!!!DEPRECATION!!!

We deprecated the old NVSHMEM-based kernels in this repo. Please check out our newer kernels in https://github.com/perplexityai/pplx-garden.

Perplexity MoE Kernels

Features:

• ✅ Cuda Graph support
• ✅ Flexible transportation layers: NVLink, IBGDA, IBRC, EFA
• ✅ Overlapping communication and computation

System Requirements

To learn how to set up the system drivers and dependencies, refer to the Install Driver and Dependencies guide.

Installation

cd pplx-kernels
TORCH_CUDA_ARCH_LIST=9.0a+PTX python3 setup.py bdist_wheel
pip install dist/*.whl

Single-node Testing and Benchmarking

Test:

pytest -svx --tb=short tests

Benchmark:

python3 -m tests.bench_all_to_all

Multi-node Testing and Benchmarking

To run an all-to-all test across multiple nodes:

export NODE_RANK= # 0, 1, ..., num_nodes-1
export WORLD_SIZE= # num_nodes * 8
export WORLD_LOCAL_SIZE=8
export MASTER_ADDR= # IP address of rank-0 node
export MASTER_PORT=29500
export NVSHMEM_IB_ENABLE_IBGDA=1

An then run the tests normally:

pytest -svx tests/test_all_to_all.py

Benchmark Results

1 token per GPU:

1 tok per GPU	EP128	EP64	EP32	EP16	EP8
NVLINK Dispatch	x	x	x	x	41.6μs ± 1.3μs
IBGDA Dispatch	125.9μs ± 0.6μs	121.0μs ± 0.2μs	115.7μs ± 1.4μs	102.7μs ± 8.7μs	x
IBRC Dispatch	488.4μs ± 51.0μs	525.0μs ± 9.4μs	421.2μs ± 35.5μs	290.5μs ± 4.7μs	x
NVLINK Combine	x	x	x	x	41.7μs ± 3.0μs
IBGDA Combine	63.2μs ± 8.3μs	58.6μs ± 1.0μs	55.4μs ± 0.8μs	62.7μs ± 0.7μs	x
IBRC Combine	786.8μs ± 149.8μs	400.0μs ± 47.9μs	122.1μs ± 38.2μs	85.9μs ± 5.3μs	x
Torch AtA	132.0μs ± 25.9μs	101.6μs ± 15.7μs	95.7μs ± 14.3μs	109.7μs ± 3.1μs	24.4μs ± 16.3μs
NVLINK NVSHMEM AtA	x	x	x	x	59.9μs ± 30.7μs
IBGDA NVSHMEM AtA	132.4μs ± 73.3μs	95.3μs ± 23.5μs	77.3μs ± 23.0μs	71.7μs ± 14.6μs	x
IBRC NVSHMEM AtA	258.8μs ± 145.3μs	98.9μs ± 57.1μs	63.2μs ± 20.3μs	55.4μs ± 12.6μs	x

128 tokens per GPU:

128 tok per GPU	EP128	EP64	EP32	EP16	EP8
DeepEP Dispatch	192μs	186μs	182μs	173μs	163μs
NVLINK Dispatch	x	x	x	x	83.6μs ± 1.0μs
IBGDA Dispatch	307.7μs ± 3.0μs	317.4μs ± 1.5μs	427.6μs ± 1.4μs	622.4μs ± 1.7μs	x
IBRC Dispatch	2038.5μs ± 77.0μs	1669.3μs ± 64.0μs	973.5μs ± 37.9μs	687.1μs ± 12.9μs	x
DeepEP Combine	369μs	353μs	350μs	329μs	318μs
NVLINK Combine	x	x	x	x	102.3μs ± 0.6μs
IBGDA Combine	593.9μs ± 6.6μs	529.9μs ± 6.7μs	481.4μs ± 3.6μs	668.1μs ± 3.4μs	x
IBRC Combine	1184.8μs ± 79.7μs	1058.5μs ± 49.6μs	916.5μs ± 45.1μs	633.4μs ± 14.0μs	x
Torch AtA	4972.0μs ± 135.8μs	5418.1μs ± 241.4μs	4225.9μs ± 69.5μs	3213.9μs ± 19.7μs	699.9μs ± 2.2μs
NVLINK NVSHMEM AtA	x	x	x	x	6585.3μs ± 2.4μs
IBGDA NVSHMEM AtA	6180.1μs ± 344.7μs	6916.3μs ± 315.4μs	4603.4μs ± 133.1μs	3444.8μs ± 15.3μs	x
IBRC NVSHMEM AtA	6378.5μs ± 375.9μs	6625.1μs ± 371.3μs	4371.3μs ± 148.8μs	3410.1μs ± 20.2μs	x

C++ Testing

To build the C++ tests and benchmarks:

cd pplx-kernels
mkdir build-cmake
cd build-cmake

export TORCH_PREFIX_PATH=$(python3 -c 'import torch; print(torch.utils.cmake_prefix_path)')

cmake ../csrc \
    -GNinja \
    -DCMAKE_PREFIX_PATH=$TORCH_PREFIX_PATH \
    -DTORCH_CUDA_ARCH_LIST=9.0a+PTX \
    -DWITH_TESTS=ON \
    -DWITH_BENCHMARKS=ON

ninja test_all_to_all bench_all_to_all

To run the all-to-all tests on one node:

NVSHMEM_REMOTE_TRANSPORT=none mpirun -np 4 ./all_to_all/test_all_to_all

To run the all-to-all benchmarks on one node:

NVSHMEM_REMOTE_TRANSPORT=none mpirun -np 4 ./all_to_all/bench_all_to_all

Citation

If you use this codebase or otherwise find our work valuable, please cite:

@misc{pplx-kernels,
  title={{pplx-kernels}: {Perplexity} {MoE} Kernels},
  author={Nandor Licker and Kevin Hu and Vladimir Zaytsev and Lequn Chen},
  year={2025},
  publisher = {GitHub},
  howpublished = {\url{https://github.com/perplexityai/pplx-kernels}},
}