICES uses UUID/MIG entries "
|
||
f"({parent_visible_spec['raw']!r}). Omit gpu_ids to use the "
|
||
"parent-visible devices."
|
||
)
|
||
|
||
if len(set(requested_ids)) != len(requested_ids):
|
||
raise ValueError(
|
||
f"Invalid gpu_ids {requested_ids}: duplicate GPU IDs are not allowed. "
|
||
f"Parent-visible GPUs: {parent_visible_ids}"
|
||
)
|
||
|
||
# Reject negative IDs.
|
||
negative_ids = [gpu_id for gpu_id in requested_ids if gpu_id < 0]
|
||
if negative_ids:
|
||
raise ValueError(
|
||
f"Invalid gpu_ids {requested_ids}: GPU IDs must be non-negative. "
|
||
f"Rejected IDs: {negative_ids}. Parent-visible GPUs: {parent_visible_ids}"
|
||
)
|
||
|
||
# Only enforce the physical upper bound when the count is reliable (nvidia-smi).
|
||
# A torch count reflects only visible devices, so it could falsely reject valid
|
||
# physical indices. The parent-visible check below is always authoritative.
|
||
if physical_gpu_count > 0 and parent_visible_ids:
|
||
max_parent_id = max(parent_visible_ids)
|
||
if physical_gpu_count > max_parent_id:
|
||
# Count is plausibly physical, so enforce it.
|
||
out_of_range = [gpu_id for gpu_id in requested_ids if gpu_id >= physical_gpu_count]
|
||
if out_of_range:
|
||
raise ValueError(
|
||
f"Invalid gpu_ids {requested_ids}: IDs must be physical GPU IDs "
|
||
f"between 0 and {physical_gpu_count - 1}. "
|
||
f"Rejected IDs: {out_of_range}. Parent-visible GPUs: {parent_visible_ids}"
|
||
)
|
||
|
||
disallowed_ids = [gpu_id for gpu_id in requested_ids if gpu_id not in parent_visible_ids]
|
||
if disallowed_ids:
|
||
raise ValueError(
|
||
f"Invalid gpu_ids {requested_ids}: requested GPUs {disallowed_ids} are "
|
||
f"outside the parent-visible set {parent_visible_ids}"
|
||
)
|
||
|
||
return requested_ids
|
||
|
||
|
||
def _resolve_model_identifier_for_gpu_estimate(
|
||
model_name: str, hf_token: Optional[str] = None
|
||
) -> str:
|
||
try:
|
||
from utils.models.model_config import ModelConfig
|
||
|
||
config = ModelConfig.from_identifier(model_name, hf_token = hf_token)
|
||
if config and config.is_lora and config.base_model:
|
||
return config.base_model
|
||
return config.identifier if config else model_name
|
||
except Exception as e:
|
||
logger.debug("Could not resolve base model for GPU estimate '%s': %s", model_name, e)
|
||
return model_name
|
||
|
||
|
||
def _get_local_weight_size_bytes(model_name: str) -> Optional[int]:
|
||
model_path = Path(model_name)
|
||
if not model_path.exists():
|
||
return None
|
||
|
||
weight_exts = (".safetensors", ".bin", ".pt", ".pth")
|
||
# Skip intermediate training checkpoints: a run dir can hold several
|
||
# checkpoint-*/global_step* snapshots, but export loads only the model at
|
||
# the root, so counting them would multiply the estimate.
|
||
skip_prefixes = ("checkpoint-", "global_step")
|
||
total = 0
|
||
for file in model_path.rglob("*"):
|
||
if not file.is_file() or file.suffix not in weight_exts:
|
||
continue
|
||
rel = file.relative_to(model_path)
|
||
if any(part.startswith(skip_prefixes) for part in rel.parts):
|
||
continue
|
||
total += file.stat().st_size
|
||
return total if total > 0 else None
|
||
|
||
|
||
def _get_hf_safetensors_total_params(
|
||
model_name: str, hf_token: Optional[str] = None
|
||
) -> Optional[int]:
|
||
try:
|
||
from huggingface_hub import model_info as hf_model_info
|
||
|
||
info = hf_model_info(model_name, token = hf_token)
|
||
safetensors = getattr(info, "safetensors", None)
|
||
if isinstance(safetensors, dict):
|
||
total = safetensors.get("total")
|
||
if total:
|
||
return int(total)
|
||
except Exception as e:
|
||
logger.warning("Could not get safetensors metadata for '%s': %s", model_name, e)
|
||
return None
|
||
|
||
|
||
def _load_config_for_gpu_estimate(model_name: str, hf_token: Optional[str] = None):
|
||
# Estimation needs only declarative config.json fields, and this probe runs
|
||
# on model selection, so read raw config.json (never run auto_map Python) and
|
||
# expose it as an attribute namespace for downstream getattr access.
|
||
try:
|
||
from utils.transformers_version import _load_config_json
|
||
|
||
cfg = _load_config_json(model_name, hf_token = hf_token)
|
||
if cfg is None:
|
||
return None
|
||
|
||
def _to_ns(d):
|
||
if isinstance(d, dict):
|
||
return types.SimpleNamespace(**{k: _to_ns(v) for k, v in d.items()})
|
||
return d
|
||
|
||
return _to_ns(cfg)
|
||
except Exception as e:
