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FlashAttention能够在BERT-large训练中节省15%，将GPT训练速度提高2/3，并且是在不需要修改代码的情况下实现的。这为LLM（大型语言模型）的研究提出了一个新的方向。可以说FlashAttention的研究方向主要集中在机器学习，特别是自然语言处理（NLP）领域，并在这个领域中寻求更有效的模型训练方法。
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《FlashAttention-大模型前沿研究》直播课内容
part1：7大核心要点
- FlashAttention是一种快速且内存高效的精确注意力机制，同时具有IO感知性。
- 它主要解决了Transformer在计算长文本时面临的平方时间复杂度问题，即处理速度慢和存储占用高的问题。
- 不同于其他Efficient Transformer通过降低模型的浮点运算来提高效能，FlashAttention将优化重点放在了降低存储访问开销上。……
part2：探索研究背景
- IO-Aware Runtime Optimization
- Efficient ML Models with Structured Matrices
- Sparse Training
- Efficient Transformer
part3：相关工作讲解
part4：算法分析
- Flash Attention算法两个主要思想
- Flash attention的计算过程（算法实现）
part5：实验结果
- Training Speed
- 模型加速测试
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直播课导师：魏导师
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魏导师有丰富的深度学习研究经验。从事新能源汽车智能座舱语音对话高级算法研发；芯片公司模型训练推理加速框架研发和高性能计算工程师；大模型算法资深工程师。
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主要研究方向: 深度学习，自然语言算法与应用，大语言模型预训练微调、模型推理加速、AIGC多模态、AI推理框架。发表学术论文多篇(均为独立发表)，工信部重点项目1项。
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可指导学生方向：大语言模型预训练和微调对齐、医疗大语言模型、code LLM算法研究。
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