* 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
- Removed driver module -- accelerator runtime is handled by pytorch
- Added basic support for ROCM based on @micmelesse 's PR -- now can execute empty kernel on AMD devices without any compile-time changes
- Now only using PREFER_SHARED for kernels when the size of shared memory is greater than 49k. Otherwise there can be poor L1 performance for broadcast tensors
This PR adds a BF16 data-type, along with FP32 <-> BF16 conversion instructions in the LLVM codegen. Other kinds of ops on bfloat16 are not yet supported.
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
* 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
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
- A100 support via mma.16816
- Thread swizzling for conflict-free shared memory accesses without
padding
- Complete overhaul of the LLVM code generation in
codegen/selection/generator.cc to remove overengineering
- Added debugging capabilities in the Python binding
- Compilation error for kernels that spill