|
||
# A 5.x-only config can't be parsed by the default transformers; that is
|
||
# expected (the worker reloads under the sidecar), so only warn for default tier.
|
||
tier = "default"
|
||
try:
|
||
from utils.transformers_version import get_transformers_tier
|
||
tier = get_transformers_tier(model_name)
|
||
except Exception:
|
||
pass
|
||
if tier != "default":
|
||
_tier_version = {"510": "5.10.x", "530": "5.3.0", "550": "5.5.0"}.get(tier, "5.x")
|
||
logger.info(
|
||
"Config for '%s' not parseable by the default transformers; "
|
||
"needs transformers %s and will be loaded with that sidecar in the worker",
|
||
model_name,
|
||
_tier_version,
|
||
)
|
||
else:
|
||
logger.warning("Could not load config for '%s': %s", model_name, e)
|
||
return None
|
||
|
||
|
||
def _determine_attention_impl_for_gpu_estimate(config) -> str:
|
||
# torch.distributed is incomplete on Windows ROCm (torch._C._distributed_c10d
|
||
# can't be imported). Inject stubs into sys.modules before importing
|
||
# torch.distributed, then patch the missing process-group helpers.
|
||
if sys.platform == "win32" and IS_ROCM:
|
||
# Dummy for any name torch.distributed imports from these stubs.
|
||
class _Dummy:
|
||
pass
|
||
|
||
for _c10d_name in (
|
||
"torch._C._distributed_c10d",
|
||
"torch._C._distributed_autograd",
|
||
"torch._C._distributed_rpc",
|
||
):
|
||
if _c10d_name not in sys.modules:
|
||
_stub = types.ModuleType(_c10d_name)
|
||
# No-op dummies for names torch.distributed imports from _distributed_c10d.
|
||
for _sym in (
|
||
"FakeProcessGroup",
|
||
"ProcessGroup",
|
||
"Work",
|
||
"Store",
|
||
"PrefixStore",
|
||
"FileStore",
|
||
"TCPStore",
|
||
"HashStore",
|
||
"Reducer",
|
||
"Logger",
|
||
"DistributedDebugLevel",
|
||
"GradBucket",
|
||
"BuiltinCommHookType",
|
||
):
|
||
setattr(_stub, _sym, _Dummy)
|
||
sys.modules[_c10d_name] = _stub
|
||
|
||
try:
|
||
import torch.distributed as _td
|
||
for _attr, _stub in (
|
||
("is_initialized", lambda: False),
|
||
("is_available", lambda: False),
|
||
("get_rank", lambda: 0),
|
||
("get_world_size", lambda: 1),
|
||
("is_torchelastic_launched", lambda: False),
|
||
):
|
||
if not hasattr(_td, _attr):
|
||
setattr(_td, _attr, _stub)
|
||
except ImportError:
|
||
pass
|
||
|
||
from unsloth.models._utils import resolve_attention_implementation
|
||
from transformers import AutoModel, AutoModelForCausalLM
|
||
|
||
# why: resolve_attention_implementation writes _attn_implementation onto the
|
||
# config and propagates to nested sub-configs; a shallow copy would still
|
||
# mutate the cached config's shared inner objects. Deepcopy isolates them.
|
||
config_copy = copy.deepcopy(config)
|
||
|
||
model_class = None
|
||
for auto_model in (AutoModelForCausalLM, AutoModel):
|
||
mapping = getattr(auto_model, "_model_mapping", None)
|
||
if mapping is None:
|
||
continue
|
||
try:
|
||
if config_copy.__class__ in mapping:
|
||
model_class = mapping[config_copy.__class__]
|
||
break
|
||
except Exception:
|
||
continue
|
||
|
||
return resolve_attention_implementation(model_class, config_copy)
|
||
|
||
|
||
def _estimate_fp16_model_size_bytes_from_config(config) -> Optional[int]:
|
||
from .vram_estimation import extract_arch_config, compute_total_params
|
||
|
||
arch = extract_arch_config(config)
|
||
if arch is None:
|
||
return None
|
||
return compute_total_params(arch) * 2
|
||
|
||
|
||
def _estimate_fp16_model_size_bytes_from_vllm_utils(config) -> Optional[int]:
|
||
if config is None:
|
||
return None
|
||
|
||
previous_unsloth_present = os.environ.get("UNSLOTH_IS_PRESENT")
|
||
os.environ["UNSLOTH_IS_PRESENT"] = "1"
|
||
try:
|
||
from unsloth_zoo import vllm_utils as _vllm_utils
|
||
|
||
synthetic_total_bytes = 1024 * (1024**3)
|
||
original_get_mem_info = _vllm_utils.get_mem_info
|
||
try:
|
||
_vllm_utils.get_mem_info = lambda: (
|
||
synthetic_total_bytes,
|
||
synthetic_total_bytes,
|
||
)
|
||
_, _, _, memory_left_for_kv_cache_gb = _vllm_utils.approximate_vllm_memory_usage(
|
||
config,
|
||
load_in_4bit = False,
|
||
load_in_8bit = False,
|
||
max_seq_length = 1,
|
||
gpu_memory_utilization = 1.0,
|
||
enable_lora = False,
|
||
account_for_gradients = False,
|
||
cuda_graph_overhead = False,
|
||
)
|
||
finally:
|
||
_vllm_utils.get_mem_info = original_get_mem_info
|
||
except Exception as e:
|
||
logger.debug("Could not estimate model size via vllm_utils: %s", e)
|
||
return None
|
||
finally:
|
||
if previous_unsloth_present is None:
|
||
os.environ.pop("UNSLOTH_IS_PRESENT", None)
|
||
else:
|
||
os.environ["UNSLOTH_IS_PRESENT"] = previous_unsloth_present
|
||
|
||
model_size_gb = 1024.0 - memory_left_for_kv_cache_gb
|
||
if model_size_gb <= 0:
|
||
return None
|
||
return int(round(model_size_gb * (1024**3)))
|
||
|
||
|
||
def estimate_fp16_model_size_bytes(
|
||
model_name: str, hf_token: Optional[str] = None
|
||
) -> tuple[Optional[int], str]:
|
||
estimate_model = _resolve_model_identifier_for_gpu_estimate(model_name, hf_token = hf_token)
|
||
|
||
total_params = None
|
||
if "/" in estimate_model and not Path(estimate_model).exists():
|
||
total_params = _get_hf_safetensors_total_params(estimate_model, hf_token = hf_token)
|
||
if total_params:
|
||
return int(total_params * 2), "safetensors"
|
||
|
||
config = _load_config_for_gpu_estimate(estimate_model, hf_token = hf_token)
|
||
config_bytes: Optional[int] = None
|
||
if config is not None:
|
||
config_bytes = _estimate_fp16_model_size_bytes_from_config(config)
|
||
|
||
local_bytes = _get_local_weight_size_bytes(estimate_model)
