# Motivation
This PR aims to deprecate the host allocator legacy API and recommend users to use the unified API `getHostAllocator(device_type)` APIs, such as:
```cpp
at::getHostAllocator(device_type)->allocate(...);
at::getHostAllocator(device_type)->empty_cache();
at::getHostAllocator(device_type)->record_event(...);
at::getHostAllocator(device_type)->get_stats();
at::getHostAllocator(device_type)->reset_accumulated_stats();
at::getHostAllocator(device_type)->reset_peak_stats();
```
# Additional Context
TODO:
- [ ] Move is_pinned from `AcceleratorHookInterface` to `HostAllocator`
- [ ] Deprecate `getPinnedMemoryAllocator` inside `AcceleratorHookInterface` and recommend using `getHostAllocator` instead.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/151437
Approved by: https://github.com/EikanWang, https://github.com/albanD
ghstack dependencies: #151403, #151431
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
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
TunableOp's rotating buffer feature cannot be properly tested because the environment variable that controls this feature is sticky. A Python API is introduced to modify this value.
Additional items in this PR:
* UT for rotating buffer API
* Clean up UTs that were setting the rotating buffer via the environment variable
* Align behavior of environment variable and Python API when a negative value (< 0) is set.
* Update documentation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143172
Approved by: https://github.com/jeffdaily
Was a bit too fast with my earlier PR, `sharedMemPerMultiprocessor` includes some memory that is reserved for the system. The amount a kernel can actually use is limited by `sharedMemPerBlockOptin`.
I also expose `sharedMemPerBlock` for completeness.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143226
Approved by: https://github.com/ezyang
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 PR adds in cuSPARSELt as a backend to PyTorch.
It is now possible to see if cuSPARSELt is available and the version if
it is with
```
torch.backends.cusparselt.is_available()
torch.backends.cusparselt.version()
```
Test Plan:
```
python test/test_sparse_semi_structured.py -k test_cusparselt_backend
```
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128534
Approved by: https://github.com/cpuhrsch, https://github.com/eqy, https://github.com/syed-ahmed
Summary: Instead of embedding the user_defined TraceEntry inside of device_traces, which causes issues when some threads may not have the proper device id set, save them into an external_annotations field by using a RingBuffer<AnnotationEntry> called annotation_buffer owned by the NativeCachingAllocator.
Test Plan: CI, resnet run, and FBR model.
Differential Revision: D59703213
Pulled By: aaronenyeshi
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130964
Approved by: https://github.com/zdevito
