2. 长上下文推断(Long-Context Inference)的相关技术 长上下文推断是指在大语言模型(LLM)中处理较长输入序列的推理过程。由于LLM在处理长序列时,注意力机制的计算开销会随着序列长度的增加而显著增加,因此如何高效地处理长序列成为了一个重要的研究方向。Flash-Decoding正是针对这一挑战提出的一种解决方案。 3
LLM inference (or “decoding”) is an iterative process: tokens are generated one at a time. Generating full sentences of N tokens requires N forward passes through the model. Fortunately, it is possible to cache previously calculated tokens: this means that a single generation step does not d...
Flash Decoding(FD)是FlashAttention(FA)针对推理场景的改进版本,它的设计思想在2023.10.13发布在如下PyTorch官方blog。如果大家了解FA原理的话会觉得FD改进非常自然。 Flash-Decoding for long-context inference 关于FlashAttention V1和V2如何加速LLM训练的技术,可以看我之前的文章: 方佳瑞:大模型训练加速之FlashAttentio...
FlashAttention-2中(后文简称FA-2)提出对batch size * head size较小(小于GPU SM个数)且 KV sequence较长的attention操作可以进行split-KV操作来提升硬件使用率,从而降低推理latency。 flash-decoding示意图 上图为Flash-Decoding for long-context inference中对Flash-decoding动画展示,FA-2issue中对Flash-decoding也...
- Flash-Decoding已集成到FlashAttention和xFormers中,可以显著加速诸如CodeLLaMa等大模型的推理速度。 - 这项技术可以减少LLM的计算成本,也使其能处理更长的文本,将会对LLM的应用产生重要影响。《Flash-Decoding for long-context inference | PyTorch》 O网页链接 #机器学习# ...
2023.10 🔥[Flash-Decoding] Flash-Decoding for long-context inference(@Stanford University etc) [blog] [flash-attention] ⭐️⭐️ 2023.11 [Flash-Decoding++] FLASHDECODING++: FASTER LARGE LANGUAGE MODEL INFERENCE ON GPUS(@Tsinghua University&Infinigence-AI) [pdf] ⚠️ ⭐️ 2023.01 [...
2023.10🔥[Flash-Decoding] Flash-Decoding for long-context inference(@Stanford University etc)[blog][flash-attention] ⭐️⭐️ 2023.11[Flash-Decoding++] FLASHDECODING++: FASTER LARGE LANGUAGE MODEL INFERENCE ON GPUS(@Tsinghua University&Infinigence-AI)[pdf]⚠️⭐️ ...
A single and static dataflow may lead to a 50.25% performance loss for GEMMs of different shapes in LLM inference. We present FlashDecoding++, a fast LLM inference engine supporting mainstream LLMs and hardware back-ends. To tackle the above challenges, FlashDecoding++ creatively proposes: (1)...
Model inference. (a) FlashDecoding++ proposes the asynchronized softmax with unified max value technique, avoiding synchronized update to previous partial attention results. (b) FlashDecoding++ optimizes flat GEMM by improving computation utilization. (c) FlashDecoding++ heuristically optimizes dataflo...
Learn what speculative decoding is, how it works, when to use it, and how to implement it using Gemma2 models. Aashi Dutt 12 min Tutorial Gemini 1.5 Pro API Tutorial: Getting Started With Google's LLM To connect to the Gemini 1.5 Pro API, obtain your API key from Google AI for Devel...