|
||
|
||
# why: config-derived bytes cover only the text tower; local safetensors
|
||
# include vision/audio towers. Take the larger so the multimodal
|
||
# extra_bytes correction can fire.
|
||
if config_bytes is not None and local_bytes is not None:
|
||
if local_bytes > config_bytes:
|
||
return local_bytes, "weight_bytes"
|
||
return config_bytes, "config"
|
||
if config_bytes is not None:
|
||
return config_bytes, "config"
|
||
if local_bytes is not None:
|
||
return local_bytes, "weight_bytes"
|
||
|
||
vllm_bytes = _estimate_fp16_model_size_bytes_from_vllm_utils(config)
|
||
if vllm_bytes is not None:
|
||
return vllm_bytes, "vllm_utils"
|
||
|
||
return None, "unavailable"
|
||
|
||
|
||
def estimate_required_model_memory_gb(
|
||
model_name: str,
|
||
*,
|
||
hf_token: Optional[str] = None,
|
||
training_type: Optional[str] = None,
|
||
load_in_4bit: bool = True,
|
||
batch_size: int = 4,
|
||
max_seq_length: int = 2048,
|
||
lora_rank: int = 16,
|
||
target_modules: Optional[list] = None,
|
||
gradient_checkpointing: str = "unsloth",
|
||
optimizer: str = "adamw_8bit",
|
||
) -> tuple[Optional[float], Dict[str, Any]]:
|
||
from .vram_estimation import (
|
||
TrainingVramConfig,
|
||
extract_arch_config,
|
||
estimate_training_vram,
|
||
compute_total_params,
|
||
compute_optimizer_bytes,
|
||
compute_gradient_bytes,
|
||
CUDA_OVERHEAD_BYTES,
|
||
QUANT_4BIT_FACTOR,
|
||
DEFAULT_TARGET_MODULES,
|
||
)
|
||
|
||
model_size_bytes, source = estimate_fp16_model_size_bytes(model_name, hf_token = hf_token)
|
||
metadata: Dict[str, Any] = {
|
||
"mode": "inference" if training_type is None else "training",
|
||
"model_size_source": source,
|
||
}
|
||
if model_size_bytes is None:
|
||
metadata["required_gb"] = None
|
||
return None, metadata
|
||
|
||
model_size_gb = model_size_bytes / (1024**3)
|
||
metadata["model_size_gb"] = round(model_size_gb, 3)
|
||
min_buffer_gb = 2.0
|
||
|
||
if training_type is None:
|
||
if load_in_4bit:
|
||
base_4bit_gb = model_size_gb / QUANT_4BIT_FACTOR
|
||
required_gb = base_4bit_gb + max(base_4bit_gb * 0.3, min_buffer_gb)
|
||
else:
|
||
required_gb = model_size_gb * 1.3
|
||
metadata["required_gb"] = round(required_gb, 3)
|
||
return required_gb, metadata
|
||
|
||
training_method = (
|
||
"full" if training_type == "Full Finetuning" else ("qlora" if load_in_4bit else "lora")
|
||
)
|
||
vram_config = TrainingVramConfig(
|
||
training_method = training_method,
|
||
batch_size = batch_size,
|
||
max_seq_length = max_seq_length,
|
||
lora_rank = lora_rank,
|
||
target_modules = target_modules or list(DEFAULT_TARGET_MODULES),
|
||
gradient_checkpointing = gradient_checkpointing,
|
||
optimizer = optimizer,
|
||
load_in_4bit = load_in_4bit,
|
||
)
|
||
|
||
estimate_model = _resolve_model_identifier_for_gpu_estimate(model_name, hf_token = hf_token)
|
||
config = _load_config_for_gpu_estimate(estimate_model, hf_token = hf_token)
|
||
if config is not None:
|
||
try:
|
||
vram_config.attention_implementation = _determine_attention_impl_for_gpu_estimate(
|
||
config
|
||
)
|
||
except Exception as e:
|
||
# Debug-level: fires every estimate on Windows ROCm (stub lacks Store);
|
||
# expected and non-actionable -- eager is the safe fallback.
|
||
logger.debug(
|
||
"Could not resolve attention implementation for '%s': %s",
|
||
estimate_model,
|
||
e,
|
||
)
|
||
# why: charge the quadratic non-flash activation path so GPU
|
||
# selection stays conservative when flash attn isn't proven usable.
|
||
vram_config.attention_implementation = "eager"
|
||
arch = extract_arch_config(config) if config is not None else None
|
||
|
||
if arch is not None:
|
||
breakdown = estimate_training_vram(arch, vram_config)
