mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-06-28 18:56:59 +00:00
0b3c297ee2
[CK Tile] Add WAVELET pipeline for forward grouped convolution (#8009) ## Motivation CK Tile forward grouped convolution trails classic CK on 3x3 convolutions whose output-channel count is not divisible by 8, where the narrow output store limits the compute CShuffle epilogue. This ports the WAVELET pipeline (added for backward-weight in #7937) to the forward kernel to close that gap. ## Technical Details - Kernel (`grouped_convolution_forward_kernel.hpp`): WAVELET load/math-wave wiring, mirroring the backward-weight implementation; the non-WAVELET path is unchanged. - Generator: implement `parse_native_fwd_instance`, the forward native-instance parser. - Registered WAVELET instances: profiler bf16 3 / fp16 5, tests 1 each. WAVELET requires input channels divisible by 8 (it does not apply to depthwise). The bf16/fp16 instance asymmetry is intentional and measured: the VecC=8 tiles never beat the compute pool in bf16 but win about 20% of divisible-by-8 3x3 shapes in fp16, so VecC=8 is registered for fp16 only. ## Test Plan - Correctness (CPU reference) for every registered profiler instance, across VecC variants. - Per-shape best-instance performance sweep over the 34 RetinaNet shapes (bf16) and a 200-shape cross-model sweep (bf16 and fp16), compared against classic CK. ## Test Result - Correctness: PASS for all instances. - RetinaNet (bf16, vs classic CK): faster on 28 of 34 shapes, geomean +19.5%; the not-divisible-by-8 shapes up to 3.7x. One 1x1 stride-2 shape stays ~20% behind classic CK, unrelated to WAVELET. - Cross-model (200 shapes): WAVELET wins 3x3 not-divisible-by-8 in both dtypes (up to 61% over the next-best compute instance); for divisible-by-8 3x3 it wins about 20% of shapes in fp16 (3-11%) and none in bf16. ## Submission Checklist - [x] Look over the contributing guidelines at https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests. Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>