74c871d109
amd-smi on iGPUs with shared/unified memory (e.g. Radeon 8060S on Strix Halo) reports only the dedicated VRAM slice (~512 MB) in its metric output, so get_visible_gpu_utilization() was returning usable_gb ≈ 0.35 GB instead of the full GTT pool (~128 GB). torch.cuda.mem_get_info() already surfaces the correct unified-pool size. Add _reconcile_rocm_unified_memory(): after amd-smi returns a valid result on a ROCm device, cross-check each device's vram_total_gb against torch.cuda.mem_get_info(). When torch reports a larger total, replace the amd-smi VRAM fields in-place. No-op for discrete AMD GPUs where the two sources agree. Fixes: "Falling back to all visible GPUs -- model may not fit" on AMD iGPU machines even when 100+ GB of unified memory is available. |
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| auth | ||
| core | ||
| loggers | ||
| models | ||
| plugins | ||
| requirements | ||
| routes | ||
| state | ||
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| tests | ||
| utils | ||
| __init__.py | ||
| _platform_compat.py | ||
| colab.py | ||
| main.py | ||
| run.py | ||
| startup_banner.py | ||