|
||
# why: extract_arch_config only sees text_config; add the vision/audio
|
||
# tower bytes that the text-arch fp16 total misses.
|
||
arch_fp16_bytes = compute_total_params(arch) * 2
|
||
extra_bytes = max(0, int(model_size_bytes) - arch_fp16_bytes)
|
||
if extra_bytes > 0:
|
||
breakdown.model_weights += extra_bytes
|
||
if training_method == "full":
|
||
# why: full fine-tuning makes extra params trainable; optimizer +
|
||
# gradient bytes scale with them.
|
||
extra_params = extra_bytes // 2
|
||
breakdown.optimizer_states += compute_optimizer_bytes(
|
||
extra_params,
|
||
vram_config.optimizer,
|
||
)
|
||
breakdown.gradients += compute_gradient_bytes(extra_params)
|
||
required_gb = breakdown.total / (1024**3)
|
||
metadata["required_gb"] = round(required_gb, 3)
|
||
metadata["estimation_mode"] = "detailed"
|
||
metadata["attention_implementation"] = vram_config.attention_implementation
|
||
metadata["vram_breakdown"] = breakdown.to_gb_dict()
|
||
max_gpus = max(1, get_visible_gpu_count())
|
||
for n_gpus in range(1, max_gpus + 1):
|
||
metadata["vram_breakdown"][f"min_per_gpu_{n_gpus}"] = round(
|
||
breakdown.min_gpu_vram(n_gpus) / (1024**3), 3
|
||
)
|
||
return required_gb, metadata
|
||
|
||
# Fallback when model config is unavailable.
|
||
overhead_gb = CUDA_OVERHEAD_BYTES / (1024**3)
|
||
if training_method == "full":
|
||
required_gb = model_size_gb * 3.5 + overhead_gb
|
||
elif training_method == "qlora":
|
||
base_4bit_gb = model_size_gb / QUANT_4BIT_FACTOR
|
||
lora_overhead_gb = model_size_gb * 0.04
|
||
act_gb = model_size_gb * 0.15 * (batch_size / 4) * (max_seq_length / 2048)
|
||
required_gb = base_4bit_gb + lora_overhead_gb + act_gb + overhead_gb
|
||
else:
|
||
lora_overhead_gb = model_size_gb * 0.04
|
||
act_gb = model_size_gb * 0.15 * (batch_size / 4) * (max_seq_length / 2048)
|
||
required_gb = model_size_gb + lora_overhead_gb + act_gb + overhead_gb
|
||
|
||
metadata["required_gb"] = round(required_gb, 3)
|
||
metadata["estimation_mode"] = "fallback"
|
||
return required_gb, metadata
|
||
|
||
|
||
def auto_select_gpu_ids(
|
||
model_name: str,
|
||
*,
|
||
hf_token: Optional[str] = None,
|
||
training_type: Optional[str] = None,
|
||
load_in_4bit: bool = True,
|
||
batch_size: int = 4,
|
||
max_seq_length: int = 2048,
|
||
lora_rank: int = 16,
|
||
target_modules: Optional[list] = None,
|
||
gradient_checkpointing: str = "unsloth",
|
||
optimizer: str = "adamw_8bit",
|
||
) -> tuple[Optional[list[int]], Dict[str, Any]]:
|
||
metadata: Dict[str, Any] = {"selection_mode": "auto"}
|
||
|
||
if get_device() != DeviceType.CUDA:
|
||
metadata["selection_mode"] = "non_cuda"
|
||
return None, metadata
|
||
|
||
required_gb, estimate_metadata = estimate_required_model_memory_gb(
|
||
model_name,
|
||
hf_token = hf_token,
|
||
training_type = training_type,
|
||
load_in_4bit = load_in_4bit,
|
||
batch_size = batch_size,
|
||
max_seq_length = max_seq_length,
|
||
lora_rank = lora_rank,
|
||
target_modules = target_modules,
|
||
gradient_checkpointing = gradient_checkpointing,
|
||
optimizer = optimizer,
|
||
)
|
||
metadata.update(estimate_metadata)
|
||
parent_visible_spec = _get_parent_visible_gpu_spec()
|
||
metadata["parent_cuda_visible_devices"] = parent_visible_spec["raw"]
|
||
|
||
if not parent_visible_spec["supports_explicit_gpu_ids"]:
|
||
metadata["selection_mode"] = "inherit_parent_visible"
|
||
metadata["selected_gpu_ids"] = None
|
||
return None, metadata
|
||
|
||
if required_gb is None:
|
||
# Can't estimate size -- use all visible GPUs rather than risk one too small.
|
||
parent_ids = get_parent_visible_gpu_ids()
|
||
metadata["selection_mode"] = "fallback_all"
|
||
metadata["selected_gpu_ids"] = parent_ids
|
||
return parent_ids, metadata
|
||
|
||
utilization = get_visible_gpu_utilization()
|
||
devices = utilization.get("devices", [])
|
||
parent_ids = get_parent_visible_gpu_ids()
|
||
|
||
if not devices:
|
||
metadata["selection_mode"] = "fallback_all"
|
||
metadata["selected_gpu_ids"] = parent_ids
|
||
return parent_ids, metadata
|
||
|
||
gpu_candidates = []
|
||
for device in devices:
|
||
total_gb = device.get("vram_total_gb")
|
||
used_gb = device.get("vram_used_gb")
|
||
if total_gb is None or used_gb is None:
|
||
continue
|
||
free_gb = max(total_gb - used_gb, 0.0)
|
||
gpu_candidates.append(
|
||
{
|
||
"index": device["index"],
|
||
"free_gb": free_gb,
|
||
}
|
||
)
|
||
|
||
if not gpu_candidates:
|
||
metadata["selection_mode"] = "fallback_all"
|
||
metadata["selected_gpu_ids"] = parent_ids
|
||
return parent_ids, metadata
|
||
|
||
ranked = sorted(gpu_candidates, key = lambda item: (-item["free_gb"], item["index"]))
|
||
free_by_index = {item["index"]: item["free_gb"] for item in ranked}
|
||
selected: list[int] = []
