[CK_TILE] Separate PermuteN epilogue from CShuffle epilogue
into standalone file (#5863)
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## Motivation
The PermuteN epilogue was previously embedded within
cshuffle_epilogue.hpp, despite having fundamentally different behaviour.
Coupling these two independent strategies in one file introduced
unnecessary complexity, SFINAE guards, and a dual operator() overload
selected at compile time via TiledMMAPermuteN_ template parameter.
This PR separates PermuteN into its own standalone
file(pertmuten_epilogue.hpp), simplifying both implementations and
making the codebase easier to maintain and extend independently.
## Technical Details
**New file: permuten_epilogue.hpp:**
contains PermuteNEpilogueProblem and PermuteNEpilogue, extracted from
the permuteN code path in cshuffle_epilogue.hpp.
**Cleanup of cshuffle_epilogue.hpp:**
- Removed the TiledMMAPermuteN_ template parameter from
[CShuffleEpilogueProblem]
- Removed the SFINAE-guarded permuteN operator() overload
- Removed the EnablePermuateN_ SFINAE alias
- CShuffle now only contains CShuffle logic; EightWave support
(independent feature) is retained
**Consumer migration :**
All consumer files now use compile-time epilogue selection via
[std::conditional_t]
`using GemmEpilogue = std::conditional_t<
TiledMMAPermuteN,
PermuteNEpilogue<PermuteNEpilogueProblem<...>>,
CShuffleEpilogue<CShuffleEpilogueProblem<...>>>;`
**Files modified:**
- flatmm_basic.cpp, moe_flatmm.cpp, a16w4_moe_flatmm.cpp,
mixed_prec_flatmm.cpp, mx_flatmm_instance.hpp — flatmm examples
- run_gemm_quant_example.inc — block-scale GEMM example
- gemm_weight_preshuffle_invoker.hpp — weight preshuffle invoker
- test_gemm_quant_fixtures.hpp, test_gemm_persistent_async_input.cpp,
test_gemm_pipeline_util.hpp — test utilities
- universal_gemm_invoker.hpp — universal GEMM invoker
- epilogue.hpp — add header updated to include permuten_epilogue.hpp
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
Add signal-based synchronization for persistent GEMM kernels where
input data becomes available incrementally. Uses modulo wraparound
(like PyTorch's AsyncMM) for chunk index calculation:
chunk_idx = ((tile_idx + tile_idx_pivot) / tiles_per_chunk) % num_chunks
Key components:
- PersistentAsyncInputScheduler struct with tiles_per_chunk_m,
chunk_signals, tile_idx_pivot_m, and num_chunks fields
- wait_eq_wave method using __builtin_amdgcn_s_sleep for power efficiency
- IsSupportedArgument validation for scheduler parameters
- Example demonstrating async input scheduling with simulated producer
- GTest unit tests covering all layout combinations
