zhen8838/composable_kernel

Composable Kernel: Performance Portable Programming Model for Machine Learning Tensor Operators

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".

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

Contributors

The list of developers and contributors is here: Contributors

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

License

CK is released under the MIT license. License File

Build CK

Build docker image

DOCKER_BUILDKIT=1 docker build -t ck:latest -f Dockerfile .

Launch docker

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

Build CK

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

 make -j examples tests
 make test

Instructions for running each individual examples are under example

Build ckProfiler

 make -j ckProfiler

Instructions for running ckProfiler are under profiler

Install CK

make install

Using CK as pre-built kernel library

Instructions for using CK as a pre-built kernel library are under 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.