MSCCL++ Integration (#22734)

Co-authored-by: Caio Rocha <caiorocha@microsof.com>
Co-authored-by: empyreus <rjsouza1995@gmail.com>

benchmark/kernels/all_reduce/README.md

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+## MSCCL++ All-Reduce Benchmark
+
+[MSCCL++](https://github.com/microsoft/mscclpp) 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++](https://github.com/microsoft/mscclpp) is already installed by default.
+2. If you are not using that Docker image (or want to install manually), install [MSCCL++](https://github.com/microsoft/mscclpp) from source (requires CMake and a CUDA toolkit):
+    ```bash
+    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.
+
+```bash
+torchrun --nproc_per_node 8 \
+    --nnodes 1 \
+    --node_rank 0 \
+    benchmark/kernels/all_reduce/benchmark_mscclpp.py
+```
+
+For multi-node (TP=16):
+```bash
+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:
+
+```bash
+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.