ZeRO-Offload: Democratizing Billion-Scale Model Training (2101.06840v1)

Abstract: Large-scale model training has been a playing ground for a limited few requiring complex model refactoring and access to prohibitively expensive GPU clusters. ZeRO-Offload changes the large model training landscape by making large model training accessible to nearly everyone. It can train models with over 13 billion parameters on a single GPU, a 10x increase in size compared to popular framework such as PyTorch, and it does so without requiring any model change from the data scientists or sacrificing computational efficiency. ZeRO-Offload enables large model training by offloading data and compute to CPU. To preserve compute efficiency, it is designed to minimize the data movement to/from GPU, and reduce CPU compute time while maximizing memory savings on GPU. As a result, ZeRO-Offload can achieve 40 TFlops/GPU on a single NVIDIA V100 GPU for 10B parameter model compared to 30TF using PyTorch alone for a 1.4B parameter model, the largest that can be trained without running out of memory. ZeRO-Offload is also designed to scale on multiple-GPUs when available, offering near linear speedup on up to 128 GPUs. Additionally, it can work together with model parallelism to train models with over 70 billion parameters on a single DGX-2 box, a 4.5x increase in model size compared to using model parallelism alone. By combining compute and memory efficiency with ease-of-use, ZeRO-Offload democratizes large-scale model training making it accessible to even data scientists with access to just a single GPU.

An Analysis of ZeRO-Offload: Advancements in Billion-Scale Model Training

The paper "ZeRO-Offload: Democratizing Billion-Scale Model Training" introduces an innovative technique aimed at making the training of large-scale models with over 13 billion parameters viable on a single GPU. This represents a significant enhancement over existing frameworks like PyTorch, which have limitations concerning model size due to memory constraints. A novel approach, ZeRO-Offload offloads a substantial portion of data and computation tasks to the CPU, thus optimizing the resources that are available on both the GPU and CPU without requiring any changes to the model architecture from the data scientist's perspective.

The authors highlight the capability of ZeRO-Offload to execute 10 billion parameter model training at an efficiency of 40 TFlops on a single NVIDIA V100 GPU. Comparison with a 1.4 billion parameter model using PyTorch, which achieves 30 TFlops, underscores the computational advantage and memory management prowess of ZeRO-Offload. This system is further engineered to operate across multiple GPUs, achieving almost linear scale-up to 128 GPUs. Moreover, it integrates effectively with existing model parallelism methods to support training of models exceeding 70 billion parameters on a single DGX-2 box.

The capability of ZeRO-Offload to significantly extend the size of neural network models that can be trained on relatively modest GPU hardware is an important contribution to the AI and machine learning community. By minimizing data transfer between CPU and GPU, and optimizing computational operations on the CPU, ZeRO-Offload successfully balances computational and memory efficiency. This achievement is particularly important as it lowers the barrier to entry, enabling data scientists with limited access to extensive GPU parallel clusters to conduct large-scale model training.

Further evaluation of ZeRO-Offload's impact reveals the potential to transform how large-scale models are developed and deployed, as it democratizes access by using existing hardware configurations more effectively. This paper also sheds light on the broader implications for computational resource management, pointing towards a future where the model size isn't bounded by the availability of high-cost computational infrastructure but rather by innovative utilization of available resources.

Looking forward, the development of tools like ZeRO-Offload suggests ongoing advancements in optimizing memory and computational workloads, potentially leading to even more efficient training paradigms. As the journey towards increasingly expansive models continues, developments of this nature will likely stimulate further exploration into heterogeneous computing systems, distributed training methodologies, and their intersection with next-generation AI infrastructures.