Summary:
If triton is available, but we can't import triton.compiler.compiler.triton_key, then we see some annoying behavior:
1) If we don't actually need to compile triton, the subprocess pool will still spew error messages about the import failure; it's unclear to users if this is an actual problem.
2) If we do need to compile triton, we a) see the error messages from above and b) get a vanilla import exception without the helpful "RuntimeError: Cannot find a working triton installation ..."
Test Plan: Ran with and without torch.compile for a) recent version of triton, b) triton 2.2, and c) no triton. In all cases, verified expected output (success or meaningful error message)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130403
Approved by: https://github.com/eellison
This PR adds an alternative backend for Inductor, adding Composable Kernel Universal GEMM instances to the autotune instance selection.
The implementation is heavily influenced by the series of PRs which adds CUTLASS backend (https://github.com/pytorch/pytorch/issues/106991). The main differences are
(1) customizing compiler for the ROCm platform
(2) customizing template code generation for Composable Kernel Universal GEMM instances.
We provide config tuning knobs for balancing between instance sources compilation time and finding the best instance.
### Testing
Install the ck library
```
pip install git+https://github.com/rocm/composable_kernel@develop
```
Run the test
```
TORCH_LOGS=+torch._inductor \
pytest --capture=tee-sys test/inductor/test_ck_backend.py
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125453
Approved by: https://github.com/eellison, https://github.com/jansel
Automated fixes to put imports that are only used in type hints into TYPE_CHECKING imports. This also enables the RUFF TCH rules which will automatically apply autofixes to move imports in and out of TYPE_CHECKING blocks as needed in the future, this will make the initial PyTorch import faster and will reduce cyclic dependencies.
Co-authored-by: Xuehai Pan <XuehaiPan@pku.edu.cn>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127688
Approved by: https://github.com/XuehaiPan, https://github.com/ezyang, https://github.com/malfet
By moving AsyncCompile to its own file, we can import codecache without running the side effects of AsyncCompile. This will be important for AOTAutogradCaching, where we want to share some implementation details with codecache.py without spawning new processes.
To conservatively maintain the same behavior elsewhere, every time we import codecache, I've added an import to torch._inductor.async_compile (except in autograd_cache.py, where the explicit goal is to not do this)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127235
Approved by: https://github.com/aorenste, https://github.com/oulgen, https://github.com/masnesral
