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[CK Tile] StreamK support for Bwd Weight grouped convolutions (#5393) ## Motivation Add StreamK work distribution to the CK Tile grouped convolution backward weight kernel. Split-K divides the K-dimension uniformly across a fixed `k_batch`, which causes load imbalance when the number of output tiles doesn't evenly fill the GPU. StreamK distributes total K-iterations evenly across workgroups, improving utilization on these shapes. ## Technical Details StreamK is added as an `if constexpr` branch in the existing kernel, selected by the `TilePartitioner_` template parameter. Two reduction strategies are supported: - **Linear**: tile-starter sequentially accumulates partials from contributing CTAs - **Tree**: pairwise binary tree reduction (O(log n) depth, faster for many contributors) Both persistent and non-persistent data-parallel (DP) sections are supported. Key changes: - `grouped_convolution_backward_weight_kernel.hpp`: StreamK execution path with `RunStreamK`/`RunStreamKLoop`, partial store/load via workspace, flag-based cross-CTA synchronization, `GridSize`/`MakeKernelArgs`/`GetWorkSpaceSize` extensions - `streamk_common.hpp`: Shared `StreamKReductionOps` (reduction helpers) and `StreamKDispatch` (persistent/non-persistent DP dispatch), used by both GEMM and Conv StreamK kernels - `streamk_gemm_kernel.hpp`: Refactored to use shared helpers - Merged split-K and StreamK example invokers via `PartitionerPolicy` template parameter - StreamK example binary with `--streamk_reduction=linear|tree` and `--streamk_persistent=0|1` - CK Builder integration: `SpecifiesStreamK` concept, `TilePartitionerType` factory helper, `InstanceTraits` with StreamK fields - 30 tests: host-side, GPU end-to-end (Linear + Tree + Persistent DP), negative, builder regression ### Performance (MI355X, gfx950) Speedup relative to best split-K (sweep over k_batch={1,2,4,8,16,32}): | Shape | 16x64 tiles | | 128x128 tiles | | |---|---|---|---|---| | | Split-K | StreamK | Split-K | StreamK | | 1x1 128x128 N=32 28x28 | 1.00x | 0.54x | 1.00x | 0.81x | | 3x3 128x128 N=32 14x14 | 1.00x | 0.59x | 1.00x | 0.62x | | 1x1 256x64 N=32 56x56 | 1.00x | 0.83x | 1.00x | 1.83x | | 3x3 512x512 N=2 7x7 | 1.00x | 1.12x | 1.00x | 0.62x | | 1x1 1024x1024 N=4 7x7 | 1.00x | 1.09x | 1.00x | 0.60x | | 3x3 128x128 N=32 28x28 | 1.00x | 0.44x | 1.00x | 0.96x | | 3x3 256x256 N=32 14x14 | 1.00x | 0.67x | 1.00x | 0.93x | | 3x3 512x512 N=32 7x7 | 1.00x | 0.98x | 1.00x | 1.16x | StreamK's value depends on tile config: with larger tiles (fewer output tiles), StreamK delivers up to 1.83x speedup on bottleneck shapes and up to 1.16x on typical large-channel convolutions. Tree reduction consistently outperforms Linear when multiple CTAs contribute to the same tile (up to 2.87x faster), due to O(log n) reduction depth vs O(n) sequential accumulation. The table reports the best of Linear and Tree for each shape. ## Test Plan ```bash ninja -C build test_ck_tile_grouped_conv_bwd_weight_streamk ./build/bin/test_ck_tile_grouped_conv_bwd_weight_streamk # Builder tests (requires CK_EXPERIMENTAL_BUILDER=ON) ninja -C build check-builder ``` 30 tests covering: - Host-side: type traits, kernel args construction, grid size, workspace size - GPU end-to-end (Linear + Tree): small/medium shapes, multi-group, stride>1, pure-DP degeneration, single-tile all-SK, large GemmK, higher occupancy - Persistent DP: Linear + Tree with persistent data-parallel dispatch - Negative: `IsSupportedArgument` rejects unaligned K and C - Builder: Create (instance string validation) + Execution (reference comparison) + instance string regression ## Test Result All 30 conv StreamK tests pass on MI355X (gfx950). 64/64 GEMM StreamK tests pass. Full `check-builder` suite passes. Tolerances computed dynamically using `calculate_rtol_atol` pattern (fp16 ULP-aware). ## Submission Checklist - [x] Look over the contributing guidelines at https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
CK Tile Testing Guide
This document describes the test organization and available test targets for CK Tile operations.
