GitHub - meta-pytorch/MSLK: MSLK (Meta Superintelligence Labs Kernels) is a collection of PyTorch GPU operator libraries that are designed and optimized for GenAI training and inference, such as FP8 row-wise quantization and collective communications.

MSLK Library

MSLK (Meta Superintelligence Labs Kernels, formerly known as FBGEMM GenAI) is a collection of high-performance kernels and optimizations built on top of PyTorch primitives for GenAI training and inference.

Installation

# Install MSLK for CUDA
pip install mslk-cuda==1.0.0
# Install MSLK for ROCm
pip install mslk-rocm==1.0.0
# Install a nightly CUDA version
pip install --pre mslk --index-url https://download.pytorch.org/whl/nightly/cu128
# Install a nightly ROCm version
pip install --pre mslk --index-url https://download.pytorch.org/whl/nightly/rocm7.1/

Release Compatibility Table

MSLK is released in accordance to the PyTorch release schedule, and each release has no guarantee to work in conjunction with PyTorch releases that are older than the one that the MSLK release corresponds to.

MSLK Release	Corresponding PyTorch Release	Supported Python Versions	Supported CUDA Versions	Supported CUDA Architectures	Supported ROCm Versions	Supported ROCm Architectures
1.0.0	2.10.x	3.10, 3.11, 3.12, 3.13, 3.14	12.6, 12.8, 12.9, 13.0	8.0, 9.0a, 10.0a, 12.0a	7.0, 7.1	gfx908, gfx90a, gfx942, gfx950

Running Benchmarks

python bench/gemm/gemm_bench.py --M 4096 --N 4096 --K 4096
python bench/quantize/quantize_bench.py --M 4096 --K 4096
python bench/conv/conv_bench.py

Running Tests

pytest test/gemm/gemm_test.py
pytest test/quantize/fp8_quantize_correctness_test.py
pytest test/conv/conv_test.py

Build From Source

We only support building on Linux. See the release compatibility table above for supported versions of Python, CUDA, ROCm.

# Clone repo
git clone https://github.com/meta-pytorch/MSLK
cd MSLK
git submodule sync
git submodule update --init --recursive
# Build and install
# The script will create a conda environment and install the required dependencies.
# The conda environment will look something like: build-py3.14-torchnightly-cuda12.9.1
./ci/integration/mslk_oss_build.bash
# After the initial environment setup, you can activate the environment and iterate faster:
conda activate build-py3.14-torchnightly-cuda12.9.1
python setup.py install

Join the MSLK community

For questions, support, news updates, or feature requests, please feel free to:

File a ticket in GitHub Issues

For contributions, please see the CONTRIBUTING file for ways to help out.

License

MSLK is BSD licensed, as found in the LICENSE file.