From e6a0e69cd7e7485acd63a679620a1f2f2b4601e5 Mon Sep 17 00:00:00 2001 From: wildkid1024 Date: Sat, 7 Dec 2024 19:12:38 +0800 Subject: [PATCH] add llm serving survey --- docs/LLM_Serving_Survey.md | 102 +++++++++++++++++++++++ docs/imgs/LLM-Serving-Survey/image-1.png | Bin 0 -> 46926 bytes docs/imgs/LLM-Serving-Survey/image-2.png | Bin 0 -> 22037 bytes docs/imgs/LLM-Serving-Survey/image-3.png | Bin 0 -> 92191 bytes docs/imgs/LLM-Serving-Survey/image.png | Bin 0 -> 44353 bytes 5 files changed, 102 insertions(+) create mode 100644 docs/LLM_Serving_Survey.md create mode 100644 docs/imgs/LLM-Serving-Survey/image-1.png create mode 100644 docs/imgs/LLM-Serving-Survey/image-2.png create mode 100644 docs/imgs/LLM-Serving-Survey/image-3.png create mode 100644 docs/imgs/LLM-Serving-Survey/image.png diff --git a/docs/LLM_Serving_Survey.md b/docs/LLM_Serving_Survey.md new file mode 100644 index 0000000..7429339 --- /dev/null +++ b/docs/LLM_Serving_Survey.md @@ -0,0 +1,102 @@ +# Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems[Arixv'23][CMU] + +### 2.2 GPU和加速器 +经典GPU架构中有很多SM,每个SM中包含了一些SRAM,用以在线程间进行数据交换和同步。 + +encompass CPU:[19, 222] +移动边缘设备:[71] +ASICs: [202, 307] +加速器:TPU[136], FPGA[284] + +### 2.4 当前推理系统面临的挑战 + +- 延迟和响应时间:低延迟快响应 +- 内存访问和模型大小:降低内存占用 +- 扩展性和吞吐:并行计算、调度、系统优化 +- 硬件兼容性和加速:硬件感知优化 +- 精度和效率均衡:在精度和效率之间做均衡 + +## 3 分类 + +分为算法创新和系统优化 + + +![Alt text](imgs/LLM-Serving-Survey/image.png) + +### 3.1 算法创新 + +#### 3.1.1 解码算法 + +- 非自回归式的解码:将自回归式的解码变成可并行的非自回归方式[97, 104, 108]。半自回归编码[98]。并行编码[217]将编码过程视为一个非线性公式,使用Jacob和Gauss-Seidel定点迭代算法加速运算。非自回归编码综述[271]。 +- 投机编码:使用小模型预测多个词进行投机解码,然后使用大的LLM进行验证。SpecInfer[177]使用了多个小模型,然后使用了一个基于树的投机预测。 +- 早停机制:也叫自适应计算,将浅层的输出特征作为输出推理,不等模型运行完成。但错误率较高。 +- 级联推理:使用了多个推理模型,当请求较容易时使用简单的推理,当请求较难时,使用大的模型。 + +![Alt text](imgs/LLM-Serving-Survey/image-1.png) + + +#### 3.1.2 架构设计 + +![Alt text](imgs/LLM-Serving-Survey/image-2.png) + +- 配置下调:使用浅层的encoder和decoder,权重共享,词表缩放 +- Attention简化:因为Attention计算复杂度为O(L^2),稀疏化、核化和分解,代表有滑窗attention(129, 299), hash Attention(198), 蒸馏Attention(74)。StreamingLLM评估了初始token并维护一个滑动窗口。FastGen将Attention头应用到不同的强调KV。 +- 激活共享:共享中间激活结果来减少计算量。MQA[220]:多个Q共享一对KV,应用PaLM,ChatGLM2;GQA[2]:分组共享KV,应用LLAMA2,Mistral-7B +- 条件计算:MOE模型,使用多个专家模型来组成一整个模型。 +- 循环单元:使用RNN代替Attention,代表RWKV[200]和RetNet[212],还有线性Attention的尝试。但这种结构对长上下文支持可能没那么好。 + +#### 3.1.3 模型压缩 + +- 知识蒸馏:分为白盒蒸馏和黑盒蒸馏。代表Alpaca和Vicuna。 +- 模型裁剪:主要是结构化裁剪,裁剪Attention头[172]。以及使用矩阵分解方法产生2:4的半结构稀疏,LoSparse[161]和DSFormer[49]使用小的稠密矩阵来近似大的稀疏矩阵。Flash-LLM[267]使用内存友好的SpMM算法。 + +### 3.2 系统优化 + +#### 3.2.1 低bit量化 + +使用低bit的数据来表示32位的数。分为QAT和PTQ。PTQ代表有W4A16在GPTQ[88],W8A8在SmoothQuant[268].量化也应用与上下文压缩CacheGen[169]和微调QloRA[70], PEQA[142]. + +#### 3.2.2 并行计算 + +- 模型并行:TP[227]把模型中的Tensor纵向分为多份,然后并行计算每一份的内容,TP可以降低模型时延。PP[189]将模型的每一层横向切割为子模型的一部分,PP可以增加模型吞吐但不降低时延。SP序列并行是指在序列长度的维度上分布式化计算和存储[124]。自动并行技巧(Alpa[303],FlexFlow[128, 250], Galvatron[179])自动进行资源划分来达到模型和资源的平衡。 +- 去中心化推理:使用模型和数据并行方式在脆弱节点上进行联合推理计算,类似于联邦学习。 + +#### 3.2.3 内存管理 + +主要是由上下文KV Cache带来的,KV Cache会动态放缩。传统的方法比如FasterTransFormer是通过定长的内存预分配来管理的。vllm[150] 提出了paged Attention将KV Cache分割为多个不连续的内存block。SpecInfer[177]提出了Tree Attention和深度优先的树遍历来评估剩余KV Cahe的内存分配,主要应用在多个请求共享prompt前缀的情况。Lightllm[21]则提出了一个细粒度的基于token block的内存管理机制来进行内存管理。 + +#### 3.2.4 请求调度 + +请求调度算法是为了最大化资源利用、保证响应时间等服务级别的目标。分为动态batch[33],preemption[114],priority[191],swapping[39],model selection[107],cost efficiency[295],负载均衡和资源分配[259]。LLM独有的特征:模型形状大,迭代的自编码机制、不确定的输出长度和上下文信息的状态管理。 + +selective batching和iteration-level scheduling策略,主要有vllm,rayLLM[27], inflight batching TensorRT-LLM。SpecInfer采用投机采样来迭代地选择下一个请求。FastServe[261]使用较短完成时间来代替FCFS。SARATHI[31]解决模型初始化时的bubble问题。 + +#### 3.2.5 Kernel优化 + +- Kernel Fusion:减少内存启动和内存访问开销。不少框架重用 1. 相同形状的GEMM,例如QKV的乘积计算 2. 加法偏置的融合,比如LN、Relu等。 +- 定制化Attention,定制化GPU kernel。比如cuDNN提供FMHA的API。开源的分为prefill和decode阶段,比如xformers[153]使用了CUTLASS[24]在prefill阶段实现了online softmax;在decode阶段的目标是最大化线程利用率和最小化HBM占用,比如使用共享内存和寄存器。FlashDecoding[249]在序列长度的维度上进行了划分,把KV Cache分为了多个block,在blcok级别进行并行计算然后reduce。 +- 采样优化:采样算法会影响到LLM的生成质量,例如贪心采样和beam-search采样,topk,topp,temperature采样等控制输出的随机性。有两方面的问题,一是KV Cache带来的内存压力,二是词表带来的效率问题。LightSeq将词表分为k个group,在每个group中重排选择候选token。 +- 变长序列:输入输出都变长,一种方法是组batch,但会出现padding的问题。打包技巧[1, 293]将序列存储为连续的内存序列,中间无padding。Ragged Tensor[84]进一步支持紧凑Tensor的计算。还有一种方法是对序列划分为bucket,不过需要复杂的内存管理和请求调度。 +- 自动编译:手写Kernel,或者自动编译的kernel,比如TVM、TensorIR等,让编译方法去发现更有效的kernel。 + +## 4. 软件框架 + +![Alt text](imgs/LLM-Serving-Survey/image-3.png) + +1. 其他硬件的定制方案:PopTransformer[17], CTranslate2[8],llama.cpp +2. 在其他框架上的部署方案:OpenLLm[26], xinference[30],Lmdeploy[20], gpt-fast[15], DeepSpeed-MII和DeepSpeed-FastGen[11]和RayLLm、RayServe[27] + +在初始阶段TensorRT-LLm基于上下文长度来选择cublas和Flash Attention。还有优化是将GEMM替换为GEMV。vllm提出是为了提高模型的吞吐,FlexFlow-serve利用SpecInfer是为了降低延迟。 + +## 6. 与其他工作关联 + +主要关注在GLM的serving问题,关注GPU上的推理高效问题[190, 297],新型编译器[78]。模型压缩和量化[126]。分布式训练[42, 175] + +## 7. 未来方向 + +- 硬件加速器的开发和改进:如何结合硬件架构进行开发和改进。 +- 高效的解码算法:解锁自编码的速度并维护生成质量,一个方向便是投机编码 +- 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