|
||
usable_gb = 0.0
|
||
# Sharding has inter-GPU overhead, so each extra GPU contributes less than
|
||
# its raw free memory (first GPU keeps full capacity). 0.85 is empirical on
|
||
# 2-8 GPU setups: covers NCCL buffers, pipeline bubbles, fragmentation.
|
||
multi_gpu_overhead = 0.85
|
||
|
||
# Per-GPU check: activations don't shard, so each GPU needs its weight shard
|
||
# + full activation cost. Uses precomputed min_per_gpu_N values.
|
||
vram_breakdown = estimate_metadata.get("vram_breakdown", {})
|
||
|
||
for candidate in ranked:
|
||
selected.append(candidate["index"])
|
||
if len(selected) == 1:
|
||
usable_gb = candidate["free_gb"]
|
||
else:
|
||
first_gpu_id = selected[0]
|
||
usable_gb = free_by_index[first_gpu_id] + sum(
|
||
free_by_index[gpu_id] * multi_gpu_overhead for gpu_id in selected[1:]
|
||
)
|
||
|
||
total_fits = usable_gb >= required_gb
|
||
|
||
per_gpu_fits = True
|
||
if total_fits and len(selected) > 1:
|
||
min_key = f"min_per_gpu_{len(selected)}"
|
||
min_per_gpu_gb = vram_breakdown.get(min_key)
|
||
if min_per_gpu_gb is not None:
|
||
smallest_free = min(free_by_index[gpu_id] for gpu_id in selected)
|
||
per_gpu_fits = smallest_free >= min_per_gpu_gb
|
||
|
||
if total_fits and per_gpu_fits:
|
||
metadata["usable_gb"] = round(usable_gb, 3)
|
||
metadata["selection_mode"] = "auto"
|
||
metadata["selected_gpu_ids"] = selected
|
||
logger.debug(
|
||
"Selected GPUs automatically",
|
||
model_name = model_name,
|
||
selected_gpu_ids = selected,
|
||
usable_gb = metadata["usable_gb"],
|
||
required_gb = metadata.get("required_gb"),
|
||
multi_gpu_overhead = multi_gpu_overhead,
|
||
)
|
||
return selected, metadata
|
||
|
||
# Use only GPUs with verified VRAM data.
|
||
fallback_all = [c["index"] for c in gpu_candidates] if gpu_candidates else parent_ids
|
||
metadata["selection_mode"] = "fallback_all"
|
||
if ranked:
|
||
fallback_usable = ranked[0]["free_gb"] + sum(
|
||
c["free_gb"] * multi_gpu_overhead for c in ranked[1:]
|
||
)
|
||
else:
|
||
fallback_usable = 0.0
|
||
metadata["usable_gb"] = round(fallback_usable, 3)
|
||
metadata["selected_gpu_ids"] = fallback_all
|
||
logger.warning(
|
||
"Falling back to all visible GPUs -- model may not fit",
|
||
model_name = model_name,
|
||
selected_gpu_ids = fallback_all,
|
||
usable_gb = metadata["usable_gb"],
|
||
required_gb = metadata.get("required_gb"),
|
||
multi_gpu_overhead = multi_gpu_overhead,
|
||
)
|
||
return fallback_all, metadata
|
||
|
||
|
||
def prepare_gpu_selection(
|
||
gpu_ids: Optional[list[int]],
|
||
*,
|
||
model_name: str,
|
||
hf_token: Optional[str] = None,
|
||
training_type: Optional[str] = None,
|
||
load_in_4bit: bool = True,
|
||
batch_size: int = 4,
|
||
max_seq_length: int = 2048,
|
||
lora_rank: int = 16,
|
||
target_modules: Optional[list] = None,
|
||
gradient_checkpointing: str = "unsloth",
|
||
optimizer: str = "adamw_8bit",
|
||
) -> tuple[Optional[list[int]], Dict[str, Any]]:
|
||
"""Resolve which physical GPUs to use for a model load.
|
||
|
||
GPU selection modes:
|
||
- **Explicit** (``gpu_ids=[5, 6, 7]``): caller chooses exact GPUs.
|
||
All listed GPUs are used and the model is sharded via
|
||
``device_map="balanced"``, even if it would fit on fewer. IDs are
|
||
validated against the parent-visible set.
|
||
- **Auto** (``gpu_ids=None`` or ``[]``): ``auto_select_gpu_ids``
|
||
estimates VRAM needs and picks the *minimum* GPUs needed,
|
||
preferring those with the most free memory.
|
||
|
||
The returned ``gpu_ids`` is later passed to ``get_device_map()`` (maps it
|
||
to a Hugging Face ``device_map`` string) and to ``apply_gpu_ids()`` in the
|
||
worker subprocess (narrows ``CUDA_VISIBLE_DEVICES`` before torch/CUDA init).
|
||
"""
|
||
if gpu_ids and get_device() != DeviceType.CUDA:
|
||
raise ValueError(
|
||
f"gpu_ids {list(gpu_ids)} is only supported on CUDA devices, "
|
||
f"but the current backend is '{get_device().value}'."
|
||
)
|
||
|
||
if gpu_ids:
|
||
resolved = resolve_requested_gpu_ids(gpu_ids)
|
||
metadata = {
|
||
"selection_mode": "explicit",
|
||
"selected_gpu_ids": resolved,
|
||
}
|
||
return resolved, metadata
|
||
|
||
selected_gpu_ids, metadata = auto_select_gpu_ids(
|
||
model_name,
|
||
hf_token = hf_token,
|
||
training_type = training_type,
|
||
load_in_4bit = load_in_4bit,
|
||
batch_size = batch_size,
|
||
max_seq_length = max_seq_length,
|
||
lora_rank = lora_rank,
|
||
target_modules = target_modules,
|
||
gradient_checkpointing = gradient_checkpointing,
|
||
optimizer = optimizer,
|
||
)
|
||
return selected_gpu_ids, metadata
|
||
|
||
|
||
def get_physical_gpu_count() -> int:
|
||
"""
|
||
Return the number of physical GPUs on the machine.
