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## Motivation We want to be able to calculate TileDistributionEncodings describing register mappings for any MmaOp. This is necessary for further integration with CK Tile. This MR adds a new struct TileDistrEncCalc, which takes an amdgcn_mma type (MmaOp) and provides ABC warp distribution encodings for mapping matrix fragment coordinates to register coordinates (lane, vector item) and vice versa. It is able to take CTranpose, Swizzle, and NumAccessA / NumAccessB template parameters for tweaking the tile distributions. Swizzle modification will be implemented later. The current implementation can deal with all intrinsic types and block-hiding. This MR also adds some additional static asserts and derived params within amdgcn_mma_base, to enforce consistency and help calculate Tile Distributions for block-hiding intrinsics. An Example was added that uses the Tile Distr Enc Calc to calc and print register layouts for Tile Distributions for some of our amdgcn_mma structs. It also makes sure that the CTranspose modifier works as intended. Some additional gfx9 intrinsics were added to test block-hiding layouts for the different types of C-block-hiding layouts. The sparse intrinsic wrappers were updated according to Chris's recent changes in another branch (https://github.com/ROCm/rocm-libraries/pull/5508), which moved the compression step outside of the intrinsic itself. This is necessary to make sure that the Calculator can deal with this new interpretation of the sparse intrinsics. I directly copied the new amdgcn structs from Chris's branch and changed nothing else to avoid more complex merges in the future. Note that this means I did not update a bunch of related sparse code since that would be a lot, and therefore I disabled test_amdgcn_sparse_mma for now. The amdgcn_mma_layout test was refactored a bit: - The old register mapping utility was removed and its use was replaced by the new TileDistrEncCalc - More tests were added to test layouts for different types of block-hiding and sparse intrinsics - The Selector method was removed and the tests were split up over target architectures, with each target arch having a direct list of amdgcn structs to be tested. This ensures that we force specific tests on specific architectures and makes sure that the selector doesn't quietly do some workarounds like creating compound intrinsics. ## Test Results Layout tests based on calculated tile distribution encodings pass on all architectures. Calculator works for all currently added amdgcn structs, which includes different types of block-hiding and sparse intrinsics. Printed layouts from new example verified by eye. CTranspose modifier tested for large set of intrinsics.
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