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An open source AI tool that promises to revolutionize LLM training by reducing GPU usage by 20%
Developing large language models requires a large investment of time and GPU resources, which directly translates into high costs. The larger the model, the more obvious these challenges become.
Recently, Yandex launched a new solution: YaFSDP, an open source tool that promises to revolutionize LLM training by significantly reducing GPU resource consumption and training time. In a pre-training scenario for a 70-billion-parameter model, using YaFSDP can save approximately 150 GPUs. This means a potential savings of approximately $0.5 to $1.5 million per month, depending on the virtual GPU provider or platform.
Yandex has publicly made YaFSDP available on GitHub. Machine learning engineers can use this tool to improve the efficiency of the LLM training process. Through the open source YaFSDP, Yandex aims to promote innovation and collaboration in the artificial intelligence community, allowing developers to train models faster and more cost-effectively.
Challenges of distributed LLM training
Training LLMs across multiple GPUs involves complex operations that lead to inefficiencies and high memory consumption. One of the main problems is the need to send and receive large amounts of data between GPUs. For example, in a typical all_reduce operation, twice the amount of gradient data as network parameters must be passed. For the Llama 70B model, this means that 280 GB of data is transferred per iteration.
In addition, weights, gradients, and optimizer states are repeated between GPUs, resulting in huge memory loads. The Llama 70B model and the Adam optimizer require more than 1TB of memory, far exceeding the typical 80GB memory capacity of most GPUs. This redundancy severely slows down the training process and often makes it impractical to put medium-sized models into GPU memory.
Introduction to YaFSDP
Yandex’s YaFSDP provides efficient solutions to these challenges. YaFSDP improves the efficiency of LLM training by focusing on optimizing memory consumption and eliminating communication bottlenecks. It works by slicing layers rather than individual parameters, maintaining efficient communication and avoiding redundant operations. In addition, YaFSDP pre-allocates buffers for all required data, ensuring that the Torch allocator does not cause inefficiencies.
YaFSDP achieves intermediate weights and gradients by utilizing two buffers, one buffer for odd-numbered layers and another buffer for even-numbered layers.
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Full text: https://marktechpost.com/2024/06/14/yandex-introduces-yafsdp-an-open-source-ai-tool-that-promises-to-revolutionize-llm-training-by-cutting-gpu-usage-by-20/
GitHub page: https://github.com/yandex/YaFSDP? tab=readme-ov-file
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