Optimizing Large Language Model Training Using FP4 Quantization

• URL: http://arxiv.org/abs/2501.17116v1
• Date: Tue, 28 Jan 2025 18:04:50 GMT
• Title: Optimizing Large Language Model Training Using FP4 Quantization
• Authors: Ruizhe Wang, Yeyun Gong, Xiao Liu, Guoshuai Zhao, Ziyue Yang, Baining Guo, Zhengjun Zha, Peng Cheng,
• Abstract summary: Quantized training presents a promising solution by enabling low-bit arithmetic operations to reduce costs. This work introduces the first FP4 training framework for large language models (LLMs)
• Score: 73.55459961002371
• License:
• Abstract: The growing computational demands of training large language models (LLMs) necessitate more efficient methods. Quantized training presents a promising solution by enabling low-bit arithmetic operations to reduce these costs. While FP8 precision has demonstrated feasibility, leveraging FP4 remains a challenge due to significant quantization errors and limited representational capacity. This work introduces the first FP4 training framework for LLMs, addressing these challenges with two key innovations: a differentiable quantization estimator for precise weight updates and an outlier clamping and compensation strategy to prevent activation collapse. To ensure stability, the framework integrates a mixed-precision training scheme and vector-wise quantization. Experimental results demonstrate that our FP4 framework achieves accuracy comparable to BF16 and FP8, with minimal degradation, scaling effectively to 13B-parameter LLMs trained on up to 100B tokens. With the emergence of next-generation hardware supporting FP4, our framework sets a foundation for efficient ultra-low precision training.

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