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composable_kernel/CHANGELOG.md
Yashvardhan Agarwal 094e5bad50 [CK_TILE] Support for elementwise kernel (#2246) 
* Elementwise kernel implementation

Co-authored-by: Sami Aario <samaario@amd.com>
Co-authored-by: Mohsen Saffari <mohsen.saffari@amd.com>
Co-authored-by: yashagar <yashagar@amd.com>

* Elementwise with generalized nDims

* Adding the n-ary input tensor feature

* Generalize dimensions on top of inputs

* Add TFLOPS + remove std usage for tuples

* 1D basecase optimization

* Cleanup code + refactoring to a common interface

* Generalize to unary and add an example

* Cleanup, refactoring and commenting

* Suggestions for LWPCK-3170: elementwise kernel improvements

* Clang-format: remod.py

* Replace InputTensorType with XDataType as the type of input_tensors

* Add Tuple::apply and use it in ElementWiseKernel::operator to call operation with the exact number of arguments in xs

* Move examples to folder 19_elementwise

* Add missing copyright headers and fix some existing ones

* Replace an assert with throw std::runtime_error in elementwise example

* Avoid reading the output by using make_static_distributed_tensor for y_tile

* Removed two unused includes

* No need to move windows to the next block when each workgroup processes a single tile

* Only copy input tensors to the device

* Use get_warp_size to obtain warp size, and use ceiling division for grid size also for the unary example

* Adding output strides to the kernel, transposition example and update the other examples

* Changes made by remod.py

* Use default template parameter values for memory operation and coherence in a call to make_naive_tensor_view

* Move binary operations to include/ck_tile/ops/elementwise/binary_elementwise_operation.hpp

* Reuse generic reference binary/unary operation in examples + refactoring the transpose reference

* Fix comments in elementwise_example.cpp

- Refer to AMD terminology except when suggesting NVIDIA alternatives in parentheses
- ElementWiseTraits was renamed to ElementWiseShape
- Adopt suggestions made by Copilot when prompted to check for factual or typographical errors

* Simplify CMakeLists.txt and remove the unused variables this uncovers

* Rename a file and fix some copyright statements

* Changes made by script/clang-format-overwrite.sh

* Add basic unit test for ElementWiseKernel

* Remove left-over uninformative comment in apply unit test

* Changes made by clang-format-overwrite.sh

* fixup! Use default template parameter values for memory operation and coherence in a call to make_naive_tensor_view

* Clean up test_tuple_apply.cpp and test_elementwise_1d.cpp

* Use make_uniform_array_with_factory to define h_xs and d_xs_mems_owner as type std::array

* Use a DeviceMem constructor that calls get_element_space_size_in_bytes internally

* Move examples to folder 20_elementwise

* Reduced register pressure on the CK tile elementwise kernel + add 4d input example to be able benchmark against old CK

* Fix CLang formating

* Bump up the elementwise example folder number

* Elementwise: add padding + minor cleanup

* Add Vector Size inference + fix issue with wrong vectorization due to missing GuaranteedLastDimensionVectorStride setting in make_naive_tensor_view

* Add isSupportedArg to Elementwise kernel + addapt example and unit tests

* Fix clang-format on the unit test file

---------

Co-authored-by: Damien Lejeune <damien.lejeune@amd.com>
Co-authored-by: Sami Aario <samaario@amd.com>
Co-authored-by: Mohsen Saffari <mohsen.saffari@amd.com>
Co-authored-by: Aviral Goel <aviral.goel@amd.com>

[ROCm/composable_kernel commit: 606b0cc947]
2025-07-24 11:21:45 +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 1.1.0 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/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 support for elementwise kernel.

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.

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

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