At the heart of the Transformer model is the self-attention mechanism, which has both time and storage complexity at the O(N2)O(N2) level in terms of sequence length. As the scale of large language models (LLMs) continues to grow, equipping LLMs with longer contextual backgrounds poses a significant engineering implementation challenge. A team of researchers from the Department of Computer Science at Stanford University and the State University of New York at Buffalo has published a novel attention algorithm called FlashAttention, which not only has a longer context than PyT

For deep learning developers and researchers, high-performance computing power is an important weapon to help their research succeed. As for the factors affecting the speed of deep learning training, it is often easy to overlook the important role of network transmission in speeding up training. Especially in large-scale clusters, distributed training scenarios, network congestion may directly lead to the failure of the GPU computing power, just as there is a section of two-way 8-lane expressway, but if the road planning is messy, the highway can only be reduced to a large parking lot. In this issue, we share a little bit of Phantom AI's thinking and optimization in this direction on the topic of network. First, let's talk about network

由于 LLM 巨大的 GPU 内存开销和计算成本，在大多数应用中，机器学习工程师通常通过内部调整（如量化和对 CUDA 核的定制）来优化。然而，由于 LLM 通过迭代生成其输出，并且 LLM 推理通常涉及内存而不是计算，因此在很多实践中，优化系统级批处理可以使性能差异达到10倍甚至更多。 一种最近提出的优化方法是连续批处理（Continuous batching），也称为动态批处理或基于迭代级的批

LLaMA 是目前备受关注的开源大语言预训练模型。最近 Meta 发布了 LLaMA 2，它是 LLaMA 的下一代版本，具有商业友好的许可证。LLaMA 2 有 3 种不同的尺寸：7B、13B 和 70B，在对话用例下微调的模型版本被称为 LLaMA 2-Chat。Llama-2-Chat 模型在大多数基准测试中都优于现有的开源聊天模型，在对有用性和安全性的人工评估中，LLama-2-Chat

GPT-4（Generative Pre-trained Transformer 4）是 OpenAI 发布的最新 GPT 系列模型。它是一个大规模多模态模型，相比 GPT-3.5 / ChatGPT，GPT-4 可以接受图像和文本两种形式的输入，产生文本输出。输出依旧是一个自回归的单词预测任务。技术上，GPT-4 采用了专家混合（MoE）技术，进一步增强模型的能力。整体来说，GPT-4 在各种专