Summary: This has been set to "subproc" for a while internally and externally, so we can remove and simplify some of the code. Note that there's no pressing need here -- just that since we've had internal outage with the legacy "fork" implementation, it doesn't seem helpful to leave it available. But if people aren't in the mood for this sort of cleanup, I won't be offended to abandon it.
Test Plan: CI
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142001
Approved by: https://github.com/eellison, https://github.com/jansel
This gets a bit messy, but this appears to be the best spot to make a
true / false decision.
Note that since we're looking at whether or not it's used, if the pool
doesn't warm up within the time it takes for a compile, we will mark the
feature use as false.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141074
Approved by: https://github.com/masnesral
ghstack dependencies: #141059
Adds a few more dynamo_timed() to measure triton compilation and load_by_key_path times.
In the case of async compilation with multiple threads, we'll generate a single `kernel_compile` event that occurs when waiting on all the parallel compiles to finish.
In the case where async parallel compilation is disabled (or, compile threads are warming up), we'll generate a `triton_compile` event for each kernel.
The `triton_compile` events is a bit questionable: do we need a row for each triton compile event? It might eat up on our already low retention, so I might just remove that. Will discuss with @slarsen.
Differential Revision: [D65215707](https://our.internmc.facebook.com/intern/diff/D65215707/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139402
Approved by: https://github.com/oulgen
Summary: Now that we have subprocess parallel compile on by default, we can change the internal compile_threads default to > 1 with a killswitch. Some jankiness so we can avoid evaluating the justknob at import.
Test Plan: Ran codecache tests with JK on, then canaried locally with JK off
Differential Revision: D62913998
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136246
Approved by: https://github.com/eellison
Summary: BrokenProcessPool means a parallel-compile subprocess exited, which we never expect. It's likely due to a crash, so print a more meaningful error message and instructions that it's probably easier to debug by turning off parallel compile. Output looks like:
```
...
File "/data/users/slarsen/pytorch/torch/_inductor/runtime/compile_tasks.py", line 45, in _reload_python_module
exec(code, mod.__dict__, mod.__dict__)
File "/tmp/torchinductor_slarsen/4q/c4qw7xk5lbb7whg5txnk4hwbc7z6kepak3o666tr3d64gcad5r5b.py", line 815, in <module>
async_compile.wait(globals())
File "/data/users/slarsen/pytorch/torch/_inductor/async_compile.py", line 265, in wait
raise RuntimeError(
RuntimeError: A compilation subprocess exited unexpectedly. This is likely due to a crash. To facilitate debugging, you can re-run with TORCHINDUCTOR_COMPILE_THREADS=1 to cause compilation to occur in the main process.
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135120
Approved by: https://github.com/Chillee
Summary: Torch-compiling a quick script can be a bit slower than it needs to be: even though we initialize the subprocess pool early, it still might not be ready by the time we try to compile the first Triton kernel. Instead, let's use the single-threaded path until the pool has successfully completed a no-op job.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133508
Approved by: https://github.com/Chillee
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
