sgl-project--sglang/benchmark/kernels/all_reduce

MSCCL++ All-Reduce Benchmark

MSCCL++ is a GPU-driven communication library that can replace NCCL for all-reduce operations. It supports CUDA graph capture and is optimized for small-to-medium message sizes commonly seen in tensor-parallel inference.

Currently supported configurations: TP=8 (single-node) and TP=16 (two-node).

Prerequisites

1. If you use the default SGLang Docker image build from docker/Dockerfile, MSCCL++ is already installed by default.
2. If you are not using that Docker image (or want to install manually), install MSCCL++ from source (requires CMake and a CUDA toolkit):

   git clone https://github.com/microsoft/mscclpp.git
   cd mscclpp && mkdir build && cd build
   cmake .. && make -j && pip install ..
   

3. Ensure mscclpp is importable in your Python environment before running the benchmark or using MSCCL++ for inference.

Running the Benchmark

The benchmark compares all-reduce latency across torch/NCCL (eager), MSCCL++ (eager and graph), and PyNccl (graph) for power-of-two message sizes.

torchrun --nproc_per_node 8 \
    --nnodes 1 \
    --node_rank 0 \
    benchmark/kernels/all_reduce/benchmark_mscclpp.py

For multi-node (TP=16):

export WORLD_SIZE=2
export MASTER_ADDR=<master-ip>
export MASTER_PORT=12345

# Run on each node with the appropriate RANK (0 or 1):
torchrun --nproc_per_node 8 \
    --nnodes $WORLD_SIZE \
    --node_rank $RANK \
    --master_addr $MASTER_ADDR \
    --master_port $MASTER_PORT \
    benchmark/kernels/all_reduce/benchmark_mscclpp.py

Inference with MSCCL++

Use the --enable-mscclpp flag to select MSCCL++ as the all-reduce backend during CUDA-graph-captured inference:

python -m sglang.launch_server \
    --model-path Qwen/Qwen3-8B \
    --tp-size 8 \
    --enable-mscclpp

Note: MSCCL++ performs auto-tuning on first initialization, which may add a few seconds to startup time. The tuned configurations are cached for the lifetime of the process.