- 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.
This revives #671 , removing the static functions that may unnecessarily hold a reference to the grid and the JITFunction object
Co-authored-by: Jason Ansel <jansel@jansel.net>
Reverts openai/triton#671
It seems like for some reason this caused out-of-memory errors on some
of our internal workloads. I'm reverting this so that HEAD can be used
in production at OpenAI, and I will work on digging into this issue
asynchronously.
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.
This PR adds several optimization capabilities in the compiler backend:
- Now using inline PTX for `tl.store`, making it possible to use things like evict_last
- For A100, mma layout can be directly converted to shared memory
- For A100, an additional "transpose" argument in `dot` allows tensors to be loaded once and used both row- and col- major.
- Fixed liveness analysis; this was broken.
- Now can load/store directly mma layout without converting. Useful for when tl.dot accumulator is initialized with DRAM data inside of an inner loop.
- `tl.dot` can now take LHS inputs in registers when it comes from a previous `tl.dot` instruction. Useful for e.g. fused attention.
This is a more stable commit that produce bitwise identical code to earlier
versions. Using commits after this one may lead to slightly different numerics
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>
* dds layout now internally re-uses dsd code path for increased code
* at_mask and kp_mask related things are now dropped from the softmax API. I couldn't think of any case where it was needed beyond is_causal. And if there is any, we should probably find a way to get it implemented statically so that users don't have to materialize masks.
* fixed bug in blocksparse matmul that caused troubles when layout had a full row/col of zeros
* blocksparse softmax now no longer modifies any data in-place
* blocksparse softmax now takes an is_dense arguments that provides better performance. Passing is_dense=True, is_causal=True is the best way to achieve triangular attention.
* unit tests now test backward pass
