- updates to support ROCm 5.2
- workarounds in tests where NV tools were used unconditionally
- implemented `get_num_blocks()` and `add_memfence()` for AMD GPU
- backported from history some atomics
- added bf16 support
- minor warnings cleanup
- added dockerfile to run on a ROCm enabled machine
Co-authored-by: B1tway <andrew.shukshov@gmail.com>
Co-authored-by: Andrey Shukshov <36711069+B1tway@users.noreply.github.com>
It is currently necessary for optimal performance in quantized workloads to add a special-purpose instruction in the IR. Backward compatibility with this instruction is *NOT* guaranteed.
Without this patch, a debug version of python complains that:
```
Fatal Python error: Python memory allocator called without holding the GIL
Python runtime state: initialized
```
This PR completely rewrites the runtime of Triton to be more lean and
clearly separate the compilation step from the just-in-time caching logic.
This should substantially reduce launch overhead.
Redo of #651 against master. Fixes#525 by catching CUDA error when we
check pytorch tensor size and rethrowing a more informative error that
says why we failed.
Moved dispatch.cc to semantic.py (@ptillet)
Integer signedness analysis was moved from C++ to python (@daadaada)
Cleaner frontend types (@daadaada)
Moved SSA construction to a separate object (@ptillet)
Co-authored-by: Yan Da <dyanab@connect.ust.hk>
A forthcoming PR will update the RNG to use these types.
Also:
- Add tests for the `//`, `<<`, and `>>` operators.
- Change `TensorWrapper` to unwrap objects when the resulting object would be simpler.
- Clean up `throw_unreachable`, since it was triggering compiler warnings.
- `BF16TyID` was missing a repr implementation.
- Throw a better exception on impossible casts.
- Add a few assertions. Tested with a debug build.
- Add `pointer_dtype.__str__` to aid kernel debugging.
Added an additional `repr` argument to the cache hook, which represents a human-readable string representation of the signature and argument attributes associated with the compiled binary.
Significantly improves the performance of `triton.ops.matmul` in memory-bound settings via the use of many more block configs coupled with a performance model to drive the auto-tuning process.
* make C++ code compatible with Windows + MSVC
* added dlfcn-win32 for cross-platform dlopen
* fixed building and pip install on Windows
* fixed shared library file name under Windows
