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[CK Conv] Wavelet gemm pipeline for bwd_weight convolution (#5652) ## Motivation In the current CShuffleV3 backward weight kernel, the in-kernel conv-to-GEMM transform generates significant INT32 VALU pressure per MFMA instruction. On VALU-heavy shapes (e.g., G=1, 3×3, C=256), these index computation ops compete with MFMA for VALU issue slots, creating a bottleneck that cannot be resolved by pipeline prefetching alone. This PR adds a wave-specialized ("wavelet") convolution backward weight kernel that splits workgroup threads into two roles: - **Load waves**: conv-to-GEMM address computation + global memory loads + LDS writes (all VALU/VMEM) - **Math waves**: LDS reads + MFMA + CShuffle epilogue (no index computation) By physically separating the two instruction classes onto different waves, VALU and MFMA execute on different hardware functional units without contention. ## Technical Details **Core kernel (new files):** - `gridwise_gemm_xdl_waveletmodel_cshuffle_conv_v3.hpp` — wave-specialized gridwise GEMM for conv bwd weight (2-way split: load + math) - `device_grouped_conv_bwd_weight_xdl_waveletmodel_cshuffle_v3.hpp` — device op following CShuffleV3 patterns; `BlockSize = TileMathThreadGroupSize` for MFMA wave assignment, `LaunchBlockSize = TileLoad + TileMath` for kernel launch **Wave pipeline (modified):** - `gridwise_gemm_waveletmodel.hpp` — load/math wave pipeline structs with `sched_group_barrier` scheduling hints to front-load VMEM reads before address-advance VALU **Two wave ratios:** - **(4,4)**: 256 load + 256 math = 512 threads (8 waves). Best on large shapes. - **(4,2)**: 256 load + 128 math = 384 threads (6 waves). Best on small shapes (fewer sync barriers, denser MFMA per math wave). **Instance coverage (F16 and BF16 symmetric):** | Ratio | Tiles | Layouts | ConvSpecs | |-------|-------|---------|-----------| | (4,4) | M128×N128, M64×N64, M128×N64, M64×N128 | 2D NHWGC, 3D NDHWGC | Default, Filter1x1Stride1Pad0 | | (4,2) | M64×N64, M128×N64, M64×N128 | 2D NHWGC | Default, Filter1x1Stride1Pad0 | **Existing wavelet model fixes:** - `BlockSize` corrected from `math::max(TileLoad, TileMath)` to `TileMathThreadGroupSize` in the flat-GEMM wavelet device op and gridwise kernel ## Test Plan - `test_grouped_convnd_bwd_weight` GTest: 34 hardcoded test cases covering 1D/2D/3D, F16/BF16, G=1/2/16, various spatial sizes - Performance benchmark: all 37 RetinaNet bwd_weight shapes on gfx950 ```bash ninja -C build test_grouped_convnd_bwd_weight ./build/bin/test_grouped_convnd_bwd_weight ``` ## Test Result **Correctness:** 34/34 GTest cases passed (F16/BF16 × 1D/2D/3D × Default/Filter1x1Stride1Pad0 × various G/N/K/C combinations). **Performance:** Wavelet is the fastest overall instance on 12/37 RetinaNet shapes — all G=1, 3×3 convolutions with C=256 (the VALU-heavy target shapes): | Shape | Uplift vs best baseline | |-------|------------------------| | K=36, 7×7 | 1.91x | | K=36, 100×100 | 1.60x | | K=36, 13×13 | 1.43x | | K=36, 25×25 | 1.38x | | K=36, 50×50 | 1.38x | | K=256, 100×100 | 1.24x | | K=256, 13×13, s=2 | 1.20x | | K=256, 25×25, s=2 | 1.20x | | K=256, 7×7 | 1.17x | | K=256, 13×13 | 1.13x | | K=2376, 50×50 | 1.05x | | K=2376, 100×100 | 1.06x | Where wavelet does not win (25/37): 1×1 convolutions (explicit kernel does host-side transform), grouped convolutions with small per-group channels, and shapes where standard CShuffleV3 already amortizes VALU overhead. ## Submission Checklist - [x] Look over the contributing guidelines at https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests. --------- Co-authored-by: jakpiase <jakpia21@gmail.com>