refactor(ck): mx gemm kernel unification
## Motivation
CK tile currently has two separate MX GEMM kernels for gfx950 and
gfx1250. This pull request refactors and modernizes the MX GEMM kernel
and example to use new scale tensor handling, improved kernel argument
structures, and updated pipeline and kernel APIs. The changes simplify
the interface and improve type safety.
JIRA ID ROCM-26313
## Technical Details
- Add support for gfx950 in MX GEMM kernel for gfx1250 and remove unused
kernel
- Unify comp async pipeline for GEMM and MX GEMM
- Unify eight waves pipeline for GEMM and MX GEMM
- Move preshuffle MX GEMM pipeline to gemm ops and remove gemm_mx ops
- Unify testing framework for MX GEMM
- Add gfx950 tests for grouped MX GEMM
## Test Plan
- `test_mx_gemm_async.cpp` for MX GEMM on gfx950
- `test_mx_grouped_gemm_comp_async.cpp` for grouped MX GEMM on gfx950
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
Users/tlakshma/ck/tile engine develop
## Motivation
This PR adds multiple new GPU kernel benchmarking operations to the CK
Tile Engine, expanding its coverage of GEMM-family operations:
- **gemm_multi_abd**: GEMM with multiple A, B, and D tensors, enabling
epilogue patterns such as scale/bias fusion.
- **batched_contraction**: Batched tensor contraction supporting
multi-dimensional batch (G), M, N, and K dimensions, targeting workloads
where the contraction indices span more than one logical axis.
- **mx_gemm**: MX-format GEMM with microscaling (e8m0) scale tensors.
- **gemm_rowcolquant**: Block-scale GEMM with row/column quantization.
- **gemm_tensor_quant**: Block-scale GEMM with tensor quantization.
- **grouped_gemm_rowcolquant**: Grouped GEMM with row/column
quantization.
- **grouped_gemm_tensorquant**: Grouped GEMM with tensor quantization.
- **batched_gemm**: Batched GEMM benchmarking support.
## Technical Details
### gemm_multi_abd
- New subdirectory: tile_engine/ops/gemm/gemm_multi_abd/
- CMakeLists.txt follows the same individual-target pattern as
gemm_universal / gemm_multi_d.
- gemm_multi_abd_instance_builder.py subclasses GemmKernelBuilder from
the shared gemm_instance_builder.py.
- gemm_multi_abd_benchmark.py delegates to the shared GemmBenchmark
parent class.
- Configs: default_config.json, default_ci_config.json,
user_provided_config.json.
- Supported GPU targets: gfx90a, gfx942, gfx950, gfx1201.
### batched_contraction
- New subdirectory: tile_engine/ops/gemm/batched_contraction/
- Extends GemmKernelBuilder via BatchedContractionKernelBuilder, adding
num_dim_g, num_dim_m, num_dim_n, num_dim_k, num_d_tensors, and
elementwise_function parameters.
- Layout string uses 3-character encoding (A+B+E), e.g. rcr.
- Self-contained benchmark sweep driver
(batched_contraction_benchmark.py) with JSON/CSV export and best-kernel
selection.
- Supported GPU targets: gfx90a, gfx942, gfx950.
### mx_gemm
- New subdirectory: tile_engine/ops/gemm/mx_gemm/
- Supports MX-format (e8m0) microscaling for A and B scale tensors.
### block_scale_gemm (gemm_rowcolquant, gemm_tensor_quant)
- New subdirectory: tile_engine/ops/gemm/block_scale_gemm/
- gemm_rowcolquant: row/column quantization epilogue.
- gemm_tensor_quant: tensor-level quantization epilogue.
### grouped_gemm_quant (grouped_gemm_rowcolquant,
grouped_gemm_tensorquant)
- New subdirectory: tile_engine/ops/gemm/grouped_gemm_quant/
- grouped_gemm_rowcolquant: grouped GEMM with row/column quantization.
- grouped_gemm_tensorquant: grouped GEMM with tensor quantization.
### batched_gemm
- New subdirectory: tile_engine/ops/gemm/batched_gemm/
- Batched GEMM benchmark support wired into the sampling/active-op
lists.
All new ops are registered in op_weights.json for budget allocation and
wired into the active-op sampling lists in CMakeLists.txt.
## Test Plan
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## Test Result
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## Submission Checklist
- [ ] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
