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AMSP: Optimizing ZeRO for Efficient LLM Training


Core Concepts
Optimizing ZeRO for efficient large language model training.
Abstract
The article introduces AMSP, a system designed to optimize ZeRO for scalable LLM training. It proposes flexible sharding strategies and efficient communication-computation overlap. Evaluations show significant improvements in Model FLOPs Utilization (MFU) compared to other systems like MiCS and ZeRO++. Challenges in large-scale LLM training with ZeRO are discussed, along with proposed solutions by AMSP.
Stats
Evaluations demonstrate up to 52% Model FLOPs Utilization (MFU) when training the LLaMA-based model on 1024 GPUs. AMSP improves the training throughput by a factor of 1.4 − 12.7 on 1024 GPUs for training LLaMA-based models. Compared to MiCS and ZeRO++, AMSP shows higher MFU rates at 51%, 52%, and 42% on LLaMA-7B, LLaMA-13B, and LLaMA-30B training.
Quotes

Key Insights Distilled From

by Qiaoling Che... at arxiv.org 03-14-2024

https://arxiv.org/pdf/2311.00257.pdf
AMSP

Deeper Inquiries

How does AMSP address the challenges of communication overhead in large-scale LLM training

AMSP addresses the challenges of communication overhead in large-scale LLM training by introducing a system designed to optimize ZeRO for scalable LLM training. It incorporates three flexible sharding strategies: Full-Replica, Full-Sharding, and Partial-Sharding, allowing each component within the model states (P, G, OS) to independently choose a sharding strategy. By providing this flexibility, AMSP can fine-tune the trade-off between communication and GPU memory usage. The system conducts a thorough analysis of communication costs and formulates an optimization problem to discover the optimal sharding strategy that minimizes communication costs while adhering to GPU memory constraints. Additionally, AMSP optimizes distributed LLM training by efficiently overlapping communication with computation. This overlap helps reduce GPU idle time and significantly enhances the training performance of LLMs.

What are the potential drawbacks or limitations of using flexible sharding strategies like those proposed by AMSP

One potential drawback or limitation of using flexible sharding strategies like those proposed by AMSP is the complexity involved in determining the optimal configuration for each component within the model states (P, G, OS). With multiple options available for sharding strategies and factors to consider such as device mesh size and number of GPUs involved, it may be challenging to find the most efficient combination without extensive computational resources or advanced algorithms. Additionally, managing different sharding configurations for parameters, gradients, and optimizer states could introduce additional overhead in terms of implementation complexity and maintenance.

How can optimizing communication placement impact overall efficiency in distributed LLM training

Optimizing communication placement can have a significant impact on overall efficiency in distributed LLM training by reducing latency and network congestion during collective communications across spine switches within a leaf-spine network architecture commonly used in GPU data centers. By strategically grouping nodes under the same leaf switches when s1p,gos > 1 instead of distributing them across different spine switches as shown in Figure 10(a), AMSP aims to minimize inter-switch communication latency. This approach helps minimize additional communication overhead introduced by sharding model states across multiple nodes or leaf switches. Ultimately, optimizing communication placement can lead to improved performance and reduced bottlenecks in large-scale distributed LLM training scenarios.
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