Root cause: torch.load() reads 6.9GB .ckpt into Python heap + model params
in CPU RAM = ~14GB peak, exceeding 16GB system RAM → OOM Killer.
Fix 1 - mmap=True on all torch.load() calls (torch 2.7 supports this):
With mmap, checkpoint storage is file-backed (not heap). Only the model
parameters (also ~7GB) exist in physical RAM during loading. Peak RAM
drops from ~14GB to ~7GB — within safe limits on 16GB machines.
Files changed: pipelines.py, hunyuan3ddit.py, model.py (×2), flow_matching_sit.py
Fix 2 - malloc_trim(0) after every gc.collect():
Forces glibc to return freed heap pages to OS immediately, so Python's
memory pool doesn't hoard freed model memory before the next load.
Fix 3 - PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True:
Prevents CUDA allocator fragmentation between model switches.
Fix 4 - Adaptive threshold recalculated:
With mmap loading, loading a model requires ~7.5GB (model params) not
14GB. CPU offload threshold lowered from 16GB → 10.5GB, enabling fast
path on machines with more headroom.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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