Recently there has been more and more report about installation issues:
- Installing Triton before upgrading pytorch can create some issues because Triton uses some torch headers
- llvm-10-dev not available on some platform; llvm-11-dev not available on e.g. Ubuntu.
absence of nightly builds
This PR should fix all these issues. Some CMake tricks are used to download and install llvm at build time. Triton Python bindings were modified to remove dependence on pytorch ops. Midnight CI job added to generate binary wheels for all Triton version and update them on pypi's new triton-nightly project.
This PR will also make it very easy to use LLVM forks in the future for whatever needs we have.
Improved handling of asynchronous copy, scheduling and synchronization for A100. Now achieving CUTLASS-like performance on large square dense matrix multiplication tasks
* Simplified `triton.kernel` API to achieve lower latency:
> .data_ptr() must now be passed as kernel argument. No more implicit
conversion from torch.tensor
> compilation options are now constant attributes, i.e., opt.d('VAR')
becomes opt.VAR
> torch.device must now be passed explicitly to triton.kernel (no
longer inferred from torch.tensor arguments)
* C++ tests moved to `python/tests/`
* C++ tutorial created in `tutorials/`
* Python tutorial created in python/tutorials/
* Version changed to 1.0alpha
* No longer copying C++ headers into the Python package
* added python/triton/ops/ package for pre-written Triton ops