Overview
CK Tile tests are organized with multiple levels of granularity to support different development workflows:
- Global test labels - Run tests across all operations
- Operation-specific umbrella targets - Run all tests for a specific operation
- Individual test executables - Run specific tests
Global Test Labels
These targets run tests across all CK operations (not just CK Tile):
ninja smoke
Run fast smoke tests (tests that complete within ~30 seconds on gfx90a).
ninja smoke
ninja regression
Run slower, more comprehensive regression tests.
ninja regression
ninja check
Run ALL available tests in the entire codebase.
ninja check
Operation-Specific Umbrella Targets
These targets allow you to run all tests for a specific CK Tile operation. This is useful when making changes to a particular operation and wanting to validate all related tests without running the entire test suite.
GEMM Operations
ck_tile_gemm_tests
Run all basic GEMM pipeline tests (memory, compute variants, persistent, etc.)
ninja ck_tile_gemm_tests
Test executables included:
test_ck_tile_gemm_pipeline_memtest_ck_tile_gemm_pipeline_compv3test_ck_tile_gemm_pipeline_compv4test_ck_tile_gemm_pipeline_persistenttest_ck_tile_gemm_pipeline_compv6test_ck_tile_gemm_pipeline_comp_async(gfx95 only)test_ck_tile_gemm_pipeline_*_wmmavariants (gfx11/gfx12 only)
ck_tile_gemm_block_scale_tests
Run all GEMM tests with block-scale quantization (AQuant, BQuant, ABQuant, etc.)
ninja ck_tile_gemm_block_scale_tests
Test executables included: 29 test executables covering:
- AQuant tests (memory pipelines, base layouts, prefill, preshuffle, transpose)
- ABQuant tests (base, padding, preshuffle)
- BQuant tests (1D/2D variants, transpose)
- BQuant with PreshuffleB (decode/prefill, 1D/2D)
- BQuant with PreshuffleQuant (decode/prefill, 1D/2D)
- RowColQuant and TensorQuant tests
ck_tile_gemm_streamk_tests
Run all GEMM StreamK tests (tile partitioner, reduction, smoke, extended)
ninja ck_tile_gemm_streamk_tests
Test executables included:
test_ck_tile_streamk_tile_partitionertest_ck_tile_streamk_reductiontest_ck_tile_streamk_smoketest_ck_tile_streamk_extended
ck_tile_grouped_gemm_quant_tests
Run all grouped GEMM quantization tests
ninja ck_tile_grouped_gemm_quant_tests
Test executables included:
test_ck_tile_grouped_gemm_quant_rowcoltest_ck_tile_grouped_gemm_quant_tensortest_ck_tile_grouped_gemm_quant_aquanttest_ck_tile_grouped_gemm_quant_bquanttest_ck_tile_grouped_gemm_quant_bquant_preshuffleb
Other Operations
ck_tile_fmha_tests
Run all FMHA (Flash Multi-Head Attention) tests
ninja ck_tile_fmha_tests
Test executables included: Forward and backward tests for fp16, bf16, fp8bf16, fp32
ck_tile_reduce_tests
Run all reduce operation tests
ninja ck_tile_reduce_tests
Test executables included:
test_ck_tile_reduce2dtest_ck_tile_multi_reduce2d_threadwisetest_ck_tile_multi_reduce2d_multiblock
Individual Test Executables
You can also build and run individual test executables:
Build a specific test
ninja test_ck_tile_gemm_pipeline_mem
Run a specific test directly
./build/bin/test_ck_tile_gemm_pipeline_mem
Run a specific test through ctest
ctest -R test_ck_tile_gemm_pipeline_mem --output-on-failure