* Load libcuda.so.1 if libcuda.so is not there. Error if both aren't
there.
* Support for multiple grad_to_none in triton.testing.do_bench
* Benchmark dataframe printed along with name
* update membar pass when data is double buffered
* Add instruction prefetch_s
* prefetch tests pass (except the 1 warp case)
* Fix the 1-warp bug
* Add back prefetch files
* Disable prefetch on a100
* Always add war barrier on sm>=80
This massively simplifies implementation of `reassociate` and also fixes
a bunch of bug. The pass could still be improved, but can already be used
to generate constant pointer offsets in eg the matmul epilogue
This PR implements a major overhaul of the frontend for Triton, and replaces Triton-C by a pure Python API in which kernels are defined as @triton.jit decorated functions. The documentation and tutorials have also been updated to accommodate these changes.
See documentations for more information on the new API
The solution proposed in #77 can create namespace conflicts when triton and triton-nightly have both been pip installed. Therefore, this PR is moving nightly releases to pre-releases in the main triton index.
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.
Before this commit, the benchmarking infrastructure used heterogeneous protocols between library (e.g., CUTLASS uses a C++ binary that reports mean TFLOPS; torch and triton use python call and report 10th, 50th and 90th quantiles). For the sake of uniformity and fair benchmark practices, this PR adds a python wrapper for auto-tuned CUTLASS matrix multiplication. Benchmarks have been rewritten to use this wrapper with `triton.testing.do_bench` rather than system calls to CUTLASS profiler. Importantly, this also ensures that all the matmuls are done on the *same* input data which should stabilize clock across providers.
kernels before synchronizing, the kernels are now enqueued one by one.
This makes it possible to clear the L2 cache before running the
workload, and also potentially collect some variance data for error bars
in plots
