Mini-batch Coresets for Memory-efficient Training of Large Language Models

• URL: http://arxiv.org/abs/2407.19580v2
• Date: Thu, 10 Oct 2024 17:25:10 GMT
• Title: Mini-batch Coresets for Memory-efficient Training of Large Language Models
• Authors: Dang Nguyen, Wenhan Yang, Rathul Anand, Yu Yang, Baharan Mirzasoleiman,
• Abstract summary: Training with larger mini-batches becomes prohibitive for Large Language Models (LLMs) We propose Coresets for Training LLMs (CoLM) CoLM reduces the memory requirement of fine-tuning by 2x and even outperforms training with 4x larger mini-batches.
• Score: 41.59038171479036
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training with larger mini-batches improves the convergence rate and can yield superior performance. However, training with large mini-batches becomes prohibitive for Large Language Models (LLMs), due to the large GPU memory requirement. To address this problem, an effective approach is finding small mini-batch coresets that closely match the gradient of larger mini-batches. However, this approach becomes infeasible and ineffective for LLMs, due to the highly imbalanced nature of the sources in language data, use of the Adam optimizer, and the very large gradient dimensionality of LLMs. In this work, we address the above challenges by proposing Coresets for Training LLMs (CoLM). First, we show that mini-batch coresets found by gradient matching do not contain representative examples of the small sources w.h.p., and thus including all examples of the small sources in the mini-batch coresets is crucial for optimal performance. Second, we normalize the gradients by their historical exponential to find mini-batch coresets for training with Adam. Finally, we leverage zeroth-order methods to find smooth gradient of the last V -projection matrix and sparsify it to keep the dimensions with the largest normalized gradient magnitude. We apply CoLM to fine-tuning Phi-2, Phi-3, and Zephyr with LoRA on MathInstruct and SuperGLUE benchmark. Remarkably, CoLM reduces the memory requirement of fine-tuning by 2x and even outperforms training with 4x larger mini-batches. Notably, CoLM easily stack with existing memory-efficient training methods, such as LoRA.

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