|
||
|
||
Uses ``nvidia-smi -L`` on NVIDIA (unaffected by CUDA_VISIBLE_DEVICES),
|
||
with a torch fallback for AMD ROCm and Intel XPU. Cached after first call.
|
||
"""
|
||
global _physical_gpu_count
|
||
if _physical_gpu_count is not None:
|
||
return _physical_gpu_count
|
||
|
||
device = get_device()
|
||
|
||
if device == DeviceType.CUDA:
|
||
try:
|
||
if IS_ROCM:
|
||
from . import amd as _smi_mod
|
||
else:
|
||
from . import nvidia as _smi_mod
|
||
count = _smi_mod.get_physical_gpu_count()
|
||
if count is not None:
|
||
_physical_gpu_count = count
|
||
return _physical_gpu_count
|
||
except Exception:
|
||
pass
|
||
# SMI unavailable -- fall back to torch.
|
||
count = _torch_get_physical_gpu_count()
|
||
_physical_gpu_count = count if count is not None else 1
|
||
return _physical_gpu_count
|
||
|
||
if device == DeviceType.XPU:
|
||
count = _torch_get_physical_gpu_count()
|
||
_physical_gpu_count = count if count is not None else 1
|
||
return _physical_gpu_count
|
||
|
||
if device == DeviceType.MLX:
|
||
_physical_gpu_count = 1
|
||
return _physical_gpu_count
|
||
|
||
_physical_gpu_count = 0
|
||
|
||
return _physical_gpu_count
|
||
|
||
|
||
def _backend_visible_devices_env() -> Optional[str]:
|
||
"""Return the raw visibility env string that applies to this backend.
|
||
|
||
On ROCm, HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES take precedence over
|
||
CUDA_VISIBLE_DEVICES; this mirrors ``_get_parent_visible_gpu_spec`` so
|
||
``backend_cuda_visible_devices`` reports the value actually narrowing the
|
||
visible device set.
|
||
"""
|
||
if IS_ROCM:
|
||
return _get_parent_visible_gpu_spec().get("raw")
|
||
return os.environ.get("CUDA_VISIBLE_DEVICES")
|
||
|
||
|
||
def get_backend_visible_gpu_info() -> Dict[str, Any]:
|
||
device = get_device()
|
||
if device in (DeviceType.CUDA, DeviceType.XPU):
|
||
parent_visible_ids = get_parent_visible_gpu_ids()
|
||
# Try native SMI first (nvidia-smi; skipped for ROCm).
|
||
if device == DeviceType.CUDA and not IS_ROCM:
|
||
try:
|
||
from . import nvidia
|
||
|
||
parent_visible_spec = _get_parent_visible_gpu_spec()
|
||
result = nvidia.get_backend_visible_gpu_info(
|
||
parent_visible_spec["numeric_ids"],
|
||
parent_visible_spec["raw"],
|
||
)
|
||
if result.get("available"):
|
||
result["backend"] = _backend_label(device)
|
||
return result
|
||
except Exception as e:
|
||
logger.warning("Backend GPU visibility query failed: %s", e)
|
||
|
||
# Torch fallback (ROCm, XPU, nvidia-smi missing). Empty parent_visible_ids
|
||
# (UUID/MIG mask) -> enumerate by torch ordinal so the UI shows devices.
|
||
if parent_visible_ids:
|
||
torch_indices = parent_visible_ids
|
||
index_kind = "physical"
|
||
else:
|
||
visible_count = _torch_get_physical_gpu_count() or 0
|
||
torch_indices = list(range(visible_count))
|
||
index_kind = "relative"
|
||
torch_devices = _torch_get_per_device_info(torch_indices)
|
||
if torch_devices:
|
||
devices = [
|
||
{
|
||
"index": td["index"],
|
||
"index_kind": index_kind,
|
||
"visible_ordinal": td["visible_ordinal"],
|
||
"name": td["name"],
|
||
"memory_total_gb": td["total_gb"],
|
||
}
|
||
for td in torch_devices
|
||
]
|
||
return {
|
||
"available": True,
|
||
"backend": _backend_label(device),
|
||
"backend_cuda_visible_devices": _backend_visible_devices_env(),
|
||
"parent_visible_gpu_ids": parent_visible_ids,
|
||
"devices": devices,
|
||
"index_kind": index_kind,
|
||
}
|
||
|
||
return {
|
||
"available": False,
|
||
"backend": _backend_label(device),
|
||
"backend_cuda_visible_devices": _backend_visible_devices_env(),
|
||
"parent_visible_gpu_ids": parent_visible_ids,
|
||
"devices": [],
|
||
"index_kind": "physical",
|
||
}
|
||
|
||
if device == DeviceType.MLX:
|
||
mem = get_gpu_memory_info()
|
||
if not mem.get("available"):
|
||
return {
|
||
"available": False,
|
||
"backend": _backend_label(device),
|
||
"backend_cuda_visible_devices": os.environ.get("CUDA_VISIBLE_DEVICES"),
|
||
"parent_visible_gpu_ids": [],
|
||
"devices": [],
|
||
"index_kind": "relative",
|
||
}
|
||
return {
|
||
"available": True,
|
||
"backend": _backend_label(device),
|
||
"backend_cuda_visible_devices": os.environ.get("CUDA_VISIBLE_DEVICES"),
|
||
"parent_visible_gpu_ids": [0],
|
||
"devices": [
|
||
{
|
||
"index": 0,
|
||
"index_kind": "relative",
|
||
"visible_ordinal": 0,
|
||
"name": mem.get("device_name", "MLX"),
|
||
"memory_total_gb": round(mem.get("total_gb", 0), 2),
|
||
}
|
||
],
|
||
"index_kind": "relative",
|
||
}
|
||
|
||
return {
|
||
"available": False,
|
||
"backend": _backend_label(device),
|
||
"backend_cuda_visible_devices": os.environ.get("CUDA_VISIBLE_DEVICES"),
|
||
"parent_visible_gpu_ids": [],
|
||
"devices": [],
|
||
"index_kind": "relative",
|
||
}
|
||
|
||
|
||
def get_visible_gpu_count() -> int:
|
||
"""
|
||
Return the number of GPUs visible to this process.
