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UCCL-EP: GPU-initiated Expert-parallel Communication

GPU-initiated expert-parallel communication (e.g., DeepEP) is the key to efficient and large-scale EP. However, it cannot run on heterogeneous platforms due to tight coupling between GPU and NIC (e.g., with IBGDA). UCCL-EP has the same interface and functionality as DeepEP, and enables GPU-initiated communication for MoE models across heterogeneous GPUs (e.g., Nvidia, AMD) and NICs (e.g., EFA, Broadcom, CX7), with superior performance to the state-of-the-art.

Prerequisite

We provide a script to install dependencies (tested on p5en, p6-b200, AMD MI300x), assuming under a Python environment:

./install_deps.sh

Build on CUDA

You can directly build and install into your Python env:

python setup.py install

You can also use make to build and install (might deprecate in the future):

make -j install

Alternatively, you can build uccl.ep wheel using docker then install:

# Under uccl
bash build.sh cu12 ep --install

Note: docker-built uccl.ep wheel currently does not work on p6-b200, see #554.

Build on ROCm

You can directly build and install into your Python env:

python setup.py install

Alternatively, you can build uccl.ep wheel for ROCm7 using docker then install:

# Under uccl
bash build.sh roc7 ep --install

Test import uccl.ep

python -c "import torch; import uccl.ep"

Note:

  • If you hit some CUDA error: invalid device function, it is likely that the GPU arch auto-detection fails. You can forcely specify the arch by setting TORCH_CUDA_ARCH_LIST=gfx950 (eg, default gfx942 for MI300X/MI325X, gfx950 for MI355X) during compilation.
  • If you hit any weird compilation errors, try python setup.py clean.

Example APIs

Dispatch and combine:

packed_recv_x, packed_recv_count, handle, event, hook = buffer.low_latency_dispatch(
    current_x,
    topk_idx,
    num_tokens,
    num_experts,
    use_fp8=dispatch_use_fp8,
    round_scale=round_scale,
    use_ue8m0=use_ue8m0,
    cumulative_local_expert_recv_stats=cumulative_local_expert_recv_stats,
    async_finish=not return_recv_hook,
    return_recv_hook=return_recv_hook,
)

combined_x, event, hook = buffer.low_latency_combine(
    simulated_gemm_x,
    topk_idx,
    topk_weights,
    handle,
    use_logfmt=use_logfmt,
    async_finish=not return_recv_hook,
    zero_copy=zero_copy,
    return_recv_hook=return_recv_hook,
    out=out,
)

Initialization and tear down:

proxies, workers = initialize_uccl(scratch, num_rdma_bytes, rank, num_ranks, group, args.num_experts)
destroy_uccl(proxies, workers)

Benchmark

In ep folder, the benchmark can be run with torchrun.

Intranode Test

torchrun --standalone --nproc_per_node=8 \
  bench/test_intranode.py --num-tokens 4096 \
  --hidden 7168 --num-topk 8 --num-experts 256

Internode Low Latency Test

torchrun --nnodes=4 --nproc_per_node=8 --node_rank=<rank> \
  --master_addr=<ip> --master_port=12355 \
  bench/test_low_latency.py --num-tokens=128 \
  --hidden=7168 --num-topk=8 --num-experts=288

Internode Normal Mode (Throughput) Test

torchrun --nnodes=4 --nproc_per_node=8 --node_rank=<rank> \
  --master_addr=<ip> --master_port=12355 \
  bench/test_internode.py  --num-tokens=4096 \
  --hidden=7168 --num-topk=8 --num-experts=288 --test-ll-compatibility

Notes:

  • To avoid possible hangs, we suggest setting env variables explicitly including NCCL_IB_GID_INDEX, UCCL_IB_GID_INDEX, NCCL_SOCKET_IFNAME, and UCCL_SOCKET_IFNAME:
    • UCCL_IB_GID_INDEX should be the same as NCCL_IB_GID_INDEX like if you were using NCCL.
    • UCCL_SOCKET_IFNAME should be the interface that you would use for the --master_addr in torchrun.
  • For Broadcom Thor-2, we suggest setting UCCL_IB_MAX_INFLIGHT_BYTES=1572864 UCCL_IB_MAX_INFLIGHT_NORMAL=1 to enforce strict flow control, avoiding CQE error 12 (Transport Retry Counter Exceeded).
  • For AMD Pollara AI NIC, we suggest setting UCCL_IB_MAX_INFLIGHT_BYTES=2097152 UCCL_IB_MAX_INFLIGHT_NORMAL=1.
  • Please refer to bench/baseline for running more baselines including Torch, NVSHMEM, and pplx-kernels on EFA.
Environment Variable Description Default Value
UCCL_SOCKET_IFNAME Boostrapping interface null
UCCL_IB_GID_INDEX GID index in RDMA network -1
UCCL_IB_MAX_INFLIGHT_BYTES Max inflight bytes per GPU/NIC SIZE_MAX (no limit)
UCCL_IB_MAX_INFLIGHT_NORMAL Max inflight writes per GPU/NIC in HT 8
UCCL_IB_MAX_INFLIGHT_LOW_LATENCY Max inflight writes per GPU/NIC in LL 32
UCCL_IB_SL Service level in RDMA network 3/8 (IB/EFA)
UCCL_IB_TC Traffic class in RDMA network 104/0 (IB/EFA)
UCCL_EP_ENABLE_AGGRESSIVE_ATOMIC Use relaxed atomics with manual fences instead of acquire/release semantics (AMD only) 0 (disabled)

Results

Normal kernels with NVLink and RDMA forwarding

On p5en

We test normal kernels on 8x H200 + 16x 200Gb/s EFA with each GPU connected to two 200 Gb/s EFA RDMA network cards. We follow the DeepSeek-V3 pretraining configuration (4096 tokens per batch, 7168 hidden, top-4 groups, top-8 experts, FP8 dispatch and BF16 combine).

