README.md

# 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 .
```

## 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_CXX_FLAGS="-O3"                                                                          \
-D CMAKE_BUILD_TYPE=Release                                                                       \
-D GPU_TARGETS="gfx908;gfx90a"                                                                    \
..
```

### Build examples and tests

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

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)

## 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.