Documentation for Composable Kernel available at https://rocm.docs.amd.com/projects/composable_kernel/en/latest/.
- Added overload of load_tile_transpose that takes reference to output tensor as output parameter
- Use data type from LDS tensor view when determining tile distribution for transpose in the GEMM pipeline
- Added preshuffleB support for abquant mode in blockscale GEMM.
- Added support for explicit GEMM in CK_TILE grouped convolution forward and backward weight.
- Added TF32 convolution support on gfx942 and gfx950 in CK. It could be enabled/disabled via
DTYPESof "tf32". - Added streamingllm sink support for FMHA FWD, include qr_ks_vs, qr_async and splitkv pipelines.
- Added support for microscaling (MX) FP8/FP4 mixed data types to Flatmm pipeline.
- Added support for fp8 dynamic tensor-wise quantization of fp8 fmha fwd kernel.
- Added FP8 KV cache support for FMHA batch prefill.
- Added support for gfx1153 target.
- Added FMHA batch prefill kernel support for several KV cache layouts, flexible page sizes, and different lookup table configurations.
- Added gpt-oss sink support for FMHA FWD, include qr_ks_vs, qr_async, qr_async_trload and splitkv pipelines.
- Added persistent async input scheduler for CK Tile universal GEMM kernels to support asynchronous input streaming.
- Added FP8 block scale quantization for FMHA forward kernel.
- Added gfx11 support for FMHA.
- Added tests for f8 x bf8 on CompV3, and f8 x bf8 with K_BlockSize 32 on CompV4
- Added CK-Tile dispatcher - a unified kernel dispatch, code generation and architecture-based kernel filtering system with with C++ and Python frontends starting with GEMM support.
- Added support for bf16 data type to grouped_gemm and grouped_gemm_preshuffle.
- Added Col-Col-Row-Col layout support for aquant mode in blockscale GEMM.
- Added support for mixed precision fp8 x bf8 universal GEMM and weight preshuffle GEMM.
- Added a compute async pipeline in the CK Tile universal GEMM on gfx950.
- Added support for B Tensor type
pk_int4_tin the CK Tile weight preshuffle GEMM. - Added the new api to load different memory sizes to SGPR.
- Added support for B Tensor preshuffle in CK Tile grouped GEMM.
- Added a basic copy kernel example and supporting documentation for new CK Tile developers.
- Added support for grouped GEMM kernels to perform Multi D elementwise operation.
- Added support for multiple ABD GEMM.
- Added benchmarking support for tile engine GEMM Multi D.
- Added block scaling support in CK Tile GEMM, allowing flexible use of quantization matrices from either A or B operands.
- Added the row-wise column-wise quantization for CK Tile GEMM and CK Tile grouped GEMM.
- Added support for f32 to FMHA (forward and backward).
- Added tensor-wise quantization for CK Tile GEMM.
- Added support for batched contraction kernel.
- Added WMMA (gfx12) support for FMHA.
- Added pooling kernel in CK_TILE
- Added top-k sigmoid kernel in CK_TILE
- Added the blockscale 2D support for CK_TILE GEMM.
- Added Flatmm pipeline for microscaling (MX) FP8/FP4 data types
- Added reduce and multi reduction kernels
- Removed
BlockSizeinmake_kernelandCShuffleEpilogueProblemto support Wave32 in CK Tile (#2594) - Added an optional template parameter
Arch(gfx9_t,gfx12_tetc.) tomake_kernelto support linking multiple object files that have the same kernel compiled for different architectures. - FMHA examples and tests can be built for multiple architectures (gfx9, gfx950, gfx12) at the same time.
- Composable Kernel will be adopting C++20 features in an upcoming ROCm release, updating the minimum compiler requirement to C++20. Ensure that your development environment complies with this requirement to facilitate a seamless transition.
- Composable Kernel will be adopting C++20 features in an upcoming ROCm release, updating the minimum compiler requirement to C++20. Ensure that your development environment complies with this requirement to facilitate a seamless transition.
- Added support for hdim as a multiple of 32 for FMHA (fwd/fwd_splitkv/bwd)
- Added support for elementwise kernel.
- Non-grouped convolutions are deprecated. Their functionality is supported by grouped convolution.
- 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/FP6/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.
- Added CK Tile Epilogue Chainer framework for composable epilogue sequences in GEMM operations
- Optimize the gemm multiply multiply preshuffle & lds bypass with Pack of KGroup and better instruction layout.
- Added Vectorize Transpose optimization for CK Tile
- Added the asynchronous copy for gfx950
- 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.
- 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)
- New performance optimizations for GEMM operations on MI200 and MI300 architectures (#1135)
- Reduced the build time for most GPU architectures (#1084)
- Fixed some conversion issues for fp8 data type (#1099)
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- Fixed a hazard associated with inline v_dot (#808)
- Fixed two bugs in grouped convolution backward data without K padding (#848 #876)
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- 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)
- Changed the grouped convolution API to maintain consistency with other convolution kernels (#817)
- Fixed a bug in 6-dimensional kernels (#555)
- Fixed a test case failure with grouped convolution backward weight (#524)
- Improved the performance of the normalization kernel
- 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)
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