AI BriefingAnalyzing the Precision Handling Differences Between DeepSpeed and FSDP
Key point
This analyzes how differences in the internal precision handling methods of DeepSpeed and PyTorch FSDP affect model training outcomes.
Details
DeepSpeed and PyTorch FSDP both implement the ZeRO algorithm, but differences in precision handling can lead to different training results.
DeepSpeed is designed to always maintain master weights in float32 internally and perform upcasting. This enables stable convergence even under low-precision settings.
FSDP, on the other hand, creates parameters based on the torch_dtype set when the model is loaded, managing precision in a different way from DeepSpeed. Due to this difference, even when using the same Learning Rate, the loss function convergence pattern can differ between the two frameworks.
Hugging Face reflects precision-related updates in its Accelerate library and provides related guides so that users can smoothly switch between the two backends during training.
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