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67dcd62310
Summary: Fixes https://github.com/pytorch/pytorch/issues/35901 This change is designed to prevent fragmentation in the Caching Allocator. Permissive block splitting in the allocator allows very large blocks to be split into many pieces. Once split too finely it is unlikely all pieces will be 'free' at that same time so the original allocation can never be returned. Anecdotally, we've seen a model run out of memory failing to alloc a 50 MB block on a 32 GB card while the caching allocator is holding 13 GB of 'split free blocks' Approach: - Large blocks above a certain size are designated "oversize". This limit is currently set 1 decade above large, 200 MB - Oversize blocks can not be split - Oversize blocks must closely match the requested size (e.g. a 200 MB request will match an existing 205 MB block, but not a 300 MB block) - In lieu of splitting oversize blocks there is a mechanism to quickly free a single oversize block (to the system allocator) to allow an appropriate size block to be allocated. This will be activated under memory pressure and will prevent _release_cached_blocks()_ from triggering Initial performance tests show this is similar or quicker than the original strategy. Additional tests are ongoing. Pull Request resolved: https://github.com/pytorch/pytorch/pull/44742 Reviewed By: ngimel Differential Revision: D23752058 Pulled By: ezyang fbshipit-source-id: ccb7c13e3cf8ef2707706726ac9aaac3a5e3d5c8 |
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| .. | ||
| amp_examples.rst | ||
| autograd.rst | ||
| broadcasting.rst | ||
| cpu_threading_runtimes.svg | ||
| cpu_threading_torchscript_inference.rst | ||
| cpu_threading_torchscript_inference.svg | ||
| cuda.rst | ||
| ddp.rst | ||
| extending.rst | ||
| faq.rst | ||
| large_scale_deployments.rst | ||
| modules.rst | ||
| multiprocessing.rst | ||
| randomness.rst | ||
| serialization.rst | ||
| windows.rst | ||
