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* Implement seek and read for pins Source pins from an mmap is pad because its its a CPU->CPU copy that attempts to fully buffer the same data twice. Instead, use seek and read which avoids the mmap buffering while usually being a faster read in the first place (avoiding mmap faulting etc). * pinned_memory: Use Aimdo pinner The aimdo pinner bypasses pytorches CPU allocator which can leak windows commit charge. * ops: bypass init() of weight for embedding layer This similarly consumes large commit charge especially for TEs. It can cause a permanement leaked commit charge which can destabilize on systems close to the commit ceiling and generally confuses the RAM stats. * model_patcher: implement pinned memory counter Implement a pinned memory counter for better accounting of what volume of memory pins have. * implement touch accounting Implement accounting of touching mmapped tensors. * mm+mp: add residency mmap getter * utils: use the aimdo mmap to load sft files * model_management: Implement tigher RAM pressure semantics Implement a pressure release on entire MMAPs as windows does perform faster when mmaps are unloaded and model loads free ramp into fully unallocated RAM. Make the concept of freeing for pins a completely separate concept. Now that pins are loadable directly from original file and don' touch the mmap, tighten the freeing budget to just the current loaded model - what you have left over. This still over-frees pins, but its a lot better than before. So after the pins are freed with that algorithm, bounce entire MMAPs to free RAM based on what the model needs, deducting off any known resident-in-mmap tensors to the free quota to keep it as tight as possible. * comfy-aimdo 0.2.11 Comfy aimdo 0.2.11 * mm: Implement file_slice path for QT * ruff * ops: put meta-tensors in place to allow custom nodes to check geo
4.2 KiB
4.2 KiB