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composable_kernel/CHANGELOG.md
msaffari-amd e9f0cc83a8 [CK Tile] contraction multi d - kernel & example (#2901) 
* Initial commit. create batched_contraction_kernel file

* initial problem definition

* implement initial example to launch kernel

* add universal gemm to contraction. initial phase

* complete implementation for special case all Dims are 1 and no Ds

* clean code

* initial changes to support multi dimensional G

* more progress in implementing multiple G

* tmp commit

* manage dynamic NumDimG in kernel

* improving example for multi M,N,K,G handling. start generalizing kernel. it is a temporary commit

* implement the example for general Multi dimension G M N K and test different reference calculation algorithms

* 2 functions for reference using multi dimensional and flat indexing

* clean the code for muti dimentional G, M, N, K contraction and add some logs

* Add Make descriptor function in kernel for merging Ms, Ns, Ks for A, B, E

* some cleaning on kernel

* clean the code for  calculating the offsets from flatten batch number

* Start adding MultiD support to kernel and example

* more changes to manage multi D in kernel and example

* manage passing multi d to kernel and testing.

* complete multi D support in kernel. modify example code to support it

* Correct algorithm to calc the correct offset values for D tensor batches and some code cleaning

* Minor fix

* Generalize example code for variable NumD tensors and apply cleanup based on review feedback

* Refactored code and addressed review feedback

* refactoring, cleaning, add documents, in kernel side and example codes

* Optimize batch offset calculation in kernel

* Inline CalculateBatchOffset in batched contraction kernel, update CHANGELOG.md

---------

Co-authored-by: Adam Osewski <19374865+aosewski@users.noreply.github.com>
2025-10-13 12:30:28 +02:00

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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 1.2.0 for ROCm 7.0.0

Added

  • Added a compute async pipeline in the CK TILE universal GEMM on gfx950
  • Added support for B Tensor type pk_int4_t in 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 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 support for Multiple ABD 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 support for hdim as a multiple of 32 for FMHA (fwd/fwd_splitkv/bwd)
  • 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 support for elementwise kernel.
  • 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 & CK_TILE Grouped GEMM.
  • Added support for f32 to FMHA (fwd/bwd).
  • Added tensor-wise quantization for CK_TILE GEMM.
  • Added support for batched contraction kernel.
  • Added pooling kernel in CK_TILE

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)
  • Added the asynchronous copy for gfx950 (#2425)

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.
  • Removed BlockSize in make_kernel and CShuffleEpilogueProblem to support Wave32 in CK_TILE (#2594)

Known issues

None

Upcoming changes

  • Non-grouped convolutions are deprecated. All of their functionality is supported by grouped convolution.

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

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