|
||
|
||
Respects ``CUDA_VISIBLE_DEVICES`` -- if set, only those GPUs count.
|
||
Falls back to physical count if unset or torch is unavailable.
|
||
Cached after the first call.
|
||
"""
|
||
global _visible_gpu_count
|
||
if _visible_gpu_count is not None:
|
||
return _visible_gpu_count
|
||
|
||
# _get_parent_visible_gpu_spec() already handles HIP_VISIBLE_DEVICES /
|
||
# ROCR_VISIBLE_DEVICES on ROCm.
|
||
visible_spec = _get_parent_visible_gpu_spec()
|
||
if visible_spec["raw"] is not None:
|
||
raw = visible_spec["raw"].strip()
|
||
if raw == "" or raw == "-1":
|
||
_visible_gpu_count = 0
|
||
elif visible_spec["numeric_ids"] is not None:
|
||
_visible_gpu_count = len(visible_spec["numeric_ids"])
|
||
else:
|
||
_visible_gpu_count = len([x for x in raw.split(",") if x.strip()])
|
||
return _visible_gpu_count
|
||
|
||
# No visibility env var set -- try torch, else physical count
|
||
try:
|
||
import torch
|
||
if get_device() == DeviceType.XPU and hasattr(torch, "xpu"):
|
||
_visible_gpu_count = torch.xpu.device_count()
|
||
else:
|
||
_visible_gpu_count = torch.cuda.device_count()
|
||
except Exception:
|
||
_visible_gpu_count = get_physical_gpu_count()
|
||
|
||
return _visible_gpu_count
|
||
|
||
|
||
def apply_gpu_ids(gpu_ids) -> None:
|
||
if gpu_ids is None:
|
||
return
|
||
|
||
# Empty list -> treat like None (inherit parent); setting CUDA_VISIBLE_DEVICES=""
|
||
# disables CUDA entirely and crashes downstream torch calls.
|
||
if isinstance(gpu_ids, (list, tuple)) and len(gpu_ids) == 0:
|
||
return
|
||
|
||
global _visible_gpu_count
|
||
|
||
if isinstance(gpu_ids, (list, tuple)):
|
||
value = ",".join(str(g) for g in gpu_ids)
|
||
else:
|
||
value = str(gpu_ids)
|
||
|
||
os.environ["CUDA_VISIBLE_DEVICES"] = value
|
||
# Keep ROCm visibility env vars in sync. Workers may call apply_gpu_ids()
|
||
# before detect_hardware() (IS_ROCM still False), so also mirror when the
|
||
# parent set a ROCm visibility var, with a torch.version.hip probe fallback.
|
||
_inherits_rocm_visibility = (
|
||
"HIP_VISIBLE_DEVICES" in os.environ or "ROCR_VISIBLE_DEVICES" in os.environ
|
||
)
|
||
_is_rocm = IS_ROCM or _inherits_rocm_visibility
|
||
if not _is_rocm:
|
||
# torch.version.hip is set on ROCm, None on CUDA; AMD SDK wheels may leave
|
||
# it unset but encode "rocm" in __version__. Broad except: never crash a worker.
|
||
try:
|
||
import torch as _torch
|
||
_is_rocm = (
|
||
getattr(_torch.version, "hip", None) is not None
|
||
or "rocm" in getattr(_torch, "__version__", "").lower()
|
||
)
|
||
except Exception as e:
|
||
logger.debug(
|
||
"apply_gpu_ids: torch ROCm probe skipped (%s: %s)",
|
||
type(e).__name__,
|
||
e,
|
||
)
|
||
if _is_rocm:
|
||
os.environ["HIP_VISIBLE_DEVICES"] = value
|
||
# ROCR_VISIBLE_DEVICES operates at the HSA agent level and uses
|
||
# different indexing semantics to HIP_VISIBLE_DEVICES. Setting it
|
||
# to a physical GPU index breaks multi-GPU ROCm systems where the
|
||
# parent already set ROCR_VISIBLE_DEVICES (e.g. "0,1"): narrowing
|
||
# to "1" causes torch.cuda.is_available() to return False in the
|
||
# worker subprocess. HIP_VISIBLE_DEVICES is sufficient for GPU
|
||
# selection on ROCm -- leave ROCR_VISIBLE_DEVICES inherited.
|
||
_visible_gpu_count = None
|
||
if _is_rocm:
|
||
logger.info("Applied gpu_ids: CUDA_VISIBLE_DEVICES='%s' (rocm)", value)
|
||
else:
|
||
logger.info("Applied gpu_ids: CUDA_VISIBLE_DEVICES='%s'", value)
|
||
|
||
|
||
def get_device_map(gpu_ids: Optional[list[int]] = None) -> str:
|
||
"""Return the Hugging Face ``device_map`` string for model loading.
