专家并行负载均衡器
When using expert parallelism (EP), different experts are assigned to different GPUs. Because the load of different experts may vary depending on the current workload, it is important to keep the load of different GPUs balanced. As described in the DeepSeek-V3 paper, we adopt a redundant experts strategy that duplicates heavy-loaded experts. Then, we heuristically pack the duplicated experts to GPUs to ensure load balancing across different GPUs. Moreover, thanks to the group-limited expert routing used in DeepSeek-V3, we also attempt to place the experts of the same group to the same node to reduce inter-node data traffic, whenever possible.
To facilitate reproduction and deployment, we open-source our deployed EP load balancing algorithm in eplb.py.
The algorithm computes a balanced expert replication and placement plan based on the estimated expert loads. Note
that the exact method to predict the loads of experts is out of this repo's scope. A common method is to use
moving average of historical statistics.
The load balancing algorithm comes with two policies used for different cases.
When the number of server nodes divides the number of expert groups, we use the hierarchical load balancing policy to harness the group-limited expert routing. We first pack the expert groups to nodes evenly, ensuring the loads of different nodes are balanced. Then, we replicate the experts within each node. Finally, we pack the replicated experts to individual GPUs to ensure different GPUs are load-balanced. The hierarchical load balancing policy can be used in prefilling stage with a smaller expert-parallel size.
In other cases, we use the global load balancing policy that replicates the experts globally regardless of expert groups, and pack the replicated experts to individual GPUs. This policy can be adopted in decoding stage with a larger expert-parallel size.
The main function of the load balancer is eplb.rebalance_experts.
The following code illustrates an example of a two-layer MoE model, and each layer contains 12 experts. We introduce 4 redundant experts per layer, and the total 16 replicas are placed on 2 nodes, and each node contains 4 GPUs.
import torch
import eplb
weight = torch.tensor([[ 90, 132, 40, 61, 104, 165, 39, 4, 73, 56, 183, 86],
[ 20, 107, 104, 64, 19, 197, 187, 157, 172, 86, 16, 27]])
num_replicas = 16
num_groups = 4
num_nodes = 2
num_gpus = 8
phy2log, log2phy, logcnt = eplb.rebalance_experts(weight, num_replicas, num_groups, num_nodes, num_gpus)
print(phy2log)
# Output:
# tensor([[ 5, 6, 5, 7, 8, 4, 3, 4, 10, 9, 10, 2, 0, 1, 11, 1],
# [ 7, 10, 6, 8, 6, 11, 8, 9, 2, 4, 5, 1, 5, 0, 3, 1]])
The output, generated by the hierarchical load balancing policy, indicates the following expert replication and placement plan.
This code repository is released under the MIT License.
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