Type Dispatch #EP Bottleneck bandwidth & latency Combine #EP Bottleneck bandwidth & latency
Intranode 8 320 GB/s (NVLink), 500 µs 8 319 GB/s (NVLink), 973 µs
Internode 16 50 GB/s (RDMA), 1196 µs 16 18 GB/s (RDMA), 6379 µs
Internode 24 53 GB/s (RDMA), 1633 µs 24 26 GB/s (RDMA), 6365 µs
Internode 32 54 GB/s (RDMA), 2022 µs 32 43 GB/s (RDMA), 4899 µs

On p6-b200

We test normal kernels on 8x B200 + 8x 400Gb/s EFA with each GPU connected to a 400Gb/s EFA RDMA network card.

Type Dispatch #EP Bottleneck bandwidth & latency Combine #EP Bottleneck bandwidth & latency
Intranode 8 280 GB/s (NVLink), 571 µs 8 426 GB/s (NVLink), 727 µs
Internode 16 53 GB/s (RDMA), 1141 µs 16 60 GB/s (RDMA), 1965 µs
Internode 24 53 GB/s (RDMA), 1637 µs 24 59 GB/s (RDMA), 2887 µs
Internode 32 53 GB/s (RDMA), 2072 µs 32 57 GB/s (RDMA), 3724 µs

On AMD MI300X with CX7 InfiniBand

Type FP8 Dispatch #EP Bottleneck bandwidth BF16 Dispatch #EP Bottleneck bandwidth Combine #EP Bottleneck bandwidth
Intranode 8 260 GB/s (xGMI) 8 295 GB/s (xGMI) 8 304 GB/s (xGMI)
Internode 16 74 GB/s (RDMA) 16 82 GB/s (RDMA) 16 78 GB/s (RDMA)
Internode 32 60 GB/s (RDMA) 32 61 GB/s (RDMA) 32 60 GB/s (RDMA)
Internode 64 52 GB/s (RDMA) 32 53 GB/s (RDMA) 64 51 GB/s (RDMA)

On AMD MI355X with Pollara AI NIC InfiniBand

Type FP8 Dispatch #EP Bottleneck bandwidth BF16 Dispatch #EP Bottleneck bandwidth Combine #EP Bottleneck bandwidth
Intranode 8 299 GB/s (xGMI) 8 336 GB/s (xGMI) 8 333 GB/s (xGMI)
Internode 16 82 GB/s (RDMA) 16 82 GB/s (RDMA) 16 82 GB/s (RDMA)
Internode 32 59 GB/s (RDMA) 32 58 GB/s (RDMA) 32 59 GB/s (RDMA)
Internode 64 50 GB/s (RDMA) 32 49 GB/s (RDMA) 64 49 GB/s (RDMA)

On AMD MI300X with Broadcom Thor2

Type FP8 Dispatch #EP Bottleneck bandwidth BF16 Dispatch #EP Bottleneck bandwidth Combine #EP Bottleneck bandwidth
Internode 16 71 GB/s (RDMA) 16 81 GB/s (RDMA) 16 45 GB/s (RDMA)
Internode 32 49 GB/s (RDMA) 32 55 GB/s (RDMA) 32 50 GB/s (RDMA)

Low-latency kernels with pure RDMA

AMD MI300X with CX7 InfiniBand

Dispatch #EP Latency RDMA bandwidth Combine #EP Latency RDMA bandwidth
8 65 us 114 GB/s 8 92 us 157 GB/s
16 136 us 55 GB/s 16 207 us 70 GB/s
32 224 us 30 GB/s 32 341 us 42 GB/s

On p6-b200

We test low-latency kernels on 8x B200 + 8x 400Gb/s EFA.

Dispatch #EP Latency RDMA bandwidth Combine #EP Latency RDMA bandwidth
16 228 µs 33 GB/s 16 318 µs 46 GB/s
24 448 µs 17 GB/s 24 566 µs 26 GB/s
32 406 µs 19 GB/s 32 617 µs 24 GB/s

On p5en

We test low-latency kernels on 8x H200 + 16x 200Gb/s EFA, following a DeepSeek-V3 inference setting (128 tokens per batch, 7168 hidden, top-8 experts, FP8 dispatch / BF16 combine).

Dispatch #EP Latency RDMA bandwidth Combine #EP Latency RDMA bandwidth
16 226 µs 36 GB/s 16 293 µs 48 GB/s
24 386 µs 20 GB/s 24 580 µs 26 GB/s
32 465 µs 16 GB/s 32 694 µs 25 GB/s