[Video] Bound H3 host weight residency on memory-constrained hosts - #582
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yangzhuxinyzx merged 1 commit intoSep 9, 2026
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Assisted-by: OpenAI Codex Signed-off-by: yangzhuxinyzx <153831768+yangzhuxinyzx@users.noreply.github.com>
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September 9, 2026 05:42
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Purpose
H3 TP4 loading exhausted the four-V100 host's 64 GiB system RAM after GPU selection and collectives succeeded. Store DiT and encoder CPU masters in reclaimable file mappings on smaller hosts, while preserving weight bytes, tensor aliases, GPU kernels and sampling. Reserve disk before mapping and immediately unlink mapped files so worker crashes cannot leave large offload files behind. Explicit pinned/mmap options remain available.
The source-wheel extraction path also retains all three H3 native operators, allowing this Python-only change to reuse the exact native libraries compiled from the current source during the same deployment.
Base:
a9cfe620127f04888400f7fba91c23699174832f. Head:9f111147204b55d35eb11b3c188842af55ed01bb.Test Plan and Result
python -m pip checkpassed.python -m pytest src/tests/video/test_h3_host_memory.py -q— 7 passed, including three byte-exact GPU load/offload cycles.Original OOM and raw deployment evidence are retained in
/home/ymzx/1cat-vllm-deploy/studio-main-20260909/; candidate evidence is retained in/home/ymzx/1cat-vllm-deploy/h3-host-memory-20260909/.Scope and Limitations
Open-PR checks found kernel/workflow changes (#571/#578), but no duplicate host-weight residency fix. This scope does not change those kernels. File-backed staging needs local disk capacity and can slow CPU→GPU transfers; transient activations still require RAM. Automatic mode uses physical host RAM, with an explicit override for constrained containers.
AI assistance: implementation and tests prepared with OpenAI Codex at the maintainer's request.