Distributed training communication optimization scheme
Problem Analysis:All-Reduce operations can become a major bottleneck when there are more than 8 GPU nodes.ColossalAI provides the following solution:
- Layered communication:pass (a bill or inspection etc)
hierarchical_allreduce=TrueEnabling intra/inter-node hierarchical aggregation - Communication compression:utilization
comm_fp16=TrueConverting gradients to FP16 transmission - Overlapping calculations:configure
overlap_communication=TrueHide communication delays
Hardware Recommendations:
- Using RDMA networks (InfiniBand) instead of TCP/IP
- Ensure that NVLink is prioritized for intra-node communication
- pass (a bill or inspection etc)
colossalai.check_network()Test Bandwidth
Tuning Methods:existconfig.pymid-range adjustmentbucket_sizeparameters (4MB-8MB recommended) and monitor NCCL logs to optimize the topology.
This answer comes from the articleColossalAI: Providing Efficient Large-Scale AI Model Training SolutionsThe
May not be reproduced without permission:AI productivity tools " How to overcome the communication efficiency bottleneck in multi-node training?
