[CK Tile] Add conv Wavelet GEMM pipeline and bwd_weight
instances (#7937)
## Motivation
CK Tile had no pipeline competitive with old CK's wavelet on the
RetinaNet K=36 C=256 3x3 conv bwd_weight class. This adds a
wave-specialized "wavelet" GEMM pipeline so CK Tile has a competitive
kernel for spatial small-K shapes.
## Technical Details
- New wavelet GEMM pipeline (`gemm_pipeline_ag_bg_cr_wavelet.hpp`):
workgroup split into math waves (LDS read + MFMA) and load waves (DRAM
read + LDS write).
- VGPR role-split: `operator()` has two top-level mutually-exclusive
`is_math` branches so the allocator overlays both roles onto the same
physical VGPRs, cutting arch VGPR ~33-40% and raising occupancy.
Correctness depends on identical `block_sync_lds` counts on both arms
plus a matching load-wave barrier stub in the epilogue
(`cshuffle_epilogue.hpp`).
- Kernel dispatch (`grouped_convolution_backward_weight_kernel.hpp`):
`kIsWavelet` path, `LaunchBlockSize`, load-wave barrier stub.
Uplift: wavelet is the fastest CK Tile pipeline on the RetinaNet K=36
C=256 3x3 family, beating the best non-wavelet CK Tile kernel by 10-27%
(googlenet K=320 by 16-23%); the role-split roughly halves the parity
gap vs old CK on the 13x13 fp16 shape.
## Test Plan
- `ckProfiler grouped_conv_bwd_weight`, NHWGC layout, fp16/bf16,
`split_k=all`, CPU verify on RetinaNet K=36 shapes (7x7, 13x13) and a
broad 2D sweep.
- Correctness: `-v=1` across `split_k` in {-1,1,2,4,8,16,32,64}
(barrier-parity / deadlock check).
- `test_grouped_convnd_bwd_weight` over the tests `.conf` wavelet
instances.
## Test Result
- All wavelet instances CPU-verify correct across the split-K sweep; no
hangs (dual-arm barrier sequence matches).
- Wavelet wins the RetinaNet K=36 C=256 3x3 family (10-27% over best
non-wavelet CK Tile) and googlenet K=320 (16-23%); at parity-or-better
vs old CK on the majority of spatial shapes.
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
[CK][CK Tile] Grouped Conv Backward Weight Streamk instances (#5904)
## Motivation
Add streamk instance to grouped convolution backward weight profiler.
## Technical Details
- New instances for grouped conv backward weight with streamk
## Test Plan
test_grouped_convnd_bwd_weight_tile
## Test Result
passed locally
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
---------
Co-authored-by: Graner, Johannes <johannes.graner@amd.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
[CK_TILE] Add CK Tile bwd weight profiler (#4797)
## Motivation
To compare old CK and CK Tile, we need to extend the current CK profiler
to support running also CK Tile instance with the same API. In order to
have the same instance coverage in CK Tile compared to the old CK, I've
added code generation from old CK configurations to CK Tile instances
using the CK Builder.
## Technical Details
- The codegen python script for CK Tile fwd convs is extended to support
also bwd weight and bwd data.
- The generated instances are added to the CMake build (target
`device_grouped_conv_bwd_weight_tile_instance`s).
- A new profiler op (`grouped_conv_bwd_weight_tile`) has been added to
the CK Profiler.
---------
Co-authored-by: Ville Pietilä <>
Co-authored-by: Bartlomiej Kocot <barkocot@amd.com>
