fix more conflits
Resolve merge conflicts
Some more build and conflict fixes
Resolve conflicts for 06-fused-attension.py
resolve merge conflicts for the tutorial group gemm example
Fixes for some LIT tests
resolve remaining conflicts in tests
Fix empty kernel
set capability 0
* optimize_epilogue
* Add config
* Remove licenses
* Comment out Hopper specific parameters when printing out configs
* Add benchmark parameters from flash-attention repo
* Add Z and H in the key of autotuner
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Co-authored-by: Lixun Zhang <lixun.zhang@amd.com>
This allow pipelining when a load is used by multiple dot in a loop.
Relax the condition to pipeline dot operands for mma v3 case. This
improves performance for the bwd pass from 260TF to 275TF. However this
expose a performance problem due to the wmma pipelining as ptxas will
now fall back to serial wgmma. A follow up PR will fix a bug in how we
emit wgmma_wait during pipelining and will bring performance to 335TF
I've add an option to yapf to do what we want for long lines, see
https://github.com/google/yapf/pull/1177. We can now have a real Python
formatter, yay!
To make this PR, I ran my modified yapf over the repository, then looked
over the full diff. Where yapf was mangling the param list of long
function decls/calls (mostly kernels), I manually added `#` to put
linebreaks where we want. I fixed up other formatting too -- mostly
adding or removing a trailing comma from lists.
Overall, trailing `#` was sufficient to get formatting similar to our
current code. I didn't have to disable yapf anywhere.
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Co-authored-by: Phil Tillet <phil@openai.com>
* add two fp8 data types `tl.float8e4b8` and `tl.float8e5b16` to triton.
* add SW type conversion between `tl.float8e4b8/tl.float8e5b16` and `fp16`
* change flashattention to support fp8 in q/k.
* rebase onto improve_fwd_fa
* Fixed a leftover from rebase
* rebase onto improve_fa_fwd
* Reduce tuning space
* Disable bwd with D=128
* Add test for d=128
* Fix an issue with get_best_config when there is only one config
* Added better configs for d=128
* Fix typos
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Co-authored-by: Lixun Zhang <lixun.zhang@amd.com>
* Attempt to absorb upstream's changes to improve causal=True
* Add autotuner
* Optimize for AMD MI250
- add pre_load_v as a tuning parameter
- do not define N_CTX as constexpr
- perform the second dot before sum
- remove qk_scale out of the inner loop
- add more configs in the autotuner
Note that bwd kernel is disabled for now. This is because we enabled
autotuning and grid becomes a function. So ctx.grid[0] no longer works.
* Enable bwd kernel
Support having chain of mma with mixed size.
Serialize the different block calculation in backward attention to
workaround problem with ptxas and wgmma.
* refine the gemm tuning scripts to reduce tuning space and better perf numbers
* added code to support tuning in full tuning space
* add a function to get best tuning config
* refine the matmul tutorial example to print out best tuning config for each input
* added even_k to gemm kernel heuristic for better performance
* address review comments
* Add fwd and bwd v2
Changes are largely from upstream.
* Split bwd kernel in dq and dk+dv
Only adds the split kernels. They are not enabled yet.
* Pull scalar multiplies out of the loop
* Enable split kernel for bwd pass
* Put back P_SEQ=128 in fwd test
Not used for bwd test
* Address review comments
* Address comments
Conditionally set causal/ splitkernel to False for bwd.
* Add block pointer semantics to bwd pass
This significantly increases perf for bwd, similar to fwd.
* Enable usage of block pointer semantics for AMD gpus
This commit enables usage of block pointer semantics by enabling
rewrite_tensor_pointer_pass that rewrites block pointer loads/stores
to legacy loads/stores.
* Update FA fwd in tutorial to use the block pointers
* use 90 compute capability for amd gpus in python/triton/compiler/compiler.py
Co-authored-by: Alexander Efimov <efimov.alexander@gmail.com>
---------
Co-authored-by: Ognjen Plavsic <ognjen.plavsic@dxc.com>
Co-authored-by: Lixun Zhang <lixun.zhang@amd.com>
Co-authored-by: Aleksandr Efimov <130555951+alefimov-amd@users.noreply.github.com>
Co-authored-by: Alexander Efimov <efimov.alexander@gmail.com>
1. Optimize the conversion and packing for 2xf32 -> 2xf16.
2. Split TMA store block into multiple slices of size 64x64.
3. Distribute the TMA store to all the warps.
4. Fix some naming issue.
* simple changes of the matmul scripts to get good performance. Specification reason for the performance boost needs futher investigation and are tracked
* fix review comments
* change the num_warps in the autotuning config for hip to workaround an error and change the rtol so correctness check passed
Implemented this situation with and without causal mask.
My implementation with causal mask looks like:
111000
111100
111110
Where only the right upper triangle part will be masked.
I added `P_SEQ` for the notation of extra sequence length for KV.
Co-authored-by: Philippe Tillet <phil@openai.com>
