01cca38c8e
[CK Tile] WAVELET pipeline for backward-data grouped convolution (#8220) ## Motivation On the RetinaNet shapes (gfx950, fp16) CK Tile backward-data conv was ~18% behind classic CK, with the gap concentrated in the K=2376 3x3 detection-head family where bwd_data spends most of its time. The WAVELET GEMM pipeline already gives uplift for forward and backward-weight conv; this ports it to backward-data and consolidates the now-shared machinery across all three directions. ## Technical Details - Backward-data wavelet support in the tile kernel: launch extra load waves when the pipeline exposes `LaunchBlockSize`, and split the epilogue into math waves (run the CShuffle epilogue) and load waves (`RunBarrierStub`). - Register 7 WAVELET instances (fp16 and bf16), tuned for backward-data's tall-skinny GEMM rather than the forward tile shapes: a big-M `256/128/64` workhorse, a `VecA=4` variant for the `K % 8 != 0` shapes, and a `NumGroupsToMerge=32` variant for grouped (depthwise-style) shapes. - Implement the native backward-data instance parser in `generate_instances.py`. - Deduplicate the wavelet machinery shared by forward, backward-data, and backward-weight: `GroupedConvLaunchBlockSize`, `is_wavelet_pipeline`, and `RunWaveletAwareEpilogue` in `grouped_convolution_utils.hpp`; the three native instance parsers collapse to one parameterized parser. The three kernels now call the shared helpers. ## Test Plan - Rebuild the full profiler instance pools for all three directions (fp16/bf16/fp32, nhwgc/ndhwgc) to exercise the shared helpers across every instantiation. - Tile GTests on gfx950: `test_grouped_convnd_fwd_tile`, `test_grouped_convnd_bwd_data_tile`, `test_grouped_convnd_bwd_weight_tile`. - Per-shape sweep of the 35 RetinaNet backward-data shapes vs classic CK and the non-wavelet tile pool (`profile_wavelet_bwd_data.py`); correctness spot-checked with GPU-reference verification on the new big-M and NumGroupsToMerge instances. ## Test Result - GTests pass: forward 9/9, backward-data 6/6, backward-weight 6/6. - Backward-data perf (3x3 g=1 region, geomean classic/tile): 0.88 -> 1.11, i.e. the tile path goes from ~12% slower than classic to ~8% faster. The largest single backward-data shape (256x100x100->2376) moves from 11% slower than classic to 12.5% faster. - The dedup refactor preserves behavior (net -174 lines across the kernels/generator), confirmed by the full rebuild and the GTests above. ## Submission Checklist - [ ] Look over the contributing guidelines at https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
Composable Kernel Tile
concept
ck_tile provides a programming model with templated abstractions to enable users to implement performance-critical kernels for machine learning workloads. introduces following basic concepts to help users building your own operator
- tensor coordinate transformation, this is the core concept of layout/index transform abstraction in both compiler time and run time.
- tile-based programming model, including tile-level api and the concept of distributed tensor.
ck_tile is independently from the old ck, located under /include/ck_tile. You don't need to include anything from old CK, ck_tile has similiar (indeed almost the same) implementations for users to build operators. We will have a transition period to pull everything from old ck into ck_tile, stay tuned.
component
ck_tile is splitted into several componenets including core, host, ops/gemm, ops/fmha... each component you only need to include a single header (e.g #include "ck_tile/core.hpp", #include "ck_tile/ops/fmha.hpp") then you are able to use the function/structure inside (different from old ck)
[core]
ck_tile/core contains all the basic data structure and function to build the kernel, you can only include this header and build your own operators that utilizing all the basic building blocks introduced in ck.
core/container
- array, store runtime variables with fixed length (tensor index, register buffer, etc...)
- tuple, same as std::tuple, hold different type of data, and one of the solution to achieve multiple buffer.
- sequence, compile time integer sequence used to build various internal structures, or to describe tile size
- other convenient structure build on top of above 3
core/numeric
- gpu data type like
fp16_t,bf16_t,fp8_t... and the conversion between each other - constexpr integer similiar to std::integral_constant to be used as compile time integer.
- math functions and numeric utilities
core/algorithm
- coordinate transformation system, used to build tensor transform and compile time indexing. This is the core idea introduced in old
ckto describe how a tensor is build by several basic transform primitives likemerge/unmerge/embedetc... and how we indexing into a ND tensor that finally mapped to 1D memory offset.
core/tensor
- tensor descriptor, to describe how a ND tensor
- distributed tensor, describe the storage of this tensor, and the distribution of how a collection of threads collaborately work for this tensor.
- tile level API, including
load_tile,store_tile,shuffle_tile,slice_tile, etc...
[host]
ck_tile/host contains all the host side utilities to launch a kernel, create the device buffer, and some reference implementations. This can be used to create examples (like that under ck_tile example folder) and simple executable to invoke this kernel, so if you only need ck_tile to build your own device library then it's OK to not include this. Based on this, it is recommended to include the specific header you needed under this folder to avoid including unwanted headers (e.g, only include ck_tile/host/kernel_launch.hpp), unless you are writing a host executable.
[ops/gemm, ops/fmha, ops/reduce...]
our implementation of different device operators.
- warp, warp tile level operator
- block, block tile level operator
- pipeline, pipeline that can achieve a customized tile level mainloop (or epilogue). By switching different pipeline to the kernel template you can have different kind of pipeline optimizations.
- kernel, template interface for users to instantiate a particular kernel
[ops/epilogue]
epilogue part of our kernel. We may extend this epilogue part to let users to build their own cutomized epilogues.
[ref]
reference implementation of cpu or gpu. This folder is supposed to include a specific header on demand.
examples
currently we put all ck_tile related example under /example/ck_tile folder. Please check each example's subfolder.