Improved codegen for the Ampere GPUs.
* Make the layout pass recognize the multistage pipelined pattern.
* Now the pipeline pass can automate the multistage pipelining transformation.
* Remove extra barriers (from the prefetch pass & WAR) on Ampere.
* Update the code generator (generator.cc) to make Triton generate n-buffered shared memory loads/stores.
Membar pass on top of master is buggy with asynchronous copy. For example, it doesn't wait for asynchronous copies to complete before recoalescing accumulator in GEMM, which leads to undefined behavior when the program doesn't enter the loop. This PR proposes
* Now using unordered instead of ordered float (fixes NaN issues)
* Bool -> int32 now converts to 1 rather than -1
* Reduce extend arguments to 32-bits if possible
* 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.
