Number of Attention Heads (A):表示每层自注意力机制的头数,影响模型的计算量。 图6:LLM inference流程(详细版) 4. 写在最后 本文以 Llama 模型为参考,详细拆解了 LLM 的整体架构,深入分析了其计算过程及核心组件的实现原理,包括 Self-Attention、FFN、RMSNorm 等模块。业界其他主流模型(如 Baichuan、Qwen、In...
这时Cache中有之前token的key和value值,每轮计算可以从Cache中读取历史token的key和value,只需计算当前token对应的key和value,并写入Cache。 图5:LLM inference流程(KVCache) 假设序列长度为S。 3.1. Attention计算量分析 输入变换和线性投影:从图5易知,Q的计算由GEMM变为GEMV,K和V由于Cache的存在,也只用计算最后...
aideep-learningartificial-intelligencelarge-language-modelsllmllmsllm-inference UpdatedDec 9, 2024 Python bentoml/OpenLLM Star10.2k Run any open-source LLMs, such as Llama, Mistral, as OpenAI compatible API endpoint in the cloud. llamamistralfine-tuningmlopsbentomlvicunallmmodel-inferencellmopsllm-...
llm-inference is a platform for publishing and managing llm inference, providing a wide range of out-of-the-box features for model deployment, such as UI, RESTful API, auto-scaling, computing resource management, monitoring, and more. - OpenCSGs/llm-infe
Generally speaking, LLM inference is a memory bandwidth bounded task for weight loading. Weight-only quantization (WOQ) is an effective performance optimization algorithm to reduce the total amount of memory access without losing accuracy. int4 GEMM with a weight-only quantization (WOQ) recipe speci...
Many of the inference challenges and corresponding solutions featured in this post concern the optimization of this decode phase: efficient attention modules, managing the keys and values effectively, and others. Different LLMs may use different tokenizers, and thus, comparing output tokens between the...
The computational challenges of Large Language Model (LLM) inference remain a significant barrier to their widespread deployment, especially as prompt lengths continue to increase. Due to the quadratic complexity of the attention computation, it takes 30 minu...
For example, MosaicML has seamlessly added specific features that it needs on top of TensorRT-LLM and integrated them into inference serving. Naveen Rao, vice president of engineering at Databricks, said, “It has been an absolute breeze.” ...
训练后的模型会用于推理或者部署。推理即使用模型用输入获得输出的过程,部署是将模型发布到恒定运行的环境中推理的过程。一般来说,LLM的推理可以直接使用PyTorch代码、使用VLLM/XInference/FastChat等框架,也可以使用llama.cpp/chatglm.cpp/qwen.cpp等c++推理框架。
训练后的模型会用于推理或者部署。推理即使用模型用输入获得输出的过程,部署是将模型发布到恒定运行的环境中推理的过程。一般来说,LLM的推理可以直接使用PyTorch代码、使用VLLM/XInference/FastChat等框架,也可以使用llama.cpp/chatglm.cpp/qwen.cpp等c++推理框架。