Altering the flag to use the correct streamType in CUDAPluggableAllocator class for ROCm gpu. The flag TORCH_HIP_VERSION does not work for ROCm as intended. This flag is replaced with USE_ROCM. This is impacting Distributed Fused Adam in Rocm/APEX when using nccl_ub feature. This has been tested with rocm/apex.
See PR https://github.com/ROCm/apex/pull/184
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150010
Approved by: https://github.com/jeffdaily
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
Summary:
LLVM has a warning `-Wunused-value` which we treat as an error because it's so often diagnostic of a code issue. Unused values often indicate a programming mistake, but can also just be unnecessary cruft that harms readability and performance.
For questions/comments, contact r-barnes.
- If you approve of this diff, please use the "Accept & Ship" button :-)
Test Plan: Sandcastle
Differential Revision: D69945678
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147555
Approved by: https://github.com/Skylion007, https://github.com/eqy
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
Summary:
PTD all_to_all uses a list of tensors, while ncclAllToAllv (provided
by NCCLX and RCCL) assumes that a single contiguous buffer is used.
These are fundamentally mismatched. The list of tensors might not be
contiguous or even ordered (buffer addresses might not be in
increasing order).
This patch removes the ncclAllToAllv specialization for PTD
all_to_all, and instead let's it directly call ncclSend/ncclRecv.
Co-authored by @pavanbalaji
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145045
Approved by: https://github.com/pavanbalaji, https://github.com/d4l3k, https://github.com/fduwjj, https://github.com/ezyang
Currently, the roctracer for Windows is not available. This PR disables any mentions of its usage for Windows, and creates dummy functions for Windows to keep compatibility with existing code, but which warn the user about the lack of Windows' availability.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143329
Approved by: https://github.com/sraikund16
Summary:
LLVM-15 has a warning `-Wunused-variable` which we treat as an error because it's so often diagnostic of a code issue. Unused variables can compromise readability or, worse, performance.
This diff either (a) removes an unused variable and, possibly, it's associated code or (b) qualifies the variable with `[[maybe_unused]]`.
- If you approve of this diff, please use the "Accept & Ship" button :-)
Test Plan: Sandcastle
Reviewed By: palmje
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143381
Approved by: https://github.com/malfet
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:
Splitting this PR into two, one for the cuSPARSELt improvements, and one
for the inductor lowering.
This PR adds in the additional cuSPARSELt bindings into pytorch.
* `torch._cslt_sparse_mm_search` will be deprecated in a future PR,
so a warning has been added
* Added a header file for cuSPARSELtOps.cpp
* max_id is now available in `torch.backends.cusparselt` via
`torch.backends.cusparselt.get_max_alg_id()`
* fixed meta registrations for float8
Test Plan:
python test/test_sparse_semi_structured.py
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137427
Approved by: https://github.com/cpuhrsch, https://github.com/eqy
Summary:
Splitting this PR into two, one for the cuSPARSELt improvements, and one
for the inductor lowering.
This PR adds in the additional cuSPARSELt bindings into pytorch.
* `torch._cslt_sparse_mm_search` will be deprecated in a future PR,
so a warning has been added
* Added a header file for cuSPARSELtOps.cpp
* max_id is now available in `torch.backends.cusparselt` via
`torch.backends.cusparselt.get_max_alg_id()`
* fixed meta registrations for float8
Test Plan:
python test/test_sparse_semi_structured.py
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137427
Approved by: https://github.com/cpuhrsch, https://github.com/eqy
Summary:
It looks RCCL does have the support for those two error types:: ncclRemoteError and ncclnProgress: https://github.com/ROCm/rccl/blob/develop/src/nccl.h.in#L57. And I do see my job throwing out those errors. But pytorch just said:
```
RuntimeError: Unconvertible NCCL type
```
Even though nccl says:
```
develop/src/init.cc.hip:502 NCCL WARN Attempt to use communicator before the previous operation returned ncclSuccess
```
Therefore just enabling those.
Test Plan: CI
Differential Revision: D66434341
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141461
Approved by: https://github.com/eqy
Summary:
Splitting this PR into two, one for the cuSPARSELt improvements, and one
for the inductor lowering.
This PR adds in the additional cuSPARSELt bindings into pytorch.
* `torch._cslt_sparse_mm_search` will be deprecated in a future PR,
so a warning has been added
* Added a header file for cuSPARSELtOps.cpp
* max_id is now available in `torch.backends.cusparselt` via
`torch.backends.cusparselt.get_max_alg_id()`
* fixed meta registrations for float8
Test Plan:
python test/test_sparse_semi_structured.py
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137427
Approved by: https://github.com/cpuhrsch, https://github.com/eqy
Summary:
LLVM-15 has a warning `-Wunused-variable` which we treat as an error because it's so often diagnostic of a code issue. Unused variables can compromise readability or, worse, performance.
This diff either (a) removes an unused variable and, possibly, it's associated code or (b) qualifies the variable with `[[maybe_unused]]`.
#buildsonlynotests - Builds are sufficient
- If you approve of this diff, please use the "Accept & Ship" button :-)
Test Plan: Sandcastle
Reviewed By: meyering
Differential Revision: D65833225
Pull Request resolved: https://github.com/pytorch/pytorch/pull/140569
Approved by: https://github.com/Skylion007
### Why use non-blocking mode in eager init?
For overlapping comm init and model init, etc.
### Why can we set non-blocking as default?
If the setting is dangling -- i.e. not passed in by user nor set via env -- `ProcessGroupNCCL` can have some preferred logic. And torch-level API semantics does not change whether the NCCL comm is blocking or non-blocking (handled within `ProcessGroupNCCL`).
### Why not make non-blocking default for lazy mode as well?
PR https://github.com/pytorch/pytorch/pull/137544 tried it.
Two reasons why that's not preferred today:
1. It is hard -- too big a blast.
2. There is no gain by doing lazy init in non-blocking mode, because the right next CPU call is a collective, and we will block there waiting for comm to be ready, so same effect as blocked init, no "opening" compared to eager mode.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/138527
Approved by: https://github.com/wconstab
ghstack dependencies: #138860
