Summary: This diffs add an API to query expandable segment size for each stream so that we can use this info to warmup the segment in advance, so we dont incur any performance penalty during steady state inference for new CUDA memory allocations.
Differential Revision: D76447308
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163771
Approved by: https://github.com/bbus
Removes MemPoolContext from custom user mempools. The ground truth for which pool should be used is in graph_pools active pool, and MemPoolContext just introduced an opportunity for the pool pointed to by MemPoolContext and active pool in graph_pools to go out of sync (see all the asserts in the code to make sure that happens, and yet it still could happen in a multithread scenario, see my recent PRs (#153990).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154042
Approved by: https://github.com/albanD, https://github.com/syed-ahmed
Summary:
Oftentimes, users complain that a bunch of extra events are prepended to their desired GPU snapshot. This is because they usually attach an OOM logger without knowing and when they go to collect the actual snapshot, it adds all the OOM logger contents. Since OOM and regular snapshot use the same backend, we currently don't have the infra in place to split these snapshots.
As a solution we add a flag to the snapshot frontend to clear out the history when starting the auto-trace record memory history.
A more thorough solution would be to have a user pass in a handle and to have snapshots per handle to seperate the events. However, this would likely be complicated and more work than it is worth as we would have to change the callbacks in the caching allocator and pass these objects between python and cpp.
Test Plan:
See diff below
Differential Revision: D71159720
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149352
Approved by: https://github.com/eqy, https://github.com/aaronenyeshi
Enables clang-tidy rule [`misc-use-internal-linkage`](https://clang.llvm.org/extra/clang-tidy/checks/misc/use-internal-linkage.html). This new check was introduced in Clang-Tidy 18 and is available due to recent update of Clang-Tidy 19.
The check marks functions and variables used only in the translation unit as static. Therefore undesired symbols are not leaked into other units, more link time optimisations are possible and the resulting binaries may be smaller.
The detected violations were mostly fixed by using static. In other cases, the symbols were indeed consumed by others files, then their declaring headers were included. Still some declarations were wrong and have been fixed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148948
Approved by: https://github.com/Skylion007
This is an initial attempt to provide some statistics for the pinned host memory allocations flowing through CachingHostAllocator. Many times in the past we have had inexplicable slowdowns that would be much easier to diagnose if we had some host memory characteristics.
This change tries very hard not to disrupt the initial design of the allocator, and it uses existing locking mechanism, whenever possible, to gather statistics "for free". Only deviation from that is on the "slow path" where we incur CUDA calls anyway, so taking a short lock is not going to hurt the performance much, especially in the steady state where most allocations will come from cache.
As mentioned before, this is the first PR, to introduce the concept and to see if it fits the right paradigm. We can always add more later.
Metrics that would require more involved changes to the code base and locks, like requested memory, have been punted for now. I also tried to reuse the Stat structure used in CUDA caching allocator, in order to maintain symmetry.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147660
Approved by: https://github.com/ngimel
This is an initial attempt to provide some statistics for the pinned host memory allocations flowing through CachingHostAllocator. Many times in the past we have had inexplicable slowdowns that would be much easier to diagnose if we had some host memory characteristics.
This change tries very hard not to disrupt the initial design of the allocator, and it uses existing locking mechanism, whenever possible, to gather statistics "for free". Only deviation from that is on the "slow path" where we incur CUDA calls anyway, so taking a short lock is not going to hurt the performance much, especially in the steady state where most allocations will come from cache.
As mentioned before, this is the first PR, to introduce the concept and to see if it fits the right paradigm. We can always add more later.
Metrics that would require more involved changes to the code base and locks, like requested memory, have been punted for now. I also tried to reuse the Stat structure used in CUDA caching allocator, in order to maintain symmetry.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147660
Approved by: https://github.com/ngimel
Certain `cpp_wrapper`-enabled tests were OOM-ing in the CI pipeline, with error messages suggesting that sufficient memory was accessible. This ultimately resulted from an internal memory limitation that was not queryable in the API. This PR adds querying for that limit.
Additionally, the failing tests had incorrect memory availability checks, and are updated with measured memory requirements.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/140620
Approved by: https://github.com/malfet, https://github.com/eqy
ghstack dependencies: #141367
Summary:
This patch prototypes a trace tracker callback mechanism based on existing TraceEntry records.
- It allows external of cache allocator to "attach" trace tracker callbacks.
- When a TraceEntry is recorded, it triggers all attached callbacks. Callbacks can selectively behave based on the trace action.
- **RISK**: The attached callback would be called within an allocator call stack (e.g., free during an allocate call). Potential deadlock may occur if other locks are called within the callback and has interdependency w/ the device allocator lock. It is the callback developer's responsibility to avoid any potential deadlock.
- **ADVICE**: The callback mechanism is designed **only for Pytorch internal use**. We should not expose it to Python layer due to Python GIL that would cause a deadlock.
See example in D50726970 that attaches NCCL register/deregister hooks via the trace tracker callback, so that all CUDA segments allocated by the allocator can be registered to NCCL communicators before any NCCL communication happens. This enables fast zero copy algorithms in NCCL.
Differential Revision: D50726971
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112238
Approved by: https://github.com/zdevito
