* use hardware instruction for type conversion between fp8 and fp32
* move gpu_matrix_core_version from semantics.py to hip_backend.py
---------
Co-authored-by: Aleksandr Efimov <efimov.alexander@gmail.com>
* [Tutorial] Fix post IFU issues with FA
* Remove redundant kernels in 06-fused-attention.py
* Added README for scripts in perf-kernels dir
* Fix bwd kernel
---------
Co-authored-by: Lixun Zhang <lixun.zhang@amd.com>
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
---------
Co-authored-by: Lixun Zhang <lixun.zhang@amd.com>
In current implementation, warpsPerCTA is always set to [numWarps, 1]
for 2 tt.dot fusion scenario. But, it is not optimal for cases such that
tt.dot doesn't have enough parallelism on row dimension but on column
dimension.
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.
---------
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.
The [hints
dispatching](218492cd65/python/triton/tools/link.py (L161))
logic currently fails for the edge case where a single kernel with no
specializations is to be linked in the [AOT
compiler](https://github.com/openai/triton/blob/main/python/triton/tools/link.py).
Since the dispatcher inserts a conditional branch for each
specialization case, this results in an `if ()` being inserted into the
`C` source, which clearly breaks downstream artifacts.
Fix:
- Added simple check for this edge case
- Added unit test that mirrors the existing
[`test_compile_link_matmul`](218492cd65/python/test/unit/tools/test_aot.py (L224))
test case save for the aforementioned condition.
Refactor the pipeliner pass in order to make it more generic. The main
change is that the pipeliner is now broken into 2 pieces one calculating
a modulo schedule and create async ops based on the IR and an expander
that will generate the pipelined IR based on the modulo schedule.
The advantage of separating the two pieces is that it will allow us to
create different schedule without having to change the expander and it
will allow for more complex schedules.
For now the schedule generated for matmul case matches rougly the
schedule picked by the previous pipeliner in order to avoid changes.
This also creates a different sequence of insert/extract slice for the
alloc. We should probably change shared alloc to use memory semantic.
For in-place kernels, neither `reset_to_zero` nor `Config.prehook`
provided in the autotuner can restore the values changed during the
tuning process, so I propose a recovery mechanism here.
---------
Co-authored-by: Chenggang Zhao <chenggangz@deepseek.com>
Co-authored-by: Keren Zhou <kerenzhou@openai.com>
[FRONTEND] Enable ruff linter instead of flake8.
This fixes a few issues automatically, and also flagged two issues to
fix manually in test_core.py: We had two duplicate function names! One
of these function bodies was a duplicate, so I deleted it. The other
function body was not a duplicate, so I gave it a new name.
AIUI all of these errors should have been picked up by flake8. I'm
confused why it wasn't working. Anyway this is working, and it's faster
than flake8, so it seems like an improvement in all dimensions.
This PR https://github.com/openai/triton/pull/2555 disabled `W503`
(means line breaks can now occur before a binary operator).
The change surprisingly didn't take any effect nor required any style
changes in `triton` main `pre-commit` stage. But our `triton-shared`
[pipeline
run](https://github.com/microsoft/triton-shared/actions/runs/6710459100/job/18236352821)
(see `Check pre-commit` stage) picked this up correctly and complained
about formatting issues. I'm not entirely sure what could be the cause
for such difference, but if we also disable `W503` in `pyproject.toml`
then the rule is picked up correctly.
[FRONTEND] Refactor jit.py.
The goal is to simplify the code and make it more flexible before we
change the kernel launch syntax to
`kernel[grid, compiler_flags(...)](...)`.
The main changes here are:
- Get rid of the eval'ed code in make_launcher. We can do everything
using bind().
- Add KernelParam and KernelArg classes, letting us get rid of the
parallel arrays/dicts indexed by parameter index.
- Get rid of duplicated kernel launch code in the cache-hit/cache-miss
branches.
We're in the process of incrementally converting from autopep8 + flake8
+ isort to ruff, on a directory-by-directory basis.
The motivation to switch away from autopep8 is that I can't get it to
wrap long lines, even with -aaa. This seems to be a known problem,
https://github.com/hhatto/autopep8/issues/497.
See more details about alternatives tried in
https://github.com/openai/triton/pull/2557.
This is a combination of 4 commits.
Works as StandAlone and Backend
Works as StandAlone and Backend
This is a combination of 13 commits.
Works StandAlone and as Backend
This is a combination of 7 commits.
backend set default dir with flag
move bitcode to backend dir
copy backend
save
empty test work in backendmode
enable backend mode when copying to upstream
clean up
fix failure
minimize diff
add skip function
fix bug with corrupted dwarf exp
match num_wraps
fix multi threaded test issue
move bitcode file out of lib
move backend to python/triton/third_party/hip
move libhsa
backend works again
restart ci
clean upstream location first before copy
match scripts
fix new error
memoize backend stuff
fix bug
* this pr adds a third party backend for triton that works on AMD
* this expose a lot of the work that has been done in our
[fork](https://github.com/ROCmSoftwarePlatform/triton)
* most unit tests on `test_core.py` pass
* it skips some unit tests for various reasons
* we plan to follow up with more prs improving Functionality and
Performance in the future
---------
Co-authored-by: Philippe Tillet <phil@openai.com>
* [MFMA] FP8 and BF8 support
This PR adds support of fp8 and bf8 in AccelerateMatmul pass and
Introduces generation of float8 mfma instructions in ttg to llvm conversion.
* add tests
* fix tests
* review fix: fix variable naming and dot operand promotion.
* review comments fixes
---------
Co-authored-by: Shucai Xiao <shucai.xiao@amd.com>
