Composable Kernel

The Composable Kernel (CK) library provides a programming model for writing performance-critical kernels for machine learning workloads across multiple architectures (GPUs, CPUs, etc.). The CK library uses general purpose kernel languages, such as HIP C++.

CK uses two concepts to achieve performance portability and code maintainability:

A tile-based programming model
Algorithm complexity reduction for complex machine learning (ML) operators. This uses an innovative technique called Tensor Coordinate Transformation.

The current CK library is structured into four layers:

Templated Tile Operators
Templated Kernel and Invoker
Instantiated Kernel and Invoker
Client API

General information

To build our documentation locally, use the following code:

cd docs
pip3 install -r sphinx/requirements.txt
python3 -m sphinx -T -E -b html -d _build/doctrees -D language=en . _build/html

You can find a list of our developers and contributors on our Contributors page.

If you use CK, cite us as follows:

* [Realizing Tensor Operators Using Coordinate Transformations and Tile Based Programming](???):
  This paper will be available on arXiv soon.
* [CITATION.cff](/CITATION.cff)

CK is released under the MIT license.

Building CK

We recommend building CK inside Docker containers, which include all necessary packages. Pre-built Docker images are available on DockerHub.

To build a new Docker image, use the Dockerfile provided with the source code:
```
DOCKER_BUILDKIT=1 docker build -t ck:latest -f Dockerfile .
```

Launch the Docker container:

docker run                                     \
-it                                            \
--privileged                                   \
--group-add sudo                               \
-w /root/workspace                             \
-v ${PATH_TO_LOCAL_WORKSPACE}:/root/workspace  \
ck:latest                                      \
/bin/bash

Clone CK source code from the GitHub repository and start the build:

git clone https://github.com/ROCm/composable_kernel.git && \
cd composable_kernel && \
mkdir build && \
cd build

You must set the GPU_TARGETS macro to specify the GPU target architecture(s) you want to run CK on. You can specify single or multiple architectures. If you specify multiple architectures, use a semicolon between each; for example, gfx908;gfx90a;gfx940.

cmake                                                                                             \
-D CMAKE_PREFIX_PATH=/opt/rocm                                                                    \
-D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc                                                         \
-D CMAKE_BUILD_TYPE=Release                                                                       \
-D GPU_TARGETS="gfx908;gfx90a"                                                                    \
..

If you don't set GPU_TARGETS on the cmake command line, CK is built for all GPU targets supported by the current compiler (this may take a long time).

Build the entire CK library:
```
make -j
```
Install CK:
```
make -j install
```

Optional post-install steps

Build examples and tests:
```
make -j examples tests
```
Build and run all examples and tests:
```
make -j check
```
You can find instructions for running each individual example in example.
Build ckProfiler:
```
make -j ckProfiler
```
You can find instructions for running ckProfiler in profiler.

Note the -j option for building with multiple threads in parallel. This speeds up the build significantly. Depending on the number of CPU cores and the amount of RAM on your system, you may want to limit the number of threads. For example, if you have a 128-core CPU and 64 Gb of RAM.

By default, -j launches one thread per CPU core, which can cause the build to run out of memory and crash. In such cases, you can reduce the number of threads to 32 by using -j32.

Additional cmake flags can be used to significantly speed-up the build:

INSTANCES_ONLY (default is OFF) must be set to ON in order to build only the instances and library while skipping all tests, examples, and profiler. This is useful in cases when you plan to use CK as a dependency and don't plan to run any examples or tests.
DTYPES (default is not set) can be set to any subset of "fp64;fp32;fp16;fp8;bf16;int8" to build instances of select data types only. The main default data types are fp32 and fp16; you can safely skip other data types.
DL_KERNELS (default is OFF) must be set to ON in order to build instances, such as gemm_dl or batched_gemm_multi_d_dl. These instances are useful on architectures like the NAVI2x, as most other platforms have faster instances, such as xdl or wmma, available.

Using sccache for building

The default CK Docker images come with a pre-installed version of sccache, which supports clang being used as hip-compiler (" -x hip"). Using sccache can help reduce the time to re-build code from hours to 1-2 minutes. In order to invoke sccache, you need to run:

 sccache --start-server

then add the following flags to the cmake command line:

 -DCMAKE_CXX_COMPILER_LAUNCHER=sccache -DCMAKE_C_COMPILER_LAUNCHER=sccache

You may need to clean up the build folder and repeat the cmake and make steps in order to take advantage of the sccache during subsequent builds.

Using CK as pre-built kernel library

You can find instructions for using CK as a pre-built kernel library in client_example.

Contributing to CK

When you contribute to CK, make sure you run clang-format on all changed files. We highly recommend using git hooks that are managed by the pre-commit framework. To install hooks, run:

sudo script/install_precommit.sh

With this approach, pre-commit adds the appropriate hooks to your local repository and automatically runs clang-format (and possibly additional checks) before any commit is created.

If you need to uninstall hooks from the repository, you can do so by running the following command:

script/uninstall_precommit.sh

If you need to temporarily disable pre-commit hooks, you can add the --no-verify option to the git commit command.