AI BriefingHugging Face Supports ND-Parallel
Key point
Hugging Face's Accelerate library has launched ND-Parallel, a feature that supports efficient multi-GPU training by combining various parallelization strategies.
Details
Hugging Face's Accelerate and Axolotl now integrate ND-Parallel, a feature that lets users easily combine various parallelization strategies.
Through the ParallelismConfig class, users can conveniently configure combinations of the following parallelization strategies:
- Data Parallelism (DP): Data parallelization
- Fully Sharded Data Parallelism (FSDP): Sharding of model parameters and optimizer states
- Tensor Parallelism (TP): Distributing tensor operations
- Context Parallelism (CP): Distributing sequences for long-context processing
Combining these strategies enables advanced configurations such as Hybrid Sharded Data Parallelism, minimizing the communication overhead that occurs when training large-scale models with hundreds of billions of parameters. Axolotl users can also apply this feature immediately by simply adding a few fields to their configuration file.
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