# Composable Kernel

## Methodology

Composable Kernel (CK) library aims to provide a programming model for writing performance critical kernels for machine learning workloads across multiple architectures including GPUs, CPUs, etc, through general purpose kernel languages, like HIP C++.

CK utilizes two concepts to achieve performance portability and code maintainability:
* A tile-based programming model
* Algorithm complexity reduction for complex ML operators, using innovative technique we call "Tensor Coordinate Transformation".

![ALT](/docs/data/ck_component.png "CK Components")

## Code Structure

Current CK library are structured into 4 layers:
* "Templated Tile Operators" layer
* "Templated Kernel and Invoker" layer
* "Instantiated Kernel and Invoker" layer
* "Client API" layer

![ALT](/docs/data/ck_layer.png "CK Layers")

## Documentation

Run the steps below to build documentation locally.

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

## Contributors

The list of developers and contributors is here: [Contributors](/CONTRIBUTORS.md)

## Citation

If you use CK, please use following citations:
* CK paper will be freely available on arXiv soon: [Realizing Tensor Operators Using Coordinate Transformations and Tile Based Programming](???)
* [CITATION.cff](/CITATION.cff)

## License

CK is released under the MIT license. [License File](/LICENSE)


# Build CK

## Build docker image

```bash
DOCKER_BUILDKIT=1 docker build -t ck:latest -f Dockerfile .
```
Pre-built dockers are available from this public repo: 
https://hub.docker.com/r/rocm/composable_kernel/tags

## Launch docker

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

## Build CK

```bash
mkdir build && cd build

# Need to specify target ID, example below is for gfx908 and gfx90a

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 GPU_TARGETS is not set on the cmake command line, CK will be built for all targets supported by the 
current compiler.

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

INSTANCES_ONLY (by 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 for libraries that use CK as a dependency.

DTYPES (by default not set) can be set to any subset of "fp64;fp32;fp16;fp8;bf16;int8" to build instances 
of select data types only. Currently, building of int8 instances is taking a lot of time (the compiler fix is in the works).

DL_KERNELS (by default is OFF) must be set to ON in order to build the gemm_dl and batched_gemm_multi_d_dl 
instances. Those instances are only needed for the NAVI2x platforms.

### Using sccache for building

The default CK dockers come with 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 the code from hours to 1 - 2 minutes. In order to
invoke sccache, you need to run

```bash
 sccache --start-server
```
and add the following flags to the cmake command line:

```bash
 -DCMAKE_CXX_COMPILER_LAUNCHER=sccache -DCMAKE_C_COMPILER_LAUNCHER=sccache
```

### Build examples and tests

```bash
 make -j examples tests
```

### Build and run all examples and tests

```bash
 make -j check
```

Instructions for running each individual examples are under [example](/example)


## Build ckProfiler

```bash
 make -j ckProfiler
```
Instructions for running ckProfiler are under [profiler](/profiler)

## Install CK

```bash
make install
```

## Using CK as pre-built kernel library

Instructions for using CK as a pre-built kernel library are under [client_example](/client_example)

## Contributing

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

```bash
sudo script/install_precommit.sh
```

This way, `pre-commit` will add the appropriate hooks to your local repository and automatically run `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:

```bash
script/uninstall_precommit.sh
```

If for any reason, you need to temporarily disable precommit hooks, you can add the `--no-verify` option to the `git commit` command.

## Caveat
### Kernel Timing and Verification

CK's own kernel timer will warn up kernel once, and then run it multiple times
to get average kernel time. For some kernels that use atomic add, this will cause
output buffer to be accumulated multiple times, causing verification failure.
To work around it, do not use CK's own timer and do verification at the same time.
CK's own timer and verification in each example and ckProfiler can be enabled or
disabled from command line.