|
||
|
||
Returns ``"balanced"`` (shard evenly across GPUs) when:
|
||
- ``gpu_ids`` explicitly lists >1 GPU, **or**
|
||
- ``CUDA_VISIBLE_DEVICES`` uses UUID/MIG identifiers (non-numeric) and
|
||
>1 GPU is visible (fallback: numeric IDs unresolvable, so assume
|
||
multi-GPU is intended).
|
||
|
||
Returns ``"sequential"`` (single device) otherwise, including non-CUDA
|
||
backends (CPU, MLX).
|
||
|
||
Use ``prepare_gpu_selection()`` upstream to determine ``gpu_ids`` -- it
|
||
handles auto-selecting the minimum GPUs needed for a model.
|
||
"""
|
||
device = get_device()
|
||
if device == DeviceType.CUDA:
|
||
multi_gpu = gpu_ids is not None and len(gpu_ids) > 1
|
||
|
||
if not multi_gpu:
|
||
# UUID/MIG masks can't be split into numeric IDs; >1 visible GPU
|
||
# means multi-GPU sharding is intended.
|
||
parent_visible_spec = _get_parent_visible_gpu_spec()
|
||
if parent_visible_spec["numeric_ids"] is None and get_visible_gpu_count() > 1:
|
||
multi_gpu = True
|
||
|
||
if multi_gpu:
|
||
return "balanced"
|
||
|
||
return "sequential"
|
||
|
||
|
||
def get_offloaded_device_map_entries(model) -> dict[str, str]:
|
||
hf_device_map = getattr(model, "hf_device_map", None)
|
||
if not isinstance(hf_device_map, dict):
|
||
return {}
|
||
return {
|
||
module_name: placement
|
||
for module_name, placement in hf_device_map.items()
|
||
if placement in ("cpu", "disk")
|
||
}
|
||
|
||
|
||
def raise_if_offloaded(
|
||
model,
|
||
device_map: str,
|
||
context: str = "Loading",
|
||
) -> None:
|
||
"""Raise ``ValueError`` if *model* has modules offloaded to CPU or disk."""
|
||
offloaded = get_offloaded_device_map_entries(model)
|
||
if not offloaded:
|
||
return
|
||
example = ", ".join(f"{name}={placement}" for name, placement in list(offloaded.items())[:5])
|
||
raise ValueError(
|
||
f"{context} does not support models loaded with CPU or disk offload. "
|
||
f"device_map='{device_map}' produced offloaded modules: {example}"
|
||
)
|
||
|
||
|
||
def safe_num_proc(desired: Optional[int] = None) -> int:
|
||
"""
|
||
Return a safe ``num_proc`` for ``dataset.map()`` calls.
|
||
|
||
On Windows always returns 1: Python uses ``spawn`` not ``fork``, so
|
||
re-importing torch/transformers/unsloth per worker is typically slower
|
||
than single-process for normal dataset sizes.
|
||
|
||
On multi-GPU machines (multiple GPUs *visible* to this process) the
|
||
NVIDIA driver spawns extra background threads, making ``os.fork()``
|
||
deadlock-prone with many workers, so this caps ``num_proc`` to 4.
|
||
The cap does not apply when ``CUDA_VISIBLE_DEVICES`` restricts to one GPU.
|
||
|
||
Args:
|
||
desired: The num_proc you *want*. If None, auto-computes from
|
||
``os.cpu_count()``.
|
||
|
||
Returns:
|
||
A safe integer ≥ 1.
|
||
"""
|
||
# Windows/macOS use 'spawn'; re-importing torch/transformers/unsloth per
|
||
# worker is typically slower than single-process.
|
||
if sys.platform in ("win32", "darwin"):
|
||
return 1
|
||
|
||
if desired is None or not isinstance(desired, int):
|
||
desired = max(1, (os.cpu_count() or 1) // 3)
|
||
|
||
visible = get_visible_gpu_count()
|
||
if visible > 1:
|
||
capped = max(1, min(4, desired))
|
||
logger.info(
|
||
f"Multi-GPU detected ({visible} visible GPUs) -- "
|
||
f"capping num_proc {desired} -> {capped} to avoid fork deadlocks"
|
||
)
|
||
return capped
|
||
|
||
return max(1, desired)
|
||
|
||
|
||
def safe_thread_num_proc(desired: Optional[int] = None) -> int:
|
||
"""
|
||
Return a safe worker count for ``ThreadPoolExecutor`` calls.
|
||
|
||
Unlike ``safe_num_proc()``, does NOT cap to 1 on macOS/Windows: threads
|
||
share the parent address space, unaffected by ``spawn`` vs ``fork``.
|
||
|
||
Args:
|
||
desired: The thread count you *want*. If None, auto-computes
|
||
from ``os.cpu_count()``.
|
||
|
||
Returns:
|
||
A safe integer >= 1.
|
||
"""
|
||
if desired is None or not isinstance(desired, int):
|
||
desired = max(1, (os.cpu_count() or 1) // 3)
|
||
|
||
return max(1, desired)
|
||
|
||
|
||
def dataset_map_num_proc(desired: Optional[int] = None) -> Optional[int]:
|
||
"""
|
||
Return a safe ``num_proc`` for ``Dataset.map()`` and ``Dataset.filter()``.
|
||
|
||
Returns ``None`` on spawn platforms (Windows, macOS) because ``datasets``
|
||
treats ``num_proc=1`` as multiprocessing (creates ``Pool(1)``); only
|
||
``num_proc=None`` guarantees in-process execution.
|
||
"""
|
||
if sys.platform in ("win32", "darwin"):
|
||
return None
|
||
return safe_num_proc(desired)
|