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* updates to support int8 in 03_gemm example * added comments, using aliases, helper functions * test(gemm_universal): add test cases for int8 gemm pipeline * fix(test_gemm): fix for failing test unit test for int8 * test(ck_tile): add int8 unit test for gemm universal * refactor(gemm_universal): GPU reference verification for GEMM code improved * style(gemm_universal): removed extra comments and did clang format * merging recent changes to universal gemm to tile_engine * ck tile engine integration work * feat(tile_engine): add int8 support to tile engine ops/gemm * feat(tile_engine): added 32 32 16 mfma instances to tile engine for int8 * style: Format code with clang-format-12 * refactor(tile_engine): address review comments * style: removed unhelpful comments & unused variables. * build: tile engine uses default config * feat: add int8 support for CK_TILE GEMM * style: added trailing commas to codegen_utils.py * refactor: tile engine * refactor: formatting and code review * refactor: code formatting for python files * fix: suppress build warning * add support for gfx950 * refactor:KWarpTile size in gemms util * Fix the branch and wrap up the k warp tile * Add bf8 integration * refactor: clang format and rebase --------- Co-authored-by: zjli2013 <leezhengjiang@gmail.com> Co-authored-by: AviralGoelAMD <aviral.goel@amd.com> Co-authored-by: Khushbu Agarwal <khuagarw@amd.com>
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Changelog for Composable Kernel
Documentation for Composable Kernel available at https://rocm.docs.amd.com/projects/composable_kernel/en/latest/.
Composable Kernel 1.1.0 for ROCm 6.5.0
Added
- Added support for bf16, f32, and f16 for 2D and 3D NGCHW grouped convolution backward data
- Added a fully asynchronous HOST (CPU) arguments copy flow for CK grouped GEMM kernels.
- Added support GKCYX layout for grouped convolution forward (NGCHW/GKCYX/NGKHW, number of instances in instance factory for NGCHW/GKYXC/NGKHW has been reduced).
- Added support for GKCYX layout for grouped convolution forward (NGCHW/GKCYX/NGKHW).
- Added support for GKCYX layout for grouped convolution backward weight (NGCHW/GKCYX/NGKHW).
- Added support for GKCYX layout for grouped convolution backward data (NGCHW/GKCYX/NGKHW).
- Added support for Stream-K version of mixed fp8/bf16 GEMM
- Added support for Multiple D GEMM
- Added GEMM pipeline for microscaling (MX) FP8/FP4 data types
- Added support for FP16 2:4 structured sparsity to universal GEMM.
- Added support for Split K for grouped convolution backward data.
- Added logit soft-capping support for fMHA forward kernels.
- Added support for hdim as a multiple of 32 for FMHA (fwd/fwd_splitkv)
- Added benchmarking support for tile engine GEMM.
- Added Ping-pong scheduler support for GEMM operation along the K dimension.
- Added rotating buffer feature for CK_Tile GEMM.
- Added int8 support for CK_TILE GEMM.
Optimized
- Optimize the gemm multiply multiply preshuffle & lds bypass with Pack of KGroup and better instruction layout. (#2166)
- Added Vectorize Transpose optimization for CK Tile (#2131)
Fixes
None
Changes
- Removed support for gfx940 and gfx941 targets (#1944)
- Replaced the raw buffer load/store intrinsics with Clang20 built-ins (#1876)
- DL and DPP kernels are now enabled by default.
- Number of instances in instance factory for grouped convolution forward NGCHW/GKYXC/NGKHW has been reduced.
- Number of instances in instance factory for grouped convolution backward weight NGCHW/GKYXC/NGKHW has been reduced.
- Number of instances in instance factory for grouped convolution backward data NGCHW/GKYXC/NGKHW has been reduced.
Known issues
None
Composable Kernel 1.1.0 for ROCm 6.1.0
Additions
- Added generic instances for GEMM XDL operations (#1161)
- Added gamma and beta parameters for the layernorm and groupnorm bwd operations (#1133)
- Introduced wrapper sublibrary (limited functionality). (#1071, #1098, #1108, #1126)
- Added an option to vary the number of warm-up cycles and iterations for ckProfiler (#1124)
Optimizations
- New performance optimizations for GEMM operations on MI200 and MI300 architectures (#1135)
Fixes
- Reduced the build time for most GPU architectures (#1084)
- Fixed some conversion issues for fp8 data type (#1099)
Changes
None
Known issues
None
Composable Kernel 1.1.0 for ROCm 6.0.0
Fixes
- Fixed a hazard associated with inline v_dot (#808)
- Fixed two bugs in grouped convolution backward data without K padding (#848 #876)
Optimizations
None
Additions
- Added an image to a column kernel (#867)
- Added a column to an image kernel (#930)
- Support for 3D grouped convolution on RDNA 3 GPUs (#935, #950, #985)
- Grouped convolution support for small K and C (#822 #879 #897)
- Support for NHWGC (2D and 3D) grouped convolution backward weight (#769 #804)
- Support for bf16/f32/f16 and NHWGC (2D and 3D) grouped convolution backward data (#757 #799)
- Support for Batched GEMM DL (#732)
Changes
- Changed the grouped convolution API to maintain consistency with other convolution kernels (#817)
Composable Kernel 0.2.0 for ROCm 5.7.0
Fixes
- Fixed a bug in 6-dimensional kernels (#555)
- Fixed a test case failure with grouped convolution backward weight (#524)
Optimizations
- Improved the performance of the normalization kernel
Additions
- New CMake flags:
- "DL_KERNELS"-* Must be set to "ON" in order to build the GEMM DL and batched_gemm_multi_d_dl instances
- "DTYPES" -- Can be set to any subset of "fp64;fp32;fp16;fp8;bf16;int8" to build an instance of the specified data types
- "INSTANCES_ONLY" -- Only builds CK library and instances without tests, examples, or profiler
- New feature: if GPU_TARGETS is not set in the CMake command line, CK will be built for all targets supported by the compiler
- Support for MI300A/MI300X
- Support for AMD RDNA 3
- New user tutorial (#563)
- Additional instances for irregular GEMM sizes (#560)
- New inter-wave consumer-producer programming model for GEMM kernels (#310)
- GEMM with support multiple elementwise fusions (multi-D) (#534)
- Multi-embeddings support (#542)
- AMD RDNA 3 blockwise GEMM and real GEMM support (#541)
- AMD RDNA grouped convolution backward weight support (#505)
- MaxPool and AvgPool forward (#815); MaxPool backward (#750)
Changes
None