Balancing Speed and Stability: The Trade-offs of FP8 vs. BF16 Training in LLMs (2411.08719v1)

Abstract: LLMs have attracted significant attention due to their human-like language understanding and generation capabilities, as well as their applicability across various domains. These models, characterized by their massive scale and extensive training data, continue to push the boundaries of what is possible in natural language processing. The Llama 3 series, for instance, exemplifies this trend with its flagship model boasting 405 billion parameters trained on 15.6 trillion tokens. The immense computational demands associated with training such models have spurred ongoing research into optimizing the efficiency of the training process, particularly through the use of lower-precision formats. NVIDIA's H100 GPU, which introduces support for FP8 in addition to the more conventional FP16 and BF16 formats, has emerged as a focal point in this optimization effort. Preliminary studies suggest that FP8 could offer substantial reductions in training time without sacrificing model performance when compared to BF16, making it a promising candidate for large-scale model training. However, the broader implications of adopting FP8, particularly in terms of training stability and downstream task performance, have yet to be fully understood. In this study, we delve into the practical trade-offs involved in adopting FP8 over BF16 for training LLMs.

• The paper demonstrates FP8 yields significant speed gains (570 vs 415 TFLOPS) but introduces unstable training loss spikes.
• Experiments with Llama-3-70B and 100B tokens on high-performance GPUs rigorously compared the precision formats.
• Results reveal task-dependent performance variations, emphasizing a need to balance efficiency with stability in LLM training.

Balancing Speed and Stability: The Trade-offs of FP8 vs. BF16 Training in LLMs

The paper "Balancing Speed and Stability: The Trade-offs of FP8 vs. BF16 Training in LLMs" presents an empirical analysis of precision formats in the context of LLM training. The authors focus on the emerging FP8 format, as introduced in NVIDIA's H100 GPU, as opposed to the more traditional BF16 format. Through detailed experimentation using Megatron-LM, the paper specifically examines the implications of adopting FP8 in terms of training speed, stability, and downstream task performance.

Key Findings and Methodology

The research emphasizes the computational gains possible with FP8, noting a substantial increase in training throughput from 415 TFLOPS (BF16) to 570 TFLOPS (FP8). Despite this improvement, the paper identifies a notable downside: the FP8 configuration employed led to unstable training loss, as evidenced by frequent spikes not seen in BF16-trained models. These findings are critical for researchers and practitioners considering the trade-offs involved in precision format selection for LLMs.

The experimental setup involved continued pre-training of Llama-3-70B on a diverse corpus using approximately 100 billion tokens. The training environment utilized top-tier computational infrastructure with NVIDIA's high-performance GPUs, leveraging distributed training strategies. Specifically, the FP8 efforts were conducted on the TSUBAME4.0 supercomputer, while BF16 experiments were executed on the AI Bridging Cloud Infrastructure (ABCI) of Japan, highlighting the pilot nature and scale of the paper.

Evaluation of Downstream Performance

The authors provide an elaborate evaluation of FP8's impact on downstream task performance across various languages and domains. The analysis reveals that FP8's influence differs by task type and language. Japanese tasks like QA showed resilience, displaying minimal performance degradation when switching to FP8. However, tasks necessitating code generation experienced marked performance declines, indicating a precision sensitivity that could inform future studies. Similar trends were observed in English tasks, although the distinctions were less pronounced. These insights necessitate a granular understanding of task-specific precision requirements when adopting FP8.

Implications and Future Directions

This work presents nuanced insights into the trade-offs between training speed and model stability, offering a pragmatic perspective on precision format choice. The implications are manifold. Practitioners may leverage FP8 for its potential to enhance efficiency but must remain cognizant of its current limitations in stability and downstream performance variance across tasks.

For future studies, this research suggests several avenues for exploration. Firstly, refining FP8's configuration parameters could mitigate stability concerns, advancing its applicability. Furthermore, a deeper investigation into task-specific precision formats could inform a more tailored approach, improving overall model robustness and performance. Finally, continued examination of FP8's impact on an even broader array of tasks and datasets would provide a comprehensive understanding, ultimately paving the way for optimized LLM training protocols.

This paper constitutes a valuable contribution to the discourse on efficient model training, underscoring the pressing need to balance emerging computational techniques with their practical implications. The findings provide a foundation for informed decision-making concerning precision formats in the context of large-scale LLMs, while also setting the stage for future innovations in the field.