Including:
- `torch/csrc/instruction_counter`
- `torch/csrc/lazy`
- `torch/csrc/monitor`
- `torch/csrc/profiler`
- `torch/csrc/dynamo`
Fixes part of #148114
Personal mistake about (PR #163317), this PR does the same thing **and PR #163317 has already been approved by @albanD.**
This is a personal mistake on my part, and I'm so sorry about that. Hope you won't mind @albanD. 🥹
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163610
Approved by: https://github.com/albanD, https://github.com/Skylion007
Fixes#161014
This bug fix introduces a fix that is consistent with the exception handling. Outlined in issue #161014, there is an edge case where the negative padding does not make the tensor size negative but still triggers the exception that the size is negative. The fix is simply adding `new_dim >=0` to include the zero dim and letting the operator return an empty tensor.
In the PR I have added the edge case where the test will now check the negative padding where the dimension gets reduced to zero. But the sample is only for the `constant` type of padding. I would like some feedback if it is necessary to put the same sample on the `reduce` type as well.
This is my first PR to contribute to PyTorch and any help/feedback will be welcome! Thank you!
@malfet @manuelcandales @janeyx99 @ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/161639
Approved by: https://github.com/manuelcandales
This PR is part of the work to deprecate torch::deploy in OSS. Effectively it does 3 things to get started.
1. Remove test_deploy_interaction as we no longer need to worry about this
2. Remove all torch._running_with_deploy checks and use the False path always (surfaced 1)
3. Remove `USE_DEPLOY` and switch to the default path always
Note: MyPy does fail on a bunch of things here as a bunch of older files are touched. It may be better to fix these things on a separate PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158288
Approved by: https://github.com/albanD
This PR is part of the work to deprecate torch::deploy in OSS. Effectively it does 3 things to get started.
1. Remove test_deploy_interaction as we no longer need to worry about this
2. Remove all torch._running_with_deploy checks and use the False path always (surfaced 1)
3. Remove `USE_DEPLOY` and switch to the default path always
Note: MyPy does fail on a bunch of things here as a bunch of older files are touched. It may be better to fix these things on a separate PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158288
Approved by: https://github.com/albanD
This PR is part of the work to deprecate torch::deploy in OSS. Effectively it does 3 things to get started.
1. Remove test_deploy_interaction as we no longer need to worry about this
2. Remove all torch._running_with_deploy checks and use the False path always (surfaced 1)
3. Remove `USE_DEPLOY` and switch to the default path always
Note: MyPy does fail on a bunch of things here as a bunch of older files are touched. It may be better to fix these things on a separate PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158288
Approved by: https://github.com/albanD
Summary:
When we compute contiguity for a tensor with dynamic shapes we first:
1) Try to compute it without guarding.
2) If all shapes hinted, compute it with potentially adding guards.
3) if any input is not hinted, compute it symbolically.
sym_is_contiguous return a SymBool that is then either evaluated or guard_or_false can be called
on it to avoid data dependent errors.
ex:
bool is_contiguous = input.sym_is_contiguous().guard_or_false(__FILE__, __LINE__);
is_contiguous_or_false is a helper function that does that.
In this PR I only handle default contiguity, will follow up with changes for other formats like channel_last .
We use this patter in this PR for several locations to avoid DDEs.
Test Plan:
contbuild & OSS CI,
Rollback Plan:
Reviewed By: malfet
Differential Revision: D77639021
Pull Request resolved: https://github.com/pytorch/pytorch/pull/157472
Approved by: https://github.com/aorenste
When we compute contiguity for a tensor with dynamic shapes we first:
1) Try to compute it without guarding.
2) If all shapes hinted, compute it with potentially adding guards.
3) if any input is not hinted, compute it symbolically.
sym_is_contiguous return a SymBool that is then either evaluated or guard_or_false can be called
on it to avoid data dependent errors.
ex:
bool is_contiguous = input.sym_is_contiguous().guard_or_false(__FILE__, __LINE__);
is_contiguous_or_false is a helper function that does that.
In this PR I only handle default contiguity, will follow up with changes for other formats like channel_last .
We use this patter in this PR for several locations to avoid DDEs.
Differential Revision: [D77183032](https://our.internmc.facebook.com/intern/diff/D77183032)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155590
Approved by: https://github.com/ezyang
When we compute contiguity for a tensor with dynamic shapes we first:
1) Try to compute it without guarding.
2) If all shapes hinted, compute it with potentially adding guards.
3) if any input is not hinted, compute it symbolically.
sym_is_contiguous return a SymBool that is then either evaluated or guard_or_false can be called
on it to avoid data dependent errors.
ex:
bool is_contiguous = input.sym_is_contiguous().guard_or_false(__FILE__, __LINE__);
is_contiguous_or_false is a helper function that does that.
In this PR I only handle default contiguity, will follow up with changes for other formats like channel_last .
We use this patter in this PR for several locations to avoid DDEs.
Differential Revision: [D77183032](https://our.internmc.facebook.com/intern/diff/D77183032)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155590
Approved by: https://github.com/ezyang
It's better because you return less date, encapsulate more, and no longer need special handling of symvec vs nonsym vec dim(). Also removes a few casts and fixes a few potential edge cases relating to unsigned comparisons
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155334
Approved by: https://github.com/ezyang
Fixes#153133
Fixes an inconsistency in torch.arange on CUDA and MPS backends when using float32 and large input values. Previously, invalid ranges (e.g., start > end with a positive step) could silently return empty tensors due to precision loss in validation logic.
The fix introduces double precision validation for checking whether the step sign is consistent with the range direction.
This ensures torch.arange behaves consistently with CPU for large float32 inputs, and raises an appropriate error when the range is invalid.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154320
Approved by: https://github.com/malfet
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 PR introduces two new methods to the LazyGraphExecutor class:
- ClearComputationCache(): Allows clearing the entire computation cache.
- RemoveFromComputationCache(hash): Enables removal of specific cache entries based on their hash.
The main objective is to expose cache management functionality for debugging cache hits and misses across different computations. For instance:
- Reset the cache state in tests, allowing reuse of the same computation client to evaluate cache logic consistently.
- Selectively remove cache entries to analyze the impact on subsequent computations.
- Improve observability into the cache behavior, aiding in the investigation of cache-related issues or optimizations.
On the XLA lazy graph executor, we want to run a series of tests that modify some parts of the HLO module proto of the computation, and we need a means to ensure that the hash is agnostic to some elements (OpMetadata in the XLA proto data). Hence, it would be easy to parameterize the test, clear the cache and validate that the resulting hash is the same between runs. Otherwise, we'd need to hardcode the resulting serialized hash.
Simultaneously, **another motivation**, is that users could also clear some computation hashes for an added flexibility in their applications, by introducing their own custom strategies for maintaining the cache (without relying on the default LRU).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144489
Approved by: https://github.com/wconstab
Summary: Failed to write the auto-tune result to `PYTORCH_TUNABLEOP_FILENAME` with this change (empty file) , reverting to unblock.
Test Plan: CI
Differential Revision: D66870750
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142226
Approved by: https://github.com/leitian
